From 9c25397371924001bc56c45aa691290d98c99af6 Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 11:55:13 -0700 Subject: [PATCH 01/24] Naming --- chainladder/core/base.py | 29 +++++++++++++++++------------ chainladder/core/triangle.py | 8 +++++--- 2 files changed, 22 insertions(+), 15 deletions(-) diff --git a/chainladder/core/base.py b/chainladder/core/base.py index eff782fb..a39cf027 100644 --- a/chainladder/core/base.py +++ b/chainladder/core/base.py @@ -128,6 +128,9 @@ def _set_odims(data_agg, date_axes): @staticmethod def _set_ddims(data_agg, date_axes): + # print("date origin", date_axes["__origin__"]) + # print("date development", date_axes["__development__"]) + if date_axes["__development__"].nunique() > 1: dev_lag = TriangleBase._development_lag( data_agg["__origin__"], data_agg["__development__"] @@ -147,6 +150,8 @@ def _set_ddims(data_agg, date_axes): ) dev_idx = np.zeros((len(data_agg), 1)) + print("ddims", ddims) + return ddims, dev_idx @staticmethod @@ -253,10 +258,10 @@ def _to_datetime(data, fields, period_end=False, format=None): return target @staticmethod - def _development_lag(origin, development): + def _development_lag(origin, valuation): """For tabular format, this will convert the origin/development difference to a development lag""" - return ((development - origin) / np.timedelta64(1, "M")).round(0).astype(int) + return ((valuation - origin) / np.timedelta64(1, "M")).round(0).astype(int) @staticmethod def _get_grain(dates, trailing=False, kind="origin"): @@ -285,8 +290,12 @@ def _get_grain(dates, trailing=False, kind="origin"): if trailing and grain != "M": if kind == "origin": end = (dates.min() - pd.DateOffset(days=1)).strftime("%b").upper() - end = 'DEC' if end in ['MAR', 'JUN', 'SEP', 'DEC'] and grain == 'Q' else end - end = 'DEC' if end in ['JUN', 'DEC'] and grain == '2Q' else end + end = ( + "DEC" + if end in ["MAR", "JUN", "SEP", "DEC"] and grain == "Q" + else end + ) + end = "DEC" if end in ["JUN", "DEC"] and grain == "2Q" else end else: # If inferred to beginning of calendar period, 1/1 from YYYY, 4/1 from YYYYQQ if ( @@ -428,16 +437,12 @@ def compute(self, *args, **kwargs): obj.array_backend = "numpy" return obj return self - + def _get_axis_value(self, axis): axis = self._get_axis(axis) - return { - 0: self.index, - 1: self.columns, - 2: self.origin, - 3: self.development - }[axis] - + return {0: self.index, 1: self.columns, 2: self.origin, 3: self.development}[ + axis + ] def is_chainladder(estimator): diff --git a/chainladder/core/triangle.py b/chainladder/core/triangle.py index 9fb64ac2..355705bd 100644 --- a/chainladder/core/triangle.py +++ b/chainladder/core/triangle.py @@ -425,11 +425,13 @@ def incr_to_cum(self, inplace=False): xp = self.get_array_module() if not self.is_cumulative: if self.is_pattern: - if hasattr(self,"is_additive"): + if hasattr(self, "is_additive"): if self.is_additive: - values = xp.nan_to_num(self.values[...,::-1]) + values = xp.nan_to_num(self.values[..., ::-1]) values = num_to_value(values, 0) - self.values = xp.cumsum(values,-1)[...,::-1] * self.nan_triangle + self.values = ( + xp.cumsum(values, -1)[..., ::-1] * self.nan_triangle + ) else: values = xp.nan_to_num(self.values[..., ::-1]) values = num_to_value(values, 1) From 656c17898b261dd1c8cf84e76169e2c9db728d8e Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 12:18:53 -0700 Subject: [PATCH 02/24] Revised development age calculation --- chainladder/core/base.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/chainladder/core/base.py b/chainladder/core/base.py index a39cf027..7c18e049 100644 --- a/chainladder/core/base.py +++ b/chainladder/core/base.py @@ -261,7 +261,21 @@ def _to_datetime(data, fields, period_end=False, format=None): def _development_lag(origin, valuation): """For tabular format, this will convert the origin/development difference to a development lag""" - return ((valuation - origin) / np.timedelta64(1, "M")).round(0).astype(int) + # temp = pd.DataFrame() + # temp["valuation"] = (valuation + pd.DateOffset(1)).dt.strftime("%m/%d/%Y") + # temp["origin"] = origin.dt.strftime("%m/%d/%Y") + # temp["age_old"] = ( + # ((valuation - origin) / np.timedelta64(1, "M")).round(0).astype(int) + # ) + # temp["age_temp"] = valuation - origin + # temp["age_temp_div"] = ( + # ((valuation - origin) / np.timedelta64(1, "Y")).round(0).astype(int) + # ) * 12 + + # print(temp) + # print(temp.dtypes) + + return ((valuation - origin) / np.timedelta64(1, "Y")).round(0).astype(int) * 12 @staticmethod def _get_grain(dates, trailing=False, kind="origin"): From 821641cf611c4a5fdc0dfaee760d7c498cb58c97 Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 12:19:51 -0700 Subject: [PATCH 03/24] Added Untitled.ipynb, the default playground NB --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index cbde3b23..3f7c4b34 100644 --- a/.gitignore +++ b/.gitignore @@ -122,3 +122,6 @@ settings.json .asv coverage_html_report + +# Random python test workbooks +Untitled.ipynb \ No newline at end of file From 6ebf574eb78f935883b5a2fcb702ee54cdd620e9 Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 12:30:50 -0700 Subject: [PATCH 04/24] Added development age test --- chainladder/core/tests/test_grain.py | 57 ++++++++++++++++------------ 1 file changed, 33 insertions(+), 24 deletions(-) diff --git a/chainladder/core/tests/test_grain.py b/chainladder/core/tests/test_grain.py index 89f7629e..59b2041a 100644 --- a/chainladder/core/tests/test_grain.py +++ b/chainladder/core/tests/test_grain.py @@ -9,19 +9,22 @@ def test_grain(qtr): actual = qtr.iloc[0, 0].grain("OYDY") xp = actual.get_array_module() nan = xp.nan - expected = xp.array([ - [44, 621, 950, 1020, 1070, 1069, 1089, 1094, 1097, 1099, 1100, 1100], - [42, 541, 1052, 1169, 1238, 1249, 1266, 1269, 1296, 1300, 1300, nan], - [17, 530, 966, 1064, 1100, 1128, 1155, 1196, 1201, 1200, nan, nan], - [10, 393, 935, 1062, 1126, 1209, 1243, 1286, 1298, nan, nan, nan], - [13, 481, 1021, 1267, 1400, 1476, 1550, 1583, nan, nan, nan, nan], - [2, 380, 788, 953, 1001, 1030, 1066, nan, nan, nan, nan, nan], - [4, 777, 1063, 1307, 1362, 1411, nan, nan, nan, nan, nan, nan], - [2, 472, 1617, 1818, 1820, nan, nan, nan, nan, nan, nan, nan], - [3, 597, 1092, 1221, nan, nan, nan, nan, nan, nan, nan, nan], - [4, 583, 1212, nan, nan, nan, nan, nan, nan, nan, nan, nan], - [21, 422, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], - [13, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]]) + expected = xp.array( + [ + [44, 621, 950, 1020, 1070, 1069, 1089, 1094, 1097, 1099, 1100, 1100], + [42, 541, 1052, 1169, 1238, 1249, 1266, 1269, 1296, 1300, 1300, nan], + [17, 530, 966, 1064, 1100, 1128, 1155, 1196, 1201, 1200, nan, nan], + [10, 393, 935, 1062, 1126, 1209, 1243, 1286, 1298, nan, nan, nan], + [13, 481, 1021, 1267, 1400, 1476, 1550, 1583, nan, nan, nan, nan], + [2, 380, 788, 953, 1001, 1030, 1066, nan, nan, nan, nan, nan], + [4, 777, 1063, 1307, 1362, 1411, nan, nan, nan, nan, nan, nan], + [2, 472, 1617, 1818, 1820, nan, nan, nan, nan, nan, nan, nan], + [3, 597, 1092, 1221, nan, nan, nan, nan, nan, nan, nan, nan], + [4, 583, 1212, nan, nan, nan, nan, nan, nan, nan, nan, nan], + [21, 422, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], + [13, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], + ] + ) xp.testing.assert_array_equal(actual.values[0, 0, :, :], expected) @@ -54,10 +57,11 @@ def test_commutative(qtr, atol): assert abs(a - b).max().max().max() < atol -@pytest.mark.parametrize('grain', - ['OYDY', 'OYDQ', 'OYDM', 'OSDS', 'OSDQ', 'OSDM', 'OQDQ', 'OQDM']) -@pytest.mark.parametrize('alt', [0, 1, 2]) -@pytest.mark.parametrize('trailing', [False, True]) +@pytest.mark.parametrize( + "grain", ["OYDY", "OYDQ", "OYDM", "OSDS", "OSDQ", "OSDM", "OQDQ", "OQDM"] +) +@pytest.mark.parametrize("alt", [0, 1, 2]) +@pytest.mark.parametrize("trailing", [False, True]) def test_different_forms_of_grain(prism_dense, grain, trailing, alt, atol): t = prism_dense["Paid"] if alt == 1: @@ -67,20 +71,25 @@ def test_different_forms_of_grain(prism_dense, grain, trailing, alt, atol): t = t[t.valuation < "2017-09"] a = t.grain(grain, trailing=trailing) b = t.incr_to_cum().grain(grain, trailing=trailing).cum_to_incr() - assert abs(a-b).sum().sum() < atol + assert abs(a - b).sum().sum() < atol a = t.incr_to_cum().grain(grain, trailing=trailing) b = t.grain(grain, trailing=trailing).incr_to_cum() - assert abs(a-b).sum().sum() < atol + assert abs(a - b).sum().sum() < atol def test_assymetrric_origin_grain(prism_dense): x = prism_dense.iloc[..., 8:, :].incr_to_cum() - x = x[x.valuation Date: Thu, 27 Apr 2023 20:55:21 -0500 Subject: [PATCH 05/24] Update references.bib --- docs/library/references.bib | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/library/references.bib b/docs/library/references.bib index 1bb6eb92..bef8e40e 100644 --- a/docs/library/references.bib +++ b/docs/library/references.bib @@ -28,7 +28,8 @@ @article{quarg2004 author = {Quarg, G. and Mack, T.}, title = {Munich {C}hain {L}adder: {A} {R}eserving {M}ethod that {R}educes the {G}ap between {IBNR}}, journal = {Variance}, - year = {2004} + year = {2004}, + url = {https://www.casact.org/sites/default/files/2021-07/Munich-Chain-Ladder-Quarg-Mack.pdf} } @article{clark2003, From 35a61725e07e89fcf63ea9e3a6eebec1edaced2b Mon Sep 17 00:00:00 2001 From: John S Bogaardt Date: Sun, 28 May 2023 19:49:27 +0000 Subject: [PATCH 06/24] fix for #409 --- chainladder/__init__.py | 2 +- chainladder/core/tests/test_grain.py | 10 +++++++++- chainladder/core/triangle.py | 19 ++++++++++++------- setup.py | 2 +- 4 files changed, 23 insertions(+), 10 deletions(-) diff --git a/chainladder/__init__.py b/chainladder/__init__.py index 38ef4681..339bd8c0 100644 --- a/chainladder/__init__.py +++ b/chainladder/__init__.py @@ -42,4 +42,4 @@ def auto_sparse(auto_sparse=True): from chainladder.methods import * # noqa (API Import) from chainladder.workflow import * # noqa (API Import) -__version__ = "0.8.15" +__version__ = "0.8.16" diff --git a/chainladder/core/tests/test_grain.py b/chainladder/core/tests/test_grain.py index 59b2041a..d8493f45 100644 --- a/chainladder/core/tests/test_grain.py +++ b/chainladder/core/tests/test_grain.py @@ -77,7 +77,7 @@ def test_different_forms_of_grain(prism_dense, grain, trailing, alt, atol): assert abs(a - b).sum().sum() < atol -def test_assymetrric_origin_grain(prism_dense): +def test_asymmetric_origin_grain(prism_dense): x = prism_dense.iloc[..., 8:, :].incr_to_cum() x = x[x.valuation < x.valuation_date] assert x.grain("OYDM").development[0] == 1 @@ -90,6 +90,14 @@ def test_vector_triangle_grain_mismatch(prism): assert (tri / exposure).development_grain == "Q" +def test_annual_trailing(prism): + tri = prism["Paid"].sum().incr_to_cum() + # (limit data to November) + tri = tri[tri.valuation < tri.valuation_date].incr_to_cum() + tri = tri.grain("OQDQ", trailing=True).grain("OYDY") + assert np.all(tri.ddims[:4] == np.array([3, 6, 9, 12])) + + def test_development_age(): raa_tri = cl.load_sample("raa") assert (raa_tri.ddims == [12, 24, 36, 48, 60, 72, 84, 96, 108, 120]).all() diff --git a/chainladder/core/triangle.py b/chainladder/core/triangle.py index 355705bd..12b0afca 100644 --- a/chainladder/core/triangle.py +++ b/chainladder/core/triangle.py @@ -670,22 +670,27 @@ def grain(self, grain="", trailing=False, inplace=False): obj = self.dev_to_val() if ograin_new != ograin_old: freq = {"Y": "A", "S": "2Q"}.get(ograin_new, ograin_new) - mn = self.origin[-1].strftime("%b").upper() if trailing else "DEC" + if trailing or obj.origin.freqstr[-3:] != "DEC": + origin_period_end = self.origin[-1].strftime("%b").upper() + else: + origin_period_end = "DEC" indices = ( pd.Series(range(len(self.origin)), index=self.origin) - .resample("-".join([freq, mn])) + .resample("-".join([freq, origin_period_end])) .indices ) groups = pd.concat( [pd.Series([k] * len(v), index=v) for k, v in indices.items()], axis=0 ).values obj = obj.groupby(groups, axis=2).sum() - obj.origin_close = mn - if len(obj.ddims) > 1 and pd.Timestamp(obj.odims[0]).strftime( - "%Y%m" - ) != obj.valuation[0].strftime("%Y%m"): + obj.origin_close = origin_period_end + d_start = pd.Period( + obj.valuation[0], + freq=dgrain_old if dgrain_old == 'M' else dgrain_old + obj.origin.freqstr[-4:] + ).to_timestamp(how='s') + if (len(obj.ddims) > 1 and obj.origin.to_timestamp(how='s')[0] != d_start): addl_ts = ( - pd.period_range(obj.odims[0], obj.valuation[0], freq="M")[:-1] + pd.period_range(obj.odims[0], obj.valuation[0], freq=dgrain_old)[:-1] .to_timestamp() .values ) diff --git a/setup.py b/setup.py index 75f3708d..ce4a3569 100644 --- a/setup.py +++ b/setup.py @@ -14,7 +14,7 @@ descr = "Chainladder Package - P&C Loss Reserving package " name = 'chainladder' url = 'https://github.com/casact/chainladder-python' -version='0.8.15' # Put this in __init__.py +version='0.8.16' # Put this in __init__.py data_path = '' setup( From 3d47fd15c81b340687611f3ddbc5d2b16f57c5ad Mon Sep 17 00:00:00 2001 From: John S Bogaardt Date: Sun, 28 May 2023 20:32:28 +0000 Subject: [PATCH 07/24] fix for #409 --- chainladder/core/tests/test_grain.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chainladder/core/tests/test_grain.py b/chainladder/core/tests/test_grain.py index d8493f45..6c8b1bdf 100644 --- a/chainladder/core/tests/test_grain.py +++ b/chainladder/core/tests/test_grain.py @@ -95,7 +95,7 @@ def test_annual_trailing(prism): # (limit data to November) tri = tri[tri.valuation < tri.valuation_date].incr_to_cum() tri = tri.grain("OQDQ", trailing=True).grain("OYDY") - assert np.all(tri.ddims[:4] == np.array([3, 6, 9, 12])) + assert np.all(tri.ddims[:4] == np.array([12, 24, 36, 48])) def test_development_age(): From afc4aed78a4c43bfde3d0d3546508c7a9724dd31 Mon Sep 17 00:00:00 2001 From: John S Bogaardt Date: Sun, 28 May 2023 20:54:51 +0000 Subject: [PATCH 08/24] fix for [BUG] #411 --- chainladder/methods/base.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/chainladder/methods/base.py b/chainladder/methods/base.py index 5d8e176d..26bf84fb 100644 --- a/chainladder/methods/base.py +++ b/chainladder/methods/base.py @@ -118,7 +118,10 @@ def predict(self, X, sample_weight=None): return X_new def intersection(self, a, b): - """ Given two Triangles with mismatched indices, this method""" + """ Given two Triangles with mismatched indices, this method aligns + their indices """ + if len(a) == 1 and len(b) == 1: + return a, b intersection = list(set(a.key_labels).intersection(set(b.key_labels))) if intersection == []: return a, b From 14cd809ba112d97e6de5131336a76f2d7c1c66ab Mon Sep 17 00:00:00 2001 From: John S Bogaardt Date: Fri, 9 Jun 2023 10:31:26 -0600 Subject: [PATCH 09/24] Update pytest_upstream_nightly.yml --- .github/workflows/pytest_upstream_nightly.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pytest_upstream_nightly.yml b/.github/workflows/pytest_upstream_nightly.yml index fb9bc3d3..6e487356 100644 --- a/.github/workflows/pytest_upstream_nightly.yml +++ b/.github/workflows/pytest_upstream_nightly.yml @@ -28,7 +28,8 @@ jobs: - name: Install repo and dependencies shell: bash -l {0} run: | - conda env create --file ./ci/environment-latest.yaml; + conda install mamba -y -c conda-forge + mamba env create --file ./ci/environment-latest.yaml; conda activate cl_latest; python setup.py develop pytest chainladder -m "not r" From 09b548eaf6735fa7c519eb0ce25c9dac2e975c64 Mon Sep 17 00:00:00 2001 From: John S Bogaardt Date: Sun, 11 Jun 2023 07:16:54 -0600 Subject: [PATCH 10/24] Update pytest_upstream_nightly.yml --- .github/workflows/pytest_upstream_nightly.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/pytest_upstream_nightly.yml b/.github/workflows/pytest_upstream_nightly.yml index 6e487356..39cb73b7 100644 --- a/.github/workflows/pytest_upstream_nightly.yml +++ b/.github/workflows/pytest_upstream_nightly.yml @@ -18,7 +18,7 @@ jobs: fail-fast: false matrix: os: ['ubuntu-latest'] #, 'macos-latest', 'windows-latest'] - python-version: ['3.5', '3.6', '3.7', '3.8', '3.9', '3.10'] + python-version: ['3.6', '3.7', '3.8', '3.9', '3.10', '3.11'] steps: - uses: actions/checkout@v1 - uses: conda-incubator/setup-miniconda@v2 @@ -28,7 +28,7 @@ jobs: - name: Install repo and dependencies shell: bash -l {0} run: | - conda install mamba -y -c conda-forge + conda install mamba -y -c conda-forge -n base mamba env create --file ./ci/environment-latest.yaml; conda activate cl_latest; python setup.py develop From 6d00e336392d1fd00caa0fc1f94b5c9587d65423 Mon Sep 17 00:00:00 2001 From: John S Bogaardt Date: Sun, 11 Jun 2023 13:47:56 -0600 Subject: [PATCH 11/24] docs --- docs/_config.yml | 1 + docs/_toc.yml | 80 +- docs/adjustments.ipynb | 1023 -- docs/conf.py | 1 + docs/development.ipynb | 2727 ---- docs/examples_toc.md | 380 - docs/guides_intro.md | 110 - docs/intro.md | 39 - docs/library/install.md | 65 - docs/library/releases.md | 37 +- docs/methods.ipynb | 1140 -- docs/modules/api.rst | 187 - docs/plot_advanced_triangle.ipynb | 1891 --- docs/plot_ave_analysis.ipynb | 1486 -- docs/plot_benktander.ipynb | 315 - 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html_admonition + - colon_fence diff --git a/docs/_toc.yml b/docs/_toc.yml index a01a205d..b20af8b3 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -2,50 +2,44 @@ # Learn more at https://jupyterbook.org/customize/toc.html format: jb-book -root: intro.md +root: index.md parts: - - caption: 4/13/2023 CAS Webinar Series - chapters: - - file: library/presentation_assets/webinar_demo.md - # - file: library/2022_cas_annual_meeting/concurrent_session.md - # - file: library/2022_cas_annual_meeting/roundtable_session.md - - - caption: Start Here - chapters: - - file: tutorials/demo.ipynb - - file: library/install.md - - file: tutorials/tutorials_intro.md + - chapters: + - file: getting_started/index.md sections: - - file: tutorials/triangle-tutorial.ipynb - - file: tutorials/development-tutorial.ipynb - - file: tutorials/tail-tutorial.ipynb - - file: tutorials/deterministic-tutorial.ipynb - - file: tutorials/stochastic-tutorial.ipynb - - file: tutorials/data-tutorial.ipynb - - - caption: References - chapters: - - file: guides_intro.md + - file: getting_started/install.md + - file: getting_started/tutorials/index.md + sections: + - file: getting_started/tutorials/triangle-tutorial.ipynb + - file: getting_started/tutorials/development-tutorial.ipynb + - file: getting_started/tutorials/tail-tutorial.ipynb + - file: getting_started/tutorials/deterministic-tutorial.ipynb + - file: getting_started/tutorials/stochastic-tutorial.ipynb + - file: getting_started/tutorials/data-tutorial.ipynb + - chapters: + - file: user_guide/index.md sections: - - file: triangle.ipynb - - file: development.ipynb - - file: tails.ipynb - - file: methods.ipynb - - file: adjustments.ipynb - - file: workflow.ipynb - - file: utilities.ipynb - - file: modules/api.rst - - file: examples_toc.md - - file: library/sample_data.md - - file: library/glossary.md - - file: library/references.md - - - caption: About - chapters: - - file: library/usage.md - - file: library/source_code.md - - file: library/releases.md - - file: library/questions_issues.md - - file: library/contributing.md - - file: library/roadmap.md + - file: user_guide/triangle.ipynb + - file: user_guide/development.ipynb + - file: user_guide/tails.ipynb + - file: user_guide/methods.ipynb + - file: user_guide/adjustments.ipynb + - file: user_guide/workflow.ipynb + - file: user_guide/utilities.ipynb + - chapters: + - file: gallery/index.md + - chapters: + - file: library/api.rst + - chapters: + - file: library/about.md + sections: + - file: library/usage.md + - file: library/references.md + - file: library/sample_data.md + - file: library/glossary.md + - file: library/questions_issues.md + - file: library/contributing.md + - file: library/roadmap.md + - chapters: + - file: library/releases.md \ No newline at end of file diff --git a/docs/adjustments.ipynb b/docs/adjustments.ipynb deleted file mode 100644 index fd183343..00000000 --- a/docs/adjustments.ipynb +++ /dev/null @@ -1,1023 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fc09520f-eda2-4f15-800e-aa5421dde199", - "metadata": {}, - "source": [ - "# Data Adjustments\n", - "\n", - "There are many useful data adjustments in reserving that are not necessarily direct\n", - "IBNR models nor development factor selections. This module covers those implemented\n", - "by ``chainladder``. In all cases, these adjustments are most useful when used\n", - "as part of a larger `workflow`.\n" - ] - }, - { - "cell_type": "markdown", - "id": "3ee2ab5b-6cc8-432a-bf25-1a1cfec00e8e", - "metadata": {}, - "source": [ - "(adjustments:bootstrapodpsample)=\n", - "## BootstrapODPSample\n", - "### Simulations\n", - "\n", - ":class:`BootstrapODPSample` is a transformer that simulates new triangles\n", - "according to the ODP Bootstrap model. That is both the ``index`` and ``column``\n", - "of the Triangle must be of unity length. Upon fitting the Estimator, the\n", - "``index`` will contain the individual simulations." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6bb3584c-279c-4401-ba30-7af10abb516c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
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" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1990-12\n", - "Grain: OYDY\n", - "Shape: (500, 1, 10, 10)\n", - "Index: [Total]\n", - "Columns: [values]" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chainladder as cl\n", - "import pandas as pd\n", - "\n", - "raa = cl.load_sample('raa')\n", - "cl.BootstrapODPSample(n_sims=500).fit_transform(raa)" - ] - }, - { - "cell_type": "markdown", - "id": "5272f97a-181f-41f8-a83e-bc983f700748", - "metadata": {}, - "source": [ - "```{note}\n", - "The `BootstrapODPSample` can only apply to single triangles as it needs the\n", - "``index`` axis to be free to hold the different triangle simluations.\n", - "```\n", - "\n", - "### Dropping\n", - "The Estimator has full dropping support consistent with the `Development` estimator.\n", - "This allows for eliminating problematic values from the sampled residuals.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "eb29be5a-26f9-4dc6-9811-1623914562b8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1990-12
Grain:OYDY
Shape:(100, 1, 10, 10)
Index:[Total]
Columns:[values]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1990-12\n", - "Grain: OYDY\n", - "Shape: (100, 1, 10, 10)\n", - "Index: [Total]\n", - "Columns: [values]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.BootstrapODPSample(n_sims=100, drop=[('1982', 12)]).fit_transform(raa)" - ] - }, - { - "cell_type": "markdown", - "id": "1e9c4598-5ab5-4cdd-ab37-ff43b6248970", - "metadata": {}, - "source": [ - "### Deterministic methods\n", - "\n", - "The class only simulates new triangles from which you can generate\n", - "statistics about parameter and process uncertainty. This allows for converting\n", - "the various deterministic IBNR methods into stochastic\n", - "methods.\n", - "\n", - "\n", - "Like the `Development` estimators, The `BootstrapODPSample` allows for ommission\n", - "of certain residuals from its sampling algorithm with a suite of \"dropping\"\n", - "parameters. See :ref:`Omitting Link Ratios`.\n", - "\n", - "### Examples\n", - ":::{panels}\n", - ":column: col-lg-4 px-2 py-2\n", - "---\n", - "**[BootstrapODPSample Variability](plot_bootstrap_comparison)**\n", - "```{glue:} plot_bootstrap_comparison\n", - "```\n", - "+++\n", - "{bdg-warning}`medium`\n", - "\n", - "---\n", - "**[Basic BootstrapODPSample](plot_bootstrap)**\n", - "```{glue:} plot_bootstrap\n", - "```\n", - "+++\n", - "{bdg-success}`easy`\n", - ":::\n", - "{cite}`shapland2016`" - ] - }, - { - "cell_type": "markdown", - "id": "9e6166f7-a83a-4d19-8823-7f647119a9bb", - "metadata": {}, - "source": [ - "(adjustments:berquistsherman)=\n", - "## BerquistSherman\n", - ":class:`BerquistSherman` provides a mechanism of restating the inner diagonals of a\n", - "triangle for changes in claims practices. These adjustments can materialize in\n", - "case incurred and paid amounts as well as closed claims count development.\n", - "\n", - "In all cases, the adjustments retain the unadjusted latest diagonal of the\n", - "triangle. For the Incurred adjustment, an assumption of the trend rate in\n", - "average open case reserves must be supplied. For the adjustments to paid\n", - "amounts and closed claim counts, an estimator, such as `Chainladder` is needed\n", - "to calulate ultimate reported count so that the ``disposal_rate_`` of the\n", - "model can be calculated.\n", - "\n", - "### Adjustments\n", - "The `BerquistSherman` technique requires values for ``paid_amount``, ``incurred_amount``,\n", - "``reported_count``, and ``closed_count`` to be available in your `Triangle`. Without\n", - "these triangles, the `BerquistSherman` model cannot be used. If these conditions\n", - "are satisfied, the `BerquistSherman` technique adjusts all triangles except the\n", - "``reported_count`` triangle.\n", - "\n", - "The Estimator wraps all adjustments up in its ``adjusted_triangle_`` property." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "aaec15bf-c98e-435e-9815-14821e2d510c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496
19691.00001.00001.00001.00001.00001.00001.00001.0000
19701.00001.00001.00001.00001.00001.00001.0000
19711.00001.00001.00001.00001.00001.0000
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" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96\n", - "1969 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n", - "1970 1.0 1.0 1.0 1.0 1.0 1.0 1.0 NaN\n", - "1971 1.0 1.0 1.0 1.0 1.0 1.0 NaN NaN\n", - "1972 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN\n", - "1973 1.0 1.0 1.0 1.0 NaN NaN NaN NaN\n", - "1974 1.0 1.0 1.0 NaN NaN NaN NaN NaN\n", - "1975 1.0 1.0 NaN NaN NaN NaN NaN NaN\n", - "1976 1.0 NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle = cl.load_sample('berqsherm').loc['MedMal']\n", - "berq = cl.BerquistSherman(\n", - " paid_amount='Paid', incurred_amount='Incurred',\n", - " reported_count='Reported', closed_count='Closed',\n", - " trend=0.15).fit(triangle)\n", - "\n", - "# Only Reported triangle is left unadjusted\n", - "(triangle / berq.adjusted_triangle_)['Reported']" - ] - }, - { - "cell_type": "markdown", - "id": "c55c1000-7647-4e44-89bc-20bd552ac611", - "metadata": {}, - "source": [ - "### Latest Diagonal\n", - "Only the inner diagonals of the `Triangle` are adjusted. This allows the\n", - "``adjusted_triangle_`` to be a drop in surrogate for the original." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a325af12-8e57-4435-9451-e555d1368548", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496
19692.07143.36351.59681.38560.75451.21050.88621.0000
19700.76032.20841.24200.99640.87291.49941.0000
19711.76634.34641.74291.28990.76291.0000
19720.71292.35141.62281.25401.0000
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" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96\n", - "1969 2.071394 3.363483 1.596756 1.385616 0.754503 1.210486 0.886243 1.0\n", - "1970 0.760317 2.208368 1.242022 0.996379 0.872924 1.499417 1.000000 NaN\n", - "1971 1.766309 4.346440 1.742887 1.289923 0.762913 1.000000 NaN NaN\n", - "1972 0.712947 2.351430 1.622842 1.253956 1.000000 NaN NaN NaN\n", - "1973 1.323418 1.602855 0.718643 1.000000 NaN NaN NaN NaN\n", - "1974 1.451868 2.851770 1.000000 NaN NaN NaN NaN NaN\n", - "1975 1.360393 1.000000 NaN NaN NaN NaN NaN NaN\n", - "1976 1.000000 NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(triangle / berq.adjusted_triangle_)['Paid']" - ] - }, - { - "cell_type": "markdown", - "id": "c1cf6de5-a21b-4b4a-ba52-14509c652cb6", - "metadata": {}, - "source": [ - "### Transform\n", - "\n", - "`BerquistSherman` is strictly a data adjustment to the `Triangle` and it does\n", - "not attempt to estimate development patterns, tails, or ultimate values. Once\n", - "``transform`` is invoked on a Triangle, the ``adjusted_triangle_`` takes the\n", - "place of the existing `Triangle`.\n", - "\n", - "\n", - "### Examples\n", - ":::{panels}\n", - ":column: col-lg-4 px-2 py-2\n", - "\n", - "---\n", - "**[BerquistSherman Adjustment](plot_berqsherm_case)**\n", - "```{glue:} plot_berqsherm_case\n", - "```\n", - "+++\n", - "{bdg-success}`easy`\n", - "\n", - ":::\n", - "{cite}`friedland2010`" - ] - }, - { - "cell_type": "markdown", - "id": "3f8b8811-0983-44f5-8640-b6625bb00c1d", - "metadata": {}, - "source": [ - "(adjustments:paralellogramolf)=\n", - "## ParallelogramOLF\n", - "\n", - "The :class:`ParallelogramOLF` estimator is used to on-level a Triangle using\n", - "the parallogram technique. It requires a \"rate history\" and supports both\n", - "vertical line estimates as well as the more common effective date estimates.\n", - "\n", - "```{eval-rst}\n", - ".. image:: images/onlevel.PNG\n", - "```\n", - "\n", - "### Rate History\n", - "The Estimator requires a rate history that has, at a minimum, the rate changes\n", - "and date-like columns reflecting the corresponding effective dates of the\n", - "rate changes." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "77b3d1ae-28f5-4afa-9071-ba360779b6d0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " EffDate RateChange\n", - "0 2016-07-15 0.02\n", - "1 2017-03-01 0.05\n", - "2 2018-01-01 -0.03\n", - "3 2019-10-31 0.10" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rate_history = pd.DataFrame({\n", - " 'EffDate': ['2016-07-15', '2017-03-01', '2018-01-01', '2019-10-31'],\n", - " 'RateChange': [0.02, 0.05, -.03, 0.1]})\n", - "rate_history" - ] - }, - { - "cell_type": "markdown", - "id": "e50ea2cc-c82b-4ee3-a782-23ae72a0380a", - "metadata": {}, - "source": [ - "The `ParallelogramOLF` maps the rate history using the ``change_col`` and ``date_col``\n", - "arguments. Once mapped, the transformer provides an ``olf_`` property\n", - "representing the on-level factors from the parallelogram on-leveling technique.\n", - "When used as a transformer, it retains your `Triangle` as it is, but then adds\n", - "the ``olf_`` property to your `Triangle`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "252ed464-a589-4a09-9bc7-937c4b0a8529", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2020
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" - ], - "text/plain": [ - " 2020\n", - "2016 1.140327\n", - "2017 1.104799\n", - "2018 1.084197\n", - "2019 1.098557\n", - "2020 1.034456" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = pd.DataFrame({\n", - " 'Year': [2016, 2017, 2018, 2019, 2020],\n", - " 'EarnedPremium': [10_000]*5})\n", - "prem_tri = cl.Triangle(data, origin='Year', columns='EarnedPremium')\n", - "prem_tri = cl.ParallelogramOLF(rate_history, change_col='RateChange', date_col='EffDate').fit_transform(prem_tri)\n", - "prem_tri.olf_" - ] - }, - { - "cell_type": "markdown", - "id": "0952cc65-fbfe-41ac-b33e-bc41c3145798", - "metadata": {}, - "source": [ - "### Input to CapeCod\n", - "\n", - "This estimator can be used within other estimators that depend on on-leveling,\n", - "such as the :class:`CapeCod` method. This is accomplished by passing a\n", - "`ParallelogramOLF` transformed `Triangle` to the `CapeCod` estimator.\n", - "\n", - "### Examples\n", - ":::{panels}\n", - ":column: col-lg-4 px-2 py-2\n", - "---\n", - "**[CapeCod Onleveling](plot_capecod_onlevel)**\n", - "```{glue:} plot_capecod_onlevel\n", - "```\n", - "+++\n", - "{bdg-warning}`medium`\n", - "\n", - ":::\n" - ] - }, - { - "cell_type": "markdown", - "id": "ebe90dc3-cd67-4a82-8bab-8fe94a131302", - "metadata": {}, - "source": [ - "(adjustments:trend)=\n", - "## Trend\n", - "\n", - "The :class:`Trend` estimator is a convenience estimator that allows for compound\n", - "trends to be used in other estimators that have a ``trend`` assumption. This\n", - "enables more complex trend assumptions to be used." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "af652141-0a29-4dcf-9807-18f252c6cd3c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ppauto_loss = cl.load_sample('clrd').groupby('LOB').sum().loc['ppauto', 'CumPaidLoss']\n", - "ppauto_prem = cl.load_sample('clrd').groupby('LOB').sum() \\\n", - " .loc['ppauto']['EarnedPremDIR'].latest_diagonal\n", - "\n", - "# Simple trend\n", - "a = cl.CapeCod(trend=0.05).fit(ppauto_loss, sample_weight=ppauto_prem).ultimate_.sum()\n", - "\n", - "# Equivalent using a Trend Estimator. This allows us to convert to more complex trends\n", - "b = cl.CapeCod().fit(cl.Trend(.05).fit_transform(ppauto_loss), sample_weight=ppauto_prem).ultimate_.sum()\n", - "a == b\n" - ] - }, - { - "cell_type": "markdown", - "id": "0cfb7d64-aa9f-4d8f-bfbf-d8d39ba99fb4", - "metadata": {}, - "source": [ - "### Multipart Trend\n", - "\n", - "A multipart trend can be achieved if passing a list of ``trends`` and corresponding\n", - "``dates``. Dates should be represented as a list of tuples (``start``, ``end``).\n", - "\n", - "The default start and end dates for a Triangle are its ``valuation_date`` and\n", - "its earliest origin date." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "dae2933e-864f-4d42-a866-e3fda52d1f2a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19881.24001.24001.24001.24001.24001.24001.24001.24001.24001.2400
19891.24001.24001.24001.24001.24001.24001.24001.24001.2400
19901.24001.24001.24001.24001.24001.24001.24001.2400
19911.24001.24001.24001.24001.24001.24001.2400
19921.23001.23001.23001.23001.23001.2300
19931.19001.19001.19001.19001.1900
19941.16001.16001.16001.1600
19951.10001.10001.1000
19961.05001.0500
19971.0000
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24\n", - "1989 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24 NaN\n", - "1990 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24 NaN NaN\n", - "1991 1.24 1.24 1.24 1.24 1.24 1.24 1.24 NaN NaN NaN\n", - "1992 1.23 1.23 1.23 1.23 1.23 1.23 NaN NaN NaN NaN\n", - "1993 1.19 1.19 1.19 1.19 1.19 NaN NaN NaN NaN NaN\n", - "1994 1.16 1.16 1.16 1.16 NaN NaN NaN NaN NaN NaN\n", - "1995 1.10 1.10 1.10 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 1.05 1.05 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 1.00 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ppauto_loss = cl.load_sample('clrd').groupby('LOB').sum().loc['ppauto', 'CumPaidLoss']\n", - "cl.Trend(\n", - " trends=[.05, .03],\n", - " dates=[('1997-12-31', '1995'),('1995', '1992-07')]\n", - ").fit(ppauto_loss).trend_.round(2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eaaf2ea2-cf4e-4549-ae83-4f1b6476e30e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.7.11 ('cl_dev')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - }, - "vscode": { - "interpreter": { - "hash": "cc8120b3d9d10be7e89b07ff6d4d09ab1619cf80570f11e1a80e248c6b69ac77" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/conf.py b/docs/conf.py index 4704fd76..268d6015 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -32,6 +32,7 @@ "sphinxcontrib.bibtex", "sphinx_jupyterbook_latex", "sphinx_copybutton", + "sphinx_design", ] external_toc_exclude_missing = False external_toc_path = "_toc.yml" diff --git a/docs/development.ipynb b/docs/development.ipynb deleted file mode 100644 index 90d5a018..00000000 --- a/docs/development.ipynb +++ /dev/null @@ -1,2727 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "colored-filter", - "metadata": {}, - "source": [ - "# Development\n" - ] - }, - { - "cell_type": "markdown", - "id": "figured-cemetery", - "metadata": {}, - "source": [ - "## Basics and Commonalities\n", - "\n", - "Before stepping into fitting development patterns, its worth reviewing the basics\n", - "of Estimators. The main modeling API implemented by chainladder follows that of\n", - "the scikit-learn estimator. An estimator is any object that learns from data." - ] - }, - { - "cell_type": "markdown", - "id": "protecting-customer", - "metadata": {}, - "source": [ - "### Scikit-Learn API\n", - "\n", - "The scikit-learn API is a common modeling interface that is used to construct and\n", - "fit a countless variety of machine learning algorithms. The common interface\n", - "allows for very quick swapping between models with minimal code changes. The\n", - "``chainladder`` package has adopted the interface to promote a standardized approach\n", - "to fitting reserving models.\n", - "\n", - "All estimator objects can optionally be configured with parameters to uniquely\n", - "specify the model being built. This is done ahead of pushing any data through\n", - "the model.\n", - "\n", - "```python\n", - "estimator = Estimator(param1=1, param2=2)\n", - "```\n", - "All estimator objects expose a `fit` method that takes a `Triangle` as input, `X`:\n", - "\n", - "```python\n", - "estimator.fit(X=data)\n", - "```\n", - "\n", - "All estimators include a `sample_weight` option to the `fit` method to specify\n", - "an exposure basis. If an exposure base is not applicable, then this argument is\n", - "ignored.\n", - "\n", - "```python\n", - "estimator.fit(X=data, sample_weight=weight)\n", - "```\n", - "\n", - "All estimators either `transform` the input Triangle or `predict` an outcome." - ] - }, - { - "cell_type": "markdown", - "id": "compatible-secondary", - "metadata": {}, - "source": [ - "### Transformers\n", - "\n", - "All transformers include a ``transform`` method. The method is used to transform a\n", - "Triangle and it will always return a Triangle with added features based on the\n", - "specifics of the transformer.\n", - "\n", - "```python\n", - "transformed_data = estimator.transform(data)\n", - "```\n", - "\n", - "Other than final IBNR models, ``chainladder`` estimators are transformers.\n", - "That is, they return your `Triangle` back to you with additional properties.\n", - "\n", - "Transforming can be done at the time of fit.\n", - "\n", - "```python\n", - "# Fitting and Transforming\n", - "estimator.fit(data)\n", - "transformed_data = estimator.transform(data)\n", - "# One line equivalent\n", - "transformed_data = estimator.fit_transform(data)\n", - "assert isinstance(transformed_data, cl.Triangle)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "common-mileage", - "metadata": {}, - "source": [ - "### Predictors\n", - "\n", - "All predictors include a ``predict`` method.\n", - "\n", - "```python\n", - "prediction = estimator.predict(new_data)\n", - "```\n", - "\n", - "Predictors are intended to create new predictions. It is not uncommon to fit a\n", - "model on a more aggregate view, say national level, of data and predict on a\n", - "more granular triangle, state or provincial." - ] - }, - { - "cell_type": "markdown", - "id": "lyric-syracuse", - "metadata": {}, - "source": [ - "### Parameter Types\n", - "\n", - "Estimator parameters: All the parameters of an estimator can be set when it is\n", - "instantiated or by modifying the corresponding attribute. These parameters\n", - "define how you'd like to fit an estimator and are chosen before the fitting\n", - "process. These are often referred to as hyperparameters in the context of\n", - "Machine Learning, and throughout these documents. Most of the hyperparameters\n", - "of the ``chainladder`` package take on sensible defaults.\n", - "\n", - "```python\n", - "estimator = Estimator(param1=1, param2=2)\n", - "assert estimator.param1 == 1\n", - "```\n", - "\n", - "Estimated parameters: When data is fitted with an estimator, parameters are\n", - "estimated from the data at hand. All the estimated parameters are attributes\n", - "of the estimator object ending by an underscore. The use of the underscore is\n", - "a key API design style of scikit-learn that allows for the quicker recognition\n", - "of fitted parameters vs hyperparameters:\n", - "\n", - "```python\n", - "estimator.estimated_param_\n", - "```\n", - "\n", - "In many cases the estimated parameters are themselves Triangles and can be\n", - "manipulated using the same methods we learned about in the `Triangle` class.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "mysterious-translation", - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6788e883-acdc-4436-ae4a-fca113442feb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "chainladder.core.triangle.Triangle" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dev = cl.Development().fit(cl.load_sample('ukmotor'))\n", - "type(dev.cdf_)" - ] - }, - { - "cell_type": "markdown", - "id": "accessible-uganda", - "metadata": {}, - "source": [ - "### Commonalities\n", - "\n", - "All \"Development Estimators\" are transformers and reveal common a set of properties\n", - "when they are fit.\n", - "\n", - "1. ``ldf_`` represents the fitted age-to-age factors of the model.\n", - "2. ``cdf_`` represents the fitted age-to-ultimate factors of the model.\n", - "3. All \"Development estimators\" implement the ``transform`` method.\n", - "\n", - "\n", - "``cdf_`` is nothing more than the cumulative representation of the ``ldf_`` vectors.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "naughty-survivor", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dev = cl.Development().fit(cl.load_sample('raa'))\n", - "dev.ldf_.incr_to_cum() == dev.cdf_" - ] - }, - { - "cell_type": "markdown", - "id": "tamil-terminology", - "metadata": {}, - "source": [ - "(development:development)=\n", - "## Development\n", - "\n", - "`Development` allows for the selection of loss development patterns. Many\n", - "of the typical averaging techniques are available in this class: ``simple``,\n", - "``volume`` and ``regression`` through the origin. Additionally, `Development`\n", - "includes patterns to allow for fine-tuned exclusion of link-ratios from the LDF\n", - "calculation.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "timely-illinois", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Development(average='simple')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa = cl.load_sample('raa')\n", - "cl.Development(average='simple')" - ] - }, - { - "cell_type": "markdown", - "id": "cosmetic-correction", - "metadata": {}, - "source": [ - "Alternatively, you can provide a list to parameterize each development period\n", - "separately. When adjusting individual development periods the list must be\n", - "the same length as your triangles ``link_ratio`` development axis." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "female-function", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(raa.link_ratio.development)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "meaningful-aviation", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Development(average=['volume', 'simple', 'simple', 'simple', 'simple', 'simple',\n", - " 'simple', 'simple', 'simple'])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(average=['volume']+['simple']*8)" - ] - }, - { - "cell_type": "markdown", - "id": "indian-bench", - "metadata": {}, - "source": [ - "This approach works for ``average``, ``n_periods``, ``drop_high`` and ``drop_low``.\n", - "\n", - "Notice where you have not specified a parameter, a sensible default\n", - "is chosen for you." - ] - }, - { - "cell_type": "markdown", - "id": "cubic-xerox", - "metadata": {}, - "source": [ - "### Omitting link ratios\n", - "\n", - "There are several arguments for dropping individual cells from the triangle as\n", - "well as excluding whole valuation periods or highs and lows. Any combination\n", - "of the 'drop' arguments is permissible.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "naughty-writing", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Development(drop_high=[True, True, True, True, True, False, False, False,\n", - " False],\n", - " drop_low=[True, True, True, True, True, False, False, False, False])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(\n", - " drop_high=[True]*5+[False]*4, \n", - " drop_low=[True]*5+[False]*4).fit(raa)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "alive-cooperative", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Development(drop_valuation='1985')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(drop_valuation='1985').fit(raa)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "restricted-herald", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Development(drop=[('1985', 12), ('1987', 24)])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(drop=[('1985', 12), ('1987', 24)]).fit(raa)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "democratic-wheel", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Development(drop=('1985', 12), drop_valuation='1988')" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(drop=('1985', 12), drop_valuation='1988').fit(raa)" - ] - }, - { - "cell_type": "markdown", - "id": "final-alpha", - "metadata": {}, - "source": [ - "When using `drop`, the earliest age of the `link_ratio` should be referenced.\n", - "For example, use `12` to drop the `12-24` ratio.\n", - "\n", - "```{note}\n", - "`drop_high` and `drop_low` are ignored in cases where the number of link ratios available for a given development period is less than 1.\n" - ] - }, - { - "cell_type": "markdown", - "id": "electrical-shell", - "metadata": {}, - "source": [ - "### Extended Link Ratio Family\n", - "\n", - "The `Development` estimator is based on the regression framework known as the\n", - "Extended Link Ratio Family (ELRF). A nice property of this family is that we\n", - "not only get estimates for our patterns (`cdf_`, and `ldf_`), but also\n", - "measures of variability of our estimates (`sigma_`, `std_err_` and `std_residuals_`\n", - "). These variability properties are used to develop the stochastic features in the\n", - "`MackChainladder` method, but even for deterministic methods these variability\n", - "estimates can be used as a diagnostic tool to validate the appropriateness of using\n", - "multiplicative link ratios.\n", - "\n", - "The `std_residuals_` in particular is described by Barnett and Zehnwirth as a diagnostic\n", - "that points to the inferiority of the chainladder method relative to the probablistic\n", - "trend family." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "045dc473-dd73-4b57-b74d-de857c3bd256", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108
1981-0.5722-0.8317-0.7489-0.34420.87041.4143-0.0003-0.6819
19822.3075-0.71611.97161.59000.1982-0.94881.09190.7315
1983-0.1267-0.2299-0.4811-0.17800.9056-0.2967-0.8987
1984-0.4305-0.83650.3723-1.30740.0012-0.1064
19851.13980.09430.6175-0.0170-1.5437
19860.29360.4633-0.68090.7825
19870.59612.0935-0.5805
19880.47170.6607
1989-0.4282
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108\n", - "1981 -0.572151 -0.831650 -0.748883 -0.344181 0.870432 1.414340 -0.000279 -0.681860 NaN\n", - "1982 2.307508 -0.716139 1.971586 1.589984 0.198229 -0.948836 1.091934 0.731482 NaN\n", - "1983 -0.126737 -0.229861 -0.481096 -0.178044 0.905637 -0.296705 -0.898711 NaN NaN\n", - "1984 -0.430543 -0.836499 0.372285 -1.307360 0.001171 -0.106393 NaN NaN NaN\n", - "1985 1.139843 0.094282 0.617466 -0.017042 -1.543656 NaN NaN NaN NaN\n", - "1986 0.293597 0.463314 -0.680936 0.782504 NaN NaN NaN NaN NaN\n", - "1987 0.596140 2.093539 -0.580547 NaN NaN NaN NaN NaN NaN\n", - "1988 0.471658 0.660667 NaN NaN NaN NaN NaN NaN NaN\n", - "1989 -0.428176 NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa = cl.load_sample('raa')\n", - "model = cl.Development().fit(raa)\n", - "model.std_residuals_" - ] - }, - { - "cell_type": "markdown", - "id": "9865a16d-f69f-4bf6-bc19-3b362b82380b", - "metadata": {}, - "source": [ - " Replicating **Fig 2.6** from their paper can be accomplished with \n", - "a bit of manipulation of the residual triangles." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "c479daf1-9209-4d0e-984e-c9ee3ceca7f4", - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-04T18:56:56.318708\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,8))\n", - "model.std_residuals_.T.plot(\n", - " style='.', color='gray', legend=False, grid=True, ax=ax00,\n", - " xlabel='Development Month', ylabel='Weighted Standardized Residuals')\n", - "model.std_residuals_.iloc[..., :-1].mean('origin').T.plot(\n", - " color='red', legend=False, grid=True, ax=ax00)\n", - "model.std_residuals_.plot(\n", - " style='.', color='gray', legend=False, grid=True, ax=ax01, xlabel='Origin Period')\n", - "model.std_residuals_.mean('development').plot(\n", - " color='red', legend=False, grid=True, ax=ax01)\n", - "model.std_residuals_.dev_to_val().T.plot(\n", - " style='.', color='gray', legend=False, grid=True, ax=ax10,\n", - " xlabel='Valuation Date', ylabel='Weighted Standardized Residuals')\n", - "\n", - "model.std_residuals_.dev_to_val().mean('origin').T.plot(color='red', legend=False, grid=True, ax=ax10)\n", - "pd.concat((\n", - " (raa[raa.valuation\n", - " \n", - " \n", - " \n", - " 12-24\n", - " 24-36\n", - " 36-48\n", - " 48-60\n", - " 60-72\n", - " 72-84\n", - " 84-96\n", - " 96-108\n", - " 108-120\n", - " \n", - " \n", - " \n", - " \n", - " 1998\n", - " 1.7925\n", - " 1.2056\n", - " 1.0956\n", - " 1.0457\n", - " 1.0189\n", - " 1.0097\n", - " 1.0048\n", - " 1.0023\n", - " 1.0019\n", - " \n", - " \n", - " 1999\n", - " 1.7683\n", - " 1.1986\n", - " 1.0902\n", - " 1.0435\n", - " 1.0194\n", - " 1.0092\n", - " 1.0050\n", - " 1.0024\n", - " 1.0019\n", - " \n", - " \n", - " 2000\n", - " 1.7620\n", - " 1.1902\n", - " 1.0900\n", - " 1.0430\n", - " 1.0191\n", - " 1.0101\n", - " 1.0046\n", - " 1.0024\n", - " 1.0020\n", - " \n", - " \n", - " 2001\n", - " 1.7439\n", - " 1.1913\n", - " 1.0906\n", - " 1.0436\n", - " 1.0187\n", - " 1.0090\n", - " 1.0042\n", - " 1.0021\n", - " 1.0017\n", - " \n", - " \n", - " 2002\n", - " 1.7348\n", - " 1.1940\n", - " 1.0892\n", - " 1.0442\n", - " 1.0186\n", - " 1.0085\n", - " 1.0041\n", - " 1.0020\n", - " 1.0016\n", - " \n", - " \n", - " 2003\n", - " 1.7189\n", - " 1.1853\n", - " 1.0920\n", - " 1.0438\n", - " 1.0186\n", - " 1.0092\n", - " 1.0045\n", - " 1.0022\n", - " 1.0018\n", - " \n", - " \n", - " 2004\n", - " 1.7025\n", - " 1.1867\n", - " 1.0922\n", - " 1.0415\n", - " 1.0179\n", - " 1.0088\n", - " 1.0043\n", - " 1.0021\n", - " 1.0017\n", - " \n", - " \n", - " 2005\n", - " 1.7012\n", - " 1.1860\n", - " 1.0859\n", - " 1.0412\n", - " 1.0177\n", - " 1.0087\n", - " 1.0042\n", - " 1.0021\n", - " 1.0017\n", - " \n", - " \n", - " 2006\n", - " 1.7028\n", - " 1.1797\n", - " 1.0857\n", - " 1.0411\n", - " 1.0177\n", - " 1.0087\n", - " 1.0042\n", - " 1.0021\n", - " 1.0017\n", - " \n", - " \n", - " 2007\n", - " 1.6693\n", - " 1.1804\n", - " 1.0860\n", - " 1.0412\n", - " 1.0178\n", - " 1.0087\n", - " 1.0042\n", - " 1.0021\n", - " 1.0017\n", - " \n", - " \n", - "" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "1998 1.792469 1.205560 1.095603 1.045669 1.018931 1.009749 1.004777 1.002288 1.001866\n", - "1999 1.768268 1.198631 1.090150 1.043455 1.019377 1.009244 1.004998 1.002392 1.001904\n", - "2000 1.761959 1.190201 1.089958 1.042975 1.019054 1.010127 1.004585 1.002444 1.001969\n", - "2001 1.743900 1.191331 1.090565 1.043566 1.018677 1.009022 1.004171 1.002074 1.001672\n", - "2002 1.734765 1.194005 1.089153 1.044183 1.018551 1.008452 1.004106 1.002042 1.001646\n", - "2003 1.718935 1.185285 1.091988 1.043790 1.018635 1.009171 1.004452 1.002213 1.001784\n", - "2004 1.702514 1.186716 1.092167 1.041492 1.017863 1.008798 1.004272 1.002124 1.001712\n", - "2005 1.701237 1.186004 1.085897 1.041211 1.017746 1.008741 1.004245 1.002111 1.001701\n", - "2006 1.702795 1.179664 1.085678 1.041114 1.017706 1.008722 1.004236 1.002106 1.001698\n", - "2007 1.669287 1.180366 1.085961 1.041239 1.017758 1.008747 1.004248 1.002112 1.001702" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.ldf_['paid']" - ] - }, - { - "cell_type": "markdown", - "id": "recovered-english", - "metadata": {}, - "source": [ - "### Incremental patterns\n", - "\n", - "The incremental patterns of the `CaseOutstanding` method are avilable as\n", - "additional properties for review. They are the `paid_to_prior_case_` and the\n", - "`case_to_prior_case_`. These are useful to review when deciding on the appropriate\n", - "hyperparameters for `paid_n_periods` and `case_n_periods`. Once you are satisfied\n", - "with your hyperparameter tuning, you can see the fitted selections in the\n", - "`paid_ldf_` and `case_ldf_` incremental patterns." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "sized-adoption", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
24-3636-4848-6060-7272-8484-9696-108108-120120-132
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24-3636-4848-6060-7272-8484-9696-108108-120120-132
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" - ], - "text/plain": [ - " 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120 120-132\n", - "(All) 0.534019 0.56385 0.529593 0.490031 0.513853 0.55064 0.631726 0.673788 0.579812" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.case_ldf_" - ] - }, - { - "cell_type": "markdown", - "id": "crucial-jefferson", - "metadata": {}, - "source": [ - "{cite}`friedland2010`" - ] - }, - { - "cell_type": "markdown", - "id": "informed-suite", - "metadata": {}, - "source": [ - "(development:tweedieglm)=\n", - "## TweedieGLM\n", - "\n", - "The `TweedieGLM` implements the GLM reserving structure discussed by Taylor and McGuire.\n", - "A nice property of the GLM framework is that it is highly flexible in terms of including\n", - "covariates that may be predictive of loss reserves while maintaining a close relationship\n", - "to traditional methods. Additionally, the framework can be extended in a straightforward\n", - "way to incorporate various approaches to measuring prediction errors. Behind the\n", - "scenes, `TweedieGLM` is using scikit-learn's `TweedieRegressor` estimator.\n", - "\n", - "### Long Format\n", - "\n", - "GLMs are fit to triangles in \"Long Format\". That is, they are converted to pandas\n", - "DataFrames behind the scenes. Each axis of the `Triangle` is included in the\n", - "dataframe. The ``origin`` and ``development`` axes are in columns of the same name.\n", - "You can inspect what your `Triangle` looks like in long format by calling `to_frame`\n", - "with ``keepdims=True``\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "bulgarian-bookmark", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GRNAMELOBorigindevelopmentIncurLossCumPaidLossBulkLossEarnedPremDIREarnedPremCededEarnedPremNet
0Adriatic Ins Coothliab1995-01-01128.0NaN8.0139.0131.08.0
1Adriatic Ins Coothliab1995-01-012411.0NaN4.0139.0131.08.0
2Adriatic Ins Coothliab1995-01-01367.03.04.0139.0131.08.0
3Adriatic Ins Coothliab1996-01-011240.0NaN40.0410.0359.051.0
4Adriatic Ins Coothliab1996-01-012440.0NaN40.0410.0359.051.0
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" - ], - "text/plain": [ - " GRNAME LOB origin development IncurLoss CumPaidLoss \\\n", - "0 Adriatic Ins Co othliab 1995-01-01 12 8.0 NaN \n", - "1 Adriatic Ins Co othliab 1995-01-01 24 11.0 NaN \n", - "2 Adriatic Ins Co othliab 1995-01-01 36 7.0 3.0 \n", - "3 Adriatic Ins Co othliab 1996-01-01 12 40.0 NaN \n", - "4 Adriatic Ins Co othliab 1996-01-01 24 40.0 NaN \n", - "\n", - " BulkLoss EarnedPremDIR EarnedPremCeded EarnedPremNet \n", - "0 8.0 139.0 131.0 8.0 \n", - "1 4.0 139.0 131.0 8.0 \n", - "2 4.0 139.0 131.0 8.0 \n", - "3 40.0 410.0 359.0 51.0 \n", - "4 40.0 410.0 359.0 51.0 " - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.load_sample('clrd').to_frame(keepdims=True).reset_index().head()" - ] - }, - { - "cell_type": "markdown", - "id": "social-price", - "metadata": {}, - "source": [ - "```{warning}\n", - "'origin', 'development', and 'valuation' are reserved keywords for the dataframe. Declaring columns with these names separately will result in error.\n", - "```\n", - "\n", - "While you can inspect the `Triangle` in long format, you will not directly convert\n", - "to long format yourself. The `TweedieGLM` does this for you. Additionally,\n", - "the `origin` of the design matrix is restated in years from the earliest origin\n", - "period. That is, is if the earliest origin is '1995-01-01' then it gets replaced with\n", - "0. Consequently, '1996-04-01' would be replaced with 1.25. This is done because\n", - "datetimes have limited support in scikit-learn. Finally, the `TweedieGLM`\n", - "will automatically convert the response to an incremental basis.\n", - "\n", - "### R-style formulas\n", - "\n", - "We use the `patsy` library to allow formulation of the the feature set `X`\n", - "of the GLM. Because `X` is a parameter that used extensively throughout\n", - "`chainladder`, the `TweedieGLM` refers to it as the `design_matrix`. Those\n", - "familiar with the R programming language will be familiar with the notation\n", - "used by `patsy`. For example, we can include both `origin` and `development`\n", - "as terms in a model." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "broad-stephen", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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coef_
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" - ], - "text/plain": [ - " coef_\n", - "Intercept 13.516322\n", - "development -0.006251\n", - "origin 0.033863" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genins = cl.load_sample('genins')\n", - "glm = cl.TweedieGLM(design_matrix='development + origin').fit(genins)\n", - "glm.coef_" - ] - }, - { - "cell_type": "markdown", - "id": "heated-cardiff", - "metadata": {}, - "source": [ - "### ODP Chainladder\n", - "\n", - "Replicating the results of the volume weighted chainladder development patterns\n", - "can be done by fitting a Poisson-log GLM to incremental paids. To do this, we\n", - "can specify the `power` and `link` of the estimator as well as the `design_matrix`.\n", - "The volume-weighted chainladder method can be replicated by including both\n", - "`origin` and `development` as categorical features." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "signed-realtor", - "metadata": {}, - "outputs": [], - "source": [ - "dev = cl.TweedieGLM(\n", - " design_matrix='C(development) + C(origin)',\n", - " power=1, link='log').fit(genins)" - ] - }, - { - "cell_type": "markdown", - "id": "mounted-judge", - "metadata": {}, - "source": [ - "A trivial comparison against the traditional `Development` estimator shows\n", - "a comparable set of `ldf_` patterns.\n", - "\n", - "\n", - "### Parsimonious modeling\n", - "\n", - "Having full access to all axes of the `Triangle` along with the powerful formulation\n", - "of `patsy` allows for substantial customization of the model fit. For example,\n", - "we can include 'LOB' interactions with piecewise linear coefficients to reduce\n", - "model complexity.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "improved-corps", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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coef_
Intercept12.549945
LOB[T.ppauto]3.202703
LOB[comauto]:C(np.minimum(development, 36))[T.24]0.578694
LOB[ppauto]:C(np.minimum(development, 36))[T.24]0.449832
LOB[comauto]:C(np.minimum(development, 36))[T.36]0.790516
LOB[ppauto]:C(np.minimum(development, 36))[T.36]0.321206
LOB[comauto]:development-0.044627
LOB[ppauto]:development-0.054814
LOB[comauto]:origin0.054581
LOB[ppauto]:origin0.057790
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" - ], - "text/plain": [ - " coef_\n", - "Intercept 12.549945\n", - "LOB[T.ppauto] 3.202703\n", - "LOB[comauto]:C(np.minimum(development, 36))[T.24] 0.578694\n", - "LOB[ppauto]:C(np.minimum(development, 36))[T.24] 0.449832\n", - "LOB[comauto]:C(np.minimum(development, 36))[T.36] 0.790516\n", - "LOB[ppauto]:C(np.minimum(development, 36))[T.36] 0.321206\n", - "LOB[comauto]:development -0.044627\n", - "LOB[ppauto]:development -0.054814\n", - "LOB[comauto]:origin 0.054581\n", - "LOB[ppauto]:origin 0.057790" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample('clrd')['CumPaidLoss'].groupby('LOB').sum()\n", - "clrd=clrd[clrd['LOB'].isin(['ppauto', 'comauto'])]\n", - "dev = cl.TweedieGLM(\n", - " design_matrix='LOB+LOB:C(np.minimum(development, 36))+LOB:development+LOB:origin',\n", - " max_iter=1000).fit(clrd)\n", - "dev.coef_" - ] - }, - { - "cell_type": "markdown", - "id": "republican-candle", - "metadata": {}, - "source": [ - "This model is limited to 10 coefficients across two lines of business. The basic\n", - "chainladder model is known to be overparameterized with at least 18 parameters\n", - "requiring estimation. Despite drastically simplifying the model, the `cdf_`\n", - "patterns of the GLM are within 1% of the traditional chainladder for every lag\n", - "and for both lines of business:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "secure-committee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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development12-Ult24-Ult36-Ult48-Ult60-Ult72-Ult84-Ult96-Ult108-Ult
LOB
comauto0.0020.003-0.010.0030.0110.0080.005-0.000-0.002
ppauto0.0060.003-0.000.0010.0020.0010.0010.0010.001
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" - ], - "text/plain": [ - "development 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult \\\n", - "LOB \n", - "comauto 0.002 0.003 -0.01 0.003 0.011 0.008 0.005 -0.000 \n", - "ppauto 0.006 0.003 -0.00 0.001 0.002 0.001 0.001 0.001 \n", - "\n", - "development 108-Ult \n", - "LOB \n", - "comauto -0.002 \n", - "ppauto 0.001 " - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "((dev.cdf_.iloc[..., 0, :] / \n", - " cl.Development().fit(clrd).cdf_) - 1\n", - ").to_frame().round(3)" - ] - }, - { - "cell_type": "markdown", - "id": "according-headset", - "metadata": {}, - "source": [ - "Like every other Development estimator, the `TweedieGLM` produces a set of `ldf_`\n", - "patterns and can be used in a larger workflow with tail extrapolation and reserve\n", - "estimation.\n", - "\n", - "\n", - ":::{panels}\n", - ":column: col-lg-4 px-2 py-2\n", - "\n", - "---\n", - "**[TweedieGLM Basics](plot_glm_ldf)**\n", - "```{glue:} plot_glm_ldf\n", - "```\n", - "+++\n", - "{bdg-success}`easy`\n", - "\n", - ":::\n", - "{cite}`taylor2016`" - ] - }, - { - "cell_type": "markdown", - "id": "killing-constitution", - "metadata": {}, - "source": [ - "(development:developmentml)=\n", - "## DevelopmentML\n", - "\n", - "`DevelopmentML` is a general development estimator that works as an interface to\n", - "scikit-learn compliant machine learning (ML) estimators. The `TweedieGLM` is\n", - "a special case of `DevelopmentML` with the ML algorithm limited to scikit-learn's\n", - "`TweedieRegressor` estimator.\n", - "\n", - "### The Interface\n", - "\n", - "ML algorithms are designed to be fit against tabular data like a pandas DataFrame\n", - "or a 2D numpy array. A `Triangle` does not meet the definition and so `DevelopmentML`\n", - "is provided to incorporate ML into a broader reserving workflow. This includes:\n", - "\n", - " 1. Automatic conversion of Triangle to a dataframe for fitting\n", - " 2. Flexibility in expressing any preprocessing as part of a scikit-learn `Pipeline`\n", - " 3. Predictions through the terminal development age of a `Triangle` to fill in the\n", - " lower half\n", - " 4. Predictions converted to `ldf_` patterns so that the results of the estimator\n", - " are compliant with the rest of `chainladder`, like tail selection and IBNR modeling.\n", - "\n", - "### Features\n", - "\n", - "Data from any axis of a `Triangle` is available to be used in the `DevelopmentML`\n", - "estimator. For example, we can use many of the scikit-learn components to\n", - "generate development patterns from both the time axes as well as the `index` of\n", - "the `Triangle`." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "middle-machinery", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2261-12
Grain:OYDY
Shape:(6, 1, 10, 9)
Index:[LOB]
Columns:[CumPaidLoss]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2261-12\n", - "Grain: OYDY\n", - "Shape: (6, 1, 10, 9)\n", - "Index: [LOB]\n", - "Columns: [CumPaidLoss]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.ensemble import RandomForestRegressor\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.compose import ColumnTransformer\n", - "\n", - "clrd = cl.load_sample('clrd').groupby('LOB').sum()['CumPaidLoss']\n", - "\n", - "# Decide how to preprocess the X (ML) dataset using sklearn\n", - "design_matrix = ColumnTransformer(transformers=[\n", - " ('dummy', OneHotEncoder(drop='first'), ['LOB', 'development']),\n", - " ('passthrough', 'passthrough', ['origin'])\n", - "])\n", - "\n", - "# Wrap preprocessing and model in a larger sklearn Pipeline\n", - "estimator_ml = Pipeline(steps=[\n", - " ('design_matrix', design_matrix),\n", - " ('model', RandomForestRegressor())\n", - "])\n", - "\n", - "# Fitting DevelopmentML fits the underlying ML model and gives access to ldf_\n", - "cl.DevelopmentML(estimator_ml=estimator_ml, y_ml='CumPaidLoss').fit(clrd).ldf_" - ] - }, - { - "cell_type": "markdown", - "id": "atlantic-venice", - "metadata": {}, - "source": [ - "### Autoregressive\n", - "\n", - "The time-series nature of loss development naturally lends to an urge for autoregressive\n", - "features. That is, features that are based on predictions, albeit on a lagged basis.\n", - "`DevelopmentML` includes an `autoregressive` parameter that can be used to\n", - "express the response as a lagged feature as well.\n", - "\n", - "```{note}\n", - "When using `autoregressive` features, you must also declare it as a column\n", - "in your `estimator_ml` Pipeline.\n", - "```\n", - "\n", - "### PatsyFormula\n", - "\n", - "While the sklearn preprocessing API is powerful, it can be tedious work with in\n", - "some instances. In particular, modeling complex interactions is much easier to do\n", - "with Patsy. The `chainladder` package includes a custom sklearn estimator\n", - "to gain access to the patsy API. This is done through the `PatsyFormula` estimator." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "civil-state", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
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" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 2.65 1.41 1.19 1.1 1.04 1.02 1.01 1.01 1.01" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "estimator_ml = Pipeline(steps=[\n", - " ('design_matrix', cl.PatsyFormula('LOB:C(origin)+LOB:C(development)+development')),\n", - " ('model', RandomForestRegressor())\n", - "])\n", - "cl.DevelopmentML(\n", - " estimator_ml=estimator_ml, \n", - " y_ml='CumPaidLoss').fit(clrd).ldf_.iloc[0, 0, 0].round(2)" - ] - }, - { - "cell_type": "markdown", - "id": "prime-teacher", - "metadata": {}, - "source": [ - "```{note}\n", - "`PatsyFormula` is not an estimator designed to work with triangles. It is an sklearn transformer designed to work with pandas DataFrames allowing it to work directly in an sklearn Pipeline.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "weird-stone", - "metadata": {}, - "source": [ - "(development:barnettzehnwirth)=\n", - "## BarnettZehnwirth\n", - "\n", - "The `BarnettZehnwirth` estimator solves for development patterns using the\n", - "Probabilistic Trend Family (PTF) regression framework. Unlike the ELRF framework,\n", - "which assumes no ``valuation`` covariate, the PTF framework allows for this.\n", - "\n", - "```{eval-rst}\n", - ".. figure:: images/prob_trend_family.PNG\n", - " :align: center\n", - " :scale: 40%\n", - "```\n", - "\n", - "Structurally, the PTF regression is different from the `ELRF` (ELRF) regression framework in\n", - "two distinct ways:\n", - "\n", - " 1. Where the ELRF fits independent regressions to each adjacent development lag, the PTF\n", - " regression is fit to the entire triangle\n", - " 2. Where the ELRF is fit to cumulative amounts, the PTF is fit to the log of\n", - " the incremental amounts of the `Triangle`.\n", - "\n", - "### Formulation\n", - "\n", - "The PTF framework is an ordinary least squares (OLS) model with the response, `y`\n", - "being the log of the incremental amounts of a Triangle. These are assumed to be\n", - "normally distributed which implies the incrementals themselves are log-normal\n", - "distributed.\n", - "\n", - "The framework includes coefficients for origin periods (alpha), development periods (gamma)\n", - "and calendar period (iota).\n", - "\n", - "\n", - "$y(i, j) = \\alpha _{i} + \\sum_{k=1}^{j}\\gamma _{k}+ \\sum_{\\iota =1}^{i+j}\\gamma _{\\iota}+ \\varepsilon _{i,j}$\n", - "\n", - "These coefficients can be categorical or continuous, and to support a wide range of\n", - "model forms, patsy formulas are used.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "innovative-attachment", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Intercept C(origin)[T.1.0] C(origin)[T.2.0] C(origin)[T.3.0] \\\n", - "coef_ 11.836863 0.178824 0.345112 0.378133 \n", - "\n", - " C(origin)[T.4.0] C(origin)[T.5.0] C(origin)[T.6.0] C(origin)[T.7.0] \\\n", - "coef_ 0.405093 0.427041 0.431076 0.660383 \n", - "\n", - " C(origin)[T.8.0] C(origin)[T.9.0] ... C(development)[T.24] \\\n", - "coef_ 0.963223 1.1568 ... 0.251091 \n", - "\n", - " C(development)[T.36] C(development)[T.48] C(development)[T.60] \\\n", - "coef_ -0.055824 -0.448589 -0.828917 \n", - "\n", - " C(development)[T.72] C(development)[T.84] C(development)[T.96] \\\n", - "coef_ -1.16913 -1.507561 -1.798345 \n", - "\n", - " C(development)[T.108] C(development)[T.120] C(development)[T.132] \n", - "coef_ -2.0231 -2.238333 -2.427672 \n", - "\n", - "[1 rows x 21 columns]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "abc = cl.load_sample('abc')\n", - "\n", - "# Discrete origin, development, valuation\n", - "cl.BarnettZehnwirth(formula='C(origin)+C(development)').fit(abc).coef_.T" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "nominated-zimbabwe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Interceptorigindevelopmentvaluation
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" - ], - "text/plain": [ - " Intercept origin development valuation\n", - "coef_ 8.359157 4.215981 0.319288 -4.116569" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Linear coefficients for origin, development, and valuation\n", - "cl.BarnettZehnwirth(formula='origin+development+valuation').fit(abc).coef_.T" - ] - }, - { - "cell_type": "markdown", - "id": "pending-breakdown", - "metadata": {}, - "source": [ - "The PTF framework is particularly useful when there is calendar period inflation\n", - "influences on loss development.\n", - "\n", - ":::{panels}\n", - ":column: col-lg-4 px-2 py-2\n", - "\n", - "---\n", - "**[PTF Residuals](plot_ptf_resid)**\n", - "```{glue:} plot_ptf_resid\n", - "```\n", - "+++\n", - "{bdg-warning}`medium`\n", - "\n", - ":::\n", - "{cite}`barnett2000`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "blank-enemy", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.7.11 ('cl_dev')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - }, - "toc-showtags": true, - "vscode": { - "interpreter": { - "hash": "cc8120b3d9d10be7e89b07ff6d4d09ab1619cf80570f11e1a80e248c6b69ac77" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples_toc.md b/docs/examples_toc.md deleted file mode 100644 index e03253af..00000000 --- a/docs/examples_toc.md +++ /dev/null @@ -1,380 +0,0 @@ -```{eval-rst} -:parenttoc: True - -.. title:: Gallery: contents - -=================== -Example Gallery -=================== - - -.. toctree:: - :hidden: - - plot_advanced_triangle - plot_ave_analysis - plot_benktander - plot_berqsherm_case - plot_bf_apriori_from_cl - plot_bondy_sensitivity - plot_bootstrap - plot_bootstrap_comparison - plot_callable_dev_constant - plot_capecod - plot_capecod_onlevel - plot_clarkldf - plot_clarkldf_resid - plot_development_periods - plot_elrf_resid - plot_exponential_smoothing - plot_exposure_triangle - plot_extrap_period - plot_glm_ldf - plot_ibnr_runoff - plot_industry_to_company - plot_mack - plot_munich - plot_munich_resid - plot_ptf_resid - plot_stochastic_bornferg - plot_tailcurve_compare - plot_triangle_from_pandas - plot_triangle_slicing - plot_value_at_risk - plot_voting_chainladder - -``` - -You'll find a collection of gallery examples here. These notebooks syntesize many of the concepts learned in the tutorials or the user guide. - -Each of these notebooks has a corresponding badge that indicates its the level of difficulty. However, they are not ordered in any way. - -* {bdg-success}`easy` examples cover the basics of the library. -* {bdg-warning}`medium` examples cover the more advanced features of the library. -* {bdg-danger}`hard` examples have more complex workflows that are not recommended for beginners. - - - -::::{grid} -:gutter: 2 - -:::{grid-item-card} -:columns: 4 -**[BornhutterFerguson vs Chainladder](plot_benktander)** -```{glue:} plot_benktander -``` -+++ -{bdg-warning}`medium` - -::: - -:::{grid-item-card} -:columns: 4 -**[Advanced Triangle Manipulation](plot_advanced_triangle)** -```{glue:} plot_advanced_triangle -``` -+++ -{bdg-danger}`hard` - -::: - -:::{grid-item-card} -:columns: 4 -**[Actual Vs Expected](plot_ave_analysis)** -```{glue:} plot_ave_analysis -``` -+++ -{bdg-success}`easy` - -::: - -:::{grid-item-card} -:columns: 4 -**[BerquistSherman Adjustment](plot_berqsherm_case)** -```{glue:} plot_berqsherm_case -``` -+++ -{bdg-success}`easy` - -::: - -:::{grid-item-card} -:columns: 4 -**[Selecting BornheutterFerguson Apriori](plot_bf_apriori_from_cl)** -```{glue:} plot_bf_apriori_from_cl -``` -+++ -{bdg-success}`easy` - -::: - -:::{grid-item-card} -:columns: 4 -**[Bondy Tail Sensitivity](plot_bondy_sensitivity)** -```{glue:} plot_bondy_sensitivity -``` -+++ -{bdg-warning}`medium` - -::: - -:::{grid-item-card} -:columns: 4 -**[BootstrapODPSample Variability](plot_bootstrap_comparison)** -```{glue:} plot_bootstrap_comparison -``` -+++ -{bdg-warning}`medium` - -::: - -:::{grid-item-card} -:columns: 4 -**[Basic BootstrapODPSample](plot_bootstrap)** -```{glue:} plot_bootstrap -``` -+++ -{bdg-success}`easy` - - -::: - -:::{grid-item-card} -:columns: 4 -**[DevelopmentConstant Callable](plot_callable_dev_constant)** -```{glue:} plot_callable_dev_constant -``` -+++ -{bdg-danger}`hard` - - -::: - -:::{grid-item-card} -:columns: 4 -**[CapeCod Onleveling](plot_capecod_onlevel)** -```{glue:} plot_capecod_onlevel -``` -+++ -{bdg-warning}`medium` - -::: - -:::{grid-item-card} -:columns: 4 -**[CapeCod Sensitivity](plot_capecod)** -```{glue:} plot_capecod -``` -+++ -{bdg-danger}`hard` - - -::: - -:::{grid-item-card} -:columns: 4 -**[ClarkLDF Residuals](plot_clarkldf_resid)** -```{glue:} plot_clarkldf_resid -``` -+++ -{bdg-warning}`medium` - - -::: - -:::{grid-item-card} -:columns: 4 -**[Clark Growth Curves](plot_clarkldf)** -```{glue:} plot_clarkldf -``` -+++ -{bdg-success}`easy` - - -::: - -:::{grid-item-card} -:columns: 4 -**[Tuning Development Patterns](plot_development_periods)** -```{glue:} plot_development_periods -``` -+++ -{bdg-warning}`medium` - - -::: - -:::{grid-item-card} -:columns: 4 -**[ELRF Residuals](plot_elrf_resid)** -```{glue:} plot_elrf_resid -``` -+++ -{bdg-success}`easy` - - -::: - -:::{grid-item-card} -:columns: 4 -**[Attachment Age Smoothing](plot_exponential_smoothing)** -```{glue:} plot_exponential_smoothing -``` -+++ -{bdg-success}`easy` - - -::: - -:::{grid-item-card} -:columns: 4 -**[Exposure Vector](plot_exposure_triangle)** -```{glue:} plot_exposure_triangle -``` -+++ -{bdg-success}`easy` - -::: - -:::{grid-item-card} -:columns: 4 -**[TailCurve Extrapolation](plot_extrap_period)** -```{glue:} plot_extrap_period -``` -+++ -{bdg-warning}`medium` - -::: - -:::{grid-item-card} -:columns: 4 -**[TweedieGLM Basics](plot_glm_ldf)** -```{glue:} plot_glm_ldf -``` -+++ -{bdg-success}`easy` - -::: - -:::{grid-item-card} -:columns: 4 -**[IBNR Runoff](plot_ibnr_runoff)** -```{glue:} plot_ibnr_runoff -``` -+++ -{bdg-success}`easy` - - -::: - -:::{grid-item-card} -:columns: 4 -**[Making Predictions](plot_industry_to_company)** -```{glue:} plot_industry_to_company -``` -+++ -{bdg-success}`easy` - -::: - -:::{grid-item-card} -:columns: 4 -**[MackChainladder Basics](plot_mack)** -```{glue:} plot_mack -``` -+++ -{bdg-success}`easy` - -::: - -:::{grid-item-card} -:columns: 4 -**[MunichAdjustment Residuals](plot_munich_resid)** -```{glue:} plot_munich_resid -``` -+++ -{bdg-warning}`medium` - - -::: - -:::{grid-item-card} -:columns: 4 -**[MunichAdjustment Basics](plot_munich)** -```{glue:} plot_munich -``` -+++ -{bdg-success}`easy` - -::: - -:::{grid-item-card} -:columns: 4 -**[PTF Residuals](plot_ptf_resid)** -```{glue:} plot_ptf_resid -``` -+++ -{bdg-warning}`medium` - -::: - -:::{grid-item-card} -:columns: 4 -**[Stochastic BornhuetterFerguson](plot_stochastic_bornferg)** -```{glue:} plot_stochastic_bornferg -``` -+++ -{bdg-warning}`medium` - -::: - -:::{grid-item-card} -:columns: 4 -**[TailCurve Basics](plot_tailcurve_compare)** -```{glue:} plot_tailcurve_compare -``` -+++ -{bdg-success}`easy` - -::: - -:::{grid-item-card} -:columns: 4 -**[Triangle Creation Basics](plot_triangle_from_pandas)** -```{glue:} plot_triangle_from_pandas -``` -+++ -{bdg-success}`easy` - -::: - -:::{grid-item-card} -:columns: 4 -**[Triangle Slicing](plot_triangle_slicing)** -```{glue:} plot_triangle_slicing -``` -+++ -{bdg-success}`easy` - -::: - -:::{grid-item-card} -:columns: 4 -**[Value At Risk](plot_value_at_risk)** -```{glue:} plot_value_at_risk -``` -+++ -{bdg-warning}`medium` - -::: - -:::{grid-item-card} -:columns: 4 -**[VotingChainladder Basics](plot_voting_chainladder)** -```{glue:} plot_voting_chainladder -``` -+++ -{bdg-warning}`medium` - -::: -:::: \ No newline at end of file diff --git a/docs/guides_intro.md b/docs/guides_intro.md deleted file mode 100644 index 1393c86f..00000000 --- a/docs/guides_intro.md +++ /dev/null @@ -1,110 +0,0 @@ -# User Guide - -Here are a few examples that we have came up with, hopefully the code is simple enough so you can fit your own needs. - -`````{grid} -:gutter: 3 - -````{grid-item-card} -:columns: 6 - -**[Triangles](triangle)** -^^^ - -Data object to manage and manipulate reserving data - -**Application**: Extend pandas syntax to manipulate reserving triangles - -```{glue:} plot_triangle_from_pandas -``` -+++ -**Classes**: **[Triangle](triangle)**, ... -```` - -````{grid-item-card} -:columns: 6 - -**[Development](development)** -^^^ -Tooling to generate loss development patterns - -**Applications**: Comprehensive library for development - -```{glue:} plot_clarkldf -``` -+++ - -**Algorithms**: [Development](development:development), [ClarkLDF](development:clarkldf), … - -```` - -````{grid-item-card} -:columns: 6 - -**[Tail Estimation](tails)** -^^^ -Extrapolate development patterns beyond the known data. - -**Applications**: Long-tailed lines of business use cases - -```{glue:} plot_exponential_smoothing -``` - -+++ -**Algorithms**: [TailCurve](tails:tailcurve), [TailConstant](tails:tailconstant), … -```` - -````{grid-item-card} -:columns: 6 - -**[IBNR Models](methods)** -^^^ - -Generate IBNR estimates and associated statistics - - -**Applications**: Constructing reserve estimates - -```{glue:} plot_mack -``` - -+++ -**Algorithms**: [Chainladder](methods:chainladder), [CapeCod](methods:capecod), … -```` - -````{grid-item-card} -:columns: 6 - -**[Adjustments](adjustments)** -^^^ -Common actuarial data adjustments - - - -**Applications**: Simulation, trending, on-leveling - -```{glue:} plot_stochastic_bornferg -``` - -+++ -**Classes**: [BootstrapODPSample](adjustments:bootstrapodpsample), [Trend](adjustments:trend), … -```` - -````{grid-item-card} -:columns: 6 - -**[Workflow](workflow)** -^^^ - -Workflow tools for complex analyses - -**Application**: Scenario testing, ensembling - -```{glue:} plot_voting_chainladder -``` - -+++ -**Utilities**: [Pipeline](workflow:pipeline), [VotingChainladder](workflow:votingchainladder), … -```` - -````` diff --git a/docs/intro.md b/docs/intro.md deleted file mode 100644 index a0abbadf..00000000 --- a/docs/intro.md +++ /dev/null @@ -1,39 +0,0 @@ -# WELCOME! - -Welcome! - -The `chainladder-python` package was built to be able to handle all of your actuarial needs in python. It consists of popular actuarial tools, such as **triangle data manipulation**, **link ratios calculation**, and **IBNR estimates** using both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications. - -This package strives to be minimalistic in needing its own API. The syntax mimics popular packages such as [pandas](https://pandas.pydata.org/) for data manipulation and [scikit-learn](https://scikit-learn.org/) for model construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work. - -Want to see examples of what you can do with the package? At the bottom of this page, you will find selected examples of what we have built using `chainladder-python`, for more examples, visit the **Example Gallery** page. - -This package is built by a group of volunteers, and we need YOUR help! If you see an error on this site, please click on the GitHub Icon at the top, and submit a ticket so we make the necessary correction. - -Don't know where to start? Head over to the **START HERE** section to start exploring. In fact, you can try out the package without having to install anything, go to **Try Chainladder-Python Online**. - - -::::{grid} -:gutter: 2 - -:::{grid-item-card} -:columns: 4 -**[Actual Vs Expected](plot_ave_analysis)** -```{glue:} plot_ave_analysis -``` -::: - -:::{grid-item-card} -:columns: 4 -**[IBNR Runoff](plot_ibnr_runoff)** -```{glue:} plot_ibnr_runoff -``` -::: - -:::{grid-item-card} -:columns: 4 -**[MackChainladder](plot_mack)** -```{glue:} plot_mack -``` -::: -:::: diff --git a/docs/library/install.md b/docs/library/install.md deleted file mode 100644 index e47874f7..00000000 --- a/docs/library/install.md +++ /dev/null @@ -1,65 +0,0 @@ -(installation-instructions)= -# Installation - - -## General Installation - - -There are two ways to install the chainladder package, using `pip` or `conda`: - -````{tab-set} -```{tab-item} pip - -[![](https://badge.fury.io/py/chainladder.svg)](https://anaconda.org/conda-forge/chainladder) -[![](https://pepy.tech/badge/chainladder)](https://pepy.tech/project/chainladder) - - -Installing `chainladder` using `pip`: - -`pip install chainladder` - -Alternatively, if you have git and want to enjoy unreleased features, you can -install directly from `Github`: - -`pip install git+https://github.com/casact/chainladder-python/` -``` - -```{tab-item} conda - -[![](https://img.shields.io/conda/vn/conda-forge/chainladder.svg)](https://anaconda.org/conda-forge/chainladder) -[![](https://img.shields.io/conda/dn/conda-forge/chainladder.svg)](https://anaconda.org/conda-forge/chainladder) - - -Installing `chainladder` using `conda`: - -`conda install -c conda-forge chainladder` -``` -```` - -## Developer Installation - - -If you're interested in contributing, please refer to [Contributing](contributing) -for information on the developer environment. - - - -## Keeping Packages Updated - - -Depends on how you first install the package, to update the package through `pip` and `conda`: - -````{tab-set} -```{tab-item} pip - - If you want to use ``pip``, the code is a bit messy, as there isn't a built-in flag yet. - - ``pip list --outdated --format=freeze | grep -v '^\-e' | cut -d = -f 1 | xargs -n1 pip install -U`` -``` -```{tab-item} conda - - Using ``conda`` is simple: - - ``conda update --all`` -``` -```` \ No newline at end of file diff --git a/docs/library/releases.md b/docs/library/releases.md index f521c2dd..62694a26 100644 --- a/docs/library/releases.md +++ b/docs/library/releases.md @@ -1,7 +1,42 @@ -# Releases & Changelog +# {octicon}`megaphone` Releases & Changelog ## Version 0.8 +### Version 0.8.15 + +Release Date: Apr 11, 2023 + +**What's Changed** +- revamping drop functions by @henrydingliu in [\#385](https://github.com/casact/chainladder-python/issues/385) +- Fixing link_ratio by @henrydingliu in [\#387](https://github.com/casact/chainladder-python/issues/387) +- adding some tests for the revamped drop function by @henrydingliu in [\#388](https://github.com/casact/chainladder-python/issues/388) +- Adding test wth CL and BF by @henrydingliu in [\#391](https://github.com/casact/chainladder-python/issues/391) +- Henrydingliu enhancement 1 2022 11 29 by @henrydingliu in [\#392](https://github.com/casact/chainladder-python/issues/392) +- w_v2_ and pipeline in Development by @henrydingliu in [\#394](https://github.com/casact/chainladder-python/issues/394) +- next step for #376 by @henrydingliu in [\#397](https://github.com/casact/chainladder-python/issues/397) +- consolidating weighted regression factor fit by @henrydingliu in [\#398](https://github.com/casact/chainladder-python/issues/398) +- cum_zeta_ by @henrydingliu in [\#401](https://github.com/casact/chainladder-python/issues/401) +- Expanded mappings to include semester sampling by @A108669 in [\#403](https://github.com/casact/chainladder-python/issues/403) +- fix one more ref to semiannual by @wleescor in [\#410](https://github.com/casact/chainladder-python/issues/410) +- TailCurve argument validation by @genedan in [\#414](https://github.com/casact/chainladder-python/issues/414) +- chore(version): specify version of numba by @pafechet in [\#418](https://github.com/casact/chainladder-python/issues/418) +- Add Friedland Sample - Auto BI Insurer (Expected Loss Method) by @genedan in [\#419](https://github.com/casact/chainladder-python/issues/419) +- Cas webinar by @kennethshsu in [\#424](https://github.com/casact/chainladder-python/issues/424) +- [BUG] Argument validation needed for TailCurve [\#413](https://github.com/casact/chainladder-python/issues/413) +- [BUG] origin_as_datetime does nothing [\#407](https://github.com/casact/chainladder-python/issues/407) +- [BUG] CapeCod predict at different index grain [\#400](https://github.com/casact/chainladder-python/issues/400) +- [BUG] Chainladder doesn't coerce valuation triangle correctly [\#399](https://github.com/casact/chainladder-python/issues/399) + +**New Contributors** +[@A108669](https://github.com/A108669) made their first contribution in [\#403](https://github.com/casact/chainladder-python/issues/403) +[@pafechet](https://github.com/pafechet) made their first contribution in [\#418](https://github.com/casact/chainladder-python/issues/418) + +### Version 0.8.14 + +Release Date: Nov 25, 2022 + + + ### Version 0.8.13 Release Date: Jun 27, 2022 diff --git a/docs/methods.ipynb b/docs/methods.ipynb deleted file mode 100644 index 6e8fefe6..00000000 --- a/docs/methods.ipynb +++ /dev/null @@ -1,1140 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "terminal-failure", - "metadata": {}, - "source": [ - "# IBNR Models\n", - "\n", - "The IBNR Estimators are the final stage in analyzing reserve estimates in the\n", - "`chainladder` package. These Estimators have a `predict` method as opposed\n", - "to a `transform` method.\n", - "\n", - "\n", - "## Basics and Commonalities\n", - "\n", - "### Ultimates\n", - "\n", - "All reserving methods determine some ultimate cost of insurance claims. These\n", - "ultimates are captured in the `ultimate_` property of the estimator." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "reverse-provision", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
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" - ], - "text/plain": [ - " 2261\n", - "1981 18834.000000\n", - "1982 16857.953917\n", - "1983 24083.370924\n", - "1984 28703.142163\n", - "1985 28926.736343\n", - "1986 19501.103184\n", - "1987 17749.302590\n", - "1988 24019.192510\n", - "1989 16044.984101\n", - "1990 18402.442529" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chainladder as cl\n", - "import pandas as pd\n", - "\n", - "cl.Chainladder().fit(cl.load_sample('raa')).ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "forced-mercury", - "metadata": {}, - "source": [ - "Ultimates are measured at a valuation date way into the future. The library is\n", - "extraordinarily conservative in picking this date, and sets it to December 31, 2261.\n", - "This is set globally and can be viewed by referencing the ``ULT_VAL``\n", - "constant." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "direct-parcel", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2261-12-31 23:59:59.999999999'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.options.get_option('ULT_VAL')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "egyptian-milwaukee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2050-12-31 23:59:59.999999999'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.options.set_option('ULT_VAL', '2050-12-31 23:59:59.999999999')\n", - "cl.options.get_option('ULT_VAL')" - ] - }, - { - "cell_type": "markdown", - "id": "retired-january", - "metadata": {}, - "source": [ - "The `ultimate_` along with most of the other properties of IBNR models are triangles\n", - "and can be manipulated. However, it is important to note that the model itself\n", - "is not a Triangle, it is an scikit-learn style Estimator. This distinction is\n", - "important when wanting to manipulate model attributes." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "scientific-farming", - "metadata": {}, - "outputs": [], - "source": [ - "triangle = cl.load_sample('quarterly')\n", - "model = cl.Chainladder().fit(triangle)\n", - "# This works since we're slicing the ultimate Triangle\n", - "ult = model.ultimate_['paid']" - ] - }, - { - "cell_type": "markdown", - "id": "selected-think", - "metadata": {}, - "source": [ - "This throws an error since the model itself is not sliceable:\n", - "```python \n", - "ult = model['paid'].ultimate_\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "measured-words", - "metadata": {}, - "source": [ - "### IBNR\n", - "\n", - "Any difference between an `ultimate_` and the `latest_diagonal` of a Triangle\n", - "is contained in the `ibnr_` property of an estimator. While technically, as in\n", - "the example of a paid triangle, there can be case reserves included in the `ibnr_`\n", - "estimate, the distinction is not made by the `chainladder` package and must be\n", - "managed by you." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "incorporated-round", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2431.2695585474003" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle = cl.load_sample('quarterly')\n", - "model = cl.Chainladder().fit(triangle)\n", - "\n", - "# Determine outstanding case reserves\n", - "case_reserves = (triangle['incurred']-triangle['paid']).latest_diagonal\n", - "\n", - "# Net case reserves off of paid IBNR\n", - "true_ibnr = model.ibnr_['paid'] - case_reserves\n", - "true_ibnr.sum()" - ] - }, - { - "cell_type": "markdown", - "id": "noble-moore", - "metadata": {}, - "source": [ - "### Complete Triangles\n", - "\n", - "The `full_triangle_` and `full_expectation_` attributes give a view of the\n", - "completed `Triangle`. While the `full_expectation_` is entirely based on\n", - "`ultimate_` values and development patterns, the `full_triangle_` is a\n", - "blend of the existing triangle. These are useful for conducting an analysis\n", - "of actual results vs model expectations." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "certified-firewall", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12243648607284
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" - ], - "text/plain": [ - " 12 24 36 48 60 72 84\n", - "2007 344.492346 557.928307 348.774627 10.847889 -11.40612 NaN NaN\n", - "2008 -21.882151 -185.514153 -340.715515 -102.582899 11.40612 NaN NaN\n", - "2009 -92.224026 -233.617500 94.508419 91.735009 NaN NaN NaN\n", - "2010 -303.438287 -209.004780 -102.567531 NaN NaN NaN NaN\n", - "2011 67.162588 70.208127 NaN NaN NaN NaN NaN\n", - "2012 5.889530 NaN NaN NaN NaN NaN NaN\n", - "2013 NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = cl.Chainladder().fit(cl.load_sample('ukmotor'))\n", - "residuals = model.full_expectation_ - model.full_triangle_\n", - "residuals[residuals.valuation<=model.X_.valuation_date]" - ] - }, - { - "cell_type": "markdown", - "id": "confidential-peoples", - "metadata": {}, - "source": [ - "Another typical analysis is to forecast the IBNR run-off for future periods." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "broken-fetish", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 2014 2015 2016\n", - "2007 NaN NaN NaN\n", - "2008 350.902024 NaN NaN\n", - "2009 661.620101 375.916667 NaN\n", - "2010 1073.335187 619.525276 351.999397\n", - "2011 1502.970266 1133.999503 654.540504\n", - "2012 2724.981102 1820.419755 1373.516924\n", - "2013 5587.058983 3351.884601 2239.221748" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "expected_3y_run_off = model.full_triangle_.dev_to_val().cum_to_incr().loc[..., '2014':'2016']\n", - "expected_3y_run_off" - ] - }, - { - "cell_type": "markdown", - "id": "running-lender", - "metadata": {}, - "source": [ - "(methods:chainladder)=\n", - "## Chainladder\n", - "\n", - "The distinguishing characteristic of the :class:`Chainladder` method is that ultimate claims for each\n", - "accident year are produced from recorded values assuming that future claims’ development is\n", - "similar to prior years’ development. In this method, the actuary uses the development triangles to\n", - "track the development history of a specific group of claims. The underlying assumption in the\n", - "development technique is that claims recorded to date will continue to develop in a similar manner\n", - "in the future – that the past is indicative of the future. That is, the development technique assumes\n", - "that the relative change in a given year’s claims from one evaluation point to the next is similar to\n", - "the relative change in prior years’ claims at similar evaluation points.\n", - "\n", - "An implicit assumption in the development technique is that, for an immature accident year, the\n", - "claims observed thus far tell you something about the claims yet to be observed. This is in\n", - "contrast to the assumptions underlying the expected claims technique.\n", - "\n", - "Other important assumptions of the development method include: consistent claim processing, a\n", - "stable mix of types of claims, stable policy limits, and stable reinsurance (or excess insurance)\n", - "retention limits throughout the experience period.\n", - "\n", - "Though the algorithm underling the basic chainladder is trivial, the properties\n", - "of the `Chainladder` estimator allow for a concise access to relevant information.\n", - "\n", - "As an example, we can use the estimator to determine actual vs expected run-off\n", - "of a subsequent valuation period.\n", - "\n", - "{cite}`friedland2010`" - ] - }, - { - "cell_type": "markdown", - "id": "surprising-interest", - "metadata": {}, - "source": [ - "(methods:mackchainladder)=\n", - "## MackChainladder\n", - "\n", - "The :class:`MackChainladder` model can be regarded as a special form of a\n", - "weighted linear regression through the origin for each development period. By using\n", - "a regression framework, statistics about the variability of the data and the parameter\n", - "estimates allows for the estimation of prediction errors. The Mack Chainladder\n", - "method is the most basic of stochastic methods.\n", - " \n", - "### Compatibility\n", - "\n", - "Because of the regression framework underlying the `MackChainladder`, it is not\n", - "compatible with all development and tail estimators of the library. In fact,\n", - "it really should only be used with the `Development` estimator and `TailCurve`\n", - "tail estimator.\n", - "\n", - "```{warning}\n", - "While the MackChainladder might not error with other options for development and\n", - "tail, the stochastic properties should be ignored, in which case the basic\n", - "`Chainladder` should be used.\n", - "```\n", - "\n", - "### Examples\n", - "\n", - ":::{panels}\n", - ":column: col-lg-4 px-2 py-2\n", - "\n", - "---\n", - "**[MackChainladder Basics](plot_mack)**\n", - "```{glue:} plot_mack\n", - "```\n", - "+++\n", - "{bdg-success}`easy`\n", - "\n", - ":::\n", - "{cite}`mack1993`\n", - "{cite}`mack1999`" - ] - }, - { - "cell_type": "markdown", - "id": "lonely-maker", - "metadata": {}, - "source": [ - "(methods:bornhuetterferguson)=\n", - "## BornhuetterFerguson\n", - "\n", - "The :class:`BornhuetterFerguson` technique is essentially a blend of the\n", - "development and expected claims techniques. In the development technique, we multiply actual\n", - "claims by a cumulative claim development factor. This technique can lead to erratic, unreliable\n", - "projections when the cumulative development factor is large because a relatively small swing in\n", - "reported claims or the reporting of an unusually large claim could result in a very large swing in\n", - "projected ultimate claims. In the expected claims technique, the unpaid claim estimate is equal to\n", - "the difference between a predetermined estimate of expected claims and the actual payments.\n", - "This has the advantage of stability, but it completely ignores actual results as reported. The\n", - "Bornhuetter-Ferguson technique combines the two techniques by splitting ultimate claims into\n", - "two components: actual reported (or paid) claims and expected unreported (or unpaid) claims. As\n", - "experience matures, more weight is given to the actual claims and the expected claims become\n", - "gradually less important.\n", - "\n", - "### Exposure base\n", - "\n", - "The :class:`BornhuetterFerguson` technique is the first we explore of the Expected\n", - "Loss techniques. In this family of techniques, we need some measure of exposure.\n", - "This is handled by passing a `Triangle` representing the exposure to the `sample_weight`\n", - "argument of the `fit` method of the Estimator.\n", - "\n", - "All scikit-learn style estimators optionally support a `sample_weight` argument\n", - "and this is used by the `chainladder` package to capture the exposure base\n", - "of these Expected Loss techniques." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "outside-resistance", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "75203.23550854485" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa = cl.load_sample('raa')\n", - "sample_weight = raa.latest_diagonal*0+40_000\n", - "cl.BornhuetterFerguson(apriori=0.7).fit(\n", - " X=raa, \n", - " sample_weight=sample_weight\n", - ").ibnr_.sum()\n" - ] - }, - { - "cell_type": "markdown", - "id": "green-pharmacology", - "metadata": {}, - "source": [ - "### Apriori\n", - "\n", - "We've fit a :class:`BornhuetterFerguson` model with the assumption that our\n", - "prior belief, or `apriori` is a 70% Loss Ratio. The method supports any constant\n", - "for the `apriori` hyperparameter. The ``apriori`` then gets\n", - "multiplied into our sample weight to determine our prior belief on expected losses\n", - "prior to considering that actual emerged to date.\n", - "\n", - "Because of the multiplicative nature of `apriori` and `sample_weight` we don't\n", - "have to limit ourselves to a single constant for the `apriori`. Instead, we\n", - "can exploit the model structure to make our `sample_weight` represent our\n", - "prior belief on ultimates while setting the `apriori` to 1.0.\n", - "\n", - "For example, we can use the :class:`Chainladder` ultimates as our prior belief\n", - "in the :class:`BornhuetterFerguson` method.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "third-installation", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2050
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" - ], - "text/plain": [ - " 2050\n", - "1981 18834.000000\n", - "1982 16898.632172\n", - "1983 24012.333266\n", - "1984 28281.843524\n", - "1985 28203.700714\n", - "1986 19840.005163\n", - "1987 18840.362337\n", - "1988 22789.948877\n", - "1989 19541.155136\n", - "1990 20986.022826" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl_ult = cl.Chainladder().fit(raa).ultimate_ # Chainladder Ultimate\n", - "apriori = cl_ult*0+(cl_ult.sum()/10) # Mean Chainladder Ultimate\n", - "cl.BornhuetterFerguson(apriori=1).fit(raa, sample_weight=apriori).ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "cosmetic-circumstances", - "metadata": {}, - "source": [ - "{cite}`friedland2010`" - ] - }, - { - "cell_type": "markdown", - "id": "removable-country", - "metadata": {}, - "source": [ - "(methods:benktander)=\n", - "## Benktander\n", - "\n", - "The :class:`Benktander` method is a credibility-weighted\n", - "average of the :class:`BornhuetterFerguson` technique and the development technique.\n", - "The advantage cited by the authors is that this method will prove more\n", - "responsive than the Bornhuetter-Ferguson technique and more stable\n", - "than the development technique.\n", - "\n", - "### Iterations\n", - "\n", - "The `Benktander` method is also known as the iterated :class:`BornhuetterFerguson`\n", - "method. This is because it is a generalization of the :class:`BornhuetterFerguson`\n", - "technique.\n", - "\n", - "The generalized formula based on `n_iters`, n is:\n", - "\n", - "$Ultimate = Apriori\\times (1-\\frac{1}{CDF})^{n} + Latest\\times \\sum_{k=0}^{n-1}(1-\\frac{1}{CDF})^{k}$\n", - "\n", - "* `n=0` yields the expected loss method \n", - "* `n=1` yields the traditional :class:`BornhuetterFerguson` method\n", - "* `n>>1` converges to the traditional :class:`Chainladder` method.\n", - "\n", - "### Examples\n", - "\n", - ":::{panels}\n", - ":column: col-lg-4 px-2 py-2\n", - "\n", - "---\n", - "**[BornhutterFerguson vs Chainladder](plot_benktander)**\n", - "```{glue:} plot_benktander\n", - "```\n", - "+++\n", - "{bdg-warning}`medium`\n", - "\n", - "\n", - ":::\n", - "\n", - "### Expected Loss Method\n", - "\n", - "Setting `n_iters` to 0 will emulate that Expected Loss method. That is to say,\n", - "the actual emerged loss experience of the Triangle will be completely ignored in\n", - "determining the ultimate. While it is a trivial calculation, it allows for\n", - "run-off patterns to be developed, which is useful for new programs new lines\n", - "of businesses.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "complete-thomas", - "metadata": {}, - "outputs": [], - "source": [ - "triangle = cl.load_sample('ukmotor')\n", - "exposure = triangle.latest_diagonal*0 + 25_000\n", - "cl.Benktander(apriori=0.75, n_iters=0).fit(\n", - " X=triangle, \n", - " sample_weight=exposure\n", - ").full_triangle_.round(0)" - ] - }, - { - "cell_type": "markdown", - "id": "maritime-shopper", - "metadata": {}, - "source": [ - "Mack noted the `Benktander` method is found to have almost always a smaller mean\n", - "squared error than the other two methods and to be almost as precise as an exact\n", - "Bayesian procedure.\n", - "\n", - "{cite}`friedland2010`" - ] - }, - { - "cell_type": "markdown", - "id": "wrong-shade", - "metadata": {}, - "source": [ - "(methods:capecod)=\n", - "## CapeCod\n", - "\n", - "The :class:`CapeCod` method, also known as the Stanard-Buhlmann method, is similar to the\n", - "Bornhuetter-Ferguson technique. The primary difference between the two methods is the\n", - "derivation of the expected claim ratio. In the Cape Cod technique, the expected claim ratio\n", - "or apriori is obtained from the triangle itself instead of an independent and often judgmental\n", - "selection as in the Bornhuetter-Ferguson technique.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "turkish-renaissance", - "metadata": {}, - "outputs": [], - "source": [ - "clrd = cl.load_sample('clrd')[['CumPaidLoss', 'EarnedPremDIR']].groupby('LOB').sum().loc['wkcomp']\n", - "loss = clrd['CumPaidLoss']\n", - "sample_weight=clrd['EarnedPremDIR'].latest_diagonal\n", - "m1 = cl.CapeCod().fit(loss, sample_weight=sample_weight)\n", - "m1.ibnr_.sum()" - ] - }, - { - "cell_type": "markdown", - "id": "automatic-midwest", - "metadata": {}, - "source": [ - "### Apriori\n", - "\n", - "The default hyperparameters for the :class:`CapeCod` method can be emulated by\n", - "the :class:`BornhuetterFerguson` method. We can manually derive the `apriori`\n", - "implicit in the CapeCod estimate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "portuguese-cartoon", - "metadata": {}, - "outputs": [], - "source": [ - "cl_ult = cl.Chainladder().fit(loss).ultimate_\n", - "apriori = loss.latest_diagonal.sum() / (sample_weight/(cl_ult/loss.latest_diagonal)).sum()\n", - "m2 = cl.BornhuetterFerguson(apriori).fit(\n", - " X=clrd['CumPaidLoss'], \n", - " sample_weight=clrd['EarnedPremDIR'].latest_diagonal)\n", - "m2.ibnr_.sum()" - ] - }, - { - "cell_type": "markdown", - "id": "national-cooler", - "metadata": {}, - "source": [ - "A parameter `apriori_sigma` can also be specified to give sampling variance to the\n", - "estimated apriori. This along with `random_state` can be used in conjuction with\n", - "the `BootstrapODPSample` estimator to build a stochastic `CapeCod` estimate.\n", - "\n", - "### Trend and On-level\n", - "\n", - "When using data implicit in the Triangle to derive the apriori, it is desirable\n", - "to bring the different origin periods to a common basis. The `CapeCod` estimator\n", - "provides a `trend` hyperparameter to allow for trending everything to the latest\n", - "origin period. However, the apriori used in the actual estimation of the IBNR is\n", - "the `detrended_apriori_` detrended back to each of the specific origin periods.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "dominant-spiritual", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Detrended Apriori Apriori\n", - "1988 0.483539 0.750128\n", - "1989 0.507716 0.750128\n", - "1990 0.533102 0.750128\n", - "1991 0.559757 0.750128\n", - "1992 0.587745 0.750128\n", - "1993 0.617132 0.750128\n", - "1994 0.647989 0.750128\n", - "1995 0.680388 0.750128\n", - "1996 0.714407 0.750128\n", - "1997 0.750128 0.750128" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m1 = cl.CapeCod(trend=0.05).fit(loss, sample_weight=sample_weight)\n", - "pd.concat((\n", - " m1.detrended_apriori_.to_frame().iloc[:, 0].rename('Detrended Apriori'),\n", - " m1.apriori_.to_frame().iloc[:, 0].rename('Apriori')), axis=1\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "polished-profile", - "metadata": {}, - "source": [ - "Simple one-part trends are supported directly in the hyperparameter selection.\n", - "If a more complex trend assumption is required or on-leveling, then passing\n", - "Triangles transformed by the :class:`Trend` and :class:`ParallelogramOLF`\n", - "estimators will capture these finer details as in this example from the\n", - "example gallery.\n", - "\n", - "\n", - "### Examples\n", - "\n", - ":::{panels}\n", - ":column: col-lg-4 px-2 py-2\n", - "\n", - "---\n", - "**[CapeCod Onleveling](plot_capecod_onlevel)**\n", - "```{glue:} plot_capecod_onlevel\n", - "```\n", - "+++\n", - "{bdg-warning}`medium`\n", - "\n", - "---\n", - "**[CapeCod Sensitivity](plot_capecod)**\n", - "```{glue:} plot_capecod\n", - "```\n", - "+++\n", - "{bdg-danger}`hard`\n", - "\n", - ":::\n", - "\n", - "\n", - "### Decay\n", - "\n", - "The default behavior of the `CapeCod` is to include all origin periods in the\n", - "estimation of the `apriori_`. A more localized approach, giving lesser weight\n", - "to origin periods that are farther from a target origin period, can be achieved\n", - "by flexing the `decay` hyperparameter.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "headed-ceramic", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " 1988 1989 1990 1991 1992 1993 1994 \\\n", - "2050 0.617945 0.613275 0.604879 0.591887 0.57637 0.559855 0.548615 \n", - "\n", - " 1995 1996 1997 \n", - "2050 0.542234 0.540979 0.541723 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.CapeCod(decay=0.8).fit(loss, sample_weight=sample_weight).apriori_.T" - ] - }, - { - "cell_type": "markdown", - "id": "built-aviation", - "metadata": {}, - "source": [ - "With a `decay` less than 1.0, we see `apriori_` estimates that vary by origin.\n", - "\n", - "{cite}`friedland2010`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "several-sherman", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/modules/api.rst b/docs/modules/api.rst deleted file mode 100644 index f4839a57..00000000 --- a/docs/modules/api.rst +++ /dev/null @@ -1,187 +0,0 @@ -.. _api_ref: - -============= -API Documentation -============= - -This is the class and function reference of chainladder. Please refer to -the full user guide for further details, as the class and -function raw specifications may not be enough to give full guidelines on their -uses. - -.. _triangle_ref: - -:mod:`chainladder.core`: Triangle -================================= - -.. automodule:: chainladder.core - :no-members: - :no-inherited-members: - - -Classes -------- -.. currentmodule:: chainladder - -.. autosummary:: - :toctree: generated/ - :template: class_inherited.rst - - Triangle - DevelopmentCorrelation - ValuationCorrelation - - -.. _development_ref: - -:mod:`chainladder.development`: Development Patterns -==================================================== - -.. automodule:: chainladder.development - :no-members: - :no-inherited-members: - - -Classes -------- -.. currentmodule:: chainladder - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - Development - DevelopmentConstant - MunichAdjustment - IncrementalAdditive - ClarkLDF - CaseOutstanding - TweedieGLM - DevelopmentML - BarnettZehnwirth - - -.. _tails_ref: - -:mod:`chainladder.tails`: Tail Factors -======================================== - -.. automodule:: chainladder.tails - :no-members: - :no-inherited-members: - - -Classes -------- -.. currentmodule:: chainladder - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - TailConstant - TailCurve - TailBondy - TailClark - - -.. _methods_ref: - -:mod:`chainladder.methods`: IBNR Methods -======================================== - -.. automodule:: chainladder.methods - :no-members: - :no-inherited-members: - - -Classes -------- -.. currentmodule:: chainladder - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - Chainladder - MackChainladder - BornhuetterFerguson - Benktander - CapeCod - -.. _adjustments_ref: - -:mod:`chainladder.workflow`: Adjustments -======================================== -.. automodule:: chainladder.workflow - :no-members: - :no-inherited-members: - -Classes -------- -.. currentmodule:: chainladder - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - BootstrapODPSample - BerquistSherman - Trend - ParallelogramOLF - -.. _workflow_ref: - -:mod:`chainladder.workflow`: Workflow -===================================== -.. automodule:: chainladder.workflow - :no-members: - :no-inherited-members: - -Classes -------- -.. currentmodule:: chainladder - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - Pipeline - VotingChainladder - GridSearch - - -.. _utils_ref: - -:mod:`chainladder.utils`: Utilities -=================================== -.. automodule:: chainladder.utils - :no-members: - :no-inherited-members: - - -Functions ---------- -.. currentmodule:: chainladder - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - load_sample - read_pickle - read_json - concat - load_sample - minimum - maximum - -Classes -------- -.. currentmodule:: chainladder - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - PatsyFormula diff --git a/docs/plot_advanced_triangle.ipynb b/docs/plot_advanced_triangle.ipynb deleted file mode 100644 index e97e4d3e..00000000 --- a/docs/plot_advanced_triangle.ipynb +++ /dev/null @@ -1,1891 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Advanced Triangle Manipulation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates several advanced data manipulation features of the\n", - "Triangle class including:\n", - "\n", - " 1. Delayed calculation using `virtual_columns`\n", - " 2. Advanced `groupby` functionality\n", - " 3. Custom sorting using `loc`\n", - "\n", - "Let's suppose we want to look at the loss ratios for the top 10 commercial auto\n", - "carriers (by premium volume) as compared to the rest of the industry.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "clrd = cl.load_sample('clrd')\n", - "clrd = clrd[clrd['LOB']=='comauto']\n", - "\n", - "# Create a loss ratio virtual column\n", - "clrd['LossRatio'] = lambda clrd: clrd['IncurLoss'] / clrd['EarnedPremDIR']\n", - "\n", - "# Identify the largest companies (by premium) for 1997\n", - "top_10 = clrd['EarnedPremDIR'].groupby('GRNAME').sum().latest_diagonal\n", - "top_10 = top_10.loc[..., '1997', :].to_frame().nlargest(10)\n", - "\n", - "# Group any companies together that are not in the top 10\n", - "clrd = clrd.groupby(clrd.index['GRNAME'].map(\n", - " lambda x: x if x in top_10.index else 'Remainder')).sum()\n", - "\n", - "# Sort by company volume, but keep Remainder as last entry\n", - "clrd = clrd.loc[top_10.index.to_list() + ['Remainder']].iloc[::-1]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-30T16:32:47.571889\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "\n", - "ax = clrd.latest_diagonal.sum('origin')['LossRatio'].plot(\n", - " kind='barh', title='Loss Ratio');" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-30T16:33:22.495636\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_advanced_triangle" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_advanced_triangle\", ax.get_figure(), display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_ave_analysis.ipynb b/docs/plot_ave_analysis.ipynb deleted file mode 100644 index 19c1e07b..00000000 --- a/docs/plot_ave_analysis.ipynb +++ /dev/null @@ -1,1486 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Actual vs Expected" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how you can slice triangle objects to perform a\n", - "typical 'Actual vs Expected' analysis. We will use Medical Malpractice\n", - "payment patterns for the demonstration." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "tri_1997 = cl.load_sample('clrd')\n", - "tri_1997 = tri_1997.groupby('LOB').sum().loc['medmal']['CumPaidLoss']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Actual vs Expected analysis is an analysis of a model in the presence of new data. Let's remove the latest diagonal of our Triangle and conduct the analysis and retain the vector of losses expected in the following period, in this case, calendar year 1997." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a triangle as of the previous valuation and build IBNR model\n", - "tri_1996 = tri_1997[tri_1997.valuation < '1997']\n", - "model_1996 = cl.Chainladder().fit(cl.TailCurve().fit_transform(tri_1996))\n", - "\n", - "# Slice the expected losses from the 1997 calendar period of the model\n", - "ave = model_1996.full_triangle_.dev_to_val()\n", - "ave = ave[ave.valuation==tri_1997.valuation_date].rename('columns', 'Expected')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can compare our expectation vs the actual emerged diagonal to see where our model over/under-predicted the actual data." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Origin')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-30T16:17:30.334903\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Slice the actual losses from the 1997 calendar period for prior AYs\n", - "ave['Actual'] = tri_1997.latest_diagonal[tri_1997.origin < '1997']\n", - "df = ave.to_frame().T.iloc[::-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plotting\n", - "fig, ax = plt.subplots()\n", - "ax.grid(axis='x')\n", - "plt.hlines(\n", - " y=df.index.astype(str), \n", - " xmin=df['Actual'], \n", - " xmax=df['Expected'],\n", - " color='grey', alpha=0.4)\n", - "plt.scatter(df['Actual'], df.index.astype(str), alpha=1, label='Actual')\n", - "plt.scatter(df['Expected'], df.index.astype(str), alpha=0.8 , label='Expected')\n", - "plt.legend()\n", - "plt.title(\"Actual vs Expected results in 1997\")\n", - "plt.xlabel('Difference')\n", - "plt.ylabel('Origin');" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-30T16:17:31.668758\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_ave_analysis" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_ave_analysis\", fig, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "815088b5be8edc0041246414015ef72f7907068f95114ad939d47d8b23d533db" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_benktander.ipynb b/docs/plot_benktander.ipynb deleted file mode 100644 index 48ebcf60..00000000 --- a/docs/plot_benktander.ipynb +++ /dev/null @@ -1,315 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "# BornhutterFerguson vs Chainladder" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates the relationship between the Chainladder and\n", - "BornhuetterFerguson methods by way fo the Benktander model. Each is a\n", - "special case of the Benktander model where ``n_iters = 1`` for BornhuetterFerguson\n", - "and as ``n_iters`` approaches infinity yields the chainladder. As ``n_iters``\n", - "increases the apriori selection becomes less relevant regardless of initial\n", - "choice." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Load Data\n", - "clrd = cl.load_sample('clrd').groupby('LOB').sum()\n", - "X = clrd.loc['medmal', 'CumPaidLoss']\n", - "sample_weight = clrd.loc['medmal', 'EarnedPremDIR'].latest_diagonal\n", - "\n", - "# Specify Model\n", - "grid = cl.GridSearch(\n", - " estimator=cl.Pipeline(steps=[\n", - " ('dev', cl.Development()),\n", - " ('tail', cl.TailCurve()),\n", - " ('model', cl.Benktander())]), \n", - " param_grid = dict(\n", - " model__n_iters=list(range(1, 100, 2)),\n", - " model__apriori=[0.50, 0.75, 1.00]), \n", - " scoring={'IBNR': lambda x: x.named_steps.model.ibnr_.sum()},\n", - " n_jobs=-1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because chainladder estimators are scikit-learn compatible, we can display them as expandable/collabsible diagrams." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
GridSearch(estimator=Pipeline(steps=[('dev', Development()),\n",
-       "                                     ('tail', TailCurve()),\n",
-       "                                     ('model', Benktander())]),\n",
-       "           n_jobs=-1,\n",
-       "           param_grid={'model__apriori': [0.5, 0.75, 1.0],\n",
-       "                       'model__n_iters': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21,\n",
-       "                                          23, 25, 27, 29, 31, 33, 35, 37, 39,\n",
-       "                                          41, 43, 45, 47, 49, 51, 53, 55, 57,\n",
-       "                                          59, ...]},\n",
-       "           scoring={'IBNR':  at 0x000002A48A55F820>})
Development()
TailCurve()
Benktander()
" - ], - "text/plain": [ - "GridSearch(estimator=Pipeline(steps=[('dev', Development()),\n", - " ('tail', TailCurve()),\n", - " ('model', Benktander())]),\n", - " n_jobs=-1,\n", - " param_grid={'model__apriori': [0.5, 0.75, 1.0],\n", - " 'model__n_iters': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21,\n", - " 23, 25, 27, 29, 31, 33, 35, 37, 39,\n", - " 41, 43, 45, 47, 49, 51, 53, 55, 57,\n", - " 59, ...]},\n", - " scoring={'IBNR': at 0x000002A48A55F820>})" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn import set_config\n", - "set_config(display='diagram')\n", - "grid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fitting this model will loop through all `param_grid` options we specified and retain the aggregate IBNR of the model we declared in our `scoring` function." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "model__apriori 0.50 0.75 1.00\n", - "model__n_iters \n", - "1 8.326572e+05 1.248986e+06 1.665314e+06\n", - "3 1.075446e+06 1.289296e+06 1.503147e+06\n", - "5 1.150192e+06 1.308018e+06 1.465843e+06\n", - "7 1.189016e+06 1.318141e+06 1.447267e+06\n", - "9 1.214161e+06 1.324979e+06 1.435798e+06" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fit Model\n", - "grid.fit(X, sample_weight=sample_weight)\n", - "\n", - "# Analyze results\n", - "output = grid.results_.pivot(\n", - " index='model__n_iters', \n", - " columns='model__apriori', \n", - " values='IBNR') \n", - "output.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot the results of our output DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-30T17:02:33.033084\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "ax = (output / 1e6).plot(\n", - " ylabel='IBNR (Millions)',\n", - " xlabel='Number of Iterations',\n", - " title='Benktander convergence to Chainladder');" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-30T17:02:33.910287\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_benktander" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_benktander\", ax.get_figure(), display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_berqsherm_case.ipynb b/docs/plot_berqsherm_case.ipynb deleted file mode 100644 index 7316cb22..00000000 --- a/docs/plot_berqsherm_case.ipynb +++ /dev/null @@ -1,153 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "\n", - "# BerquistSherman Adjustment" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "This example demonstrates the adjustment to case reserves using the Berquist-Sherman\n", - "method. A key assumption, and highly sensitive one at that, is the selection of\n", - "a trend factor representative of the trend in average open case reserves from\n", - "year to year.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Load data\n", - "triangle = cl.load_sample('berqsherm').loc['MedMal']\n", - "\n", - "# Specify Berquist-Sherman model\n", - "berq = cl.BerquistSherman(\n", - " paid_amount='Paid', incurred_amount='Incurred',\n", - " reported_count='Reported', closed_count='Closed',\n", - " trend=0.15)\n", - "\n", - "# Adjust our triangle data\n", - "berq_triangle = berq.fit_transform(triangle)\n", - "berq_cdf = cl.Development().fit(berq_triangle['Incurred']).cdf_\n", - "orig_cdf = cl.Development().fit(triangle['Incurred']).cdf_" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T07:44:42.039695\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plot the results\n", - "ax = (berq_cdf / orig_cdf).T.plot(\n", - " kind='bar', grid=True, legend=False,\n", - " title='Berquist Sherman CDF to Unadjusted CDF',\n", - " xlabel='Age to Ultimate', \n", - " ylabel='Case Incurred CDF Adjustment');" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [ - "hide-input", - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T07:44:42.663043\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_berqsherm_case" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_berqsherm_case\", ax.get_figure(), display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_bf_apriori_from_cl.ipynb b/docs/plot_bf_apriori_from_cl.ipynb deleted file mode 100644 index 747a8509..00000000 --- a/docs/plot_bf_apriori_from_cl.ipynb +++ /dev/null @@ -1,148 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Selecting BornhuetterFerguson Apriori" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how you can can use the output of one method as the\n", - "apriori selection for the Bornhuetter-Ferguson Method.\n", - "\n", - "We use basic arithmetic to build up an Apriori rather than initializing it explicitely with `cl.Triangle`" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Create Aprioris as the mean AY chainladder ultimate\n", - "raa = cl.load_sample('RAA')\n", - "\n", - "cl_ult = cl.Chainladder().fit(raa).ultimate_ # Chainladder Ultimate\n", - "apriori = cl_ult * 0 + (cl_ult.sum() / 10) # Mean Chainladder Ultimate\n", - "bf_ult = cl.BornhuetterFerguson(apriori=1).fit(raa, sample_weight=apriori).ultimate_\n", - "\n", - "output = pd.concat(\n", - " (cl_ult.to_frame().rename({'2261': 'Chainladder'}, axis=1),\n", - " bf_ult.to_frame().rename({'2261': 'BornhuetterFerguson'}, axis=1)),\n", - " axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T07:51:54.164056\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plot of Ultimates\n", - "\n", - "ax = output.plot(grid=True, marker='o', \n", - " xlabel='Accident Year', ylabel='Ultimate');" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T07:51:54.968377\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_bf_apriori_from_cl" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_bf_apriori_from_cl\", ax.get_figure(), display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_bondy_sensitivity.ipynb b/docs/plot_bondy_sensitivity.ipynb deleted file mode 100644 index 419ceeee..00000000 --- a/docs/plot_bondy_sensitivity.ipynb +++ /dev/null @@ -1,151 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "# Bondy Tail Sensitivity" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "This example demonstrates the usage of the `TailBondy` estimator as well as\n", - "passing multiple scoring functions to `GridSearch`. When the ``earliest_age``\n", - "is set to the last available in the Triangle, the estimator reverts to the\n", - "traditional Bondy method.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Fit basic development to a triangle\n", - "tri = cl.load_sample('tail_sample')['paid']\n", - "dev = cl.Development(average='simple').fit_transform(tri)\n", - "\n", - "# Return both the tail factor and the Bondy exponent in the scoring function\n", - "scoring = {\n", - " 'tail_factor': lambda x: x.tail_.values[0,0],\n", - " 'bondy_exponent': lambda x : x.b_.values[0,0]}\n", - "\n", - "# Vary the 'earliest_age' assumption in GridSearch\n", - "param_grid=dict(earliest_age=list(range(12, 120, 12)))\n", - "grid = cl.GridSearch(cl.TailBondy(), param_grid, scoring)\n", - "results = grid.fit(dev).results_\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T09:33:06.811293\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "ax = results.plot(x='earliest_age', y='bondy_exponent',\n", - " title='Bondy Assumption Sensitivity', marker='o')\n", - "results.plot(x='earliest_age', y='tail_factor', grid=True,\n", - " secondary_y=True, ax=ax, marker='o');" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T09:33:13.510174\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_benktander" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_bondy_sensitivity\", ax.get_figure(), display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_bootstrap.ipynb b/docs/plot_bootstrap.ipynb deleted file mode 100644 index 0bb6e756..00000000 --- a/docs/plot_bootstrap.ipynb +++ /dev/null @@ -1,159 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Basic BootstrapODPSample" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how you can can use the Overdispersed Poisson\n", - "Bootstrap sampler and get various properties about parameter uncertainty.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "\n", - "# Grab a Triangle\n", - "tri = cl.load_sample('genins')\n", - "\n", - "# Generate bootstrap samples\n", - "sims = cl.BootstrapODPSample(random_state=42).fit_transform(tri)\n", - "\n", - "# Calculate LDF for each simulation\n", - "sim_ldf = cl.Development().fit(sims).ldf_\n", - "\n", - "plot1 = tri.T / 1e6\n", - "plot2 = (sims.sum() / 1000).T / 1e6\n", - "plot3a = sim_ldf.T\n", - "plot3b = cl.Development().fit(tri).ldf_.drop_duplicates().T\n", - "plot4 = sim_ldf.T.loc['12-24']" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T10:48:28.234819\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n 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\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plot the Data\n", - "fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))\n", - "\n", - "# Plot 1\n", - "plot1.plot(ax=ax00, title='Raw Data', xlabel='Development', ylabel='Incurred (Millions)')\n", - "\n", - "# Plot 2\n", - "plot2.plot(ax=ax01, title='Mean Simulation (Millions)', xlabel='Development')\n", - "\n", - "# Plot 3\n", - "plot3a.plot(legend=False, color='lightgray', ax=ax10, \n", - " title='Simulated LDF', xlabel='Development', ylabel='LDF')\n", - "plot3b.plot(legend=False, ax=ax10, grid=True)\n", - "\n", - "# Plot 4\n", - "plot4.plot(\n", - " kind='hist', bins=50, alpha=0.5, ax=ax11,\n", - " title='Age 12-24 LDF Distribution', xlabel='LDF');" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T10:46:44.621692\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n 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\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_bootstrap" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_bootstrap\", fig, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_bootstrap_comparison.ipynb b/docs/plot_bootstrap_comparison.ipynb deleted file mode 100644 index 9f5185fc..00000000 --- a/docs/plot_bootstrap_comparison.ipynb +++ /dev/null @@ -1,129 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "# BootstrapODPSample Variability" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how you can drop the outlier link ratios from the\n", - "`BootstrapODPSample`. This has a direct consquence of reducing reserve variability estimates." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Load triangle\n", - "triangle = cl.load_sample('genins')\n", - "\n", - "# Use bootstrap sampler to get resampled triangles\n", - "s1 = cl.BootstrapODPSample(\n", - " n_sims=5000, random_state=42).fit(triangle).resampled_triangles_\n", - "\n", - "## Alternatively use fit_transform() to access resampled triangles dropping\n", - "# outlier link-ratios from resampler\n", - "s2 = cl.BootstrapODPSample(\n", - " drop_high=[True] * 5+ [False] * 4, \n", - " drop_low=[True] * 5 + [False] * 4,\n", - " n_sims=5000, random_state=42).fit_transform(triangle)\n", - "\n", - "# Summarize results of first model\n", - "results = cl.Chainladder().fit(s1).ibnr_.sum('origin').rename('columns', ['Original'])\n", - "# Add another column to triangle with second set of results.\n", - "results['Dropped'] = cl.Chainladder().fit(s2).ibnr_.sum('origin')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T09:39:59.958288\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "# Plot both IBNR distributions\n", - "ax = results.to_frame().plot(\n", - " kind='hist', bins=50, alpha=0.5, \n", - " grid=True, xlabel='Ultimate',\n", - " title='Reserve Variability')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_bootstrap_comparison\", ax.get_figure(), display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_callable_dev_constant.ipynb b/docs/plot_callable_dev_constant.ipynb deleted file mode 100644 index 67a95568..00000000 --- a/docs/plot_callable_dev_constant.ipynb +++ /dev/null @@ -1,173 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "# DevelopmentConstant Callable" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`DevelopmentConstant` takes a dictionary of development factors and stores them\n", - "in a development estimator. This example demonstrates how a function can be\n", - "passed into `DevelopmentConstant` rather than a static dictionary of patterns.\n", - "The function should return development patterns for each element of the Triangle's\n", - "index. When passing a function to the estimator, it behaves as if calling the\n", - "pandas ``apply`` method on the Triangle's index.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "DevelopmentConstant(callable_axis=1,\n", - " patterns=,\n", - " style='cdf')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Sample Data\n", - "agway = cl.load_sample('clrd').loc['Agway Ins Co', 'CumPaidLoss']\n", - "\n", - "def paid_cdfs(x):\n", - " \"\"\" A function that returns different CDFs depending on a specified LOB \"\"\"\n", - " cdfs = {\n", - " 'comauto': [3.832, 1.874, 1.386, 1.181, 1.085, 1.043, 1.022, 1.013, 1.007, 1],\n", - " 'medmal': [24.168, 4.127, 2.103, 1.528, 1.275, 1.161, 1.088, 1.047, 1.018, 1],\n", - " 'othliab': [10.887, 3.416, 1.957, 1.433, 1.231, 1.119, 1.06, 1.031, 1.011, 1],\n", - " 'ppauto': [2.559, 1.417, 1.181, 1.084, 1.04, 1.019, 1.009, 1.004, 1.001, 1],\n", - " 'prodliab': [13.703, 5.613, 2.92, 1.765, 1.385, 1.177, 1.072, 1.034, 1.008, 1],\n", - " 'wkcomp': [4.106, 1.865, 1.418, 1.234, 1.141, 1.09, 1.056, 1.03, 1.01, 1]}\n", - " patterns = pd.DataFrame(cdfs, index=range(12, 132, 12)).T\n", - " return patterns.loc[x.loc['LOB']].to_dict()\n", - "\n", - "# If it works with pandas apply on the triangle index...\n", - "agway.index.apply(paid_cdfs, axis=1)\n", - "# ... then it will work in DevelopmentConstant\n", - "model = cl.DevelopmentConstant(patterns=paid_cdfs, callable_axis=1, style='cdf')\n", - "\n", - "model.fit(agway)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T11:08:56.691934\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "ax = model.ldf_.T.plot(kind='bar', title='Agway Insurance LDFs');" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T11:08:56.884297\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_callable_dev_constant" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_callable_dev_constant\", ax.get_figure(), display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.7.11 ('cl_dev')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - }, - "vscode": { - "interpreter": { - "hash": "cc8120b3d9d10be7e89b07ff6d4d09ab1619cf80570f11e1a80e248c6b69ac77" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_capecod.ipynb b/docs/plot_capecod.ipynb deleted file mode 100644 index 5b25ec3b..00000000 --- a/docs/plot_capecod.ipynb +++ /dev/null @@ -1,3124 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "# CapeCod Apriori Sensitivity" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates the usage of the deterministic CapeCod method and\n", - "shows the sensitivity of the apriori expectation to various choices of ``trend``\n", - "and ``decay``. Instead of using `GridSearch` we declare our own looping." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Grab data\n", - "ppauto_loss = cl.load_sample('clrd').groupby('LOB').sum().loc['ppauto', 'CumPaidLoss']\n", - "ppauto_prem = cl.load_sample('clrd').groupby('LOB').sum() \\\n", - " .loc['ppauto']['EarnedPremDIR'].latest_diagonal\n", - "\n", - "def get_apriori(decay, trend):\n", - " \"\"\" Function to grab apriori array from cape cod method \"\"\"\n", - " cc = cl.CapeCod(decay=decay, trend=trend)\n", - " cc.fit(ppauto_loss, sample_weight=ppauto_prem)\n", - " return cc.detrended_apriori_.to_frame()\n", - "\n", - "def get_plot_data(trend):\n", - " \"\"\" Function to grab plot data \"\"\"\n", - " # Initial apriori DataFrame\n", - " detrended_aprioris = get_apriori(0,trend)\n", - " detrended_aprioris.columns=['decay: 0%']\n", - "\n", - " # Add columns to apriori DataFrame\n", - " for item in [25, 50, 75, 100]:\n", - " detrended_aprioris[f'decay: {item}%'] = get_apriori(item/100, trend)\n", - " return detrended_aprioris\n", - "\n", - "# Plot Data\n", - "plot1 = get_plot_data(-0.05)\n", - "plot2 = get_plot_data(-.025)\n", - "plot3 = get_plot_data(0)\n", - "plot4 = get_plot_data(0.025)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T11:04:27.372046\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(\n", - " ncols=2, nrows=2, sharex=True, figsize=(10,10))\n", - "fig.suptitle(\"Private Passenger Auto Cape Cod Detrended Aprioris\");\n", - "plot1.plot(ax=ax00, title='Trend: -5.0%')\n", - "plot2.plot(ax=ax01, title='Trend: -2.5%')\n", - "plot3.plot(ax=ax10, title='Trend: 0.0%')\n", - "plot4.plot(ax=ax11, title='Trend: 2.5%')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'ax' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_4348/318167634.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmyst_nb\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mglue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mglue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"plot_capecod\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mNameError\u001b[0m: name 'ax' is not defined" - ] - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_capecod\", fig, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_capecod_onlevel.ipynb b/docs/plot_capecod_onlevel.ipynb deleted file mode 100644 index 114c178e..00000000 --- a/docs/plot_capecod_onlevel.ipynb +++ /dev/null @@ -1,1773 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "# CapeCod Onleveling" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how to incorporate on-leveling into the `CapeCod`\n", - "estimator. The on-level approach emulates the approach taken by Friedland in\n", - "\"Estimating Unpaid Claims Using Basic Techniques\" Chapter 10. The `ParallelogramOLF`\n", - "estimator is new in chainladder 0.7.9 as is the ``xyz`` triangle.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "\n", - "# Grab a triangle\n", - "xyz = cl.load_sample('xyz')\n", - "\n", - "# Premium on-leveling factors\n", - "rate_history = pd.DataFrame({\n", - " 'date': ['1/1/1999', '1/1/2000', '1/1/2001', '1/1/2002', '1/1/2003',\n", - " '1/1/2004', '1/1/2005', '1/1/2006', '1/1/2007', '1/1/2008'],\n", - " 'rate_change': [.02, .02, .02, .02, .05, .075, .15, .1, -.2, -.2]\n", - "})\n", - "\n", - "# Loss on-leveling factors\n", - "tort_reform = pd.DataFrame({\n", - " 'date': ['1/1/2006', '1/1/2007'],\n", - " 'rate_change': [-0.1067, -.25]\n", - "})\n", - "\n", - "# In addition to development, include onlevel estimator in pipeline for loss\n", - "pipe = cl.Pipeline(steps=[\n", - " ('olf', cl.ParallelogramOLF(tort_reform, change_col='rate_change', date_col='date', vertical_line=True)),\n", - " ('dev', cl.Development(n_periods=2)),\n", - " ('model', cl.CapeCod(trend=0.034))\n", - "])\n", - "\n", - "# Define X\n", - "X = cl.load_sample('xyz')['Incurred']\n", - "\n", - "# Separately apply on-level factors for premium\n", - "sample_weight = cl.ParallelogramOLF(\n", - " rate_history, change_col='rate_change', date_col='date',\n", - " vertical_line=True).fit_transform(xyz['Premium'].latest_diagonal)\n", - "\n", - "# Fit Cod Estimator\n", - "pipe.fit(X, sample_weight=sample_weight).named_steps.model.ultimate_\n", - "\n", - "# Create a Cape Cod pipeline without onleveling\n", - "pipe2 = cl.Pipeline(steps=[\n", - " ('dev', cl.Development(n_periods=2)),\n", - " ('model', cl.CapeCod(trend=0.034))\n", - "])\n", - "\n", - "# Finally fit Cod Estimator without on-leveling\n", - "pipe2.fit(X, sample_weight=xyz['Premium'].latest_diagonal).named_steps.model.ultimate_\n", - "\n", - "# Plot results\n", - "results = cl.concat((\n", - " pipe.named_steps.model.ultimate_.rename('columns', ['With On-level']),\n", - " pipe2.named_steps.model.ultimate_.rename('columns', ['Without On-level'])), 1).T" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T11:09:23.081153\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "ax = results.plot(\n", - " kind='bar', title='Cape Cod sensitivity to on-leveling', \n", - " subplots=True, legend=False);" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T11:09:51.075922\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_capecod_onlevel" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "\n", - "fig = ax[0].get_figure()\n", - "glue(\"plot_capecod_onlevel\", fig, display=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_clarkldf.ipynb b/docs/plot_clarkldf.ipynb deleted file mode 100644 index df51b82b..00000000 --- a/docs/plot_clarkldf.ipynb +++ /dev/null @@ -1,1715 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Clark Growth Curves" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import numpy as np\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates one of the attributes of the :class:`ClarkLDF`. We can\n", - "use the growth curve ``G_`` to estimate the percent of ultimate at any given\n", - "age.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Grab Industry triangles\n", - "clrd = cl.load_sample('clrd').groupby('LOB').sum()\n", - "\n", - "# Fit Clark Cape Cod method\n", - "model = cl.ClarkLDF(growth='loglogistic').fit(\n", - " clrd['CumPaidLoss'],\n", - " sample_weight=clrd['EarnedPremDIR'].latest_diagonal)\n", - "\n", - "# sample ages\n", - "ages = np.linspace(1, 300, 30)\n", - "\n", - "# Plot results\n", - "results = model.G_(ages).T" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T11:25:46.430279\r\n", - " image/svg+xml\r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "ax = results.plot(\n", - " title='Loglogistic Growth Curves',\n", - " xlabel='Age', ylabel='% of Ultimate');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_clarkldf\", ax.get_figure(), display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_clarkldf_resid.ipynb b/docs/plot_clarkldf_resid.ipynb deleted file mode 100644 index 40590b64..00000000 --- a/docs/plot_clarkldf_resid.ipynb +++ /dev/null @@ -1,1876 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "# Clark Residual Plots" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how to recreate the normalized residual plots in\n", - "Clarks LDF Curve-Fitting paper (2003).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Fit the basic model\n", - "genins = cl.load_sample('genins')\n", - "genins = cl.ClarkLDF().fit(genins)\n", - "\n", - "# Grab Normalized Residuals as a DataFrame\n", - "norm_resid = genins.norm_resid_.melt(\n", - " var_name='Development Age',\n", - " value_name='Normalized Residual').dropna()\n", - "\n", - "# Grab Fitted Incremental values as a DataFrame\n", - "incremental_fits = genins.incremental_fits_.melt(\n", - " var_name='Development Age',\n", - " value_name='Expected Incremental Loss').dropna()\n", - "incremental_fits = incremental_fits.merge(\n", - " norm_resid, how='inner', left_index=True, right_index=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T11:18:14.802409\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plot the residuals vs Age and vs Expected Incrementals\n", - "fig, ((ax0, ax1)) = plt.subplots(ncols=2, figsize=(10,5))\n", - "\n", - "# Left plot\n", - "norm_resid.plot(\n", - " x='Development Age', y='Normalized Residual',\n", - " kind='scatter', grid=True, ylim=(-4, 4), ax=ax0)\n", - "# Right plot\n", - "incremental_fits.plot(\n", - " x='Expected Incremental Loss', y='Normalized Residual',\n", - " kind='scatter', grid=True, ylim=(-4, 4), ax=ax1)\n", - "fig.suptitle(\"Clark LDF Normalized Residual Plots\");" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_clarkldf_resid\", fig, display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_development_periods.ipynb b/docs/plot_development_periods.ipynb deleted file mode 100644 index 8ddde305..00000000 --- a/docs/plot_development_periods.ipynb +++ /dev/null @@ -1,146 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "# Tuning Development Patterns" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates testing multiple number of periods and averages in the\n", - "development transformer to see its influence on the overall ultimate estimate.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "tri = cl.load_sample('abc')\n", - "\n", - "# Set up Pipeline\n", - "pipe = cl.Pipeline(steps=[\n", - " ('dev',cl.Development()),\n", - " ('chainladder',cl.Chainladder())])\n", - "\n", - "# Develop scoring function that returns an Ultimate/Incurred Ratio\n", - "\n", - "# Set up a GridSearch space\n", - "grid = cl.GridSearch(\n", - " estimator=pipe,\n", - " param_grid=dict(\n", - " dev__n_periods=[item for item in range(2,11)],\n", - " dev__average=['simple', 'volume', 'regression']),\n", - " scoring=lambda x: (\n", - " x.named_steps.chainladder.ultimate_.sum() /\n", - " tri.latest_diagonal.sum()))\n", - "grid.fit(tri)\n", - "\n", - "# Plot data\n", - "results = pd.pivot_table(grid.results_, index='dev__n_periods',\n", - " columns='dev__average', values='score')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T11:32:27.137141\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "fig, ax = plt.subplots()\n", - "im = ax.imshow(results.T)\n", - "ax.set_yticks(np.arange(len(results.columns)))\n", - "ax.set_xticks(np.arange(len(results.index)))\n", - "ax.set_yticklabels(results.columns)\n", - "ax.set_xticklabels(results.index)\n", - "for i in range(len(results.index)):\n", - " for j in range(len(results.columns)):\n", - " text = ax.text(i, j, results.round(2).values[i, j],\n", - " ha=\"center\", va=\"center\", color=\"w\")\n", - "ax.set_title(\"Ultimate to Incurred Ratio\")\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_development_periods\", fig, display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_elrf_resid.ipynb b/docs/plot_elrf_resid.ipynb deleted file mode 100644 index 03f2e28f..00000000 --- a/docs/plot_elrf_resid.ipynb +++ /dev/null @@ -1,156 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ELRF Residuals" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example replicates the diagnostic residuals from Barnett and Zehnwirth's\n", - "\"Best Estimates for Reserves\" paper in which they describe the Extended Link\n", - "Ratio Family (ELRF) model. This `Development` estimator is based on the ELRF\n", - "model.\n", - "\n", - "The weighted standardized residuals are contained in the ``std_residuals_``\n", - "property of the fitted estimator. Using these, we can replicate Figure 2.6\n", - "from the paper.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\jboga\\documents\\github\\chainladder-python\\chainladder\\core\\pandas.py:314: RuntimeWarning: Mean of empty slice\n", - " obj.values = func(obj.values, axis=axis, *args, **kwargs)\n" - ] - } - ], - "source": [ - "raa = cl.load_sample('raa')\n", - "model = cl.Development().fit(raa)\n", - "\n", - "plot1a = model.std_residuals_.T\n", - "plot1b = model.std_residuals_.mean('origin').T\n", - "plot2a = model.std_residuals_\n", - "plot2b = model.std_residuals_.mean('development')\n", - "plot3a = model.std_residuals_.dev_to_val().T\n", - "plot3b = model.std_residuals_.dev_to_val().mean('origin').T\n", - "plot4 = pd.concat((\n", - " (raa[raa.valuation < raa.valuation_date] * \n", - " model.ldf_.values).unstack().rename('Fitted Values'),\n", - " model.std_residuals_.unstack().rename('Residual')), axis=1).dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T11:47:08.210210\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n 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\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(13,10))\n", - "fig.suptitle(\"Barnett Zehnwirth\\nStandardized residuals of the Extended Link Ratio Family (ELRF)\\n(Fig 2.6)\");\n", - "\n", - "\n", - "plot1a.plot(\n", - " style='.', color='gray', legend=False, ax=ax00,\n", - " xlabel='Development Month', ylabel='Weighted Standardized Residuals')\n", - "plot1b.plot(\n", - " color='red', legend=False, ax=ax00)\n", - "plot2a.plot(\n", - " style='.', color='gray', legend=False, ax=ax01, xlabel='Origin Period')\n", - "plot2b.plot(\n", - " color='red', legend=False, ax=ax01)\n", - "plot3a.plot(\n", - " style='.', color='gray', legend=False, ax=ax10,\n", - " xlabel='Valuation Date', ylabel='Weighted Standardized Residuals')\n", - "plot3b.plot(color='red', legend=False, grid=True, ax=ax10)\n", - "plot4.plot(kind='scatter', marker='o', color='gray', \n", - " x='Fitted Values', y='Residual', ax=ax11, sharey=True);\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_elrf_resid\", fig, display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_exponential_smoothing.ipynb b/docs/plot_exponential_smoothing.ipynb deleted file mode 100644 index 6f0b2465..00000000 --- a/docs/plot_exponential_smoothing.ipynb +++ /dev/null @@ -1,1448 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Attachment Age Smoothing" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This simple example demonstrates how a Tail ``attachment_age`` can be used to\n", - "smooth over development patterns within the triangle. Regardless of where\n", - "the ``attachment_age`` is set, the patterns will always extrapolate one year\n", - "past the highest known lag of the `Triangle` before applying a terminal tail\n", - "factor.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "raa = cl.load_sample('raa')\n", - "\n", - "results = pd.concat((\n", - " cl.TailCurve().fit(raa).ldf_.T.iloc[:, 0].rename('Unsmoothed'),\n", - " cl.TailCurve(attachment_age=12).fit(raa).ldf_.T.iloc[:, 0].rename('Curve Fit')\n", - "), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T11:52:10.540974\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "ax = results.plot(title='Exponential Smoothing of LDF');" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T11:52:11.218568\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_exponential_smoothing" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_exponential_smoothing\", ax.get_figure(), display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_exposure_triangle.ipynb b/docs/plot_exposure_triangle.ipynb deleted file mode 100644 index c7f88a54..00000000 --- a/docs/plot_exposure_triangle.ipynb +++ /dev/null @@ -1,931 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Exposure Triangle" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although triangles have both origin and development attributes, it is often\n", - "convenient to create premium or exposure vectors that can work with loss\n", - "triangles. The `Triangle` class treats the development parameter as\n", - "optional. This example instantiates a 'premium' triangle as a single vector.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 1987\n", - "1977 3000000.0\n", - "1978 3000000.0\n", - "1979 3000000.0\n", - "1980 3000000.0\n", - "1981 3000000.0\n", - "1982 3000000.0\n", - "1983 3000000.0\n", - "1984 3000000.0\n", - "1985 3000000.0\n", - "1986 3000000.0\n", - "1987 3000000.0" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Raw premium data in pandas\n", - "premium_df = pd.DataFrame(\n", - " {'AccYear':[item for item in range(1977, 1988)],\n", - " 'premium': [3000000]*11})\n", - "\n", - "# Create a premium 'triangle' with no development\n", - "premium = cl.Triangle(premium_df, origin='AccYear', columns='premium')\n", - "premium" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Create some loss triangle\n", - "loss = cl.load_sample('abc')\n", - "ultimate = cl.Chainladder().fit(loss).ultimate_\n", - "\n", - "loss_ratios = (ultimate / premium).to_frame()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2022-02-23T16:58:51.698263\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.5.0, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "fig, ax = plt.subplots()\n", - "plt.stem(loss_ratios.index.astype(str), loss_ratios.iloc[:, 0])\n", - "ax.grid(axis='y')\n", - "for spine in ax.spines:\n", - " ax.spines[spine].set_visible(False)\n", - "plt.show();" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\n\n\n \n \n \n \n 2022-02-23T16:58:52.793938\n image/svg+xml\n \n \n Matplotlib v3.5.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_exposure_triangle" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_exposure_triangle\", fig, display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_extrap_period.ipynb b/docs/plot_extrap_period.ipynb deleted file mode 100644 index 7d34f7cb..00000000 --- a/docs/plot_extrap_period.ipynb +++ /dev/null @@ -1,1304 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "# Extrapolation Period Sensitivity" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates the ``extrap_periods`` functionality of the `TailCurve`\n", - "estimator. The estimator defaults to extrapolating out 100 periods. However,\n", - "we can see that the \"Inverse Power\" curve fit doesn't converge to its asymptotic\n", - "value.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "tri = cl.load_sample('clrd').groupby('LOB').sum().loc['medmal', 'CumPaidLoss']\n", - "\n", - "# Create a fuction to grab the scalar tail value.\n", - "def scoring(model):\n", - " \"\"\" Scoring functions must return a scalar \"\"\"\n", - " return model.tail_.iloc[0, 0]\n", - "\n", - "# Create a grid of scenarios\n", - "param_grid = dict(\n", - " extrap_periods=list(range(1, 100, 6)),\n", - " curve=['inverse_power', 'exponential'])\n", - "\n", - "# Fit Grid\n", - "model = cl.GridSearch(cl.TailCurve(), param_grid=param_grid, scoring=scoring).fit(tri)\n", - "\n", - "# Plot results\n", - "results = model.results_.pivot(columns='curve', index='extrap_periods', values='score')" - ] - }, - { - "cell_type": "code", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "ax = results.plot(\n", - " ylim=(1,None), ylabel='Tail Factor',\n", - " title='Curve Fit Sensitivity to Extrapolation Period');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_extrap_period\", ax.get_figure(), display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_glm_ldf.ipynb b/docs/plot_glm_ldf.ipynb deleted file mode 100644 index eadae9d1..00000000 --- a/docs/plot_glm_ldf.ipynb +++ /dev/null @@ -1,1419 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# GLM Reserving" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how you can use the `TweedieGLM` estimator to incorporate\n", - "the GLM framework into a ``chainladder`` workflow. The specific case of the Over-dispersed\n", - "poisson GLM fit to incremental paids. It is further shown to match the basic\n", - "chainladder `Development` estimator.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "genins = cl.load_sample('genins')\n", - "\n", - "# Fit an ODP GLM\n", - "dev = cl.TweedieGLM(\n", - " design_matrix='C(development) + C(origin)',\n", - " link='log', power=1).fit(genins)\n", - "\n", - "# Grab LDFs vs traditional approach\n", - "glm = dev.ldf_.iloc[..., 0, :].T.iloc[:, 0].rename('GLM')\n", - "traditional = cl.Development().fit(genins).ldf_.T.iloc[:, 0].rename('Traditional')\n", - "\n", - "# Plot data\n", - "results = pd.concat((glm, traditional), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T12:10:35.530585\r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "ax = results.plot(kind='bar', title='LDF: Poisson GLM vs Traditional');" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T12:10:59.640131\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_glm_ldf" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_glm_ldf\", ax.get_figure(), display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_ibnr_runoff.ipynb b/docs/plot_ibnr_runoff.ipynb deleted file mode 100644 index adf9e87f..00000000 --- a/docs/plot_ibnr_runoff.ipynb +++ /dev/null @@ -1,1295 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# IBNR Runoff" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All IBNR models spin off several results triangles including ``inbr_``,\n", - "``ultimate_``, ``full_expectation``, and ``full_triangle_``. These can be\n", - "manipulated into a variety of formats. This example demonstrates how to\n", - "create a calendar year runoff of IBNR.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Create a triangle\n", - "triangle = cl.load_sample('genins')\n", - "\n", - "# Fit a model\n", - "model = cl.Chainladder().fit(triangle)\n", - "\n", - "# Develop IBNR runoff triangle\n", - "runoff = (model.full_triangle_.cum_to_incr() - triangle.cum_to_incr())\n", - "\n", - "# Convert to calendar period and aggregate across all accident years\n", - "cal_yr_runoff = runoff[runoff.valuation > triangle.valuation_date]\n", - "cal_yr_runoff = cal_yr_runoff.dev_to_val().sum(axis='origin')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2022-02-23T17:00:30.564009\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plot results\n", - "ax = cal_yr_runoff.dropna().T.plot(\n", - " kind='bar', legend=False,\n", - " title='GenIns: IBNR Run-off', alpha=0.7,\n", - " xlabel='Calendar Year', ylabel='IBNR');" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\n\n\n \n \n \n \n 2022-02-23T17:00:31.723966\n image/svg+xml\n \n \n Matplotlib v3.5.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_ibnr_runoff" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_ibnr_runoff\", ax.get_figure(), display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_industry_to_company.ipynb b/docs/plot_industry_to_company.ipynb deleted file mode 100644 index 9ffd03ab..00000000 --- a/docs/plot_industry_to_company.ipynb +++ /dev/null @@ -1,184 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Making Predictions" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how you can create development patterns at a\n", - "particular ``index`` grain and apply them to another.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Industry to Company LDF Diff
1988
1989-203
1990-4,401
1991-5,781
1992-6,286
1993-4,793
1994-6,653
1995-8,327
1996-7,170
1997-273
" - ], - "text/plain": [ - " Industry to Company LDF Diff\n", - "1988 NaN\n", - "1989 -202.830662\n", - "1990 -4400.765402\n", - "1991 -5781.419855\n", - "1992 -6286.251679\n", - "1993 -4792.896607\n", - "1994 -6652.981043\n", - "1995 -8327.246649\n", - "1996 -7169.608047\n", - "1997 -273.269090" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample('clrd')['CumPaidLoss']\n", - "clrd = clrd[clrd['LOB'] == 'wkcomp']\n", - "\n", - "industry = clrd.sum()\n", - "\n", - "allstate_industry_cl = cl.Chainladder().fit(industry).predict(clrd.loc['Allstate Ins Co Grp']).ultimate_\n", - "allstate_company_cl = cl.Chainladder().fit(clrd.loc['Allstate Ins Co Grp']).ultimate_\n", - "\n", - "diff = (allstate_industry_cl - allstate_company_cl)\n", - "\n", - "output = diff.rename('development',['Industry to Company LDF Diff'])\n", - "output" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Industry to Company LDF Diff
1995-8327
1996-7169
1997-273
\n
", - "application/papermill.record/text/plain": " Industry to Company LDF Diff\n1995 -8327\n1996 -7169\n1997 -273" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_industry_to_company" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "out = output.to_frame().tail(3).astype(int)\n", - "glue(\"plot_industry_to_company\", out, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_mack.ipynb b/docs/plot_mack.ipynb deleted file mode 100644 index 7c446df0..00000000 --- a/docs/plot_mack.ipynb +++ /dev/null @@ -1,1700 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "# MackChainladder Example" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how you can can use the Mack Chainladder method." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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LatestIBNRUltimateMack Std Err
198118834.0NaN18834.00000019.174776
198216704.0153.95391716857.953917144.036555
198323466.0617.37092424083.370924592.583721
198427067.01636.14216328703.142163713.318620
198526180.02746.73634328926.7363431452.321097
198615852.03649.10318419501.1031841995.084397
198712314.05435.30259017749.3025902203.915501
198813112.010907.19251024019.1925105354.388503
19895395.010649.98410116044.9841016331.566502
19902063.016339.44252918402.44252924565.782962
\n", - "
" - ], - "text/plain": [ - " Latest IBNR Ultimate Mack Std Err\n", - "1981 18834.0 NaN 18834.000000 19.174776\n", - "1982 16704.0 153.953917 16857.953917 144.036555\n", - "1983 23466.0 617.370924 24083.370924 592.583721\n", - "1984 27067.0 1636.142163 28703.142163 713.318620\n", - "1985 26180.0 2746.736343 28926.736343 1452.321097\n", - "1986 15852.0 3649.103184 19501.103184 1995.084397\n", - "1987 12314.0 5435.302590 17749.302590 2203.915501\n", - "1988 13112.0 10907.192510 24019.192510 5354.388503\n", - "1989 5395.0 10649.984101 16044.984101 6331.566502\n", - "1990 2063.0 16339.442529 18402.442529 24565.782962" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Load the data\n", - "data = cl.load_sample('raa')\n", - "\n", - "# Compute Mack Chainladder ultimates and Std Err using 'volume' average\n", - "mack = cl.MackChainladder()\n", - "dev = cl.Development(average='volume')\n", - "mack.fit(dev.fit_transform(data))\n", - "\n", - "plot_data = mack.summary_.to_frame()\n", - "plot_data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T12:33:48.536962\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "ax = plot_data[['Latest', 'IBNR']].plot(\n", - " kind='bar', stacked=True, ylim=(0, None), grid=True,\n", - " yerr=pd.DataFrame({'latest': plot_data['Mack Std Err']*0,\n", - " 'IBNR': plot_data['Mack Std Err']}),\n", - " title='Mack Chainladder Ultimate',\n", - " xlabel='Accident Year', ylabel='Loss');" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T12:34:08.542373\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_mack" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_mack\", ax.get_figure(), display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_munich.ipynb b/docs/plot_munich.ipynb deleted file mode 100644 index f5b90c62..00000000 --- a/docs/plot_munich.ipynb +++ /dev/null @@ -1,2124 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MunichAdjustment Basics" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how to adjust LDFs by the relationship between Paid\n", - "and Incurred using the MunichAdjustment.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Load data\n", - "mcl = cl.load_sample('mcl')\n", - "\n", - "# Traditional Chainladder\n", - "cl_traditional = cl.Chainladder().fit(mcl).ultimate_\n", - "\n", - "# Munich Adjustment\n", - "dev_munich = cl.MunichAdjustment(paid_to_incurred=('paid', 'incurred')).fit_transform(mcl)\n", - "cl_munich = cl.Chainladder().fit(dev_munich).ultimate_\n", - "\n", - "plot1_data = cl_munich.to_frame().T.rename(\n", - " {'incurred':'Ultimate Incurred', 'paid': 'Ultimate Paid'}, axis=1)\n", - "\n", - "plot2_data = pd.concat(\n", - " ((cl_munich['paid'] / cl_munich['incurred']).to_frame().rename(\n", - " columns={'2261': 'Munich'}),\n", - " (cl_traditional['paid'] / cl_traditional['incurred']).to_frame().rename(\n", - " columns={'2261': 'Traditional'})), axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see how the Paid / Incurred Ultimates stay close to `1.0` for all origin periods under the `MunichAdjustment` while they diverge under the traditional `Development`." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-11-02T19:55:40.749790\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - 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" \r\n", - "\r\n" - ], - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plot data\n", - "fig, (ax0, ax1) = plt.subplots(ncols=2, sharex=True, figsize=(10,5))\n", - "plot_kw = dict(kind='bar', alpha=0.7)\n", - "\n", - "plot1_data.plot(\n", - " title='Munich Chainladder', ax=ax0, **plot_kw).set(\n", - " ylabel='Ultimate', xlabel='Accident Year')\n", - "plot2_data.plot(\n", - " title='P/I Ratio Comparison', ax=ax1, ylim=(0,1.25), **plot_kw).set(\n", - " ylabel='Paid Ultimate / Incurred Ultimate', xlabel='Accident Year');" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-11-02T19:55:44.214018\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n 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\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_munich" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_munich\", fig, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_munich_resid.ipynb b/docs/plot_munich_resid.ipynb deleted file mode 100644 index 7103c162..00000000 --- a/docs/plot_munich_resid.ipynb +++ /dev/null @@ -1,2472 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MunichAdjustment Correlation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how to recreate the the residual correlation plots\n", - "of the Munich Chainladder paper.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T12:40:39.411702\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Fit Munich Model\n", - "mcl = cl.load_sample('mcl')\n", - "model = cl.MunichAdjustment([('paid', 'incurred')]).fit(mcl)\n", - "\n", - "# Paid lambda line\n", - "paid_lambda = pd.DataFrame(\n", - " {'(P/I)': np.linspace(-2,2,2),\n", - " 'P': np.linspace(-2,2,2)*model.lambda_.loc['paid']})\n", - "\n", - "# Paid scatter\n", - "paid_plot = pd.concat(\n", - " (model.resids_['paid'].melt(value_name='P')['P'],\n", - " model.q_resids_['paid'].melt(value_name='(P/I)')['(P/I)']),\n", - " axis=1)\n", - "\n", - "# Incurred lambda line\n", - "inc_lambda = pd.DataFrame(\n", - " {'(I/P)': np.linspace(-2,2,2),\n", - " 'I': np.linspace(-2,2,2)*model.lambda_.loc['incurred']})\n", - "\n", - "# Incurred scatter\n", - "incurred_plot = pd.concat(\n", - " (model.resids_['incurred'].melt(value_name='I')['I'],\n", - " model.q_resids_['incurred'].melt(value_name='(I/P)')['(I/P)']),\n", - " axis=1)" - ] - }, - { - "cell_type": "code", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plot Data\n", - "fig, ((ax0, ax1)) = plt.subplots(ncols=2, figsize=(10,5))\n", - "\n", - "paid_lambda.plot(x='(P/I)', y='P', legend=False, ax=ax0)\n", - "paid_plot.plot(\n", - " kind='scatter', y='P', x='(P/I)', ax=ax0,\n", - " xlim=(-2,2), ylim=(-2,2), title='Paid')\n", - "\n", - "inc_lambda.plot(x='(I/P)', y='I', ax=ax1, legend=False);\n", - "incurred_plot.plot(\n", - " kind='scatter', y='I', x='(I/P)', ax=ax1,\n", - " xlim=(-2,2), ylim=(-2,2), title='Incurred');\n", - "fig.suptitle(\"Munich Chainladder Residual Correlations\");" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T12:42:30.768671\r\n image/svg+xml\r\n 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\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_munich_resid" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_munich_resid\", fig, display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_ptf_resid.ipynb b/docs/plot_ptf_resid.ipynb deleted file mode 100644 index 5574e0f9..00000000 --- a/docs/plot_ptf_resid.ipynb +++ /dev/null @@ -1,2752 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PTF Residuals" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example replicates the diagnostic residuals from Barnett and Zehnwirth's\n", - "\"Best Estimates for Reserves\" paper in which they describe the Probabilistic\n", - "Trend Family (PTF) model. With the \"ABC\" triangle, they show that the basic\n", - "chainladder, which ignores trend along the valuation axis, fails to have iid\n", - "weighted standardized residuals along the valuation of the Triangle.\n", - "\n", - "We fit a \"diagnostic\" model that deliberately ignores modeling the valuation\n", - "vector. This is done by specifying the patsy formula ``C(origin)+C(development)``\n", - "which fits origin and development as categorical features.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "abc = cl.load_sample('abc')\n", - "model = cl.BarnettZehnwirth(formula='C(origin) + C(development)').fit(abc)\n", - "\n", - "plot1a = model.std_residuals_.T\n", - "plot1b = plot1a.T.mean()\n", - "\n", - "plot2a = model.std_residuals_\n", - "plot2b = plot2a.T.mean()\n", - "\n", - "plot3a = model.std_residuals_.dev_to_val().T\n", - "plot3b = model.std_residuals_.dev_to_val().mean('origin').T\n", - "\n", - "plot4 = pd.concat((\n", - " model.triangle_ml_[model.triangle_ml_.valuation<=abc.valuation_date].log().unstack().rename('Fitted Values'),\n", - " model.std_residuals_.unstack().rename('Residual')), axis=1).dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(14,10))\n", - "fig.suptitle(\"Barnett Zehnwirth\\nStandardized residuals of the statistical chainladder model\\n(Fig 3.11)\");\n", - "\n", - "plot1a.plot(\n", - " style='.', color='gray', legend=False, ax=ax00,\n", - " xlabel='Development Month', ylabel='Weighted Standardized Residuals')\n", - "plot1b.plot(color='red', legend=False, ax=ax00)\n", - "\n", - "plot2a.plot(style='.', color='gray', legend=False, ax=ax01, xlabel='Origin Period')\n", - "plot2b.plot(color='red', legend=False, ax=ax01)\n", - "plot3a.plot(\n", - " style='.', color='gray', legend=False, ax=ax10,\n", - " xlabel='Valuation Date', ylabel='Weighted Standardized Residuals')\n", - "plot3b.plot(color='red', legend=False, ax=ax10)\n", - "\n", - "plot4.plot(kind='scatter', marker='o', color='gray', \n", - " x='Fitted Values', y='Residual', ax=ax11, sharey=True);\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T13:10:25.284984\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n 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\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_ptf_resid" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_ptf_resid\", fig, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_stochastic_bornferg.ipynb b/docs/plot_stochastic_bornferg.ipynb deleted file mode 100644 index 2338d391..00000000 --- a/docs/plot_stochastic_bornferg.ipynb +++ /dev/null @@ -1,5421 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Stochastic BornhuetterFerguson" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see how to use the :class:`BootstrapODPSample` and :class:`BornhuetterFerguson`\n", - "to come up with a stochastic view of the Bornhuetter-Ferguson method. This can\n", - "be done with any deterministic IBNR method which makes bootstraping so versatile.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "\n", - "# Simulation parameters\n", - "random_state = 42\n", - "n_sims = 1000\n", - "\n", - "# Get data\n", - "loss = cl.load_sample('genins')\n", - "premium = loss.latest_diagonal * 0 + 8e6\n", - "\n", - "# Simulate loss triangles\n", - "sim = cl.BootstrapODPSample(random_state=random_state, n_sims=n_sims)\n", - "sim.fit(loss, sample_weight=premium)\n", - "\n", - "\n", - "# Fit Bornhuetter-Ferguson to stochastically generated data\n", - "model = cl.BornhuetterFerguson(0.65, apriori_sigma=0.10)\n", - "model.fit(sim.resampled_triangles_, sample_weight=premium)\n", - "\n", - "# Grab completed triangle replacing simulated known data with actual known data\n", - "full_triangle = (model.full_triangle_ - model.X_ + loss) / premium\n", - "\n", - "# Limiting to the current year for plotting\n", - "current_year = full_triangle[full_triangle.origin==full_triangle.origin.max()].to_frame().T" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T13:20:16.687941\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plot the data\n", - "ax = current_year.iloc[:-1, :300].plot(\n", - " legend=False, alpha=0.1, color='red', \n", - " xlabel='Development Age', ylabel='Loss Ratio',\n", - " title='Current Accident Year BornFerg Distribution');" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T13:20:17.659415\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n 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\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_stochastic_bornferg" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_stochastic_bornferg\", ax.get_figure(), display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_tailcurve_compare.ipynb b/docs/plot_tailcurve_compare.ipynb deleted file mode 100644 index 12627469..00000000 --- a/docs/plot_tailcurve_compare.ipynb +++ /dev/null @@ -1,1424 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# TailCurve Basics" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how the ``inverse_power`` curve generally produces more\n", - "conservative tail factors than the ``exponential`` fit.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "clrd = cl.load_sample('clrd').groupby('LOB').sum()['CumPaidLoss']\n", - "cdf_ip = cl.TailCurve(curve='inverse_power').fit(clrd)\n", - "cdf_xp = cl.TailCurve(curve='exponential').fit(clrd)\n", - "\n", - "result = pd.concat((cdf_ip.tail_.rename(\"Inverse Power\"),\n", - " cdf_xp.tail_.rename(\"Exponential\")), axis=1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T13:48:56.829613\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "ax = result.plot(\n", - " kind='bar', title='Curve Fit Comparison',\n", - " xlabel='Industry', ylabel='Tail Factor');" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T13:50:04.542911\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_tailcurve_compare" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_tailcurve_compare\", ax.get_figure(), display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_triangle_from_pandas.ipynb b/docs/plot_triangle_from_pandas.ipynb deleted file mode 100644 index 767410fb..00000000 --- a/docs/plot_triangle_from_pandas.ipynb +++ /dev/null @@ -1,2219 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Triangle Creation Basics" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates the typical way you'd ingest data into a Triangle.\n", - "Data in tabular form in a pandas DataFrame is required. At a minimum, columns\n", - "specifying origin and development, and a value must be present. Note, you can\n", - "include more than one column as a list as well as any number of indices for\n", - "creating triangle subgroups.\n", - "\n", - "In this example, we create a triangle object with triangles for each company\n", - "in the CAS Loss Reserve Database for Workers' Compensation.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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GRCODEGRNAMEAccidentYearDevelopmentYearDevelopmentLagIncurLossCumPaidLossBulkLossEarnedPremDIREarnedPremCededEarnedPremNetSinglePostedReserve97LOB
086Allstate Ins Co Grp1988198813674047057112773740069959573947420281872wkcomp
186Allstate Ins Co Grp1988198923629881559056017340069959573947420281872wkcomp
286Allstate Ins Co Grp1988199033472882207442776340069959573947420281872wkcomp
386Allstate Ins Co Grp1988199143306482515951528040069959573947420281872wkcomp
486Allstate Ins Co Grp1988199253546902741562768940069959573947420281872wkcomp
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" - ], - "text/plain": [ - " GRCODE GRNAME AccidentYear DevelopmentYear DevelopmentLag \\\n", - "0 86 Allstate Ins Co Grp 1988 1988 1 \n", - "1 86 Allstate Ins Co Grp 1988 1989 2 \n", - "2 86 Allstate Ins Co Grp 1988 1990 3 \n", - "3 86 Allstate Ins Co Grp 1988 1991 4 \n", - "4 86 Allstate Ins Co Grp 1988 1992 5 \n", - "\n", - " IncurLoss CumPaidLoss BulkLoss EarnedPremDIR EarnedPremCeded \\\n", - "0 367404 70571 127737 400699 5957 \n", - "1 362988 155905 60173 400699 5957 \n", - "2 347288 220744 27763 400699 5957 \n", - "3 330648 251595 15280 400699 5957 \n", - "4 354690 274156 27689 400699 5957 \n", - "\n", - " EarnedPremNet Single PostedReserve97 LOB \n", - "0 394742 0 281872 wkcomp \n", - "1 394742 0 281872 wkcomp \n", - "2 394742 0 281872 wkcomp \n", - "3 394742 0 281872 wkcomp \n", - "4 394742 0 281872 wkcomp " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Read in the data\n", - "data = pd.read_csv(r'https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/clrd.csv')\n", - "\n", - "# Output\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(376, 3, 10, 10)
Index:[GRNAME]
Columns:[IncurLoss, CumPaidLoss, EarnedPremDIR]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (376, 3, 10, 10)\n", - "Index: [GRNAME]\n", - "Columns: [IncurLoss, CumPaidLoss, EarnedPremDIR]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create a triangle\n", - "triangle = cl.Triangle(\n", - " data, origin='AccidentYear', development='DevelopmentYear',\n", - " index=['GRNAME'], columns=['IncurLoss','CumPaidLoss','EarnedPremDIR'])\n", - "\n", - "triangle\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19883,577,7807,059,9668,826,1519,862,68710,474,69810,814,57610,994,01411,091,36311,171,59011,203,949
19894,090,6807,964,7029,937,52011,098,58811,766,48812,118,79012,311,62912,434,82612,492,899
19904,578,4428,808,48610,985,34712,229,00112,878,54513,238,66713,452,99313,559,557
19914,648,7568,961,75511,154,24412,409,59213,092,03713,447,48113,642,414
19925,139,1429,757,69912,027,98313,289,48513,992,82114,347,271
19935,653,37910,599,42312,953,81214,292,51615,005,138
19946,246,44711,394,96013,845,76415,249,326
19956,473,84311,612,15114,010,098
19966,591,59911,473,912
19976,451,896
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plot data\n", - "ax = triangle['CumPaidLoss'].sum().T.plot(\n", - " marker='.', grid=True,\n", - " title='CAS Loss Reserve Database: Workers Compensation',\n", - " xlabel='Development Period', ylabel='Cumulative Paid Loss');" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T13:51:44.248711\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_triangle_from_pandas" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_triangle_from_pandas\", ax.get_figure(), display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_triangle_slicing.ipynb b/docs/plot_triangle_slicing.ipynb deleted file mode 100644 index 58a159c6..00000000 --- a/docs/plot_triangle_slicing.ipynb +++ /dev/null @@ -1,1896 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "# Triangle Slicing" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates the familiarity of the pandas API applied to a\n", - ":class:`Triangle` instance.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(6, 6, 10, 10)
Index:[LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (6, 6, 10, 10)\n", - "Index: [LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Load data\n", - "clrd = cl.load_sample('clrd')\n", - "\n", - "# pandas-style Aggregations\n", - "clrd = clrd.groupby('LOB').sum()\n", - "clrd" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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"# pandas loc-style index slicing\n", - "clrd = clrd.loc['medmal']\n", - "clrd" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T13:55:39.824006\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plot\n", - "ax = clrd.link_ratio.T.plot(\n", - " marker='o', \n", - " title='Medical Malpractice Link Ratios',\n", - " ylabel='Link Ratio', xlabel='Accident Year');" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T13:56:37.329076\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_triangle_slicing" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_triangle_slicing\", ax.get_figure(), display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_value_at_risk.ipynb b/docs/plot_value_at_risk.ipynb deleted file mode 100644 index b49bec3e..00000000 --- a/docs/plot_value_at_risk.ipynb +++ /dev/null @@ -1,1491 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Value at Risk" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example uses the `BootstrapODPSample` to simulate new triangles that\n", - "are then used to simulate an IBNR distribution from which we can do\n", - "Value at Risk percentile lookups.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Load triangle\n", - "triangle = cl.load_sample('genins')\n", - "\n", - "# Create 1000 bootstrap samples of the triangle\n", - "resampled_triangles = cl.BootstrapODPSample(random_state=42).fit_transform(triangle)\n", - "\n", - "# Create 1000 IBNR estimates\n", - "sim_ibnr = cl.Chainladder().fit(resampled_triangles).ibnr_.sum('origin')\n", - "\n", - "# X - mu\n", - "sim_ibnr = (sim_ibnr - sim_ibnr.mean()).to_frame().sort_values()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T13:57:48.997780\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "# Plot data\n", - "fig, ax = plt.subplots()\n", - "sim_ibnr.index = [item/1000 for item in range(1000)]\n", - "(sim_ibnr/1e6).loc[0.90:].plot(kind='area', alpha=0.5,\n", - " title='Bootstrap VaR (90% and above)', ax=ax).set(\n", - " xlabel='Percentile', xlim=(0.899, 1.0), ylabel='Value (Millions)');\n", - "ax.grid(axis='y')\n", - "for spine in ax.spines:\n", - " ax.spines[spine].set_visible(False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T13:58:18.968022\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_value_at_risk" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_value_at_risk\", ax.get_figure(), display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/plot_voting_chainladder.ipynb b/docs/plot_voting_chainladder.ipynb deleted file mode 100644 index f1a4a68f..00000000 --- a/docs/plot_voting_chainladder.ipynb +++ /dev/null @@ -1,1693 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# VotingChainladder Basics" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates how you can can use the Voting Chainladder method.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Load the data\n", - "raa = cl.load_sample('raa')\n", - "cl_ult = cl.Chainladder().fit(raa).ultimate_ # Chainladder Ultimate\n", - "apriori = cl_ult * 0 + (float(cl_ult.sum()) / 10) # Mean Chainladder Ultimate\n", - "\n", - "# Load estimators to vote between\n", - "bcl = cl.Chainladder()\n", - "cc = cl.CapeCod()\n", - "estimators = [('bcl', bcl), ('cc', cc)]\n", - "\n", - "# Fit VotingChainladder using CC after 1987 and a blend of BCL and CC otherwise\n", - "vot = cl.VotingChainladder(\n", - " estimators=estimators,\n", - " weights=lambda origin: (0, 1) if origin.year > 1987 else (0.5, 0.5)\n", - " )\n", - "vot.fit(raa, sample_weight=apriori)\n", - "\n", - "# Plotting\n", - "bcl_ibnr = bcl.fit(raa).ibnr_.to_frame()\n", - "cc_ibnr = cc.fit(raa, sample_weight=apriori).ibnr_.to_frame()\n", - "vot_ibnr = vot.ibnr_.to_frame()\n", - "\n", - "plot_ibnr = pd.concat([bcl_ibnr, vot_ibnr, cc_ibnr], axis=1)\n", - "plot_ibnr.columns = ['BCL', 'Voting', 'CC']" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2021-10-31T13:59:41.444371\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.4.2, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "\n", - "ax = plot_ibnr.plot(\n", - " kind='bar', ylim=(0, None), \n", - " title='Voting Chainladder IBNR',\n", - " xlabel='Accident Year', ylabel='Loss');" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "application/papermill.record/image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-31T14:00:11.438665\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "plot_voting_chainladder" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from myst_nb import glue\n", - "glue(\"plot_voting_chainladder\", ax.get_figure(), display=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tails.ipynb b/docs/tails.ipynb deleted file mode 100644 index 5abb8287..00000000 --- a/docs/tails.ipynb +++ /dev/null @@ -1,1541 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0a0a49ca-8a9d-42e0-966f-283362624557", - "metadata": {}, - "source": [ - "# Tail Estimators\n", - "Unobserved loss experience beyond the edge of a Triangle can be substantial and is a necessary part of an actuary's analysis. This is particularly so for long tailed lines more common in commercial insurance or excess layers of loss.\n", - "\n", - "With all tail estimation, we are *extrapolating* beyond the known data which comes with its own challenges. It tends to be more difficult to validate assumptions when performing tail estimation. Additionally, many of the techniques carry a high degree of sensitivity to the assumptions you will choose when conducting analysis. \n", - "\n", - "Nevertheless, it is a necessary part of the actuary's toolkit in estimating reserve liabilities." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5ee2a1e0-80ae-455e-85fa-487c838fd07d", - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'" - ] - }, - { - "cell_type": "markdown", - "id": "2f3423ed-d0fd-4d5e-b4de-f708f832b074", - "metadata": {}, - "source": [ - "## Basics and Commonalities\n", - "As with the `Development` family of estimators, the `Tail` module provides a variety of tail **transformers** that allow for the extrapolation of development patterns beyond the end of the triangle. Being transformers, we can expect that each estimator has a `fit` and a `transform` method. It is also expected that the `transform` method will give us back our Triangle with additional estimated parameters for incorporating our tail review in our IBNR estimates. \n", - "\n", - "### Tail\n", - "Every tail estimator produced a `tail_` attribute which represents the point estimate of the tail of the Triangle.\n", - "```{tip}\n", - "Recall that the trailing `underscore_` implies that these parameters are only available once we've `fit` our estimator.\n", - "```\n", - "\n", - "We will demonstrate this property using the [TailConstant](tails:tailconstant) estimator which we explore further below." - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "6bc8412b-d6bb-4083-b12f-480481ddf6ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
120-Ult
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" - ], - "text/plain": [ - " 120-Ult\n", - "(All) 1.1" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle = cl.load_sample('genins')\n", - "tail = cl.TailConstant(1.10).fit_transform(triangle)\n", - "tail.tail_" - ] - }, - { - "cell_type": "markdown", - "id": "658908cf-c915-4c62-adb2-b62675a506a2", - "metadata": {}, - "source": [ - "### Run Off\n", - "\n", - "In addition to point estimates, tail estimators support run-off analysis. The\n", - "`ldf_` and `cdf_` attribute splits the tail point estimate into enough\n", - "development patterns to allow for tail run-off to be examined for at least another\n", - "valuation year by default. For an annual `development_grain` grain, the development pattterns\n", - "include two additional patterns." - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "e106a1a3-d18c-4467-ad8d-3db2bf46178e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult 120-Ult 132-Ult\n", - "(All) 15.891235 4.552571 2.60544 1.787716 1.522949 1.379703 1.27013 1.205201 1.119497 1.1 1.051394" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tail.cdf_" - ] - }, - { - "cell_type": "markdown", - "id": "2a15cc1a-1eaf-4830-be69-8b4dfaa281d9", - "metadata": {}, - "source": [ - "```{note}\n", - "When fitting a tail estimator without first specifying a `Development` estimator first, `chainladder` assumes a volume-weighted `Development`. To override this, you should declare transform your triangle with a development transformer first.\n", - "```\n", - "\n", - "For quarterly grain, there are five additional development patterns and for monthly\n", - "there are thirteen.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "09b4ef13-881f-4475-b724-987a071729a3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
135-Ult138-Ult141-Ult144-Ult147-Ult
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" - ], - "text/plain": [ - " 135-Ult 138-Ult 141-Ult 144-Ult 147-Ult\n", - "(All) 1.00065 1.000531 1.000434 1.000355 1.00029" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle = cl.load_sample('quarterly')['paid']\n", - "tail = cl.TailCurve().fit(triangle)\n", - "# Slice just the tail entries\n", - "tail.cdf_[~tail.ldf_.development.isin(triangle.link_ratio.development)]" - ] - }, - { - "cell_type": "markdown", - "id": "60d40444-6bbb-42db-9335-f31634322224", - "metadata": {}, - "source": [ - "```{note}\n", - "The point estimate `tail_` of the tail is the CDF for 135-Ult. The remaining CDFs simply represent the run-off of this tail as specified by the Tail estimator in use.\n", - "```\n", - "\n", - "### Attachment Age\n", - "\n", - "By default, tail estimators attach to the oldest `development` age of the `Triangle`.\n", - "In practice, the last several known development factors of a `Triangle` can be\n", - "unreliable and attaching the tail earlier and using it as a smoothing mechanism\n", - "is preferred. All tail estimators have an `attachment_age` parameter that\n", - "allows you to select the development age to which the tail will attach." - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "f8aa14fb-a12f-489d-aa28-548877085bff", - "metadata": {}, - "outputs": [], - "source": [ - "triangle = cl.load_sample('genins')\n", - "unsmoothed = cl.TailCurve().fit(triangle).ldf_\n", - "smoothed = cl.TailCurve(attachment_age=24).fit(triangle).ldf_" - ] - }, - { - "cell_type": "markdown", - "id": "daf318e0-f6b5-4126-876e-883910af86e3", - "metadata": {}, - "source": [ - "In this way, we can smooth over any volatile development patterns." - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "27bd974b-481b-4899-bffe-59fc7289510c", - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-06T18:17:54.184267\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pd.concat((\n", - " unsmoothed.T.iloc[:, 0].rename('Unsmoothed'),\n", - " smoothed.T.iloc[:, 0].rename('Age 24+ Smoothed')),\n", - " axis=1).plot(marker='o', title='Selected Link Ratio', ylabel='LDF');" - ] - }, - { - "cell_type": "markdown", - "id": "6081c268-58a0-449f-83c0-0bd72e65c3d5", - "metadata": {}, - "source": [ - "### Projection period\n", - "\n", - "Regardless of where you attach a tail, there will be incremental patterns to at\n", - "least one year past the end of the `Triangle` to support run-off analysis.\n", - "\n", - "This accommodates views of run-off for the oldest origin periods in your triangle for at least another year. As actuarial reviews typically occur no less than annually, this should be sufficient for examining run-off performance between actuarial valuations.\n", - "\n", - "Cases arise where modeling run-off on a longer time horizon is desired. For these cases, it is possible to modify the `projection_period` (in months) of all Tail Esimtators by specifying the number of months you wish to extend your patterns." - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "1ffb22de-fd73-46fc-a1f1-5e61514315be", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
120-132132-144144-156156-168168-180
(All)1.01191.00711.00421.00251.0036
" - ], - "text/plain": [ - " 120-132 132-144 144-156 156-168 168-180\n", - "(All) 1.011946 1.007056 1.004167 1.002461 1.003555" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Extend tail run-off 4 years past end of Triangle.\n", - "tail = cl.TailCurve(projection_period=12*4).fit(triangle)\n", - "tail.ldf_[~tail.ldf_.development.isin(triangle.link_ratio.development)]" - ] - }, - { - "cell_type": "markdown", - "id": "d21599f9-b4e2-4b28-8429-bd71e4be6bcd", - "metadata": {}, - "source": [ - "In this example, we see a higher development pattern for the last period than the ones prior. This is because the true tail run-off exceeds four years. To be clear, the `projection_period` has no effect on the actual estimated tail, it simply provides a longer time horizon for measuring run-off." - ] - }, - { - "cell_type": "markdown", - "id": "4c8969bc-e01e-40cc-a794-37a13104b49a", - "metadata": {}, - "source": [ - "(tails:tailconstant)=\n", - "## TailConstant\n", - "\n", - "`TailConstant` allows you to input a tail factor as a constant. This is\n", - "useful when relying on tail selections from an external source like industry data.\n", - "\n", - "The tail factor supplied applies to all individual triangles contained within\n", - "the Triangle object. If this is not the desired outcome, slicing individual\n", - "triangles and applying `TailConstant` separately to each can be done.\n", - "\n", - "### Decay\n", - "\n", - "For run-off analysis, you can control the decay of your tail. An exponential\n", - "`decay` parameter is also available to facilitate the run off analysis described\n", - "above." - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "531ffe4a-8c12-4c34-a519-2cbcf0fae4be", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120120-132132-144
(All)3.49061.74731.45741.17391.10381.08631.05391.07661.01771.00241.0474
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120 120-132 132-144\n", - "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725 1.002436 1.047448" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tail = cl.TailConstant(tail=1.05, decay=0.95).fit_transform(triangle)\n", - "tail.ldf_" - ] - }, - { - "cell_type": "markdown", - "id": "c20918a2-42d1-4b51-9420-98b8b0de316e", - "metadata": {}, - "source": [ - "As we can see in the example, the 5% tail in the example is split between the\n", - "amount to run-off over the subsequent calendar period **132-144**, and the\n", - "remainder, **144-Ult**. The split is controlled by the `decay` parameter. We\n", - "can always reference our `tail_` point estimate." - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "5cb579be-7e25-40d1-8756-816d54ddeb95", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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120-Ult
(All)1.05
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" - ], - "text/plain": [ - " 120-Ult\n", - "(All) 1.05" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tail.tail_" - ] - }, - { - "cell_type": "markdown", - "id": "40e0f469-249b-4ddd-9a45-6bee5edadcc9", - "metadata": {}, - "source": [ - "### Examples\n", - "\n", - ":::{panels}\n", - ":column: col-lg-4 px-2 py-2\n", - "\n", - "---\n", - "**[DevelopmentConstant Callable](plot_callable_dev_constant)**\n", - "```{glue:} plot_callable_dev_constant\n", - "```\n", - "+++\n", - "{bdg-danger}`hard`\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(tails:tailcurve)=\n", - "## TailCurve\n", - "\n", - "`TailCurve` allows for extrapolating a tail factor using curve fitting.\n", - "Currently, exponential decay of LDFs and inverse power curve are supported." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "736e0edd-293a-4a0f-a4cb-661804518431", - "metadata": {}, - "outputs": [], - "source": [ - "clrd = cl.load_sample('clrd').groupby('LOB').sum()['CumPaidLoss']\n", - "cdf_ip = cl.TailCurve(curve='inverse_power').fit(clrd).tail_\n", - "cdf_xp = cl.TailCurve(curve='exponential').fit(clrd).tail_" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "aaba2663-25c5-45eb-ac76-99440baa1b1c", - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-06T11:19:23.654974\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = pd.concat((cdf_ip.rename(\"Inverse Power\"),\n", - " cdf_xp.rename(\"Exponential\")), axis=1).plot(\n", - " kind='bar', title='Curve Fit Comparison',\n", - " xlabel='Industry', ylabel='Tail Factor')\n", - "\n", - "ax.legend(loc='center', bbox_to_anchor=(.5, -.35), facecolor='white', ncol=2);" - ] - }, - { - "cell_type": "markdown", - "id": "82043cfd-22a0-48e5-82be-b947687f65e8", - "metadata": {}, - "source": [ - "### Regression parameters\n", - "\n", - "Underlying the curve fit is an OLS regression which generates both a `slope_`\n", - "and `intercept_` term.\n", - "\n", - "For the `exponential` curve fit with slope, $\\beta$ and intercept $\\alpha$, the tail factor\n", - "is:\n", - "\n", - "\n", - "$$\n", - "\\prod_{i}^{N}1+exp(\\beta X+\\alpha )\n", - "$$ (tail_exponential_eq)\n", - "\n", - "For the `inverse_power` curve, the tail factor is:\n", - "\n", - "$$\n", - "\\prod_{i}^{N}1+exp(\\alpha) X^{\\beta}\n", - "$$ (tail_inverse_power_eq)\n", - "\n", - "Where $X$ is your selected link ratios and $N$ is the number of years you wish to extrapolate the `tail_`\n", - "\n", - "Deriving the `tail_` factor manually:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4dba8958-8c9a-4d98-8b93-050bddf35269", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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120-Ult
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" - ], - "text/plain": [ - " 120-Ult\n", - "(All) 1.029499" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle = cl.load_sample('genins')\n", - "tail = cl.TailCurve('exponential').fit(triangle)\n", - "tail.tail_" - ] - }, - { - "cell_type": "markdown", - "id": "f3c3aef3-c653-40b0-9828-111769bd91a3", - "metadata": {}, - "source": [ - "A manual calculation using {eq}`tail_exponential_eq` above:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "22d0b670-35ac-4472-9d45-a65a636029ea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0294991710529175" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.prod(\n", - " (1+np.exp(\n", - " np.arange(triangle.shape[-1],\n", - " triangle.shape[-1] + tail.extrap_periods) * \n", - " tail.slope_.values + tail.intercept_.values)\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "82b944f8-835b-4c57-991d-43ff3032ee8b", - "metadata": {}, - "source": [ - "For completeness, let's also confirm {eq}`tail_inverse_power_eq`:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "4d4423e8-0311-4df4-9e0b-b25a2e91f03f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " 120-Ult\n", - "(All) 1.29243" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tail = cl.TailCurve('inverse_power').fit(triangle)\n", - "tail.tail_" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "8a3a1e46-e982-4971-9f85-db8fc60f06d0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.292430311543694" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.prod(\n", - " 1 + np.exp(tail.intercept_.values) * \n", - " (\n", - " np.arange(\n", - " triangle.shape[-1], \n", - " triangle.shape[-1] + tail.extrap_periods\n", - " ) ** tail.slope_.values)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "c798b3b0-590c-4687-9d21-9ddbad185480", - "metadata": {}, - "source": [ - "### Extrapolation Period\n", - "\n", - "From these formulas, the actuary has control over the parameter, $N$ which \n", - "represents how far out how far beyond the edge of Results for the `inverse_power`\n", - "curve are sensitive to this parameter as it tends to converge slowly to its\n", - "asymptotic value. This parameter can be controlled using the `extrap_periods`\n", - "hyperparamter.\n", - "\n", - "```{tip}\n", - "Exponential decay is generally rapid and is less sensitive to the `extrap_period` \n", - "argument than the inverse power fit.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "8a41dcc1-cefd-4967-bab9-a8d8ad5d6a40", - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-06T11:19:25.779404\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tri = cl.load_sample('clrd').groupby('LOB').sum().loc['medmal', 'CumPaidLoss']\n", - "\n", - "# Create a fuction to grab the scalar tail value.\n", - "def scoring(model):\n", - " \"\"\" Scoring functions must return a scalar \"\"\"\n", - " return model.tail_.iloc[0, 0]\n", - "\n", - "# Create a grid of scenarios\n", - "param_grid = dict(\n", - " extrap_periods=list(range(1, 100, 6)),\n", - " curve=['inverse_power', 'exponential'])\n", - "\n", - "# Fit Grid\n", - "model = cl.GridSearch(cl.TailCurve(), param_grid=param_grid, scoring=scoring).fit(tri)\n", - "\n", - "# Plot results\n", - "model.results_.pivot(columns='curve', index='extrap_periods', values='score').plot(\n", - " grid=True, ylim=(1,None), title='Curve Fit Sensitivity to Extrapolation Period').set(\n", - " ylabel='Tail Factor');" - ] - }, - { - "cell_type": "markdown", - "id": "72d11c89-b369-4539-bb4c-409238836974", - "metadata": {}, - "source": [ - "This example demonstrates the `extrap_periods` functionality of the TailCurve estimator. The estimator defaults to extrapolating out 100 periods. However, we can see that the \"Inverse Power\" curve fit doesn't converge to its asymptotic value even with 100 periods whereas the \"exponential\" converges within 10 periods." - ] - }, - { - "cell_type": "markdown", - "id": "89e12919-831e-4754-badd-0aef16b4255c", - "metadata": {}, - "source": [ - "### Fit period\n", - "The default behavior of `TailCurve` is to include all `cdf_` patterns from the Triangle in extrapolating the tail. Often, the data assumptions of linearity will be violated when using all \n", - "selected patterns. In those cases, you can use `fit_period=(start_age, end_age)` for fitting to a contiguous set of patterns." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e742020e-084f-432e-aa08-98729531909b", - "metadata": {}, - "outputs": [], - "source": [ - "dev = cl.Development().fit_transform(cl.load_sample('quarterly')['paid'])\n", - "fit_all = cl.TailCurve().fit(dev)\n", - "exclude = cl.TailCurve(fit_period=(36, None)).fit(dev)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "a5c04d20-ca44-4a4f-8984-a965ee65e319", - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-06T11:21:50.267521\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "obs = (dev.ldf_ - 1).T.iloc[:, 0]\n", - "obs[obs < 0] = np.nan\n", - "\n", - "ax = np.log(obs).rename('Selected LDF').plot(\n", - " style=' ', marker='o', title=f'Fit Period affect on Tail Estimate');\n", - "pd.Series(\n", - " np.arange(1, dev.ldf_.shape[-1] + 1) * exclude.slope_.sum().values + exclude.intercept_.sum().values, \n", - " index=dev.ldf_.development, \n", - " name=f'Ages after 36: {round(exclude.tail_.values[0,0], 3)}').plot(\n", - " ax=ax, style = '--', legend=True);\n", - "pd.Series(\n", - " np.arange(1, dev.ldf_.shape[-1] + 1) * fit_all.slope_.sum().values + fit_all.intercept_.sum().values, \n", - " index=dev.ldf_.development, \n", - " name=f'All Periods: {round(fit_all.tail_.values[0,0], 3)}').plot(\n", - " ax=ax, style = '-.', legend=True);" - ] - }, - { - "cell_type": "markdown", - "id": "be565e57-32f9-4b71-9b7c-cdf36b6dc6dd", - "metadata": {}, - "source": [ - "```{warning}\n", - "The nature of curve fitting with `log` and `exp` transforms is inappropriate for development factors less\n", - "than 1.0. The default behavior of `TailCurve` is to ignore these observations when determining\n", - "the OLS regression parameters. You can force errors by setting the `errors` argument to \"raise\".\n", - "```\n", - "```python\n", - "dev = cl.TailCurve(errors='raise')\n", - "# This will fail because of LDFs < 1 and our choice to 'raise' errors\n", - "dev.fit(cl.load_sample('quarterly')['paid'])\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "65a9d484-cde0-4932-967d-3c1668a7ca3c", - "metadata": {}, - "source": [ - "You can also use a list to of booleans to specify which values you want to include. This allows for finer grain control over excluding outliers from your analysis. The following two estimators are\n", - "equivalent." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "8b5ff0eb-44c0-4ab3-add6-ec4f4412f257", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(cl.TailCurve(fit_period=[False] * 11 + [True] * 33).fit(dev).cdf_ ==\n", - " cl.TailCurve(fit_period=(36, None)).fit(dev).cdf_)" - ] - }, - { - "cell_type": "markdown", - "id": "ff111142-d7a4-4e23-b4b9-7c76127f966d", - "metadata": {}, - "source": [ - "### Examples\n", - "\n", - ":::{panels}\n", - ":column: col-lg-4 px-2 py-2\n", - "\n", - "---\n", - "**[Attachment Age Smoothing](plot_exponential_smoothing)**\n", - "```{glue:} plot_exponential_smoothing\n", - "```\n", - "+++\n", - "{bdg-success}`easy`\n", - "\n", - "---\n", - "**[TailCurve Extrapolation](plot_extrap_period)**\n", - "```{glue:} plot_extrap_period\n", - "```\n", - "+++\n", - "{bdg-warning}`medium`\n", - "\n", - "---\n", - "**[TailCurve Basics](plot_tailcurve_compare)**\n", - "```{glue:} plot_tailcurve_compare\n", - "```\n", - "+++\n", - "{bdg-success}`easy`\n", - ":::\n", - "{cite}`CAS_TFWP2013`" - ] - }, - { - "cell_type": "markdown", - "id": "38788926-acdd-4cf9-a12b-39931c5041da", - "metadata": { - "tags": [] - }, - "source": [ - "(tails:tailbondy)=\n", - "## TailBondy\n", - "Most people are familiar with the Bondy tail know it as a basic method that assumes the \n", - "`tail_` is just a repeat of the last available `ldf_`.\n", - "\n", - "Indeed, without any hyperparameter selection, this is how `TailBondy` works.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "234713f3-53c7-4213-83e1-c570b4487bc3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 120-Ult\n", - "(All) 1.017725" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tail = cl.TailBondy().fit(triangle)\n", - "tail.tail_" - ] - }, - { - "cell_type": "markdown", - "id": "5f6da5e3-ca60-47a4-927e-1f1219ff23c7", - "metadata": {}, - "source": [ - "This simple relationship has a more generalized form. If we define the `ldf_` at time $n$ as $f(n-1)$\n", - "and assume that $f(n) = f(n-1)^{B}$, it follows that the tail $F(n)$ can be defined as:\n", - "\n", - "$$\n", - "F(n)=f(n-1)^{B}f(n-1)^{B^{2}}...=f(n-1)^{\\frac{B}{1-B}}\n", - "$$ (tail_bondy_eq)\n", - "\n", - "where $B$ is some constant, and if $B$ is $\\frac{1}{2}$, then we obtain Bondy's original tail formulation.\n", - "\n", - "Rather than using the only the last known development factor or setting $B=\\frac{1}{2}$, we\n", - "can stablize things using more `ldf_` using the `earliest_age` parameter. We can then minimize\n", - "the difference in the the assumed relationship $f(n) = f(n-1)^{B}$ and our data to estimate $B$.\n", - "\n", - "This is often refered to as the *Generalized Bondy* method." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "fbad0192-78ba-49ce-ba8d-25a00a534972", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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120-Ult
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" - ], - "text/plain": [ - " 120-Ult\n", - "(All) 1.027763" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle = cl.load_sample('tail_sample')['paid']\n", - "dev = cl.Development(average='simple').fit_transform(triangle)\n", - "\n", - "# Estimate the Bondy Tail\n", - "tail = cl.TailBondy(earliest_age=12).fit(dev)\n", - "tail.tail_" - ] - }, - { - "cell_type": "markdown", - "id": "790d9977-e8d2-44f7-9d79-7160436ed211", - "metadata": {}, - "source": [ - "Using our formulation {eq}`tail_bondy_eq` above, we can manually estimate our tail using our earliest `ldf_` \n", - "and our Bondy constant `b_`." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "caaae7b4-647e-440e-9512-26251df7ac6a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " paid\n", - "Total \n", - "Total 1.027756" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get last fitted LDF of the model\n", - "last_fitted_ldf = (tail.earliest_ldf_ ** (tail.b_ ** 8))\n", - "\n", - "# Calculate the tail using the Bondy formula above\n", - "last_fitted_ldf ** (tail.b_ / (1-tail.b_))" - ] - }, - { - "cell_type": "markdown", - "id": "5174eef9-78ee-4bca-9a1d-70e6bfcee369", - "metadata": {}, - "source": [ - "{cite}`CAS_TFWP2013`" - ] - }, - { - "cell_type": "markdown", - "id": "af580a90-ae7b-4601-a29e-c293fe57f186", - "metadata": {}, - "source": [ - "(tails:tailclark)=\n", - "## TailClark\n", - "\n", - "`TailClark` is a continuation of the `ClarkLDF` model. Familiarity\n", - "with [ClarkLDF](development:clarkldf) will aid in understanding this Estimator.\n", - "The growth curve approach used by Clark produces development patterns for any\n", - "age including ages beyond the edge of the Triangle.\n", - "\n", - "An example completing Clark's model:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c24a18be-332e-47da-bfbf-d971d0df5089", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120120-132132-144
(All)4.09491.72051.34251.20051.13061.09101.06651.05041.03931.03131.2540
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120 120-132 132-144\n", - "(All) 4.094936 1.72047 1.342452 1.200518 1.130614 1.091014 1.066512 1.050383 1.039257 1.031297 1.254023" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genins = cl.load_sample('genins')\n", - "dev = cl.ClarkLDF()\n", - "tail = cl.TailClark()\n", - "tail.fit(dev.fit_transform(genins)).ldf_" - ] - }, - { - "cell_type": "markdown", - "id": "7ade6734-fb9f-4fe6-8867-9ee2ebe1642f", - "metadata": {}, - "source": [ - "Coupled with `attachment_age` one could use any Development estimator and replicate the `ClarkLDF` estimator." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9b7e36ee-815c-42ff-9382-6eb23eb2a02a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120120-132132-144
(All)4.09491.72051.34251.20051.13061.09101.06651.05041.03931.03131.2540
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120 120-132 132-144\n", - "(All) 4.094936 1.72047 1.342452 1.200518 1.130614 1.091014 1.066512 1.050383 1.039257 1.031297 1.254023" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genins = cl.load_sample('genins')\n", - "tail = cl.TailClark(attachment_age=12)\n", - "tail.fit(cl.Development().fit_transform(genins)).ldf_" - ] - }, - { - "cell_type": "markdown", - "id": "a14edaef-3960-44e2-8f9e-e0cc9e46d952", - "metadata": {}, - "source": [ - "### Truncated patterns\n", - "\n", - "Clark warns of the dangers of extrapolating too far beyond the edge of a Triangle,\n", - "particularly with a heavy tailed distribution. In these cases, it is suggested that\n", - "a suitable cut-off age or ``truncation_age`` be established where losses are considered\n", - "fully developed. This is very similar to the `extrap_periods` parameter of `TailCurve`,\n", - "however, it is expressed as an age (in months) as opposed to a period length." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "ef6c4d79-7e7f-4ba9-8a7c-c286df0ef9df", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-Ult24-Ult36-Ult48-Ult60-Ult72-Ult84-Ult96-Ult108-Ult120-Ult132-Ult
(All)19.20934.69102.72662.03101.69181.49641.37151.28601.22431.17811.1423
" - ], - "text/plain": [ - " 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult 120-Ult 132-Ult\n", - "(All) 19.209306 4.69099 2.726575 2.031041 1.691804 1.496359 1.371531 1.285997 1.224313 1.178065 1.142313" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tail = cl.TailClark(truncation_age=245)\n", - "tail.fit(dev.fit_transform(genins)).cdf_" - ] - }, - { - "cell_type": "markdown", - "id": "99501ed3-abd3-47e6-91bd-f43d970e23c9", - "metadata": {}, - "source": [ - "{cite}`clark2003`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98a75fe5-edbc-40ea-b545-885282ddbe29", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/triangle.ipynb b/docs/triangle.ipynb deleted file mode 100644 index 06109f3b..00000000 --- a/docs/triangle.ipynb +++ /dev/null @@ -1,4194 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a3117a95", - "metadata": {}, - "source": [ - "(triangle)=\n", - "# Triangle\n", - "\n", - "## Introduction\n", - "\n", - "With this User Guide, we will be covering all of the core functionality of `chainladder`.\n", - "All important user functionality can be referenced from the top level of the library and\n", - "so importing the library as the `cl` namespace is the preferred way of importing from `chainladder`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0f1fb6f6", - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'" - ] - }, - { - "cell_type": "markdown", - "id": "ecab7331", - "metadata": {}, - "source": [ - "```{epigraph}\n", - "Analysis is the intersection of data, models, and assumptions.\n", - "```\n", - "\n", - "Reserving analysis is no different and the loss Triangle is the most ubiquitous data construct used by actuaries today. The chainladder package has its own :class:`Triangle` data structure that behaves much like a pandas ``DataFrame``. \n", - "\n", - "### Why not Pandas?\n", - "This begs the question, why not just use pandas?\n", - "There are several advantages over having a dedicated Triangle object:\n", - "\n", - " * Actuaries work with sets of triangles. DataFrames, being two dimensional, support single triangles with grace but become unwieldy with multiple triangles.\n", - " * We can carry through the meaningful pandas functionality while also supporting triangle specific methods not found in pandas\n", - " * Improved memory footprint with sparse array representation in backend\n", - " * Calculated fields with \"virtual\" columns allows for lazy column evaluation of Triangles as well as improved memory footprint for larger triangles.\n", - " * Ability to support GPU-based backends.\n", - "\n", - "Ultimately, there are a lot of things pandas can do that are not relevant to reserving, and there are a lot of things a Triangle needs to do that are not handled easily with pandas." - ] - }, - { - "cell_type": "markdown", - "id": "666cc8c0", - "metadata": {}, - "source": [ - "### Structure\n", - "\n", - "The :class:`Triangle` is the data structure of the chainladder package. Just as\n", - "Scikit-learn likes to only consume numpy arrays, Chainladder only likes\n", - "Triangles. It is a 4D data structure with labeled axes. These axes are its\n", - "index, columns, origin, development.\n", - "\n", - "``index`` (axis 0):\n", - " The index is the lowest grain at which you want to manage the triangle.\n", - " These can be things like state or company. Like a ``pandas.multiIndex``, you\n", - " can throw more than one column into the index.\n", - "\n", - "``columns`` (axis 1):\n", - " Columns are where you would want to store the different numeric values of your\n", - " data. Paid, Incurred, Counts are all reasonable choices for the columns of your\n", - " triangle.\n", - "\n", - "``origin`` (axis 2):\n", - " The origin is the period of time from which your columns originate. It can\n", - " be an Accident Month, Report Year, Policy Quarter or any other period-like vector.\n", - "\n", - "``development`` (axis 3):\n", - " Development represents the development age or date of your triangle.\n", - " Valuation Month, Valuation Year, Valuation Quarter are good choices.\n", - "\n", - "Despite this structure, you interact with it in the style of pandas. You would\n", - "use ``index`` and ``columns`` in the same way you would for a pandas DataFrame.\n", - "You can think of the 4D structure as a pandas DataFrame where each cell (row,\n", - "column) is its own triangle.\n", - "\n", - "```{eval-rst}\n", - ".. image:: images/triangle_graphic.PNG\n", - "```\n", - "\n", - "Like pandas, you can access the ``values`` property of a triangle to get its numpy\n", - "representation, however the Triangle class provides many helper methods to\n", - "keep the shape of the numpy representation in sync with the other Triangle\n", - "properties.\n", - "\n", - "## Creating a Triangle\n", - "\n", - "### Basic requirements\n", - "You must have a pandas DataFrame on hand to create a triangle. While\n", - "data can come in a variety of forms those formats should be coerced to a pandas\n", - "DataFrame before creating a triangle. The DataFrame also **must** be in tabular\n", - "(long) format, not triangle (wide) format:\n", - "\n", - "```{eval-rst}\n", - ".. image:: images/triangle_bad_good.PNG\n", - "```\n", - "\n", - "At a minimum, the DataFrame must also:\n", - "\n", - " 1. have \"date-like\" columns for the ``origin`` and ``development`` period of the triangle.\n", - " 2. Have a numeric column(s) representing the amount(s) of the triangle.\n", - "\n", - "The reason for these restriction is that the :class:`Triangle` infers a lot of\n", - "useful properties from your DataFrame. For example, it will determine the ``grain``\n", - "and ``valuation_date`` of your triangle which in turn are used to derive many\n", - "other properties of your triangle without further prompting from you.\n", - "\n", - "### Date Inference\n", - "\n", - "When instantiating a :class:`Triangle`, the ``origin`` and ``development``\n", - "arguments can take a ``str`` representing the column name in your pandas DataFrame\n", - "that contains the relevant information. Alternatively, the arguments can also\n", - "take a ``list`` in the case where your DataFrame includes multiple columns that\n", - "represent the dimension, e.g. ``['accident_year','accident_quarter']`` can be\n", - "supplied to create an ``origin`` dimension at the accident quarter grain.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "a390063e", - "metadata": {}, - "source": [ - "```python\n", - "cl.Triangle(data, origin='Acc Year', development=['Cal Year', 'Cal Month'], columns=['Paid Loss'])\n", - "```\n", - " The :class:`Triangle` relies heavily on pandas date inference. In fact,\n", - " ``pd.to_datetime(date_like)`` is exactly how it works. While pandas is excellent\n", - " at inference, it is not perfect. When initializing a Triangle you can always\n", - " use the ``origin_format`` and/or ``development_format`` arguments to force\n", - " the inference. For example, ``origin_format='%Y/%m/%d'``\n", - "\n", - "### Multidimensional Triangle\n", - "So far we've seen how to create a single Triangle, but as described in the Intro\n", - "the Triangle class can hold multiple triangles at once. These triangles share the\n", - "same ``origin`` and ``development`` axes and act as individual cells would in a\n", - "pandas DataFrame. By specifying one or more ``column`` and one or more ``index``,\n", - "we can fill out the 4D triangle structure.\n", - "\n", - "```python\n", - "cl.Triangle(data, origin='Acc Year', development='Cal Year',\n", - " columns=['Paid Loss', 'Incurred Loss'],\n", - " index=['Line of Business', 'State'])\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "9ad267aa", - "metadata": {}, - "source": [ - "### Sample Data\n", - "\n", - "The ``chainladder`` package has several sample triangles. Many of these come\n", - "from existing papers and can be used to verify the results of those papers.\n", - "Additionally, They are a quick way of exploring the functionality of the package.\n", - "These triangles can be called by name using the :func:`~chainladder.load_sample` function." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2737052c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (775, 6, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.load_sample('clrd')" - ] - }, - { - "cell_type": "markdown", - "id": "55948bd3", - "metadata": {}, - "source": [ - "### Other Parameters\n", - "\n", - "Whether a triangle is cumulative or incremental in nature cannot be inferred\n", - "from the \"date-like\" vectors of your DataFrame. You can optionally specify this\n", - "property with the ``cumulative`` parameter.\n", - "```python\n", - "cl.Triangle(\n", - " data, origin='Acc Year', development=['Cal Year'], \n", - " columns=['PaidLoss'], cumulative=True)\n", - "```\n", - "\n", - "```{note}\n", - "The ``cumulative`` parameter is completely optional. If it is not specified,\n", - "the Triangle will infer its cumulative/incremental status at the point you\n", - "call on the `cum_to_incr` or `incr_to_cum` methods discussed below. Some methods\n", - "may not work until the cumulative/incremental status is known.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "23bd8268", - "metadata": {}, - "source": [ - "**Backends**\n", - "\n", - "`Triangle` is built on numpy which serves as the array backend by default.\n", - "However, you can now swap array_backend between numpy, cupy, and sparse to switch\n", - "between CPU and GPU-based computations or dense and sparse backends.\n", - "\n", - "Array backends can be set globally:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "60d34839", - "metadata": {}, - "outputs": [], - "source": [ - "cl.options.set_option('array_backend', 'cupy')\n", - "cl.options.reset_option()" - ] - }, - { - "cell_type": "markdown", - "id": "be8dabf1", - "metadata": {}, - "source": [ - "Alternatively, they can be set per Triangle instance.\n", - "\n", - "```python\n", - "cl.Triangle(..., array_backend='cupy')\n", - "```\n", - "\n", - "```{note}\n", - "You must have a CUDA-enabled graphics card and CuPY installed to use the GPU\n", - "backend. These are optional dependencies of chainladder.\n", - "```\n", - "\n", - "Chainladder by default will swap between the ``numpy`` and ``sparse`` backends. This\n", - "substantially improves the memory footprint of chainladder substantially beyond\n", - "what can be achieved with pandas alone. When a Triangle becomes sufficiently large\n", - "and has a lot of 0 or null entries, the triangle will silently swap between the\n", - "``numpy`` and ``sparse`` backends." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "baf3b810", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(34244, 4, 120, 120)
Index:[ClaimNo, Line, Type, ClaimLiability, Limit, Deductible]
Columns:[reportedCount, closedPaidCount, Paid, Incurred]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2017-12\n", - "Grain: OMDM\n", - "Shape: (34244, 4, 120, 120)\n", - "Index: [ClaimNo, Line, Type, ClaimLiability, Limit, D...\n", - "Columns: [reportedCount, closedPaidCount, Paid, Incurred]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism = cl.load_sample('prism')\n", - "prism" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d6027a4e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'sparse'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism.array_backend" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4b42e2ea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'numpy'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism.sum().array_backend" - ] - }, - { - "cell_type": "markdown", - "id": "d4922160", - "metadata": {}, - "source": [ - "You can globally disable the backend swapping by invoking ``auto_sparse(False)``.\n", - "Any triangle with a cupy backend will not invoke auto_sparse. In the future it may be supported\n", - "when there is better sparse-GPU array support.:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "bc2151fc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'sparse'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.options.set_option('auto_sparse', False)\n", - "prism = cl.load_sample('prism', array_backend='sparse')\n", - "prism.array_backend" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4e9529e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'numpy'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism.sum().array_backend" - ] - }, - { - "cell_type": "markdown", - "id": "fc6ab53c", - "metadata": {}, - "source": [ - "```{warning}\n", - "Loading 'prism' with the numpy backend will consume all of your systems memory.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "5ac3e02e", - "metadata": {}, - "source": [ - "## Basic Functionality\n", - "\n", - "### Representation\n", - "\n", - "The `Triangle` has two different representations. When only a single\n", - "``index`` AND single ``column`` is selected. The triangle is the typical 2-dimensional\n", - "representation we typically think of." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a387dd5d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1, 1, 7, 7)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12243648607284
20073,5116,7268,99210,70411,76312,35012,690
20084,0017,7039,98111,16112,11712,746
20094,3558,28710,23311,75512,993
20104,2957,7509,77311,093
20114,1507,89710,217
20125,1029,650
20136,283
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84\n", - "2007 3511.0 6726.0 8992.0 10704.0 11763.0 12350.0 12690.0\n", - "2008 4001.0 7703.0 9981.0 11161.0 12117.0 12746.0 NaN\n", - "2009 4355.0 8287.0 10233.0 11755.0 12993.0 NaN NaN\n", - "2010 4295.0 7750.0 9773.0 11093.0 NaN NaN NaN\n", - "2011 4150.0 7897.0 10217.0 NaN NaN NaN NaN\n", - "2012 5102.0 9650.0 NaN NaN NaN NaN NaN\n", - "2013 6283.0 NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle = cl.load_sample('ukmotor')\n", - "print(triangle.shape)\n", - "triangle" - ] - }, - { - "cell_type": "markdown", - "id": "db401c5a", - "metadata": {}, - "source": [ - "If more than one ``index`` or more than one column is present, then the Triangle\n", - "takes on more of a summary view." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "0b38a9f6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(775, 6, 10, 10)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (775, 6, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle = cl.load_sample('CLRD')\n", - "print(triangle.shape)\n", - "triangle" - ] - }, - { - "cell_type": "markdown", - "id": "ec566948", - "metadata": {}, - "source": [ - "### Valuation vs Development\n", - "\n", - "While most Estimators that use triangles expect the development period to be\n", - "expressed as an origin age, it is possible to transform a triangle into a valuation\n", - "triangle where the development periods are converted to valuation periods. Expressing\n", - "triangles this way may provide a more convenient view of valuation slices.\n", - "Switching between a development triangle and a valuation triangle can be\n", - "accomplished with the method `dev_to_val` and its inverse `val_to_dev`." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d2a9b8a9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1981198219831984198519861987198819891990
19815,0128,26910,90711,80513,53916,18118,00918,60818,66218,834
19821064,2855,39610,66613,78215,59915,49616,16916,704
19833,4108,99213,87316,14118,73522,21422,86323,466
19845,65511,55515,76621,26623,42526,08327,067
19851,0929,56515,83622,16925,95526,180
19861,5136,44511,70212,93515,852
19875574,02010,94612,314
19881,3516,94713,112
19893,1335,395
19902,063
" - ], - "text/plain": [ - " 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990\n", - "1981 5012.0 8269.0 10907.0 11805.0 13539.0 16181.0 18009.0 18608.0 18662.0 18834.0\n", - "1982 NaN 106.0 4285.0 5396.0 10666.0 13782.0 15599.0 15496.0 16169.0 16704.0\n", - "1983 NaN NaN 3410.0 8992.0 13873.0 16141.0 18735.0 22214.0 22863.0 23466.0\n", - "1984 NaN NaN NaN 5655.0 11555.0 15766.0 21266.0 23425.0 26083.0 27067.0\n", - "1985 NaN NaN NaN NaN 1092.0 9565.0 15836.0 22169.0 25955.0 26180.0\n", - "1986 NaN NaN NaN NaN NaN 1513.0 6445.0 11702.0 12935.0 15852.0\n", - "1987 NaN NaN NaN NaN NaN NaN 557.0 4020.0 10946.0 12314.0\n", - "1988 NaN NaN NaN NaN NaN NaN NaN 1351.0 6947.0 13112.0\n", - "1989 NaN NaN NaN NaN NaN NaN NaN NaN 3133.0 5395.0\n", - "1990 NaN NaN NaN NaN NaN NaN NaN NaN NaN 2063.0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.load_sample('raa').dev_to_val()" - ] - }, - { - "cell_type": "markdown", - "id": "a1f4a01d", - "metadata": {}, - "source": [ - "Triangles have the ``is_val_tri`` property that denotes whether a triangle is in valuation\n", - "mode. The latest diagonal of a Triangle is a valuation triangle.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "dd17b4de", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.load_sample('raa').latest_diagonal.is_val_tri" - ] - }, - { - "cell_type": "markdown", - "id": "8db64393", - "metadata": {}, - "source": [ - "### Incremental vs Cumulative\n", - "\n", - "A triangle is either cumulative or incremental. The ``is_cumulative``\n", - "property will identify this trait. Accumulating an incremental triangle can\n", - "be acomplished with `incr_to_cum`. The inverse operation is `cum_to_incr`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "07a035ce", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19815,0123,2572,6388981,7342,6421,82859954172
19821064,1791,1115,2703,1161,817-103673535
19833,4105,5824,8812,2682,5943,479649603
19845,6555,9004,2115,5002,1592,658984
19851,0928,4736,2716,3333,786225
19861,5134,9325,2571,2332,917
19875573,4636,9261,368
19881,3515,5966,165
19893,1332,262
19902,063
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1981 5012.0 3257.0 2638.0 898.0 1734.0 2642.0 1828.0 599.0 54.0 172.0\n", - "1982 106.0 4179.0 1111.0 5270.0 3116.0 1817.0 -103.0 673.0 535.0 NaN\n", - "1983 3410.0 5582.0 4881.0 2268.0 2594.0 3479.0 649.0 603.0 NaN NaN\n", - "1984 5655.0 5900.0 4211.0 5500.0 2159.0 2658.0 984.0 NaN NaN NaN\n", - "1985 1092.0 8473.0 6271.0 6333.0 3786.0 225.0 NaN NaN NaN NaN\n", - "1986 1513.0 4932.0 5257.0 1233.0 2917.0 NaN NaN NaN NaN NaN\n", - "1987 557.0 3463.0 6926.0 1368.0 NaN NaN NaN NaN NaN NaN\n", - "1988 1351.0 5596.0 6165.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1989 3133.0 2262.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa = cl.load_sample('raa')\n", - "print(raa.is_cumulative)\n", - "raa.cum_to_incr()" - ] - }, - { - "cell_type": "markdown", - "id": "fb4e488d", - "metadata": {}, - "source": [ - "### Triangle Grain\n", - "\n", - "If your triangle has origin and development grains that are more frequent then\n", - "yearly, you can easily swap to a higher grain using the `grain` method of the\n", - "`Triangle`. The `grain` method recognizes Yearly (Y), Quarterly (Q), and\n", - "Monthly (M) grains for both the origin period and development period." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e94c7362", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2006-03
Grain:OYDQ
Shape:(1, 2, 12, 45)
Index:[Total]
Columns:[incurred, paid]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2006-03\n", - "Grain: OYDQ\n", - "Shape: (1, 2, 12, 45)\n", - "Index: [Total]\n", - "Columns: [incurred, paid]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.load_sample('quarterly')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "948168c7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2006-03
Grain:OYDY
Shape:(1, 2, 12, 12)
Index:[Total]
Columns:[incurred, paid]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2006-03\n", - "Grain: OYDY\n", - "Shape: (1, 2, 12, 12)\n", - "Index: [Total]\n", - "Columns: [incurred, paid]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.load_sample('quarterly').grain('OYDY')" - ] - }, - { - "cell_type": "markdown", - "id": "2f3e684f", - "metadata": {}, - "source": [ - "It is generally a good practice to bring your data in at the lowest grain available,\n", - "so that you have full flexibility in aggregating to the grain of your choosing for\n", - "analysis and separately, the grain of your choosing for reporting and communication." - ] - }, - { - "cell_type": "markdown", - "id": "a6a578af", - "metadata": {}, - "source": [ - "### Link Ratios\n", - "\n", - "The age-to-age factors or link ratios of a Triangle can be accessed with the\n", - "``link_ratio`` property. Triangles also have a `heatmap` method that can optionally\n", - "be called to apply conditional formatting to triangle values along an axis. The\n", - "heatmap method requires IPython/Jupyter notebook to render.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "4614dc7f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 12-2424-3636-4848-6060-7272-8484-9696-108108-120120-132
19772.22631.39331.18351.10701.06791.04681.03041.02311.01961.0163
19782.26821.39241.18571.10691.06491.04511.03011.02671.0207
19792.23311.40141.18711.10681.06491.04181.03521.0276
19802.19881.39441.20211.10141.07021.04961.0394
19812.21151.40041.17641.10941.07061.0536
19822.22901.38721.20371.12641.0956
19832.29181.42961.21971.1337
19842.37311.46541.2283
19852.44571.4704
19862.4031
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle = cl.load_sample('abc')\n", - "triangle.link_ratio.heatmap()" - ] - }, - { - "cell_type": "markdown", - "id": "79a55776", - "metadata": {}, - "source": [ - "The Triangle ``link_ratio`` property has unique properties from regular triangles.\n", - "They are considered patterns.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "37df4e8d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle.link_ratio.is_pattern" - ] - }, - { - "cell_type": "markdown", - "id": "c3e85bb3", - "metadata": {}, - "source": [ - "They are considered incremental, and accumulate in a multiplicative fashion.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "cc660eee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-Ult24-Ult36-Ult48-Ult60-Ult72-Ult84-Ult96-Ult108-Ult120-Ult
19774.96332.22931.60001.35191.22131.14361.09241.06011.03621.0163
19784.97952.19541.57671.32981.20141.12821.07951.04791.0207
19794.85282.17311.55071.30631.18021.10831.06381.0276
19804.73942.15551.54581.28591.16751.09101.0394
19814.55942.06161.47221.25141.12801.0536
19824.59322.06071.48551.23411.0956
19834.53021.97671.38271.1337
19844.27151.80001.2283
19853.59621.4704
19862.4031
" - ], - "text/plain": [ - " 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult 120-Ult\n", - "1977 4.963251 2.229335 1.600020 1.351932 1.221266 1.143601 1.092423 1.060146 1.036200 1.016259\n", - "1978 4.979511 2.195398 1.576722 1.329820 1.201420 1.128249 1.079510 1.047930 1.020665 NaN\n", - "1979 4.852844 2.173120 1.550690 1.306262 1.180228 1.108299 1.063799 1.027606 NaN NaN\n", - "1980 4.739387 2.155451 1.545787 1.285875 1.167534 1.090977 1.039414 NaN NaN NaN\n", - "1981 4.559365 2.061644 1.472161 1.251394 1.128033 1.053617 NaN NaN NaN NaN\n", - "1982 4.593220 2.060676 1.485462 1.234099 1.095568 NaN NaN NaN NaN NaN\n", - "1983 4.530235 1.976722 1.382740 1.133654 NaN NaN NaN NaN NaN NaN\n", - "1984 4.271461 1.799967 1.228344 NaN NaN NaN NaN NaN NaN NaN\n", - "1985 3.596180 1.470398 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1986 2.403115 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle = cl.load_sample('abc')\n", - "triangle.link_ratio.incr_to_cum()" - ] - }, - { - "cell_type": "markdown", - "id": "039c77e5", - "metadata": {}, - "source": [ - "### Commutativity\n", - "\n", - "Where possible, the triangle methods are designed to be commutative. For example,\n", - "each of these operations is functionally equivalent." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "2f1c7680", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tri = cl.load_sample('quarterly')\n", - "# Functionally equivalent transformations\n", - "tri.grain('OYDY').val_to_dev() == tri.val_to_dev().grain('OYDY')\n", - "tri.cum_to_incr().grain('OYDY').val_to_dev() == tri.val_to_dev().cum_to_incr().grain('OYDY')\n", - "tri.grain('OYDY').cum_to_incr().val_to_dev().incr_to_cum() == tri.val_to_dev().grain('OYDY')" - ] - }, - { - "cell_type": "markdown", - "id": "6ac27f4f", - "metadata": {}, - "source": [ - "### Performance Tips\n", - "\n", - "Being mindful of commutativity and computational intensity can really help improve\n", - "the performance of the package, particularly for really large triangles. Consider\n", - "these examples that produce identical outputs but with drastically different\n", - "performance. In general, aggregations reduce the number of cells in a Triangle\n", - "and should come as early in your method chain as possible.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "655571ad", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "25.725541299999996" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import timeit\n", - "prism = cl.load_sample('prism')\n", - "# Accumulation before aggregation - BAD\n", - "timeit.timeit(lambda : prism.incr_to_cum().sum(), number=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "fff6b895", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.05498839999999916" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Aggregation before accumulation - GOOD\n", - "timeit.timeit(lambda : prism.sum().incr_to_cum(), number=1)" - ] - }, - { - "cell_type": "markdown", - "id": "cc2c6c2f", - "metadata": {}, - "source": [ - "In other cases, querying the Triangle in clever ways can improve performance.\n", - "Consider that the ``latest_diagonal`` of a cumulative Triangle is equal to the\n", - "sum of its incremental values along the 'development' axis." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "f041d366", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19.788624399999996" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Accumulating a large triangle to get latest_diagonal - BAD\n", - "timeit.timeit(lambda : prism.incr_to_cum().latest_diagonal, number=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "cf24c186", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.07332869999999758" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Summing incrementals of a large triangle to get latest_diagonal - GOOD\n", - "timeit.timeit(lambda : prism.sum('development'), number=1)" - ] - }, - { - "cell_type": "markdown", - "id": "a0566f5f", - "metadata": {}, - "source": [ - "### Trend\n", - "A uniform `trend` factor can also be applied to a Triangle. The trend can\n", - "be applied along the ``origin`` or ``valuation`` axes." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "9e095a5c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12243648607284
20071.34011.27631.21551.15761.10251.05001.0000
20081.27631.21551.15761.10251.05001.0000
20091.21551.15761.10251.05001.0000
20101.15761.10251.05001.0000
20111.10251.05001.0000
20121.05001.0000
20131.0000
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84\n", - "2007 1.340096 1.276282 1.215506 1.157625 1.1025 1.05 1.0\n", - "2008 1.276282 1.215506 1.157625 1.102500 1.0500 1.00 NaN\n", - "2009 1.215506 1.157625 1.102500 1.050000 1.0000 NaN NaN\n", - "2010 1.157625 1.102500 1.050000 1.000000 NaN NaN NaN\n", - "2011 1.102500 1.050000 1.000000 NaN NaN NaN NaN\n", - "2012 1.050000 1.000000 NaN NaN NaN NaN NaN\n", - "2013 1.000000 NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tri = cl.load_sample('ukmotor')\n", - "# Dividing by original triangle to show the trend factor\n", - "tri.trend(0.05, axis='valuation')/tri" - ] - }, - { - "cell_type": "markdown", - "id": "8a18e43b", - "metadata": {}, - "source": [ - "While the `trend` method only allows for a single trend, you can create\n", - "compound trends using ``start`` and ``end`` arguments and chaining them together." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "db88249f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12243648607284
20071.61421.46741.33401.21281.10251.05001.0000
20081.46741.33401.21281.10251.05001.0000
20091.33401.21271.10251.05001.0000
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20111.10251.05001.0000
20121.05001.0000
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" - ], - "text/plain": [ - " 12 24 36 48 60 72 84\n", - "2007 1.614170 1.467428 1.334025 1.21275 1.1025 1.05 1.0\n", - "2008 1.467428 1.334025 1.212750 1.10250 1.0500 1.00 NaN\n", - "2009 1.334025 1.212750 1.102500 1.05000 1.0000 NaN NaN\n", - "2010 1.212750 1.102500 1.050000 1.00000 NaN NaN NaN\n", - "2011 1.102500 1.050000 1.000000 NaN NaN NaN NaN\n", - "2012 1.050000 1.000000 NaN NaN NaN NaN NaN\n", - "2013 1.000000 NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tri.trend(0.05, axis='valuation', start=tri.valuation_date, end='2011-12-31') \\\n", - " .trend(0.10, axis='valuation', start='2011-12-31')/tri" - ] - }, - { - "cell_type": "markdown", - "id": "0d68323c", - "metadata": {}, - "source": [ - "### Correlation Tests\n", - "\n", - "The multiplicative chainladder method is based on the strong assumptions of\n", - "independence across origin years and across valuation years. Mack developed\n", - "tests to verify if these assumptions hold.\n", - "\n", - "These tests are included as methods on the triangle class `valuation_correlation`\n", - "and `development_correlation`. ``False`` indicates that correlation between years\n", - "is not sufficiently large." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "924a3385", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
198219831984198519861987198819891990
1981FalseFalseFalseFalseFalseFalseFalseFalseFalse
" - ], - "text/plain": [ - " 1982 1983 1984 1985 1986 1987 1988 1989 1990\n", - "1981 False False False False False False False False False" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "triangle = cl.load_sample('raa')\n", - "triangle.valuation_correlation().z_critical" - ] - }, - { - "cell_type": "markdown", - "id": "d5bf6b74", - "metadata": {}, - "source": [ - "triangle.development_correlation().t_critical" - ] - }, - { - "cell_type": "markdown", - "id": "6ee130c8", - "metadata": {}, - "source": [ - "There are many properties of these correlation tests and they've been included\n", - "as their own classes. Refer to `ValuationCorrelation` and\n", - "`DevelopmentCorrelation` for additional information.\n", - "\n", - "{cite}`mack1994`" - ] - }, - { - "cell_type": "markdown", - "id": "bad9cfbc", - "metadata": {}, - "source": [ - "## Pandas-style syntax\n", - "\n", - "We've chosen to keep as close as possible to pandas syntax for Triangle data\n", - "manipulation. Relying on the most widely used data manipulation library in Python\n", - "gives us two benefits. This not only allows for easier adoption, but also provides\n", - "stability to the ``chainladder`` API.\n" - ] - }, - { - "cell_type": "markdown", - "id": "3ad2d431", - "metadata": {}, - "source": [ - "### Slicing and Filtering\n", - "\n", - "With a newly minted `Triangle`, individual triangles can be sliced out\n", - "of the object using pandas-style ``loc``/``iloc`` or boolean filtering." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "65edc1e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 1, 10, 10)
Index:[GRNAME, LOB]
Columns:[EarnedPremDIR]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (775, 1, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [EarnedPremDIR]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample('clrd')\n", - "clrd.iloc[0,1]\n", - "clrd[clrd['LOB']=='othliab']\n", - "clrd['EarnedPremDIR']" - ] - }, - { - "cell_type": "markdown", - "id": "ce507e3f", - "metadata": {}, - "source": [ - "```{note}\n", - "Boolean filtering on non-index columns in pandas feels natural. We've exposed\n", - "the same syntax specifically for the index column(s) of the Triangle without the\n", - "need for ``reset_index()`` or trying to boolean-filter a ``MultiIndex``. This is\n", - "a divergence from the pandas API.\n", - "```\n", - "\n", - "As of version ``0.7.6``, four-dimensional slicing is supported:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "720ffc06", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(5, 5, 5, 4)
Index:[GRNAME, LOB]
Columns:[CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (5, 5, 5, 4)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedP..." - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample('clrd')\n", - "clrd.iloc[[0, 10, 3], 1:8, :5, :]\n", - "clrd.loc[:'Aegis Grp', 'CumPaidLoss':, '1990':'1994', :48]" - ] - }, - { - "cell_type": "markdown", - "id": "caeec05e", - "metadata": {}, - "source": [ - "As of version ``0.8.3``, ``.iat`` and ``at`` functionality have been added. Similar\n", - "to pandas, one can use these for value assignment for a single cell of a `Triangle`.\n", - "When a 'sparse' backend is in use, these accessors are the only way to modify individual\n", - "cells of a triangle.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "622a6ee4", - "metadata": {}, - "outputs": [], - "source": [ - "raa = cl.load_sample('raa').set_backend('sparse')\n", - "# To modify a sparse triangle, we need to use at or iat\n", - "raa.at['Total', 'values', '1985', 12] = 10000" - ] - }, - { - "cell_type": "markdown", - "id": "f58b53d6", - "metadata": {}, - "source": [ - "### Arithmetic\n", - "\n", - "Most arithmetic operations can be used to create new triangles within your\n", - "triangle instance. Like with pandas, these can automatically be added as new\n", - "columns to your `Triangle`." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "3de7969a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 7, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncur]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (775, 7, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample('clrd')\n", - "clrd['CaseIncur'] = clrd['IncurLoss']-clrd['BulkLoss']\n", - "clrd" - ] - }, - { - "cell_type": "markdown", - "id": "4ccc132e", - "metadata": {}, - "source": [ - "For `origin` and `development` axes, arithmetic follows `numpy broadcasting `_\n", - "rules. If broadcasting fails, arithmetic operations will rely on ``origin`` and\n", - "``development`` vectors to determine whether an operation is legal.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "38eac8d6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1981 5012.0 8269.0 10907.0 11805.0 13539.0 16181.0 18009.0 18608.0 18662.0 18834.0\n", - "1982 106.0 4285.0 5396.0 10666.0 13782.0 15599.0 15496.0 16169.0 16704.0 NaN\n", - "1983 3410.0 8992.0 13873.0 16141.0 18735.0 22214.0 22863.0 23466.0 NaN NaN\n", - "1984 5655.0 11555.0 15766.0 21266.0 23425.0 26083.0 27067.0 NaN NaN NaN\n", - "1985 1092.0 9565.0 15836.0 22169.0 25955.0 26180.0 NaN NaN NaN NaN\n", - "1986 1513.0 6445.0 11702.0 12935.0 15852.0 NaN NaN NaN NaN NaN\n", - "1987 557.0 4020.0 10946.0 12314.0 NaN NaN NaN NaN NaN NaN\n", - "1988 1351.0 6947.0 13112.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1989 3133.0 5395.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa = cl.load_sample('raa')\n", - "# Allow for arithmetic beyond numpy broadcasting rules\n", - "raa[raa.origin<'1985']+raa[raa.origin>='1985']" - ] - }, - { - "cell_type": "markdown", - "id": "865ba7a9", - "metadata": {}, - "source": [ - "```python\n", - "# Numpy broadcasting equivalent fails\n", - "raa[raa.origin<'1985'].values+raa[raa.origin>='1985'].values\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "b4e37742", - "metadata": {}, - "source": [ - "Arithmetic between two Triangles with different labels will align the axes\n", - "of each Triangle consistent with arithmetic of a pandas Series. Bypassing\n", - "index matching can be accomplished with arithmetic between an Triangle and an array.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "4f0a11be", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s1 = cl.load_sample('clrd').iloc[:3]\n", - "s2 = s1.sort_index(ascending=False)\n", - "s1 + s2 == 2 * s1" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "9d0fd807", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s1 + s2.values == 2 * s1" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "36c1149f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s3 = s1.iloc[:, ::-1]\n", - "s1 + s3 == 2 * s1" - ] - }, - { - "cell_type": "markdown", - "id": "f4602c4f", - "metadata": {}, - "source": [ - "### Virtual Columns\n", - "There are instances where we want to defer calculations, we can create \"virtual\"\n", - "columns that defer calculation to when needed. These columns can be created by wrapping a\n", - "normal column in a function. Lambda expressions work as a tidy representation of\n", - "virtual columns." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "c26c1f24", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 0.242399 0.478321 0.597982 0.668209 0.709673 0.732700 0.744857 0.751453 0.756888 0.759081\n", - "1989 0.251711 0.490090 0.611483 0.682927 0.724025 0.745703 0.757569 0.765150 0.768723 NaN\n", - "1990 0.254824 0.490257 0.611415 0.680634 0.716786 0.736829 0.748758 0.754689 NaN NaN\n", - "1991 0.236422 0.455768 0.567272 0.631115 0.665822 0.683899 0.693812 NaN NaN NaN\n", - "1992 0.242317 0.460087 0.567134 0.626615 0.659779 0.676491 NaN NaN NaN NaN\n", - "1993 0.246314 0.461809 0.564389 0.622715 0.653763 NaN NaN NaN NaN NaN\n", - "1994 0.252294 0.460242 0.559230 0.615920 NaN NaN NaN NaN NaN NaN\n", - "1995 0.247836 0.444543 0.536343 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 0.245855 0.427956 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 0.238284 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample('clrd')\n", - "# A physical column with immediate evaluation\n", - "clrd['PaidLossRatio'] = clrd['CumPaidLoss'] / clrd['EarnedPremDIR']\n", - "# A virtual column with deferred evaluation\n", - "clrd['PaidLossRatio'] = lambda clrd : clrd['CumPaidLoss'] / clrd['EarnedPremDIR']\n", - "# Good - Defer loss ratio calculation until after summing premiums and losses\n", - "clrd.sum()['PaidLossRatio']" - ] - }, - { - "cell_type": "markdown", - "id": "2021199b", - "metadata": {}, - "source": [ - "Virtual column expressions should only reference other columns in the same triangle.\n", - "A Triangle without all the underlying columns will fail.\n", - "\n", - "```python\n", - "# Eliminating EarnedPremDIR will result in a calculation failure\n", - "clrd[['CumPaidLoss', 'PaidLossRatio']].sum()['PaidLossRatio']\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "eaa8edfc", - "metadata": {}, - "source": [ - "When used in tandem with the 'sparse' backend, virtual columns can also substantially\n", - "reduce the memory footprint of your Triangle. This is because the calculation\n", - "expression is the only thing in memory.\n" - ] - }, - { - "cell_type": "markdown", - "id": "0f1a957d", - "metadata": {}, - "source": [ - "### Aggregations\n", - "\n", - "It is generally good practice to bring your data into ``chainladder`` at a ganularity\n", - "that is comfortably supported by your system RAM. This provides the greatest flexibility\n", - "in analyzing your data within the ``chainladder`` framework. However, not everything\n", - "needs to be analyzed at the most granular level. Like pandas, you can aggregate\n", - "multiple triangles within a `Triangle` by using `sum()` which can\n", - "optionally be coupled with `groupby()`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "6bd8ff1a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(1, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (1, 6, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample('clrd')\n", - "clrd.sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "e6e5e294", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(6, 6, 10, 10)
Index:[LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (6, 6, 10, 10)\n", - "Index: [LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd.groupby('LOB').sum()" - ] - }, - { - "cell_type": "markdown", - "id": "c068a6d9", - "metadata": {}, - "source": [ - "By default, the aggregation will apply to the first axis with a length greater\n", - "than 1. Alternatively, you can specify the axis using the ``axis`` argument of\n", - "the aggregate method.\n", - "\n", - "Like pandas, the `groupby` method supports any groupable list. This allows for\n", - "complex groupings that can be derived dynamically.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "416ccea3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(11, 6, 10, 10)
Index:[GRNAME]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (11, 6, 10, 10)\n", - "Index: [GRNAME]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample('clrd')\n", - "clrd = clrd[clrd['LOB']=='comauto']\n", - "# Identify the largest commercial auto carriers (by premium) for 1997\n", - "top_10 = clrd['EarnedPremDIR'].groupby('GRNAME').sum().latest_diagonal.loc[..., '1997', :].to_frame().nlargest(10)\n", - "# Group any companies together that are not in the top 10\n", - "clrd.groupby(clrd.index['GRNAME'].map(lambda x: x if x in top_10.index else 'Remainder')).sum()" - ] - }, - { - "cell_type": "markdown", - "id": "368d5a78", - "metadata": {}, - "source": [ - "### Converting to DataFrame\n", - "When a triangle is presented with a single index level and single column, it\n", - "becomes a 2D object. As such, its display format changes to that similar to a\n", - "dataframe. These 2D triangles can easily be converted to a pandas dataframe\n", - "using the `to_frame` method.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "5b11e8eb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1224364860728496108120
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" - ], - "text/plain": [ - "origin 1988 1989 1990 1991 1992 \\\n", - "LOB \n", - "comauto 626097.0 674441.0 718396.0 711762.0 731033.0 \n", - "medmal 217239.0 222707.0 235717.0 275923.0 267007.0 \n", - "othliab 317889.0 350684.0 361103.0 426085.0 389250.0 \n", - "ppauto 8690036.0 9823747.0 10728411.0 10713621.0 11555121.0 \n", - "prodliab 110973.0 112614.0 121255.0 100276.0 76059.0 \n", - "wkcomp 1241715.0 1308706.0 1394675.0 1414747.0 1328801.0 \n", - "\n", - "origin 1993 1994 1995 1996 1997 \n", - "LOB \n", - "comauto 762039.0 768095.0 675166.0 510191.0 272342.0 \n", - "medmal 276235.0 252449.0 209222.0 107474.0 20361.0 \n", - "othliab 434995.0 402244.0 294332.0 191258.0 54130.0 \n", - "ppauto 12249826.0 12600432.0 11807279.0 9900842.0 5754249.0 \n", - "prodliab 94462.0 111264.0 62018.0 28107.0 10682.0 \n", - "wkcomp 1187581.0 1114842.0 962081.0 736040.0 340132.0 " - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd['CumPaidLoss'].groupby('LOB').sum().latest_diagonal.to_frame()" - ] - }, - { - "cell_type": "markdown", - "id": "0dfcaa8e", - "metadata": {}, - "source": [ - "The entire 4D triangle can be flattened to a DataFrame in long format. This can\n", - "be handy for moving back and forth between pandas and chainladder.\n", - "\n", - "Because ``chainladder`` only supports valuation dates when creating new triangles,\n", - "it is often helpful converting to a valuation format before moving to pandas.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "a740a308", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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originvaluationIncurLossCumPaidLossBulkLossEarnedPremDIREarnedPremCededEarnedPremNet
GRNAMELOB
Adriatic Ins Coothliab1995-01-011995-12-31 23:59:59.9999999998.0NaN8.0139.0131.08.0
othliab1995-01-011996-12-31 23:59:59.9999999993.0NaN-4.0NaNNaNNaN
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" - ], - "text/plain": [ - " origin valuation IncurLoss \\\n", - "GRNAME LOB \n", - "Adriatic Ins Co othliab 1995-01-01 1995-12-31 23:59:59.999999999 8.0 \n", - " othliab 1995-01-01 1996-12-31 23:59:59.999999999 3.0 \n", - " othliab 1995-01-01 1997-12-31 23:59:59.999999999 -4.0 \n", - " othliab 1996-01-01 1996-12-31 23:59:59.999999999 40.0 \n", - " othliab 1997-01-01 1997-12-31 23:59:59.999999999 67.0 \n", - "\n", - " CumPaidLoss BulkLoss EarnedPremDIR \\\n", - "GRNAME LOB \n", - "Adriatic Ins Co othliab NaN 8.0 139.0 \n", - " othliab NaN -4.0 NaN \n", - " othliab 3.0 NaN NaN \n", - " othliab NaN 40.0 410.0 \n", - " othliab NaN 31.0 458.0 \n", - "\n", - " EarnedPremCeded EarnedPremNet \n", - "GRNAME LOB \n", - "Adriatic Ins Co othliab 131.0 8.0 \n", - " othliab NaN NaN \n", - " othliab NaN NaN \n", - " othliab 359.0 51.0 \n", - " othliab 425.0 33.0 " - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample('clrd')\n", - "df = clrd.dev_to_val().cum_to_incr().to_frame()\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "5f66b8b2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[BulkLoss, CumPaidLoss, EarnedPremCeded, EarnedPremDIR, EarnedPremNet, IncurLoss]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (775, 6, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [BulkLoss, CumPaidLoss, EarnedPremCeded, Earne..." - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Triangle(\n", - " df.reset_index(), index=['GRNAME', 'LOB'], \n", - " origin='origin', development='valuation',\n", - " columns=['BulkLoss', 'CumPaidLoss', 'EarnedPremCeded', \n", - " 'EarnedPremDIR', 'EarnedPremNet', 'IncurLoss']\n", - ").incr_to_cum()" - ] - }, - { - "cell_type": "markdown", - "id": "bb732da6", - "metadata": {}, - "source": [ - "To enforce long format when moving to pandas the ``keepdims`` argument guarantees\n", - "that all 4 dimensions of the Triangle will be preserved when moving to pandas.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "ccefc128", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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origindevelopmentvalues
Total
Total1981-01-01125012.0
Total1981-01-01248269.0
Total1981-01-013610907.0
Total1981-01-014811805.0
Total1981-01-016013539.0
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" - ], - "text/plain": [ - " origin development values\n", - "Total \n", - "Total 1981-01-01 12 5012.0\n", - "Total 1981-01-01 24 8269.0\n", - "Total 1981-01-01 36 10907.0\n", - "Total 1981-01-01 48 11805.0\n", - "Total 1981-01-01 60 13539.0" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.load_sample('raa').to_frame(keepdims=True).head()" - ] - }, - { - "cell_type": "markdown", - "id": "bb8b4462", - "metadata": {}, - "source": [ - "### Exposing Pandas functionality\n", - "\n", - "The ability to move from a triangle to a pandas DataFrame opens up the full\n", - "suite of pandas functionality to you. For the more commonly used\n", - "functionality, we handle the `to_frame()` for you. For example,\n", - "``triangle.to_frame().plot()`` is equivalent to ``triangle.plot()``.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "06742d63", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-04T07:02:22.695112\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cl.load_sample('clrd').groupby('LOB').sum().loc['wkcomp', 'CumPaidLoss'].T.plot(\n", - " marker='.',\n", - " title='CAS Loss Reserve Database: Workers Compensation').set(\n", - " xlabel='Development Period', ylabel='Cumulative Paid Loss');" - ] - }, - { - "cell_type": "markdown", - "id": "53bd3df0", - "metadata": {}, - "source": [ - "Many of the more commonly used pandas methods are passed through in this way\n", - "allowing for working with triangles as DataFrames.\n", - "\n", - "\n", - "|Aggregations | IO | Shaping | Other|\n", - "|-------------|------------------|-------------|---------------------|\n", - "|``sum`` | ``to_clipboard`` | ``unstack`` | ``plot``|\n", - "|``mean`` | ``to_csv`` | ``pivot`` | ``rename``|\n", - "|``median`` | ``to_excel`` | ``melt`` | ``pct_chg``|\n", - "|``max`` | ``to_json`` | ``T`` | ``round``|\n", - "|``min`` | ``to_html`` | ``drop`` | ``hvplot``|\n", - "|``prod`` | ``to_dict`` | ``dropna`` | ``drop_duplicates``|\n", - "|``var`` | | | ``describe``|\n", - "|``std`` | | | | \n", - "|``cumsum`` | | | | \n", - "|``quantile`` | | | | \n", - "\n", - "```{note}\n", - "While some of these methods have been rewritten to return a Triangle, Many\n", - "are pandas methods and will have return values consistent with pandas.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "0e7f85e2", - "metadata": {}, - "source": [ - "### Accessors\n", - "\n", - "Like pandas ``.str`` and ``.dt`` accessor functions, you can also perform operations\n", - "on the ``origin``, ``development`` or ``valuation`` of a triangle. For example, all\n", - "of these operations are legal." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "c54f8d9e", - "metadata": {}, - "outputs": [], - "source": [ - "raa = cl.load_sample('raa')\n", - "x = raa[raa.origin=='1986']\n", - "x = raa[(raa.development>=24)&(raa.development<=48)]\n", - "x = raa[raa.origin<='1985-JUN']\n", - "x = raa[raa.origin>'1987-01-01'][raa.development<=36]\n", - "x = raa[raa.valuation'1987-01-01')&(raa.development<=36)]\n", - "# Instead, chain the boolean filters together.\n", - "x = raa[raa.origin>'1987-01-01'][raa.development<=36]\n", - "``` \n", - "\n", - "When using the accessors to filter a triangle, you may be left with empty portions\n", - "of the triangle that need to be trimmed up. The `dropna` method will look for\n", - "any origin periods or development periods that are fully empty at the edges\n", - "of the triangle and eliminate them for you." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "c73d271e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 1351.0 6947.0 13112.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1989 3133.0 5395.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa = cl.load_sample('raa')\n", - "raa[raa.origin>'1987-01-01']" - ] - }, - { - "cell_type": "markdown", - "id": "57689670", - "metadata": {}, - "source": [ - "There are many more methods available to manipulate triangles. The complete\n", - "list of methods is available under the `Triangle` docstrings.\n" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "815088b5be8edc0041246414015ef72f7907068f95114ad939d47d8b23d533db" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/data-tutorial.ipynb b/docs/tutorials/data-tutorial.ipynb deleted file mode 100644 index 1b0a6c77..00000000 --- a/docs/tutorials/data-tutorial.ipynb +++ /dev/null @@ -1,1619 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Preparation\n", - "## Getting Started\n", - "This tutorial focuses on data, including a brief discussion on how to best prepare your data so it works well with the `chainladder` package. \n", - "\n", - "Be sure to make sure your packages are updated. For more info on how to update your pakages, visit [Keeping Packages Updated](https://chainladder-python.readthedocs.io/en/latest/library/install.html#keeping-packages-updated)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pandas: 1.4.2\n", - "numpy: 1.22.4\n", - "chainladder: 0.8.12\n" - ] - } - ], - "source": [ - "# Black linter, optional\n", - "%load_ext lab_black\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import chainladder as cl\n", - "import matplotlib.pyplot as plt\n", - "\n", - "print(\"pandas: \" + pd.__version__)\n", - "print(\"numpy: \" + np.__version__)\n", - "print(\"chainladder: \" + cl.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Disclaimer\n", - "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Converting Triangle Data into Long Format\n", - "\n", - "One of the most commonly asked questions is that if the data needs to be in the tabular long format as opposed to the already processed triangle format when we are loading the data for use. \n", - "\n", - "Unfortunately, the chainladder package requires the data to be in long form. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Suppose you have a wide triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " level_0 level_1 0\n", - "0 12 1981-01-01 5012.0\n", - "1 12 1982-01-01 106.0\n", - "2 12 1983-01-01 3410.0\n", - "3 12 1984-01-01 5655.0\n", - "4 12 1985-01-01 1092.0\n", - "5 12 1986-01-01 1513.0\n", - "6 12 1987-01-01 557.0\n", - "7 12 1988-01-01 1351.0\n", - "8 12 1989-01-01 3133.0\n", - "9 12 1990-01-01 2063.0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = df.unstack().dropna().reset_index()\n", - "df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's clean up our column names before we get too far." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ageoriginvalues
0121981-01-015012.0
1121982-01-01106.0
2121983-01-013410.0
3121984-01-015655.0
4121985-01-011092.0
\n", - "
" - ], - "text/plain": [ - " age origin values\n", - "0 12 1981-01-01 5012.0\n", - "1 12 1982-01-01 106.0\n", - "2 12 1983-01-01 3410.0\n", - "3 12 1984-01-01 5655.0\n", - "4 12 1985-01-01 1092.0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns = [\"age\", \"origin\", \"values\"]\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will need a valuation column (think Schedule P style triangle)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ageoriginvaluesvaluation
0121981-01-015012.01981
1121982-01-01106.01982
2121983-01-013410.01983
3121984-01-015655.01984
4121985-01-011092.01985
\n", - "
" - ], - "text/plain": [ - " age origin values valuation\n", - "0 12 1981-01-01 5012.0 1981\n", - "1 12 1982-01-01 106.0 1982\n", - "2 12 1983-01-01 3410.0 1983\n", - "3 12 1984-01-01 5655.0 1984\n", - "4 12 1985-01-01 1092.0 1985" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[\"valuation\"] = (df[\"origin\"].dt.year + df[\"age\"] / 12 - 1).astype(int)\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we are finally ready to load it into the chainladder package!" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19815,0128,26910,90711,80513,53916,18118,00918,60818,66218,834
19821064,2855,39610,66613,78215,59915,49616,16916,704
19833,4108,99213,87316,14118,73522,21422,86323,466
19845,65511,55515,76621,26623,42526,08327,067
19851,0929,56515,83622,16925,95526,180
19861,5136,44511,70212,93515,852
19875574,02010,94612,314
19881,3516,94713,112
19893,1335,395
19902,063
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1981 5012.0 8269.0 10907.0 11805.0 13539.0 16181.0 18009.0 18608.0 18662.0 18834.0\n", - "1982 106.0 4285.0 5396.0 10666.0 13782.0 15599.0 15496.0 16169.0 16704.0 NaN\n", - "1983 3410.0 8992.0 13873.0 16141.0 18735.0 22214.0 22863.0 23466.0 NaN NaN\n", - "1984 5655.0 11555.0 15766.0 21266.0 23425.0 26083.0 27067.0 NaN NaN NaN\n", - "1985 1092.0 9565.0 15836.0 22169.0 25955.0 26180.0 NaN NaN NaN NaN\n", - "1986 1513.0 6445.0 11702.0 12935.0 15852.0 NaN NaN NaN NaN NaN\n", - "1987 557.0 4020.0 10946.0 12314.0 NaN NaN NaN NaN NaN NaN\n", - "1988 1351.0 6947.0 13112.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1989 3133.0 5395.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Triangle(\n", - " df, origin=\"origin\", development=\"valuation\", columns=\"values\", cumulative=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sparse Triangles\n", - "By default, the chainladder `Triangle` is a wrapper around a numpy array. Numpy is optimized for high performance and this allows chainladder to achieve decent computational speeds. Despite being fast, numpy can become memory inefficient with triangle data because triangles are inherently sparse (when memory is being allocated yet no data is stored).\n", - "\n", - "The lower half of a an incomplete triangle is generally blank and that means about 50% of an array size is wasted on empty space. As we include granular index and column values in our `Triangle`, the sparsity of the triangle increases further consuming RAM unnecessarily. Chainladder automatically eliminates this extraneous consumption of memory by resorting to a sparse array representation when the Triangle becomes sufficiently large.\n", - "\n", - "Let's load the `prism` dataset and include each claim number in the index of the Triangle. The dataset is claim level and includes over 130,000 triangles." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(34244, 4, 120, 120)
Index:[ClaimNo, Line, Type, ClaimLiability, Limit, Deductible]
Columns:[reportedCount, closedPaidCount, Paid, Incurred]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2017-12\n", - "Grain: OMDM\n", - "Shape: (34244, 4, 120, 120)\n", - "Index: [ClaimNo, Line, Type, ClaimLiability, Limit, D...\n", - "Columns: [reportedCount, closedPaidCount, Paid, Incurred]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism = cl.load_sample(\"prism\")\n", - "prism" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's also look at the array representation of the Triangle and notice how it is no longer a numpy array, but instead a sparse array." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Formatcoo
Data Typefloat64
Shape(34244, 4, 120, 120)
nnz121178
Density6.143513381095148e-05
Read-onlyTrue
Size4.6M
Storage ratio0.0
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism.values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The sparse array consumes about 4.6Mb of memory. We can also see its density is very low, this is because individual claims will at most exist in only one origin period. Let's approximate the size of this Triangle assuming we used a dense array representation. Approximation can be done by assuming 8 bytes (for float64) of memory are used for each cell in the array." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dense array size: 15779.6352 MB.\n", - "Sparse array size: 4.84712 MB.\n", - "Dense array is 3255.5 times larger!\n" - ] - } - ], - "source": [ - "print(\"Dense array size:\", np.prod(prism.shape) / 1e6 * 8, \"MB.\")\n", - "print(\"Sparse array size:\", prism.values.nbytes / 1e6, \"MB.\")\n", - "print(\n", - " \"Dense array is\",\n", - " round((np.prod(prism.shape) / 1e6 * 8) / (prism.values.nbytes / 1e6), 1),\n", - " \"times larger!\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Incremental vs Cumulative Triangles\n", - "Cumulative triangles are naturally denser than those stored in an incremental fashion. While almost all actuarial techniques rely on cumulative triangles, it may be worthwhile to maintain and manipulate triangles as incremental triangles until you are ready to apply a model." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Formatcoo
Data Typefloat64
Shape(34244, 4, 120, 120)
nnz121178
Density6.143513381095148e-05
Read-onlyTrue
Size4.6M
Storage ratio0.0
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism.values" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Formatcoo
Data Typefloat64
Shape(34244, 4, 120, 120)
nnz5750047
Density0.00291517360299939
Read-onlyTrue
Size219.3M
Storage ratio0.0
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism = prism.incr_to_cum()\n", - "prism.values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our incremental triangle is under 5MB, but when we convert to a cumulative triangle it becomes an astonishingly large 219MB and this is despite still maintaining a sparsity of under 0.3%!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Claim-Level Data\n", - "The sparse representation of triangles allows for substantially more data to be pushed through chainladder. This gives us some nice capabilities that we would not otherwise be able to do with aggregate data.\n", - "\n", - "For example, we can now drill into the individual claim makeup of any cell in our Triangle. Let's look at January 2017 claim details at age 12." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ClaimNoLineTypeClaimLiabilityLimitDeductiblereportedCountclosedPaidCountPaidIncurred
038339AutoPDFalse8000.010001.00.00.0000000.000000
138436AutoPDTrue15000.010001.01.08337.8758638337.875863
238142AutoPDTrue8000.010001.01.07000.0000007000.000000
338195AutoPDTrue20000.010001.01.019000.00000019000.000000
438158AutoPDTrue20000.010001.01.010686.22942010686.229420
.................................
15538393AutoPDTrue8000.010001.01.07000.0000007000.000000
15638396AutoPDTrue8000.010001.01.07000.0000007000.000000
15738455AutoPDTrue20000.010001.01.09927.3512249927.351224
15838457AutoPDTrue15000.010001.01.07874.8790707874.879070
15938460AutoPDTrue15000.010001.01.03125.8406723125.840672
\n", - "

160 rows × 10 columns

\n", - "
" - ], - "text/plain": [ - " ClaimNo Line Type ClaimLiability Limit Deductible reportedCount \\\n", - "0 38339 Auto PD False 8000.0 1000 1.0 \n", - "1 38436 Auto PD True 15000.0 1000 1.0 \n", - "2 38142 Auto PD True 8000.0 1000 1.0 \n", - "3 38195 Auto PD True 20000.0 1000 1.0 \n", - "4 38158 Auto PD True 20000.0 1000 1.0 \n", - ".. ... ... ... ... ... ... ... \n", - "155 38393 Auto PD True 8000.0 1000 1.0 \n", - "156 38396 Auto PD True 8000.0 1000 1.0 \n", - "157 38455 Auto PD True 20000.0 1000 1.0 \n", - "158 38457 Auto PD True 15000.0 1000 1.0 \n", - "159 38460 Auto PD True 15000.0 1000 1.0 \n", - "\n", - " closedPaidCount Paid Incurred \n", - "0 0.0 0.000000 0.000000 \n", - "1 1.0 8337.875863 8337.875863 \n", - "2 1.0 7000.000000 7000.000000 \n", - "3 1.0 19000.000000 19000.000000 \n", - "4 1.0 10686.229420 10686.229420 \n", - ".. ... ... ... \n", - "155 1.0 7000.000000 7000.000000 \n", - "156 1.0 7000.000000 7000.000000 \n", - "157 1.0 9927.351224 9927.351224 \n", - "158 1.0 7874.879070 7874.879070 \n", - "159 1.0 3125.840672 3125.840672 \n", - "\n", - "[160 rows x 10 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "claims = prism[prism.origin == \"2017-01\"][prism.development == 12].to_frame(\n", - " origin_as_datetime=True\n", - ")\n", - "claims[abs(claims).sum(axis=\"columns\") != 0].reset_index()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also examine the data as the usual aggregated Triangle, by applying `sum()`. We'll also apply `grain()` so we can better visualize the data." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(prism[\"Paid\"].sum().grain(\"OYDM\").to_frame(origin_as_datetime=True).T)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With claim level data, we can set a claim large loss cap or create an excess Triangle on the fly." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "prism[\"Capped 100k Paid\"] = cl.minimum(prism[\"Paid\"], 100000)\n", - "prism[\"Excess 100k Paid\"] = prism[\"Paid\"] - prism[\"Capped 100k Paid\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(\n", - " prism[\"Excess 100k Paid\"].sum().grain(\"OYDM\").to_frame(origin_as_datetime=True).T\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Claim-Level IBNR Estimates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see how we can use the API to create claim-level IBNR estimates. When using aggregate actuarial techniques, it really makes sense to perform the model fitting at an aggregate level. \n", - "\n", - "We use aggregate data to fit the model to generate reasonable development patterns." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/dunders.py:321: RuntimeWarning: divide by zero encountered in true_divide\n", - " obj.values = obj.values / other\n", - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/slice.py:250: RuntimeWarning: invalid value encountered in multiply\n", - " obj.values = num_to_nan(obj.values * obj.get_array_module().array(key))\n" - ] - } - ], - "source": [ - "agg_data = prism.sum()[[\"Paid\", \"reportedCount\"]]\n", - "model_cl = cl.Chainladder().fit(agg_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the fitted model, we are not limited to predicting ultimates at the aggregated grain. Let's predict chainladder ultimates at a claim level. Here, we are using `model_cl`, which was built using `agg_data` to make prediction on `prism`, which is claim-level data." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2261-12
Grain:OMDM
Shape:(34244, 2, 120, 1)
Index:[ClaimNo, Line, Type, ClaimLiability, Limit, Deductible]
Columns:[Paid, reportedCount]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2261-12\n", - "Grain: OMDM\n", - "Shape: (34244, 2, 120, 1)\n", - "Index: [ClaimNo, Line, Type, ClaimLiability, Limit, D...\n", - "Columns: [Paid, reportedCount]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl_ults = model_cl.predict(prism[[\"Paid\", \"reportedCount\"]]).ultimate_\n", - "cl_ults" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We could stop here, but let's try a Bornhuetter-Ferguson method as well. We will infer an a-priori severity from our chainladder model, `model_cl` above." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(\n", - " (model_cl.ultimate_[\"Paid\"] / model_cl.ultimate_[\"reportedCount\"]).to_frame(\n", - " origin_as_datetime=True\n", - " ),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "40K seems a reasonable a-priori (at least for the last two years or so, between 2016 - 2017)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's fit an aggregate Bornhuetter-Ferguson model. Like the chainladder example, we fit the model in aggregate (summing all claims) to create a stable model from which we can generate granular predictions. We will use our Chainladder ultimate claim counts as our `sample_weight` (exposure) for the BornhuetterFerguson method." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/dunders.py:321: RuntimeWarning: divide by zero encountered in true_divide\n", - " obj.values = obj.values / other\n", - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/slice.py:250: RuntimeWarning: invalid value encountered in multiply\n", - " obj.values = num_to_nan(obj.values * obj.get_array_module().array(key))\n" - ] - } - ], - "source": [ - "paid_bf = cl.BornhuetterFerguson(apriori=40000).fit(\n", - " X=prism[\"Paid\"].sum().incr_to_cum(), sample_weight=cl_ults[\"reportedCount\"].sum()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(\n", - " paid_bf.ultimate_.grain(\"OYDM\").to_frame(origin_as_datetime=False).index.year,\n", - " paid_bf.ultimate_.grain(\"OYDM\").to_frame(origin_as_datetime=False)[\"2261-12\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now create claim-level BornhuetterFerguson predictions using our claim-level Triangle. Ideally, the results should tie to the aggregate results." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "bf_ults = paid_bf.predict(\n", - " prism[\"Paid\"].incr_to_cum(), sample_weight=cl_ults[\"reportedCount\"]\n", - ").ultimate_" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(\n", - " bf_ults.sum().grain(\"OYDM\").to_frame(origin_as_datetime=False).index.year,\n", - " bf_ults.sum().grain(\"OYDM\").to_frame(origin_as_datetime=False)[\"2261-12\"],\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/demo-blank.ipynb b/docs/tutorials/demo-blank.ipynb deleted file mode 100644 index 093bff94..00000000 --- a/docs/tutorials/demo-blank.ipynb +++ /dev/null @@ -1,979 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b8f3f4ac-6c32-4fa3-be83-a829c070e7aa", - "metadata": {}, - "source": [ - "# Link to the Instruction Page\n", - "[Click here](https://chainladder-python.readthedocs.io/en/latest/tutorials/demo.html) to view the filled-in demo code." - ] - }, - { - "cell_type": "markdown", - "id": "d8f38e79-5010-4190-b38c-cbc1d85bde47", - "metadata": { - "tags": [] - }, - "source": [ - "# Setting Up\n", - "We will first need to install the package, as Google Colab's default environment doesn't have the chainladder package pre-installed. You will need to run this step using your terminal instead of using a python notebook when you are ready to install the package on your machine.\n", - "\n", - "Simply execute `pip install chainladder`, Colab is smart enough to know that this is not a piece of python code, but to execute it in shell. FYI, `pip` stands for \"Package Installer for Python\"." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be51a379-5efe-420e-b689-3bf93b96ebc8", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "011ee825-ca6d-4efc-b782-5e6f2a14bead", - "metadata": {}, - "source": [ - "Other commonly used packages, such as `numpy`, `pandas`, and `matplotlib` are already pre-installed, we just need to load them into our environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "03fdf8fd-ecd1-4df4-b9cf-a4bf01d978f0", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "id": "38e0a2cc-24a4-4e38-a5a3-2e20d22707b8", - "metadata": {}, - "source": [ - "Let's check the version of chainladder that we have." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a2e2450f-f52a-422b-8a92-61b7dde6860c", - "metadata": {}, - "outputs": [], - "source": [ - "print(\"chainladder:\", _____)" - ] - }, - { - "cell_type": "markdown", - "id": "42e0f37f-6d82-46ed-9f80-647cc7233046", - "metadata": {}, - "source": [ - "# Your Journey Begins" - ] - }, - { - "cell_type": "markdown", - "id": "c9a3a636-979a-4205-9762-469e8afb7e46", - "metadata": {}, - "source": [ - "Let's begin by looking at a sample dataset, called `clrd`, which is hosted on https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/clrd.csv.\n", - "\n", - "Let's load the dataset into the memory with `pandas`, then inspect it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa2c95b8-86b4-4846-b950-12c402477ec1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "clrd_df = pd.read_csv(_____)\n", - "clrd_df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "996795b6-9361-4b5c-a00d-d9b6391b115f", - "metadata": {}, - "source": [ - "Can you list all of the unique accident years? How many are there?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4c11052c-291e-439f-ac0f-6736bb2b0b68", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_df[_____].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cfeca5a6-366f-4abb-b3e9-51c91e7b9336", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_df[_____].nunique()" - ] - }, - { - "cell_type": "markdown", - "id": "8f870f4f-117c-467d-b3d7-d2941f964f23", - "metadata": {}, - "source": [ - "# Triangle Basics" - ] - }, - { - "cell_type": "markdown", - "id": "4d4ebbf6-bcdc-4c4f-be8c-168c4e7883ea", - "metadata": {}, - "source": [ - "Let's instantiate the `clrd_df` dataset and build a triangle object. Let's call it `clrd_tri`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b51e0b6-c1d3-4976-8866-4800b15d27ec", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri = cl.Triangle(\n", - " _____,\n", - " origin=_____,\n", - " development=_____,\n", - " columns=[\"IncurLoss\", \"CumPaidLoss\", \"BulkLoss\", \"EarnedPremDIR\", \"EarnedPremCeded\", \"EarnedPremNet\"],\n", - " index = \"LOB\",\n", - " cumulative=True,\n", - ")\n", - "\n", - "clrd_tri" - ] - }, - { - "cell_type": "markdown", - "id": "2c404d26-4418-43b8-8687-58be1b6423f1", - "metadata": {}, - "source": [ - "What does the paid triangle look like?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe9309fe-2744-4e4d-beff-0a36c1182386", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri[_____].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "ed9811e6-5761-4258-9942-19a620540361", - "metadata": {}, - "source": [ - "How about incurred?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "278856cf-6d84-4fa6-ac57-4f57755580b8", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri[_____].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "29c84099-36e1-43c8-8b74-584c4f76ddc8", - "metadata": {}, - "source": [ - "The `clrd` dataset's `IncurLoss` actually includes IBNR, to get the case incurred, we would have to take out `BulkLoss`. Let's try to get the case incurred triangle." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a221e5f9-9f22-4db3-bfdb-dcf90d24ba39", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri[_____].sum() - clrd_tri[_____].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "04114ff8-107a-4c56-ab9a-8c36f53553df", - "metadata": {}, - "source": [ - "# Pandas-like Operations" - ] - }, - { - "cell_type": "markdown", - "id": "433b8ae8-1968-4dfc-a176-c8a8c93c5f97", - "metadata": {}, - "source": [ - "Let's see how `.iloc[...]` and `.loc[...]` similarly to pandas. They take 4 parameters: [index, column, origin, valuation]." - ] - }, - { - "cell_type": "markdown", - "id": "f0452527-796d-4185-929a-97241329b377", - "metadata": {}, - "source": [ - "What if we want the row from AY 1988 `CumPaidLoss` data?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a13a157b-3fe9-4254-bc72-11d4e1705f29", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri.iloc[_____, _____, _____, _____].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "08b8557c-66fe-4a25-a8bf-5413ca1c1fbb", - "metadata": {}, - "source": [ - "What if you only want the value at age 60?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb20eda1-4e4a-431d-8c8a-21cc87b8c472", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri.iloc[_____, _____, _____, _____].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "56683ffb-01ef-4e18-ba27-1b8ab31b9ae7", - "metadata": {}, - "source": [ - "Let's use `.loc[...]` to get the `CumPaidLoss` triangle." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8116ded-c788-483c-b2af-fde45b72ee4a", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri._____[_____, _____, _____, _____].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "8b695f3e-283f-437c-82fb-4ef24a96314e", - "metadata": {}, - "source": [ - "What if you only want the row from 1992 accident year?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1ac75b6-d268-4f30-b7e0-35fcbeef01d7", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri._____[_____, _____, _____, _____].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "c9d515b7-c9a3-4045-ad79-78af1574be8a", - "metadata": {}, - "source": [ - "How do we get the latest `CumPaidLoss` diagonal only?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5bce08b8-bf34-418e-ac3b-db253db44898", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum()._____" - ] - }, - { - "cell_type": "markdown", - "id": "31b56210-cbcd-4bbb-af9f-063a3788867a", - "metadata": {}, - "source": [ - "Very often, we want incremental triangles instead. Let's convert the Incurred triangle to the incremental form." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2766e7b-b1e6-4574-bfa7-fd70ccd556d7", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum()._____" - ] - }, - { - "cell_type": "markdown", - "id": "6235668f-9025-4108-b987-f867f93c8ce6", - "metadata": {}, - "source": [ - "We can also convert the triangle to the valuation format, what we often see on Schedule Ps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72487c9a-4438-4ab7-8a24-245485d4c637", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum()._____" - ] - }, - { - "cell_type": "markdown", - "id": "6e404747-8e22-42c0-a1b5-45c95d702730", - "metadata": {}, - "source": [ - "Another function that is often useful is the `.heatmap()` method. Let's inspect the incremental paid amount and see if there are trends." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f20ed887-e5b1-40f7-81b5-14bd840cca23", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum()._____._____" - ] - }, - { - "cell_type": "markdown", - "id": "c67b5285-ae9c-48be-8bbc-ef84b6a50e3c", - "metadata": {}, - "source": [ - "Often, we need to get out of the chainladder Triangle object, and back to pandas, we can simply use `.to_frame()` to get the object back as a DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "309293b1-4361-4981-b810-da97c741409b", - "metadata": {}, - "outputs": [], - "source": [ - "print(type(clrd_tri))\n", - "print(type(clrd_tri._____))" - ] - }, - { - "cell_type": "markdown", - "id": "27d110d2-ee73-4bb5-a411-3d27c0dd7673", - "metadata": {}, - "source": [ - "# Development" - ] - }, - { - "cell_type": "markdown", - "id": "a0d0950f-bec7-406d-b253-4cf1bfd925dd", - "metadata": {}, - "source": [ - "How can we get the `CumPaidLoss` link ratios?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec16d0fd-ac17-4280-aabf-ad5795114d5f", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum()._____" - ] - }, - { - "cell_type": "markdown", - "id": "c74c5352-a95b-4403-8322-962ded312e39", - "metadata": {}, - "source": [ - "We can also apply a `.heatmap()` to make it too, to help us visualize the highs and lows." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "172c70be-2324-472f-b89c-29963695179a", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum()._____._____" - ] - }, - { - "cell_type": "markdown", - "id": "f5f212b0-3769-49cd-b7cc-b484f2877aa2", - "metadata": {}, - "source": [ - "Let's get the LDFs for our paid triangle." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba0b96cb-77eb-472c-84fd-c5c8c5c11e10", - "metadata": {}, - "outputs": [], - "source": [ - "cl.Development().fit(_____).ldf_" - ] - }, - { - "cell_type": "markdown", - "id": "0c4baafd-e141-4566-a4ae-2f0a44ef828e", - "metadata": {}, - "source": [ - "How about the CDFs?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b156f84b-dd0d-49d6-8eec-070d0143f40c", - "metadata": {}, - "outputs": [], - "source": [ - "cl.Development().fit(_____)._____" - ] - }, - { - "cell_type": "markdown", - "id": "d51e5664-3106-41d1-b77f-8afa9ee94ff7", - "metadata": {}, - "source": [ - "We can also use only the latest 3 periods in the calculation of LDFs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de88fdad-5d89-4cc2-adb0-bbeb7c77bbcb", - "metadata": {}, - "outputs": [], - "source": [ - "cl.Development(n_periods=_____).fit(_____).ldf_" - ] - }, - { - "cell_type": "markdown", - "id": "b018bae9-6070-4795-8af6-b5e196aa1af1", - "metadata": {}, - "source": [ - "# Deterministic Models" - ] - }, - { - "cell_type": "markdown", - "id": "e7c7b88e-205d-45c8-b9e6-4586f29041a4", - "metadata": {}, - "source": [ - "Before we can build any models, we need to use `fit_transform()`, so that the object is actually modified with our the development pattern(s).\n", - "\n", - "Set the development of the triangle to use only 3 periods." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e5136d2-0c3c-44da-8440-57ca3cfbbb9d", - "metadata": {}, - "outputs": [], - "source": [ - "cl.Development(n_periods=_____)._____(_____)" - ] - }, - { - "cell_type": "markdown", - "id": "1bd89481-e5c7-4a84-b2cc-a2e386ccdb15", - "metadata": {}, - "source": [ - "Let's fit a chainladder model to our paid triangle." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "022e22e9-92a8-427c-bf5c-cf352df1437c", - "metadata": {}, - "outputs": [], - "source": [ - "cl_mod = cl.Chainladder().fit(_____)\n", - "cl_mod" - ] - }, - { - "cell_type": "markdown", - "id": "7b710342-5f86-408e-bf7e-76382b37f2d1", - "metadata": {}, - "source": [ - "How can we get the model's ultimate estimate?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69f18923-73b1-4b80-9148-60a7bab5b118", - "metadata": {}, - "outputs": [], - "source": [ - "cl_mod._____" - ] - }, - { - "cell_type": "markdown", - "id": "b416a404-8d0f-46fc-a3e7-f5b5b884b4b4", - "metadata": {}, - "source": [ - "How about just the IBNR?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5fad3aa0-03bc-4f84-a8b7-1a00dbdebe8d", - "metadata": {}, - "outputs": [], - "source": [ - "cl_mod._____" - ] - }, - { - "cell_type": "markdown", - "id": "70d8c018-21ca-4f2c-a764-433e310bb44a", - "metadata": {}, - "source": [ - "Let's fit an Expected Loss model, with an aprior of 90% on Premium, and get its ultimates." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "22eba9fa-1890-4f6f-8a10-281142d2d58d", - "metadata": {}, - "outputs": [], - "source": [ - "cl._____(apriori=_____).fit(\n", - " _____, \n", - " sample_weight=_____\n", - ").ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "eb20b72a-4e49-4eaa-b8e8-d3801833e2d3", - "metadata": {}, - "source": [ - "Try it on the incurred triangle, which again, is `IncurLoss` - `BulkLoss`, do you get the same estimates?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "411f48b0-8b86-4175-80f2-f5f4a19e6c46", - "metadata": {}, - "outputs": [], - "source": [ - "cl._____(apriori=0.90).fit(\n", - " _____ - _____, \n", - " sample_weight=_____\n", - ").ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "fb1d7eda-f4c6-4990-9488-47235492001a", - "metadata": {}, - "source": [ - "How about a Bornhuetter-Ferguson model?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d66c7c9a-71eb-4d56-beea-f275da062fc0", - "metadata": {}, - "outputs": [], - "source": [ - "cl._____(apriori=_____).fit(\n", - " _____, \n", - " sample_weight=_____\n", - ").ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "5564ead9-d059-4d2c-839a-f988238e50ee", - "metadata": {}, - "source": [ - "How about Benktander, with 2 iterations?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d504e48d-1f5d-4fd6-975b-155235ffb577", - "metadata": {}, - "outputs": [], - "source": [ - "cl._____(apriori=_____, n_iters=_____).fit(\n", - " _____, \n", - " sample_weight=_____\n", - ").ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "002a76c2-7989-46ba-954b-d84c09b4675a", - "metadata": {}, - "source": [ - "How about Cape Cod?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7089ea42-ad28-4edc-9e83-723a7bc25443", - "metadata": {}, - "outputs": [], - "source": [ - "cl._____().fit(\n", - " _____, \n", - " sample_weight=_____\n", - ").ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "5a0d73a2-0e05-4be2-91f0-9ef1ef56a7be", - "metadata": {}, - "source": [ - "Let's store the Cape Cod model as `cc_result`. We can also use `.to_frame()` to leave `chainladder` and go to a `DataFrame`. Let's make a bar chart over origin years to see what they look like." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f2cd9f8c-454d-4b9f-b936-a2f39e8fefde", - "metadata": {}, - "outputs": [], - "source": [ - "cc_result = cl.CapeCod().fit(\n", - " clrd_tri[\"CumPaidLoss\"].sum(), sample_weight=clrd_tri[\"EarnedPremDIR\"].sum().latest_diagonal\n", - ")\n", - "\n", - "cc_result_ult = cc_result.ultimate_.to_frame()\n", - "\n", - "plt.bar(\n", - " _____, #x-axis, accident year\n", - " _____, #y-axis, ultimates\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "3d1328b3-d578-4d50-9a71-eb7bc4cb1275", - "metadata": {}, - "source": [ - "# Pipelines" - ] - }, - { - "cell_type": "markdown", - "id": "234a2157-599a-4e7f-9c75-d287a303f8a0", - "metadata": {}, - "source": [ - "Pipeline is a tool that can streamline your workflow, it is a way to wrap multiple estimators into a single compact object. Let's see an example that includes a selection of development pattern and the chainladder model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf8b573e-12ec-43f8-82f1-65f528c90b2c", - "metadata": {}, - "outputs": [], - "source": [ - "pipe = cl.Pipeline(\n", - " steps=[\n", - " ('dev', cl.Development(average='simple')),\n", - " ('model', cl.Chainladder())])\n", - "\n", - "pipe" - ] - }, - { - "cell_type": "markdown", - "id": "49af39f9-b13b-444f-93f5-d2b04bf78b68", - "metadata": {}, - "source": [ - "Once the pipeline is built, we can apply to any dataset we like, let's try it on the paid triangle and get their ultimate estimates." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2654f3f0-d945-47f3-86a9-b6f0c4532604", - "metadata": {}, - "outputs": [], - "source": [ - "pipe.fit(_____).named_steps.model.ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "dc52b094-1386-4769-8e51-484b8aca8626", - "metadata": {}, - "source": [ - "You can use the same pipeline on other datasets as well, let's try it on the incurred triangle, which again, is `IncurLoss` - `BulkLoss`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4db3ac56-b70c-4e33-9a14-ca2824cbf5b2", - "metadata": {}, - "outputs": [], - "source": [ - "pipe.fit(_____).named_steps.model.ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "3f9e62f8-225b-4046-8847-a6e8d971e14d", - "metadata": {}, - "source": [ - "# Stochastic Models" - ] - }, - { - "cell_type": "markdown", - "id": "36105614-e317-4a87-a42d-282f59b1d339", - "metadata": {}, - "source": [ - "The Mack's Chainladder model is available." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e008ebdb-243d-4ed0-9256-86331df1070a", - "metadata": {}, - "outputs": [], - "source": [ - "mcl_mod = cl._____().fit(_____)\n", - "mcl_mod" - ] - }, - { - "cell_type": "markdown", - "id": "3298c63c-5356-4d69-afa3-058b68daf777", - "metadata": {}, - "source": [ - "There are many attributes that are available, such as `full_std_err_`, `total_process_risk_`, `total_parameter_risk_`, `mack_std_err_` and `total_mack_std_err_`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "67f5d99b-7a5e-4640-a6e0-f8b654e6ce27", - "metadata": {}, - "outputs": [], - "source": [ - "mcl_mod._____" - ] - }, - { - "cell_type": "markdown", - "id": "bdb08c81-5921-4c41-ad63-96168ffd48b7", - "metadata": {}, - "source": [ - "MackChainladder also has a `summary_` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81fc38c1-d5b7-4262-94ae-bce5c7ac17e1", - "metadata": {}, - "outputs": [], - "source": [ - "mcl_mod._____" - ] - }, - { - "cell_type": "markdown", - "id": "0e285585-62b6-48e4-8b1d-c5824ae5df46", - "metadata": {}, - "source": [ - "Let's make a graph, that shows the Paid and Unpaid as stacked bars, and error bars showing Mack Standard Errors." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e615b86e-a907-4445-9e95-645090719f76", - "metadata": {}, - "outputs": [], - "source": [ - "mcl_mod_df = mcl_mod.summary_.to_frame()\n", - "\n", - "# bottom bar chart\n", - "plt.bar(\n", - " _____, # category, year\n", - " _____, # height, Latest (paid)\n", - " label=\"Paid\",\n", - ")\n", - "\n", - "# top bar chart\n", - "plt.bar(\n", - " _____, # category, year\n", - " _____, # height, IBNR (unpaid)\n", - " bottom=_____, # height, Latest (paid)\n", - " yerr=_____, # Mack's standard error\n", - " label=\"Unpaid\",\n", - ")\n", - "plt.legend(loc=\"upper left\")" - ] - }, - { - "cell_type": "markdown", - "id": "785120ad-03cf-48a7-90d8-d1d56a75ef88", - "metadata": {}, - "source": [ - "ODP Bootstrap is also available. Let's bootstrap 10,000 paid triangles." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "859e19f3-d526-435c-a845-4845a7a3956d", - "metadata": {}, - "outputs": [], - "source": [ - "clrd_tri_sampled = (\n", - " cl._____(n_sims=_____).fit(_____).resampled_triangles_\n", - ")\n", - "clrd_tri_sampled" - ] - }, - { - "cell_type": "markdown", - "id": "4391f730-5309-49b2-9c19-0801e3e66c7c", - "metadata": {}, - "source": [ - "We can fit a basic chainladder to all sampled triangles. We now have 10,000 simulated chainladder models, all (most) with unique LDFs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe6dbe70-1b2a-4fb0-aa6b-56380534704f", - "metadata": {}, - "outputs": [], - "source": [ - "cl_mod_bootstrapped = cl.Chainladder().fit(_____)\n", - "cl_mod_bootstrapped" - ] - }, - { - "cell_type": "markdown", - "id": "a6f81ac6-a2ab-496d-8d1f-4604aa464370", - "metadata": {}, - "source": [ - "We can use `predict()` to use the model characteristics (their unique LDFs) to predict our basic Incurred triangle. Let's inspect the prediction's means. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f304cf4-f973-4e98-b76f-8266de8659b6", - "metadata": {}, - "outputs": [], - "source": [ - "cl_mod_bootstrapped.predict(_____).ultimate_._____" - ] - }, - { - "cell_type": "markdown", - "id": "bb3d7c32-9e75-4ae4-ab23-0ca3f2a436b5", - "metadata": {}, - "source": [ - "Let's make another graph." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "edeba1db-97e6-43df-b1c0-590c2d7cd098", - "metadata": {}, - "outputs": [], - "source": [ - "plt.bar(\n", - " _____, #x-axis, accident year\n", - " _____, #y-axis, ultimates\n", - " yerr=_____, #ultimates standard deviation\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/demo.ipynb b/docs/tutorials/demo.ipynb deleted file mode 100644 index afedc605..00000000 --- a/docs/tutorials/demo.ipynb +++ /dev/null @@ -1,4627 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "681cc0c7-9312-41f6-8935-3e5cf2daa1c1", - "metadata": { - "tags": [] - }, - "source": [ - "# Try Chainladder-Python Online\n", - "If you are here to try `chainladder-python` for the first time, you are in the right place!\n", - "\n", - "With the help of Google Colab, you will be able to try the package online, without any installation or additional setup.\n", - "\n", - "You should use a blank copy of this workbook.\n", - "\n", - "It's best that you work on your version of the workbook, while comparing this site directly for additional commentary and check your solutions to the exercises." - ] - }, - { - "cell_type": "markdown", - "id": "245eb9d8-55e3-46ec-943b-a3f73baf70f3", - "metadata": {}, - "source": [ - "# Disclaimer\n", - "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." - ] - }, - { - "cell_type": "markdown", - "id": "d8f38e79-5010-4190-b38c-cbc1d85bde47", - "metadata": {}, - "source": [ - "# Setting Up\n", - "We will first need to install the package, as Google Colab's default environment doesn't have the chainladder package pre-installed. You will need to run this step using your terminal instead of using a python notebook when you are ready to install the package on your machine.\n", - "\n", - "Simply execute `pip install chainladder`, Colab is smart enough to know that this is not a piece of python code, but to execute it in shell. FYI, `pip` stands for \"Package Installer for Python\"." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "be51a379-5efe-420e-b689-3bf93b96ebc8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: chainladder in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (0.8.15)\n", - "Requirement already satisfied: matplotlib in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from chainladder) (3.7.1)\n", - "Requirement already satisfied: patsy in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from chainladder) (0.5.3)\n", - "Requirement already satisfied: dill in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from chainladder) (0.3.6)\n", - "Requirement already satisfied: pandas>=0.23 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from chainladder) (1.5.3)\n", - "Requirement already satisfied: scikit-learn>=0.23 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from chainladder) (1.1.3)\n", - "Requirement already satisfied: numba>0.54 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from chainladder) (0.56.4)\n", - "Requirement already satisfied: sparse>=0.9 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from chainladder) (0.13.0)\n", - "Requirement already satisfied: numpy<1.24,>=1.18 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from numba>0.54->chainladder) (1.22.4)\n", - "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from numba>0.54->chainladder) (0.39.1)\n", - "Requirement already satisfied: setuptools in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from numba>0.54->chainladder) (65.6.3)\n", - "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.23->chainladder) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.23->chainladder) (2022.7)\n", - "Requirement already satisfied: joblib>=1.0.0 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from scikit-learn>=0.23->chainladder) (1.2.0)\n", - "Requirement already satisfied: scipy>=1.3.2 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from scikit-learn>=0.23->chainladder) (1.9.3)\n", - "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from scikit-learn>=0.23->chainladder) (3.1.0)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from matplotlib->chainladder) (1.0.7)\n", - "Requirement already satisfied: packaging>=20.0 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from matplotlib->chainladder) (23.0)\n", - "Requirement already satisfied: cycler>=0.10 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from matplotlib->chainladder) (0.11.0)\n", - "Requirement already satisfied: pillow>=6.2.0 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from matplotlib->chainladder) (9.4.0)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from matplotlib->chainladder) (1.4.4)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from matplotlib->chainladder) (4.39.3)\n", - "Requirement already satisfied: importlib-resources>=3.2.0 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from matplotlib->chainladder) (5.12.0)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from matplotlib->chainladder) (3.0.9)\n", - "Requirement already satisfied: six in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from patsy->chainladder) (1.16.0)\n", - "Requirement already satisfied: zipp>=3.1.0 in /Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib->chainladder) (3.11.0)\n", - "\u001b[33mWARNING: Error parsing requirements for itemadapter: [Errno 2] No such file or directory: '/Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages/itemadapter-0.7.0.dist-info/METADATA'\u001b[0m\u001b[33m\n", - "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "pip install chainladder" - ] - }, - { - "cell_type": "markdown", - "id": "011ee825-ca6d-4efc-b782-5e6f2a14bead", - "metadata": {}, - "source": [ - "Other commonly used packages, such as `numpy`, `pandas`, and `matplotlib` are already pre-installed, we just need to load them into our environment." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "03fdf8fd-ecd1-4df4-b9cf-a4bf01d978f0", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import chainladder as cl" - ] - }, - { - "cell_type": "markdown", - "id": "fc9db757-81d0-46cc-bc1a-608bac9e661c", - "metadata": {}, - "source": [ - "Let's check the version of chainladder that we have." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "53af1c0f-7dfd-45ac-b8d1-e9334656eaae", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "chainladder 0.8.15\n" - ] - } - ], - "source": [ - "print(\"chainladder\", cl.__version__)" - ] - }, - { - "cell_type": "markdown", - "id": "42e0f37f-6d82-46ed-9f80-647cc7233046", - "metadata": {}, - "source": [ - "# Your Journey Begins" - ] - }, - { - "cell_type": "markdown", - "id": "c9a3a636-979a-4205-9762-469e8afb7e46", - "metadata": {}, - "source": [ - "Let's begin by looking at a sample dataset, called `clrd`, which is hosted on https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/clrd.csv.\n", - "\n", - "Let's load the dataset into the memory with `pandas`, then inspect it." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "aa2c95b8-86b4-4846-b950-12c402477ec1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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GRCODEGRNAMEAccidentYearDevelopmentYearDevelopmentLagIncurLossCumPaidLossBulkLossEarnedPremDIREarnedPremCededEarnedPremNetSinglePostedReserve97LOB
086Allstate Ins Co Grp1988198813674047057112773740069959573947420281872wkcomp
186Allstate Ins Co Grp1988198923629881559056017340069959573947420281872wkcomp
286Allstate Ins Co Grp1988199033472882207442776340069959573947420281872wkcomp
386Allstate Ins Co Grp1988199143306482515951528040069959573947420281872wkcomp
486Allstate Ins Co Grp1988199253546902741562768940069959573947420281872wkcomp
\n", - "
" - ], - "text/plain": [ - " GRCODE GRNAME AccidentYear DevelopmentYear DevelopmentLag \\\n", - "0 86 Allstate Ins Co Grp 1988 1988 1 \n", - "1 86 Allstate Ins Co Grp 1988 1989 2 \n", - "2 86 Allstate Ins Co Grp 1988 1990 3 \n", - "3 86 Allstate Ins Co Grp 1988 1991 4 \n", - "4 86 Allstate Ins Co Grp 1988 1992 5 \n", - "\n", - " IncurLoss CumPaidLoss BulkLoss EarnedPremDIR EarnedPremCeded \\\n", - "0 367404 70571 127737 400699 5957 \n", - "1 362988 155905 60173 400699 5957 \n", - "2 347288 220744 27763 400699 5957 \n", - "3 330648 251595 15280 400699 5957 \n", - "4 354690 274156 27689 400699 5957 \n", - "\n", - " EarnedPremNet Single PostedReserve97 LOB \n", - "0 394742 0 281872 wkcomp \n", - "1 394742 0 281872 wkcomp \n", - "2 394742 0 281872 wkcomp \n", - "3 394742 0 281872 wkcomp \n", - "4 394742 0 281872 wkcomp " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_df = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/clrd.csv\"\n", - ")\n", - "clrd_df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "996795b6-9361-4b5c-a00d-d9b6391b115f", - "metadata": {}, - "source": [ - "Can you list all of the unique accident years? How many are there?" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4c11052c-291e-439f-ac0f-6736bb2b0b68", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_df[\"AccidentYear\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cfeca5a6-366f-4abb-b3e9-51c91e7b9336", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_df[\"AccidentYear\"].nunique()" - ] - }, - { - "cell_type": "markdown", - "id": "8f870f4f-117c-467d-b3d7-d2941f964f23", - "metadata": {}, - "source": [ - "# Triangle Basics" - ] - }, - { - "cell_type": "markdown", - "id": "4d4ebbf6-bcdc-4c4f-be8c-168c4e7883ea", - "metadata": {}, - "source": [ - "Let's instantiate the `clrd_df` dataset and build a triangle object. Let's call it `clrd_tri`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "0a5af8fa-9fc3-464c-b187-28e0793d3d92", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(6, 6, 10, 10)
Index:[LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (6, 6, 10, 10)\n", - "Index: [LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri = cl.Triangle(\n", - " clrd_df,\n", - " origin=\"AccidentYear\",\n", - " development=\"DevelopmentYear\",\n", - " columns=[\"IncurLoss\", \"CumPaidLoss\", \"BulkLoss\", \"EarnedPremDIR\", \"EarnedPremCeded\", \"EarnedPremNet\"],\n", - " index = \"LOB\",\n", - " cumulative=True,\n", - ")\n", - "clrd_tri" - ] - }, - { - "cell_type": "markdown", - "id": "2c404d26-4418-43b8-8687-58be1b6423f1", - "metadata": {}, - "source": [ - "What does the paid triangle look like?" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "fe9309fe-2744-4e4d-beff-0a36c1182386", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19883,577,7807,059,9668,826,1519,862,68710,474,69810,814,57610,994,01411,091,36311,171,59011,203,949
19894,090,6807,964,7029,937,52011,098,58811,766,48812,118,79012,311,62912,434,82612,492,899
19904,578,4428,808,48610,985,34712,229,00112,878,54513,238,66713,452,99313,559,557
19914,648,7568,961,75511,154,24412,409,59213,092,03713,447,48113,642,414
19925,139,1429,757,69912,027,98313,289,48513,992,82114,347,271
19935,653,37910,599,42312,953,81214,292,51615,005,138
19946,246,44711,394,96013,845,76415,249,326
19956,473,84311,612,15114,010,098
19966,591,59911,473,912
19976,451,896
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1224364860728496108120
198811,644,99511,674,24011,653,59711,630,88211,593,86811,551,62511,463,31211,420,23811,415,56011,396,981
198913,123,29013,118,78913,113,02413,050,14412,959,03712,866,70912,787,37212,757,42012,743,440
199014,776,07914,670,69014,479,69914,324,68014,183,17814,033,49813,948,13913,925,679
199115,318,37315,112,54714,877,66214,615,54014,380,20514,205,77814,154,882
199216,828,85716,457,30715,999,38515,538,21415,249,28615,161,066
199318,169,37017,590,90217,080,18716,485,46716,281,774
199419,414,89818,609,08917,854,17817,521,037
199519,502,85018,668,38817,901,550
199619,142,09017,910,743
199718,113,581
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 11644995.0 11674240.0 11653597.0 11630882.0 11593868.0 11551625.0 11463312.0 11420238.0 11415560.0 11396981.0\n", - "1989 13123290.0 13118789.0 13113024.0 13050144.0 12959037.0 12866709.0 12787372.0 12757420.0 12743440.0 NaN\n", - "1990 14776079.0 14670690.0 14479699.0 14324680.0 14183178.0 14033498.0 13948139.0 13925679.0 NaN NaN\n", - "1991 15318373.0 15112547.0 14877662.0 14615540.0 14380205.0 14205778.0 14154882.0 NaN NaN NaN\n", - "1992 16828857.0 16457307.0 15999385.0 15538214.0 15249286.0 15161066.0 NaN NaN NaN NaN\n", - "1993 18169370.0 17590902.0 17080187.0 16485467.0 16281774.0 NaN NaN NaN NaN NaN\n", - "1994 19414898.0 18609089.0 17854178.0 17521037.0 NaN NaN NaN NaN NaN NaN\n", - "1995 19502850.0 18668388.0 17901550.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 19142090.0 17910743.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 18113581.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri[\"IncurLoss\"].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "a7f48065-9a14-4f0b-b2b5-a33cc0740531", - "metadata": {}, - "source": [ - "The `clrd` dataset's `IncurLoss` actually includes IBNR, to get the case incurred, we would have to take out `BulkLoss`. Let's try to get the case incurred triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c26ac035-0e72-461b-ae70-1f94c8eed136", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19887,778,3989,872,87610,537,70710,973,80811,175,39111,265,52411,288,28811,305,02311,323,99511,327,627
19898,734,31910,844,72011,822,13612,279,31112,481,50512,567,54312,608,48712,633,53912,639,258
19909,325,25211,913,46112,985,11313,459,84313,646,07713,718,44513,755,87913,768,960
19919,564,48612,159,82613,216,38313,659,54113,821,03213,903,08413,964,163
199210,539,61913,125,93014,120,97114,563,96414,755,40514,850,140
199311,402,44814,043,34315,095,23215,576,08615,775,057
199412,411,10715,005,42416,095,69916,650,937
199512,686,39415,140,09916,223,016
199612,627,29314,956,778
199712,705,993
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 7778398.0 9872876.0 10537707.0 10973808.0 11175391.0 11265524.0 11288288.0 11305023.0 11323995.0 11327627.0\n", - "1989 8734319.0 10844720.0 11822136.0 12279311.0 12481505.0 12567543.0 12608487.0 12633539.0 12639258.0 NaN\n", - "1990 9325252.0 11913461.0 12985113.0 13459843.0 13646077.0 13718445.0 13755879.0 13768960.0 NaN NaN\n", - "1991 9564486.0 12159826.0 13216383.0 13659541.0 13821032.0 13903084.0 13964163.0 NaN NaN NaN\n", - "1992 10539619.0 13125930.0 14120971.0 14563964.0 14755405.0 14850140.0 NaN NaN NaN NaN\n", - "1993 11402448.0 14043343.0 15095232.0 15576086.0 15775057.0 NaN NaN NaN NaN NaN\n", - "1994 12411107.0 15005424.0 16095699.0 16650937.0 NaN NaN NaN NaN NaN NaN\n", - "1995 12686394.0 15140099.0 16223016.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 12627293.0 14956778.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 12705993.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri[\"IncurLoss\"].sum() - clrd_tri[\"BulkLoss\"].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "04114ff8-107a-4c56-ab9a-8c36f53553df", - "metadata": {}, - "source": [ - "# Pandas-like Operations" - ] - }, - { - "cell_type": "markdown", - "id": "433b8ae8-1968-4dfc-a176-c8a8c93c5f97", - "metadata": {}, - "source": [ - "Let's see how `.iloc[...]` and `.loc[...]` similarly to pandas. They take 4 parameters: [index, column, origin, valuation]." - ] - }, - { - "cell_type": "markdown", - "id": "f0452527-796d-4185-929a-97241329b377", - "metadata": {}, - "source": [ - "What if we want the row from AY 1988 `CumPaidLoss` data?" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a13a157b-3fe9-4254-bc72-11d4e1705f29", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19883,577,7807,059,9668,826,1519,862,68710,474,69810,814,57610,994,01411,091,36311,171,59011,203,949
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 3577780.0 7059966.0 8826151.0 9862687.0 10474698.0 10814576.0 10994014.0 11091363.0 11171590.0 11203949.0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri.iloc[:, 1, 0, :].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "08b8557c-66fe-4a25-a8bf-5413ca1c1fbb", - "metadata": {}, - "source": [ - "What if you only want the value at age 60?" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "fb20eda1-4e4a-431d-8c8a-21cc87b8c472", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10474698.0" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri.iloc[:, 1, 0, 4].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "56683ffb-01ef-4e18-ba27-1b8ab31b9ae7", - "metadata": {}, - "source": [ - "Let's use `.loc[...]` to get the `CumPaidLoss` triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b8116ded-c788-483c-b2af-fde45b72ee4a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19883,577,7807,059,9668,826,1519,862,68710,474,69810,814,57610,994,01411,091,36311,171,59011,203,949
19894,090,6807,964,7029,937,52011,098,58811,766,48812,118,79012,311,62912,434,82612,492,899
19904,578,4428,808,48610,985,34712,229,00112,878,54513,238,66713,452,99313,559,557
19914,648,7568,961,75511,154,24412,409,59213,092,03713,447,48113,642,414
19925,139,1429,757,69912,027,98313,289,48513,992,82114,347,271
19935,653,37910,599,42312,953,81214,292,51615,005,138
19946,246,44711,394,96013,845,76415,249,326
19956,473,84311,612,15114,010,098
19966,591,59911,473,912
19976,451,896
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 3577780.0 7059966.0 8826151.0 9862687.0 10474698.0 10814576.0 10994014.0 11091363.0 11171590.0 11203949.0\n", - "1989 4090680.0 7964702.0 9937520.0 11098588.0 11766488.0 12118790.0 12311629.0 12434826.0 12492899.0 NaN\n", - "1990 4578442.0 8808486.0 10985347.0 12229001.0 12878545.0 13238667.0 13452993.0 13559557.0 NaN NaN\n", - "1991 4648756.0 8961755.0 11154244.0 12409592.0 13092037.0 13447481.0 13642414.0 NaN NaN NaN\n", - "1992 5139142.0 9757699.0 12027983.0 13289485.0 13992821.0 14347271.0 NaN NaN NaN NaN\n", - "1993 5653379.0 10599423.0 12953812.0 14292516.0 15005138.0 NaN NaN NaN NaN NaN\n", - "1994 6246447.0 11394960.0 13845764.0 15249326.0 NaN NaN NaN NaN NaN NaN\n", - "1995 6473843.0 11612151.0 14010098.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 6591599.0 11473912.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 6451896.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri.loc[:, \"CumPaidLoss\", :, :].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "39c240e0-a910-4d14-98bc-23ae9387b0f9", - "metadata": {}, - "source": [ - "What if you only want the row from 1992 accident year?" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "2d07e862-368a-472d-b3ca-76e50adef5dd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19925,139,1429,757,69912,027,98313,289,48513,992,82114,347,271
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1992 5139142.0 9757699.0 12027983.0 13289485.0 13992821.0 14347271.0 NaN NaN NaN NaN" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri.loc[:, \"CumPaidLoss\", \"1992\", :].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "c9d515b7-c9a3-4045-ad79-78af1574be8a", - "metadata": {}, - "source": [ - "How do we get the latest `CumPaidLoss` diagonal only?" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "5bce08b8-bf34-418e-ac3b-db253db44898", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1997
198811,203,949
198912,492,899
199013,559,557
199113,642,414
199214,347,271
199315,005,138
199415,249,326
199514,010,098
199611,473,912
19976,451,896
" - ], - "text/plain": [ - " 1997\n", - "1988 11203949.0\n", - "1989 12492899.0\n", - "1990 13559557.0\n", - "1991 13642414.0\n", - "1992 14347271.0\n", - "1993 15005138.0\n", - "1994 15249326.0\n", - "1995 14010098.0\n", - "1996 11473912.0\n", - "1997 6451896.0" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum().latest_diagonal" - ] - }, - { - "cell_type": "markdown", - "id": "31b56210-cbcd-4bbb-af9f-063a3788867a", - "metadata": {}, - "source": [ - "Very often, we want incremental triangles instead. Let's convert the `CumPaidLoss` triangle to the incremental form." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b2766e7b-b1e6-4574-bfa7-fd70ccd556d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19883,577,7803,482,1861,766,1851,036,536612,011339,878179,43897,34980,22732,359
19894,090,6803,874,0221,972,8181,161,068667,900352,302192,839123,19758,073
19904,578,4424,230,0442,176,8611,243,654649,544360,122214,326106,564
19914,648,7564,312,9992,192,4891,255,348682,445355,444194,933
19925,139,1424,618,5572,270,2841,261,502703,336354,450
19935,653,3794,946,0442,354,3891,338,704712,622
19946,246,4475,148,5132,450,8041,403,562
19956,473,8435,138,3082,397,947
19966,591,5994,882,313
19976,451,896
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 3577780.0 3482186.0 1766185.0 1036536.0 612011.0 339878.0 179438.0 97349.0 80227.0 32359.0\n", - "1989 4090680.0 3874022.0 1972818.0 1161068.0 667900.0 352302.0 192839.0 123197.0 58073.0 NaN\n", - "1990 4578442.0 4230044.0 2176861.0 1243654.0 649544.0 360122.0 214326.0 106564.0 NaN NaN\n", - "1991 4648756.0 4312999.0 2192489.0 1255348.0 682445.0 355444.0 194933.0 NaN NaN NaN\n", - "1992 5139142.0 4618557.0 2270284.0 1261502.0 703336.0 354450.0 NaN NaN NaN NaN\n", - "1993 5653379.0 4946044.0 2354389.0 1338704.0 712622.0 NaN NaN NaN NaN NaN\n", - "1994 6246447.0 5148513.0 2450804.0 1403562.0 NaN NaN NaN NaN NaN NaN\n", - "1995 6473843.0 5138308.0 2397947.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 6591599.0 4882313.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 6451896.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum().cum_to_incr()" - ] - }, - { - "cell_type": "markdown", - "id": "6235668f-9025-4108-b987-f867f93c8ce6", - "metadata": {}, - "source": [ - "We can also convert the triangle to the valuation format, what we often see on Schedule Ps." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "72487c9a-4438-4ab7-8a24-245485d4c637", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1988198919901991199219931994199519961997
19883,577,7807,059,9668,826,1519,862,68710,474,69810,814,57610,994,01411,091,36311,171,59011,203,949
19894,090,6807,964,7029,937,52011,098,58811,766,48812,118,79012,311,62912,434,82612,492,899
19904,578,4428,808,48610,985,34712,229,00112,878,54513,238,66713,452,99313,559,557
19914,648,7568,961,75511,154,24412,409,59213,092,03713,447,48113,642,414
19925,139,1429,757,69912,027,98313,289,48513,992,82114,347,271
19935,653,37910,599,42312,953,81214,292,51615,005,138
19946,246,44711,394,96013,845,76415,249,326
19956,473,84311,612,15114,010,098
19966,591,59911,473,912
19976,451,896
" - ], - "text/plain": [ - " 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997\n", - "1988 3577780.0 7059966.0 8826151.0 9862687.0 10474698.0 10814576.0 10994014.0 11091363.0 11171590.0 11203949.0\n", - "1989 NaN 4090680.0 7964702.0 9937520.0 11098588.0 11766488.0 12118790.0 12311629.0 12434826.0 12492899.0\n", - "1990 NaN NaN 4578442.0 8808486.0 10985347.0 12229001.0 12878545.0 13238667.0 13452993.0 13559557.0\n", - "1991 NaN NaN NaN 4648756.0 8961755.0 11154244.0 12409592.0 13092037.0 13447481.0 13642414.0\n", - "1992 NaN NaN NaN NaN 5139142.0 9757699.0 12027983.0 13289485.0 13992821.0 14347271.0\n", - "1993 NaN NaN NaN NaN NaN 5653379.0 10599423.0 12953812.0 14292516.0 15005138.0\n", - "1994 NaN NaN NaN NaN NaN NaN 6246447.0 11394960.0 13845764.0 15249326.0\n", - "1995 NaN NaN NaN NaN NaN NaN NaN 6473843.0 11612151.0 14010098.0\n", - "1996 NaN NaN NaN NaN NaN NaN NaN NaN 6591599.0 11473912.0\n", - "1997 NaN NaN NaN NaN NaN NaN NaN NaN NaN 6451896.0" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum().dev_to_val()" - ] - }, - { - "cell_type": "markdown", - "id": "6e404747-8e22-42c0-a1b5-45c95d702730", - "metadata": {}, - "source": [ - "Another function that is often useful is the `.heatmap()` method. Let's inspect the incremental paid amount and see if there are trends." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "f20ed887-e5b1-40f7-81b5-14bd840cca23", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 1224364860728496108120
19883,577,7803,482,1861,766,1851,036,536612,011339,878179,43897,34980,22732,359
19894,090,6803,874,0221,972,8181,161,068667,900352,302192,839123,19758,073
19904,578,4424,230,0442,176,8611,243,654649,544360,122214,326106,564
19914,648,7564,312,9992,192,4891,255,348682,445355,444194,933
19925,139,1424,618,5572,270,2841,261,502703,336354,450
19935,653,3794,946,0442,354,3891,338,704712,622
19946,246,4475,148,5132,450,8041,403,562
19956,473,8435,138,3082,397,947
19966,591,5994,882,313
19976,451,896
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum().cum_to_incr().heatmap()" - ] - }, - { - "cell_type": "markdown", - "id": "b3f0b4b5-2232-4348-96bb-44ea9f4a3f74", - "metadata": {}, - "source": [ - "Often, we need to get out of the chainladder Triangle object, and back to pandas, we can simply use `.to_frame()` to get the object back as a DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "594b0efa-f645-4d40-a3f4-92b75915e900", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n" - ] - } - ], - "source": [ - "print(type(clrd_tri))\n", - "print(type(clrd_tri.to_frame()))" - ] - }, - { - "cell_type": "markdown", - "id": "27d110d2-ee73-4bb5-a411-3d27c0dd7673", - "metadata": {}, - "source": [ - "# Development" - ] - }, - { - "cell_type": "markdown", - "id": "a0d0950f-bec7-406d-b253-4cf1bfd925dd", - "metadata": {}, - "source": [ - "How can we get the `CumPaidLoss` link ratios?" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "ec16d0fd-ac17-4280-aabf-ad5795114d5f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
19881.97331.25021.11741.06211.03241.01661.00891.00721.0029
19891.94701.24771.11681.06021.02991.01591.01001.0047
19901.92391.24711.11321.05311.02801.01621.0079
19911.92781.24461.11251.05501.02711.0145
19921.89871.23271.10491.05291.0253
19931.87491.22211.10331.0499
19941.82421.21511.1014
19951.79371.2065
19961.7407
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "1988 1.973281 1.250169 1.117439 1.062053 1.032448 1.016592 1.008855 1.007233 1.002897\n", - "1989 1.947036 1.247695 1.116837 1.060179 1.029941 1.015912 1.010007 1.004670 NaN\n", - "1990 1.923905 1.247132 1.113210 1.053115 1.027963 1.016189 1.007921 NaN NaN\n", - "1991 1.927775 1.244650 1.112544 1.054993 1.027150 1.014496 NaN NaN NaN\n", - "1992 1.898702 1.232666 1.104881 1.052924 1.025331 NaN NaN NaN NaN\n", - "1993 1.874883 1.222124 1.103344 1.049860 NaN NaN NaN NaN NaN\n", - "1994 1.824231 1.215078 1.101371 NaN NaN NaN NaN NaN NaN\n", - "1995 1.793703 1.206503 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 1.740687 NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum().link_ratio" - ] - }, - { - "cell_type": "markdown", - "id": "c74c5352-a95b-4403-8322-962ded312e39", - "metadata": {}, - "source": [ - "We can also apply a `.heatmap()` to make it too, to help us visualize the highs and lows." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "172c70be-2324-472f-b89c-29963695179a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 12-2424-3636-4848-6060-7272-8484-9696-108108-120
19881.97331.25021.11741.06211.03241.01661.00891.00721.0029
19891.94701.24771.11681.06021.02991.01591.01001.0047
19901.92391.24711.11321.05311.02801.01621.0079
19911.92781.24461.11251.05501.02711.0145
19921.89871.23271.10491.05291.0253
19931.87491.22211.10331.0499
19941.82421.21511.1014
19951.79371.2065
19961.7407
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri[\"CumPaidLoss\"].sum().link_ratio.heatmap()" - ] - }, - { - "cell_type": "markdown", - "id": "f5f212b0-3769-49cd-b7cc-b484f2877aa2", - "metadata": {}, - "source": [ - "Let's get the LDFs for our paid triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "ba0b96cb-77eb-472c-84fd-c5c8c5c11e10", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)1.86451.23091.10911.05501.02831.01581.00891.00591.0029
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 1.86453 1.230856 1.109122 1.055039 1.028329 1.015751 1.008899 1.005879 1.002897" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development().fit(clrd_tri[\"CumPaidLoss\"].sum()).ldf_" - ] - }, - { - "cell_type": "markdown", - "id": "0c4baafd-e141-4566-a4ae-2f0a44ef828e", - "metadata": {}, - "source": [ - "How about the CDFs?" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "b156f84b-dd0d-49d6-8eec-070d0143f40c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-Ult24-Ult36-Ult48-Ult60-Ult72-Ult84-Ult96-Ult108-Ult
(All)2.85491.53121.24401.12161.06311.03381.01781.00881.0029
" - ], - "text/plain": [ - " 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult\n", - "(All) 2.854913 1.53117 1.243988 1.121597 1.063086 1.0338 1.017769 1.008792 1.002897" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development().fit(clrd_tri[\"CumPaidLoss\"].sum()).cdf_" - ] - }, - { - "cell_type": "markdown", - "id": "d51e5664-3106-41d1-b77f-8afa9ee94ff7", - "metadata": {}, - "source": [ - "We can also use only the latest 3 periods in the calculation of LDFs." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "de88fdad-5d89-4cc2-adb0-bbeb7c77bbcb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)1.78551.21431.10311.05251.02681.01551.00891.00591.0029
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 1.785482 1.214337 1.103117 1.052471 1.026775 1.015516 1.008899 1.005879 1.002897" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(n_periods=3).fit(clrd_tri[\"CumPaidLoss\"].sum()).ldf_" - ] - }, - { - "cell_type": "markdown", - "id": "b018bae9-6070-4795-8af6-b5e196aa1af1", - "metadata": {}, - "source": [ - "# Deterministic Models" - ] - }, - { - "cell_type": "markdown", - "id": "e7c7b88e-205d-45c8-b9e6-4586f29041a4", - "metadata": {}, - "source": [ - "Before we can build any models, we need to use `fit_transform()`, so that the object is actually modified with our the development pattern(s).\n", - "\n", - "Set the development of the triangle to use only 3 periods." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "9e5136d2-0c3c-44da-8440-57ca3cfbbb9d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19883,577,7807,059,9668,826,1519,862,68710,474,69810,814,57610,994,01411,091,36311,171,59011,203,949
19894,090,6807,964,7029,937,52011,098,58811,766,48812,118,79012,311,62912,434,82612,492,899
19904,578,4428,808,48610,985,34712,229,00112,878,54513,238,66713,452,99313,559,557
19914,648,7568,961,75511,154,24412,409,59213,092,03713,447,48113,642,414
19925,139,1429,757,69912,027,98313,289,48513,992,82114,347,271
19935,653,37910,599,42312,953,81214,292,51615,005,138
19946,246,44711,394,96013,845,76415,249,326
19956,473,84311,612,15114,010,098
19966,591,59911,473,912
19976,451,896
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 3577780.0 7059966.0 8826151.0 9862687.0 10474698.0 10814576.0 10994014.0 11091363.0 11171590.0 11203949.0\n", - "1989 4090680.0 7964702.0 9937520.0 11098588.0 11766488.0 12118790.0 12311629.0 12434826.0 12492899.0 NaN\n", - "1990 4578442.0 8808486.0 10985347.0 12229001.0 12878545.0 13238667.0 13452993.0 13559557.0 NaN NaN\n", - "1991 4648756.0 8961755.0 11154244.0 12409592.0 13092037.0 13447481.0 13642414.0 NaN NaN NaN\n", - "1992 5139142.0 9757699.0 12027983.0 13289485.0 13992821.0 14347271.0 NaN NaN NaN NaN\n", - "1993 5653379.0 10599423.0 12953812.0 14292516.0 15005138.0 NaN NaN NaN NaN NaN\n", - "1994 6246447.0 11394960.0 13845764.0 15249326.0 NaN NaN NaN NaN NaN NaN\n", - "1995 6473843.0 11612151.0 14010098.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 6591599.0 11473912.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 6451896.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(n_periods=3).fit_transform(clrd_tri[\"CumPaidLoss\"].sum())" - ] - }, - { - "cell_type": "markdown", - "id": "1bd89481-e5c7-4a84-b2cc-a2e386ccdb15", - "metadata": {}, - "source": [ - "Let's fit a chainladder model to our paid triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "022e22e9-92a8-427c-bf5c-cf352df1437c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Chainladder()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Chainladder()" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl_mod = cl.Chainladder().fit(clrd_tri[\"CumPaidLoss\"].sum())\n", - "cl_mod" - ] - }, - { - "cell_type": "markdown", - "id": "7b710342-5f86-408e-bf7e-76382b37f2d1", - "metadata": {}, - "source": [ - "How can we get the model's ultimate estimates?" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "69f18923-73b1-4b80-9148-60a7bab5b118", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
198811,203,949
198912,529,085
199013,678,774
199113,884,829
199214,832,204
199315,951,756
199417,103,605
199517,428,397
199617,568,511
199718,419,602
" - ], - "text/plain": [ - " 2261\n", - "1988 1.120395e+07\n", - "1989 1.252909e+07\n", - "1990 1.367877e+07\n", - "1991 1.388483e+07\n", - "1992 1.483220e+07\n", - "1993 1.595176e+07\n", - "1994 1.710361e+07\n", - "1995 1.742840e+07\n", - "1996 1.756851e+07\n", - "1997 1.841960e+07" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl_mod.ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "b416a404-8d0f-46fc-a3e7-f5b5b884b4b4", - "metadata": {}, - "source": [ - "How about just the IBNR?" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "5fad3aa0-03bc-4f84-a8b7-1a00dbdebe8d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
1988
198936,186
1990119,217
1991242,415
1992484,933
1993946,618
19941,854,279
19953,418,299
19966,094,599
199711,967,706
" - ], - "text/plain": [ - " 2261\n", - "1988 NaN\n", - "1989 3.618623e+04\n", - "1990 1.192173e+05\n", - "1991 2.424152e+05\n", - "1992 4.849332e+05\n", - "1993 9.466176e+05\n", - "1994 1.854279e+06\n", - "1995 3.418299e+06\n", - "1996 6.094599e+06\n", - "1997 1.196771e+07" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl_mod.ibnr_" - ] - }, - { - "cell_type": "markdown", - "id": "70d8c018-21ca-4f2c-a764-433e310bb44a", - "metadata": {}, - "source": [ - "Let's fit an Expected Loss model, with an aprior of 90% on `EarnedPremDIR`, and get its ultimates." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "22eba9fa-1890-4f6f-8a10-281142d2d58d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
198813,283,902
198914,626,345
199016,170,372
199117,696,674
199219,087,522
199320,656,746
199422,282,752
199523,509,366
199624,129,860
199724,368,800
" - ], - "text/plain": [ - " 2261\n", - "1988 13283901.9\n", - "1989 14626344.6\n", - "1990 16170372.0\n", - "1991 17696673.9\n", - "1992 19087522.2\n", - "1993 20656746.0\n", - "1994 22282751.7\n", - "1995 23509366.2\n", - "1996 24129860.4\n", - "1997 24368799.6" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.ExpectedLoss(apriori=0.90).fit(\n", - " clrd_tri[\"CumPaidLoss\"].sum(), \n", - " sample_weight=clrd_tri[\"EarnedPremDIR\"].sum().latest_diagonal\n", - ").ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "eb20b72a-4e49-4eaa-b8e8-d3801833e2d3", - "metadata": {}, - "source": [ - "Try it on the incurred triangle, which again, is `IncurLoss` - `BulkLoss`, do you get the same estimates?" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "411f48b0-8b86-4175-80f2-f5f4a19e6c46", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
198813,283,902
198914,626,345
199016,170,372
199117,696,674
199219,087,522
199320,656,746
199422,282,752
199523,509,366
199624,129,860
199724,368,800
" - ], - "text/plain": [ - " 2261\n", - "1988 13283901.9\n", - "1989 14626344.6\n", - "1990 16170372.0\n", - "1991 17696673.9\n", - "1992 19087522.2\n", - "1993 20656746.0\n", - "1994 22282751.7\n", - "1995 23509366.2\n", - "1996 24129860.4\n", - "1997 24368799.6" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.ExpectedLoss(apriori=0.90).fit(\n", - " clrd_tri[\"IncurLoss\"].sum() - clrd_tri[\"BulkLoss\"].sum(), \n", - " sample_weight=clrd_tri[\"EarnedPremDIR\"].sum().latest_diagonal\n", - ").ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "fb1d7eda-f4c6-4990-9488-47235492001a", - "metadata": {}, - "source": [ - "How about a Bornhuetter-Ferguson model?" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "d66c7c9a-71eb-4d56-beea-f275da062fc0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
198811,203,949
198912,535,142
199013,700,490
199113,951,380
199214,971,330
199316,230,962
199417,665,100
199518,621,081
199619,844,674
199722,284,954
" - ], - "text/plain": [ - " 2261\n", - "1988 1.120395e+07\n", - "1989 1.253514e+07\n", - "1990 1.370049e+07\n", - "1991 1.395138e+07\n", - "1992 1.497133e+07\n", - "1993 1.623096e+07\n", - "1994 1.766510e+07\n", - "1995 1.862108e+07\n", - "1996 1.984467e+07\n", - "1997 2.228495e+07" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.BornhuetterFerguson(apriori=0.90).fit(\n", - " clrd_tri[\"CumPaidLoss\"].sum(), sample_weight=clrd_tri[\"EarnedPremDIR\"].sum().latest_diagonal\n", - ").ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "5564ead9-d059-4d2c-839a-f988238e50ee", - "metadata": {}, - "source": [ - "How about Benktander, with 2 iterations?" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "d504e48d-1f5d-4fd6-975b-155235ffb577", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
198811,203,949
198912,529,103
199013,678,964
199113,885,991
199214,836,753
199315,968,324
199417,164,479
199517,662,323
199618,358,122
199720,931,025
" - ], - "text/plain": [ - " 2261\n", - "1988 1.120395e+07\n", - "1989 1.252910e+07\n", - "1990 1.367896e+07\n", - "1991 1.388599e+07\n", - "1992 1.483675e+07\n", - "1993 1.596832e+07\n", - "1994 1.716448e+07\n", - "1995 1.766232e+07\n", - "1996 1.835812e+07\n", - "1997 2.093102e+07" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Benktander(apriori=0.90, n_iters=2).fit(\n", - " clrd_tri[\"CumPaidLoss\"].sum(), \n", - " sample_weight=clrd_tri[\"EarnedPremDIR\"].sum().latest_diagonal\n", - ").ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "002a76c2-7989-46ba-954b-d84c09b4675a", - "metadata": {}, - "source": [ - "How about Cape Cod?" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "7089ea42-ad28-4edc-9e83-723a7bc25443", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
198811,203,949
198912,526,081
199013,670,257
199113,885,101
199214,837,458
199315,968,001
199417,146,874
199517,631,944
199618,048,998
199718,888,485
" - ], - "text/plain": [ - " 2261\n", - "1988 1.120395e+07\n", - "1989 1.252608e+07\n", - "1990 1.367026e+07\n", - "1991 1.388510e+07\n", - "1992 1.483746e+07\n", - "1993 1.596800e+07\n", - "1994 1.714687e+07\n", - "1995 1.763194e+07\n", - "1996 1.804900e+07\n", - "1997 1.888848e+07" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.CapeCod().fit(\n", - " clrd_tri[\"CumPaidLoss\"].sum(), \n", - " sample_weight=clrd_tri[\"EarnedPremDIR\"].sum().latest_diagonal\n", - ").ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "5a0d73a2-0e05-4be2-91f0-9ef1ef56a7be", - "metadata": {}, - "source": [ - "Let's store the Cape Cod model as `cc_result`. We can also use `.to_frame()` to leave `chainladder` and go to a `DataFrame`. Let's make a bar chart over origin years to see what they look like." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "f2cd9f8c-454d-4b9f-b936-a2f39e8fefde", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cc_result = cl.CapeCod().fit(\n", - " clrd_tri[\"CumPaidLoss\"].sum(), sample_weight=clrd_tri[\"EarnedPremDIR\"].sum().latest_diagonal\n", - ")\n", - "\n", - "cc_result_ult = cc_result.ultimate_.to_frame()\n", - "\n", - "plt.bar(\n", - " cc_result_ult.index.year,\n", - " cc_result_ult[\"2261\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "cb905192-e96f-453d-a9bd-ef107f735d2a", - "metadata": {}, - "source": [ - "# Pipelines" - ] - }, - { - "cell_type": "markdown", - "id": "97c50acb-cb65-408f-823e-abb8aa5a5614", - "metadata": {}, - "source": [ - "Pipeline is a tool that can streamline your workflow, it is a way to wrap multiple estimators into a single compact object. Let's see an example that includes a selection of development pattern and the chainladder model." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "9e4e11ec-6c73-4d02-a7b9-484b1d4abf4e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('dev', Development(average='simple')),\n",
-       "                ('model', Chainladder())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Pipeline(steps=[('dev', Development(average='simple')),\n", - " ('model', Chainladder())])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe = cl.Pipeline(\n", - " steps=[\n", - " ('dev', cl.Development(average='simple')),\n", - " ('model', cl.Chainladder())])\n", - "\n", - "pipe" - ] - }, - { - "cell_type": "markdown", - "id": "2703d62e-2abc-47f9-a9e9-d6f7d54d7706", - "metadata": {}, - "source": [ - "Once the pipeline is built, we can apply to any dataset we like, let's try it on the paid triangle and get their ultimate estimates." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "12241691-d5c7-4fb8-a390-b8979424cc8e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 2261\n", - "1988 1.120395e+07\n", - "1989 1.252909e+07\n", - "1990 1.367977e+07\n", - "1991 1.388623e+07\n", - "1992 1.483439e+07\n", - "1993 1.595779e+07\n", - "1994 1.711789e+07\n", - "1995 1.745592e+07\n", - "1996 1.763052e+07\n", - "1997 1.862057e+07" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe.fit(clrd_tri[\"CumPaidLoss\"].sum()).named_steps.model.ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "c1b3dd0d-396b-4dd6-a380-bc75a066e1c0", - "metadata": {}, - "source": [ - "You can use the same pipeline on other datasets as well, let's try it on the incurred triangle, which again, is `IncurLoss` - `BulkLoss`." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "12f4c6bc-92b2-4896-b4cc-ea6c5e248e3c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
198811,327,627
198912,643,312
199013,788,051
199114,004,129
199214,938,810
199315,972,778
199417,102,452
199517,252,958
199617,157,374
199718,014,765
" - ], - "text/plain": [ - " 2261\n", - "1988 1.132763e+07\n", - "1989 1.264331e+07\n", - "1990 1.378805e+07\n", - "1991 1.400413e+07\n", - "1992 1.493881e+07\n", - "1993 1.597278e+07\n", - "1994 1.710245e+07\n", - "1995 1.725296e+07\n", - "1996 1.715737e+07\n", - "1997 1.801477e+07" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe.fit(clrd_tri[\"IncurLoss\"].sum() - clrd_tri[\"BulkLoss\"].sum()).named_steps.model.ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "3f9e62f8-225b-4046-8847-a6e8d971e14d", - "metadata": {}, - "source": [ - "# Stochastic Models" - ] - }, - { - "cell_type": "markdown", - "id": "36105614-e317-4a87-a42d-282f59b1d339", - "metadata": {}, - "source": [ - "The Mack's Chainladder model is available." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "e008ebdb-243d-4ed0-9256-86331df1070a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
MackChainladder()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "MackChainladder()" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mcl_mod = cl.MackChainladder().fit(clrd_tri[\"CumPaidLoss\"].sum())\n", - "mcl_mod" - ] - }, - { - "cell_type": "markdown", - "id": "3298c63c-5356-4d69-afa3-058b68daf777", - "metadata": {}, - "source": [ - "There are many attributes that are available, such as `full_std_err_`, `total_process_risk_`, `total_parameter_risk_`, `mack_std_err_` and `total_mack_std_err_`." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "67f5d99b-7a5e-4640-a6e0-f8b654e6ce27", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19880.09440.01990.00750.00510.00290.00100.00110.00190.00040.0000
19890.08830.01870.00710.00480.00270.00090.00110.00180.00040.0000
19900.08350.01780.00670.00460.00260.00090.00100.00170.00040.0000
19910.08280.01760.00670.00450.00260.00090.00100.00170.00040.0000
19920.07880.01690.00640.00440.00250.00090.00100.00160.00040.0000
19930.07510.01620.00620.00420.00240.00080.00090.00160.00040.0000
19940.07150.01560.00600.00410.00230.00080.00090.00150.00040.0000
19950.07020.01550.00600.00400.00230.00080.00090.00150.00040.0000
19960.06960.01560.00590.00400.00230.00080.00090.00150.00040.0000
19970.07030.01520.00580.00390.00230.00080.00090.00150.00030.0000
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 0.094429 0.019861 0.007523 0.005068 0.002907 0.000988 0.001128 0.001863 0.000443 0.0\n", - "1989 0.088311 0.018699 0.007090 0.004777 0.002742 0.000934 0.001066 0.001760 0.000419 0.0\n", - "1990 0.083474 0.017781 0.006743 0.004551 0.002621 0.000893 0.001020 0.001685 0.000401 0.0\n", - "1991 0.082840 0.017628 0.006692 0.004518 0.002600 0.000886 0.001013 0.001673 0.000398 0.0\n", - "1992 0.078789 0.016894 0.006444 0.004366 0.002515 0.000858 0.000980 0.001618 0.000385 0.0\n", - "1993 0.075120 0.016209 0.006210 0.004210 0.002428 0.000827 0.000945 0.001561 0.000371 0.0\n", - "1994 0.071465 0.015633 0.006006 0.004076 0.002345 0.000799 0.000913 0.001507 0.000358 0.0\n", - "1995 0.070199 0.015486 0.005971 0.004038 0.002323 0.000792 0.000904 0.001493 0.000355 0.0\n", - "1996 0.069569 0.015579 0.005947 0.004021 0.002314 0.000789 0.000900 0.001487 0.000354 0.0\n", - "1997 0.070318 0.015215 0.005808 0.003927 0.002260 0.000770 0.000879 0.001452 0.000345 0.0" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mcl_mod.full_std_err_" - ] - }, - { - "cell_type": "markdown", - "id": "bdb08c81-5921-4c41-ad63-96168ffd48b7", - "metadata": {}, - "source": [ - "MackChainladder also has a `summary_` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "81fc38c1-d5b7-4262-94ae-bce5c7ac17e1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LatestIBNRUltimateMack Std Err
198811,203,94911,203,949
198912,492,89936,18612,529,0857,613
199013,559,557119,21713,678,77429,905
199113,642,414242,41513,884,82934,341
199214,347,271484,93314,832,20438,639
199315,005,138946,61815,951,75658,394
199415,249,3261,854,27917,103,60594,894
199514,010,0983,418,29917,428,397139,873
199611,473,9126,094,59917,568,511276,854
19976,451,89611,967,70618,419,602793,559
" - ], - "text/plain": [ - " Latest IBNR Ultimate Mack Std Err\n", - "1988 11203949.0 NaN 1.120395e+07 NaN\n", - "1989 12492899.0 3.618623e+04 1.252909e+07 7612.690223\n", - "1990 13559557.0 1.192173e+05 1.367877e+07 29904.992210\n", - "1991 13642414.0 2.424152e+05 1.388483e+07 34341.474494\n", - "1992 14347271.0 4.849332e+05 1.483220e+07 38638.874014\n", - "1993 15005138.0 9.466176e+05 1.595176e+07 58393.849416\n", - "1994 15249326.0 1.854279e+06 1.710361e+07 94894.193048\n", - "1995 14010098.0 3.418299e+06 1.742840e+07 139872.884208\n", - "1996 11473912.0 6.094599e+06 1.756851e+07 276854.086589\n", - "1997 6451896.0 1.196771e+07 1.841960e+07 793559.126941" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mcl_mod.summary_" - ] - }, - { - "cell_type": "markdown", - "id": "0e285585-62b6-48e4-8b1d-c5824ae5df46", - "metadata": {}, - "source": [ - "Let's make a graph, that shows the Paid and Unpaid as stacked bars, and error bars showing Mack Standard Errors." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "8463501c-1341-4413-9a5c-468dbe583e7e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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XSyNHjtTq1aubVb9y5Uo5nU73Vl5ervDwcK8VgrCwMI86p9PJLzQAAFqoR5BN1uIwWYvDzj+sGMDnS0KpqalKTU1tdr3dbvd4/8zGjRt19OhRrxUVm83GV78DQAfWXlcqVPedCxHLoiRDVyo6Or/fw7J27VpNmjRJsbGxHu01NTWKjY3VmTNndNlll+mRRx5RfHx8o/3U1taqtrbWvV9dXX3BxtzeFRQUtPUQAAA4L359SsjpdOovf/mL5syZ49E+dOhQORwObd68WTk5OQoJCdGVV16p/fv3N9rX8uXL3as3drtd0dHRF3r4AACgjfh1hcXhcKhXr15eN+kmJiYqMTHRvX/llVfq8ssv16pVq5Sdnd1gXxkZGUpPT3fvV1dXnzO08GVnbY//B0A7xaUVtDG/BRbLsvSHP/xBaWlpCgoKarK2S5cuuuKKK5pcYQkODlZwcHCzfnZAQICks9/a2q1bt+YPGq3uxIkTkry/nRcAgKb4LbBs375dBw4c0OzZs89Za1mWSkpKdOmll7bKz+7atau6d++ur7/+WoGBgerShe/L8zfLsnTixAkdOnRIvXr1codIADDdt0/boG35HFhqamp04MAB935paalKSkoUHh6umJgYZWRkqKKiQuvXr/c4bu3atRozZoxGjBjh1efSpUuVmJioQYMGqbq6WtnZ2SopKdGaNWtaMCVvNptNUVFRKi0t1b/+9a9W6RMt06tXL54GAzopfvHjfPgcWHbv3q0JEya497+9j+TWW2+Vw+GQ0+n0esNvVVWV8vLytHLlygb7PHbsmO68805VVlbKbrcrPj5ehYWFGj16tK/Da1RQUJAGDRrU6Mv8cOEFBgaysgK0gjZ5PBhoYz4HlvHjxzd546TD4fBqs9vt7nsXGpKZmanMzExfh+KzLl268GV0AAC0Q537XUIA4G88bQO0CHefAgAA47HCAgDtDDevojNihQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACoNNyuVyy2Wyy2WxyuVxtPRwATeja1gMAgBZZYj//Puqs//vvZVFSkO38+ltSdX7HA2gUgQVAp9UjyCZrcVhbDwNAM3BJCAAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADG8/ldQoWFhXryySe1Z88eOZ1ObdiwQddee22j9QUFBZowYYJX+yeffKKhQ4e69/Py8vTQQw/p4MGDGjhwoJYtW6brrrvO1+EBgNEGnHylrYfg5Yu2HgDQDD6vsLhcLo0cOVKrV6/26bh9+/bJ6XS6t0GDBrk/Kyoq0syZM5WWlqa9e/cqLS1NM2bM0K5du3wdHgAA6IB8XmFJTU1Vamqqzz+oX79+6tWrV4OfZWVlafLkycrIyJAkZWRkaPv27crKylJOTo7PPwsAAHQsfruHJT4+XlFRUZo4caLee+89j8+KioqUkpLi0TZlyhTt3Lmz0f5qa2tVXV3tsQEAgI7J5xUWX0VFRemFF15QQkKCamtr9cc//lETJ05UQUGBxo0bJ0mqrKxURESEx3ERERGqrKxstN/ly5dr6dKlF3TsAMzFvSBA53LBA8uQIUM0ZMgQ935SUpLKy8u1YsUKd2CRJJvN5nGcZVlebd+VkZGh9PR09351dbWio6NbceQAAMAUbfJYc2Jiovbv3+/ej4yM9FpNOXTokNeqy3cFBwcrLCzMYwMAAB1TmwSW4uJiRUVFufeTkpKUn5/vUfPOO+8oOTnZ30MD0AIul0s2m002m00ul6uthwOgA/L5klBNTY0OHDjg3i8tLVVJSYnCw8MVExOjjIwMVVRUaP369ZLOPgE0YMAAXXLJJaqrq9PLL7+svLw85eXlufuYP3++xo0bpyeeeELTp0/Xpk2btG3bNr3//vutMEUATVpiP+8uekiyFv//Vc4n+593f1pSdf59AOhQfA4su3fv9vgiuG/vI7n11lvlcDjkdDpVVlbm/ryurk733nuvKioq1K1bN11yySV66623dPXVV7trkpOT9eqrr2rRokV66KGHNHDgQOXm5mrMmDHnMzcAANBB+BxYxo8fL8uyGv3c4XB47C9cuFALFy48Z7833HCDbrjhBl+HAwAAOgHeJQQAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgX/Kv5AZiNd/IAaA9YYQEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6PNQMAzonH39HWWGEBDOJyuWSz2WSz2eRyudp6OABgDFZYgNayxH7+fdRZ//ffy6KkINv59bek6vyOBwBDEFgAg/QIsslaHNbWwwAA43BJCAAAGI/Agg6Je0EAoGPhkhDM0wr3gvSQ/u/SypP9z7u/5twLwlMUAHDhsMICAACMxwoLjMNKBQDg+1hhAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADCez4GlsLBQ06ZNU//+/WWz2bRx48Ym619//XVNnjxZffv2VVhYmJKSkvT222971DgcDtlsNq/t5MmTvg4PAAB0QD4HFpfLpZEjR2r16tXNqi8sLNTkyZO1ZcsW7dmzRxMmTNC0adNUXFzsURcWFian0+mxhYSE+Do8AADQAXX19YDU1FSlpqY2uz4rK8tj/7HHHtOmTZv0xhtvKD4+3t1us9kUGRnp63AAAEAn4Pd7WOrr63X8+HGFh4d7tNfU1Cg2NlYXXXSRpk6d6rUC8321tbWqrq722AAAQMfk8wrL+Xrqqafkcrk0Y8YMd9vQoUPlcDh06aWXqrq6WitXrtSVV16pvXv3atCgQQ32s3z5ci1dutRfw26XBtz/VlsPwcsXj1/T1kMAALRDfl1hycnJ0ZIlS5Sbm6t+/fq52xMTE/WLX/xCI0eO1NixY/WnP/1JgwcP1qpVqxrtKyMjQ1VVVe6tvLzcH1MAAABtwG8rLLm5uZo9e7Zee+01TZo0qcnaLl266IorrtD+/fsbrQkODlZwcHBrDxMAABjILyssOTk5mjVrll555RVdc825LwlYlqWSkhJFRUX5YXQAAMB0Pq+w1NTU6MCBA+790tJSlZSUKDw8XDExMcrIyFBFRYXWr18v6WxYueWWW7Ry5UolJiaqsrJSktStWzfZ7XZJ0tKlS5WYmKhBgwapurpa2dnZKikp0Zo1a1pjjgAAoJ3zeYVl9+7dio+Pdz+SnJ6ervj4eP32t7+VJDmdTpWVlbnrn3/+eZ0+fVp33XWXoqKi3Nv8+fPdNceOHdOdd96pYcOGKSUlRRUVFSosLNTo0aPPd34AAKAD8HmFZfz48bIsq9HPHQ6Hx35BQcE5+8zMzFRmZqavQwEAAJ0E7xICAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHg+v/wQAID2YsDJV9p6CF6+aOsBtFOssAAAAOMRWAAAgPEILAAAwHgEFgAAYDxuum2GAfe/1dZD8PLF49e09RAAAPAbVlgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8XwOLIWFhZo2bZr69+8vm82mjRs3nvOY7du3KyEhQSEhIbr44ov13HPPedXk5eVp+PDhCg4O1vDhw7VhwwZfhwYAADoonwOLy+XSyJEjtXr16mbVl5aW6uqrr9bYsWNVXFysBx54QHfffbfy8vLcNUVFRZo5c6bS0tK0d+9epaWlacaMGdq1a5evwwMAAB1QV18PSE1NVWpqarPrn3vuOcXExCgrK0uSNGzYMO3evVsrVqzQz3/+c0lSVlaWJk+erIyMDElSRkaGtm/frqysLOXk5Pg6RAAA0MFc8HtYioqKlJKS4tE2ZcoU7d69W6dOnWqyZufOnY32W1tbq+rqao8NAAB0TBc8sFRWVioiIsKjLSIiQqdPn9bhw4ebrKmsrGy03+XLl8tut7u36Ojo1h88AAAwgl+eErLZbB77lmV5tTdU8/2278rIyFBVVZV7Ky8vb8URAwAAk/h8D4uvIiMjvVZKDh06pK5du6p3795N1nx/1eW7goODFRwc3PoDBgAAxrngKyxJSUnKz8/3aHvnnXc0atQoBQYGNlmTnJx8oYcHAADaAZ9XWGpqanTgwAH3fmlpqUpKShQeHq6YmBhlZGSooqJC69evlyTNnTtXq1evVnp6uu644w4VFRVp7dq1Hk//zJ8/X+PGjdMTTzyh6dOna9OmTdq2bZvef//9VpgiAABo73xeYdm9e7fi4+MVHx8vSUpPT1d8fLx++9vfSpKcTqfKysrc9XFxcdqyZYsKCgp02WWX6ZFHHlF2drb7kWZJSk5O1quvvqp169bpxz/+sRwOh3JzczVmzJjznR8AAOgAfF5hGT9+vPum2YY4HA6vtp/+9Kf6+9//3mS/N9xwg2644QZfhwMAADoB3iUEAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOO1KLA888wziouLU0hIiBISErRjx45Ga2fNmiWbzea1XXLJJe4ah8PRYM3JkydbMjwAANDB+BxYcnNztWDBAj344IMqLi7W2LFjlZqaqrKysgbrV65cKafT6d7Ky8sVHh6uG2+80aMuLCzMo87pdCokJKRlswIAAB2Kz4Hl6aef1uzZszVnzhwNGzZMWVlZio6O1rPPPttgvd1uV2RkpHvbvXu3jh49qttuu82jzmazedRFRka2bEYAAKDD8Smw1NXVac+ePUpJSfFoT0lJ0c6dO5vVx9q1azVp0iTFxsZ6tNfU1Cg2NlYXXXSRpk6dquLi4ib7qa2tVXV1tccGAAA6Jp8Cy+HDh3XmzBlFRER4tEdERKiysvKcxzudTv3lL3/RnDlzPNqHDh0qh8OhzZs3KycnRyEhIbryyiu1f//+Rvtavny57Ha7e4uOjvZlKgAAoB1p0U23NpvNY9+yLK+2hjgcDvXq1UvXXnutR3tiYqJ+8YtfaOTIkRo7dqz+9Kc/afDgwVq1alWjfWVkZKiqqsq9lZeXt2QqAACgHejqS3GfPn0UEBDgtZpy6NAhr1WX77MsS3/4wx+UlpamoKCgJmu7dOmiK664oskVluDgYAUHBzd/8AAAoN3yaYUlKChICQkJys/P92jPz89XcnJyk8du375dBw4c0OzZs8/5cyzLUklJiaKionwZHgAA6KB8WmGRpPT0dKWlpWnUqFFKSkrSCy+8oLKyMs2dO1fS2Us1FRUVWr9+vcdxa9eu1ZgxYzRixAivPpcuXarExEQNGjRI1dXVys7OVklJidasWdPCaQEAgI7E58Ayc+ZMHTlyRA8//LCcTqdGjBihLVu2uJ/6cTqdXt/JUlVVpby8PK1cubLBPo8dO6Y777xTlZWVstvtio+PV2FhoUaPHt2CKQEAgI7G58AiSfPmzdO8efMa/MzhcHi12e12nThxotH+MjMzlZmZ2ZKhAACAToB3CQEAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgvBa9/BAAAFw4A06+0tZD8PJFG/98VlgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACM16LA8swzzyguLk4hISFKSEjQjh07Gq0tKCiQzWbz2j799FOPury8PA0fPlzBwcEaPny4NmzY0JKhAQCADsjnwJKbm6sFCxbowQcfVHFxscaOHavU1FSVlZU1edy+ffvkdDrd26BBg9yfFRUVaebMmUpLS9PevXuVlpamGTNmaNeuXb7PCAAAdDg+B5ann35as2fP1pw5czRs2DBlZWUpOjpazz77bJPH9evXT5GRke4tICDA/VlWVpYmT56sjIwMDR06VBkZGZo4caKysrJ8nhAAAOh4fAosdXV12rNnj1JSUjzaU1JStHPnziaPjY+PV1RUlCZOnKj33nvP47OioiKvPqdMmdJkn7W1taqurvbYAABAx+RTYDl8+LDOnDmjiIgIj/aIiAhVVlY2eExUVJReeOEF5eXl6fXXX9eQIUM0ceJEFRYWumsqKyt96lOSli9fLrvd7t6io6N9mQoAAGhHurbkIJvN5rFvWZZX27eGDBmiIUOGuPeTkpJUXl6uFStWaNy4cS3qU5IyMjKUnp7u3q+uria0AADQQfm0wtKnTx8FBAR4rXwcOnTIa4WkKYmJidq/f797PzIy0uc+g4ODFRYW5rEBAICOyafAEhQUpISEBOXn53u05+fnKzk5udn9FBcXKyoqyr2flJTk1ec777zjU58AAKDj8vmSUHp6utLS0jRq1CglJSXphRdeUFlZmebOnSvp7KWaiooKrV+/XtLZJ4AGDBigSy65RHV1dXr55ZeVl5envLw8d5/z58/XuHHj9MQTT2j69OnatGmTtm3bpvfff7+VpgkAANoznwPLzJkzdeTIET388MNyOp0aMWKEtmzZotjYWEmS0+n0+E6Wuro63XvvvaqoqFC3bt10ySWX6K233tLVV1/trklOTtarr76qRYsW6aGHHtLAgQOVm5urMWPGtMIUAQBAe9eim27nzZunefPmNfiZw+Hw2F+4cKEWLlx4zj5vuOEG3XDDDS0ZDgAA6OB4lxAAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGK9FgeWZZ55RXFycQkJClJCQoB07djRa+/rrr2vy5Mnq27evwsLClJSUpLffftujxuFwyGazeW0nT55syfAAAEAH43Ngyc3N1YIFC/Tggw+quLhYY8eOVWpqqsrKyhqsLyws1OTJk7Vlyxbt2bNHEyZM0LRp01RcXOxRFxYWJqfT6bGFhIS0bFYAAKBD6errAU8//bRmz56tOXPmSJKysrL09ttv69lnn9Xy5cu96rOysjz2H3vsMW3atElvvPGG4uPj3e02m02RkZG+DgcAAHQCPq2w1NXVac+ePUpJSfFoT0lJ0c6dO5vVR319vY4fP67w8HCP9pqaGsXGxuqiiy7S1KlTvVZgvq+2tlbV1dUeGwAA6Jh8CiyHDx/WmTNnFBER4dEeERGhysrKZvXx1FNPyeVyacaMGe62oUOHyuFwaPPmzcrJyVFISIiuvPJK7d+/v9F+li9fLrvd7t6io6N9mQoAAGhHWnTTrc1m89i3LMurrSE5OTlasmSJcnNz1a9fP3d7YmKifvGLX2jkyJEaO3as/vSnP2nw4MFatWpVo31lZGSoqqrKvZWXl7dkKgAAoB3w6R6WPn36KCAgwGs15dChQ16rLt+Xm5ur2bNn67XXXtOkSZOarO3SpYuuuOKKJldYgoODFRwc3PzBAwCAdsunFZagoCAlJCQoPz/foz0/P1/JycmNHpeTk6NZs2bplVde0TXXXHPOn2NZlkpKShQVFeXL8AAAQAfl81NC6enpSktL06hRo5SUlKQXXnhBZWVlmjt3rqSzl2oqKiq0fv16SWfDyi233KKVK1cqMTHRvTrTrVs32e12SdLSpUuVmJioQYMGqbq6WtnZ2SopKdGaNWtaa54AAKAd8zmwzJw5U0eOHNHDDz8sp9OpESNGaMuWLYqNjZUkOZ1Oj+9kef7553X69Gnddddduuuuu9ztt956qxwOhyTp2LFjuvPOO1VZWSm73a74+HgVFhZq9OjR5zk9AADQEfgcWCRp3rx5mjdvXoOffRtCvlVQUHDO/jIzM5WZmdmSoQAAgE6AdwkBAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgtCizPPPOM4uLiFBISooSEBO3YsaPJ+u3btyshIUEhISG6+OKL9dxzz3nV5OXlafjw4QoODtbw4cO1YcOGlgwNAAB0QD4HltzcXC1YsEAPPvigiouLNXbsWKWmpqqsrKzB+tLSUl199dUaO3asiouL9cADD+juu+9WXl6eu6aoqEgzZ85UWlqa9u7dq7S0NM2YMUO7du1q+cwAAECH4XNgefrppzV79mzNmTNHw4YNU1ZWlqKjo/Xss882WP/cc88pJiZGWVlZGjZsmObMmaPbb79dK1ascNdkZWVp8uTJysjI0NChQ5WRkaGJEycqKyurxRMDAAAdR1dfiuvq6rRnzx7df//9Hu0pKSnauXNng8cUFRUpJSXFo23KlClau3atTp06pcDAQBUVFemee+7xqmkqsNTW1qq2tta9X1VVJUmqrq72ZUrNUl97otX7PF/NmSfjbj2M278Yt38xbv/qyOM+n34ty2qyzqfAcvjwYZ05c0YREREe7REREaqsrGzwmMrKygbrT58+rcOHDysqKqrRmsb6lKTly5dr6dKlXu3R0dHNnU67Zs9q6xG0DOP2L8btX4zbvxi3f13ocR8/flx2u73Rz30KLN+y2Wwe+5ZlebWdq/777b72mZGRofT0dPd+fX29vvnmG/Xu3bvJ49pSdXW1oqOjVV5errCwsLYeTofH+fYvzrd/cb79i/N94ViWpePHj6t///5N1vkUWPr06aOAgACvlY9Dhw55rZB8KzIyssH6rl27qnfv3k3WNNanJAUHBys4ONijrVevXs2dSpsKCwvjD7wfcb79i/PtX5xv/+J8XxhNrax8y6ebboOCgpSQkKD8/HyP9vz8fCUnJzd4TFJSklf9O++8o1GjRikwMLDJmsb6BAAAnYvPl4TS09OVlpamUaNGKSkpSS+88ILKyso0d+5cSWcv1VRUVGj9+vWSpLlz52r16tVKT0/XHXfcoaKiIq1du1Y5OTnuPufPn69x48bpiSee0PTp07Vp0yZt27ZN77//fitNEwAAtGc+B5aZM2fqyJEjevjhh+V0OjVixAht2bJFsbGxkiSn0+nxnSxxcXHasmWL7rnnHq1Zs0b9+/dXdna2fv7zn7trkpOT9eqrr2rRokV66KGHNHDgQOXm5mrMmDGtMEVzBAcHa/HixV6XsnBhcL79i/PtX5xv/+J8tz2bda7niAAAANoY7xICAADGI7AAAADjEVgAAIDxCCwAAMB4BBYfFBYWatq0aerfv79sNps2btzo8flXX32lWbNmqX///urevbuuuuoq7d+/36OmsrJSaWlpioyMVI8ePXT55Zfrz3/+s0fNZ599punTp6tPnz4KCwvTlVdeqffee+9CT89IrXHODx48qOuuu059+/ZVWFiYZsyYoa+++sqj5ujRo0pLS5PdbpfdbldaWpqOHTt2gWdnHn+c7y+++EKzZ89WXFycunXrpoEDB2rx4sWqq6vzxxSN4q8/39+qra3VZZddJpvNppKSkgs0K3P583y/9dZbGjNmjLp166Y+ffro+uuvv5BT6xQILD5wuVwaOXKkVq9e7fWZZVm69tpr9fnnn2vTpk0qLi5WbGysJk2aJJfL5a5LS0vTvn37tHnzZn300Ue6/vrrNXPmTBUXF7trrrnmGp0+fVrvvvuu9uzZo8suu0xTp05t8t1KHdX5nnOXy6WUlBTZbDa9++67+utf/6q6ujpNmzZN9fX17r5uuukmlZSUaOvWrdq6datKSkqUlpbmt3mawh/n+9NPP1V9fb2ef/55ffzxx8rMzNRzzz2nBx54wK9zNYG//nx/a+HChef8+vOOzF/nOy8vT2lpabrtttu0d+9e/fWvf9VNN93kt3l2WBZaRJK1YcMG9/6+ffssSdY//vEPd9vp06et8PBw6/e//727rUePHtb69es9+goPD7defPFFy7Is6+uvv7YkWYWFhe7Pq6urLUnWtm3bLtBs2oeWnPO3337b6tKli1VVVeWu+eabbyxJVn5+vmVZlvXPf/7TkmR98MEH7pqioiJLkvXpp59e4FmZ60Kd74b87ne/s+Li4lp/Eu3IhT7fW7ZssYYOHWp9/PHHliSruLj4gs7HdBfqfJ86dcr64Q9/6P47Ha2HFZZWUltbK0kKCQlxtwUEBCgoKMjjG3t/8pOfKDc3V998843q6+v16quvqra2VuPHj5ck9e7dW8OGDdP69evlcrl0+vRpPf/884qIiFBCQoJf52S65pzz2tpa2Ww2jy97CgkJUZcuXdw1RUVFstvtHl9UmJiYKLvdrp07d/pjKu1Ca53vhlRVVSk8PPwCjbx9as3z/dVXX+mOO+7QH//4R3Xv3t1PM2hfWut8//3vf1dFRYW6dOmi+Ph4RUVFKTU1VR9//LEfZ9MxEVhaydChQxUbG6uMjAwdPXpUdXV1evzxx1VZWSmn0+muy83N1enTp9W7d28FBwfrl7/8pTZs2KCBAwdKOvvW6vz8fBUXFys0NFQhISHKzMzU1q1b283LHf2lOec8MTFRPXr00H333acTJ07I5XLpN7/5jerr6901lZWV6tevn1f//fr165SX4RrTWuf7+w4ePKhVq1a5X++Bs1rrfFuWpVmzZmnu3LkaNWpUW07JaK11vj///HNJ0pIlS7Ro0SK9+eab+sEPfqCf/vSn+uabb9psfh0BgaWVBAYGKi8vT5999pnCw8PVvXt3FRQUKDU1VQEBAe66RYsW6ejRo9q2bZt2796t9PR03Xjjjfroo48knf3LZd68eerXr5927Nihv/3tb5o+fbqmTp3a6F/4nVVzznnfvn312muv6Y033lDPnj1lt9tVVVWlyy+/3OP/i81m8+rfsqwG2zur1jzf3/ryyy911VVX6cYbb9ScOXP8PSWjtdb5XrVqlaqrq5WRkdGW0zFea53vb+9lefDBB/Xzn/9cCQkJWrdunWw2m1577bU2m19H4PO7hNC4hIQElZSUqKqqSnV1derbt6/GjBnj/lfNwYMHtXr1av3jH//QJZdcIkkaOXKkduzYoTVr1ui5557Tu+++qzfffFNHjx51v8L8mWeeUX5+vl566SXdf//9bTY/E53rnEtSSkqKDh48qMOHD6tr167q1auXIiMjFRcXJ0mKjIxs8C7/r7/+WhEREX6bS3vQGuf7W19++aUmTJjgfokqvLXG+X733Xf1wQcfeL0DZ9SoUbr55pv10ksv+XVOJmuN8x0VFSVJGj58uPuY4OBgXXzxxR7v2YPvWGG5AOx2u/r27av9+/dr9+7dmj59uiTpxIkTkqQuXTxPe0BAgDuVN1bTpUuXBu/6x1mNnfPv6tOnj3r16qV3331Xhw4d0s9+9jNJUlJSkqqqqvS3v/3NXbtr1y5VVVUpOTnZb3NoT87nfEtSRUWFxo8fr8svv1zr1q3z+vMOT+dzvrOzs7V3716VlJSopKREW7ZskXT28vSyZcv8Oo/24nzOd0JCgoKDg7Vv3z537alTp/TFF1+4XxKMlmGFxQc1NTU6cOCAe7+0tFQlJSUKDw9XTEyMXnvtNfXt21cxMTH66KOPNH/+fF177bVKSUmRdPYa6Y9+9CP98pe/1IoVK9S7d29t3LhR+fn5evPNNyWd/eX5gx/8QLfeeqt++9vfqlu3bvr973+v0tJSXXPNNW0y77Z0vudcktatW6dhw4apb9++Kioq0vz583XPPfdoyJAhkqRhw4bpqquu0h133KHnn39eknTnnXdq6tSp7prOwh/n+8svv9T48eMVExOjFStW6Ouvv3YfGxkZ6b/JGsAf5zsmJsbjZ/bs2VOSNHDgQF100UV+mKU5/HG+w8LCNHfuXC1evFjR0dGKjY3Vk08+KUm68cYb/TvhjqaNn1JqV9577z1Lktd26623WpZlWStXrrQuuugiKzAw0IqJibEWLVpk1dbWevTx2WefWddff73Vr18/q3v37taPf/xjr8ecP/zwQyslJcUKDw+3QkNDrcTERGvLli3+mqZRWuOc33fffVZERIQVGBhoDRo0yHrqqaes+vp6j5ojR45YN998sxUaGmqFhoZaN998s3X06FE/zdIc/jjf69ata/BndMa/jvz15/u7SktLO+1jzf4633V1ddZ//ud/Wv369bNCQ0OtSZMmeTwujZaxWZZlXfhYBAAA0HJcOAYAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeP8PBcgrW+efYmYAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mcl_mod_df = mcl_mod.summary_.to_frame()\n", - "\n", - "# bottom bar chart\n", - "plt.bar(\n", - " mcl_mod_df.index.year, # category, year\n", - " mcl_mod_df[\"Latest\"], # height, Latest (paid)\n", - " label=\"Paid\",\n", - ")\n", - "\n", - "# top bar chart\n", - "plt.bar(\n", - " mcl_mod_df.index.year, # category, year\n", - " mcl_mod_df[\"IBNR\"], # height, IBNR (unpaid)\n", - " bottom=mcl_mod_df[\"Latest\"], # height, Latest (paid)\n", - " yerr=mcl_mod_df[\"Mack Std Err\"], # Mack's standard error\n", - " label=\"Unpaid\",\n", - ")\n", - "\n", - "plt.legend(loc=\"upper left\")" - ] - }, - { - "cell_type": "markdown", - "id": "785120ad-03cf-48a7-90d8-d1d56a75ef88", - "metadata": {}, - "source": [ - "ODP Bootstrap is also available. Let's bootstrap 10,000 paid triangles." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "859e19f3-d526-435c-a845-4845a7a3956d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(10000, 1, 10, 10)
Index:[LOB]
Columns:[CumPaidLoss]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (10000, 1, 10, 10)\n", - "Index: [LOB]\n", - "Columns: [CumPaidLoss]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_tri_sampled = (\n", - " cl.BootstrapODPSample(n_sims=10000).fit(clrd_tri[\"CumPaidLoss\"].sum()).resampled_triangles_\n", - ")\n", - "clrd_tri_sampled" - ] - }, - { - "cell_type": "markdown", - "id": "4391f730-5309-49b2-9c19-0801e3e66c7c", - "metadata": {}, - "source": [ - "We can fit a basic chainladder to all sampled triangles. We now have 10,000 simulated chainladder models, all (most) with unique LDFs." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "fe6dbe70-1b2a-4fb0-aa6b-56380534704f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Chainladder()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Chainladder()" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl_mod_bootstrapped = cl.Chainladder().fit(clrd_tri_sampled)\n", - "cl_mod_bootstrapped" - ] - }, - { - "cell_type": "markdown", - "id": "a6f81ac6-a2ab-496d-8d1f-4604aa464370", - "metadata": {}, - "source": [ - "We can use `predict()` to use the model characteristics (their unique LDFs) to predict our basic Incurred triangle. Let's inspect the prediction's means. " - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "4f304cf4-f973-4e98-b76f-8266de8659b6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
198811,203,949
198912,529,242
199013,679,039
199113,885,632
199214,833,163
199315,953,124
199417,104,934
199517,430,911
199617,571,158
199718,424,816
" - ], - "text/plain": [ - " 2261\n", - "1988 1.120395e+07\n", - "1989 1.252924e+07\n", - "1990 1.367904e+07\n", - "1991 1.388563e+07\n", - "1992 1.483316e+07\n", - "1993 1.595312e+07\n", - "1994 1.710493e+07\n", - "1995 1.743091e+07\n", - "1996 1.757116e+07\n", - "1997 1.842482e+07" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl_mod_bootstrapped.predict(clrd_tri[\"CumPaidLoss\"].sum()).ultimate_.mean()" - ] - }, - { - "cell_type": "markdown", - "id": "bb3d7c32-9e75-4ae4-ab23-0ca3f2a436b5", - "metadata": {}, - "source": [ - "Let's make another graph." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "edeba1db-97e6-43df-b1c0-590c2d7cd098", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(\n", - " cl_mod_bootstrapped.ultimate_.mean().to_frame().index.year, #x-axis, accident year\n", - " cl_mod_bootstrapped.ultimate_.mean().to_frame()[\"2261\"], #y-axis, ultimates\n", - " yerr=cl_mod_bootstrapped.ultimate_.std().to_frame()[\"2261\"], #ultimates standard deviation\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/deterministic-tutorial.ipynb b/docs/tutorials/deterministic-tutorial.ipynb deleted file mode 100644 index 44f8a1a1..00000000 --- a/docs/tutorials/deterministic-tutorial.ipynb +++ /dev/null @@ -1,4289 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Applying Deterministic Methods\n", - "## Getting Started\n", - "This tutorial focuses on using deterministic methods to square a triangle. \n", - "\n", - "Be sure to make sure your packages are updated. For more info on how to update your pakages, visit [Keeping Packages Updated](https://chainladder-python.readthedocs.io/en/latest/library/install.html#keeping-packages-updated)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pandas: 1.4.2\n", - "numpy: 1.22.4\n", - "chainladder: 0.8.12\n" - ] - } - ], - "source": [ - "# Black linter, optional\n", - "%load_ext lab_black\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import chainladder as cl\n", - "import matplotlib.pyplot as plt\n", - "\n", - "print(\"pandas: \" + pd.__version__)\n", - "print(\"numpy: \" + np.__version__)\n", - "print(\"chainladder: \" + cl.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Disclaimer\n", - "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Chainladder Method\n", - "\n", - "The basic chainladder method is entirely specified by its development pattern selections. For this reason, the `Chainladder` estimator takes no additional assumptions, i.e. no additional arguments. Let's start by loading an example dataset and creating an Triangle with `Development` patterns and a `TailCurve`. Recall, we can bundle these two estimators into a single `Pipeline` if we wish." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "genins = cl.load_sample(\"genins\")\n", - "\n", - "genins_dev = cl.Pipeline(\n", - " [(\"dev\", cl.Development()), (\"tail\", cl.TailCurve())]\n", - ").fit_transform(genins)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now use the basic `Chainladder` estimator to estimate `ultimate_` values of our `Triangle`." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 2261\n", - "2001 1.150899e+05\n", - "2002 2.549240e+05\n", - "2003 6.281822e+05\n", - "2004 8.659217e+05\n", - "2005 1.128202e+06\n", - "2006 1.570235e+06\n", - "2007 2.344629e+06\n", - "2008 4.120447e+06\n", - "2009 4.445414e+06\n", - "2010 4.772416e+06" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genins_model.ibnr_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is often useful to see the completed `Triangle` and this can be accomplished by inspecting the `full_triangle_`. As with most other estimator properties, the `full_triangle_` is itself a `Triangle` and can be manipulated as such." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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2001357,8481,124,7881,735,3302,218,2702,745,5963,319,9943,466,3363,606,2863,833,5153,901,463...3,948,0713,948,0713,948,0713,948,0713,948,0713,948,0713,948,0713,948,0713,948,0714,016,553
2002352,1181,236,1392,170,0333,353,3223,799,0674,120,0634,647,8674,914,0395,339,085...5,498,6325,498,6325,498,6325,498,6325,498,6325,498,6325,498,6325,498,6325,498,6325,594,009
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2004310,6081,418,8582,195,0473,757,4474,029,9294,381,9824,588,268...5,205,6375,297,9065,361,1975,361,1975,361,1975,361,1975,361,1975,361,1975,361,1975,454,190
2005443,1601,136,3502,128,3332,897,8213,402,6723,873,311...4,434,1334,773,5894,858,2004,916,2374,916,2374,916,2374,916,2374,916,2374,916,2375,001,513
2006396,1321,333,2172,180,7152,985,7523,691,712...4,426,5464,665,0235,022,1555,111,1715,172,2315,172,2315,172,2315,172,2315,172,2315,261,947
2007440,8321,288,4632,419,8613,483,130...4,513,1794,902,5285,166,6495,562,1825,660,7715,728,3965,728,3965,728,3965,728,3965,827,759
2008359,4801,421,1282,864,498...4,900,5455,409,3375,875,9976,192,5626,666,6356,784,7996,865,8536,865,8536,865,8536,984,945
2009376,6861,363,294...3,471,7444,075,3134,498,4264,886,5025,149,7605,544,0005,642,2665,709,6715,709,6715,808,708
2010344,014...2,098,2283,057,9843,589,6203,962,3074,304,1324,536,0154,883,2704,969,8255,029,1965,116,430
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While ultimates will generally be realized before this date, the `chainladder` package picks the highest allowable date available for its `ultimate_` valuation. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp('2261-12-31 23:59:59.999999999')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genins_model.full_triangle_.valuation_date" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can further manipulate the \"triangle\", such as applying `cum_to_incr()`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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2001357,848766,940610,542482,940527,326574,398146,342139,950227,22967,948...68,482
2002352,118884,021933,8941,183,289445,745320,996527,804266,172425,046...64,91395,377
2003290,5071,001,799926,2191,016,654750,816146,923495,992280,405...93,67864,25794,413
2004310,6081,108,250776,1891,562,400272,482352,053206,286...370,17992,26863,29192,993
2005443,160693,190991,983769,488504,851470,639...226,674339,45684,61158,03885,275
2006396,132937,085847,498805,037705,960...351,548238,477357,13289,01661,06089,715
2007440,832847,6311,131,3981,063,269...424,501389,349264,121395,53498,58867,62699,362
2008359,4801,061,6481,443,370...725,788508,792466,660316,566474,073118,16481,054119,092
2009376,686986,608...1,089,616603,569423,113388,076263,257394,24198,26667,40599,038
2010344,014...897,410959,756531,636372,687341,826231,882347,25586,55559,37187,234
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2009-13,875
2010
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "2001 87786.584355 182109.854207 88157.704861 -182340.046069 -72364.206180 209463.211490 87461.697305 45377.021916 4.656613e-10 NaN\n", - "2002 -24007.006253 -76765.409669 -124047.730972 9899.295819 -125615.456548 -212093.852125 -58022.270396 -45377.021916 NaN NaN\n", - "2003 -81818.315504 -7335.184258 -52380.464273 -74467.773015 100960.477986 -155474.528980 -29439.426909 NaN NaN NaN\n", - "2004 -56115.956096 138768.957566 -41694.368640 497591.418491 203341.953249 158105.169615 NaN NaN NaN NaN\n", - "2005 106872.747755 -37496.484673 77232.720516 -91478.875281 -106322.768507 NaN NaN NaN NaN NaN\n", - "2006 42333.899273 98247.032955 22811.664568 -159204.019945 NaN NaN NaN NaN NaN NaN\n", - "2007 48990.342828 -79302.054276 29920.473940 NaN NaN NaN NaN NaN NaN NaN\n", - "2008 -110167.520951 -218226.711852 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009 -13874.775407 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(\n", - " genins_model.full_triangle_[\n", - " genins_model.full_triangle_.valuation <= genins.valuation_date\n", - " ]\n", - " - genins_model.full_expectation_[\n", - " genins_model.full_triangle_.valuation <= genins.valuation_date\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Getting comfortable with manipulating `Triangle`s will greatly improve our ability to extract value out of the `chainladder` package. Here is another way of getting the same answer." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
200187,787182,11088,158-182,340-72,364209,46387,46245,3770
2002-24,007-76,765-124,0489,899-125,615-212,094-58,022-45,377
2003-81,818-7,335-52,380-74,468100,960-155,475-29,439
2004-56,116138,769-41,694497,591203,342158,105
2005106,873-37,49677,233-91,479-106,323
200642,33498,24722,812-159,204
200748,990-79,30229,920
2008-110,168-218,227
2009-13,875
2010
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "2001 87786.584355 182109.854207 88157.704861 -182340.046069 -72364.206180 209463.211490 87461.697305 45377.021916 4.656613e-10 NaN\n", - "2002 -24007.006253 -76765.409669 -124047.730972 9899.295819 -125615.456548 -212093.852125 -58022.270396 -45377.021916 NaN NaN\n", - "2003 -81818.315504 -7335.184258 -52380.464273 -74467.773015 100960.477986 -155474.528980 -29439.426909 NaN NaN NaN\n", - "2004 -56115.956096 138768.957566 -41694.368640 497591.418491 203341.953249 158105.169615 NaN NaN NaN NaN\n", - "2005 106872.747755 -37496.484673 77232.720516 -91478.875281 -106322.768507 NaN NaN NaN NaN NaN\n", - "2006 42333.899273 98247.032955 22811.664568 -159204.019945 NaN NaN NaN NaN NaN NaN\n", - "2007 48990.342828 -79302.054276 29920.473940 NaN NaN NaN NaN NaN NaN NaN\n", - "2008 -110167.520951 -218226.711852 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009 -13874.775407 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genins_AvE = genins - genins_model.full_expectation_\n", - "genins_AvE[genins_AvE.valuation <= genins.valuation_date]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also filter out the lower right part of the triangle with `[genins_model.full_triangle_.valuation <= genins.valuation_date]` before applying the `heatmap()`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/display.py:134: FutureWarning: this method is deprecated in favour of `Styler.to_html()`\n", - " .render()\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 1224364860728496108120
200187,787182,11088,158-182,340-72,364209,46387,46245,3770
2002-24,007-76,765-124,0489,899-125,615-212,094-58,022-45,377
2003-81,818-7,335-52,380-74,468100,960-155,475-29,439
2004-56,116138,769-41,694497,591203,342158,105
2005106,873-37,49677,233-91,479-106,323
200642,33498,24722,812-159,204
200748,990-79,30229,920
2008-110,168-218,227
2009-13,875
2010
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genins_AvE[genins_AvE.valuation <= genins.valuation_date].heatmap()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Can you figure out how to get the expected IBNR runoff in the upcoming year?" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2011
200146,608
200294,634
2003375,833
2004247,190
2005334,148
2006383,287
2007605,548
20081,310,258
20091,018,834
2010856,804
" - ], - "text/plain": [ - " 2011\n", - "2001 4.660832e+04\n", - "2002 9.463381e+04\n", - "2003 3.758335e+05\n", - "2004 2.471900e+05\n", - "2005 3.341481e+05\n", - "2006 3.832866e+05\n", - "2007 6.055481e+05\n", - "2008 1.310258e+06\n", - "2009 1.018834e+06\n", - "2010 8.568035e+05" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cal_yr_ibnr = genins_model.full_triangle_.dev_to_val().cum_to_incr()\n", - "cal_yr_ibnr[cal_yr_ibnr.valuation.year == 2011]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Expected Loss Method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's talk about the expected loss method, where we know the ultimate loss already (but then why are you trying to estimate your ultimate losses?). The `ExpectedLoss` model estimator has many of the same attributes as the `Chainladder` estimator. It comes with one input assumption, the a priori (`apriori`). This is a scalar multiplier that will be applied to an exposure vector, which will produce an a priori ultimate estimate vector that we can use for the model.\n", - "\n", - "Earlier, we used the `Chainladder` method on the `genins` data. Let's use the average of the `Chainladder` ultimate to help us derive an a priori in the expected loss method. \n", - "\n", - "Below, we use `genins_model.ultimate_ * 0` and add the mean to it to preserve the index values of years 2001 - 2020." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
20015,460,355
20025,460,355
20035,460,355
20045,460,355
20055,460,355
20065,460,355
20075,460,355
20085,460,355
20095,460,355
20105,460,355
" - ], - "text/plain": [ - " 2261\n", - "2001 5.460355e+06\n", - "2002 5.460355e+06\n", - "2003 5.460355e+06\n", - "2004 5.460355e+06\n", - "2005 5.460355e+06\n", - "2006 5.460355e+06\n", - "2007 5.460355e+06\n", - "2008 5.460355e+06\n", - "2009 5.460355e+06\n", - "2010 5.460355e+06" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "expected_loss_apriori = genins_model.ultimate_ * 0 + genins_model.ultimate_.mean()\n", - "expected_loss_apriori" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's assume that we know our expected loss will be 95% of the a priori, which is common if the a priori is a function of something else, like earned premium. We set `apriori` with 0.95 inside the function, then call `fit` on the data, `genins`, and assigh the `sample_weight` with our vector of a prioris, `expected_loss_apriori`." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
20015,187,337
20025,187,337
20035,187,337
20045,187,337
20055,187,337
20065,187,337
20075,187,337
20085,187,337
20095,187,337
20105,187,337
" - ], - "text/plain": [ - " 2261\n", - "2001 5.187337e+06\n", - "2002 5.187337e+06\n", - "2003 5.187337e+06\n", - "2004 5.187337e+06\n", - "2005 5.187337e+06\n", - "2006 5.187337e+06\n", - "2007 5.187337e+06\n", - "2008 5.187337e+06\n", - "2009 5.187337e+06\n", - "2010 5.187337e+06" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "EL_model = cl.ExpectedLoss(apriori=0.95).fit(\n", - " genins, sample_weight=expected_loss_apriori\n", - ")\n", - "EL_model.ultimate_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bornhuetter-Ferguson Method\n", - "The `BornhuetterFerguson` estimator is another deterministic method having many of the same attributes as the `Chainladder` estimator. It comes with one input assumption, the a priori (`apriori`). This is a scalar multiplier that will be applied to an exposure vector, which will produce an a priori ultimate estimate vector that we can use for the model.\n", - "\n", - "Since the CAS Loss Reserve Database has premium, we will use it as an example. Let's grab the paid loss and net earned premium for the commercial auto line of business." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remember that `apriori` is a scaler, which we need to apply it to a vector of exposures. Let's assume that the a priori is 0.75, for 75% loss ratio." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's set an apriori Loss Ratio estimate of 75%" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `BornhuetterFerguson` method along with all other expected loss methods like `CapeCod` and `Benktander` (discussed later), need to take in an exposure vector. The exposure vector has to be a `Triangle` itself. Remember that the `Triangle` class supports single exposure vectors." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
BornhuetterFerguson(apriori=0.75)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "BornhuetterFerguson(apriori=0.75)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "comauto = cl.load_sample(\"clrd\").groupby(\"LOB\").sum().loc[\"comauto\"]\n", - "\n", - "bf_model = cl.BornhuetterFerguson(apriori=0.75)\n", - "bf_model.fit(\n", - " comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
1988626,097
1989679,224
1990728,363
1991729,927
1992767,610
1993833,686
1994918,582
1995954,377
1996985,280
19971,031,637
" - ], - "text/plain": [ - " 2261\n", - "1988 6.260970e+05\n", - "1989 6.792242e+05\n", - "1990 7.283626e+05\n", - "1991 7.299271e+05\n", - "1992 7.676100e+05\n", - "1993 8.336865e+05\n", - "1994 9.185817e+05\n", - "1995 9.543771e+05\n", - "1996 9.852804e+05\n", - "1997 1.031637e+06" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bf_model.ultimate_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Having an `apriori` that takes on only a constant for all origins can be limiting. This shouldn't stop the practitioner from exploiting the fact that the `apriori` can be embedded directly in the exposure vector itself allowing full cusomization of the `apriori`." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b1 = cl.BornhuetterFerguson(apriori=0.75).fit(\n", - " comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", - ")\n", - "\n", - "b2 = cl.BornhuetterFerguson(apriori=1.00).fit(\n", - " comauto[\"CumPaidLoss\"],\n", - " sample_weight=0.75 * comauto[\"EarnedPremNet\"].latest_diagonal,\n", - ")\n", - "\n", - "b1.ultimate_ == b2.ultimate_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we need to create a new colume, such as `AdjEarnedPrmNet` with varying implied loss ratios. It is recommend that we perform any data modification in `pandas` instead of `Triangle` forms.\n", - "\n", - "Let's perform the estimate using `Chainladder` and compare the results." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Int64Index([1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997], dtype='int64')" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bf_model.ultimate_.to_frame(origin_as_datetime=True).index.year" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(\n", - " bf_model.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", - " bf_model.ultimate_.to_frame(origin_as_datetime=True),\n", - " label=\"BF\",\n", - ")\n", - "\n", - "cl_model = cl.Chainladder().fit(comauto[\"CumPaidLoss\"])\n", - "plt.plot(\n", - " cl_model.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", - " cl_model.ultimate_.to_frame(origin_as_datetime=True),\n", - " label=\"CL\",\n", - ")\n", - "\n", - "plt.legend(loc=\"upper left\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Benktander Method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `Benktander` method is similar to the `BornhuetterFerguson` method, but allows for the specification of one additional assumption, `n_iters`, the number of iterations to recalculate the ultimates. The Benktander method generalizes both the `BornhuetterFerguson` and the `Chainladder` estimator through this assumption.\n", - "\n", - "- When `n_iters = 1`, the result is equivalent to the `BornhuetterFerguson` estimator.\n", - "- When `n_iters` is sufficiently large, the result converges to the `Chainladder` estimator." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Benktander(apriori=0.75, n_iters=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Benktander(apriori=0.75, n_iters=2)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bk_model = cl.Benktander(apriori=0.75, n_iters=2)\n", - "bk_model.fit(\n", - " comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fitting the `Benktander` method looks identical to the other methods." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Benktander(apriori=0.75, n_iters=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Benktander(apriori=0.75, n_iters=2)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bk_model.fit(\n", - " X=comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(\n", - " bf_model.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", - " bf_model.ultimate_.to_frame(origin_as_datetime=True),\n", - " label=\"BF\",\n", - ")\n", - "plt.plot(\n", - " cl_model.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", - " cl_model.ultimate_.to_frame(origin_as_datetime=True),\n", - " label=\"CL\",\n", - ")\n", - "plt.plot(\n", - " bk_model.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", - " bk_model.ultimate_.to_frame(origin_as_datetime=True),\n", - " label=\"BK\",\n", - ")\n", - "plt.legend(loc=\"upper left\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cape Cod Method\n", - "The `CapeCod` method is similar to the `BornhuetterFerguson` method, except its `apriori` is computed from the `Triangle` itself. Instead of specifying an `apriori`, `decay` and `trend` need to be specified. \n", - "\n", - " - `decay` is the rate that gives weights to earlier origin periods, this parameter is required by the Generalized Cape Cod Method, as discussed in [Using Best Practices to Determine a Best Reserve Estimate](https://www.casact.org/sites/default/files/database/forum_98fforum_struhuss.pdf) by Struzzieri and Hussian. As the `decay` factor approaches 1 (the default value), the result approaches the traditional Cape Cod method. As the `decay` factor approaches 0, the result approaches the `Chainladder` method. \n", - " - `trend` is the trend rate along the origin axis to reflect systematic inflationary impacts on the a priori." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When we `fit` a `CapeCod` method, we can see the `apriori` it computes with the given `decay` and `trend` assumptions. Since it is an array of estimated parameters, this `CapeCod` attribute is called the `apriori_`, with a trailing underscore." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
CapeCod()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "CapeCod()" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cc_model = cl.CapeCod()\n", - "cc_model.fit(\n", - " comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With `decay=1`, each `origin` period gets the same `apriori_` (this is the traditional Cape Cod). The `apriori_` is calculated using the latest diagonal over the used-up exposure, where the used-up exposure is the exposure vector / CDF. Let's validate the calculation of the a priori." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6856862224535671" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "latest_diagonal = comauto[\"CumPaidLoss\"].latest_diagonal\n", - "\n", - "cdf_as_origin_vector = (\n", - " cl.Chainladder().fit(comauto[\"CumPaidLoss\"]).ultimate_\n", - " / comauto[\"CumPaidLoss\"].latest_diagonal\n", - ")\n", - "\n", - "latest_diagonal.sum() / (\n", - " comauto[\"EarnedPremNet\"].latest_diagonal / cdf_as_origin_vector\n", - ").sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With `decay=0`, the `apriori_` for each `origin` period stands on its own." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
19880.6853
19890.7041
19900.6903
19910.6478
19920.6518
19930.6815
19940.6925
19950.7004
19960.7039
19970.7619
" - ], - "text/plain": [ - " 2261\n", - "1988 0.685281\n", - "1989 0.704094\n", - "1990 0.690279\n", - "1991 0.647802\n", - "1992 0.651835\n", - "1993 0.681518\n", - "1994 0.692516\n", - "1995 0.700403\n", - "1996 0.703893\n", - "1997 0.761919" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cc_model = cl.CapeCod(decay=0, trend=0).fit(\n", - " X=comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", - ")\n", - "cc_model.apriori_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Doing the same on our manually calculated `apriori_` yields the same result." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1997
19880.6853
19890.7041
19900.6903
19910.6478
19920.6518
19930.6815
19940.6925
19950.7004
19960.7039
19970.7619
" - ], - "text/plain": [ - " 1997\n", - "1988 0.685281\n", - "1989 0.704094\n", - "1990 0.690279\n", - "1991 0.647802\n", - "1992 0.651835\n", - "1993 0.681518\n", - "1994 0.692516\n", - "1995 0.700403\n", - "1996 0.703893\n", - "1997 0.761919" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "latest_diagonal / (comauto[\"EarnedPremNet\"].latest_diagonal / cdf_as_origin_vector)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's verify the result of this Cape Cod model's result with the Chainladder's." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
1988
1989
1990
1991
19920.0000
19930.0000
1994
19950.0000
1996
19970.0000
" - ], - "text/plain": [ - " 2261\n", - "1988 NaN\n", - "1989 NaN\n", - "1990 NaN\n", - "1991 NaN\n", - "1992 1.164153e-10\n", - "1993 1.164153e-10\n", - "1994 NaN\n", - "1995 1.164153e-10\n", - "1996 NaN\n", - "1997 1.164153e-10" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cc_model.ultimate_ - cl_model.ultimate_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can examine the `apriori_`s to see whether there exhibit any trends over time." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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hqlYkMjU0NrFt72GKyv7V5bK+vJrSqiOfzJPSMYGRflvnI/qlkN0nhS4d1PPs72RBH8iamgwUO+e2+J5sLnAVcNygB64HfuS7fzGw2Dm3z7fsYmAm8Hzg5Ueeiupabnx8GTv21TD7pkmcN7z3KZfplBTPTz8/hvOGp/H9eflc8T/vcd8lI7h5aqZ21Eq74pxj2dZ9zFm2g9fWlXPUt3M0Ps4YkpbMxEHduWFKBiN9wd4vtaP+RtookKAfAOz0e1wCTDnejGY2CBgMvHmSZQccZ7k7gDsAMjIyAijJO7uqjnDjE8vYfbCWp289i6lDTq/P/YKRfVh013S+P28NP36lkLc2VPLwdTn0TondPf4iAFU1dfz9o1LmLNvO5srDpHRM4NpJ6UzM6M6IfikM7d1FXZohEuzfPrOAec65xtNZyDk3G5gNzV03Qa4paHbuq+H6xz/kQE09z3x1MrmZPc7oedJSOvDULWfx7Ifb+VleETMfeZeHrsnhwlF9glyxiLecc3y0Yz/PLdtBXn4ZRxuaGD+wGw9fm8PlOf3plKRgD4dAgr4UGOj3ON3XdjyzgG+0Wva8Vsu+HXh5kWNL5SFufGIZNXWNPHf7FHLSu7Xp+cyMr5yTyTlDevKduau57ZmV3DAlgx9eNpLOSep7lOh2sLaelz4uZc6yHawvr6ZLhwSuy03nhsmDGNW/q9fltTuB7IxNoHln7AU0B/cK4Abn3LpW840AFgGDne9JfTtjVwETfbN9RPPO2H0ner1I3Bm7obyaG59YhnOOZ2+bwsh+wf2g1jU08evFG5i9ZAuDeybzu1kTGJueGtTXEAmH/JIqnvtwB/PX7OJIfSNjBnTlximDuHJcf5K18zSk2rQz1jnXYGbfBF6j+fDKp5xz68zsAWClc26+b9ZZwFzn983hnNtnZj+l+csB4IGThXwkWlt6gK88uYzE+Djm3HE2Q3sH/0iZpIQ47rtkJDOy0/iPF9bwhT8t5e6LsvnajCHt9lAxiR6Hjzbw8updzFm+nbWlB+mUGM+V4/pz49kZbf7lK8GhE6ZO4uMd+7n5qeWkdEzkudumkNkr9OPWHKip5wcvFZCXX8bkwT34zRfHxdz1KyU2rNt1gDnLdvDy6l0cOtrAiL4p3Dglg6smDKBrx0Svy2t32nzCVDhFStAv27KXr/55Bb1SOvDcbVPCGrbOOV78qJQfzV+HGfzs82O4avynDlYSCbsjdY28kr+LOct2sHpnFR0S4rgspx83ThnExIxuOgzSQ209jr7deW/THm57ZgUDunXiudvOpm9qeA99NDOumZTO5ME9uOuvq/nO3NW8tb6CBz4/RltK4omNu6uZs2wHf/+ohOraBoakJXP/5aO4euIAjeEUBRT0rby5fjdfe/Yjsnol8+xtU+jVpYNntQzs0Zm/3nE2f3p7M7/75yZWbNvPb780nsmDz+ywTpHTUVvfyKK15Ty3bDsrtu0nKT6OmWP6cuOUDCYP7qGt9yiirhs/rxaU8e25HzOib1ee+epkukfQQEgf79jPXX9dzc59Nfz7eUO468JsEuN13RgJvi2Vhz7Zet9fU09mz85cPzmDayel09PDDR85OXXdBODl1aV894U1jB/YjadvPSviukgmZHRn4ben8cArhfzxrc28u2kPj3xpPFlpXbwuTWJAXUMTrxeW89yHO/hgy14S4ozPje7DjVMGcU5WT+J09FdU0xY98MKKnfzni/lMGdyDJ28+K+KP9120tox7XyzgaH0T918xillnDdTPaDkjO/bW8PyKHfxt5U72HKojvXsnrp+cwXW56RqWI8poi/4knvlgG/e/vI7p2Wk89uVJUXFK9swx/Rg/sDvf+9sa7nuxgDfXV/DLa3LaxZjb0nZNTY43inbz7LIdLNlYSXycccGI3twwJYPpw9K09R6D2vUW/eNLtvDgwiIuGtWHP9wwIeoGVGpqcjy1dCsPLdpAaudEfnXdOGZkp3ldlkSwFdv28bMFhawpOUC/1I7MOiuDL501MOxHlknw6Tj6Vpxz/M+bxfxm8UYuy+nHI18aH9U7NovKDnLX3NVs2F3NLVMzufeSEXRMjK4vLQmtHXtr+MWiIhYWlNO3a0fuuXg4V43vT0IUf+7lWOq68eOc4+HXNvCntzdz9cQBPHRNTtR/2Ef268rL3zyXXy5az9NLt/H+5j38btaEoI/JI9HnwJF6/vRWMU8v3UZ8nHH3hdncPn2wBs5rZ9rVFr1zjp8uKOKppVu5YUoGP7tqTMz1R76zsZLv/W0NB2rque/SEdx67mCvSxIPNDQ28fzyHfz2jU3sr6nj2onpfO/i4fTpqi6aWKUtepr7s3/48lrmLNvBredmcv/lo2LySJUZ2Wks+s40vj8vn5+8UsikQd01sFQ74pzj7Q2VPLiwiOKKQ5yd1YMfXjaKMQM0Gmp7Ft19FgFqbHLcMy+fOct28O/nDYnZkG/Rs0sHHpk1npSOCTy2ZIvX5UiYrC8/yE1PLefWP6+gsckx+yuTeP72sxXyEvtb9PWNTdz919UsyC/juxdl863zh8Z0yLdI6ZjIl88exGPvbGb73sMM6hn6kTfFG5XVR/nN4o38dcUOUjomcv/lo/jy2YNISmgX23ESgJgO+qMNjXxzzscsLtzNfZeM4M4ZQ7wuKaxunZrJk+9u5Yl3t/LTz4/xuhwJstr6Rp58byt/equYow1N3Dw1k+9cMEyDjMmnxGzQ19Y3cudfVvHOxkp+cuVobp6a6XVJYde7a0e+MGEAf1u1k7suHKZxSmKEc45X8sv45avrKa06wkWj+nDfJSM0HIacUEz+tjt8tIFbn17Bkk2V/PKase0y5FvcPj2L2vomnvlgu9elSBCs2r6fq//3fb79/Mekdkpkzu1TePymXIW8nFTMbdEfrK3n1qdXsHpnFb/94ng+P6F9X7BjaO8uXDiyD898sI2vzRgSFUM8yKft3FfDLxetZ0F+Gb1TOvDQtTlcMzFdl5qUgMRU0FfV1HHTU8sp3HWQP1w/gUvG9vO6pIjwtRlZXPvobv62aic3nZPpdTlyGqpr6/nT25t58r2txBl8+4Jh3Dk9K+IH3pPIEjOflj2HjvLlJ5axpfIwj31lEheM7ON1SREjN7MHkwZ15/F3t3DD5IyoPxO4PWhobOKvK3fym9c3svdwHVdPGMD3Lh5O/26dvC5NolDMBH28GZ2S4nnyllymDdPAXq3dMT2LO/+yilfXlnPFuP5elyMn8c7GSh7MK2Tj7kNMzuzB07eO1Elv0iYxE/Tdk5N48d+ntotj5M/ERSP7kNUrmdlLtnB5Tj+tpwi0aXc1Dy4s4u0NlWT06MyjX57IxaP76v9K2ixmgh7QH8RJxMUZd0zP4t4XC/hg816mDu3ldUnis/fQUX77xkaeX76Tzknx/NelI7lp6qCoGzZbIldMBb2c3OcnDOBXr2/k0SVbFPQR4GhDI39euo0/vFlMTX0jX56SwXcuzNYFZCToFPTtSMfEeG49N5OHX9tAUdlBDWPsEeccCwvK+cWiInbuO8L5I3rzg0tHMrS3joWX0NDhF+3Ml6cMonNSPLM12Jkn8kuquO7RD/jGnI9ITkrgL/82maduOUshLyEVUNCb2Uwz22BmxWZ27wnm+aKZFZrZOjOb49f+kK+tyMx+b+pI91Rq50Sun5zB/DW7KK064nU57cqm3dVc++gHbNtbwy+uHkvet6fpCDEJi1MGvZnFA38ELgFGAdeb2ahW8wwD7gPOdc6NBu7ytU8FzgVygDHAWcCMINYvZ+CrnxmMAU+9t9XrUtqNlqGyk5PiefU705g1OUNntUrYBLJFPxkods5tcc7VAXOBq1rNczvwR+fcfgDnXIWv3QEdgSSgA5AI7A5G4XLmBnTrxBXj+vP88h0cqKn3upx24an3trJ6ZxU/vnI0aSkaXE7CK5CgHwDs9Htc4mvzlw1km9lSM/vQzGYCOOc+AN4Cyny315xzRa1fwMzuMLOVZraysrLyTN6HnKY7pmdRU9fIs8s02Fmobd1zmF+9voELR/bhSp2sJh4I1s7YBGAYcB5wPfC4mXUzs6HASCCd5i+H881sWuuFnXOznXO5zrnctDT1WYbDyH5dmZGdxtNLt1Fb3+h1OTGrqcnxn/Py6ZAQx4NfGKNzPcQTgQR9KTDQ73G6r81fCTDfOVfvnNsKbKQ5+L8AfOicO+ScOwS8CpzT9rIlGO6cnsWeQ0f5x8et/zslWP7y4XaWb9vH/7t8lC7MLZ4JJOhXAMPMbLCZJQGzgPmt5nmJ5q15zKwXzV05W4AdwAwzSzCzRJp3xH6q60a8cc6QnowdkMrjS7bQ1OS8LifmtAwtPD07jWsnpXtdjrRjpwx651wD8E3gNZpD+gXn3Doze8DMrvTN9hqw18wKae6Tv8c5txeYB2wGCoA1wBrn3CsheB9yBsyMO2dksWXPYRYXaR95MDnnuPfFfOLM+PnVY9VlI54K6MxY59xCYGGrtvv97jvgu76b/zyNwJ1tL1NCZebovgzs0YlH39nM50b1USAFydwVO1lavJcHvzCGARpaWDymM2PbuYT4OG6flsXHO6pYuX2/1+XEhF1VR3gwr4hzsnpy/VkZXpcjoqAXuG7SQLp3TuSxdzQsQls55/jBPwpobHL88poc4nRSlEQABb3QKSmem87J5I2i3RRXVHtdTlR78aNS3t5QyfdnDiejZ2evyxEBFPTic9M5g+iYGKfBztqg4mAtP3llHbmDunOzrs0rEURBLwD07NKBL+YO5KWPd7H7YK3X5UQd5xw/fGktRxuaeOhaddlIZFHQyydu+0wWDU1NPL10m9elRJ0F+WW8Xrib716UTVaahhyWyKKgl09k9OzMJWP78dyH26mu1WBngdp76Cg/mr+OcQO7cdu0LK/LEfkUBb0c487pWVQfbWDu8p2nnlkA+NH8dVTX1vPwtTkaelgikoJejpGT3o1zsnry5HtbqWto8rqciLdobTkL8sv49vnDyO6T4nU5IseloJdPuXNGFuUHa5m/ZpfXpUS0qpo6fvjSWkb168rXzhvidTkiJ6Sgl0+ZkZ3GiL4pzF6ymebRLeR4HlhQSFVNHQ9fl0NivP6UJHLp0ymfYmbcMT2LjbsP8fYGXQjmeN5cv5sXPyrl6+cNYXT/VK/LETkpBb0c1xXj+tM/tSOPvrPZ61IizsHaen7w4lqy+3ThG+cP9bockVNS0MtxJcbH8dXPDGbZ1n2s3lnldTkR5ecLi6ioruXha8fRISHe63JETklBLyc0a3IGKR0TmL1EW/Ut3tu0h+eX7+T26VmMG9jN63JEAqKglxPq0iGBr5w9iFfXlrNtz2Gvy/Hc4aMN/Off88nqlczdF2Z7XY5IwBT0clK3nJtJYlwcT7ynwc4eWrSeXQeO8NC1OXRMVJeNRA8FvZxU75SOXD1xAH9bWcKeQ0e9Lsczy7bs5f8+2M4tUzPJzezhdTkip0VBL6d0+/Qs6hqbeOb9bV6X4okjdY3859/zyejRmXsuHu51OSKnTUEvpzQkrQsXjezDMx9up6auwetywu7Xr29g294afnHNWDonBXSZZZGIoqCXgNw5I4uqmnpeWNG+Bjv7aMd+nly6lRunZDB1SC+vyxE5Iwp6CcikQT3IHdSdJ97bSkNj+xjsrLa+ke/Py6df147ce8kIr8sROWMKegnYnTOGULL/CAvXlntdSlj8/p+bKK44xM+vySGlY6LX5YicMQW9BOyCEb0ZkpbMY+/E/mBnBSUHeGzJFq6blM6M7DSvyxFpEwW9BCwurnmws3W7DrK0eK/X5YRMXUMT98xbQ8/kJH542SivyxFpMwW9nJbPTxhAWkoHHovhYRH+9HYx68urefALY0ntrC4biX4BBb2ZzTSzDWZWbGb3nmCeL5pZoZmtM7M5fu0ZZva6mRX5pmcGqXbxQIeEeG49N5N3N+1h3a4DXpcTdEVlB/nDm8VcNb4/F43q43U5IkFxyqA3s3jgj8AlwCjgejMb1WqeYcB9wLnOudHAXX6TnwEeds6NBCYDFcEpXbxy45RBJCfFM3tJbA2L0NDYxPfn5dOtcyI/vmK01+WIBE0gW/STgWLn3BbnXB0wF7iq1Ty3A390zu0HcM5VAPi+EBKcc4t97YecczVBq148kdopkRumZLAgv4yS/bHz3zn73S0UlB7ggavG0D05yetyRIImkKAfAPifJVPia/OXDWSb2VIz+9DMZvq1V5nZi2b2sZk97PuFcAwzu8PMVprZyspKXdEoGtx67mAMePK9rV6XEhTFFdU88sYmLhnTl0vH9vO6HJGgCtbO2ARgGHAecD3wuJl187VPA74HnAVkAbe0Xtg5N9s5l+ucy01L06Fs0aB/t05cOb4/c5fvpKqmzuty2qSxyXHPvHw6J8XzwFVjvC5HJOgCCfpSYKDf43Rfm78SYL5zrt45txXYSHPwlwCrfd0+DcBLwMQ2Vy0R4Y7pWRypb+TZD7d7XUqbPL10Kx/vqOLHV4wmLaWD1+WIBF0gQb8CGGZmg80sCZgFzG81z0s0b81jZr1o7rLZ4lu2m5m1bKafDxS2vWyJBCP6duW84Wn8+f1t1NY3el3OGdm25zC/en0DF4zozVXj+3tdjkhInDLofVvi3wReA4qAF5xz68zsATO70jfba8BeMysE3gLucc7tdc410txt808zKwAMeDwUb0S8cef0Iew5VMffPyrxupTT1tTk+P7f80mMj+PBL4zFzLwuSSQkAhpz1Tm3EFjYqu1+v/sO+K7v1nrZxUBO28qUSHV2Vg9y0lN54t2tzDorg/i46AnL55ZtZ/nWfTx0TQ59Uzt6XY5IyOjMWGkTM+PO6UPYuucwiwujZ7Cznftq+Pmr65k2rBfX5aZ7XY5ISCnopc1mjulLRo/OPPrOlqgY7Mw5x30vFmDAz69Wl43EPgW9tFl8nHH7tMGs3lnFim37vS7nlP66YifvFe/h3ktHkt69s9fliIScgl6C4tpJA+mRnMRj70T2YGdlB47wYF4RZ2f14MbJGV6XIxIWCnoJik5J8dx8Tib/XF/Bpt3VXpdzXM45fvBiAfVNTfzymhziomjHsUhbKOglaL5yziA6JsZF7GBn//i4lLc2VHLPxSMY1DPZ63JEwkZBL0HTIzmJL+UO5KXVpZQfqPW6nGNUVNfyk1cKmTSoO7dMzfS6HJGwUtBLUN02LYvGJsfTS70d7Mw5x859NSzI38V/LyzixseXcaS+kV9ekxNVx/qLBENAJ0yJBGpgj85cOrYfc5bt4BvnD6VrmC6qXXGwljUlBygoqWJNyQHyS6rYX1MPQFJ8HCP7d+XX141jaO8uYalHJJIo6CXo7pw+hAX5ZTy/bAd3zhgS9Oc/UFNPfmkV+SUHWLOz+d/yg81dRXEG2X1SuGhUH3LSuzEuvRvD+6aQlKAfr9J+Kegl6Mamp3Lu0J48tXQrt547uE0hW1PXwNrSg+SXNAd6fkkV2/b+62Ing3slMyWrhy/UUxnVvyudk/SxFvGnvwgJiTumD+Hmp5bz8upSrssdeOoFgLqGJtaXH2zuevFtqW+qqKbJd7Jtv9SO5KSncl3uQMald2PsgFRdvFskAAp6CYnpw3oxom8Ks5ds4ZqJ6Z86Zr2xyVFcceiYLfWismrqGpuA5iN4ctJTuXhMX3IGpJIzMJXeKRp4TORMKOglJMyMr80Ywl1/Xc1bGyoY2rvLMVvqa3cdoKaueQz7Lh0SGDOgK7eem0lOejdy0lNJ795JY9CIBImCXkLmspx+PLRoPbc9s5KWsc6SEuIY3b8rX8wdyNgBqYwbmEpWry46S1UkhBT0EjKJ8XE8ePVYFhfuZkz/VHLSUxneN4XEeB0BIxJOCnoJqc8O781nh/f2ugyRdk2bViIiMU5BLyIS4xT0IiIxTkEvIhLjFPQiIjFOQS8iEuMU9CIiMU5BLyIS48y1nJseIcysEtjehqfoBewJUjnRTuviWFofx9L6+JdYWBeDnHNpx5sQcUHfVma20jmX63UdkUDr4lhaH8fS+viXWF8X6roREYlxCnoRkRgXi0E/2+sCIojWxbG0Po6l9fEvMb0uYq6PXkREjhWLW/QiIuJHQS8iEuMiPujN7CkzqzCztX5t48zsAzMrMLNXzKyrrz3RzP7P115kZvf5LXO3ma0zs7Vm9ryZReWVpk9zfSSZ2dO+9jVmdp7fMpN87cVm9nuLwgu0BmNdmFlnM8szs/W+z8cvvHk3bResz4bfsvP9nyvaBPFvJcnMZpvZRt/n5Jrwv5s2cs5F9A2YDkwE1vq1rQBm+O5/Ffip7/4NwFzf/c7ANiATGABsBTr5pr0A3OL1ewvD+vgG8LTvfm9gFRDne7wcOBsw4FXgEq/fmxfrwvc5+ayvPQl4NxrXRTA/G762q4E5/s8Vbbcg/q38BPiZ734c0Mvr93a6t4jfonfOLQH2tWrOBpb47i8GWr5hHZBsZglAJ6AOOOiblgB08k3rDOwKZd2hcprrYxTwpm+5CqAKyDWzfkBX59yHrvnT+wzw+dBWHnzBWBfOuRrn3Fu+9jrgIyA9tJWHRjDWB4CZdQG+C/wstBWHVrDWB81fCD/3TWtyzkXdGbQRH/QnsA64ynf/OmCg7/484DBQBuwAfuWc2+ecKwV+5WsrAw44514Pb8khdaL1sQa40swSzGwwMMk3bQBQ4rd8ia8tFpzuuviEmXUDrgD+GZ5Sw+JM1sdPgV8DNeEsNExOa334PhMAPzWzj8zsb2bWJ6wVB0G0Bv1Xga+b2SogheYtd4DJQCPQHxgM/IeZZZlZd5r/cwf7piWb2ZfDX3bInGh9PEVziK8EHgHep3n9xLIzWhe+X3rPA793zm0JZ8Ehdlrrw8zGA0Occ/8If6lhcbqfjwSaf+G975ybCHxA80ZjVEnwuoAz4ZxbD3wOwMyygct8k24AFjnn6oEKM1tK888vB2x1zlX6lnkRmAo8G+7aQ+FE68M51wDc3TKfmb0PbAT2c2z3RDpQGq56Q+kM1kWL2cAm59wjYSs2DM5gfcyguXtvG8350NvM3nbOnRfeykPjDNbHXpp/2bzom/Q34N/CWHJQROUWvZn19v0bB/wQeNQ3aQdwvm9aMs07G9f72s/2HWFhwAVAUbjrDpUTrQ/f+0323b8IaHDOFTrnyoCDZna2b33cBLzsTfXBdbrrwvf4Z0AqcJcXNYfSGXw2/tc51985lwl8BtgYKyEPZ7Q+HPAKcJ7vKS4ACsNdd5t5vTf4VDeaf06XAfU0/7T6N+A7NH/bbgR+wb/O8O1C8zfuOpr/M+7xe56f0Bz6a4G/AB28fm9hWB+ZwAaav9TeoHkY05bnyfWti83AH1qWiaZbMNYFzb9mnK99te92m9fvzcvPht/zZRLdR90E629lEM07cPNp3n+T4fV7O92bhkAQEYlxUdl1IyIigVPQi4jEOAW9iEiMU9CLiMQ4Bb2ISIxT0IuIxDgFvYhIjPv/V1NLLxfTBNEAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(\n", - " cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", - " cc_model.apriori_.to_frame(origin_as_datetime=True),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks like there is a small positive trend, let's judgementally select the `trend` as 1%." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "trended_cc_model = cl.CapeCod(decay=0, trend=0.01).fit(\n", - " X=comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", - ")\n", - "\n", - "plt.plot(\n", - " cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", - " cc_model.apriori_.to_frame(origin_as_datetime=True),\n", - " label=\"Untrended\",\n", - ")\n", - "plt.plot(\n", - " trended_cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", - " trended_cc_model.apriori_.to_frame(origin_as_datetime=True),\n", - " label=\"Trended\",\n", - ")\n", - "plt.legend(loc=\"lower right\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can of course utilize both the `trend` and the `decay` parameters together. Adding `trend` to the `CapeCod` method is intended to adjust the `apriori_`s to a common level. Once at a common level, the `apriori_` can be estimated from multiple origin periods using the `decay` factor." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "trended_cc_model = cl.CapeCod(decay=0, trend=0.01).fit(\n", - " X=comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", - ")\n", - "\n", - "trended_decayed_cc_model = cl.CapeCod(decay=0.75, trend=0.01).fit(\n", - " X=comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", - ")\n", - "\n", - "plt.plot(\n", - " cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", - " cc_model.apriori_.to_frame(origin_as_datetime=True),\n", - " label=\"Untrended\",\n", - ")\n", - "plt.plot(\n", - " trended_cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", - " trended_cc_model.apriori_.to_frame(origin_as_datetime=True),\n", - " label=\"Trended\",\n", - ")\n", - "plt.plot(\n", - " trended_decayed_cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", - " trended_decayed_cc_model.apriori_.to_frame(origin_as_datetime=True),\n", - " label=\"Trended and Decayed\",\n", - ")\n", - "plt.legend(loc=\"lower right\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once estimated, it is necessary to detrend our `apriori_`s back to their untrended levels and these are contained in `detrended_apriori_`. It is the `detrended_apriori_` that gets used in the calculation of `ultimate_` losses." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(\n", - " trended_cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", - " trended_cc_model.apriori_.to_frame(origin_as_datetime=True),\n", - " label=\"Trended\",\n", - ")\n", - "plt.plot(\n", - " trended_cc_model.detrended_apriori_.to_frame(origin_as_datetime=True).index.year,\n", - " trended_cc_model.detrended_apriori_.to_frame(origin_as_datetime=True),\n", - " label=\"Detended to Original\",\n", - ")\n", - "plt.legend(loc=\"lower right\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `detrended_apriori_` is a much smoother estimate of the initial expected `ultimate_`. With the `detrended_apriori_` in hand, the `CapeCod` method estimator behaves exactly like our the `BornhuetterFerguson` model." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bf_model = cl.BornhuetterFerguson().fit(\n", - " X=comauto[\"CumPaidLoss\"],\n", - " sample_weight=trended_cc_model.detrended_apriori_\n", - " * comauto[\"EarnedPremNet\"].latest_diagonal,\n", - ")\n", - "\n", - "bf_model.ultimate_.sum() == trended_cc_model.ultimate_.sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Recap\n", - "\n", - "All the deterministic estimators have `ultimate_`, `ibnr_`, `full_expecation_` and `full_triangle_` attributes that are themselves `Triangle`s. These can be manipulated in a variety of ways to gain additional insights from our model. The expected loss methods take in an exposure vector, which itself is a `Triangle` through the `sample_weight` argument of the `fit` method. The `CapeCod` method has the additional attributes `apriori_` and `detrended_apriori_` to accommodate the selection of its `trend` and `decay` assumptions.\n", - "\n", - "Finally, these estimators work very well with the transformers discussed in previous tutorials. Let's demonstrate the compositional nature of these estimators." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(1, 2, 10, 10)
Index:[LOB]
Columns:[CumPaidLoss, EarnedPremNet]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (1, 2, 10, 10)\n", - "Index: [LOB]\n", - "Columns: [CumPaidLoss, EarnedPremNet]" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wkcomp = (\n", - " cl.load_sample(\"clrd\")\n", - " .groupby(\"LOB\")\n", - " .sum()\n", - " .loc[\"wkcomp\"][[\"CumPaidLoss\", \"EarnedPremNet\"]]\n", - ")\n", - "wkcomp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's calculate the age-to-age factors:\n", - "- Without the the 1995 valuation period\n", - "- Using volume weighted for the first 5 factors, and simple average for the next 4 factors (for a total of 9 age-to-age factors)\n", - "- Using no more than 7 periods (with `n_periods`)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "patterns = cl.Pipeline(\n", - " [\n", - " (\n", - " \"dev\",\n", - " cl.Development(\n", - " average=[\"volume\"] * 5 + [\"simple\"] * 4,\n", - " n_periods=7,\n", - " drop_valuation=\"1995\",\n", - " ),\n", - " ),\n", - " (\"tail\", cl.TailCurve(curve=\"inverse_power\", extrap_periods=80)),\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2261
19881,331,221
19891,416,505
19901,523,470
19911,581,962
19921,541,458
19931,484,168
19941,525,963
19951,548,534
19961,541,068
19971,507,592
" - ], - "text/plain": [ - " 2261\n", - "1988 1.331221e+06\n", - "1989 1.416505e+06\n", - "1990 1.523470e+06\n", - "1991 1.581962e+06\n", - "1992 1.541458e+06\n", - "1993 1.484168e+06\n", - "1994 1.525963e+06\n", - "1995 1.548534e+06\n", - "1996 1.541068e+06\n", - "1997 1.507592e+06" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cc = cl.CapeCod(decay=0.8, trend=0.02).fit(\n", - " X=patterns.fit_transform(wkcomp[\"CumPaidLoss\"]),\n", - " sample_weight=wkcomp[\"EarnedPremNet\"].latest_diagonal,\n", - ")\n", - "cc.ultimate_" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(\n", - " cc.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", - " cc.ultimate_.to_frame(origin_as_datetime=True)[\"2261\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Voting Chainladder\n", - "\n", - "A `VotingChainladder` is an ensemble meta-estimator that fits several base chainladder methods, each on the whole triangle. Then it combines the individual predictions based on a matrix of weights to form a final prediction.\n", - "\n", - "Let's begin by loading the `raa` dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "raa = cl.load_sample(\"raa\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the Chainladder's estimator." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "cl_mod = cl.Chainladder()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the Expected Loss's estimator." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "el_mod = cl.ExpectedLoss(apriori=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the Bornhuetter-Ferguson's estimator. Remember that the `BornhuetterFerguson` requires one argument, the `apriori`." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "bf_mod = cl.BornhuetterFerguson(apriori=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the Cape Cod's estimator and their required arguments." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "cc_mod = cl.CapeCod(decay=1, trend=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the Benktander's estimator and their required arguments." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "bk_mod = cl.Benktander(apriori=1, n_iters=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's prepare the `estimators` variable. The `estimators` parameter in `VotingChainladder` must be in an array of tuples, with (estimator_name, estimator) pairing." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "estimators = [\n", - " (\"cl\", cl_mod),\n", - " (\"el\", el_mod),\n", - " (\"bf\", bf_mod),\n", - " (\"cc\", cc_mod),\n", - " (\"bk\", bk_mod),\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recall that some estimators (in this case, `BornhuetterFerguson`, `CapeCod`, and `Benktander`) also require the variable `sample_weight`, let's use the mean of `Chainladder`'s average ultimate estimate." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 2261\n", - "1981 21312.222826\n", - "1982 21312.222826\n", - "1983 21312.222826\n", - "1984 21312.222826\n", - "1985 21312.222826\n", - "1986 21312.222826\n", - "1987 21312.222826\n", - "1988 21312.222826\n", - "1989 21312.222826\n", - "1990 21312.222826" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample_weight = cl_mod.fit(raa).ultimate_ * 0 + (\n", - " float(cl_mod.fit(raa).ultimate_.sum()) / 10\n", - ")\n", - "sample_weight" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 2261\n", - "1981 18834.000000\n", - "1982 16875.500226\n", - "1983 24058.534810\n", - "1984 28542.580970\n", - "1985 28236.843134\n", - "1986 19905.317262\n", - "1987 18947.245455\n", - "1988 23106.943030\n", - "1989 20004.502125\n", - "1990 21605.832631" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_weights = np.array(\n", - " [[0.6, 0, 0.2, 0.2, 0]] * 4 + [[0, 0, 0.5, 0.5, 0]] * 3 + [[0, 0, 0, 1, 0]] * 3\n", - ")\n", - "\n", - "vot_mod = cl.VotingChainladder(estimators=estimators, weights=model_weights).fit(\n", - " raa, sample_weight=sample_weight\n", - ")\n", - "vot_mod.ultimate_" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(\n", - " cl_mod.fit(raa).ultimate_.to_frame(origin_as_datetime=True).index.year,\n", - " cl_mod.fit(raa).ultimate_.to_frame(origin_as_datetime=True),\n", - " label=\"Chainladder\",\n", - " linestyle=\"dashed\",\n", - " marker=\"o\",\n", - ")\n", - "plt.plot(\n", - " el_mod.fit(raa, sample_weight=sample_weight)\n", - " .ultimate_.to_frame(origin_as_datetime=True)\n", - " .index.year,\n", - " el_mod.fit(raa, sample_weight=sample_weight).ultimate_.to_frame(\n", - " origin_as_datetime=True\n", - " ),\n", - " label=\"Expected Loss\",\n", - " linestyle=\"dashed\",\n", - " marker=\"o\",\n", - ")\n", - "plt.plot(\n", - " bf_mod.fit(raa, sample_weight=sample_weight)\n", - " .ultimate_.to_frame(origin_as_datetime=True)\n", - " .index.year,\n", - " bf_mod.fit(raa, sample_weight=sample_weight).ultimate_.to_frame(\n", - " origin_as_datetime=True\n", - " ),\n", - " label=\"Bornhuetter-Ferguson\",\n", - " linestyle=\"dashed\",\n", - " marker=\"o\",\n", - ")\n", - "plt.plot(\n", - " cc_mod.fit(raa, sample_weight=sample_weight)\n", - " .ultimate_.to_frame(origin_as_datetime=True)\n", - " .index.year,\n", - " cc_mod.fit(raa, sample_weight=sample_weight).ultimate_.to_frame(\n", - " origin_as_datetime=True\n", - " ),\n", - " label=\"Cape Cod\",\n", - " linestyle=\"dashed\",\n", - " marker=\"o\",\n", - ")\n", - "plt.plot(\n", - " bk_mod.fit(raa, sample_weight=sample_weight)\n", - " .ultimate_.to_frame(origin_as_datetime=True)\n", - " .index.year,\n", - " bk_mod.fit(raa, sample_weight=sample_weight).ultimate_.to_frame(\n", - " origin_as_datetime=True\n", - " ),\n", - " label=\"Benktander\",\n", - " linestyle=\"dashed\",\n", - " marker=\"o\",\n", - ")\n", - "plt.plot(\n", - " vot_mod.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", - " vot_mod.ultimate_.to_frame(origin_as_datetime=True),\n", - " label=\"Selected\",\n", - ")\n", - "plt.legend(loc=\"best\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also call the `weights` attribute to confirm the weights being used by the `VotingChainladder` ensemble model." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.6, 0. , 0.2, 0.2, 0. ],\n", - " [0.6, 0. , 0.2, 0.2, 0. ],\n", - " [0.6, 0. , 0.2, 0.2, 0. ],\n", - " [0.6, 0. , 0.2, 0.2, 0. ],\n", - " [0. , 0. , 0.5, 0.5, 0. ],\n", - " [0. , 0. , 0.5, 0.5, 0. ],\n", - " [0. , 0. , 0.5, 0.5, 0. ],\n", - " [0. , 0. , 0. , 1. , 0. ],\n", - " [0. , 0. , 0. , 1. , 0. ],\n", - " [0. , 0. , 0. , 1. , 0. ]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vot_mod.weights" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/development-tutorial.ipynb b/docs/tutorials/development-tutorial.ipynb deleted file mode 100644 index 228555b7..00000000 --- a/docs/tutorials/development-tutorial.ipynb +++ /dev/null @@ -1,3321 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Development Tutorial\n", - "## Getting Started\n", - "This tutorial focuses on selecting the development factors. \n", - "\n", - "Be sure to make sure your packages are updated. For more info on how to update your pakages, visit [Keeping Packages Updated](https://chainladder-python.readthedocs.io/en/latest/library/install.html#keeping-packages-updated)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pandas: 1.4.2\n", - "numpy: 1.22.4\n", - "chainladder: 0.8.12\n" - ] - } - ], - "source": [ - "# Black linter, optional\n", - "%load_ext lab_black\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import chainladder as cl\n", - "\n", - "print(\"pandas: \" + pd.__version__)\n", - "print(\"numpy: \" + np.__version__)\n", - "print(\"chainladder: \" + cl.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Disclaimer\n", - "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Testing for Violation of Chain Ladder's Assumptions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The chain ladder method is based on the strong assumptions of independence across origin periods and across valuation periods. Mack developed tests to verify if these assumptions hold, and these tests have been implemented in the `chainladder` package.\n", - "\n", - "Before the chain ladder model can be used, we should verify that the data satisfies the underlying assumptions using tests at the desired confidence interval level. If assumptions are violated, we should consider if ultimates can be estimated using other models.\n", - "\n", - "There are two main tests that we need to perform:\n", - "- The `valuation_correlation` test: \n", - " - This test tests for the assumption of independence of accident years. In fact, it tests for correlation across calendar periods (diagonals), and by extension, origin periods (rows).\n", - " - An additional parameter, `total`, can be passed, depending on if we want to calculate valuation correlation in total across all origins (`True`), or for each origin separately (`False`).\n", - " - The test uses Z-statistic.\n", - "- The `development_correlation` test:\n", - " - This test tests for the assumption of independence of the chain ladder method that assumes that subsequent development factors are not correlated (columns).\n", - " - The test uses T-statistic." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Are valuation years correlated? Or, are the origins correlated? [[False]]\n", - "Are development periods coorelated? [[False]]\n" - ] - } - ], - "source": [ - "raa = cl.load_sample(\"raa\")\n", - "print(\n", - " \"Are valuation years correlated? Or, are the origins correlated?\",\n", - " raa.valuation_correlation(p_critical=0.1, total=True).z_critical.values,\n", - ")\n", - "print(\n", - " \"Are development periods coorelated?\",\n", - " raa.development_correlation(p_critical=0.5).t_critical.values,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above tests show that the `raa` triangle is independent in both cases, suggesting that there is no evidence that the chain ladder model is not an appropriate method to develop the ultimate amounts. It is suggested to review Mack's papers to ensure a proper understanding of the methodology and the choice of `p_critical`.\n", - "\n", - "Mack also demonstrated that we can test for valuation years' correlation. To test for each valuation year's correlation individually, we set `total` to `False`." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 1982 1983 1984 1985 1986 1987 1988 1989 1990\n", - "1981 False False False False False False False False False" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa.valuation_correlation(p_critical=0.1, total=False).z_critical" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the tests are run on the entire 4 dimensions of the `triangle`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Estimator Basics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All development methods follow the `sklearn` estimator API. These estimators have a few properties that are worth getting used to.\n", - "\n", - "We instantiate the estimator with your choice of assumptions. In the case where we don't opt for any assumptions, defaults are chosen for you.\n", - "\n", - "At this point, we've chosen an estimator and assumptions (even if default) but we have not shown our estimator a `Triangle`. At this point it is merely instructions on how to fit development patterns, but no patterns exist as of yet.\n", - "\n", - "All estimators have a `fit` method and you can pass a triangle to your estimator. Let's `fit` a `Triangle` in a `Development` estimator. Let's also assign the estimator to a variable so we can reference attributes about it." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "genins = cl.load_sample(\"genins\")\n", - "dev = cl.Development().fit(genins)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have `fit` a `Development` estimator, it has many additional properties that didn't exist before fitting. For example, \n", - "we can view the `ldf_`" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.49061.74731.45741.17391.10381.08631.05391.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dev.cdf_.cum_to_incr()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice these attributes have a trailing underscore (`_`). This is scikit-learn's API convention, as its [documentation](https://scikit-learn.org/dev/developers/develop.html) states, \"attributes that have been estimated from the data must always have a name ending with trailing underscore, for example the coefficients of some regression estimator would be stored in a `coef_` attribute after `fit` has been called.\" In summary, the trailing underscore in class attributes is a scikit-learn's convention to denote that the attributes are estimated, or to denote that they are fitted attributes." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Assumption parameter (no underscore): volume\n", - "Estimated parameter (underscore):\n", - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725\n" - ] - } - ], - "source": [ - "print(\"Assumption parameter (no underscore):\", dev.average)\n", - "print(\"Estimated parameter (underscore):\\n\", dev.ldf_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Development Averaging" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have a grounding in triangle manipulation and the basics of estimators, we can start getting more creative with customizing our development factors.\n", - "\n", - "The basic `Development` estimator uses a weighted regression through the origin for estimating parameters. Mack showed that using weighted regressions allows for:\n", - "1. `volume` weighted average development patterns
\n", - "2. `simple` average development factors
\n", - "3. OLS `regression` estimate of development factor where the regression equation is Y = mX + 0
\n", - "\n", - "While he posited this framework to suggest the `MackChainladder` stochastic method, it is an elegant form even for deterministic development pattern selection." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
2001357,8481,124,7881,735,3302,218,2702,745,5963,319,9943,466,3363,606,2863,833,5153,901,463
2002352,1181,236,1392,170,0333,353,3223,799,0674,120,0634,647,8674,914,0395,339,085
2003290,5071,292,3062,218,5253,235,1793,985,9954,132,9184,628,9104,909,315
2004310,6081,418,8582,195,0473,757,4474,029,9294,381,9824,588,268
2005443,1601,136,3502,128,3332,897,8213,402,6723,873,311
2006396,1321,333,2172,180,7152,985,7523,691,712
2007440,8321,288,4632,419,8613,483,130
2008359,4801,421,1282,864,498
2009376,6861,363,294
2010344,014
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "2001 357848.0 1124788.0 1735330.0 2218270.0 2745596.0 3319994.0 3466336.0 3606286.0 3833515.0 3901463.0\n", - "2002 352118.0 1236139.0 2170033.0 3353322.0 3799067.0 4120063.0 4647867.0 4914039.0 5339085.0 NaN\n", - "2003 290507.0 1292306.0 2218525.0 3235179.0 3985995.0 4132918.0 4628910.0 4909315.0 NaN NaN\n", - "2004 310608.0 1418858.0 2195047.0 3757447.0 4029929.0 4381982.0 4588268.0 NaN NaN NaN\n", - "2005 443160.0 1136350.0 2128333.0 2897821.0 3402672.0 3873311.0 NaN NaN NaN NaN\n", - "2006 396132.0 1333217.0 2180715.0 2985752.0 3691712.0 NaN NaN NaN NaN NaN\n", - "2007 440832.0 1288463.0 2419861.0 3483130.0 NaN NaN NaN NaN NaN NaN\n", - "2008 359480.0 1421128.0 2864498.0 NaN NaN NaN NaN NaN NaN NaN\n", - "2009 376686.0 1363294.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010 344014.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genins = cl.load_sample(\"genins\")\n", - "genins" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also print the `age_to_age` factors." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
20013.14321.54281.27831.23771.20921.04411.04041.06301.0177
20023.51061.75551.54531.13291.08451.12811.05731.0865
20034.44851.71671.45831.23211.03691.12001.0606
20044.56801.54711.71181.07251.08741.0471
20052.56421.87301.36151.17421.1383
20063.36561.63571.36921.2364
20072.92281.87811.4394
20083.95332.0157
20093.6192
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "2001 3.143200 1.542806 1.278299 1.237719 1.209207 1.044079 1.040374 1.063009 1.017725\n", - "2002 3.510582 1.755493 1.545286 1.132926 1.084493 1.128106 1.057268 1.086496 NaN\n", - "2003 4.448450 1.716718 1.458257 1.232079 1.036860 1.120010 1.060577 NaN NaN\n", - "2004 4.568002 1.547052 1.711784 1.072518 1.087360 1.047076 NaN NaN NaN\n", - "2005 2.564198 1.872956 1.361545 1.174217 1.138315 NaN NaN NaN NaN\n", - "2006 3.365588 1.635679 1.369162 1.236443 NaN NaN NaN NaN NaN\n", - "2007 2.922798 1.878099 1.439393 NaN NaN NaN NaN NaN NaN\n", - "2008 3.953288 2.015651 NaN NaN NaN NaN NaN NaN NaN\n", - "2009 3.619179 NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genins.age_to_age" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And colorcode with `heatmap()`." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/display.py:134: FutureWarning: this method is deprecated in favour of `Styler.to_html()`\n", - " .render()\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 12-2424-3636-4848-6060-7272-8484-9696-108108-120
20013.14321.54281.27831.23771.20921.04411.04041.06301.0177
20023.51061.75551.54531.13291.08451.12811.05731.0865
20034.44851.71671.45831.23211.03691.12001.0606
20044.56801.54711.71181.07251.08741.0471
20052.56421.87301.36151.17421.1383
20063.36561.63571.36921.2364
20072.92281.87811.4394
20083.95332.0157
20093.6192
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genins.age_to_age.heatmap()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.49061.74731.45741.17391.10381.08631.05391.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vol = cl.Development(average=\"volume\").fit(genins).ldf_\n", - "vol" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.56611.74561.45201.18101.11121.08481.05271.07481.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.566143 1.745557 1.451961 1.180984 1.111247 1.084818 1.052739 1.074753 1.017725" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = cl.Development(average=\"simple\").fit(genins).ldf_\n", - "sim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In most cases, estimator attributes are `Triangle`s themselves and can be manipulated with just like raw triangles." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LDF Type: \n", - "Difference between volume and simple average:\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)-0.07550.00180.0055-0.0071-0.00740.00150.00110.0018
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) -0.075536 0.001776 0.005452 -0.007132 -0.007423 0.001452 0.001135 0.001802 NaN" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(\"LDF Type: \", type(vol))\n", - "print(\"Difference between volume and simple average:\")\n", - "vol - sim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can specify how the LDFs are averaged independently for each age-to-age period. For example, we can use `volume` averaging on the first pattern, `simple` the second, `regression` the third, and then repeat the cycle three times for the 9 age-to-age factors that we need. Note that the array of selected method must be of the same length as the number of age-to-age factors." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.49061.74561.46191.17391.11121.08731.05391.07481.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.745557 1.461852 1.173852 1.111247 1.087341 1.053874 1.074753 1.017725" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(average=[\"volume\", \"simple\", \"regression\"] * 3).fit(genins).ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another example, using `volume`-weighting for the first factor, `simple`-weighting for the next 5 factors, and `volume`-weighting for the last 3 factors." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.49061.74561.45201.18101.11121.08481.05391.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.745557 1.451961 1.180984 1.111247 1.084818 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(average=[\"volume\"] + [\"simple\"] * 5 + [\"volume\"] * 3).fit(genins).ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Averaging Period" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`Development` comes with an `n_periods` parameter that allows you to select the latest `n` origin periods for fitting your development patterns. `n_periods=-1` is used to indicate the usage of all available periods, which is also the default if the parameter is not specified. The units of `n_periods` follows the `origin_grain` of the underlying triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.49061.74731.45741.17391.10381.08631.05391.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development().fit(genins).ldf_" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.49061.74731.45741.17391.10381.08631.05391.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(n_periods=-1).fit(genins).ldf_" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
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" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.460401 1.846507 1.392009 1.153852 1.084915 1.097355 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(n_periods=3).fit(genins).ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Much like `average`, `n_periods` can also be set for each age-to-age individually." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.53251.95021.48081.16511.10381.08251.05391.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.532471 1.950242 1.480761 1.165122 1.103824 1.082476 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(n_periods=[8, 2, 6, 5, -1, 2, -1, -1, 5]).fit(genins).ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that if we provide `n_periods` that is greater than what is available for any particular age-to-age period, all available periods will be used instead." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(n_periods=[1, 2, 3, 4, 5, 6, 7, 8, 9]).fit(\n", - " genins\n", - ").ldf_ == cl.Development(n_periods=[1, 2, 3, 4, 5, 4, 3, 2, 1]).fit(genins).ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Discarding Problematic Link Ratios\n", - "\n", - "Even with `n_periods`, there are situations where you might want to be more surgical in our selections. For example, you could have a valuation period with bad data and wish to omit the entire diagonal from your averaging." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.37971.75171.44261.16511.10381.08631.05391.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.379677 1.751749 1.442605 1.165122 1.103824 1.086269 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(drop_valuation=\"2004\").fit(genins).ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also do an olympic averaging (i.e. exluding high and low from each period)." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/development/base.py:144: UserWarning: Some exclusions have been ignored. At least 1 (use preserve = ...) link ratio(s) is required for development estimation.\n", - " warnings.warn(warning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.52011.72771.43511.19301.10181.08251.05731.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.520098 1.727701 1.435147 1.193021 1.101827 1.082476 1.057268 1.076555 1.017725" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(drop_high=True, drop_low=True).fit(genins).ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The function also accepts intergers. For example, if we want to drop the highest 3 factors from each period." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/development/base.py:144: UserWarning: Some exclusions have been ignored. At least 1 (use preserve = ...) link ratio(s) is required for development estimation.\n", - " warnings.warn(warning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.16141.63921.36871.12221.06011.04411.05391.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.161368 1.639211 1.368696 1.122203 1.060105 1.044079 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(drop_high=3).fit(genins).ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There's a `preserve` that we can use, this variable allows us to specified the minimum number of LDFs required for calculation. If this minimum is not yet, the `drop_high` and `drop_low` for that age will be ignored. This is especially useful in the tail when the data is thin." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/development/base.py:144: UserWarning: Some exclusions have been ignored. At least 2 link ratio(s) is required for development estimation.\n", - " warnings.warn(warning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
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" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.410772 1.701152 1.406103 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(drop_high=3, drop_low=2, preserve=2).fit(genins).ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also use an array of booleans or ints." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.52011.76851.45741.23421.10381.08631.05391.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.520098 1.768474 1.457413 1.234173 1.103824 1.086269 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(drop_high=[True, True, False, True], drop_low=[1, 2, 0, 3]).fit(\n", - " genins\n", - ").ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or maybe there is just a single outlier link-ratio that you don't think is indicative of future development. For these, you can specify the intersection of the origin and development age of the **denominator** of the link-ratio to `drop`." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/display.py:134: FutureWarning: this method is deprecated in favour of `Styler.to_html()`\n", - " .render()\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genins.age_to_age.heatmap()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's say we believe the 4.5680 factor from origin 2004 between age 12 and 24 should be dropped, we can use `drop=('2004', 12)`." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.37971.74731.45741.17391.10381.08631.05391.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.379677 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(drop=(\"2004\", 12)).fit(genins).ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If there are more than one outliers, you can also pass an array of array to the `drop` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.37971.70411.45741.17391.10381.08631.05391.07661.0177
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.379677 1.704149 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(drop=[(\"2004\", 12), (\"2008\", 24)]).fit(genins).ldf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Transformers\n", - "In `sklearn`, there are two types of estimators: transformers and predictors. A transformer transforms the input data (X) in some ways, and a predictor predicts a new value (or values, Y) by using the input data X.\n", - "\n", - "`Development` is a transformer, as the returned object is a means to create development patterns, which is used to estimate ultimates, but itself is not a reserving model (predictor). \n", - "\n", - "Transformers come with the `tranform` and `fit_transform` method. These will return a `Triangle` object, but augment it with additional information for use in a subsequent IBNR model (a predictor). `drop_high` (and `drop_low`) can take an array of boolean variables, indicating if the highest factor should be dropped for each of the LDF calculation." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "2001 357848.0 1124788.0 1735330.0 2218270.0 2745596.0 3319994.0 3466336.0 3606286.0 3833515.0 3901463.0\n", - "2002 352118.0 1236139.0 2170033.0 3353322.0 3799067.0 4120063.0 4647867.0 4914039.0 5339085.0 NaN\n", - "2003 290507.0 1292306.0 2218525.0 3235179.0 3985995.0 4132918.0 4628910.0 4909315.0 NaN NaN\n", - "2004 310608.0 1418858.0 2195047.0 3757447.0 4029929.0 4381982.0 4588268.0 NaN NaN NaN\n", - "2005 443160.0 1136350.0 2128333.0 2897821.0 3402672.0 3873311.0 NaN NaN NaN NaN\n", - "2006 396132.0 1333217.0 2180715.0 2985752.0 3691712.0 NaN NaN NaN NaN NaN\n", - "2007 440832.0 1288463.0 2419861.0 3483130.0 NaN NaN NaN NaN NaN NaN\n", - "2008 359480.0 1421128.0 2864498.0 NaN NaN NaN NaN NaN NaN NaN\n", - "2009 376686.0 1363294.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010 344014.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transformed_triangle = cl.Development(drop_high=[True] * 4 + [False] * 5).fit_transform(\n", - " genins\n", - ")\n", - "transformed_triangle" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
20013.14321.54281.27831.20921.04411.04041.06301.0177
20023.51061.75551.54531.13291.08451.12811.05731.0865
20034.44851.71671.45831.23211.03691.12001.0606
20041.54711.07251.08741.0471
20052.56421.87301.36151.17421.1383
20063.36561.63571.36921.2364
20072.92281.87811.4394
20083.9533
20093.6192
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "2001 3.143200 1.542806 1.278299 NaN 1.209207 1.044079 1.040374 1.063009 1.017725\n", - "2002 3.510582 1.755493 1.545286 1.132926 1.084493 1.128106 1.057268 1.086496 NaN\n", - "2003 4.448450 1.716718 1.458257 1.232079 1.036860 1.120010 1.060577 NaN NaN\n", - "2004 NaN 1.547052 NaN 1.072518 1.087360 1.047076 NaN NaN NaN\n", - "2005 2.564198 1.872956 1.361545 1.174217 1.138315 NaN NaN NaN NaN\n", - "2006 3.365588 1.635679 1.369162 1.236443 NaN NaN NaN NaN NaN\n", - "2007 2.922798 1.878099 1.439393 NaN NaN NaN NaN NaN NaN\n", - "2008 3.953288 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009 3.619179 NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transformed_triangle.link_ratio" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our transformed triangle behaves as our original `genins` triangle. However, notice the link_ratios exclude any droppped values you specified." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/display.py:134: FutureWarning: this method is deprecated in favour of `Styler.to_html()`\n", - " .render()\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 12-2424-3636-4848-6060-7272-8484-9696-108108-120
20013.14321.54281.27831.20921.04411.04041.06301.0177
20023.51061.75551.54531.13291.08451.12811.05731.0865
20034.44851.71671.45831.23211.03691.12001.0606
20041.54711.07251.08741.0471
20052.56421.87301.36151.17421.1383
20063.36561.63571.36921.2364
20072.92281.87811.4394
20083.9533
20093.6192
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transformed_triangle.link_ratio.heatmap()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2010
20013,901,463
20025,339,085
20034,909,315
20044,588,268
20053,873,311
20063,691,712
20073,483,130
20082,864,498
20091,363,294
2010344,014
" - ], - "text/plain": [ - " 2010\n", - "2001 3901463.0\n", - "2002 5339085.0\n", - "2003 4909315.0\n", - "2004 4588268.0\n", - "2005 3873311.0\n", - "2006 3691712.0\n", - "2007 3483130.0\n", - "2008 2864498.0\n", - "2009 1363294.0\n", - "2010 344014.0" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(type(transformed_triangle))\n", - "transformed_triangle.latest_diagonal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, it has other attributes that make it IBNR model-ready." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-Ult24-Ult36-Ult48-Ult60-Ult72-Ult84-Ult96-Ult108-Ult
(All)13.13673.88702.28091.61311.38451.25431.15471.09561.0177
" - ], - "text/plain": [ - " 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult\n", - "(All) 13.136729 3.886978 2.28089 1.61311 1.384499 1.254276 1.154664 1.095637 1.017725" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transformed_triangle.cdf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`fit_transform()` is equivalent to calling `fit` and `transform` in succession on the same triangle. Again, this should feel very familiar to the `sklearn` practitioner." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development().fit_transform(genins) == cl.Development().fit(genins).transform(genins)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The reason you might want want to use `fit` and `transform` separately would be when you want to apply development patterns to a a different triangle. For example, we can:\n", - "\n", - "1. Extract the commercial auto triangles from the `clrd` dataset
\n", - "2. Summarize to an industry level and `fit` a `Development` object
\n", - "3. We can then `transform` the individual company triangles with the industry development patterns
" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(157, 1, 10, 10)
Index:[GRNAME, LOB]
Columns:[CumPaidLoss]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (157, 1, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [CumPaidLoss]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample(\"clrd\")\n", - "comauto = clrd[clrd[\"LOB\"] == \"comauto\"][\"CumPaidLoss\"]\n", - "\n", - "comauto_industry = comauto.sum()\n", - "industry_dev = cl.Development().fit(comauto_industry)\n", - "\n", - "industry_dev.transform(comauto)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Working with Multidimensional Triangles\n", - "\n", - "Several (though not all) of the estimators in `chainladder` can be fit to several triangles simultaneously. While this can be a convenient shorthand, all these estimators use the same assumptions across every triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting to 6 industries simultaneously.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2261-12
Grain:OYDY
Shape:(6, 1, 1, 9)
Index:[LOB]
Columns:[CumPaidLoss]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2261-12\n", - "Grain: OYDY\n", - "Shape: (6, 1, 1, 9)\n", - "Index: [LOB]\n", - "Columns: [CumPaidLoss]" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample(\"clrd\").groupby(\"LOB\").sum()[\"CumPaidLoss\"]\n", - "print(\"Fitting to \" + str(len(clrd.index)) + \" industries simultaneously.\")\n", - "cl.Development().fit_transform(clrd).cdf_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For greater control, you can slice individual triangles out and fit separate patterns to each." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Development(average='simple')\n", - "Development(n_periods=4)\n", - "Development(average='regression', n_periods=6)\n" - ] - } - ], - "source": [ - "print(cl.Development(average=\"simple\").fit(clrd.loc[\"wkcomp\"]))\n", - "print(cl.Development(n_periods=4).fit(clrd.loc[\"ppauto\"]))\n", - "print(cl.Development(average=\"regression\", n_periods=6).fit(clrd.loc[\"comauto\"]))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/stochastic-tutorial.ipynb b/docs/tutorials/stochastic-tutorial.ipynb deleted file mode 100644 index e1274f4e..00000000 --- a/docs/tutorials/stochastic-tutorial.ipynb +++ /dev/null @@ -1,2939 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Applying Stochastic Methods\n", - "## Getting Started\n", - "This tutorial focuses on using stochastic methods to estimate ultimates. \n", - "\n", - "Be sure to make sure your packages are updated. For more info on how to update your pakages, visit [Keeping Packages Updated](https://chainladder-python.readthedocs.io/en/latest/library/install.html#keeping-packages-updated)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pandas: 1.4.2\n", - "numpy: 1.22.4\n", - "chainladder: 0.8.12\n" - ] - } - ], - "source": [ - "# Black linter, optional\n", - "%load_ext lab_black\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import chainladder as cl\n", - "import matplotlib.pyplot as plt\n", - "import statsmodels.api as sm\n", - "\n", - "print(\"pandas: \" + pd.__version__)\n", - "print(\"numpy: \" + np.__version__)\n", - "print(\"chainladder: \" + cl.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Disclaimer\n", - "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Intro to MackChainladder\n", - "\n", - "Like the basic `Chainladder` method, the `MackChainladder` is entirely specified by its selected development pattern. In fact, it is the basic `Chainladder`, but with extra features." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = (\n", - " cl.load_sample(\"clrd\")\n", - " .groupby(\"LOB\")\n", - " .sum()\n", - " .loc[\"wkcomp\", [\"CumPaidLoss\", \"EarnedPremNet\"]]\n", - ")\n", - "\n", - "cl.Chainladder().fit(clrd[\"CumPaidLoss\"]).ultimate_ == cl.MackChainladder().fit(\n", - " clrd[\"CumPaidLoss\"]\n", - ").ultimate_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's create a Mack's Chainladder model." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "mack = cl.MackChainladder().fit(clrd[\"CumPaidLoss\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "MackChainladder has the following additional fitted features that the deterministic `Chainladder` does not:\n", - "\n", - "- `full_std_err_`: The full standard error\n", - "- `total_process_risk_`: The total process error\n", - "- `total_parameter_risk_`: The total parameter error\n", - "- `mack_std_err_`: The total prediction error by origin period\n", - "- `total_mack_std_err_`: The total prediction error across all origin periods\n", - "\n", - "Notice these are all measures of uncertainty, but where can they be applied? Let's start by examining the `link_ratios` underlying the triangle between age 12 and 24." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224
1988285,804638,532
1989307,720684,140
1990320,124757,479
1991347,417793,749
1992342,982781,402
1993342,385743,433
1994351,060750,392
1995343,841768,575
1996381,484736,040
" - ], - "text/plain": [ - " 12 24\n", - "1988 285804.0 638532.0\n", - "1989 307720.0 684140.0\n", - "1990 320124.0 757479.0\n", - "1991 347417.0 793749.0\n", - "1992 342982.0 781402.0\n", - "1993 342385.0 743433.0\n", - "1994 351060.0 750392.0\n", - "1995 343841.0 768575.0\n", - "1996 381484.0 736040.0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_first_lags = clrd[clrd.development <= 24][clrd.origin < \"1997\"][\"CumPaidLoss\"]\n", - "clrd_first_lags" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A simple average link-ratio can be directly computed." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.2066789527531494" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_first_lags.link_ratio.to_frame(origin_as_datetime=True).mean()[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also verify that the result is the same as the `Development` object." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.2066789527531494" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.Development(average=\"simple\").fit(clrd[\"CumPaidLoss\"]).ldf_.to_frame(\n", - " origin_as_datetime=False\n", - ").values[0, 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Linear Regression Framework\n", - "\n", - "Mack noted that the estimate for the LDF is really just a linear regression fit. In the case of using the `simple` average, it is a weighted regression where the weight is $\\left (\\frac{1}{X} \\right )^{2}$.\n", - "\n", - "Let's take a look at the fitted coefficient and verify that this ties to the direct calculations that we made earlier.\n", - "With the regression framework in hand, we can get more information about our LDF estimate than just the coefficient." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1477: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n", - " warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
WLS Regression Results
Dep. Variable: y R-squared (uncentered): 0.997
Model: WLS Adj. R-squared (uncentered): 0.997
Method: Least Squares F-statistic: 2887.
Date: Wed, 22 Jun 2022 Prob (F-statistic): 1.60e-11
Time: 22:25:53 Log-Likelihood: -107.89
No. Observations: 9 AIC: 217.8
Df Residuals: 8 BIC: 218.0
Df Model: 1
Covariance Type: nonrobust
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err t P>|t| [0.025 0.975]
x1 2.2067 0.041 53.735 0.000 2.112 2.301
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Omnibus: 7.448 Durbin-Watson: 1.177
Prob(Omnibus): 0.024 Jarque-Bera (JB): 2.533
Skew: -1.187 Prob(JB): 0.282
Kurtosis: 4.058 Cond. No. 1.00


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " WLS Regression Results \n", - "=======================================================================================\n", - "Dep. Variable: y R-squared (uncentered): 0.997\n", - "Model: WLS Adj. R-squared (uncentered): 0.997\n", - "Method: Least Squares F-statistic: 2887.\n", - "Date: Wed, 22 Jun 2022 Prob (F-statistic): 1.60e-11\n", - "Time: 22:25:53 Log-Likelihood: -107.89\n", - "No. Observations: 9 AIC: 217.8\n", - "Df Residuals: 8 BIC: 218.0\n", - "Df Model: 1 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "x1 2.2067 0.041 53.735 0.000 2.112 2.301\n", - "==============================================================================\n", - "Omnibus: 7.448 Durbin-Watson: 1.177\n", - "Prob(Omnibus): 0.024 Jarque-Bera (JB): 2.533\n", - "Skew: -1.187 Prob(JB): 0.282\n", - "Kurtosis: 4.058 Cond. No. 1.00\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", - "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y = clrd_first_lags.to_frame(origin_as_datetime=True).values[:, 1]\n", - "x = clrd_first_lags.to_frame(origin_as_datetime=True).values[:, 0]\n", - "\n", - "model = sm.WLS(y, x, weights=(1 / x) ** 2)\n", - "results = model.fit()\n", - "results.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By toggling the weights of our regression, we can handle the most common types of averaging used in picking loss development factors.\n", - "- For simple average, the weights are $\\left (\\frac{1}{X} \\right )^{2}$\n", - "- For volume-weighted average, the weights are $\\left (\\frac{1}{X} \\right )$\n", - "- For \"regression\" average, the weights are 1" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simple average:\n", - "True\n", - "Volume-weighted average:\n", - "True\n", - "Regression average:\n", - "True\n" - ] - } - ], - "source": [ - "print(\"Simple average:\")\n", - "print(\n", - " round(\n", - " cl.Development(average=\"simple\")\n", - " .fit(clrd_first_lags)\n", - " .ldf_.to_frame(origin_as_datetime=False)\n", - " .values[0, 0],\n", - " 10,\n", - " )\n", - " == round(sm.WLS(y, x, weights=(1 / x) ** 2).fit().params[0], 10)\n", - ")\n", - "\n", - "print(\"Volume-weighted average:\")\n", - "print(\n", - " round(\n", - " cl.Development(average=\"volume\")\n", - " .fit(clrd_first_lags)\n", - " .ldf_.to_frame(origin_as_datetime=False)\n", - " .values[0, 0],\n", - " 10,\n", - " )\n", - " == round(sm.WLS(y, x, weights=(1 / x)).fit().params[0], 10)\n", - ")\n", - "\n", - "print(\"Regression average:\")\n", - "print(\n", - " round(\n", - " cl.Development(average=\"regression\")\n", - " .fit(clrd_first_lags)\n", - " .ldf_.to_frame(origin_as_datetime=False)\n", - " .values[0, 0],\n", - " 10,\n", - " )\n", - " == round(sm.OLS(y, x).fit().params[0], 10)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The regression framework is what the `Development` estimator uses to set development patterns. Although we discard the information in the deterministic methods, in the stochastic methods, `Development` has two useful statistics for estimating reserve variability, both of which come from the regression framework. The stastics are `sigma_` and `std_err_` , and they are used by the `MackChainladder` estimator to determine the prediction error of our reserves." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "dev = cl.Development(average=\"simple\").fit(clrd[\"CumPaidLoss\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)0.12320.03400.01350.00910.00740.00670.00730.00970.0032
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12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)0.04110.01200.00510.00370.00330.00330.00420.00680.0032
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 0.041066 0.012024 0.005101 0.003734 0.003303 0.003337 0.00419 0.006831 0.003222" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dev.std_err_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remember that `std_err_` is calculated as $\\frac{\\sigma}{\\sqrt{N}}$." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.0411, 0.012 , 0.0051, 0.0037, 0.0033, 0.0033, 0.0042, 0.0068,\n", - " 0.0032])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.round(\n", - " dev.sigma_.to_frame(origin_as_datetime=False).transpose()[\"(All)\"].values\n", - " / np.sqrt(\n", - " clrd[\"CumPaidLoss\"].age_to_age.to_frame(origin_as_datetime=False).count()\n", - " ).values,\n", - " 4,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the regression framework uses the weighting method, we can easily turn \"on and off\" any observation we want using the dropping capabilities such as `drop_valuation` in the `Development` estimator. Dropping link ratios not only affects the `ldf_` and `cdf_`, but also the `std_err_` and `sigma` of the estimates.\n", - "\n", - "Can we eliminate the 1988 valuation from our triangle, which is identical to eliminating the first observation from our 12-24 regression fit? Let's calculate the `std_err` for the `ldf_` of ages 12-24, and compare it to the value calculated using the weighted least squares regression." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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1988285,804638,532865,100996,3631,084,3511,133,1881,169,7491,196,9171,229,2031,241,715
1989307,720684,140916,9961,065,6741,154,0721,210,4791,249,8861,291,5121,308,706
1990320,124757,4791,017,1441,169,0141,258,9751,315,3681,368,3741,394,675
1991347,417793,7491,053,4141,209,5561,307,1641,381,6451,414,747
1992342,982781,4021,014,9821,172,9151,281,8641,328,801
1993342,385743,433959,1471,113,3141,187,581
1994351,060750,392993,7511,114,842
1995343,841768,575962,081
1996381,484736,040
1997340,132
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 285804.0 638532.0 865100.0 996363.0 1084351.0 1133188.0 1169749.0 1196917.0 1229203.0 1241715.0\n", - "1989 307720.0 684140.0 916996.0 1065674.0 1154072.0 1210479.0 1249886.0 1291512.0 1308706.0 NaN\n", - "1990 320124.0 757479.0 1017144.0 1169014.0 1258975.0 1315368.0 1368374.0 1394675.0 NaN NaN\n", - "1991 347417.0 793749.0 1053414.0 1209556.0 1307164.0 1381645.0 1414747.0 NaN NaN NaN\n", - "1992 342982.0 781402.0 1014982.0 1172915.0 1281864.0 1328801.0 NaN NaN NaN NaN\n", - "1993 342385.0 743433.0 959147.0 1113314.0 1187581.0 NaN NaN NaN NaN NaN\n", - "1994 351060.0 750392.0 993751.0 1114842.0 NaN NaN NaN NaN NaN NaN\n", - "1995 343841.0 768575.0 962081.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 381484.0 736040.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 340132.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[\"CumPaidLoss\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "round(\n", - " cl.Development(average=\"volume\", drop_valuation=\"1988\")\n", - " .fit(clrd[\"CumPaidLoss\"])\n", - " .std_err_.to_frame(origin_as_datetime=False)\n", - " .values[0, 0],\n", - " 8,\n", - ") == round(sm.WLS(y[1:], x[1:], weights=(1 / x[1:])).fit().bse[0], 8)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With `sigma_` and `std_err_` in hand, Mack goes on to develop recursive formulas to estimate `parameter_risk_` and `process_risk_`." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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198800000000000
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12243648607284961081209999
1988
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" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120 9999\n", - "1988 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00\n", - "1989 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 5.347463e+07 5.347463e+07\n", - "1990 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.523151e+08 3.182022e+08 3.182022e+08\n", - "1991 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.316778e+08 4.030941e+08 4.758368e+08 4.758368e+08\n", - "1992 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.008802e+08 2.326034e+08 4.970409e+08 5.686924e+08 5.686924e+08\n", - "1993 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 9.980184e+07 1.994012e+08 3.259040e+08 5.720068e+08 6.392100e+08 6.392100e+08\n", - "1994 0.0 0.000000e+00 0.000000e+00 0.000000e+00 1.218746e+08 2.350337e+08 3.451579e+08 4.812806e+08 7.383803e+08 8.102704e+08 8.102704e+08\n", - "1995 0.0 0.000000e+00 0.000000e+00 1.958799e+08 3.498288e+08 4.837585e+08 6.092460e+08 7.576315e+08 1.023326e+09 1.100370e+09 1.100370e+09\n", - "1996 0.0 0.000000e+00 7.038581e+08 1.127627e+09 1.440168e+09 1.678592e+09 1.882844e+09 2.096884e+09 2.418319e+09 2.524435e+09 2.524435e+09\n", - "1997 0.0 2.078584e+09 4.312427e+09 5.901422e+09 7.024557e+09 7.796507e+09 8.402465e+09 8.950516e+09 9.552740e+09 9.806329e+09 9.806329e+09" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mack.mack_std_err_**2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Second, `total_process_risk_` 2 $= \\sum_{origin} $ `process_risk_` 2, the process risk is assumed to be independent between origins." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12243648607284961081209999
198801,868,353,5814,494,250,6616,460,788,0437,973,402,7039,155,354,14610,211,709,42011,360,087,73013,081,563,68813,595,400,48713,595,400,487
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120 9999\n", - "1988 0.0 1.868354e+09 4.494251e+09 6.460788e+09 7.973403e+09 9.155354e+09 1.021171e+10 1.136009e+10 1.308156e+10 1.359540e+10 1.359540e+10" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mack.total_process_risk_**2" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12243648607284961081209999
19881,868,353,5814,494,250,6616,460,788,0437,973,402,7039,155,354,14610,211,709,42011,360,087,73013,081,563,68813,595,400,48713,595,400,487
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120 9999\n", - "1988 NaN 1.868354e+09 4.494251e+09 6.460788e+09 7.973403e+09 9.155354e+09 1.021171e+10 1.136009e+10 1.308156e+10 1.359540e+10 1.359540e+10" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(mack.process_risk_**2).sum(axis=\"origin\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lastly, independence is also assumed to apply to the overall standard error of reserves, as expected." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "106117274249.93411" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(mack.parameter_risk_**2 + mack.process_risk_**2).sum(axis=2).sum(axis=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "106117274249.93411" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(mack.mack_std_err_**2).sum(axis=2).sum(axis=3)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "This over-reliance on independence is one of the weaknesses of the `MackChainladder` method. Nevertheless, if the data align with this assumption, then `total_mack_std_err_` is a reasonable esimator of reserve variability.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Mack Reserve Variability\n", - "The `mack_std_err_` at ultimate is the reserve variability for each `origin` period." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
9999
1988
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199121,814
199223,847
199325,283
199428,465
199533,172
199650,244
199799,027
" - ], - "text/plain": [ - " 9999\n", - "1988 NaN\n", - "1989 7312.634869\n", - "1990 17838.223062\n", - "1991 21813.683826\n", - "1992 23847.273221\n", - "1993 25282.602592\n", - "1994 28465.249566\n", - "1995 33171.832916\n", - "1996 50243.750958\n", - "1997 99026.911753" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mack.mack_std_err_[\n", - " mack.mack_std_err_.development == mack.mack_std_err_.development.max()\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the `summary_` method, we can more easily look at the result of the `MackChainladder` model." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LatestIBNRUltimateMack Std Err
19881,241,7151,241,715
19891,308,70613,3211,322,0277,313
19901,394,67542,2101,436,88517,838
19911,414,74779,4091,494,15621,814
19921,328,801119,7091,448,51023,847
19931,187,581167,1921,354,77325,283
19941,114,842260,4011,375,24328,465
1995962,081402,4031,364,48433,172
1996736,040636,8341,372,87450,244
1997340,1321,056,3351,396,46799,027
" - ], - "text/plain": [ - " Latest IBNR Ultimate Mack Std Err\n", - "1988 1241715.0 NaN 1.241715e+06 NaN\n", - "1989 1308706.0 1.332126e+04 1.322027e+06 7312.634869\n", - "1990 1394675.0 4.221037e+04 1.436885e+06 17838.223062\n", - "1991 1414747.0 7.940888e+04 1.494156e+06 21813.683826\n", - "1992 1328801.0 1.197087e+05 1.448510e+06 23847.273221\n", - "1993 1187581.0 1.671916e+05 1.354773e+06 25282.602592\n", - "1994 1114842.0 2.604007e+05 1.375243e+06 28465.249566\n", - "1995 962081.0 4.024025e+05 1.364484e+06 33171.832916\n", - "1996 736040.0 6.368335e+05 1.372874e+06 50243.750958\n", - "1997 340132.0 1.056335e+06 1.396467e+06 99026.911753" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mack.summary_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's visualize the paid to date, the estimated reserves, and their standard errors with a histogram." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 1800000.0)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(\n", - " mack.summary_.to_frame(origin_as_datetime=True).index.year,\n", - " mack.summary_.to_frame(origin_as_datetime=True)[\"Latest\"],\n", - " label=\"Paid\",\n", - ")\n", - "plt.bar(\n", - " mack.summary_.to_frame(origin_as_datetime=True).index.year,\n", - " mack.summary_.to_frame(origin_as_datetime=True)[\"IBNR\"],\n", - " bottom=mack.summary_.to_frame(origin_as_datetime=True)[\"Latest\"],\n", - " yerr=mack.summary_.to_frame(origin_as_datetime=True)[\"Mack Std Err\"],\n", - " label=\"Reserves\",\n", - ")\n", - "plt.legend(loc=\"upper left\")\n", - "plt.ylim(0, 1800000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also simulate the (assumed) normally distributed IBNR with `np.random.normal()`." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 1., 2., 8., 7., 13., 17., 30., 29., 41., 72., 66.,\n", - " 94., 142., 185., 195., 274., 312., 351., 392., 419., 509., 491.,\n", - " 546., 540., 577., 537., 534., 524., 483., 448., 401., 348., 300.,\n", - " 268., 210., 163., 118., 94., 68., 59., 41., 25., 19., 18.,\n", - " 7., 5., 3., 5., 5., 4.]),\n", - " array([2209836.99059939, 2233093.4303693 , 2256349.87013921,\n", - " 2279606.30990912, 2302862.74967904, 2326119.18944895,\n", - " 2349375.62921886, 2372632.06898877, 2395888.50875869,\n", - " 2419144.9485286 , 2442401.38829851, 2465657.82806842,\n", - " 2488914.26783834, 2512170.70760825, 2535427.14737816,\n", - " 2558683.58714807, 2581940.02691799, 2605196.4666879 ,\n", - " 2628452.90645781, 2651709.34622773, 2674965.78599764,\n", - " 2698222.22576755, 2721478.66553746, 2744735.10530738,\n", - " 2767991.54507729, 2791247.9848472 , 2814504.42461711,\n", - " 2837760.86438702, 2861017.30415694, 2884273.74392685,\n", - " 2907530.18369676, 2930786.62346667, 2954043.06323659,\n", - " 2977299.5030065 , 3000555.94277641, 3023812.38254632,\n", - " 3047068.82231624, 3070325.26208615, 3093581.70185606,\n", - " 3116838.14162597, 3140094.58139589, 3163351.0211658 ,\n", - " 3186607.46093571, 3209863.90070562, 3233120.34047554,\n", - " 3256376.78024545, 3279633.22001536, 3302889.65978527,\n", - " 3326146.09955519, 3349402.5393251 , 3372658.97909501]),\n", - " )" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ibnr_mean = mack.ibnr_.sum()\n", - "ibnr_sd = mack.total_mack_std_err_.values[0, 0]\n", - "n_trials = 10000\n", - "\n", - "np.random.seed(2021)\n", - "dist = np.random.normal(ibnr_mean, ibnr_sd, size=n_trials)\n", - "\n", - "plt.hist(dist, bins=50)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ODP Bootstrap Model\n", - "The `MackChainladder` focuses on a regression framework for determining the variability of reserve estimates. An alternative approach is to use the statistical bootstrapping, or sampling from a triangle with replacement to simulate new triangles.\n", - "\n", - "Bootstrapping imposes less model constraints than the `MackChainladder`, which allows for greater applicability in different scenarios. Sampling new triangles can be accomplished through the `BootstrapODPSample` estimator. This estimator will take a single triangle and simulate new ones from it. To simulate new triangles randomly from an existing triangle, we specify `n_sims` with how many triangles we want to simulate, and access the `resampled_triangles_` attribute to get the simulated triangles. Notice that the shape of `resampled_triangles_` matches `n_sims` at the first index." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(10000, 1, 10, 10)
Index:[LOB]
Columns:[CumPaidLoss]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (10000, 1, 10, 10)\n", - "Index: [LOB]\n", - "Columns: [CumPaidLoss]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "samples = (\n", - " cl.BootstrapODPSample(n_sims=10000).fit(clrd[\"CumPaidLoss\"]).resampled_triangles_\n", - ")\n", - "samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, we could use `BootstrapODPSample` to transform our triangle into a resampled set." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The notion of the ODP Bootstrap is that as our simulations approach infinity, we should expect our mean simulation to converge on the basic `Chainladder` estimate of of reserves.\n", - "\n", - "Let's apply the basic chainladder to our original triangle and also to our simulated triangles to see whether this holds true." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Chainladder's IBNR estimate: 2777812.6890986315\n", - "BootstrapODPSample's mean IBNR estimate: 2780734.7397505655\n", - "Difference $: -2922.050651933998\n", - "Difference %: 0.0010519250140232348\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:120: RuntimeWarning: overflow encountered in exp\n", - " sigma_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:124: RuntimeWarning: overflow encountered in exp\n", - " std_err_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:127: RuntimeWarning: invalid value encountered in multiply\n", - " sigma_ = sigma_ * 0\n", - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:128: RuntimeWarning: invalid value encountered in multiply\n", - " std_err_ = std_err_* 0\n" - ] - } - ], - "source": [ - "ibnr_cl = cl.Chainladder().fit(clrd[\"CumPaidLoss\"]).ibnr_.sum()\n", - "ibnr_bootstrap = cl.Chainladder().fit(samples).ibnr_.sum(\"origin\").mean()\n", - "\n", - "print(\n", - " \"Chainladder's IBNR estimate:\",\n", - " ibnr_cl,\n", - ")\n", - "print(\n", - " \"BootstrapODPSample's mean IBNR estimate:\",\n", - " ibnr_bootstrap,\n", - ")\n", - "print(\"Difference $:\", ibnr_cl - ibnr_bootstrap)\n", - "print(\"Difference %:\", abs(ibnr_cl - ibnr_bootstrap) / ibnr_cl)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using Deterministic Methods with Bootstrapped Samples\n", - "Our `samples` is just another triangle object with all the functionality of a regular triangle. This means we can apply any functionality we want to our `samples` including any deterministic methods we learned about previously." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('dev', Development(average='simple')),\n",
-       "                ('tail', TailConstant(tail=1.05))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Pipeline(steps=[('dev', Development(average='simple')),\n", - " ('tail', TailConstant(tail=1.05))])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe = cl.Pipeline(\n", - " steps=[(\"dev\", cl.Development(average=\"simple\")), (\"tail\", cl.TailConstant(1.05))]\n", - ")\n", - "\n", - "pipe.fit(samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now instead of a single `ldf_` (and `cdf_`) array across developmental ages, we have 10,000 arrays of `ldf_` (and `cdf_`)." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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12-2424-3636-4848-6060-7272-8484-9696-108108-120
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" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 2.247069 1.319373 1.152251 1.085909 1.051881 1.030913 1.02611 1.023726 1.004063" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe.named_steps.dev.ldf_.iloc[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)2.31121.32141.15891.08091.04841.02861.02861.01481.0099
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 2.311246 1.321449 1.15891 1.080946 1.04835 1.028581 1.028582 1.014817 1.009899" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe.named_steps.dev.ldf_.iloc[9999]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This allows us to look at the varibility of any fitted property used. Let's look at the distribution of the age-to-age factor between age 12 and 24, and compare it against the LDF calculated from the non-bootstrapped triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "resampled_ldf = pipe.named_steps.dev.ldf_\n", - "plt.hist(pipe.named_steps.dev.ldf_.values[:, 0, 0, 0], bins=50)\n", - "\n", - "orig_dev = cl.Development(average=\"simple\").fit(clrd[\"CumPaidLoss\"])\n", - "plt.axvline(orig_dev.ldf_.values[0, 0, 0, 0], color=\"black\", linestyle=\"dashed\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Bootstrap vs Mack\n", - "We can approximate some of the Mack's parameters calculated using the regression framework." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Std_BootstrapStd_Mack
12-240.0433750.041066
24-360.0122540.012024
36-480.0072700.005101
48-600.0052050.003734
60-720.0041300.003303
72-840.0036990.003337
84-960.0037520.004190
96-1080.0041380.006831
108-1200.0042310.003222
\n", - "
" - ], - "text/plain": [ - " Std_Bootstrap Std_Mack\n", - "12-24 0.043375 0.041066\n", - "24-36 0.012254 0.012024\n", - "36-48 0.007270 0.005101\n", - "48-60 0.005205 0.003734\n", - "60-72 0.004130 0.003303\n", - "72-84 0.003699 0.003337\n", - "84-96 0.003752 0.004190\n", - "96-108 0.004138 0.006831\n", - "108-120 0.004231 0.003222" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bootstrap_vs_mack = resampled_ldf.std(\"index\").to_frame(origin_as_datetime=False).T\n", - "bootstrap_vs_mack.rename(columns={\"(All)\": \"Std_Bootstrap\"}, inplace=True)\n", - "bootstrap_vs_mack = bootstrap_vs_mack.merge(\n", - " orig_dev.std_err_.to_frame(origin_as_datetime=False).T,\n", - " left_index=True,\n", - " right_index=True,\n", - ")\n", - "bootstrap_vs_mack.rename(columns={\"(All)\": \"Std_Mack\"}, inplace=True)\n", - "\n", - "bootstrap_vs_mack" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ],\n", - " [Text(0, 0, '12-24'),\n", - " Text(1, 0, '24-36'),\n", - " Text(2, 0, '36-48'),\n", - " Text(3, 0, '48-60'),\n", - " Text(4, 0, '60-72'),\n", - " Text(5, 0, '72-84'),\n", - " Text(6, 0, '84-96'),\n", - " Text(7, 0, '96-108'),\n", - " Text(8, 0, '108-120')])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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MBPoA48ysT061S4BP3P1wYAYwLZRvBn4ETGyyFouISFGy7NEPBta6+zvu/iUwDxidU2c0MDtMLwCGm5m5++fu/jxJ4IuIyG6QJeh7AO+lHteEsrx13H0LsAEoy9oIM7vczCrNrHLdunVZFxMRkQxaxclYd5/p7hXuXtG9e/fd3RwRkahkCfr3gV6pxz1DWd46ZtYG6Aysb4oGiohI42QJ+leAI8yst5m1A8YCC3PqLATGh+mzgafd3ZuumSIisqsavHulu28xswnA40AJcJ+7rzCzKUCluy8EZgFzzGwt8DHJlwEAZlYN7A+0M7MxwHfc/c0m3xIREckr022K3X0RsCin7ObU9GbgnALLljeifSIi0kit4mSsiIg0HwW9iEjkFPQiIpFT0IuIRE5BLyISOQW9iEjkFPQiIpHLdB297PnKJz+SuW711NObsSUi0tK0Ry8iEjnt0YtIfG7pXETdDc3XjlZCe/QiIpFT0IuIRE5BLyISOQW9iEjkFPQiIpFT0IuIRE5BLyISOV1HLztrgWuQs/5SV7/SFWk87dGLiEROQS8iEjkFvYhI5BT0IiKR08lY2evpFs5NJOtJ/GY+gQ9QXbpLT1GUPemCAu3Ri4hETnv00rrtZbebbW1HF61tL1p2jfboRUQipz16kWI08zh0Ufayo509Vit4n7RHLyISOQW9iEjkFPQiIpFT0IuIRE5BLyISOQW9iEjkFPQiIpFT0IuIRE5BLyISOQW9iEjkFPQiIpFT0IuIRC5T0JvZCDNbbWZrzWxynvntzWx+mL/EzMpT824M5avN7O+asO0iIpJBg0FvZiXAXcBIoA8wzsz65FS7BPjE3Q8HZgDTwrJ9gLHAscAI4O6wPhERaSFZ9ugHA2vd/R13/xKYB4zOqTMamB2mFwDDzcxC+Tx3/8Ld/wSsDesTEZEWYu5efwWzs4ER7n5pePwPwBB3n5CqUxXq1ITHbwNDgFuAl9z9gVA+C3jU3RfkPMflwOXh4VHA6sZvWoO6AR+1wPNk1Zrao7YU1pra05raAq2rPXtjW77u7t3zzWgV//GIu88EZrbkc5pZpbtXtORz1qc1tUdtKaw1tac1tQVaV3vUlh1lGbp5H+iVetwzlOWtY2ZtgM7A+ozLiohIM8oS9K8AR5hZbzNrR3JydWFOnYXA+DB9NvC0J2NCC4Gx4aqc3sARwMtN03QREcmiwaEbd99iZhOAx4ES4D53X2FmU4BKd18IzALmmNla4GOSLwNCvYeAN4EtwFXuvrWZtqVYLTpUlEFrao/aUlhrak9ragu0rvaoLSkNnowVEZE9m34ZKyISOQW9iEjkogh6M7vPzP4SruevLfuJma0ys+Vm9p9mdkCBZeutZ2aHmtlGM5uYsS29zOy/zOxNM1thZtfmzP8/ZuZm1q3A8rPMbFlozwIz65Sa979S652boS2lZvZyWN8KM7s1lJuZ/bOZvWVmK83smgbW83Mz25h6fGjYxtdDO0c11JbUsiVhuYfD4+Fm9pqZLTWz583s8ALLtTOzmaHNq8zsrFBe8PYbGdpyQHiNV4XXYaiZdTWzJ81sTfi3S4FlnwttXmpmH5jZ70L5+eE1ecPMXjCzARnacVRqXUvN7DMzu66IPjzQzF4Ky1aa2eCc+YPMbIslv4nJ+tpcH/pMlZk9aGalqXk79Ic8y54b2rzCzKblzMvUh83s2vDcK8zsulT51eE1WWFm0wsse06Yv83MKnLm5b0lS57tnW07Z0revmFmnc3s96nP2fcKtOvk0Nd3eC/C+/diWHa5mZ2bmtc79Ou1oZ+3K/Sa1cvd9/g/4GTgG0BVquw7QJswPQ2YVmDZeuuR/NL3N8DEjG05BPhGmN4PeAvoEx73Ijmp/S7QrcDy+6em/wWYHKaPAF4HuoTHB2ZoiwGdwnRbYAlwPPA94N+AfRpaF1ABzAE2pspmAt8P032A6iLeqx8Ac4GHw+O3gGPC9JXA/QWWuxW4LUzvU/v6hWXuCdNjgflFtGU2cGmYbgccAExPveaTC/WbnPX8B3BhmD4h9R6NBJYU2ZdLgD8DXy+iDz8BjAzTo4DFOet7GlgEnJ2xDT2APwEdwuOHgIsK9YecZcuA/wa6p17j4cX0YaAvUAXsS3LByFPA4cCpYbp9A8sfQ/LDy8VARaq8D7AMaA/0Bt4Or0++7b2dnTMlb98AbkpNdye5IKVdnnaVA/1JPntnp8qPBI4I018DPgQOSLVlbJi+h/C5K/Yvij16d3+W5MVNlz3h7lvCw5dIruHPt2zBemY2hqQDrCiiLR+6+2th+q/ASpKOBMl9gCYBBc+Au/tn4bkN6JCqexlwl7t/Eur9JUNb3N1r97zahj8Hvg9Mcfdt9a3LkvsS/SS0eYdVA/uH6c7ABw21JayvJ3A68KtdWNfFwP8N7d3m7rW/NCx0+42G2tKZZAdhVljnl+7+ac76ZgNjGljP/sC3gd+F9bxQ+x5RT7+rx3DgbXd/N2sfpv7X8GqSL6IG+0uONkAHS34Xsy/wQT39Ie0wYI27rwuPnwLOCtNZ+/AxJF+Qfwvb/wzwP0n67VR3/6K+5d19pbvn+3V9fbdkyd3exeRkCoX7hgP7hX7XKSy3JWdZ3L3a3ZcD23LK33L3NWH6A5L3qntY37dJ+nXucxYliqDP4GLg0WLqWTJk8kOSPcldEoYRjgOWmNlo4H13X5ZhuV+T7NUdDfwiFB8JHGlmfwyH6SMytqHEzJaSdJ4n3X0J8D+Ac8Nh/qNmdkSBxScAC939w5zyW4ALzKyGZE/x6ixtAX5KEhLpjn4psCis6x+AqXm24YAw+U/h0Pc3ZnZQKOsBvAfJpcDABpK9yob0BtYBv7ZkKOlXZtYROCi1vX8GDiq4hsQY4A+1X9A5LiFbv0sbCzyYp7y+Pnwd8BMzew+4A7gRwMx6AGcC/1pMA9z9/bCe/ybZu9zg7k9QuD+krQWOMrPyEJpj2P6jyax9uAoYZmZlZrYvyVFKr7D8sDCU8YyZDSpmu0j1laAG6FHP9uYq1DfuJPly+gB4A7i2dieqWGHYrR3J0UYZ8Gnqy76G7TuNRYk+6M3sH0m+Xf+9yHq3ADNSe8TFPm8nkj2p68J6bwJuzrKsu3+P5BBuJVA7XteG5ND3FGAccK8VGLPNWddWdx9Isjc42Mz6khy6bvbkZ9n3Avflaf/XgHPY/kWTNo5kiKUnyYdwjpnV25fM7O+Bv7j7qzmzrgdGhXX9mmS4Kleb0P4X3P0bwIskH8zGaENyaP6v7n4c8DnJ4XgdT46XG7r+eBx5gtnMTiUJ+h9mbVAYfz2DZKgwXd5QH/4+cL279yJ5PWeF8p8CPyw2dMLY82iSL8OvAR3N7EIK94c6YW/9+8B84DmgGqj97UymPuzuK0mGqp4AHgOWhnW0AbqSDD/eADyU5eitIQW294L6lsnpG38X2vg1YCBwZzjSK7Ydh5AMi31vV78oCtqV8Z7W+Ecy/lWVU3YRSSjsmyr7NcmbsqiBerWdtBr4lORwbELGtrQlGYv/QXjcj2SPunZ9W0j2Hg4O9ZYCv8qznpPZPpZ9T+gAtfP+AAwq8jW6GZgIrAJ6hzIj2YMh3RaSIZY/p9q8jeQuppAMZfVKrfcdGjhnQDLsUhPW9Wfgb8AjJMMUtXUOJflxXUlox1JgSmjj52w/p9ALWJFq89Aw3Ybk5lGW4bU4mNS5BWBYaM9q4JBQdgiwOve1SS3TjeRWH6U56+5Pskd2ZJHvz2jgiWL7MMlRTO1vYgz4LEz/KfX+bSTpg2MytOMcYFbq8YVhXTv1h9z3Ks+6LgemN6YPk4yXX0kS+qemyt8mGRPf6TMd5i9mxzH6G4EbU48fB4YW2N67ycmUevrGI8CwVL2nSYaE/rn2tclp1/3knC8hGXp7jR3H7o2kP9eepxkKPF5Mn6pb164s1Br/8rwpI0hCo3sDyzVYj2TvPuvJWCM52fLTeupUk+dkbFj28NT0HcAdqXbODtPdSA5ByxpoS3e2n9TpQPLl9fckwyMXh/JTgFcybFf6ZOyjbD85V3vI2mC4ppY/BXiY7cF8ZCi/BPiPAsvMA74dpi8CfhOmr2LHk7EPFdGO54CjUu/xT8Jf+oTb9HqWv6L2PUmVHUoSgCfsQh+ex45BmLUPrwROCdPDgVfz1Lmf7Cdjh5B8me8b+uFs4OpC/SHP8geGf7uQBN2Rqe3J1IdT6ziUZMfkgPB6TwnlR4blC/Y7dg76Y9nxZOw7JF9UebeXnTMlb98gGRq7JUwfRHI/r7wXW+R7L0iGav4AXJen7m/Y8WTslcX2K/dIgp7k0PlD4CuSvcZLwoftPbbvbdxTYNkG61Fc0J9Ecki3PLXOUTl1qvN1BJKhtD+SjPNVkRyq7x/mGcmwxpth/tgMbelPcpXD8rC+m0P5ASR7IW+Q7C0OyLCudND3Ce1cFrbvO0W+X6ew/UjlzNCOZeGDeViBZb4OPBu25Q/AoaG8NHwY1pLcRynv8gXWORCoDOv8HUkwlYX1ryE5kdi1nuUXk9yeO132K+CT1HtfmbEtHUmODjoX0zdTfe7V8BouAb6Zp879ZAz6UP9WkoCtIhlOaF+oPxT4PL4Z/samyjP3YZIv4TfDNtVetdMOeCC06TXCF3+eZc8kyYEvgP9Hai8Y+EeSI4HVhCuVCmzvfHbOlLx9g2TI5gm2f24vKNCuQWFdn4f3uvao9ILwPEtTfwPDvMNI+vVakn7evtBrVt+fboEgIhK56E/Giojs7RT0IiKRU9CLiEROQS8iEjkFvYhI5BT0IiKRU9CLiETu/wOD8dHWf+az/QAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "width = 0.3\n", - "ages = np.arange(len(bootstrap_vs_mack))\n", - "\n", - "plt.bar(\n", - " ages - width / 2,\n", - " bootstrap_vs_mack[\"Std_Bootstrap\"],\n", - " width=width,\n", - " label=\"Bootstrap\",\n", - ")\n", - "plt.bar(ages + width / 2, bootstrap_vs_mack[\"Std_Mack\"], width=width, label=\"Mack\")\n", - "plt.legend(loc=\"upper right\")\n", - "plt.xticks(ages, bootstrap_vs_mack.index)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While the `MackChainladder` produces statistics about the mean and variance of reserve estimates, the variance or precentile of reserves would need to be fited using maximum likelihood estimation or method of moments. However, for `BootstrapODPSample` based fits, we can use the empirical distribution from the samples directly to get information about variance or the precentile of the IBNR reserves." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:120: RuntimeWarning: overflow encountered in exp\n", - " sigma_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:124: RuntimeWarning: overflow encountered in exp\n", - " std_err_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:127: RuntimeWarning: invalid value encountered in multiply\n", - " sigma_ = sigma_ * 0\n", - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:128: RuntimeWarning: invalid value encountered in multiply\n", - " std_err_ = std_err_* 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Standard deviation of reserve estimate: 138,546.0\n", - "99th percentile of reserve estimate: 3,111,513.0\n" - ] - } - ], - "source": [ - "ibnr = cl.Chainladder().fit(samples).ibnr_.sum(\"origin\")\n", - "\n", - "ibnr_std = ibnr.std()\n", - "print(\"Standard deviation of reserve estimate: \" + f\"{round(ibnr_std,0):,}\")\n", - "ibnr_99 = ibnr.quantile(q=0.99)\n", - "print(\"99th percentile of reserve estimate: \" + f\"{round(ibnr_99,0):,}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see how the `MackChainladder` reserve distribution compares to the `BootstrapODPSample` reserve distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(ibnr.to_frame(origin_as_datetime=True), bins=50, label=\"Bootstrap\", alpha=0.3)\n", - "plt.hist(dist, bins=50, label=\"Mack\", alpha=0.3)\n", - "plt.legend(loc=\"upper right\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Expected Loss Methods with Bootstrap\n", - "\n", - "So far, we've only applied the multiplicative methods (i.e. basic chainladder) in a stochastic context. It is possible to use an expected loss method like the `BornhuetterFerguson` method does. \n", - "\n", - "To do this, we will need an exposure vector." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "sample_weight = clrd[\"EarnedPremNet\"].latest_diagonal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Passing an `apriori_sigma` to the `BornhuetterFerguson` estimator tells it to consider the apriori selection itself as a random variable. Fitting a stochastic `BornhuetterFerguson` looks very much like the determinsitic version." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:120: RuntimeWarning: overflow encountered in exp\n", - " sigma_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:124: RuntimeWarning: overflow encountered in exp\n", - " std_err_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:127: RuntimeWarning: invalid value encountered in multiply\n", - " sigma_ = sigma_ * 0\n", - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:128: RuntimeWarning: invalid value encountered in multiply\n", - " std_err_ = std_err_* 0\n" - ] - }, - { - "data": { - "text/html": [ - "
BornhuetterFerguson(apriori=0.65, apriori_sigma=0.1)
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" - ], - "text/plain": [ - "BornhuetterFerguson(apriori=0.65, apriori_sigma=0.1)" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fit Bornhuetter-Ferguson to stochastically generated data\n", - "bf = cl.BornhuetterFerguson(0.65, apriori_sigma=0.10)\n", - "bf.fit(samples, sample_weight=sample_weight)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use our knowledge of `Triangle` manipulation to grab most things we would want out of our model." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# Grab completed triangle replacing simulated known data with actual known data\n", - "full_triangle = bf.full_triangle_ - bf.X_ + clrd[\"CumPaidLoss\"]\n", - "# Limiting to the current year for plotting\n", - "current_year = (\n", - " full_triangle[full_triangle.origin == full_triangle.origin.max()]\n", - " .to_frame(origin_as_datetime=True)\n", - " .T\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As expected, plotting the expected development of our full triangle over time from the Bootstrap `BornhuetterFerguson` model fans out to greater uncertainty the farther we get from our valuation date." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the data\n", - "current_year.iloc[:, :200].reset_index(drop=True).plot(\n", - " color=\"red\",\n", - " legend=False,\n", - " alpha=0.1,\n", - " title=\"Current Accident Year Expected Development Distribution\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Recap\n", - "- The Mack method approaches stochastic reserving from a regression point of view
\n", - "- Bootstrap methods approach stochastic reserving from a simulation point of view
\n", - "- Where they assumptions of each model are not violated, they produce resonably consistent estimates of reserve variability
\n", - "- Mack does impose more assumptions (i.e. constraints) on the reserve estimate making the Bootstrap approach more suitable in a broader set of applciations
\n", - "- Both methods converge to their corresponding deterministic point estimates
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/tail-tutorial.ipynb b/docs/tutorials/tail-tutorial.ipynb deleted file mode 100644 index d51d39dc..00000000 --- a/docs/tutorials/tail-tutorial.ipynb +++ /dev/null @@ -1,1369 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Extending Development Patterns with Tails\n", - "## Getting Started\n", - "This tutorial focuses on extending the developent patterns beyond the tail. \n", - "\n", - "Be sure to make sure your packages are updated. For more info on how to update your pakages, visit [Keeping Packages Updated](https://chainladder-python.readthedocs.io/en/latest/library/install.html#keeping-packages-updated)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pandas: 1.4.2\n", - "numpy: 1.22.4\n", - "chainladder: 0.8.12\n" - ] - } - ], - "source": [ - "# Black linter, optional\n", - "%load_ext lab_black\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import chainladder as cl\n", - "import matplotlib.pyplot as plt\n", - "\n", - "print(\"pandas: \" + pd.__version__)\n", - "print(\"numpy: \" + np.__version__)\n", - "print(\"chainladder: \" + cl.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Disclaimer\n", - "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Basic Tail Fitting\n", - "\n", - "Tails are another class of transformers. Similar to the `Development` estimator, they come with `fit`, `transform` and `fit_transform` methods. Also, like our `Development` estimator, you can define a tail in the absence of data or if you believe development will continue beyond your latest evaluation period." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
36912151821242730...108111114117120123126129132135
19953.0024.0065.00141.00273.00418.00550.00692.00814.00876.00...1,099.001,099.001,100.001,098.001,098.001,098.001,099.001,099.001,100.001,100.00
19961.0016.0054.00135.00260.00398.00594.00758.00871.00964.00...1,296.001,296.001,297.001,298.001,298.001,298.00
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19981.0011.0040.0093.00185.00343.00474.00643.00744.00831.00...
19991.0014.0047.00113.00225.00379.00570.00715.00832.00955.00...
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20031.009.0031.0094.00192.00299.00408.00792.00873.00949.00...
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3-66-99-1212-1515-1818-2121-2424-2727-3030-33...120-123123-126126-129129-132132-135135-138138-141141-144144-147147-150
(All)8.56253.55472.76591.93321.60551.40111.32701.16581.10981.0780...1.00001.00091.00001.00091.00001.00011.00011.00011.00011.0003
" - ], - "text/plain": [ - " 3-6 6-9 9-12 12-15 15-18 18-21 21-24 24-27 27-30 30-33 33-36 36-39 39-42 42-45 45-48 48-51 51-54 54-57 57-60 60-63 63-66 66-69 69-72 72-75 75-78 78-81 81-84 84-87 87-90 90-93 93-96 96-99 99-102 102-105 105-108 108-111 111-114 114-117 117-120 120-123 123-126 126-129 129-132 132-135 135-138 138-141 141-144 144-147 147-150\n", - "(All) 8.5625 3.554745 2.765914 1.933185 1.605535 1.401081 1.327029 1.165767 1.109803 1.077971 1.054556 1.046539 1.03534 1.03039 1.028639 1.017245 1.01494 1.010918 1.014197 1.008495 1.013206 1.008549 1.007365 1.005104 1.006258 1.009568 1.006634 1.002668 1.001661 1.002694 1.005374 1.001645 1.000838 1.001117 1.001673 1.000278 1.000835 0.999583 1.0 1.0 1.000911 1.0 1.00091 1.0 1.000119 1.000097 1.000079 1.000065 1.00029" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tail = cl.TailCurve()\n", - "tail.fit(quarterly)\n", - "\n", - "print(\"Triangle latest\", quarterly.development.max())\n", - "tail.fit(quarterly).ldf_[\"paid\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These extra twelve months (147 - 135, or one year) of development patterns are included as it is typical to want to track IBNR run-off over a 1-year time horizon from the valuation date. The one-year extension is currently fixed at one year and there is no ability to extend it even further. However, a subsequent version of `chainladder` will look to address this issue. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Curve Fitting\n", - "\n", - "Curve fitting takes selected development patterns and extrapolates them using either an `exponential` or `inverse_power` fit. In most cases, the `inverse_power` produces a thicker (more conservative) tail." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " 135-Ult\n", - "(All) 1.00065" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exp = cl.TailCurve(curve=\"exponential\").fit(quarterly[\"paid\"])\n", - "exp.tail_" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " 135-Ult\n", - "(All) 1.021283" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inv = cl.TailCurve(curve=\"inverse_power\").fit(quarterly[\"paid\"])\n", - "inv.tail_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When fitting a tail, by default, all of the data will be used; however, we can specify which period of development patterns we want to begin including in the curve fitting process with `fit_period`. \n", - "\n", - "Patterns will also be generated for 100 periods beyond the end of the triangle by default, or we can specify how far beyond the triangle to project the tail factor to before dropping the age-to-age factor down to 1.0 using `extrap_periods`.\n", - "\n", - "Note that even though we can extrapolate the curve many years beyond the end of the triangle for computational purposes, the resultant development factors will compress all `ldf_` beyond one year into a single age-ultimate factor." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
36912151821242730...108111114117120123126129132135
199544.0096.00194.00420.00621.00715.00748.00906.00950.00973.00...1,098.001,099.001,103.001,100.001,098.001,100.001,100.001,098.001,101.001,100.00
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199717.0043.00135.00380.00530.00714.00813.00945.00966.001,008.00...1,203.001,200.00
199810.0043.00107.00238.00393.00574.00732.00894.00935.00967.00...
199913.0041.00109.00306.00481.00657.00821.001,007.001,021.001,141.00...
20002.0029.0088.00254.00380.00501.00615.00735.00788.00842.00...
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20022.0034.00115.00290.00472.00809.001,054.001,543.001,617.001,505.00...
20033.0019.0090.00692.00597.00929.00883.001,117.001,092.001,176.00...
20044.0038.00138.00371.00583.00756.00902.001,111.001,212.00...
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Like its inspiration `sklearn`, we can create an overall estimator known as a `Pipeline` that combines multiple transformers and optional predictors in one estimator. " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "sequence = [\n", - " (\"simple_dev\", cl.Development(average=\"simple\")),\n", - " (\"inverse_power_tail\", cl.TailCurve(curve=\"inverse_power\")),\n", - "]\n", - "\n", - "pipe = cl.Pipeline(steps=sequence).fit(quarterly)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`Pipeline` keeps references to each step with its `named_steps` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Development(average='simple')\n", - "TailCurve(curve='inverse_power')\n" - ] - } - ], - "source": [ - "print(pipe.named_steps.simple_dev)\n", - "print(pipe.named_steps.inverse_power_tail)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `Pipeline` estimator is almost an exact replica of the `sklearn Pipeline`, and the docs for `sklearn` are very comprehensive. To learn more about `Pipeline`, [reference their docs](https://scikit-learn.org/stable/modules/compose.html#pipeline).\n", - "\n", - "With a `Triangle` transformed to include development patterns and tails, we are now ready to start fitting our suite of IBNR models." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/triangle-tutorial.ipynb b/docs/tutorials/triangle-tutorial.ipynb deleted file mode 100644 index b4efe067..00000000 --- a/docs/tutorials/triangle-tutorial.ipynb +++ /dev/null @@ -1,13403 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Working with Triangles\n", - "## Getting Started\n", - "Welcome! We drafted these tutorials to help you get familiar with some of the common functionalities that most actuaries can use in their day-to-day responsibilities. The package also comes with pre-installed datasets that you can play with, which are also used in the tutorials here.\n", - "\n", - "The tutorials assume that you have the basic understanding of commonly used actuarial terms, and can independently perform an actuarial analysis in another tool, such as Microsoft Excel or another actuarial software. Furthermore, it is assumed that you already have some familiarity with Python, and that you have the basic knowledge and experience in using some common packages that are popular in the Python community, such as `pandas` and `numpy`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tutorial is linted using [black](https://github.com/psf/black) via [nb_black](https://github.com/dnanhkhoa/nb_black). This step is optional." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext lab_black" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All tutorials and exercises rely on chainladder v0.8.12 and later. It is highly recomended that you keep your packages up-to-date.\n", - "\n", - "For more info on how to update your pakages, visit [Keeping Packages Updated](https://chainladder-python.readthedocs.io/en/latest/library/install.html#keeping-packages-updated)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pandas: 1.4.2\n", - "numpy: 1.22.4\n", - "chainladder: 0.8.12\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import chainladder as cl\n", - "\n", - "print(\"pandas: \" + pd.__version__)\n", - "print(\"numpy: \" + np.__version__)\n", - "print(\"chainladder: \" + cl.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we will be plotting for quite a bit, here's a magic function in IPython, which sets the backend of matplotlib to the 'inline' backend. With this backend, the output of plotting commands is displayed inline within frontends like the Jupyter notebook, directly below the code cell that produced it. The resulting plots will then also be stored in the notebook document." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Disclaimer\n", - "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Working with a Triangle\n", - "Let's begin by looking at an unprocessed triangle data and load it into a `pandas.DataFrame`. We'll use the data `raa`, which is available from the repository." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " development origin values\n", - "0 1981 1981 5012.0\n", - "1 1982 1982 106.0\n", - "2 1983 1983 3410.0\n", - "3 1984 1984 5655.0\n", - "4 1985 1985 1092.0\n", - "5 1986 1986 1513.0\n", - "6 1987 1987 557.0\n", - "7 1988 1988 1351.0\n", - "8 1989 1989 3133.0\n", - "9 1990 1990 2063.0\n", - "10 1982 1981 8269.0\n", - "11 1983 1982 4285.0\n", - "12 1984 1983 8992.0\n", - "13 1985 1984 11555.0\n", - "14 1986 1985 9565.0\n", - "15 1987 1986 6445.0\n", - "16 1988 1987 4020.0\n", - "17 1989 1988 6947.0\n", - "18 1990 1989 5395.0\n", - "19 1983 1981 10907.0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa_df = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/raa.csv\"\n", - ")\n", - "raa_df.head(20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data has three columns: \n", - "* development: or valuation time, in this case, valuation year\n", - "* origin: or accident date, in this case, accident year\n", - "* values: the values recorded for the specific accident date at the specific valuation time (such as incurred losses, paid losses, or claim counts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A table of loss experience showing total losses for a certain period (origin) at various, regular valuation dates (development), reflect the change in amounts as claims mature and emerge. Older periods in the table will have one more entry than the next youngest period, leading to the triangle shape of the data in the table or any other measure that matures over time from an origin date. Loss triangles can be used to determine loss development for a given risk." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's put our data into the triangle format." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19815,0128,26910,90711,80513,53916,18118,00918,60818,66218,834
19821064,2855,39610,66613,78215,59915,49616,16916,704
19833,4108,99213,87316,14118,73522,21422,86323,466
19845,65511,55515,76621,26623,42526,08327,067
19851,0929,56515,83622,16925,95526,180
19861,5136,44511,70212,93515,852
19875574,02010,94612,314
19881,3516,94713,112
19893,1335,395
19902,063
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1981 5012.0 8269.0 10907.0 11805.0 13539.0 16181.0 18009.0 18608.0 18662.0 18834.0\n", - "1982 106.0 4285.0 5396.0 10666.0 13782.0 15599.0 15496.0 16169.0 16704.0 NaN\n", - "1983 3410.0 8992.0 13873.0 16141.0 18735.0 22214.0 22863.0 23466.0 NaN NaN\n", - "1984 5655.0 11555.0 15766.0 21266.0 23425.0 26083.0 27067.0 NaN NaN NaN\n", - "1985 1092.0 9565.0 15836.0 22169.0 25955.0 26180.0 NaN NaN NaN NaN\n", - "1986 1513.0 6445.0 11702.0 12935.0 15852.0 NaN NaN NaN NaN NaN\n", - "1987 557.0 4020.0 10946.0 12314.0 NaN NaN NaN NaN NaN NaN\n", - "1988 1351.0 6947.0 13112.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1989 3133.0 5395.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa = cl.Triangle(\n", - " raa_df,\n", - " origin=\"origin\",\n", - " development=\"development\",\n", - " columns=\"values\",\n", - " cumulative=True,\n", - ")\n", - "raa" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also load the example data directly, using `load_sample`:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19815,0128,26910,90711,80513,53916,18118,00918,60818,66218,834
19821064,2855,39610,66613,78215,59915,49616,16916,704
19833,4108,99213,87316,14118,73522,21422,86323,466
19845,65511,55515,76621,26623,42526,08327,067
19851,0929,56515,83622,16925,95526,180
19861,5136,44511,70212,93515,852
19875574,02010,94612,314
19881,3516,94713,112
19893,1335,395
19902,063
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1981 5012.0 8269.0 10907.0 11805.0 13539.0 16181.0 18009.0 18608.0 18662.0 18834.0\n", - "1982 106.0 4285.0 5396.0 10666.0 13782.0 15599.0 15496.0 16169.0 16704.0 NaN\n", - "1983 3410.0 8992.0 13873.0 16141.0 18735.0 22214.0 22863.0 23466.0 NaN NaN\n", - "1984 5655.0 11555.0 15766.0 21266.0 23425.0 26083.0 27067.0 NaN NaN NaN\n", - "1985 1092.0 9565.0 15836.0 22169.0 25955.0 26180.0 NaN NaN NaN NaN\n", - "1986 1513.0 6445.0 11702.0 12935.0 15852.0 NaN NaN NaN NaN NaN\n", - "1987 557.0 4020.0 10946.0 12314.0 NaN NaN NaN NaN NaN NaN\n", - "1988 1351.0 6947.0 13112.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1989 3133.0 5395.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa = cl.load_sample(\"raa\")\n", - "raa" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A triangle has more properties than just what is displayed. For example we can see the underlying `link_ratio`s, which represent the multiplicative change in amounts from one development period to the next." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
19811.64981.31901.08231.14691.19511.11301.03331.00291.0092
198240.42451.25931.97661.29211.13180.99341.04341.0331
19832.63701.54281.16351.16071.18571.02921.0264
19842.04331.36441.34891.10151.11351.0377
19858.75921.65561.39991.17081.0087
19864.25971.81571.10541.2255
19877.21722.72291.1250
19885.14211.8874
19891.7220
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "1981 1.649840 1.319023 1.082332 1.146887 1.195140 1.112972 1.033261 1.002902 1.009217\n", - "1982 40.424528 1.259277 1.976649 1.292143 1.131839 0.993397 1.043431 1.033088 NaN\n", - "1983 2.636950 1.542816 1.163483 1.160709 1.185695 1.029216 1.026374 NaN NaN\n", - "1984 2.043324 1.364431 1.348852 1.101524 1.113469 1.037726 NaN NaN NaN\n", - "1985 8.759158 1.655619 1.399912 1.170779 1.008669 NaN NaN NaN NaN\n", - "1986 4.259749 1.815671 1.105367 1.225512 NaN NaN NaN NaN NaN\n", - "1987 7.217235 2.722886 1.124977 NaN NaN NaN NaN NaN NaN\n", - "1988 5.142117 1.887433 NaN NaN NaN NaN NaN NaN NaN\n", - "1989 1.721992 NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa.link_ratio" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also view (and manipulate) the `latest_diagonal` of the triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1990
198118,834
198216,704
198323,466
198427,067
198526,180
198615,852
198712,314
198813,112
19895,395
19902,063
" - ], - "text/plain": [ - " 1990\n", - "1981 18834.0\n", - "1982 16704.0\n", - "1983 23466.0\n", - "1984 27067.0\n", - "1985 26180.0\n", - "1986 15852.0\n", - "1987 12314.0\n", - "1988 13112.0\n", - "1989 5395.0\n", - "1990 2063.0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa.latest_diagonal" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1990
198118.83
198216.70
198323.47
198427.07
198526.18
198615.85
198712.31
198813.11
19895.39
19902.06
" - ], - "text/plain": [ - " 1990\n", - "1981 18.834\n", - "1982 16.704\n", - "1983 23.466\n", - "1984 27.067\n", - "1985 26.180\n", - "1986 15.852\n", - "1987 12.314\n", - "1988 13.112\n", - "1989 5.395\n", - "1990 2.063" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa.latest_diagonal / 1000" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The latest diagonal also corresponds to a `valuation_date`. Note that 'valuation_date' is a datetime that is at the terminal timestamp of the period (i.e. the last split second of the year)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp('1990-12-31 23:59:59.999999999')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa.valuation_date" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also tell whether our triangle:\n", - "* `is_cumulative`: returns True if the data across the development periods is cumulative, or False if it is incremental.\n", - "* `is_ultimate`: returns True if the ultimate values are contained in the triangle.\n", - "* `is_val_tri`: returns True if the development period is stated as a valuation data as opposed to an age, i.e. Schedule P style triangle (True) or the more commonly used triangle by development age (False).\n", - "* `is_full`: returns True if the triangle has been \"squared\"." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Is triangle cumulative? True\n", - "Does triangle contain ultimate projections? False\n", - "Is this a valuation triangle? False\n", - "Has the triangle been \"squared\"? False\n" - ] - } - ], - "source": [ - "print(\"Is triangle cumulative?\", raa.is_cumulative)\n", - "print(\"Does triangle contain ultimate projections?\", raa.is_ultimate)\n", - "print(\"Is this a valuation triangle?\", raa.is_val_tri)\n", - "print('Has the triangle been \"squared\"?', raa.is_full)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also inspect the triangle to understand its data granularity with `origin_grain` and `development_grain`." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Origin grain: Y\n", - "Development grain: Y\n" - ] - } - ], - "source": [ - "print(\"Origin grain:\", raa.origin_grain)\n", - "print(\"Development grain:\", raa.development_grain)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The package supports monthly (\"M\"), quarterly (\"Q\"), semester (or semi-annually), (\"S\") and yearly (\"Y\") grains for both `origin_grain` and `development_grain`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Triangle Structure\n", - "The triangle described so far is a two-dimensional (accident date by valuation date) structure that spans multiple cells of data. This is a useful structure for exploring individual triangles, but becomes more problematic when working with **sets** of triangles. `Pandas` does not have a triangle `dtype`, but if it did, working with sets of triangles would be much more convenient. To facilitate working with more than one triangle at a time the `chainladder.Triangle` acts like a pandas dataframe (with an index and columns) where each cell (row x col) is an individual triangle. This structure manifests itself as a four-dimensional space. Let's take a look at another dataset `clrd`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GRCODEGRNAMEAccidentYearDevelopmentYearDevelopmentLagIncurLossCumPaidLossBulkLossEarnedPremDIREarnedPremCededEarnedPremNetSinglePostedReserve97LOB
086Allstate Ins Co Grp1988198813674047057112773740069959573947420281872wkcomp
186Allstate Ins Co Grp1988198923629881559056017340069959573947420281872wkcomp
286Allstate Ins Co Grp1988199033472882207442776340069959573947420281872wkcomp
386Allstate Ins Co Grp1988199143306482515951528040069959573947420281872wkcomp
486Allstate Ins Co Grp1988199253546902741562768940069959573947420281872wkcomp
.............................................
4284044598College Liability Ins Co Ltd RRG1995199623432498239703971630othliab
4284144598College Liability Ins Co Ltd RRG19951997383957519039703971630othliab
4284244598College Liability Ins Co Ltd RRG19961996112569825702571630othliab
4284344598College Liability Ins Co Ltd RRG19961997295172825702571630othliab
4284444598College Liability Ins Co Ltd RRG1997199718347725602561630othliab
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42845 rows × 14 columns

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" - ], - "text/plain": [ - " GRCODE GRNAME AccidentYear \\\n", - "0 86 Allstate Ins Co Grp 1988 \n", - "1 86 Allstate Ins Co Grp 1988 \n", - "2 86 Allstate Ins Co Grp 1988 \n", - "3 86 Allstate Ins Co Grp 1988 \n", - "4 86 Allstate Ins Co Grp 1988 \n", - "... ... ... ... \n", - "42840 44598 College Liability Ins Co Ltd RRG 1995 \n", - "42841 44598 College Liability Ins Co Ltd RRG 1995 \n", - "42842 44598 College Liability Ins Co Ltd RRG 1996 \n", - "42843 44598 College Liability Ins Co Ltd RRG 1996 \n", - "42844 44598 College Liability Ins Co Ltd RRG 1997 \n", - "\n", - " DevelopmentYear DevelopmentLag IncurLoss CumPaidLoss BulkLoss \\\n", - "0 1988 1 367404 70571 127737 \n", - "1 1989 2 362988 155905 60173 \n", - "2 1990 3 347288 220744 27763 \n", - "3 1991 4 330648 251595 15280 \n", - "4 1992 5 354690 274156 27689 \n", - "... ... ... ... ... ... \n", - "42840 1996 2 343 249 82 \n", - "42841 1997 3 839 575 190 \n", - "42842 1996 1 125 6 98 \n", - "42843 1997 2 95 17 28 \n", - "42844 1997 1 83 4 77 \n", - "\n", - " EarnedPremDIR EarnedPremCeded EarnedPremNet Single PostedReserve97 \\\n", - "0 400699 5957 394742 0 281872 \n", - "1 400699 5957 394742 0 281872 \n", - "2 400699 5957 394742 0 281872 \n", - "3 400699 5957 394742 0 281872 \n", - "4 400699 5957 394742 0 281872 \n", - "... ... ... ... ... ... \n", - "42840 397 0 397 1 630 \n", - "42841 397 0 397 1 630 \n", - "42842 257 0 257 1 630 \n", - "42843 257 0 257 1 630 \n", - "42844 256 0 256 1 630 \n", - "\n", - " LOB \n", - "0 wkcomp \n", - "1 wkcomp \n", - "2 wkcomp \n", - "3 wkcomp \n", - "4 wkcomp \n", - "... ... \n", - "42840 othliab \n", - "42841 othliab \n", - "42842 othliab \n", - "42843 othliab \n", - "42844 othliab \n", - "\n", - "[42845 rows x 14 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd_df = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/clrd.csv\"\n", - ")\n", - "clrd_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's load the data into the sets of triangles." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (775, 6, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.Triangle(\n", - " clrd_df,\n", - " origin=\"AccidentYear\",\n", - " development=\"DevelopmentYear\",\n", - " columns=[\n", - " \"IncurLoss\",\n", - " \"CumPaidLoss\",\n", - " \"BulkLoss\",\n", - " \"EarnedPremDIR\",\n", - " \"EarnedPremCeded\",\n", - " \"EarnedPremNet\",\n", - " ],\n", - " index=[\"GRNAME\", \"LOB\"],\n", - " cumulative=True,\n", - ")\n", - "clrd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since 4D strucures do not fit nicely on 2D screens, we see a summary view instead that describes the structure rather than the underlying data itself. \n", - "\n", - "We see 5 rows of information:\n", - "* Valuation: the valuation date.\n", - "* Grain: the granularity of the data, O stands for origin, and D stands for development, `OYDY` represents triangles with accident year by development year.\n", - "* Shape: contains 4 numbers, represents the 4-D structure. This sample triangle represents a collection of 775x6 or 4,650 triangles that are themselves 10 accident years by 10 development periods.\n", - " * 775: the number of segments, which is the combination of `index`, that represents the data segments. In this case, it is each of the `GRNAME` and `LOB` unique combination.\n", - " * 6: the number of triangles for each segment, which is also the columns `[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]`. They could be paid amounts, incurred amounts, reported counts, loss ratios, closure rates, excess losses, premium, etc.\n", - " * 10: the number of accident periods.\n", - " * 10: the number of valuation periods.\n", - "* Index: the segmentation level of the triangles.\n", - "* Columns: the value types recorded in the triangles.\n", - " \n", - "To summarize the set of triangles:\n", - "* We have a total of 775 segments, which are at the `GRNAME` and `LOB` level.\n", - "* Each segment contains 6 triangles, which are `IncurLoss`, `CumPaidLoss`, `BulkLoss`, `EarnedPremDIR`, `EarnedPremCeded`, `EarnedPremNet`.\n", - "* Each triangle is 10 accident years x 10 development periods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using `index.head()` allows us to see the first 5 segments in the set of triangles. Note that as the data is loaded, the triangle are sorted by `index`, in this case, `GRNAME` first, then by `LOB`." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " GRNAME LOB\n", - "0 Adriatic Ins Co othliab\n", - "1 Adriatic Ins Co ppauto\n", - "2 Aegis Grp comauto\n", - "3 Aegis Grp othliab\n", - "4 Aegis Grp ppauto" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd.index.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Under the hood, the data structure is a `numpy.ndarray` with the equivalent shape. Like pandas, you can directly access the underlying numpy structure with the `values` property. By exposing the underlying `ndarray` you are free to manipulate the underlying data directly with numpy should that be an easier route to solving a problem." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data structure of clrd: \n", - "Sum of all data values: 3661713596.0\n" - ] - } - ], - "source": [ - "print(\"Data structure of clrd:\", type(clrd.values))\n", - "print(\"Sum of all data values:\", np.nansum(clrd.values))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Keep in mind though, the `chainladder.Triangle` has several methods and properties beyond the raw numpy representation and these are kept in sync by using the `chainladder.Triangle` directly." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pandas-Style Slicing\n", - "As mentioned, the 4D structure is intended to behave like a pandas `DataFrame`. Like pandas, we can subset a dataframe by referencing individual columns by name." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 3, 10, 10)
Index:[GRNAME, LOB]
Columns:[CumPaidLoss, IncurLoss, BulkLoss]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (775, 3, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [CumPaidLoss, IncurLoss, BulkLoss]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[[\"CumPaidLoss\", \"IncurLoss\", \"BulkLoss\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also boolean-index the rows of the Triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(132, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (132, 6, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[clrd[\"LOB\"] == \"wkcomp\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can even use the typical `loc`, `iloc` functionality similar to `pandas` to access subsets of data. These features can be chained together as much as you want." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
198870,571155,905220,744251,595274,156287,676298,499304,873321,808325,322
198966,547136,447179,142211,343231,430244,750254,557270,059273,873
199052,233133,370178,444204,442222,193232,940253,337256,788
199159,315128,051169,793196,685213,165234,676239,195
199239,99189,873114,117133,003154,362159,496
199319,74447,22961,90985,09987,215
199420,37946,77388,63691,077
199518,75684,71287,311
199642,60944,916
1997691
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 70571.0 155905.0 220744.0 251595.0 274156.0 287676.0 298499.0 304873.0 321808.0 325322.0\n", - "1989 66547.0 136447.0 179142.0 211343.0 231430.0 244750.0 254557.0 270059.0 273873.0 NaN\n", - "1990 52233.0 133370.0 178444.0 204442.0 222193.0 232940.0 253337.0 256788.0 NaN NaN\n", - "1991 59315.0 128051.0 169793.0 196685.0 213165.0 234676.0 239195.0 NaN NaN NaN\n", - "1992 39991.0 89873.0 114117.0 133003.0 154362.0 159496.0 NaN NaN NaN NaN\n", - "1993 19744.0 47229.0 61909.0 85099.0 87215.0 NaN NaN NaN NaN NaN\n", - "1994 20379.0 46773.0 88636.0 91077.0 NaN NaN NaN NaN NaN NaN\n", - "1995 18756.0 84712.0 87311.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 42609.0 44916.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 691.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd.loc[\"Allstate Ins Co Grp\"].iloc[-1][\"CumPaidLoss\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pandas-Style Arithmetic\n", - "With complete flexibility in the ability to slice subsets of triangles, we can use basic arithmetic to derive new triangles, which is commonly used as diagnostics to explore trends." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 2, 10, 10)
Index:[GRNAME, LOB]
Columns:[CaseIncurLoss, PaidToInc]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (775, 2, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [CaseIncurLoss, PaidToInc]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[\"CaseIncurLoss\"] = clrd[\"IncurLoss\"] - clrd[\"BulkLoss\"]\n", - "clrd[\"PaidToInc\"] = clrd[\"CumPaidLoss\"] / clrd[\"CaseIncurLoss\"]\n", - "clrd[[\"CaseIncurLoss\", \"PaidToInc\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also aggregating the values across all triangles into one triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19883,577,7807,059,9668,826,1519,862,68710,474,69810,814,57610,994,01411,091,36311,171,59011,203,949
19894,090,6807,964,7029,937,52011,098,58811,766,48812,118,79012,311,62912,434,82612,492,899
19904,578,4428,808,48610,985,34712,229,00112,878,54513,238,66713,452,99313,559,557
19914,648,7568,961,75511,154,24412,409,59213,092,03713,447,48113,642,414
19925,139,1429,757,69912,027,98313,289,48513,992,82114,347,271
19935,653,37910,599,42312,953,81214,292,51615,005,138
19946,246,44711,394,96013,845,76415,249,326
19956,473,84311,612,15114,010,098
19966,591,59911,473,912
19976,451,896
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 3577780.0 7059966.0 8826151.0 9862687.0 10474698.0 10814576.0 10994014.0 11091363.0 11171590.0 11203949.0\n", - "1989 4090680.0 7964702.0 9937520.0 11098588.0 11766488.0 12118790.0 12311629.0 12434826.0 12492899.0 NaN\n", - "1990 4578442.0 8808486.0 10985347.0 12229001.0 12878545.0 13238667.0 13452993.0 13559557.0 NaN NaN\n", - "1991 4648756.0 8961755.0 11154244.0 12409592.0 13092037.0 13447481.0 13642414.0 NaN NaN NaN\n", - "1992 5139142.0 9757699.0 12027983.0 13289485.0 13992821.0 14347271.0 NaN NaN NaN NaN\n", - "1993 5653379.0 10599423.0 12953812.0 14292516.0 15005138.0 NaN NaN NaN NaN NaN\n", - "1994 6246447.0 11394960.0 13845764.0 15249326.0 NaN NaN NaN NaN NaN NaN\n", - "1995 6473843.0 11612151.0 14010098.0 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 6591599.0 11473912.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 6451896.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[\"CumPaidLoss\"].sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also construct a paid loss ratio triangle against `EarnedPremNet`." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
19880.25810.50930.63680.71150.75570.78020.79320.80020.80600.8083
19890.26860.52300.65260.72880.77270.79580.80850.81660.8204
19900.27280.52490.65460.72870.76740.78880.80160.8080
19910.25400.48960.60930.67790.71520.73460.7453
19920.25950.49270.60740.67110.70660.7245
19930.26450.49590.60610.66870.7021
19940.27090.49420.60050.6614
19950.26510.47550.5737
19960.26350.4586
19970.2552
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 0.258115 0.509334 0.636753 0.711533 0.755686 0.780206 0.793152 0.800175 0.805963 0.808297\n", - "1989 0.268634 0.523039 0.652594 0.728841 0.772701 0.795837 0.808501 0.816591 0.820405 NaN\n", - "1990 0.272814 0.524868 0.654580 0.728685 0.767389 0.788847 0.801618 0.807968 NaN NaN\n", - "1991 0.253951 0.489561 0.609332 0.677909 0.715189 0.734607 0.745255 NaN NaN NaN\n", - "1992 0.259518 0.492747 0.607392 0.671096 0.706613 0.724512 NaN NaN NaN NaN\n", - "1993 0.264522 0.495948 0.606111 0.668749 0.702092 NaN NaN NaN NaN NaN\n", - "1994 0.270903 0.494190 0.600480 0.661351 NaN NaN NaN NaN NaN NaN\n", - "1995 0.265095 0.475502 0.573694 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 0.263451 0.458585 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 0.255201 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[\"CumPaidLoss\"].sum() / clrd[\"EarnedPremNet\"].sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Aggregating all segments together is interesting, but it is often more useful to aggregate across segments using `groupby`. For example, we may want to group the triangles by line of business and get a sum across all companies for each industry." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(6, 8, 10, 10)
Index:[LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncurLoss, PaidToInc]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (6, 8, 10, 10)\n", - "Index: [LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd.groupby(\"LOB\").sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The shape is (**6**, 8, 10, 10) because now we have 6 LOBs with 8 triangles for each LOB. We can also note that `index` is now on `[LOB]` only." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['comauto', 'medmal', 'othliab', 'ppauto', 'prodliab', 'wkcomp'],\n", - " dtype=object)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.unique(clrd[\"LOB\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The aggregation functions, e.g. `sum`, `mean`, `std`, `min`, `max`, etc. don't have to just apply to the `index` axis. You can apply them to any of the four axes in the triangle object, which are `segments` (axis `0`, or the index axis), `columns` (axis `1`, the various financial fields), `origin` (axis `2`, across triangle rows), and `development` period (axis `3`, across triangle columns). You can also use either the axis name or number. Let's try to sum all of the segments (`[GRNAME, LOB]`) and columns (`[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]`) using axis name and number, respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(1, 8, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncurLoss, PaidToInc]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (1, 8, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd.sum(axis=\"index\").sum(axis=\"segments\")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
198856,387,73459,928,52061,653,45862,644,63163,182,71063,438,08063,440,90463,452,11163,522,98163,518,183
198962,840,44866,705,57068,666,92869,702,28370,188,01370,355,67970,389,85770,453,15870,483,269
199070,064,96774,084,38875,879,27676,812,99077,179,57277,240,32177,283,93777,345,588
199174,611,61178,513,12180,235,90880,967,06181,178,87681,185,48481,278,636
199281,213,79185,089,37086,443,88286,783,10786,908,61087,086,637
199387,896,22791,685,45693,018,48593,167,81093,473,079
199494,593,70298,130,71899,071,78199,809,122
199597,722,814101,192,332102,056,680
199698,497,932100,917,690
199796,832,221
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "1988 5.638773e+07 5.992852e+07 6.165346e+07 6.264463e+07 6.318271e+07 6.343808e+07 6.344090e+07 6.345211e+07 6.352298e+07 6.351818e+07\n", - "1989 6.284045e+07 6.670557e+07 6.866693e+07 6.970228e+07 7.018801e+07 7.035568e+07 7.038986e+07 7.045316e+07 7.048327e+07 NaN\n", - "1990 7.006497e+07 7.408439e+07 7.587928e+07 7.681299e+07 7.717957e+07 7.724032e+07 7.728394e+07 7.734559e+07 NaN NaN\n", - "1991 7.461161e+07 7.851312e+07 8.023591e+07 8.096706e+07 8.117888e+07 8.118548e+07 8.127864e+07 NaN NaN NaN\n", - "1992 8.121379e+07 8.508937e+07 8.644388e+07 8.678311e+07 8.690861e+07 8.708664e+07 NaN NaN NaN NaN\n", - "1993 8.789623e+07 9.168546e+07 9.301849e+07 9.316781e+07 9.347308e+07 NaN NaN NaN NaN NaN\n", - "1994 9.459370e+07 9.813072e+07 9.907178e+07 9.980912e+07 NaN NaN NaN NaN NaN NaN\n", - "1995 9.772281e+07 1.011923e+08 1.020567e+08 NaN NaN NaN NaN NaN NaN NaN\n", - "1996 9.849793e+07 1.009177e+08 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "1997 9.683222e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd.sum(axis=0).sum(axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Accessor Methods\n", - "\n", - "`Pandas` has special \"accessor\" methods for `str` and `dt`. These allow for the manipulation of data within each cell of data:\n", - "\n", - "```python\n", - "# splits lastname from first name by a comma-delimiter\n", - "df['Last_First'].str.split(',')\n", - "\n", - "# pulls the year out of each date in a dataframe column\n", - "df['Accident Date'].dt.year \n", - "```\n", - "\n", - "`chainladder` also has special \"accessor\" methods to help us manipulate the `origin`, `development` and `valuation` vectors of a triangle." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We may want to extract only the latest accident period for every triangle." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 8, 1, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncurLoss, PaidToInc]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (775, 8, 1, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[clrd.origin == clrd.origin.max()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that this triangle has only 1 row; however, all of the columns would exist, but only the youngest age would have values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We may want to extract particular diagonals from our triangles using its `valuation` vector." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12243648607284
198810,994,014
198912,118,790
199012,878,545
199112,409,592
199212,027,983
199310,599,423
19946,246,447
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84\n", - "1988 NaN NaN NaN NaN NaN NaN 10994014.0\n", - "1989 NaN NaN NaN NaN NaN 12118790.0 NaN\n", - "1990 NaN NaN NaN NaN 12878545.0 NaN NaN\n", - "1991 NaN NaN NaN 12409592.0 NaN NaN NaN\n", - "1992 NaN NaN 12027983.0 NaN NaN NaN NaN\n", - "1993 NaN 10599423.0 NaN NaN NaN NaN NaN\n", - "1994 6246447.0 NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[(clrd.valuation >= \"1994\") & (clrd.valuation < \"1995\")][\"CumPaidLoss\"].sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We may even want to slice particular development periods to explore aspects of our data by development age. For example, we can look at the development factors between ages 24 and 36." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
24-36
19881.2502
19891.2477
19901.2471
19911.2446
19921.2327
19931.2221
19941.2151
19951.2065
1996
1997
" - ], - "text/plain": [ - " 24-36\n", - "1988 1.250169\n", - "1989 1.247695\n", - "1990 1.247132\n", - "1991 1.244650\n", - "1992 1.232666\n", - "1993 1.222124\n", - "1994 1.215078\n", - "1995 1.206503\n", - "1996 NaN\n", - "1997 NaN" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[(clrd.development > 12) & (clrd.development <= 36)][\"CumPaidLoss\"].sum().link_ratio" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Moving Back to Pandas\n", - "When the shape of a `Triangle` object can be expressed as a 2D structure (i.e. two of its four axes have a length of 1) or less, you can use the `to_frame` method to convert your data into a `pandas.DataFrame`. Let's pick only one financial field, `CumPaidLoss` and only the latest diagonal, with `latest_diagonal`. We are now left with LOBs and origin period as our 2 axes." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(6, 1, 10, 1)
Index:[LOB]
Columns:[CumPaidLoss]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (6, 1, 10, 1)\n", - "Index: [LOB]\n", - "Columns: [CumPaidLoss]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd.groupby(\"LOB\").sum().latest_diagonal[\"CumPaidLoss\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "origin 1988 1989 1990 1991 1992 1993 1994 \\\n", - "LOB \n", - "comauto 626097 674441 718396 711762 731033 762039 768095 \n", - "medmal 217239 222707 235717 275923 267007 276235 252449 \n", - "othliab 317889 350684 361103 426085 389250 434995 402244 \n", - "ppauto 8690036 9823747 10728411 10713621 11555121 12249826 12600432 \n", - "prodliab 110973 112614 121255 100276 76059 94462 111264 \n", - "wkcomp 1241715 1308706 1394675 1414747 1328801 1187581 1114842 \n", - "\n", - "origin 1995 1996 1997 \n", - "LOB \n", - "comauto 675166 510191 272342 \n", - "medmal 209222 107474 20361 \n", - "othliab 294332 191258 54130 \n", - "ppauto 11807279 9900842 5754249 \n", - "prodliab 62018 28107 10682 \n", - "wkcomp 962081 736040 340132 " - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd.groupby(\"LOB\").sum().latest_diagonal[\"CumPaidLoss\"].to_frame(\n", - " origin_as_datetime=True\n", - ").astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that we added `origin_as_datetime=True` inside of `to_frame(...)` as the package is under-going a deprecation cycle. You can also execute the same code without the `origin_as_datetime=...`, but you will get a warning." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/pandas.py:57: UserWarning: In an upcoming version of the package, `origin_as_datetime` will be defaulted to `True` in to_frame(...), use `origin_as_datetime=False` to preserve current setting.\n", - " warnings.warn(warning)\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "origin 1988 1989 1990 1991 1992 1993 1994 \\\n", - "LOB \n", - "comauto 626097 674441 718396 711762 731033 762039 768095 \n", - "medmal 217239 222707 235717 275923 267007 276235 252449 \n", - "othliab 317889 350684 361103 426085 389250 434995 402244 \n", - "ppauto 8690036 9823747 10728411 10713621 11555121 12249826 12600432 \n", - "prodliab 110973 112614 121255 100276 76059 94462 111264 \n", - "wkcomp 1241715 1308706 1394675 1414747 1328801 1187581 1114842 \n", - "\n", - "origin 1995 1996 1997 \n", - "LOB \n", - "comauto 675166 510191 272342 \n", - "medmal 209222 107474 20361 \n", - "othliab 294332 191258 54130 \n", - "ppauto 11807279 9900842 5754249 \n", - "prodliab 62018 28107 10682 \n", - "wkcomp 962081 736040 340132 " - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd.groupby(\"LOB\").sum().latest_diagonal[\"CumPaidLoss\"].to_frame().astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also aggregate process away 3 dimensions, then use `to_frame`." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LOB\n", - "comauto 718396\n", - "medmal 235717\n", - "othliab 361103\n", - "ppauto 10728411\n", - "prodliab 121255\n", - "wkcomp 1394675\n", - "dtype: int64" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[clrd.origin == \"1990\"].groupby(\"LOB\").sum().latest_diagonal[\n", - " \"CumPaidLoss\"\n", - "].to_frame(origin_as_datetime=True).astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercises\n", - "Use the `clrd` dataset for all of the exercises." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. How do we create a new column named \"NetPaidLossRatio\" in the triangle using the existing columns?" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "clrd[\"NetPaidLossRatio\"] = clrd[\"CumPaidLoss\"] / clrd[\"EarnedPremNet\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. What is the highest paid loss ratio for across all segments for origin 1997 at age 12?" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4.769123134328358" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[clrd.origin == \"1997\"][clrd.development == 12][\"NetPaidLossRatio\"].max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3. How do we subset the overall triangle to just include \"Alaska Nat Ins Co\"?" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(4, 9, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncurLoss, PaidToInc, NetPaidLossRatio]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (4, 9, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[clrd[\"GRNAME\"] == \"Alaska Nat Ins Co\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4. How do we create a triangle subset that includes all triangles for companies with names starting with the letter \"B\"?" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(31, 9, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncurLoss, PaidToInc, NetPaidLossRatio]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (31, 9, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd[clrd[\"GRNAME\"].str[0] == \"B\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "5. Which are the top 5 companies by net premium share for in 1990?" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GRNAME\n", - "State Farm Mut Grp 10532675.0\n", - "United Services Automobile Asn Grp 1378791.0\n", - "Federal Ins Co Grp 477150.0\n", - "New Jersey Manufacturers Grp 383870.0\n", - "FL Farm Bureau Grp 342036.0\n", - "dtype: float64" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd.latest_diagonal.groupby(\"GRNAME\").sum()[clrd.origin == \"1990\"][\n", - " \"EarnedPremNet\"\n", - "].to_frame(origin_as_datetime=True).sort_values(ascending=False).iloc[0:5]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initializing a Triangle With Your Own Data\n", - "The `chainladder.Triangle` class is designed to ingest `pandas.DataFrame` objects. However, you do not need to worry about shaping the dataframe into triangle format yourself. This happens at the time you ingest the data.\n", - "\n", - "Let's look at the initialization signature and its docstring." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mInit signature:\u001b[0m\n", - "\u001b[0mcl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTriangle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdevelopment\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0morigin_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdevelopment_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mcumulative\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0marray_backend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mpattern\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mtrailing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m \n", - "The core data structure of the chainladder package\n", - "\n", - "Parameters\n", - "----------\n", - "data: DataFrame\n", - " A single dataframe that contains columns represeting all other\n", - " arguments to the Triangle constructor\n", - "origin: str or list\n", - " A representation of the accident, reporting or more generally the\n", - " origin period of the triangle that will map to the Origin dimension\n", - "development: str or list\n", - " A representation of the development/valuation periods of the triangle\n", - " that will map to the Development dimension\n", - "columns: str or list\n", - " A representation of the numeric data of the triangle that will map to\n", - " the columns dimension. If None, then a single 'Total' key will be\n", - " generated.\n", - "index: str or list or None\n", - " A representation of the index of the triangle that will map to the\n", - " index dimension. If None, then a single 'Total' key will be generated.\n", - "origin_format: optional str\n", - " A string representation of the date format of the origin arg. If\n", - " omitted then date format will be inferred by pandas.\n", - "development_format: optional str\n", - " A string representation of the date format of the development arg. If\n", - " omitted then date format will be inferred by pandas.\n", - "cumulative: bool\n", - " Whether the triangle is cumulative or incremental. This attribute is\n", - " required to use the ``grain`` and ``dev_to_val`` methods and will be\n", - " automatically set when invoking ``cum_to_incr`` or ``incr_to_cum`` methods.\n", - "trailing: bool\n", - " When partial origin periods are present, setting trailing to True will\n", - " ensure the most recent origin period is a full period and the oldest\n", - " origin is partial. If full origin periods are present in the data, then\n", - " trailing has no effect.\n", - "\n", - "Attributes\n", - "----------\n", - "index: Series\n", - " Represents all available levels of the index dimension.\n", - "columns: Series\n", - " Represents all available levels of the value dimension.\n", - "origin: DatetimeIndex\n", - " Represents all available levels of the origin dimension.\n", - "development: Series\n", - " Represents all available levels of the development dimension.\n", - "key_labels: list\n", - " Represents the ``index`` axis labels\n", - "virtual_columns: Series\n", - " Represents the subset of columns of the triangle that are virtual.\n", - "valuation: DatetimeIndex\n", - " Represents all valuation dates of each cell in the Triangle.\n", - "origin_grain: str\n", - " The grain of the origin vector ('Y', 'S', 'Q', 'M')\n", - "development_grain: str\n", - " The grain of the development vector ('Y', 'S', 'Q', 'M')\n", - "shape: tuple\n", - " The 4D shape of the triangle instance with axes corresponding to (index, columns, origin, development)\n", - "link_ratio, age_to_age\n", - " Displays age-to-age ratios for the triangle.\n", - "valuation_date : date\n", - " The latest valuation date of the data\n", - "loc: Triangle\n", - " pandas-style ``loc`` accessor\n", - "iloc: Triangle\n", - " pandas-style ``iloc`` accessor\n", - "latest_diagonal: Triangle\n", - " The latest diagonal of the triangle\n", - "is_cumulative: bool\n", - " Whether the triangle is cumulative or not\n", - "is_ultimate: bool\n", - " Whether the triangle has an ultimate valuation\n", - "is_full: bool\n", - " Whether lower half of Triangle has been filled in\n", - "is_val_tri:\n", - " Whether the triangle development period is expressed as valuation\n", - " periods.\n", - "values: array\n", - " 4D numpy array underlying the Triangle instance\n", - "T: Triangle\n", - " Transpose index and columns of object. Only available when Triangle is\n", - " convertible to DataFrame.\n", - "\u001b[0;31mFile:\u001b[0m /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/triangle.py\n", - "\u001b[0;31mType:\u001b[0m type\n", - "\u001b[0;31mSubclasses:\u001b[0m \n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cl.Triangle?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's use a new dataset `prism` to construct our triangles." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ClaimNoAccidentDateReportDateLineTypeClaimLiabilityLimitDeductibleTotalPaymentPaymentDateCloseDateStatusreportedCountclosedPaidCountclosedUnPaidCountopenCountPaidOutstandingIncurred
012008-01-224/19/2010HomeDwellingFalse300000.0200000.000002010-10-0810/8/2010CLOSED10100.0000000.00000
122008-01-024/20/2010HomeDwellingFalse200000.0200000.000002010-11-3011/30/2010CLOSED10100.0000000.00000
232008-01-019/23/2009HomeDwellingTrue200000.020000115744.773702010-02-172/17/2010CLOSED1100115744.773700115744.77370
342008-01-027/25/2009HomeDwellingTrue200000.02000063678.877132009-11-1811/18/2009CLOSED110063678.87713063678.87713
452008-01-1612/7/2009HomeDwellingTrue200000.020000112175.555902010-04-304/30/2010CLOSED1100112175.555900112175.55590
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" - ], - "text/plain": [ - " ClaimNo AccidentDate ReportDate Line Type ClaimLiability Limit \\\n", - "0 1 2008-01-22 4/19/2010 Home Dwelling False 300000.0 \n", - "1 2 2008-01-02 4/20/2010 Home Dwelling False 200000.0 \n", - "2 3 2008-01-01 9/23/2009 Home Dwelling True 200000.0 \n", - "3 4 2008-01-02 7/25/2009 Home Dwelling True 200000.0 \n", - "4 5 2008-01-16 12/7/2009 Home Dwelling True 200000.0 \n", - "\n", - " Deductible TotalPayment PaymentDate CloseDate Status reportedCount \\\n", - "0 20000 0.00000 2010-10-08 10/8/2010 CLOSED 1 \n", - "1 20000 0.00000 2010-11-30 11/30/2010 CLOSED 1 \n", - "2 20000 115744.77370 2010-02-17 2/17/2010 CLOSED 1 \n", - "3 20000 63678.87713 2009-11-18 11/18/2009 CLOSED 1 \n", - "4 20000 112175.55590 2010-04-30 4/30/2010 CLOSED 1 \n", - "\n", - " closedPaidCount closedUnPaidCount openCount Paid Outstanding \\\n", - "0 0 1 0 0.00000 0 \n", - "1 0 1 0 0.00000 0 \n", - "2 1 0 0 115744.77370 0 \n", - "3 1 0 0 63678.87713 0 \n", - "4 1 0 0 112175.55590 0 \n", - "\n", - " Incurred \n", - "0 0.00000 \n", - "1 0.00000 \n", - "2 115744.77370 \n", - "3 63678.87713 \n", - "4 112175.55590 " - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_df = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/prism.csv\"\n", - ")\n", - "prism_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ClaimNo int64\n", - "AccidentDate object\n", - "ReportDate object\n", - "Line object\n", - "Type object\n", - "ClaimLiability bool\n", - "Limit float64\n", - "Deductible int64\n", - "TotalPayment float64\n", - "PaymentDate object\n", - "CloseDate object\n", - "Status object\n", - "reportedCount int64\n", - "closedPaidCount int64\n", - "closedUnPaidCount int64\n", - "openCount int64\n", - "Paid float64\n", - "Outstanding int64\n", - "Incurred float64\n", - "dtype: object" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_df.dtypes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We must specify the `origin`, `development`, `columns`, and `cumulative` to create a triangle object. By limiting our columns to one measure and not specifying an index, we can create a single triangle. For example, if we are only interested in the paid triangle. Because the data we have is transactional data, the `cumulative` variable should be set to `False`." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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"2013-06 6851.046160 41898.804443 67218.094579 97914.582813 48493.323275 31632.046719 120446.003248 33589.342398 63140.204796 71564.846290 75411.635269 80843.009094 54798.567934 79716.078008 29432.766275 46001.478449 70200.629154 43242.600876 102238.324050 53310.872961 54364.769222 31299.246162 232560.715391 6218.406140 17133.088677 515366.455560 1.538436e+05 1.606199e+05 7.995187e+05 5.126378e+05 3.193492e+06 2.132832e+06 2.456402e+06 1.378216e+06 1.400631e+06 4.828153e+05 1.291986e+05 NaN NaN NaN 19980.751516 7000.000000 NaN NaN NaN 6664.802824 NaN NaN 4945.434290 11116.651450 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-07 6782.902842 48280.849566 91311.499293 74150.344557 82262.819010 98736.828029 63087.483602 68951.825023 35752.940307 21273.949454 132177.344525 26925.746999 61660.798146 57839.394301 68521.201926 101282.440507 92303.924364 44123.390018 28977.041621 18380.124511 30097.010268 16955.457255 52771.796159 118823.692910 508910.159177 29638.996986 6.148862e+05 1.300326e+06 9.194265e+05 1.301019e+06 9.253742e+05 8.694741e+05 7.884189e+05 1.948190e+06 1.393049e+06 8.008854e+05 7.539347e+05 180000.000000 544.672072 NaN NaN NaN 5663.891540 NaN NaN 19224.174763 NaN NaN 3120.678535 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-08 21493.736878 15520.709041 60462.608119 113823.496642 96250.337738 70673.718765 52833.515895 135529.308357 99627.391574 61677.189433 110307.083592 65173.960075 47224.263162 79447.106080 39518.654241 40089.870085 60167.675982 46023.912980 60686.793205 39306.276608 40861.153269 2969.304927 165891.199558 417417.317446 348152.951950 286622.018173 1.048359e+06 2.785873e+05 1.436090e+06 9.839315e+05 9.276911e+05 2.025566e+06 1.106302e+06 1.432920e+06 1.376046e+06 9.298977e+05 5.988390e+05 20751.441887 428176.003012 14000.000000 27367.433670 5208.698183 NaN NaN NaN 18113.364750 14000.000000 NaN NaN NaN 4611.832924 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-09 10687.554570 29806.407073 67036.804484 74913.242413 37983.861100 61431.122064 119461.170674 73677.286680 105033.067693 68282.468316 112486.336092 115616.373055 50785.512645 97859.409438 60040.006441 75755.746128 44698.390708 33810.813312 30546.918445 63270.036651 29637.262930 18692.616484 378817.974245 4687.092943 149460.562961 286108.385440 7.543974e+05 4.307409e+05 1.137927e+06 1.593949e+06 1.359168e+06 1.291332e+06 8.274125e+05 1.099277e+06 4.715512e+05 1.825929e+06 5.676615e+05 325509.104363 19000.000000 11916.257459 12024.665561 NaN 22340.768270 24248.289040 7334.295552 NaN NaN 6540.121014 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - 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"2013-12 16508.968579 55117.462232 21956.205867 87867.308461 68005.873030 70145.946226 55663.787074 84040.311812 79976.440325 42145.200734 56707.222234 36945.403614 116400.145708 44294.325625 8337.139896 49437.910850 78667.587248 18079.165367 19803.871191 39941.860865 238984.087444 57841.919696 204109.009972 386664.454000 117220.879514 43709.801633 5.248744e+05 2.203477e+05 6.789611e+05 6.491085e+05 7.098767e+05 1.519165e+06 1.763656e+06 8.954874e+05 1.280474e+06 2.972427e+05 4.316323e+05 2173.166532 9744.078912 27655.254283 5155.768750 NaN 19000.000000 11884.290480 NaN 2561.507702 NaN 3262.315463 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-01 18193.615004 21745.616003 37123.325715 133688.641994 114663.352432 108694.112636 65123.078266 82659.798140 54213.211334 95810.771672 72691.211877 90497.611881 39496.394383 63116.935792 55961.095364 63921.094983 76625.933648 33921.288564 24108.316330 135790.918991 53083.886251 220750.025098 10978.363967 151287.418912 297354.581769 212117.796074 5.566719e+05 6.929247e+05 2.044277e+05 6.132758e+05 2.254386e+06 1.335350e+06 9.339803e+05 9.181987e+05 1.597495e+06 9.045387e+05 4.059007e+05 643443.135354 NaN 33327.603111 1457.147080 7780.537762 1505.277880 7000.000000 NaN 7000.000000 2200.973747 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-02 11026.197800 45821.827640 93402.484775 112069.606984 135704.201050 110294.513685 46842.315555 95022.205509 66433.409682 85841.689536 108572.446159 71043.368174 61142.947348 78813.624070 77590.425240 63514.033532 81413.768117 60865.062717 4112.207544 20858.982796 NaN 21153.034828 220320.183278 163996.451958 371253.474145 221742.157122 4.592018e+05 9.535892e+05 5.916407e+05 1.149968e+05 1.861001e+06 1.228837e+06 1.245592e+06 1.746955e+06 1.111973e+06 8.784765e+05 1.921667e+05 11242.330154 7554.100571 NaN 17332.509080 NaN 21594.097695 5916.661647 NaN 3571.597883 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-03 41856.032839 24013.075063 103901.773484 104195.373641 114878.266529 155698.857019 87384.330321 132560.629505 76107.028759 118163.694097 61297.159908 47225.304934 76461.714189 65991.709744 60794.858101 61796.942326 45437.707943 32754.713275 12519.623950 107184.643067 277040.807773 36150.536860 199393.909519 4723.559636 141956.497095 29800.642097 2.485032e+05 3.161585e+05 4.921938e+05 8.354045e+05 1.304642e+06 1.238798e+06 1.305639e+06 1.721676e+06 5.997509e+05 1.071144e+06 1.259519e+05 381231.752129 172514.759135 21333.624691 26290.760815 5955.249459 16877.510340 21924.855746 NaN 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-04 25232.184583 14000.000000 60052.904826 81449.449158 98592.403509 83581.205827 87931.778124 104719.749933 83453.671453 98973.927374 58561.568221 58437.774627 76255.738051 101390.269022 69617.702510 40110.635040 38710.569752 25366.465042 61955.884883 25009.266000 31598.187314 54048.803677 11851.394530 141845.787481 524859.850658 693237.065059 1.309337e+05 3.152621e+05 5.084428e+05 9.868936e+05 1.107499e+06 1.628701e+06 9.459133e+05 1.994376e+06 1.248361e+06 7.000000e+03 2.861492e+05 NaN NaN 3823.738205 7000.000000 NaN NaN NaN 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-05 NaN 17227.928985 27750.821450 64920.312930 52425.305222 104786.922285 51431.543443 75141.609363 94043.024894 110458.538993 91977.050437 94845.116068 42100.162714 37766.071179 70352.134251 38031.178080 27324.267980 43493.409227 73484.344127 47879.400389 40091.072625 43696.871650 197505.830961 354590.402357 796275.221621 501822.287745 9.407318e+04 2.086054e+05 8.400150e+05 5.353851e+05 1.222320e+06 9.372390e+05 1.124267e+06 1.809481e+06 7.518762e+05 1.112245e+06 7.122555e+05 5503.027815 NaN NaN NaN 19000.000000 NaN 12407.219270 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-06 NaN 32351.229848 31836.316782 66289.251427 81787.337215 107053.192243 143595.368132 59589.625334 9302.495418 76650.181977 124141.065286 83364.436760 41465.820645 86065.819186 34686.068726 55951.600356 83707.079866 55792.122597 165451.433796 60042.224137 31799.379255 22992.429547 10304.208798 47204.539329 309685.077359 295442.552497 1.044091e+06 6.892586e+05 8.172055e+05 1.459404e+06 7.957606e+05 1.894393e+06 1.258487e+06 1.485590e+06 1.291553e+06 8.570753e+05 5.338876e+05 466078.465078 NaN 295421.578684 10671.210804 17585.537431 17936.294110 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-07 17395.126262 43535.215270 94354.149357 87021.707853 92540.914175 106502.437536 59634.837614 111315.719528 94168.086970 52377.829211 58395.495132 128251.420261 80860.585272 46192.051861 37533.072122 61260.917809 56111.977217 19568.079190 52970.665605 14526.357084 45278.152241 91969.711148 316389.200871 23371.563584 352996.439150 367989.597703 4.783819e+05 4.344751e+05 8.478634e+05 1.255281e+06 6.505439e+05 1.765331e+06 1.514355e+06 1.560650e+06 8.758149e+05 9.140881e+05 3.634125e+05 NaN 8398.633653 2863.147884 28156.702124 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-08 17118.276431 42752.625229 84450.652063 114023.717706 152833.806072 114121.212678 43485.638169 218119.103107 86276.046200 62235.259926 87107.280149 105437.946739 68083.995035 83227.247222 11694.651496 40618.768296 94119.673368 86156.536419 185570.010872 58978.612847 25249.083375 160572.315930 287744.976689 147455.676455 179415.635379 386769.056379 1.047920e+06 5.811764e+05 6.703268e+05 7.402687e+05 9.899028e+05 1.345040e+06 1.048110e+06 5.172193e+05 5.718047e+05 7.412821e+05 4.745204e+05 553602.058553 NaN 4739.020481 8852.389127 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-09 11766.356540 70613.585057 90230.081727 101852.600266 76066.255398 89862.791351 151175.866617 100473.424656 103208.746962 58935.152592 97079.614731 129682.745064 57545.028781 86011.194043 52566.195198 33350.920165 77655.827773 28722.831894 36532.136163 28919.582885 60075.572549 72839.456149 21977.481030 130712.385050 481402.426382 34469.897880 1.898491e+05 1.294468e+06 7.848245e+05 1.604948e+06 7.730516e+05 1.003704e+06 2.264034e+06 9.690580e+05 2.075566e+06 8.627908e+05 4.877316e+05 560000.000000 6225.141106 14392.718433 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-10 7686.279785 47288.690974 61263.717332 58860.540843 100249.116388 60196.918544 136339.434198 104905.947161 158469.829867 120334.556147 92613.523206 86727.441392 103286.805116 49770.280858 106875.401280 60855.470633 63987.812014 37601.528987 44265.371000 28034.890801 84165.344156 18401.552009 114425.800160 18925.289270 41378.655775 29410.028093 3.377927e+05 3.458116e+05 1.406488e+06 1.178639e+06 6.524484e+05 1.298710e+06 1.201304e+06 5.332826e+05 NaN 7.457256e+05 5.735299e+05 280000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-11 NaN 40085.299101 51551.273656 20923.904293 141741.326787 111531.853596 77865.577974 65298.455492 151870.053321 82061.009567 61697.287153 93122.910777 83803.651162 43913.672786 77276.779451 43448.623639 36094.214541 68528.384644 16350.415372 182462.893770 63248.816014 299616.780888 26978.323945 159313.694759 309841.096392 465701.774370 2.721091e+05 8.752746e+05 6.730805e+05 7.601739e+05 1.433160e+06 8.290349e+05 2.283413e+06 1.305887e+06 1.464814e+06 1.178750e+06 4.047749e+05 206112.848459 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-12 2338.009450 42696.189137 56894.439643 95114.713327 45989.840560 98958.952206 76403.265792 110120.637922 142920.802261 102379.619600 60846.409583 55040.740891 46001.614251 71422.187096 29877.757318 45891.358951 24222.513506 84149.338348 35899.811225 56938.134099 298184.935001 42779.811143 36294.889113 92641.894669 11117.833168 199758.341469 3.807108e+04 6.771645e+05 5.766198e+05 1.113827e+06 1.795436e+06 1.338659e+06 1.300858e+06 4.778822e+05 1.320780e+06 3.478895e+05 6.115183e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-01 NaN 18809.131752 43643.076263 143476.365405 134608.627856 72838.540601 134335.866536 90809.617198 76374.529652 44317.805600 44963.949373 116092.938968 72389.838029 77531.956013 92559.582908 70792.363429 93422.566104 19364.960856 51135.080708 58307.591149 98557.046637 171828.994625 16357.206094 164580.481733 271290.207578 263908.608181 7.310979e+05 2.617977e+05 4.444601e+05 9.430313e+05 6.540549e+05 1.983315e+06 1.100738e+06 9.984796e+05 1.853317e+06 9.114406e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-02 12412.355755 38406.845556 33393.782768 67837.760684 133775.471117 130118.675093 63956.354202 103581.623904 102121.561858 147880.848889 94519.467458 70703.530746 78163.588148 55992.518926 33768.401630 60343.512442 55206.045005 43734.718542 84779.334566 76371.753878 154218.819587 48242.969027 27646.576463 159038.475259 10543.399407 568771.844307 7.952086e+05 4.205424e+05 7.719330e+05 9.968293e+05 1.272497e+06 1.707763e+06 1.679003e+06 7.249045e+05 5.715680e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-03 4368.139941 61775.887520 30136.005063 144623.195652 55422.389557 104909.697280 55208.760267 55274.698545 92856.578086 124600.672630 77953.801804 107434.771768 43840.814860 79732.787937 38050.018025 63033.915023 79369.665209 16071.851556 61872.697863 47902.333668 49743.103874 24480.870749 25348.099244 140844.245330 277158.288574 448419.242716 1.582105e+06 1.128602e+06 5.532564e+05 1.515552e+06 1.199394e+06 1.429829e+06 1.789496e+05 1.904034e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-04 4120.397718 44016.083166 42098.174698 80937.176395 111497.252309 101099.525247 119115.739368 128819.592112 102164.280538 96918.413806 50660.778972 72949.065053 74423.421664 27681.022975 82385.876087 32462.299607 30549.388036 87759.712991 59149.196196 61675.427831 49913.956218 158963.301057 21810.137418 10729.064205 949117.308496 278402.094726 6.002861e+05 5.903465e+05 1.671649e+06 1.222692e+06 8.994191e+05 1.634694e+06 1.214017e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-05 NaN 16343.780275 134683.935192 147597.361003 166878.862224 127495.519739 80628.987804 54868.793800 119863.863223 114562.929158 57458.774398 60464.764226 74781.341514 137108.989006 84540.621394 63839.882522 53321.314742 84600.798385 42817.982258 30342.687954 23479.032326 25995.936780 32150.807773 57185.820235 373004.903124 256129.870303 5.249663e+05 4.073522e+05 8.464785e+05 1.405456e+06 1.404862e+06 8.756084e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-06 9191.127816 34263.624861 27620.306251 139037.915115 113553.257273 93092.045776 67707.398150 159902.536216 103246.353092 190275.446707 60978.815043 105951.864825 91456.906547 51097.242328 59742.359319 41047.145476 48048.962654 65014.351742 55238.028473 53775.598296 74711.669645 65403.459055 516914.261126 338918.453160 32508.514160 24680.414930 2.730094e+05 4.430222e+05 4.957713e+05 1.260089e+06 4.740520e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-07 5872.315796 61431.291838 113522.374176 96030.478574 133266.844718 130305.038331 64905.254190 58480.126318 94984.665207 72183.521869 78983.569059 74241.045574 52909.888868 90725.673217 73757.223592 53223.803895 81529.001882 36539.587446 19516.717846 44597.550143 153507.609854 38940.929237 32784.556837 58657.933191 21181.943763 542757.405428 4.857625e+05 7.320445e+05 1.689266e+06 8.333349e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-08 12275.876950 73665.971167 69771.521645 156546.149062 96828.635293 122892.653522 77525.291302 120597.162868 42213.457509 81603.139232 64318.685029 82587.536028 70822.690426 54069.221152 86280.324966 61411.904761 29181.467011 93807.472649 10591.579043 120473.835509 44354.048698 57071.138285 55456.304615 68991.650804 208825.674298 52347.075661 5.573485e+05 4.825662e+05 1.013729e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-09 29401.764323 45522.015799 76968.040204 146839.948554 171673.679256 109429.096009 86090.895235 98514.084480 83464.884211 61289.279897 106945.334949 123620.792997 36005.695177 111817.545698 97812.012630 71550.378735 133081.814697 52059.620013 69381.213861 206638.571652 46697.251204 24755.437891 41026.125804 405652.063289 127807.538226 150591.819723 5.798513e+05 4.420908e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-10 14000.000000 18823.580135 64808.886479 86112.369720 33951.887363 94798.147426 41473.460244 114279.219218 95470.452262 95322.271315 84571.513563 87283.315644 56826.229766 46418.390772 102357.785120 36633.859700 55331.328885 61585.508226 42312.614360 19390.945153 58927.385073 104921.071975 19228.360716 156208.667108 54768.101002 306567.082809 5.968770e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-11 NaN 24865.758304 74507.452518 52944.105766 109359.561135 98135.981690 160380.544193 46511.473410 206774.167009 128625.196728 125749.283046 79550.899988 76191.422212 89872.173002 67238.315529 60955.421537 24439.191831 21047.667487 38010.055279 67547.641213 17088.640029 11994.180304 39623.869759 136232.327155 419937.900185 19556.256954 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-12 14000.000000 34498.369406 85505.412309 113845.813135 118239.229726 121277.218444 179033.274623 131329.638216 130356.868676 89312.029471 50001.263705 131720.019853 114096.053064 80505.435581 47345.897743 107853.847684 51370.371877 62388.220925 22932.368203 48682.707999 164287.941261 156485.280890 34689.496090 171021.042677 388756.671990 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-01 3213.543940 16520.684619 48605.226108 63442.108722 64330.296181 156535.525771 70535.750329 90185.961127 88647.097698 87277.722080 37585.548112 129838.454174 49775.368916 99477.619056 79483.384567 77709.064810 56914.923493 49974.636491 49813.369829 11723.025526 42432.701569 92444.945865 65657.025561 302148.945562 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-02 13891.629340 20721.691370 112166.316749 154318.566251 93667.327677 117578.257984 114468.238241 77207.158186 79019.522681 85925.357894 62818.373369 94653.274923 98626.259639 14412.050378 71903.488396 63442.861487 52747.037286 60027.653938 40008.382480 206021.036486 46890.568780 45358.088045 166532.167813 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-03 18353.550375 48494.631841 70674.791516 115885.521080 83612.153482 45276.454051 123735.672613 114338.914912 79022.075494 96179.004264 104153.365919 78420.661052 171254.111316 64222.446520 43806.575540 34980.385712 16933.353509 36318.272021 67986.442666 32546.776357 54145.301365 13493.482671 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-04 14780.520614 35741.458577 51817.548576 124194.948588 76219.573225 168821.419700 117504.571796 82978.012887 99148.493702 112663.905688 47357.387177 91977.866046 59685.252396 118258.978619 38365.804252 58137.086830 54237.801338 78418.236388 71326.721028 59282.610260 85403.308987 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-05 NaN 32920.776360 80573.646233 128677.203503 98491.267711 138235.571025 110125.330488 110887.267995 150280.984291 74848.724902 54051.819831 105564.489585 42873.921065 51942.612413 34218.518171 94820.525215 81052.688242 78267.767639 13675.949254 25982.914677 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-06 17958.638920 9031.290904 35720.570300 163643.856247 172139.047804 157652.546874 154167.597823 120193.385494 100214.093197 62572.447623 82107.496478 108154.178521 70208.521568 37141.252390 86357.027860 69921.829085 62875.428241 20423.255196 24443.879967 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-07 36454.457248 24782.897875 105234.099377 116615.976124 64114.451842 118425.785680 107282.550162 101314.686533 136164.358434 95887.527655 160987.563259 65113.829226 110219.023402 104432.987704 50962.266199 136896.654248 80842.270086 95857.222846 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-08 45280.373036 14224.178374 93540.570848 147757.945387 88369.564695 195297.446098 117493.748560 57655.840012 132907.265603 100460.044301 84736.522917 101950.878263 81994.715213 58406.357588 55984.279463 54491.161014 87532.982072 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-09 13543.632993 56468.414754 84514.463120 106895.796922 127087.926687 89314.051675 132691.450853 153691.498737 62591.301678 133208.213752 88052.362757 64577.593705 63639.041274 97667.426442 102568.832260 92328.114290 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-10 1765.824878 61195.914947 80436.515750 123346.321677 82946.887915 158136.929363 168589.126365 110018.518191 86504.029415 162549.196934 80314.320545 131253.273865 95130.219265 22490.001498 61574.451140 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-11 NaN 55837.251699 111441.725643 110741.296392 111772.167542 44847.384719 164147.485673 180125.984382 90888.981098 71973.259517 92692.770710 204372.431433 43569.263275 35989.538721 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-12 NaN 52805.659963 55386.352687 116174.812373 107610.806877 109759.341040 74635.362208 145938.259214 100703.178078 114896.817293 98215.783375 74192.406168 145322.425729 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-01 8337.875863 26000.000000 121221.366085 104138.452826 113299.545815 86085.752607 139369.099191 127013.485742 66154.704652 111713.185860 83569.214341 161872.714059 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-02 NaN 50237.487443 101651.411862 103478.579796 94797.505236 94285.239288 160239.006593 141801.930962 131584.577665 94257.374842 59566.901626 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-03 4476.248773 20775.191311 74392.325782 72249.752924 143323.397539 56307.436025 210407.467883 117053.877647 101546.367791 93076.579694 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-04 NaN 30750.642034 126807.879895 154838.817832 157090.350860 72105.725147 102587.339458 91454.397901 142688.966324 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-05 3022.335184 36811.264195 54334.199788 80436.891802 81314.614006 160837.572465 180651.804749 88441.462952 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-06 NaN 31858.167598 37910.528589 81228.553673 121618.983828 85184.796073 110103.351522 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-07 28827.335193 30987.279514 126229.848204 72825.784504 80075.315891 119390.465916 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-08 NaN 14000.000000 105952.492729 81991.210907 174870.150263 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-09 18589.176575 33316.659725 86056.984325 152143.727464 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-10 NaN 35037.196888 104444.385044 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-11 4088.116947 10599.333280 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-12 10748.025150 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism = cl.Triangle(\n", - " data=prism_df,\n", - " origin=\"AccidentDate\",\n", - " development=\"PaymentDate\",\n", - " columns=\"Paid\",\n", - " cumulative=False,\n", - ")\n", - "prism" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the lowest (most-detailed) grain supported is the monthly grain, so the triangle above is aggregated to the OMDM level." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'M'" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism.origin_grain" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'M'" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism.development_grain" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we want to include more columns or indices we can certainly do so. Note that as we do this, we move into the 4D arena changing the display of the overall object." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(1, 2, 120, 120)
Index:[Total]
Columns:[Paid, Incurred]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2017-12\n", - "Grain: OMDM\n", - "Shape: (1, 2, 120, 120)\n", - "Index: [Total]\n", - "Columns: [Paid, Incurred]" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism = cl.Triangle(\n", - " data=prism_df,\n", - " origin=\"AccidentDate\",\n", - " development=\"PaymentDate\",\n", - " columns=[\"Paid\", \"Incurred\"],\n", - " cumulative=False,\n", - ")\n", - "prism" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`Pandas` has wonderful datetime inference functionality that the `Triangle` heavily uses to infer origin and development granularity. Even still, there are occassions where date format inferences can fail. It is often better to explicitly tell the triangle the date format, and is usually good pratice to explicitly state the date format instead." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ClaimNoAccidentDateReportDateLineTypeClaimLiabilityLimitDeductibleTotalPaymentPaymentDateCloseDateStatusreportedCountclosedPaidCountclosedUnPaidCountopenCountPaidOutstandingIncurred
012008-01-224/19/2010HomeDwellingFalse300000.0200000.000002010-10-0810/8/2010CLOSED10100.0000000.00000
122008-01-024/20/2010HomeDwellingFalse200000.0200000.000002010-11-3011/30/2010CLOSED10100.0000000.00000
232008-01-019/23/2009HomeDwellingTrue200000.020000115744.773702010-02-172/17/2010CLOSED1100115744.773700115744.77370
342008-01-027/25/2009HomeDwellingTrue200000.02000063678.877132009-11-1811/18/2009CLOSED110063678.87713063678.87713
452008-01-1612/7/2009HomeDwellingTrue200000.020000112175.555902010-04-304/30/2010CLOSED1100112175.555900112175.55590
\n", - "
" - ], - "text/plain": [ - " ClaimNo AccidentDate ReportDate Line Type ClaimLiability Limit \\\n", - "0 1 2008-01-22 4/19/2010 Home Dwelling False 300000.0 \n", - "1 2 2008-01-02 4/20/2010 Home Dwelling False 200000.0 \n", - "2 3 2008-01-01 9/23/2009 Home Dwelling True 200000.0 \n", - "3 4 2008-01-02 7/25/2009 Home Dwelling True 200000.0 \n", - "4 5 2008-01-16 12/7/2009 Home Dwelling True 200000.0 \n", - "\n", - " Deductible TotalPayment PaymentDate CloseDate Status reportedCount \\\n", - "0 20000 0.00000 2010-10-08 10/8/2010 CLOSED 1 \n", - "1 20000 0.00000 2010-11-30 11/30/2010 CLOSED 1 \n", - "2 20000 115744.77370 2010-02-17 2/17/2010 CLOSED 1 \n", - "3 20000 63678.87713 2009-11-18 11/18/2009 CLOSED 1 \n", - "4 20000 112175.55590 2010-04-30 4/30/2010 CLOSED 1 \n", - "\n", - " closedPaidCount closedUnPaidCount openCount Paid Outstanding \\\n", - "0 0 1 0 0.00000 0 \n", - "1 0 1 0 0.00000 0 \n", - "2 1 0 0 115744.77370 0 \n", - "3 1 0 0 63678.87713 0 \n", - "4 1 0 0 112175.55590 0 \n", - "\n", - " Incurred \n", - "0 0.00000 \n", - "1 0.00000 \n", - "2 115744.77370 \n", - "3 63678.87713 \n", - "4 112175.55590 " - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(1, 2, 120, 120)
Index:[Total]
Columns:[Paid, Incurred]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2017-12\n", - "Grain: OMDM\n", - "Shape: (1, 2, 120, 120)\n", - "Index: [Total]\n", - "Columns: [Paid, Incurred]" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism = cl.Triangle(\n", - " data=prism_df,\n", - " origin=\"AccidentDate\",\n", - " origin_format=\"%Y-%m-%d\",\n", - " development=\"PaymentDate\",\n", - " development_format=\"%Y-%m-%d\",\n", - " columns=[\"Paid\", \"Incurred\"],\n", - " cumulative=False,\n", - ")\n", - "prism" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Up until now, we've been playing with symmetric triangles (i.e. `orgin` and `development` periods have the same grain). However, nothing precludes us from having a different grain. Often times in practice the `development` axis is more granular than the `origin` axis. All the functionality available to symmetric triangles works equally well for asymmetric triangles." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
12345678910...111112113114115116117118119120
200875,66490,814194,956300,085347,487400,011356,795598,532...7,000
200924,32780,376136,857175,737297,690358,624325,462529,832540,217...
201022,19284,802148,299200,733208,237456,692519,691604,946484,188...
201124,93460,404107,198233,346268,904401,595530,490501,836513,133...
201218,79498,320165,689296,865335,643320,954529,733592,985686,689...
201370,95999,628238,991180,138302,213477,224585,963648,734637,203...
201418,19432,772124,801276,336344,635425,875449,242541,203587,789819,083...
201531,22186,418242,766276,598418,870575,634620,098858,771787,203...
20163,21430,41287,680238,884325,065468,785521,981560,611853,548976,906...
20178,33826,000175,935226,565324,943416,752646,836649,985811,832957,490...
" - ], - "text/plain": [ - " 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120\n", - "2008 NaN NaN 75664.380214 90813.742423 194956.103471 300085.418824 347486.651714 400010.617578 356794.502531 598532.326449 641536.425988 3.983742e+05 7.370244e+05 7.671280e+05 7.341962e+05 5.731743e+05 6.011886e+05 507487.238890 5.262003e+05 454101.038664 4.592687e+05 5.712807e+05 947098.698678 9.086826e+05 3.701474e+05 1.562406e+06 2.415006e+06 2.784466e+06 2.704362e+06 4.546247e+06 4.878159e+06 6.097529e+06 6.037435e+06 1.253314e+07 8.906121e+06 1.058691e+07 7.956213e+06 9.153212e+06 1.077999e+07 1.013129e+07 7.953316e+06 9.281836e+06 5.951154e+06 6.502800e+06 3.651396e+06 2.434386e+06 8.061479e+05 1.128002e+06 317472.619664 26873.221313 75318.053240 54505.399964 40676.115005 10118.603300 11607.561736 23143.087336 7000.000000 42680.332727 30727.022825 NaN 35182.690060 11722.264203 19491.773256 8390.432613 NaN NaN 1578.971749 29980.600067 34636.956810 NaN 5932.496097 22936.033691 NaN 8351.518321 NaN 21563.293691 NaN 13274.484130 6905.158619 7051.450295 NaN NaN NaN 19000.000000 NaN NaN NaN NaN 4751.868062 7000.0 19000.0 5182.60652 NaN NaN NaN NaN NaN NaN NaN 12411.369291 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 7000.0 NaN NaN NaN NaN NaN NaN\n", - "2009 NaN 24326.814665 80376.045998 136856.648098 175736.898977 297690.337140 358624.070468 325461.635602 529831.969293 540216.723927 556830.668443 5.834332e+05 7.671930e+05 4.859745e+05 7.038561e+05 7.887551e+05 8.759424e+05 714801.340039 5.985041e+05 423752.861179 3.619214e+05 5.596524e+05 634659.052995 4.785295e+05 4.712591e+05 1.647277e+06 1.521688e+06 1.757110e+06 3.142084e+06 4.045939e+06 5.090424e+06 8.524774e+06 8.998482e+06 9.623510e+06 1.323659e+07 1.166428e+07 1.111210e+07 1.004107e+07 1.042377e+07 1.159419e+07 7.894366e+06 6.431454e+06 6.561179e+06 6.370337e+06 2.442785e+06 2.164221e+06 1.056090e+06 1.528802e+05 31965.269691 295714.535436 51422.895883 52724.444105 45404.464826 20357.031655 25607.355442 30396.154054 3462.412866 18329.878692 31737.059878 21549.657455 12265.587530 7254.649009 14735.452863 4555.892746 3911.686172 17247.273140 12347.917460 23092.780648 2222.591893 NaN NaN NaN NaN NaN NaN 1656.505479 NaN 2041.114146 NaN NaN 28594.422270 7000.00000 NaN NaN NaN NaN 7000.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5349.086903 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010 NaN 22192.478687 84802.181881 148299.169900 200733.035063 208236.728733 456691.819036 519691.226862 604945.654745 484187.788542 632780.962596 7.047602e+05 8.144448e+05 6.401543e+05 8.163333e+05 8.905976e+05 7.457935e+05 586407.574605 6.040598e+05 539012.348748 5.471848e+05 1.055450e+06 501031.917698 5.889855e+05 1.297282e+06 1.232999e+06 2.276618e+06 1.789553e+06 3.719662e+06 4.295247e+06 5.120118e+06 7.224881e+06 5.819877e+06 7.528983e+06 1.020199e+07 1.130605e+07 1.547650e+07 9.001666e+06 1.172561e+07 1.024250e+07 1.061860e+07 7.433986e+06 7.475057e+06 6.116192e+06 3.595327e+06 2.584875e+06 1.979548e+06 5.896805e+05 119130.792114 13248.866623 74152.929302 73547.340893 57667.875344 27694.390061 46271.125512 38644.000529 6774.022366 56907.252677 15184.845737 62644.193703 8365.787771 NaN 35637.752310 16363.714469 NaN 30254.175910 NaN 10027.426360 20663.718512 24368.511522 NaN NaN 12139.267537 19000.000000 NaN 16734.589225 5786.159693 NaN NaN NaN 5918.473408 NaN 7000.0 5852.411423 NaN NaN NaN 2695.500913 NaN NaN NaN NaN 7894.971774 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011 NaN 24933.634889 60404.332056 107197.618088 233345.896165 268904.260500 401595.186080 530489.997271 501835.902981 513132.913397 781921.386985 7.014708e+05 8.908045e+05 8.514825e+05 7.776805e+05 1.008484e+06 5.969821e+05 785700.336605 6.557450e+05 696571.952709 6.807257e+05 6.773898e+05 490390.383019 9.459548e+05 1.122673e+06 1.851037e+06 8.995094e+05 1.702261e+06 3.582882e+06 5.331703e+06 5.225728e+06 6.785511e+06 8.506086e+06 1.102146e+07 1.032496e+07 1.170282e+07 1.269239e+07 7.393561e+06 1.143101e+07 1.143921e+07 9.328851e+06 6.473384e+06 6.381945e+06 5.097188e+06 3.492771e+06 3.288215e+06 2.309752e+06 8.448624e+05 335629.098815 50466.914460 60748.707389 29769.789302 51247.018679 22380.244650 23877.106919 72278.522573 63465.796970 26128.125781 35426.082156 3298.677356 10717.789818 41864.867430 29560.679193 NaN 27689.634704 22594.615119 8586.632697 37348.678914 22122.313895 4684.319750 3364.271744 21296.869792 4287.447633 18459.818138 21833.460868 NaN 8618.973329 NaN 2975.908600 NaN NaN 4214.10132 NaN 12831.808143 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012 NaN 18794.078501 98319.589540 165689.365207 296864.998459 335642.536754 320954.395644 529732.563253 592985.177062 686688.855866 686447.031981 8.519169e+05 1.044212e+06 9.544724e+05 7.541892e+05 7.004901e+05 8.139560e+05 735316.703403 8.434435e+05 610979.771313 7.650731e+05 9.001753e+05 524305.875585 7.705284e+05 7.828616e+05 7.899929e+05 1.103369e+06 2.186602e+06 3.443066e+06 4.221423e+06 7.178965e+06 7.656785e+06 5.590090e+06 8.982027e+06 8.261696e+06 1.359647e+07 1.032970e+07 8.257640e+06 9.321772e+06 8.861009e+06 8.026075e+06 1.151095e+07 5.959513e+06 4.613599e+06 3.586089e+06 2.294259e+06 1.359515e+06 2.037343e+05 15753.578835 4825.817052 35572.887800 47025.437821 43406.274633 79811.940412 63600.307257 46519.756172 21303.389450 11012.139648 58720.963899 42153.387526 30589.656750 6840.626511 7168.640275 37329.362031 21325.172999 42416.968478 14000.000000 30497.072560 8423.825983 12509.598410 13281.714657 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013 NaN 70958.929207 99628.008281 238991.375169 180138.458080 302213.477793 477223.596123 585963.166386 648734.411994 637203.114079 682368.117405 9.662005e+05 7.309051e+05 7.880407e+05 9.338613e+05 8.508193e+05 9.270194e+05 711364.608560 1.126966e+06 742502.695017 5.909403e+05 8.881174e+05 706265.588894 7.213157e+05 9.861389e+05 9.881487e+05 1.225893e+06 2.064617e+06 3.226128e+06 3.337927e+06 7.487120e+06 5.812563e+06 7.699736e+06 1.429176e+07 9.297106e+06 1.339363e+07 1.017609e+07 1.108135e+07 9.348480e+06 8.782365e+06 8.332327e+06 7.631190e+06 5.444235e+06 6.958137e+06 4.240059e+06 2.547624e+06 8.930230e+05 1.256927e+06 65875.866878 49276.243262 76963.405478 95595.756248 66386.070242 44070.197828 81623.175278 13540.121014 16310.835649 25538.965303 22116.041412 5427.390291 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014 18193.615004 32771.813803 124801.186194 276336.386415 344634.732900 425874.521138 449242.485198 541203.495636 587788.640766 819083.073241 841927.820373 1.084300e+06 8.273738e+05 7.725394e+05 1.198817e+06 1.035734e+06 9.333597e+05 900446.997494 9.926431e+05 845639.783362 6.242145e+05 9.333139e+05 801369.552914 9.965165e+05 1.241265e+06 1.041576e+06 1.543091e+06 2.354836e+06 3.608809e+06 3.150000e+06 4.227372e+06 7.183240e+06 7.418863e+06 7.616530e+06 1.101130e+07 1.045179e+07 9.890481e+06 1.290310e+07 8.542991e+06 8.713766e+06 9.246068e+06 6.866563e+06 7.833314e+06 3.985606e+06 4.056370e+06 3.694519e+06 1.095143e+06 8.852198e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015 NaN 31221.487507 86418.061760 242766.433412 276598.476769 418870.290158 575633.983430 620097.757031 858771.326075 787202.598401 973846.616159 1.037602e+06 1.152322e+06 9.494363e+05 1.132752e+06 8.509203e+05 1.040219e+06 969420.903319 1.018208e+06 942013.142755 8.851064e+05 1.003449e+06 778933.792936 7.958000e+05 1.122678e+06 8.086954e+05 2.222553e+06 3.433307e+06 3.693775e+06 4.020768e+06 3.646026e+06 8.476120e+06 7.538322e+06 8.573633e+06 1.070603e+07 9.245065e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016 3213.543940 30412.313959 87680.467853 238883.577926 325065.112525 468785.338384 521980.504768 560611.271358 853548.382899 976906.136902 910871.647045 1.103004e+06 1.348982e+06 1.056142e+06 1.186347e+06 1.164726e+06 1.141416e+06 922542.939622 9.558699e+05 827461.089202 1.057683e+06 1.072619e+06 636696.163982 1.136609e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017 8337.875863 26000.000000 175935.102301 226565.055999 324943.428611 416752.154857 646836.256429 649984.979125 811832.021102 957490.473881 923912.780655 1.227946e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_df[\"AccYr\"] = prism_df[\"AccidentDate\"].str[:4]\n", - "\n", - "prism = cl.Triangle(\n", - " data=prism_df,\n", - " origin=\"AccYr\",\n", - " origin_format=\"%Y\",\n", - " development=\"PaymentDate\",\n", - " development_format=\"%Y-%m-%d\",\n", - " columns=[\"Paid\", \"Incurred\"],\n", - " cumulative=False,\n", - ")\n", - "prism[\"Paid\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While exposure triangles make sense for auditable lines such as workers' compensation lines, most other lines of business' exposures can be expressed as a 1D array (along origin period) as exposures do not develop over time. `chainladder` arithmetic requires that operations happen between a triangle and either an `int`, `float`, or another `Triangle`. To create a 1D exposure array, simply omit the `development` argument at initialization.\n", - "\n", - "The `prism` data does not consist of exposure data, but we can contrive one. Let's assume that the premium is thrice the incurred amount." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2017-12
2008-0141,872,294
2008-0243,542,684
2008-0335,920,802
2008-0430,521,592
2008-0546,807,611
2008-0635,926,236
2008-0733,305,612
2008-0835,667,952
2008-0936,773,174
2008-1036,858,772
2008-1141,939,041
2008-1234,716,883
2009-0140,435,087
2009-0251,803,670
2009-0338,273,957
2009-0444,045,041
2009-0538,573,516
2009-0634,674,998
2009-0739,372,633
2009-0841,936,689
2009-0939,120,076
2009-1033,485,416
2009-1135,884,763
2009-1235,640,359
2010-0139,187,508
2010-0236,408,113
2010-0346,100,329
2010-0439,311,907
2010-0538,617,436
2010-0638,204,654
......
2015-0718,075,734
2015-0812,204,463
2015-0911,209,737
2015-107,647,748
2015-116,591,418
2015-127,948,603
2016-015,502,819
2016-025,677,216
2016-034,541,502
2016-044,938,965
2016-054,522,476
2016-064,664,779
2016-075,134,766
2016-084,554,252
2016-094,406,520
2016-104,278,755
2016-113,955,199
2016-123,586,924
2017-013,446,326
2017-023,095,700
2017-032,680,826
2017-042,634,972
2017-052,057,550
2017-061,403,713
2017-071,375,008
2017-081,130,442
2017-09870,320
2017-10418,445
2017-1144,062
2017-1232,244
" - ], - "text/plain": [ - " 2017-12\n", - "2008-01 4.187229e+07\n", - "2008-02 4.354268e+07\n", - "2008-03 3.592080e+07\n", - "2008-04 3.052159e+07\n", - "2008-05 4.680761e+07\n", - "2008-06 3.592624e+07\n", - "2008-07 3.330561e+07\n", - "2008-08 3.566795e+07\n", - "2008-09 3.677317e+07\n", - "2008-10 3.685877e+07\n", - "2008-11 4.193904e+07\n", - "2008-12 3.471688e+07\n", - "2009-01 4.043509e+07\n", - "2009-02 5.180367e+07\n", - "2009-03 3.827396e+07\n", - "2009-04 4.404504e+07\n", - "2009-05 3.857352e+07\n", - "2009-06 3.467500e+07\n", - "2009-07 3.937263e+07\n", - "2009-08 4.193669e+07\n", - "2009-09 3.912008e+07\n", - "2009-10 3.348542e+07\n", - "2009-11 3.588476e+07\n", - "2009-12 3.564036e+07\n", - "2010-01 3.918751e+07\n", - "2010-02 3.640811e+07\n", - "2010-03 4.610033e+07\n", - "2010-04 3.931191e+07\n", - "2010-05 3.861744e+07\n", - "2010-06 3.820465e+07\n", - "2010-07 3.901837e+07\n", - "2010-08 4.156632e+07\n", - "2010-09 3.908993e+07\n", - "2010-10 4.236063e+07\n", - "2010-11 4.164149e+07\n", - "2010-12 4.410378e+07\n", - "2011-01 4.455028e+07\n", - "2011-02 4.022343e+07\n", - "2011-03 4.083951e+07\n", - "2011-04 4.290512e+07\n", - "2011-05 3.799259e+07\n", - "2011-06 4.000331e+07\n", - "2011-07 4.249876e+07\n", - "2011-08 3.861800e+07\n", - "2011-09 4.187191e+07\n", - "2011-10 4.184378e+07\n", - "2011-11 3.917826e+07\n", - "2011-12 3.694711e+07\n", - "2012-01 3.788083e+07\n", - "2012-02 4.270029e+07\n", - "2012-03 4.653762e+07\n", - "2012-04 3.785465e+07\n", - "2012-05 3.693307e+07\n", - "2012-06 3.914957e+07\n", - "2012-07 3.893848e+07\n", - "2012-08 3.586909e+07\n", - "2012-09 3.110809e+07\n", - "2012-10 4.205510e+07\n", - "2012-11 3.560778e+07\n", - "2012-12 3.382385e+07\n", - "2013-01 4.813855e+07\n", - "2013-02 3.458431e+07\n", - "2013-03 4.096955e+07\n", - "2013-04 3.508083e+07\n", - "2013-05 4.537744e+07\n", - "2013-06 4.477441e+07\n", - "2013-07 4.143155e+07\n", - "2013-08 4.576263e+07\n", - "2013-09 4.198754e+07\n", - "2013-10 4.120169e+07\n", - "2013-11 3.225811e+07\n", - "2013-12 3.345250e+07\n", - "2014-01 4.036345e+07\n", - "2014-02 3.864147e+07\n", - "2014-03 3.639684e+07\n", - "2014-04 3.578460e+07\n", - "2014-05 3.745228e+07\n", - "2014-06 4.515285e+07\n", - "2014-07 3.963638e+07\n", - "2014-08 3.671515e+07\n", - "2014-09 4.552311e+07\n", - "2014-10 3.117016e+07\n", - "2014-11 4.338274e+07\n", - "2014-12 3.469077e+07\n", - "2015-01 3.697209e+07\n", - "2015-02 3.418734e+07\n", - "2015-03 3.540646e+07\n", - "2015-04 3.213757e+07\n", - "2015-05 2.365461e+07\n", - "2015-06 1.670796e+07\n", - "2015-07 1.807573e+07\n", - "2015-08 1.220446e+07\n", - "2015-09 1.120974e+07\n", - "2015-10 7.647748e+06\n", - "2015-11 6.591418e+06\n", - "2015-12 7.948603e+06\n", - "2016-01 5.502819e+06\n", - "2016-02 5.677216e+06\n", - "2016-03 4.541502e+06\n", - "2016-04 4.938965e+06\n", - "2016-05 4.522476e+06\n", - "2016-06 4.664779e+06\n", - "2016-07 5.134766e+06\n", - "2016-08 4.554252e+06\n", - "2016-09 4.406520e+06\n", - "2016-10 4.278755e+06\n", - "2016-11 3.955199e+06\n", - "2016-12 3.586924e+06\n", - "2017-01 3.446326e+06\n", - "2017-02 3.095700e+06\n", - "2017-03 2.680826e+06\n", - "2017-04 2.634972e+06\n", - "2017-05 2.057550e+06\n", - "2017-06 1.403713e+06\n", - "2017-07 1.375008e+06\n", - "2017-08 1.130442e+06\n", - "2017-09 8.703196e+05\n", - "2017-10 4.184447e+05\n", - "2017-11 4.406235e+04\n", - "2017-12 3.224408e+04" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_df[\"Premium\"] = 3 * prism_df[\"Incurred\"]\n", - "\n", - "prism = cl.Triangle(\n", - " data=prism_df,\n", - " origin=\"AccidentDate\",\n", - " origin_format=\"%Y-%m-%d\",\n", - " columns=\"Premium\",\n", - " cumulative=False,\n", - ")\n", - "\n", - "prism" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's seperate our data by segments using `index`:" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(2, 2, 120, 120)
Index:[Line]
Columns:[Paid, Incurred]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2017-12\n", - "Grain: OMDM\n", - "Shape: (2, 2, 120, 120)\n", - "Index: [Line]\n", - "Columns: [Paid, Incurred]" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism = cl.Triangle(\n", - " data=prism_df,\n", - " origin=\"AccidentDate\",\n", - " origin_format=\"%Y-%m-%d\",\n", - " development=\"PaymentDate\",\n", - " development_format=\"%Y-%m-%d\",\n", - " columns=[\"Paid\", \"Incurred\"],\n", - " index=\"Line\",\n", - " cumulative=False,\n", - ")\n", - "prism" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can futher `index` by coverages, or sublines:" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(2, 2, 120, 120)
Index:[Line, Type]
Columns:[Paid, Incurred]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2017-12\n", - "Grain: OMDM\n", - "Shape: (2, 2, 120, 120)\n", - "Index: [Line, Type]\n", - "Columns: [Paid, Incurred]" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism = cl.Triangle(\n", - " data=prism_df,\n", - " origin=\"AccidentDate\",\n", - " origin_format=\"%Y-%m-%d\",\n", - " development=\"PaymentDate\",\n", - " development_format=\"%Y-%m-%d\",\n", - " columns=[\"Paid\", \"Incurred\"],\n", - " index=[\"Line\", \"Type\"],\n", - " cumulative=False,\n", - ")\n", - "prism" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Triangle Methods not Available in Pandas\n", - "Up until now, we've kept pretty close to the pandas API for triangle manipulation. However, there are data transformations commonly applied to triangles that don't have a nice `pandas` analogy.\n", - "\n", - "For example, we often want to convert a triangle from an incremental view into a cumulative view and vice versa. This can be accomplished with the `incr_to_cum` and `cum_to_incr` methods." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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2008-0146,91519,89957,21689,78318,30295,05410,33291,772...
2008-0228,74922,10979,03363,45559,99364,68368,50233,69571,670...
2008-0348,80627,94990,41354,55783,50712,59172,03570,35335,223...
2008-0430,75817,76370,87230,23339,49466,729122,10020,99838,218...
2008-0538,67286,97420,48358,400112,01531,35422,45753,77893,303...
2008-0656,78973,35197,84064,81661,08341,68587,17261,41863,097...
2008-0727,86745,80461,49573,27971,591111,80780,24940,63232,434...
2008-084,83223,83152,51162,64945,955133,13381,73339,709112,11521,367...
2008-0943,46486,15752,66489,92888,73855,61726,59969,52246,788...
2008-1012,48821,93953,38852,41592,08645,78172,24283,35947,594...
2008-1145,4739,41678,96784,82639,54941,01174,00033,25735,802...
2008-123,6409,90554,79287,05770,68654,77531,41244,05759,44831,643...
2009-0114,02145,35051,20835,52081,65554,27541,53874,12947,962...
2009-0210,30535,02637,28958,77367,57957,589115,08357,14265,31555,019...
2009-0348,36040,50274,34264,53849,77885,59182,58256,44988,307...
2009-0440,94347,51764,02427,57362,63454,05163,30561,33970,406...
2009-0516,94474,47863,85659,26679,70763,85759,32688,81750,656...
2009-069,65543,72012,80579,40446,21946,86836,23435,65424,67626,641...
2009-0714,82848,60590,52754,50857,88164,90516,71746,88089,721...
2009-0842,06132,24764,17463,44365,42895,43291,809131,90467,284...
2009-0921,00027,60734,67937,52356,30542,85172,40454,36667,54371,032...
2009-1014,00062,04045,13497,01654,35620,84863,52178,49467,44122,107...
2009-1122,02764,18920,83633,30350,59479,85254,870111,59270,473...
2009-1238,59417,902100,47043,866102,05156,90133,87640,41654,734...
2010-0115,19253,33857,39456,98562,17584,62484,959102,73426,495...
2010-027,00031,46432,94238,10828,707100,09197,79598,31453,25996,477...
2010-0336,93764,55161,77439,58081,25335,75187,78841,076117,835...
2010-0421,02632,42246,661103,44289,86096,97651,11689,35250,59513,981...
2010-058,6688,920116,37661,586148,64714,28846,84051,20582,7144,240...
2010-0612,57892,15644,495112,86879,73273,74361,81473,71323,724...
..................................................................
2015-075,87261,431113,52296,030133,267130,30564,90558,48094,98572,184...
2015-0812,27673,66669,772156,54696,829122,89377,525120,59742,21381,603...
2015-0929,40245,52276,968146,840171,674109,42986,09198,51483,46561,289...
2015-1014,00018,82464,80986,11233,95294,79841,473114,27995,47095,322...
2015-1124,86674,50752,944109,36098,136160,38146,511206,774128,625...
2015-1214,00034,49885,505113,846118,239121,277179,033131,330130,35789,312...
2016-013,21416,52148,60563,44264,330156,53670,53690,18688,64787,278...
2016-0213,89220,722112,166154,31993,667117,578114,46877,20779,02085,925...
2016-0318,35448,49570,675115,88683,61245,276123,736114,33979,02296,179...
2016-0414,78135,74151,818124,19576,220168,821117,50582,97899,148112,664...
2016-0532,92180,574128,67798,491138,236110,125110,887150,28174,849...
2016-0617,9599,03135,721163,644172,139157,653154,168120,193100,21462,572...
2016-0736,45424,783105,234116,61664,114118,426107,283101,315136,16495,888...
2016-0845,28014,22493,541147,75888,370195,297117,49457,656132,907100,460...
2016-0913,54456,46884,514106,896127,08889,314132,691153,69162,591133,208...
2016-101,76661,19680,437123,34682,947158,137168,589110,01986,504162,549...
2016-1155,837111,442110,741111,77244,847164,147180,12690,88971,973...
2016-1252,80655,386116,175107,611109,75974,635145,938100,703114,897...
2017-018,33826,000121,221104,138113,30086,086139,369127,01366,155111,713...
2017-0250,237101,651103,47994,79894,285160,239141,802131,58594,257...
2017-034,47620,77574,39272,250143,32356,307210,407117,054101,54693,077...
2017-0430,751126,808154,839157,09072,106102,58791,454142,689...
2017-053,02236,81154,33480,43781,315160,838180,65288,441...
2017-0631,85837,91181,229121,61985,185110,103...
2017-0728,82730,987126,23072,82680,075119,390...
2017-0814,000105,95281,991174,870...
2017-0918,58933,31786,057152,144...
2017-1035,037104,444...
2017-114,08810,599...
2017-1210,748...
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"2010-04 21025.798787 32421.851560 46660.631924 103442.075327 89860.023089 96975.875989 51116.071026 89351.703690 50594.584408 13980.927808 106144.098179 84800.868360 36229.015759 34250.291590 36505.850440 57638.316048 69578.372502 33015.749836 39651.388706 21228.610958 56240.414174 45467.260399 127542.490962 259334.605124 333066.502210 421893.028249 3.166384e+05 6.333296e+05 6.727883e+05 1.358490e+06 2.011214e+06 1.679033e+06 1.017398e+06 9.677325e+05 6.161601e+05 1.102074e+06 1.876161e+03 6157.833723 280000.000000 NaN NaN NaN NaN 3085.796448 19000.000000 NaN NaN 7853.574699 24351.559930 14000.000000 NaN NaN NaN NaN 6874.985259 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 7894.971774 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-05 8667.892025 8919.511175 116375.946230 61586.081722 148646.789430 14287.641314 46840.233175 51205.386600 82714.201546 4239.561528 53778.397507 82219.079985 32879.686630 37245.942488 11094.944503 39052.240059 22088.093176 52674.453124 49404.433106 47221.111682 53916.130777 25532.164837 390895.377549 34237.151048 322771.654242 401054.546964 5.919012e+05 5.796570e+05 2.732109e+05 1.014531e+06 8.184709e+05 2.163058e+06 2.107439e+06 1.084333e+06 7.677619e+05 9.735848e+05 1.800000e+05 7000.000000 25414.254320 13146.748060 NaN 16045.785304 NaN NaN 10230.696576 NaN 6414.503123 8718.530498 2527.228856 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 12457.448590 NaN 10027.426360 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - 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"2010-08 5699.233143 26209.224424 45890.405723 69173.188463 55511.369501 80425.939928 33133.785414 47254.211352 78262.484498 95433.303584 58511.510642 54399.763546 38357.221016 14971.133539 62398.577205 22288.793384 38439.989384 71453.867046 11709.587806 80888.734320 13328.132680 4547.917779 362751.219555 11632.329370 119223.782300 4311.879333 1.957425e+05 1.881358e+05 1.144064e+06 2.032181e+06 1.797426e+06 1.527757e+06 1.223990e+06 1.319875e+06 3.947015e+05 2.256358e+06 NaN 164940.620910 7000.000000 21616.562078 10270.396630 NaN 8297.704119 NaN NaN 7000.000000 NaN 7000.000000 NaN 725.740593 NaN 2798.187880 17104.030233 NaN NaN 14000.000000 3220.950892 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5026.874645 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-09 18547.088350 30144.612056 63287.858606 97818.468281 61706.925367 49721.448081 114628.735167 115183.502698 90869.599744 72768.554901 79515.131173 59945.902141 35075.229500 68805.706289 30444.643237 54962.652897 69113.287732 47682.350455 56285.096434 38229.711118 27948.038901 21099.470253 47091.043858 271169.549096 34123.202937 231779.596450 2.873676e+05 1.440934e+05 1.232081e+06 1.141942e+06 1.477778e+06 1.269932e+06 1.549878e+06 1.730864e+06 7.226326e+05 4.783313e+05 4.918770e+05 228667.538200 290906.103499 28146.607206 17027.618820 4951.162504 NaN NaN 13163.771626 3558.089888 3589.664771 NaN NaN 6242.821014 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 19000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - 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"2014-04 25232.184583 14000.000000 60052.904826 81449.449158 98592.403509 83581.205827 87931.778124 104719.749933 83453.671453 98973.927374 58561.568221 58437.774627 76255.738051 101390.269022 69617.702510 40110.635040 38710.569752 25366.465042 61955.884883 25009.266000 31598.187314 54048.803677 11851.394530 141845.787481 524859.850658 693237.065059 1.309337e+05 3.152621e+05 5.084428e+05 9.868936e+05 1.107499e+06 1.628701e+06 9.459133e+05 1.994376e+06 1.248361e+06 7.000000e+03 2.861492e+05 NaN NaN 3823.738205 7000.000000 NaN NaN NaN 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-05 NaN 17227.928985 27750.821450 64920.312930 52425.305222 104786.922285 51431.543443 75141.609363 94043.024894 110458.538993 91977.050437 94845.116068 42100.162714 37766.071179 70352.134251 38031.178080 27324.267980 43493.409227 73484.344127 47879.400389 40091.072625 43696.871650 197505.830961 354590.402357 796275.221621 501822.287745 9.407318e+04 2.086054e+05 8.400150e+05 5.353851e+05 1.222320e+06 9.372390e+05 1.124267e+06 1.809481e+06 7.518762e+05 1.112245e+06 7.122555e+05 5503.027815 NaN NaN NaN 19000.000000 NaN 12407.219270 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-06 NaN 32351.229848 31836.316782 66289.251427 81787.337215 107053.192243 143595.368132 59589.625334 9302.495418 76650.181977 124141.065286 83364.436760 41465.820645 86065.819186 34686.068726 55951.600356 83707.079866 55792.122597 165451.433796 60042.224137 31799.379255 22992.429547 10304.208798 47204.539329 309685.077359 295442.552497 1.044091e+06 6.892586e+05 8.172055e+05 1.459404e+06 7.957606e+05 1.894393e+06 1.258487e+06 1.485590e+06 1.291553e+06 8.570753e+05 5.338876e+05 466078.465078 NaN 295421.578684 10671.210804 17585.537431 17936.294110 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-07 17395.126262 43535.215270 94354.149357 87021.707853 92540.914175 106502.437536 59634.837614 111315.719528 94168.086970 52377.829211 58395.495132 128251.420261 80860.585272 46192.051861 37533.072122 61260.917809 56111.977217 19568.079190 52970.665605 14526.357084 45278.152241 91969.711148 316389.200871 23371.563584 352996.439150 367989.597703 4.783819e+05 4.344751e+05 8.478634e+05 1.255281e+06 6.505439e+05 1.765331e+06 1.514355e+06 1.560650e+06 8.758149e+05 9.140881e+05 3.634125e+05 NaN 8398.633653 2863.147884 28156.702124 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-08 17118.276431 42752.625229 84450.652063 114023.717706 152833.806072 114121.212678 43485.638169 218119.103107 86276.046200 62235.259926 87107.280149 105437.946739 68083.995035 83227.247222 11694.651496 40618.768296 94119.673368 86156.536419 185570.010872 58978.612847 25249.083375 160572.315930 287744.976689 147455.676455 179415.635379 386769.056379 1.047920e+06 5.811764e+05 6.703268e+05 7.402687e+05 9.899028e+05 1.345040e+06 1.048110e+06 5.172193e+05 5.718047e+05 7.412821e+05 4.745204e+05 553602.058553 NaN 4739.020481 8852.389127 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-09 11766.356540 70613.585057 90230.081727 101852.600266 76066.255398 89862.791351 151175.866617 100473.424656 103208.746962 58935.152592 97079.614731 129682.745064 57545.028781 86011.194043 52566.195198 33350.920165 77655.827773 28722.831894 36532.136163 28919.582885 60075.572549 72839.456149 21977.481030 130712.385050 481402.426382 34469.897880 1.898491e+05 1.294468e+06 7.848245e+05 1.604948e+06 7.730516e+05 1.003704e+06 2.264034e+06 9.690580e+05 2.075566e+06 8.627908e+05 4.877316e+05 560000.000000 6225.141106 14392.718433 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-10 7686.279785 47288.690974 61263.717332 58860.540843 100249.116388 60196.918544 136339.434198 104905.947161 158469.829867 120334.556147 92613.523206 86727.441392 103286.805116 49770.280858 106875.401280 60855.470633 63987.812014 37601.528987 44265.371000 28034.890801 84165.344156 18401.552009 114425.800160 18925.289270 41378.655775 29410.028093 3.377927e+05 3.458116e+05 1.406488e+06 1.178639e+06 6.524484e+05 1.298710e+06 1.201304e+06 5.332826e+05 NaN 7.457256e+05 5.735299e+05 280000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-11 NaN 40085.299101 51551.273656 20923.904293 141741.326787 111531.853596 77865.577974 65298.455492 151870.053321 82061.009567 61697.287153 93122.910777 83803.651162 43913.672786 77276.779451 43448.623639 36094.214541 68528.384644 16350.415372 182462.893770 63248.816014 299616.780888 26978.323945 159313.694759 309841.096392 465701.774370 2.721091e+05 8.752746e+05 6.730805e+05 7.601739e+05 1.433160e+06 8.290349e+05 2.283413e+06 1.305887e+06 1.464814e+06 1.178750e+06 4.047749e+05 206112.848459 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-12 2338.009450 42696.189137 56894.439643 95114.713327 45989.840560 98958.952206 76403.265792 110120.637922 142920.802261 102379.619600 60846.409583 55040.740891 46001.614251 71422.187096 29877.757318 45891.358951 24222.513506 84149.338348 35899.811225 56938.134099 298184.935001 42779.811143 36294.889113 92641.894669 11117.833168 199758.341469 3.807108e+04 6.771645e+05 5.766198e+05 1.113827e+06 1.795436e+06 1.338659e+06 1.300858e+06 4.778822e+05 1.320780e+06 3.478895e+05 6.115183e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-01 NaN 18809.131752 43643.076263 143476.365405 134608.627856 72838.540601 134335.866536 90809.617198 76374.529652 44317.805600 44963.949373 116092.938968 72389.838029 77531.956013 92559.582908 70792.363429 93422.566104 19364.960856 51135.080708 58307.591149 98557.046637 171828.994625 16357.206094 164580.481733 271290.207578 263908.608181 7.310979e+05 2.617977e+05 4.444601e+05 9.430313e+05 6.540549e+05 1.983315e+06 1.100738e+06 9.984796e+05 1.853317e+06 9.114406e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-02 12412.355755 38406.845556 33393.782768 67837.760684 133775.471117 130118.675093 63956.354202 103581.623904 102121.561858 147880.848889 94519.467458 70703.530746 78163.588148 55992.518926 33768.401630 60343.512442 55206.045005 43734.718542 84779.334566 76371.753878 154218.819587 48242.969027 27646.576463 159038.475259 10543.399407 568771.844307 7.952086e+05 4.205424e+05 7.719330e+05 9.968293e+05 1.272497e+06 1.707763e+06 1.679003e+06 7.249045e+05 5.715680e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-03 4368.139941 61775.887520 30136.005063 144623.195652 55422.389557 104909.697280 55208.760267 55274.698545 92856.578086 124600.672630 77953.801804 107434.771768 43840.814860 79732.787937 38050.018025 63033.915023 79369.665209 16071.851556 61872.697863 47902.333668 49743.103874 24480.870749 25348.099244 140844.245330 277158.288574 448419.242716 1.582105e+06 1.128602e+06 5.532564e+05 1.515552e+06 1.199394e+06 1.429829e+06 1.789496e+05 1.904034e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-04 4120.397718 44016.083166 42098.174698 80937.176395 111497.252309 101099.525247 119115.739368 128819.592112 102164.280538 96918.413806 50660.778972 72949.065053 74423.421664 27681.022975 82385.876087 32462.299607 30549.388036 87759.712991 59149.196196 61675.427831 49913.956218 158963.301057 21810.137418 10729.064205 949117.308496 278402.094726 6.002861e+05 5.903465e+05 1.671649e+06 1.222692e+06 8.994191e+05 1.634694e+06 1.214017e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-05 NaN 16343.780275 134683.935192 147597.361003 166878.862224 127495.519739 80628.987804 54868.793800 119863.863223 114562.929158 57458.774398 60464.764226 74781.341514 137108.989006 84540.621394 63839.882522 53321.314742 84600.798385 42817.982258 30342.687954 23479.032326 25995.936780 32150.807773 57185.820235 373004.903124 256129.870303 5.249663e+05 4.073522e+05 8.464785e+05 1.405456e+06 1.404862e+06 8.756084e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-06 9191.127816 34263.624861 27620.306251 139037.915115 113553.257273 93092.045776 67707.398150 159902.536216 103246.353092 190275.446707 60978.815043 105951.864825 91456.906547 51097.242328 59742.359319 41047.145476 48048.962654 65014.351742 55238.028473 53775.598296 74711.669645 65403.459055 516914.261126 338918.453160 32508.514160 24680.414930 2.730094e+05 4.430222e+05 4.957713e+05 1.260089e+06 4.740520e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-07 5872.315796 61431.291838 113522.374176 96030.478574 133266.844718 130305.038331 64905.254190 58480.126318 94984.665207 72183.521869 78983.569059 74241.045574 52909.888868 90725.673217 73757.223592 53223.803895 81529.001882 36539.587446 19516.717846 44597.550143 153507.609854 38940.929237 32784.556837 58657.933191 21181.943763 542757.405428 4.857625e+05 7.320445e+05 1.689266e+06 8.333349e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-08 12275.876950 73665.971167 69771.521645 156546.149062 96828.635293 122892.653522 77525.291302 120597.162868 42213.457509 81603.139232 64318.685029 82587.536028 70822.690426 54069.221152 86280.324966 61411.904761 29181.467011 93807.472649 10591.579043 120473.835509 44354.048698 57071.138285 55456.304615 68991.650804 208825.674298 52347.075661 5.573485e+05 4.825662e+05 1.013729e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-09 29401.764323 45522.015799 76968.040204 146839.948554 171673.679256 109429.096009 86090.895235 98514.084480 83464.884211 61289.279897 106945.334949 123620.792997 36005.695177 111817.545698 97812.012630 71550.378735 133081.814697 52059.620013 69381.213861 206638.571652 46697.251204 24755.437891 41026.125804 405652.063289 127807.538226 150591.819723 5.798513e+05 4.420908e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - 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12345678910...111112113114115116117118119120
2008-0146,91566,814124,030213,813232,115327,169337,502429,274...13,957,43113,957,43113,957,43113,957,43113,957,43113,957,43113,957,43113,957,43113,957,43113,957,431
2008-0228,74950,859129,891193,346253,339318,022386,523420,218491,888...14,514,22814,514,22814,514,22814,514,22814,514,22814,514,22814,514,22814,514,22814,514,228
2008-0348,80676,755167,168221,724305,232317,823389,858460,211495,434...11,973,60111,973,60111,973,60111,973,60111,973,60111,973,60111,973,60111,973,601
2008-0430,75848,521119,393149,626189,120255,849377,949398,947437,165...10,173,86410,173,86410,173,86410,173,86410,173,86410,173,86410,173,864
2008-0538,672125,646146,129204,529316,543347,897370,354424,131517,435...15,602,53715,602,53715,602,53715,602,53715,602,53715,602,537
2008-0656,789130,140227,980292,796353,879395,565482,736544,154607,251...11,975,41211,975,41211,975,41211,975,41211,975,412
2008-0727,86773,671135,167208,446280,037391,844472,093512,725545,159...11,101,87111,101,87111,101,87111,101,871
2008-084,83228,66381,174143,824189,779322,912404,645444,354556,469577,837...11,889,31711,889,31711,889,317
2008-0943,464129,621182,285272,213360,952416,569443,168512,690559,478...12,257,72512,257,725
2008-1012,48834,42787,815140,229232,315278,096350,338433,697481,291...12,286,257
2008-1145,47354,888133,855218,682258,231299,241373,241406,498442,300...
2008-123,64013,54468,336155,393226,080280,855312,267356,324415,772447,415...
2009-0114,02159,371110,579146,099227,754282,028323,566397,696445,657...
2009-0210,30545,33182,621141,394208,972266,561381,645438,786504,101559,120...
2009-0348,36088,861163,203227,741277,519363,110445,692502,141590,448...
2009-0440,94388,459152,483180,056242,690296,742360,047421,386491,791...
2009-0516,94491,422155,278214,543294,250358,107417,433506,250556,906...
2009-069,65553,37466,180145,584191,803238,671274,905310,559335,235361,875...
2009-0714,82863,434153,961208,469266,350331,255347,971394,851484,572...
2009-0842,06174,308138,483201,926267,354362,786454,595586,499653,783...
2009-0921,00048,60783,286120,808177,113219,965292,369346,735414,277485,309...
2009-1014,00076,040121,174218,190272,546293,395356,915435,409502,850524,957...
2009-1122,02786,215107,052140,355190,949270,801325,670437,262507,735...
2009-1238,59456,496156,966200,832302,883359,784393,660434,076488,811...
2010-0115,19268,531125,925182,910245,085329,709414,668517,402543,897...
2010-027,00038,46471,406109,514138,221238,312336,107434,422487,681584,158...
2010-0336,937101,487163,262202,842284,095319,846407,635448,711566,546...
2010-0421,02653,448100,108203,550293,410390,386441,502530,854581,449595,430...
2010-058,66817,587133,963195,549344,196358,484405,324456,529539,244543,483...
2010-0612,578104,735149,230262,098341,830415,573477,388551,101574,825...
..................................................................
2015-075,87267,304180,826276,856410,123540,428605,334663,814758,798830,982...
2015-0812,27685,942155,713312,260409,088531,981609,506730,103772,317853,920...
2015-0929,40274,924151,892298,732470,405579,835665,925764,440847,904909,194...
2015-1014,00032,82497,632183,745217,697312,495353,968468,248563,718659,040...
2015-1124,86699,373152,317261,677359,813520,193566,705773,479902,104...
2015-1214,00048,498134,004247,850366,089487,366666,399797,729928,0861,017,398...
2016-013,21419,73468,339131,782196,112352,647423,183513,369602,016689,294...
2016-0213,89234,613146,780301,098394,766512,344626,812704,019783,039868,964...
2016-0318,35466,848137,523253,408337,021382,297506,033620,372699,394795,573...
2016-0414,78150,522102,340226,534302,754471,575589,080672,058771,207883,870...
2016-0532,921113,494242,172340,663478,898589,024699,911850,192925,041...
2016-0617,95926,99062,711226,354398,493556,146710,314830,507930,721993,293...
2016-0736,45461,237166,471283,087347,202465,628572,910674,225810,389906,277...
2016-0845,28059,505153,045300,803389,173584,470701,964759,620892,527992,987...
2016-0913,54470,012154,527261,422388,510477,824610,516764,207826,799960,007...
2016-101,76662,962143,398266,745349,691507,828676,418786,436872,9401,035,489...
2016-1155,837167,279278,020389,792434,640598,787778,913869,802941,776...
2016-1252,806108,192224,367331,978441,737516,372662,311763,014877,911...
2017-018,33834,338155,559259,698372,997459,083598,452725,466791,620903,333...
2017-0250,237151,889255,367350,165444,450604,689746,491878,076972,333...
2017-034,47625,25199,644171,894315,217371,524581,932698,986800,532893,609...
2017-0430,751157,559312,397469,488541,593644,181735,635878,324...
2017-053,02239,83494,168174,605255,919416,757597,409685,850...
2017-0631,85869,769150,997272,616357,801467,904...
2017-0728,82759,815186,044258,870338,946458,336...
2017-0814,000119,952201,944376,814...
2017-0918,58951,906137,963290,107...
2017-1035,037139,482...
2017-114,08814,687...
2017-1210,748...
" - ], - "text/plain": [ - " 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120\n", - "2008-01 NaN NaN 46915.015123 66813.794095 124029.778031 213813.036115 232114.863761 327169.296655 337501.659808 4.292739e+05 4.796771e+05 5.055815e+05 5.259446e+05 5.799184e+05 6.227641e+05 6.956515e+05 7.408990e+05 7.827367e+05 8.017367e+05 8.224186e+05 8.298561e+05 8.964788e+05 1.174073e+06 1.195511e+06 1.227306e+06 2.045397e+06 2.422956e+06 3.046078e+06 3.542270e+06 3.915234e+06 5.528275e+06 7.038966e+06 8.502826e+06 1.120826e+07 1.246686e+07 1.308236e+07 1.344861e+07 1.373430e+07 1.392130e+07 1.392130e+07 1.393003e+07 1.393003e+07 1.393735e+07 1.393735e+07 1.393735e+07 1.393735e+07 1.393735e+07 1.393735e+07 1.393735e+07 1.394426e+07 1.394426e+07 1.394426e+07 1.394426e+07 1.394426e+07 1.394426e+07 1.394426e+07 1.394426e+07 1.394992e+07 1.394992e+07 1.394992e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07 1.395743e+07\n", - "2008-02 NaN 28749.365091 50858.541608 129891.171812 193345.861036 253338.970037 318021.666494 386523.335857 420217.864164 4.918880e+05 5.027340e+05 5.797263e+05 6.248450e+05 6.561054e+05 6.877342e+05 7.324803e+05 7.379179e+05 7.664770e+05 7.870821e+05 8.108586e+05 1.054594e+06 1.182256e+06 1.245914e+06 1.269630e+06 1.610775e+06 2.574606e+06 3.844160e+06 4.619005e+06 5.582045e+06 7.315823e+06 8.523665e+06 9.565918e+06 1.120352e+07 1.195874e+07 1.284232e+07 1.368227e+07 1.386227e+07 1.404227e+07 1.440227e+07 1.440227e+07 1.441627e+07 1.441627e+07 1.441627e+07 1.441627e+07 1.442327e+07 1.442327e+07 1.443674e+07 1.444604e+07 1.444604e+07 1.444873e+07 1.444873e+07 1.444873e+07 1.444873e+07 1.445334e+07 1.447234e+07 1.447234e+07 1.450174e+07 1.450174e+07 1.450174e+07 1.450174e+07 1.450174e+07 1.450174e+07 1.450174e+07 1.450174e+07 1.450174e+07 1.450174e+07 1.450174e+07 1.450742e+07 1.450742e+07 1.450742e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 NaN\n", - "2008-03 NaN 48805.786934 76755.240277 167167.815200 221724.495661 305231.865291 317823.348760 389858.357958 460211.248501 4.954341e+05 5.483588e+05 5.996805e+05 6.724811e+05 7.224684e+05 7.780023e+05 8.294898e+05 8.916120e+05 9.521292e+05 1.039422e+06 1.039990e+06 1.249532e+06 1.444610e+06 1.472516e+06 1.510659e+06 2.041926e+06 2.046461e+06 2.634575e+06 4.568893e+06 5.036836e+06 5.830768e+06 5.970527e+06 7.417107e+06 9.868766e+06 9.941854e+06 1.082229e+07 1.139791e+07 1.189091e+07 1.189279e+07 1.189279e+07 1.189279e+07 1.189379e+07 1.190276e+07 1.192723e+07 1.192723e+07 1.193898e+07 1.193898e+07 1.193898e+07 1.193898e+07 1.193898e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.196498e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197160e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 NaN NaN\n", - "2008-04 NaN 30758.035988 48520.929757 119393.019268 149625.626993 189119.953667 255848.995299 377949.004270 398946.805487 4.371651e+05 4.622643e+05 5.193805e+05 5.346946e+05 5.749489e+05 5.940823e+05 6.200823e+05 6.388159e+05 6.921848e+05 7.140977e+05 7.244041e+05 9.495755e+05 9.791133e+05 9.939603e+05 1.160635e+06 1.525297e+06 1.649316e+06 2.148392e+06 2.538246e+06 3.568434e+06 3.649220e+06 4.980251e+06 5.207339e+06 6.769557e+06 7.521467e+06 8.600802e+06 9.462043e+06 1.009061e+07 1.010755e+07 1.011460e+07 1.011460e+07 1.011460e+07 1.012976e+07 1.012976e+07 1.012976e+07 1.012976e+07 1.014394e+07 1.014932e+07 1.014932e+07 1.015632e+07 1.016088e+07 1.016088e+07 1.016088e+07 1.016399e+07 1.016399e+07 1.016399e+07 1.016399e+07 1.016399e+07 1.016399e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 NaN NaN NaN\n", - "2008-05 NaN 38672.002824 125645.996032 146128.738080 204528.632057 316543.260018 347897.029551 370353.736152 424131.312183 5.174347e+05 5.845825e+05 6.407680e+05 6.982131e+05 7.457809e+05 7.878126e+05 8.385251e+05 9.312740e+05 9.682768e+05 1.023066e+06 1.038387e+06 1.095324e+06 1.099322e+06 1.288538e+06 1.577088e+06 1.945239e+06 2.371558e+06 2.703630e+06 3.645908e+06 4.562933e+06 5.408148e+06 6.726099e+06 8.690866e+06 1.022057e+07 1.069539e+07 1.325201e+07 1.447174e+07 1.476643e+07 1.524805e+07 1.554674e+07 1.554674e+07 1.555273e+07 1.555696e+07 1.556396e+07 1.556513e+07 1.556513e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.557396e+07 1.557396e+07 1.557396e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 NaN NaN NaN NaN\n", - "2008-06 NaN 56788.951887 130139.935043 227979.947798 292796.177958 353879.350283 395564.597226 482736.376780 544154.045979 6.072512e+05 6.272579e+05 6.743334e+05 7.266210e+05 7.408913e+05 7.636275e+05 7.800010e+05 8.201195e+05 8.438632e+05 9.314041e+05 9.476107e+05 1.049446e+06 1.057185e+06 1.136050e+06 1.160931e+06 1.170714e+06 1.359927e+06 1.529584e+06 2.390170e+06 3.678164e+06 5.134112e+06 6.334022e+06 7.050686e+06 8.452380e+06 9.900435e+06 1.073379e+07 1.153280e+07 1.173383e+07 1.173383e+07 1.191383e+07 1.191899e+07 1.191899e+07 1.193032e+07 1.193032e+07 1.193032e+07 1.193032e+07 1.194617e+07 1.195158e+07 1.195158e+07 1.195158e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 NaN NaN NaN NaN NaN\n", - "2008-07 NaN 27867.438010 73671.186639 135166.601705 208445.856653 280036.614963 391843.736734 472092.885838 512724.773221 5.451587e+05 5.971282e+05 6.319173e+05 6.856040e+05 7.281393e+05 7.685142e+05 7.827472e+05 8.022870e+05 8.249618e+05 8.338679e+05 8.643874e+05 8.853149e+05 8.920278e+05 9.202900e+05 1.016942e+06 1.030942e+06 1.165921e+06 1.609253e+06 2.992635e+06 3.503504e+06 4.848671e+06 5.313555e+06 6.048233e+06 8.015246e+06 9.067337e+06 1.009970e+07 1.080196e+07 1.099157e+07 1.099857e+07 1.100207e+07 1.100207e+07 1.100207e+07 1.101282e+07 1.101282e+07 1.101282e+07 1.104242e+07 1.105305e+07 1.105531e+07 1.106543e+07 1.106543e+07 1.106646e+07 1.106646e+07 1.106646e+07 1.107403e+07 1.107403e+07 1.108002e+07 1.108002e+07 1.108002e+07 1.108002e+07 1.108002e+07 1.108002e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 NaN NaN NaN NaN NaN NaN\n", - "2008-08 4832.347658 28663.352169 81174.325149 143823.753969 189778.775105 322911.952061 404644.628455 444354.082037 556469.471645 5.778366e+05 5.972233e+05 6.339826e+05 6.655116e+05 7.068361e+05 7.171831e+05 7.421081e+05 7.475395e+05 7.781453e+05 8.136729e+05 8.352770e+05 8.501195e+05 1.000239e+06 1.163244e+06 1.173219e+06 1.209483e+06 1.473016e+06 2.240575e+06 2.787657e+06 3.333590e+06 4.434400e+06 5.584938e+06 6.704678e+06 8.343224e+06 9.018195e+06 1.062627e+07 1.111357e+07 1.167463e+07 1.183841e+07 1.183841e+07 1.185003e+07 1.185003e+07 1.185003e+07 1.185003e+07 1.186403e+07 1.186403e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 NaN NaN NaN NaN NaN NaN NaN\n", - "2008-09 NaN 43464.271322 129621.055093 182285.166689 272213.301731 360951.618287 416569.049140 443168.263249 512689.862488 5.594782e+05 6.783411e+05 7.202981e+05 7.572994e+05 7.803084e+05 8.243695e+05 8.941447e+05 9.111255e+05 9.622760e+05 9.947346e+05 9.947346e+05 1.006547e+06 1.033435e+06 1.045216e+06 1.240862e+06 1.618396e+06 2.277027e+06 2.375991e+06 3.140532e+06 3.865015e+06 5.102801e+06 5.522613e+06 7.034454e+06 8.635246e+06 1.008857e+07 1.082684e+07 1.137027e+07 1.173546e+07 1.219762e+07 1.220238e+07 1.220238e+07 1.220238e+07 1.221209e+07 1.221209e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.223034e+07 1.223416e+07 1.223416e+07 1.223672e+07 1.223672e+07 1.223672e+07 1.223672e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225772e+07 1.225772e+07 1.225772e+07 1.225772e+07 1.225772e+07 1.225772e+07 1.225772e+07 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2008-10 NaN 12487.697845 34426.791815 87814.693479 140229.326978 232315.157186 278095.749566 350337.902162 433697.325778 4.812914e+05 5.301353e+05 5.392550e+05 5.778721e+05 6.435479e+05 7.366678e+05 7.499255e+05 8.160420e+05 8.695962e+05 9.143625e+05 9.488226e+05 9.558226e+05 9.826618e+05 1.006112e+06 1.290040e+06 1.433842e+06 1.534245e+06 1.995597e+06 2.553863e+06 3.169586e+06 3.682326e+06 4.892921e+06 6.387080e+06 7.884095e+06 9.483269e+06 1.084657e+07 1.138943e+07 1.190921e+07 1.222433e+07 1.222433e+07 1.222433e+07 1.222433e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.223678e+07 1.223678e+07 1.223678e+07 1.224172e+07 1.224172e+07 1.224172e+07 1.224172e+07 1.224172e+07 1.224172e+07 1.224172e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2008-11 NaN 45472.755295 54888.373254 133855.398409 218681.672120 258230.577504 299241.312490 373241.011129 406497.852661 4.422999e+05 4.611072e+05 4.912394e+05 5.312573e+05 5.901824e+05 6.714825e+05 6.936988e+05 7.298105e+05 7.762239e+05 8.271901e+05 8.497877e+05 9.093462e+05 9.236183e+05 9.369501e+05 1.231004e+06 1.285912e+06 1.686881e+06 1.695053e+06 2.587116e+06 3.417424e+06 4.425015e+06 6.040233e+06 8.030328e+06 9.844783e+06 1.101158e+07 1.236885e+07 1.303878e+07 1.335224e+07 1.391896e+07 1.393296e+07 1.393296e+07 1.394105e+07 1.394105e+07 1.394105e+07 1.394105e+07 1.394105e+07 1.394105e+07 1.394805e+07 1.394805e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395769e+07 1.397382e+07 1.397382e+07 1.397382e+07 1.397382e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2008-12 3639.502909 13544.208461 68336.048127 155393.350775 226079.814261 280854.909998 312267.261509 356324.270548 415771.824145 4.474146e+05 4.923965e+05 5.416381e+05 5.921881e+05 6.251871e+05 6.640022e+05 6.780640e+05 7.205090e+05 7.730488e+05 7.976530e+05 8.277561e+05 8.660880e+05 1.017595e+06 1.049446e+06 1.176880e+06 1.946762e+06 1.961451e+06 2.486723e+06 2.691175e+06 3.358255e+06 3.774708e+06 5.102067e+06 5.917394e+06 8.589631e+06 9.757664e+06 1.052896e+07 1.066007e+07 1.119596e+07 1.147596e+07 1.147596e+07 1.147596e+07 1.147596e+07 1.149195e+07 1.149195e+07 1.149195e+07 1.149195e+07 1.149195e+07 1.149561e+07 1.150768e+07 1.150768e+07 1.151406e+07 1.151406e+07 1.151406e+07 1.151988e+07 1.151988e+07 1.151988e+07 1.151988e+07 1.152924e+07 1.154824e+07 1.154824e+07 1.154824e+07 1.154824e+07 1.154824e+07 1.155659e+07 1.155659e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-01 NaN 14021.486015 59371.444785 110579.070674 146098.773783 227753.601115 282028.358920 323566.201835 397695.547254 4.456574e+05 5.015892e+05 5.669825e+05 6.309729e+05 6.854833e+05 7.483043e+05 7.959443e+05 7.959443e+05 8.265397e+05 8.647928e+05 8.769435e+05 9.083034e+05 9.206849e+05 9.568760e+05 9.989937e+05 1.004197e+06 1.219933e+06 1.643390e+06 2.099041e+06 3.590344e+06 5.143511e+06 6.094230e+06 7.247691e+06 8.302948e+06 1.008292e+07 1.133991e+07 1.278914e+07 1.322562e+07 1.339307e+07 1.340007e+07 1.341913e+07 1.342613e+07 1.344013e+07 1.344013e+07 1.344253e+07 1.345425e+07 1.345425e+07 1.345425e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346846e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-02 10305.328650 45331.415878 82620.776579 141393.638930 208972.295172 266561.450681 381644.589580 438786.125012 504100.892148 5.591196e+05 6.059468e+05 6.904966e+05 7.320151e+05 8.393178e+05 8.666051e+05 9.384845e+05 1.008686e+06 1.021074e+06 1.041079e+06 1.071899e+06 1.119428e+06 1.139939e+06 1.144219e+06 1.169127e+06 1.447555e+06 1.670682e+06 1.796705e+06 2.648363e+06 2.872013e+06 3.960665e+06 5.653328e+06 8.767975e+06 1.066698e+07 1.354423e+07 1.551456e+07 1.592077e+07 1.610651e+07 1.666651e+07 1.694651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.723602e+07 1.723602e+07 1.725147e+07 1.725147e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-03 NaN 48359.661508 88861.303637 163202.894412 227741.355370 277519.417249 363110.487751 445692.217863 502140.847640 5.904475e+05 6.277867e+05 6.650249e+05 7.299666e+05 7.495817e+05 8.013200e+05 8.337004e+05 8.460156e+05 8.579232e+05 8.830195e+05 9.162880e+05 1.084062e+06 1.115314e+06 1.286074e+06 1.506419e+06 1.506419e+06 1.752266e+06 2.216819e+06 3.082124e+06 3.552359e+06 5.131311e+06 6.244795e+06 7.479860e+06 9.032015e+06 1.044146e+07 1.142125e+07 1.212187e+07 1.272204e+07 1.272204e+07 1.272530e+07 1.272530e+07 1.272530e+07 1.273515e+07 1.273918e+07 1.274092e+07 1.274320e+07 1.274320e+07 1.274320e+07 1.274320e+07 1.274320e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-04 NaN 40942.691388 88459.201714 152483.051146 180056.368888 242690.185901 296741.643219 360047.083766 421385.604356 4.917912e+05 5.210717e+05 5.810379e+05 6.661736e+05 7.557554e+05 8.327863e+05 8.670049e+05 9.191808e+05 9.346273e+05 9.763242e+05 1.018561e+06 1.067710e+06 1.106159e+06 1.516181e+06 1.700887e+06 1.978208e+06 2.013377e+06 2.197165e+06 3.198193e+06 4.708962e+06 5.883665e+06 6.293452e+06 8.161962e+06 9.567093e+06 1.101763e+07 1.214080e+07 1.301418e+07 1.443712e+07 1.464745e+07 1.464745e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466569e+07 1.467395e+07 1.467395e+07 1.467395e+07 1.467395e+07 1.467395e+07 1.467395e+07 1.468008e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-05 NaN 16943.995031 91422.237490 155277.776640 214543.369647 294249.972625 358106.808695 417432.917056 506249.809112 5.569062e+05 5.733751e+05 6.279816e+05 6.842673e+05 7.387687e+05 7.779736e+05 8.330171e+05 8.478414e+05 8.671377e+05 9.642019e+05 9.758671e+05 9.916414e+05 1.049927e+06 1.384055e+06 1.756717e+06 1.942920e+06 1.958237e+06 2.169146e+06 3.190747e+06 4.576051e+06 6.036987e+06 6.944497e+06 7.450359e+06 9.152315e+06 1.035728e+07 1.153600e+07 1.212329e+07 1.248140e+07 1.276140e+07 1.278040e+07 1.278898e+07 1.279598e+07 1.281498e+07 1.281498e+07 1.283398e+07 1.283398e+07 1.283398e+07 1.283889e+07 1.283889e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-06 9654.757434 53374.361739 66179.663536 145584.002432 191803.068685 238671.184931 274905.215675 310559.330253 335234.966257 3.618755e+05 4.823756e+05 6.131614e+05 7.019967e+05 7.710071e+05 7.898330e+05 8.034114e+05 8.283521e+05 8.473208e+05 9.199272e+05 9.441240e+05 9.865883e+05 1.069848e+06 1.203248e+06 1.217805e+06 1.902551e+06 2.562978e+06 3.154397e+06 3.346111e+06 4.668753e+06 5.474823e+06 6.452820e+06 7.975885e+06 8.894291e+06 9.876455e+06 1.021350e+07 1.099451e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.150846e+07 1.151546e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-07 NaN 14828.433220 63433.822772 153961.033084 208468.889255 266349.529233 331254.681602 347971.496960 394851.242928 4.845718e+05 5.650205e+05 6.518535e+05 6.932173e+05 7.020563e+05 7.341949e+05 7.790193e+05 8.038361e+05 8.429726e+05 8.537775e+05 1.124488e+06 1.182916e+06 1.185770e+06 1.199899e+06 1.308624e+06 1.578925e+06 1.831957e+06 2.017518e+06 2.228647e+06 3.488772e+06 5.308304e+06 7.221543e+06 8.254964e+06 9.166061e+06 1.140133e+07 1.224734e+07 1.285849e+07 1.303281e+07 1.304001e+07 1.304001e+07 1.306155e+07 1.306650e+07 1.307522e+07 1.307522e+07 1.307522e+07 1.308029e+07 1.308029e+07 1.310235e+07 1.311146e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-08 NaN 42060.879472 74308.207021 138482.705665 201925.828181 267353.734828 362785.812915 454595.027216 586499.084445 6.537828e+05 6.779625e+05 7.591168e+05 7.908580e+05 8.139143e+05 8.948135e+05 9.215840e+05 9.530409e+05 9.703950e+05 1.013849e+06 1.051244e+06 1.051244e+06 1.080010e+06 1.260010e+06 1.484249e+06 1.490416e+06 1.846818e+06 2.504589e+06 3.556965e+06 4.336342e+06 5.516612e+06 7.522441e+06 8.157652e+06 9.327930e+06 1.113169e+07 1.203414e+07 1.311398e+07 1.376114e+07 1.389873e+07 1.391670e+07 1.391670e+07 1.392324e+07 1.392745e+07 1.392745e+07 1.392745e+07 1.392745e+07 1.392745e+07 1.392745e+07 1.392745e+07 1.393858e+07 1.393858e+07 1.393858e+07 1.394340e+07 1.395822e+07 1.395822e+07 1.396165e+07 1.396165e+07 1.396165e+07 1.396165e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-09 21000.000000 48606.750645 83285.571507 120808.491963 177113.398012 219964.714007 292368.997629 346734.626390 414277.239627 4.853089e+05 5.883301e+05 6.526994e+05 6.617424e+05 7.336440e+05 7.537455e+05 8.028366e+05 8.549329e+05 8.804500e+05 9.342814e+05 9.492684e+05 9.529178e+05 9.624659e+05 1.097772e+06 1.477267e+06 1.482648e+06 1.912293e+06 2.722875e+06 2.840940e+06 3.579894e+06 4.988712e+06 6.981906e+06 9.298142e+06 1.057529e+07 1.113809e+07 1.236229e+07 1.280326e+07 1.280326e+07 1.297918e+07 1.299837e+07 1.300127e+07 1.301080e+07 1.301080e+07 1.301519e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-10 14000.000000 76039.934850 121174.024940 218190.328235 272546.475900 293394.807472 356915.447258 435408.980482 502849.808060 5.249569e+05 5.627629e+05 5.869442e+05 6.396963e+05 6.847946e+05 7.194251e+05 7.424249e+05 7.691565e+05 8.181874e+05 8.331508e+05 8.416259e+05 1.036796e+06 1.055796e+06 1.074686e+06 1.103095e+06 1.137368e+06 1.880577e+06 2.750372e+06 3.168263e+06 3.601795e+06 4.509291e+06 5.666306e+06 7.033016e+06 7.749980e+06 8.858300e+06 9.898297e+06 1.061573e+07 1.086048e+07 1.114392e+07 1.114392e+07 1.114392e+07 1.114392e+07 1.115432e+07 1.115584e+07 1.115584e+07 1.115584e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-11 NaN 22026.547150 86215.491450 107051.511211 140354.912560 190948.789914 270800.504739 325670.175072 437261.737161 5.077352e+05 5.953774e+05 6.391908e+05 6.797463e+05 7.519877e+05 7.983108e+05 8.186670e+05 8.487393e+05 8.964305e+05 9.142940e+05 9.370984e+05 9.579518e+05 1.259320e+06 1.617367e+06 1.782933e+06 1.856815e+06 2.128450e+06 2.325060e+06 2.662016e+06 3.400874e+06 4.407815e+06 4.827843e+06 6.111492e+06 7.449114e+06 1.039874e+07 1.084195e+07 1.177540e+07 1.178940e+07 1.187333e+07 1.188204e+07 1.188204e+07 1.188204e+07 1.188204e+07 1.188436e+07 1.189296e+07 1.190946e+07 1.190946e+07 1.190946e+07 1.191523e+07 1.192223e+07 1.192223e+07 1.193450e+07 1.193832e+07 1.194303e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009-12 NaN 38593.890837 56496.390306 156966.409082 200832.007436 302883.169353 359784.297023 393660.259514 434076.185488 4.888106e+05 5.751590e+05 6.697303e+05 7.106325e+05 7.530221e+05 7.882492e+05 8.325017e+05 8.982119e+05 9.239700e+05 9.276885e+05 9.664426e+05 9.833986e+05 1.012972e+06 1.030696e+06 1.058622e+06 1.146511e+06 1.313611e+06 1.835769e+06 2.872244e+06 3.934356e+06 4.475365e+06 6.030671e+06 7.634509e+06 8.899059e+06 1.002086e+07 1.077070e+07 1.150293e+07 1.152834e+07 1.152834e+07 1.181524e+07 1.181524e+07 1.181576e+07 1.182076e+07 1.182341e+07 1.182341e+07 1.183486e+07 1.183486e+07 1.184129e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-01 NaN 15192.478687 68530.905343 125925.359623 182910.440134 245085.129610 329709.058011 414668.308627 517401.824085 5.438970e+05 5.725160e+05 6.512245e+05 7.263250e+05 7.827737e+05 8.342398e+05 8.919351e+05 9.386821e+05 1.039243e+06 1.047679e+06 1.096746e+06 1.119723e+06 1.655309e+06 1.678486e+06 1.709990e+06 1.914689e+06 2.110134e+06 2.956936e+06 3.313306e+06 4.834482e+06 5.844906e+06 7.090529e+06 8.623409e+06 1.005589e+07 1.097883e+07 1.170447e+07 1.228071e+07 1.277474e+07 1.279374e+07 1.280171e+07 1.281133e+07 1.302937e+07 1.303703e+07 1.303703e+07 1.304234e+07 1.304234e+07 1.304234e+07 1.304234e+07 1.305476e+07 1.305476e+07 1.305476e+07 1.305476e+07 1.305476e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-02 7000.000000 38463.755225 71405.930126 109513.586548 138221.020801 238312.341665 336107.394308 434421.873414 487680.694486 5.841582e+05 6.116884e+05 7.064166e+05 7.558891e+05 7.904674e+05 8.338100e+05 8.772753e+05 8.987391e+05 9.137035e+05 9.706375e+05 9.934472e+05 1.021625e+06 1.089200e+06 1.089200e+06 1.662665e+06 1.908055e+06 2.171196e+06 2.251818e+06 2.957715e+06 3.608963e+06 4.745549e+06 5.903183e+06 7.466563e+06 8.333997e+06 9.548416e+06 1.040240e+07 1.148569e+07 1.203255e+07 1.203888e+07 1.203888e+07 1.203888e+07 1.205288e+07 1.206676e+07 1.206676e+07 1.208231e+07 1.208231e+07 1.208647e+07 1.209222e+07 1.209222e+07 1.209222e+07 1.209222e+07 1.209875e+07 1.209875e+07 1.211277e+07 1.211277e+07 1.211720e+07 1.211720e+07 1.211720e+07 1.211720e+07 1.211720e+07 1.211720e+07 1.211720e+07 1.212842e+07 1.212842e+07 1.212842e+07 1.212842e+07 1.212842e+07 1.212842e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-03 NaN 36936.741932 101487.296477 163261.758382 202841.816717 284095.256613 319846.477099 407634.879837 448711.322288 5.665459e+05 6.511617e+05 6.945765e+05 7.652033e+05 8.336253e+05 8.700769e+05 8.796623e+05 9.498897e+05 9.928409e+05 1.065584e+06 1.078214e+06 1.102201e+06 1.146306e+06 1.205784e+06 1.603525e+06 1.817033e+06 2.398648e+06 2.967450e+06 4.071580e+06 4.638323e+06 6.011081e+06 6.167047e+06 7.408491e+06 1.008166e+07 1.137128e+07 1.360160e+07 1.441862e+07 1.527065e+07 1.527496e+07 1.528521e+07 1.529116e+07 1.529154e+07 1.529154e+07 1.529154e+07 1.529154e+07 1.530354e+07 1.530354e+07 1.530354e+07 1.530354e+07 1.531734e+07 1.531734e+07 1.531734e+07 1.531734e+07 1.531734e+07 1.531734e+07 1.531734e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.535154e+07 1.535978e+07 1.535978e+07 1.535978e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-04 21025.798787 53447.650347 100108.282271 203550.357598 293410.380687 390386.256676 441502.327702 530854.031392 581448.615800 5.954295e+05 7.015736e+05 7.863745e+05 8.226035e+05 8.568538e+05 8.933597e+05 9.509980e+05 1.020576e+06 1.053592e+06 1.093243e+06 1.114472e+06 1.170713e+06 1.216180e+06 1.343722e+06 1.603057e+06 1.936123e+06 2.358016e+06 2.674655e+06 3.307984e+06 3.980773e+06 5.339263e+06 7.350477e+06 9.029510e+06 1.004691e+07 1.101464e+07 1.163080e+07 1.273287e+07 1.273475e+07 1.274091e+07 1.302091e+07 1.302091e+07 1.302091e+07 1.302091e+07 1.302091e+07 1.302399e+07 1.304299e+07 1.304299e+07 1.304299e+07 1.305085e+07 1.307520e+07 1.308920e+07 1.308920e+07 1.308920e+07 1.308920e+07 1.308920e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.310397e+07 1.310397e+07 1.310397e+07 1.310397e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-05 8667.892025 17587.403200 133963.349430 195549.431152 344196.220582 358483.861896 405324.095071 456529.481671 539243.683217 5.434832e+05 5.972616e+05 6.794807e+05 7.123604e+05 7.496064e+05 7.607013e+05 7.997535e+05 8.218416e+05 8.745161e+05 9.239205e+05 9.711416e+05 1.025058e+06 1.050590e+06 1.441485e+06 1.475722e+06 1.798494e+06 2.199549e+06 2.791450e+06 3.371107e+06 3.644318e+06 4.658849e+06 5.477320e+06 7.640377e+06 9.747816e+06 1.083215e+07 1.159991e+07 1.257350e+07 1.275350e+07 1.276050e+07 1.278591e+07 1.279906e+07 1.279906e+07 1.281510e+07 1.281510e+07 1.281510e+07 1.282533e+07 1.282533e+07 1.283175e+07 1.284047e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.285545e+07 1.285545e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-06 NaN 12578.489879 104734.811973 149229.514700 262097.759079 341830.188298 415573.168758 477387.655861 551101.021689 5.748247e+05 6.811285e+05 7.399254e+05 7.887626e+05 8.576940e+05 8.930362e+05 9.406751e+05 9.797502e+05 1.038560e+06 1.070367e+06 1.078487e+06 1.136077e+06 1.162903e+06 1.342387e+06 1.385391e+06 1.487523e+06 1.929807e+06 2.432781e+06 2.880149e+06 3.494679e+06 5.086449e+06 6.698690e+06 8.734897e+06 9.467942e+06 1.056977e+07 1.148323e+07 1.223821e+07 1.263825e+07 1.264580e+07 1.264580e+07 1.265975e+07 1.265975e+07 1.267183e+07 1.268258e+07 1.268258e+07 1.268258e+07 1.268958e+07 1.269602e+07 1.269602e+07 1.269602e+07 1.269602e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271709e+07 1.271709e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-07 NaN 6381.823659 39654.582433 101993.041439 165489.117040 263829.530954 360199.553821 421473.795777 572091.437047 6.481498e+05 7.122969e+05 7.689687e+05 7.849470e+05 8.150021e+05 8.609778e+05 9.022331e+05 9.305704e+05 9.685439e+05 9.961908e+05 1.012279e+06 1.056738e+06 1.077857e+06 1.089187e+06 1.297185e+06 1.538361e+06 2.306672e+06 2.809188e+06 2.998837e+06 3.178789e+06 4.476703e+06 6.517327e+06 6.998929e+06 9.267948e+06 1.085478e+07 1.227655e+07 1.245655e+07 1.290615e+07 1.292015e+07 1.292859e+07 1.294259e+07 1.294259e+07 1.295060e+07 1.297422e+07 1.297422e+07 1.297422e+07 1.298838e+07 1.299129e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-08 5699.233143 31908.457567 77798.863290 146972.051753 202483.421254 282909.361182 316043.146596 363297.357948 441559.842446 5.369931e+05 5.955047e+05 6.499044e+05 6.882616e+05 7.032328e+05 7.656314e+05 7.879201e+05 8.263601e+05 8.978140e+05 9.095236e+05 9.904123e+05 1.003740e+06 1.008288e+06 1.371040e+06 1.382672e+06 1.501896e+06 1.506208e+06 1.701950e+06 1.890086e+06 3.034150e+06 5.066331e+06 6.863757e+06 8.391515e+06 9.615505e+06 1.093538e+07 1.133008e+07 1.358644e+07 1.358644e+07 1.375138e+07 1.375838e+07 1.378000e+07 1.379027e+07 1.379027e+07 1.379856e+07 1.379856e+07 1.379856e+07 1.380556e+07 1.380556e+07 1.381256e+07 1.381256e+07 1.381329e+07 1.381329e+07 1.381609e+07 1.383319e+07 1.383319e+07 1.383319e+07 1.384719e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-09 18547.088350 48691.700406 111979.559012 209798.027293 271504.952660 321226.400741 435855.135908 551038.638606 641908.238350 7.146768e+05 7.941919e+05 8.541378e+05 8.892131e+05 9.580188e+05 9.884634e+05 1.043426e+06 1.112539e+06 1.160222e+06 1.216507e+06 1.254737e+06 1.282685e+06 1.303784e+06 1.350875e+06 1.622045e+06 1.656168e+06 1.887947e+06 2.175315e+06 2.319408e+06 3.551489e+06 4.693431e+06 6.171209e+06 7.441141e+06 8.991020e+06 1.072188e+07 1.144452e+07 1.192285e+07 1.241472e+07 1.264339e+07 1.293430e+07 1.296244e+07 1.297947e+07 1.298442e+07 1.298442e+07 1.298442e+07 1.299759e+07 1.300115e+07 1.300473e+07 1.300473e+07 1.300473e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-10 NaN 54726.513076 85405.854628 163756.093451 207564.918242 279292.938246 346942.548955 426523.966191 501958.854821 5.408842e+05 5.899772e+05 6.237525e+05 6.724372e+05 7.092039e+05 8.048044e+05 8.643060e+05 8.943391e+05 9.444745e+05 1.004661e+06 1.040223e+06 1.058083e+06 1.132911e+06 1.167192e+06 1.184052e+06 1.392052e+06 1.863881e+06 2.841982e+06 3.026758e+06 3.545800e+06 4.756933e+06 6.673352e+06 8.655088e+06 1.049613e+07 1.119918e+07 1.213215e+07 1.288093e+07 1.374539e+07 1.403413e+07 1.403413e+07 1.406010e+07 1.406010e+07 1.406010e+07 1.406010e+07 1.406010e+07 1.406010e+07 1.406776e+07 1.406776e+07 1.406776e+07 1.408381e+07 1.409620e+07 1.409620e+07 1.409620e+07 1.409620e+07 1.409620e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410850e+07 1.410850e+07 1.410850e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-11 NaN 13508.711378 54321.133799 124198.585581 199640.089461 255934.626113 320190.932099 364145.776831 482359.203401 5.000009e+05 6.347390e+05 7.163326e+05 7.851899e+05 8.513736e+05 8.957894e+05 9.454921e+05 9.694748e+05 1.003383e+06 1.044711e+06 1.096672e+06 1.200355e+06 1.379582e+06 1.388305e+06 1.398910e+06 1.563274e+06 1.670687e+06 2.413681e+06 3.251451e+06 4.322566e+06 6.002265e+06 7.456257e+06 8.890760e+06 9.924880e+06 1.176561e+07 1.294822e+07 1.335507e+07 1.364310e+07 1.373043e+07 1.375643e+07 1.375643e+07 1.378330e+07 1.379665e+07 1.380365e+07 1.380733e+07 1.381635e+07 1.383485e+07 1.384090e+07 1.384090e+07 1.384090e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.386419e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.387465e+07 1.387465e+07 1.387465e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-12 9285.681508 53110.769690 102016.178794 139705.381727 242642.755350 341557.583475 366424.012692 432159.959918 483154.032135 5.445314e+05 5.894485e+05 6.596034e+05 7.445519e+05 8.245571e+05 8.530997e+05 8.734600e+05 9.308463e+05 9.462484e+05 9.942000e+05 1.019439e+06 1.053417e+06 1.075862e+06 1.096982e+06 1.304820e+06 1.426754e+06 1.751579e+06 2.159043e+06 2.489852e+06 3.152637e+06 4.874548e+06 6.012783e+06 8.274855e+06 1.110657e+07 1.207574e+07 1.312359e+07 1.418251e+07 1.459052e+07 1.460681e+07 1.460681e+07 1.461902e+07 1.461902e+07 1.462235e+07 1.462235e+07 1.464135e+07 1.464135e+07 1.464135e+07 1.464135e+07 1.464135e+07 1.467289e+07 1.468125e+07 1.468125e+07 1.468327e+07 1.468388e+07 1.468388e+07 1.468388e+07 1.468388e+07 1.468388e+07 1.468388e+07 1.469547e+07 1.469547e+07 1.469547e+07 1.469547e+07 1.469547e+07 1.469547e+07 1.469547e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-01 NaN 19101.745807 41160.494365 72959.334214 107392.349064 142114.220833 195200.244706 273995.548877 338386.632395 4.203691e+05 5.043649e+05 5.206321e+05 5.983234e+05 6.497836e+05 7.140311e+05 9.139438e+05 9.346046e+05 9.851061e+05 1.004106e+06 1.035927e+06 1.057010e+06 1.088147e+06 1.102147e+06 1.238963e+06 1.546111e+06 2.326634e+06 2.899831e+06 3.608096e+06 4.450848e+06 5.883626e+06 6.450860e+06 8.571664e+06 1.064438e+07 1.343881e+07 1.344276e+07 1.410899e+07 1.457927e+07 1.457927e+07 1.476658e+07 1.476658e+07 1.477634e+07 1.477634e+07 1.478334e+07 1.478334e+07 1.478334e+07 1.480687e+07 1.481845e+07 1.482551e+07 1.482814e+07 1.482814e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.485009e+07 1.485009e+07 1.485009e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-02 5831.889082 28053.654180 87067.238462 189119.354787 278099.280079 350083.060370 412692.469731 441418.208047 501365.092934 5.962157e+05 6.493019e+05 7.195880e+05 8.182901e+05 8.623865e+05 9.175737e+05 9.441554e+05 1.040093e+06 1.081677e+06 1.102372e+06 1.193128e+06 1.301222e+06 1.332360e+06 1.382601e+06 1.597537e+06 2.150189e+06 2.182873e+06 2.475535e+06 3.261908e+06 4.972937e+06 5.329165e+06 6.732461e+06 7.804236e+06 9.649937e+06 1.140937e+07 1.269257e+07 1.297257e+07 1.326291e+07 1.327691e+07 1.329953e+07 1.330312e+07 1.332312e+07 1.332591e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336956e+07 1.336956e+07 1.337710e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339572e+07 1.339572e+07 1.339572e+07 1.340234e+07 1.340234e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-03 16123.818400 27670.995026 106255.744459 186615.744170 263401.664484 304293.715721 368852.220625 466455.573462 516190.011105 5.658252e+05 6.186063e+05 6.681244e+05 7.499626e+05 7.899559e+05 8.451226e+05 9.495912e+05 1.015705e+06 1.080412e+06 1.101935e+06 1.108935e+06 1.124154e+06 1.177935e+06 1.192716e+06 1.239913e+06 1.243670e+06 1.627715e+06 1.922758e+06 2.263876e+06 3.744920e+06 4.856131e+06 5.655887e+06 7.608230e+06 9.429245e+06 1.110058e+07 1.273057e+07 1.309075e+07 1.355075e+07 1.355775e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.357342e+07 1.357342e+07 1.357342e+07 1.357342e+07 1.357342e+07 1.357342e+07 1.358140e+07 1.358140e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-04 4838.017331 23114.032888 61987.244500 136897.118875 270364.285023 368358.836729 442964.227486 543522.835399 657518.433226 7.126609e+05 7.677243e+05 8.767519e+05 9.181269e+05 9.780924e+05 9.942741e+05 1.110282e+06 1.167157e+06 1.185615e+06 1.231003e+06 1.237013e+06 1.248898e+06 1.438419e+06 1.624281e+06 1.639675e+06 1.737094e+06 2.499125e+06 3.288566e+06 4.001112e+06 4.505067e+06 5.263091e+06 6.810471e+06 8.361025e+06 9.914343e+06 1.162237e+07 1.251148e+07 1.330832e+07 1.393403e+07 1.422957e+07 1.422957e+07 1.423773e+07 1.424492e+07 1.425192e+07 1.425192e+07 1.425192e+07 1.425192e+07 1.425192e+07 1.426218e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.428074e+07 1.428074e+07 1.428074e+07 1.428074e+07 1.429626e+07 1.429626e+07 1.429626e+07 1.429626e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.430171e+07 1.430171e+07 1.430171e+07 1.430171e+07 1.430171e+07 1.430171e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-05 NaN 25969.252116 78175.143479 154095.553299 240098.394156 288395.261595 420794.978271 465779.053434 545209.770017 5.800742e+05 6.385637e+05 7.165541e+05 7.680540e+05 8.337151e+05 8.535742e+05 8.725341e+05 9.168707e+05 9.933424e+05 1.041421e+06 1.076686e+06 1.101574e+06 1.104153e+06 1.138851e+06 1.156562e+06 1.192243e+06 1.599668e+06 2.545950e+06 3.279290e+06 4.097467e+06 5.054867e+06 5.509446e+06 7.041759e+06 8.744281e+06 9.266679e+06 1.053559e+07 1.142647e+07 1.241287e+07 1.242509e+07 1.261733e+07 1.261733e+07 1.261733e+07 1.262247e+07 1.262247e+07 1.262947e+07 1.263647e+07 1.263647e+07 1.264208e+07 1.264208e+07 1.264208e+07 1.264208e+07 1.264208e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.266420e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-06 NaN 52177.772742 157182.932638 212788.668588 256945.637034 324926.843610 412259.948897 485708.568616 583417.128856 6.484978e+05 7.639250e+05 8.179749e+05 8.480037e+05 8.823934e+05 9.093657e+05 1.020841e+06 1.069318e+06 1.123153e+06 1.179227e+06 1.207227e+06 1.237655e+06 1.247811e+06 1.260217e+06 1.625181e+06 1.814341e+06 2.157082e+06 2.472835e+06 3.718418e+06 4.584337e+06 5.210524e+06 5.730757e+06 7.135392e+06 8.230722e+06 9.800849e+06 1.201413e+07 1.279738e+07 1.308968e+07 1.325236e+07 1.325995e+07 1.327079e+07 1.327784e+07 1.327784e+07 1.327784e+07 1.328560e+07 1.329232e+07 1.330485e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.331647e+07 1.332347e+07 1.332347e+07 1.332347e+07 1.332347e+07 1.332347e+07 1.332861e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-07 20445.923122 54246.419760 91025.062822 153045.982656 229529.975932 271703.480109 395459.317808 512269.726925 564181.748268 6.323235e+05 6.918065e+05 7.457673e+05 8.083736e+05 8.477697e+05 9.049143e+05 9.676758e+05 9.886758e+05 1.221099e+06 1.295405e+06 1.355246e+06 1.376466e+06 1.401756e+06 1.418567e+06 1.466177e+06 1.865005e+06 1.900775e+06 2.384575e+06 2.844575e+06 3.884598e+06 5.364595e+06 7.823422e+06 9.428651e+06 1.083024e+07 1.198015e+07 1.344805e+07 1.362948e+07 1.406833e+07 1.406833e+07 1.406833e+07 1.406833e+07 1.407533e+07 1.408933e+07 1.408933e+07 1.409633e+07 1.409759e+07 1.409759e+07 1.411159e+07 1.411159e+07 1.411159e+07 1.411159e+07 1.412256e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.414039e+07 1.414835e+07 1.414835e+07 1.414835e+07 1.414835e+07 1.414835e+07 1.415360e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-08 NaN 59161.814775 59161.814775 121366.004189 234249.168070 319317.109063 416573.781419 489063.917741 571647.444056 6.030010e+05 6.813769e+05 7.673878e+05 8.340224e+05 8.915414e+05 9.457041e+05 9.803649e+05 1.039811e+06 1.071930e+06 1.115320e+06 1.171001e+06 1.180341e+06 1.394408e+06 1.691706e+06 1.780497e+06 2.105713e+06 2.813917e+06 2.824082e+06 4.178650e+06 4.780048e+06 5.652616e+06 6.469829e+06 8.255926e+06 9.438589e+06 9.725589e+06 1.098130e+07 1.196748e+07 1.246177e+07 1.266429e+07 1.278718e+07 1.278718e+07 1.278718e+07 1.278718e+07 1.279190e+07 1.279190e+07 1.279190e+07 1.279978e+07 1.279978e+07 1.279978e+07 1.282103e+07 1.282645e+07 1.282645e+07 1.283303e+07 1.283303e+07 1.283303e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.287267e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-09 8616.989893 41195.424916 111931.583669 199014.044456 259139.404961 287016.878984 360921.699066 415577.192426 507841.344599 5.569931e+05 5.980730e+05 6.902388e+05 7.454212e+05 8.046835e+05 8.497900e+05 9.145130e+05 9.417029e+05 9.695326e+05 1.000611e+06 1.023670e+06 1.082764e+06 1.094362e+06 1.248678e+06 1.336363e+06 1.360127e+06 1.588664e+06 3.055229e+06 3.721462e+06 4.207027e+06 4.853752e+06 6.709248e+06 7.910928e+06 9.498153e+06 1.123462e+07 1.215049e+07 1.252848e+07 1.300107e+07 1.371083e+07 1.390999e+07 1.390999e+07 1.390999e+07 1.391669e+07 1.392702e+07 1.392702e+07 1.393492e+07 1.394586e+07 1.394586e+07 1.394586e+07 1.394586e+07 1.394586e+07 1.394586e+07 1.394586e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-10 11941.597417 54918.291800 121394.787021 230029.515493 348929.283946 407559.665800 433414.890401 494418.560462 533126.386993 5.909520e+05 6.285762e+05 6.968370e+05 7.481720e+05 8.050799e+05 8.671493e+05 8.944724e+05 9.443727e+05 1.000959e+06 1.028918e+06 1.072608e+06 1.103524e+06 1.237894e+06 1.308795e+06 1.735846e+06 1.753554e+06 1.962963e+06 2.964036e+06 3.702861e+06 4.331939e+06 4.955532e+06 6.478636e+06 7.965934e+06 9.695020e+06 1.115079e+07 1.174434e+07 1.220910e+07 1.301337e+07 1.329337e+07 1.383337e+07 1.384037e+07 1.385135e+07 1.385135e+07 1.386904e+07 1.387518e+07 1.387518e+07 1.387518e+07 1.387518e+07 1.388131e+07 1.389001e+07 1.389001e+07 1.389001e+07 1.389001e+07 1.389888e+07 1.389888e+07 1.389888e+07 1.391005e+07 1.392405e+07 1.392503e+07 1.392503e+07 1.393429e+07 1.393429e+07 1.393765e+07 1.394468e+07 1.394468e+07 1.394468e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-11 NaN 27553.838792 105006.902823 171603.105271 204435.403562 330160.254895 368698.386252 440125.871042 512007.276897 5.894240e+05 6.729196e+05 7.550056e+05 8.085635e+05 9.313430e+05 1.046908e+06 1.099404e+06 1.130779e+06 1.185324e+06 1.196417e+06 1.200808e+06 1.220542e+06 1.254654e+06 1.308869e+06 1.637050e+06 1.643557e+06 2.336508e+06 2.642508e+06 2.822508e+06 3.728294e+06 5.424972e+06 6.925456e+06 7.362006e+06 8.382006e+06 1.048410e+07 1.120528e+07 1.166247e+07 1.268481e+07 1.269875e+07 1.299698e+07 1.299698e+07 1.299698e+07 1.299698e+07 1.299698e+07 1.300842e+07 1.301369e+07 1.301369e+07 1.302161e+07 1.303399e+07 1.303399e+07 1.303399e+07 1.303399e+07 1.303399e+07 1.304803e+07 1.304803e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305942e+07 1.305942e+07 1.305942e+07 1.305942e+07 1.305942e+07 1.305942e+07 1.305942e+07 1.305942e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2011-12 NaN 26987.348220 63712.414351 128843.426450 250481.100767 296896.131911 428193.181979 467579.675150 630883.752272 6.823751e+05 7.335912e+05 8.444677e+05 9.049189e+05 9.718150e+05 9.901548e+05 1.023835e+06 1.073395e+06 1.224679e+06 1.293617e+06 1.317231e+06 1.360700e+06 1.403723e+06 1.417410e+06 1.449591e+06 1.484123e+06 2.119267e+06 2.477225e+06 3.038492e+06 3.964172e+06 4.881915e+06 5.691535e+06 6.883943e+06 8.358723e+06 9.972595e+06 1.113098e+07 1.192067e+07 1.218352e+07 1.218352e+07 1.218761e+07 1.219264e+07 1.220009e+07 1.221148e+07 1.221148e+07 1.221457e+07 1.223919e+07 1.224905e+07 1.224905e+07 1.227089e+07 1.227089e+07 1.227089e+07 1.227089e+07 1.227089e+07 1.227089e+07 1.228463e+07 1.228463e+07 1.228463e+07 1.230336e+07 1.230336e+07 1.230805e+07 1.230805e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-01 NaN 5686.987821 73042.172897 146748.277238 244447.261643 346307.665156 424226.557354 505400.057748 612559.350187 6.628856e+05 7.165620e+05 7.567891e+05 7.745374e+05 8.340645e+05 8.566990e+05 8.838164e+05 8.996067e+05 9.109208e+05 9.640703e+05 1.027045e+06 1.048597e+06 1.286409e+06 1.315259e+06 1.595805e+06 1.623071e+06 1.826616e+06 2.068501e+06 3.018092e+06 3.520224e+06 4.502966e+06 5.295910e+06 7.039475e+06 8.541053e+06 9.989760e+06 1.108067e+07 1.211276e+07 1.234259e+07 1.237549e+07 1.258568e+07 1.258568e+07 1.259262e+07 1.259262e+07 1.260446e+07 1.260626e+07 1.260626e+07 1.260626e+07 1.261208e+07 1.261667e+07 1.261667e+07 1.261667e+07 1.261667e+07 1.261924e+07 1.261924e+07 1.261924e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-02 13107.090680 25071.495144 62505.304351 163568.982079 270728.715818 344043.222508 521862.484567 590357.560986 658389.571544 7.053722e+05 7.751899e+05 9.442802e+05 1.021797e+06 1.069076e+06 1.110702e+06 1.182849e+06 1.238456e+06 1.281408e+06 1.308697e+06 1.451086e+06 1.478088e+06 1.489863e+06 1.499828e+06 1.624367e+06 1.634134e+06 1.835148e+06 2.098294e+06 2.605301e+06 3.582310e+06 6.193329e+06 7.479964e+06 8.576370e+06 1.042570e+07 1.155037e+07 1.286929e+07 1.363146e+07 1.387132e+07 1.414869e+07 1.415139e+07 1.416996e+07 1.417911e+07 1.418784e+07 1.420811e+07 1.421150e+07 1.421432e+07 1.421992e+07 1.421992e+07 1.421992e+07 1.421992e+07 1.421992e+07 1.421992e+07 1.421992e+07 1.422314e+07 1.422314e+07 1.422314e+07 1.422314e+07 1.422314e+07 1.422618e+07 1.422618e+07 1.422618e+07 1.423001e+07 1.423001e+07 1.423001e+07 1.423001e+07 1.423001e+07 1.423001e+07 1.423001e+07 1.423343e+07 1.423343e+07 1.423343e+07 1.423343e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-03 19000.000000 55631.237977 100520.304487 164333.965744 212242.702240 252184.633074 333213.452394 436909.971606 557629.911842 6.583601e+05 7.639663e+05 8.656471e+05 9.616740e+05 1.061373e+06 1.091794e+06 1.159120e+06 1.214192e+06 1.226030e+06 1.268999e+06 1.340652e+06 1.398246e+06 1.417605e+06 1.578419e+06 1.817252e+06 2.071770e+06 2.283731e+06 3.643158e+06 4.216553e+06 5.371941e+06 6.859345e+06 7.452627e+06 8.930044e+06 1.076893e+07 1.321090e+07 1.409563e+07 1.528693e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545904e+07 1.545904e+07 1.546604e+07 1.548504e+07 1.548504e+07 1.548504e+07 1.548504e+07 1.548504e+07 1.548504e+07 1.548858e+07 1.548858e+07 1.548858e+07 1.548858e+07 1.549528e+07 1.549528e+07 1.550082e+07 1.550082e+07 1.551254e+07 1.551254e+07 1.551254e+07 1.551254e+07 1.551254e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-04 17918.213682 71131.483498 130713.525758 178223.949159 245423.069125 321962.047814 399902.339759 526821.716271 622079.480539 7.544929e+05 8.520942e+05 9.208654e+05 9.842332e+05 1.060035e+06 1.093765e+06 1.135778e+06 1.185268e+06 1.226276e+06 1.255656e+06 1.269656e+06 1.321728e+06 1.340728e+06 1.358083e+06 1.372373e+06 1.611062e+06 2.075196e+06 2.674711e+06 2.914226e+06 3.780211e+06 4.427168e+06 6.114147e+06 6.303677e+06 8.734064e+06 9.269435e+06 1.076452e+07 1.132530e+07 1.197479e+07 1.197479e+07 1.253479e+07 1.255579e+07 1.256742e+07 1.257654e+07 1.257654e+07 1.258323e+07 1.259023e+07 1.259023e+07 1.259023e+07 1.259023e+07 1.259199e+07 1.259199e+07 1.259199e+07 1.259199e+07 1.260935e+07 1.260935e+07 1.261541e+07 1.261541e+07 1.261541e+07 1.261541e+07 1.261541e+07 1.261541e+07 1.261541e+07 1.261822e+07 1.261822e+07 1.261822e+07 1.261822e+07 1.261822e+07 1.261822e+07 1.261822e+07 1.261822e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-05 NaN 3226.695985 26191.588533 99994.256991 148346.443041 279589.662986 364781.858289 466238.011329 510949.980948 5.797950e+05 6.700173e+05 7.250597e+05 7.752739e+05 8.476722e+05 8.945051e+05 9.316010e+05 9.879291e+05 1.019212e+06 1.046300e+06 1.091918e+06 1.130833e+06 1.182793e+06 1.201793e+06 1.427597e+06 1.461971e+06 1.491356e+06 2.343250e+06 3.320405e+06 3.697091e+06 4.103408e+06 5.433988e+06 8.398247e+06 9.963994e+06 1.050709e+07 1.127244e+07 1.145798e+07 1.198296e+07 1.226943e+07 1.227812e+07 1.227812e+07 1.228551e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229660e+07 1.229660e+07 1.229660e+07 1.230285e+07 1.230285e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.231102e+07 1.231102e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-06 NaN 44336.944311 122316.370328 189321.587648 284404.896049 383194.460715 470511.240633 541658.196295 606660.775311 6.726998e+05 7.096006e+05 7.782762e+05 8.531763e+05 9.021134e+05 9.468536e+05 9.657455e+05 1.039464e+06 1.074760e+06 1.145933e+06 1.149063e+06 1.178384e+06 1.405470e+06 1.531945e+06 1.675000e+06 2.135000e+06 2.904485e+06 2.940886e+06 3.316617e+06 4.656003e+06 5.489658e+06 6.168191e+06 8.336974e+06 9.278123e+06 9.885747e+06 1.078799e+07 1.167835e+07 1.265670e+07 1.293670e+07 1.293670e+07 1.296108e+07 1.297199e+07 1.298447e+07 1.298447e+07 1.298447e+07 1.298447e+07 1.298783e+07 1.298783e+07 1.299528e+07 1.301089e+07 1.301789e+07 1.302604e+07 1.302604e+07 1.302604e+07 1.302604e+07 1.302604e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304986e+07 1.304986e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-07 7000.000000 17155.497667 104920.349670 162785.813919 197290.277500 338390.171046 408094.284127 478363.805707 518694.271574 5.540758e+05 6.008477e+05 6.579571e+05 7.344526e+05 8.005145e+05 8.594334e+05 9.034338e+05 9.787942e+05 1.001732e+06 1.023852e+06 1.036630e+06 1.041616e+06 1.069028e+06 1.129312e+06 1.187190e+06 1.226218e+06 2.014114e+06 2.137562e+06 2.433664e+06 2.936166e+06 4.044137e+06 4.767722e+06 6.265648e+06 7.920444e+06 9.445169e+06 1.040629e+07 1.162784e+07 1.257761e+07 1.285761e+07 1.286532e+07 1.288661e+07 1.289359e+07 1.289359e+07 1.289359e+07 1.289359e+07 1.289359e+07 1.289918e+07 1.291818e+07 1.291818e+07 1.293797e+07 1.293797e+07 1.294184e+07 1.294184e+07 1.294184e+07 1.294422e+07 1.294422e+07 1.294724e+07 1.294724e+07 1.296124e+07 1.296124e+07 1.297949e+07 1.297949e+07 1.297949e+07 1.297949e+07 1.297949e+07 1.297949e+07 1.297949e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-08 1661.157858 53519.589000 119748.109570 203374.125137 271043.138575 362065.002575 435782.172223 506023.633700 589243.789510 6.671597e+05 7.082445e+05 7.883987e+05 8.308116e+05 9.194695e+05 9.898777e+05 1.031681e+06 1.100833e+06 1.233995e+06 1.276363e+06 1.305155e+06 1.332318e+06 1.488463e+06 1.525042e+06 1.585332e+06 1.742132e+06 2.070032e+06 2.087829e+06 3.103498e+06 3.925317e+06 4.861634e+06 5.702901e+06 6.847026e+06 8.238648e+06 8.847989e+06 1.063678e+07 1.139216e+07 1.185623e+07 1.187090e+07 1.187090e+07 1.187790e+07 1.187790e+07 1.187790e+07 1.187790e+07 1.189270e+07 1.189270e+07 1.189270e+07 1.190903e+07 1.190903e+07 1.190903e+07 1.190903e+07 1.190903e+07 1.191735e+07 1.192435e+07 1.193450e+07 1.193450e+07 1.193450e+07 1.193450e+07 1.194565e+07 1.195265e+07 1.195265e+07 1.195265e+07 1.195265e+07 1.195636e+07 1.195636e+07 1.195636e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-09 4782.323680 27662.766529 39092.299225 86594.374820 163637.584146 251298.852396 317355.936609 396729.984967 535688.724185 5.735548e+05 6.912174e+05 7.423651e+05 8.279133e+05 8.736395e+05 9.201414e+05 9.363366e+05 9.791285e+05 1.037310e+06 1.052837e+06 1.077735e+06 1.146025e+06 1.548024e+06 2.061830e+06 2.223588e+06 2.478457e+06 2.493172e+06 2.772918e+06 2.902672e+06 3.517841e+06 3.805350e+06 4.913289e+06 6.357328e+06 7.857139e+06 9.518671e+06 1.016371e+07 1.025783e+07 1.026226e+07 1.027802e+07 1.027802e+07 1.028502e+07 1.028502e+07 1.028502e+07 1.028502e+07 1.028502e+07 1.028502e+07 1.028502e+07 1.030147e+07 1.030147e+07 1.030147e+07 1.030147e+07 1.030751e+07 1.032476e+07 1.032476e+07 1.032476e+07 1.032476e+07 1.032476e+07 1.033212e+07 1.033683e+07 1.035083e+07 1.036236e+07 1.036236e+07 1.036236e+07 1.036236e+07 1.036936e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-10 13392.784604 37999.786344 88414.327105 212665.812054 314474.681534 406709.342868 480783.526021 529447.755536 619717.865078 6.855869e+05 8.072495e+05 8.835255e+05 9.672502e+05 9.987685e+05 1.064904e+06 1.113668e+06 1.148777e+06 1.195815e+06 1.203935e+06 1.215583e+06 1.230136e+06 1.265179e+06 1.298115e+06 1.498842e+06 1.784161e+06 1.808229e+06 2.334130e+06 2.988355e+06 3.812712e+06 4.825244e+06 6.146147e+06 6.621202e+06 8.302078e+06 9.911238e+06 1.089825e+07 1.271749e+07 1.309617e+07 1.373716e+07 1.391716e+07 1.393224e+07 1.393224e+07 1.393224e+07 1.395654e+07 1.395900e+07 1.396907e+07 1.396907e+07 1.396907e+07 1.396907e+07 1.397403e+07 1.399294e+07 1.399294e+07 1.399294e+07 1.399294e+07 1.399294e+07 1.400956e+07 1.400956e+07 1.400956e+07 1.400956e+07 1.400956e+07 1.400956e+07 1.401837e+07 1.401837e+07 1.401837e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-11 NaN 23836.213272 72628.438203 127971.500892 161279.813503 225616.873385 300199.274272 430629.845211 567984.559855 6.249572e+05 6.883896e+05 7.746843e+05 8.615866e+05 9.334334e+05 1.017758e+06 1.047284e+06 1.060880e+06 1.085924e+06 1.141294e+06 1.212452e+06 1.240749e+06 1.256492e+06 1.312706e+06 1.470647e+06 1.483962e+06 1.619878e+06 2.061884e+06 2.414375e+06 3.313679e+06 4.277779e+06 5.528228e+06 7.874400e+06 8.505362e+06 9.684289e+06 1.007612e+07 1.150727e+07 1.179288e+07 1.179288e+07 1.179356e+07 1.179356e+07 1.180154e+07 1.180854e+07 1.180854e+07 1.181535e+07 1.183516e+07 1.183516e+07 1.183516e+07 1.183516e+07 1.185108e+07 1.185935e+07 1.186234e+07 1.186234e+07 1.186234e+07 1.186234e+07 1.186234e+07 1.186926e+07 1.186926e+07 1.186926e+07 1.186926e+07 1.186926e+07 1.186926e+07 1.186926e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012-12 26589.428550 119271.763714 214770.993496 275815.407157 316164.518512 430178.651171 493457.591885 570408.980158 609702.647390 6.788052e+05 7.779778e+05 8.455946e+05 8.911229e+05 9.691583e+05 1.030406e+06 1.066043e+06 1.124343e+06 1.205542e+06 1.222751e+06 1.305007e+06 1.409513e+06 1.445805e+06 1.447821e+06 1.465980e+06 1.474936e+06 2.286704e+06 2.297400e+06 3.213580e+06 3.689234e+06 5.478679e+06 6.456751e+06 7.495672e+06 9.071454e+06 1.037537e+07 1.080048e+07 1.118883e+07 1.119397e+07 1.119397e+07 1.119880e+07 1.120171e+07 1.120752e+07 1.121501e+07 1.122126e+07 1.122126e+07 1.124226e+07 1.125345e+07 1.125345e+07 1.125345e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.126441e+07 1.126941e+07 1.126941e+07 1.127462e+07 1.127462e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-01 NaN 42970.866185 109658.175147 269447.919867 316189.381460 346611.784658 435423.735461 536576.579542 655240.090352 7.822457e+05 8.017010e+05 8.783724e+05 9.665048e+05 1.061201e+06 1.106377e+06 1.165500e+06 1.200121e+06 1.233019e+06 1.296292e+06 1.318702e+06 1.348652e+06 1.465572e+06 1.602941e+06 1.616271e+06 1.870512e+06 2.292950e+06 2.730927e+06 2.967593e+06 3.864725e+06 5.687584e+06 8.942028e+06 1.011819e+07 1.184798e+07 1.397787e+07 1.521801e+07 1.569334e+07 1.597334e+07 1.597334e+07 1.598611e+07 1.598611e+07 1.598611e+07 1.598611e+07 1.598611e+07 1.600112e+07 1.600112e+07 1.601396e+07 1.601396e+07 1.602248e+07 1.602248e+07 1.602755e+07 1.602755e+07 1.603426e+07 1.603426e+07 1.603682e+07 1.603682e+07 1.603682e+07 1.604357e+07 1.604357e+07 1.604357e+07 1.604618e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-02 27988.063022 43617.896631 72566.065601 133608.630201 195523.863557 292490.487355 373774.127950 501460.065698 544760.351691 6.111267e+05 6.725518e+05 7.289007e+05 7.840076e+05 8.633298e+05 8.905116e+05 1.017592e+06 1.041671e+06 1.099918e+06 1.131177e+06 1.167072e+06 1.224046e+06 1.264682e+06 1.337533e+06 1.407973e+06 1.502143e+06 1.825635e+06 2.360840e+06 2.871276e+06 3.332841e+06 4.465368e+06 5.678972e+06 6.471929e+06 8.087396e+06 9.679313e+06 1.038244e+07 1.102244e+07 1.102244e+07 1.120244e+07 1.143628e+07 1.145586e+07 1.146594e+07 1.147110e+07 1.148405e+07 1.149280e+07 1.149280e+07 1.149980e+07 1.149980e+07 1.150920e+07 1.150920e+07 1.150920e+07 1.150920e+07 1.150920e+07 1.150920e+07 1.152810e+07 1.152810e+07 1.152810e+07 1.152810e+07 1.152810e+07 1.152810e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-03 17310.865710 62350.792473 78984.467469 169514.822507 251116.870701 320451.114103 361716.293417 454513.802107 556431.478462 6.185079e+05 6.350550e+05 7.256064e+05 7.711168e+05 8.266865e+05 9.034755e+05 9.718165e+05 1.310080e+06 1.365954e+06 1.388032e+06 1.439207e+06 1.499026e+06 1.509641e+06 1.564600e+06 1.582027e+06 1.597449e+06 1.827321e+06 2.344464e+06 2.885014e+06 3.274169e+06 3.809110e+06 5.983849e+06 8.805923e+06 9.648623e+06 1.081448e+07 1.210077e+07 1.295581e+07 1.295581e+07 1.322114e+07 1.358114e+07 1.358114e+07 1.358114e+07 1.358174e+07 1.358554e+07 1.360103e+07 1.360743e+07 1.361635e+07 1.363395e+07 1.363395e+07 1.363395e+07 1.363395e+07 1.364889e+07 1.364889e+07 1.364889e+07 1.364889e+07 1.364889e+07 1.365652e+07 1.365652e+07 1.365652e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-04 5213.534716 47467.983075 115619.453207 219286.922589 288557.786829 338098.594876 375043.233238 417122.540637 576373.008690 6.504329e+05 7.126937e+05 7.674279e+05 8.296378e+05 8.922551e+05 9.200228e+05 9.666319e+05 1.016964e+06 1.073423e+06 1.127420e+06 1.202770e+06 1.240333e+06 1.301322e+06 1.484444e+06 1.639827e+06 1.671938e+06 2.098844e+06 2.198512e+06 2.492866e+06 3.341801e+06 4.327619e+06 5.973460e+06 7.270875e+06 8.947007e+06 9.719981e+06 1.039949e+07 1.115061e+07 1.143061e+07 1.160240e+07 1.160746e+07 1.160746e+07 1.160746e+07 1.163546e+07 1.164543e+07 1.164543e+07 1.164543e+07 1.165924e+07 1.166682e+07 1.166682e+07 1.166682e+07 1.168582e+07 1.168938e+07 1.169361e+07 1.169361e+07 1.169361e+07 1.169361e+07 1.169361e+07 1.169361e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-05 13466.308532 57809.278441 115303.075102 243231.968147 339376.598505 463618.956908 545429.161179 640179.484699 694708.645164 7.925077e+05 8.603242e+05 9.451780e+05 1.025486e+06 1.077879e+06 1.139377e+06 1.207785e+06 1.235926e+06 1.300529e+06 1.328436e+06 1.408718e+06 1.583665e+06 1.618517e+06 1.646328e+06 2.001017e+06 2.730401e+06 2.751487e+06 3.084486e+06 4.024117e+06 4.695874e+06 6.658382e+06 7.762826e+06 1.039841e+07 1.099892e+07 1.269907e+07 1.433354e+07 1.477080e+07 1.505780e+07 1.505780e+07 1.505780e+07 1.506936e+07 1.509196e+07 1.510478e+07 1.510478e+07 1.511115e+07 1.511115e+07 1.511951e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-06 6851.046160 48749.850603 115967.945182 213882.527995 262375.851270 294007.897989 414453.901237 448043.243635 511183.448431 5.827483e+05 6.581599e+05 7.390029e+05 7.938015e+05 8.735176e+05 9.029504e+05 9.489518e+05 1.019152e+06 1.062395e+06 1.164633e+06 1.217944e+06 1.272309e+06 1.303608e+06 1.536169e+06 1.542387e+06 1.559520e+06 2.074887e+06 2.228731e+06 2.389350e+06 3.188869e+06 3.701507e+06 6.894999e+06 9.027832e+06 1.148423e+07 1.286245e+07 1.426308e+07 1.474590e+07 1.487509e+07 1.487509e+07 1.487509e+07 1.487509e+07 1.489508e+07 1.490208e+07 1.490208e+07 1.490208e+07 1.490208e+07 1.490874e+07 1.490874e+07 1.490874e+07 1.491369e+07 1.492480e+07 1.492480e+07 1.492480e+07 1.492480e+07 1.492480e+07 1.492480e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-07 6782.902842 55063.752408 146375.251701 220525.596258 302788.415268 401525.243297 464612.726899 533564.551922 569317.492229 5.905914e+05 7.227688e+05 7.496945e+05 8.113553e+05 8.691947e+05 9.377159e+05 1.038998e+06 1.131302e+06 1.175426e+06 1.204403e+06 1.222783e+06 1.252880e+06 1.269835e+06 1.322607e+06 1.441431e+06 1.950341e+06 1.979980e+06 2.594866e+06 3.895193e+06 4.814619e+06 6.115638e+06 7.041012e+06 7.910486e+06 8.698905e+06 1.064710e+07 1.204014e+07 1.284103e+07 1.359496e+07 1.377496e+07 1.377551e+07 1.377551e+07 1.377551e+07 1.377551e+07 1.378117e+07 1.378117e+07 1.378117e+07 1.380040e+07 1.380040e+07 1.380040e+07 1.380352e+07 1.381052e+07 1.381052e+07 1.381052e+07 1.381052e+07 1.381052e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-08 21493.736878 37014.445919 97477.054038 211300.550680 307550.888418 378224.607183 431058.123078 566587.431435 666214.823009 7.278920e+05 8.381991e+05 9.033731e+05 9.505973e+05 1.030044e+06 1.069563e+06 1.109653e+06 1.169821e+06 1.215845e+06 1.276531e+06 1.315838e+06 1.356699e+06 1.359668e+06 1.525559e+06 1.942977e+06 2.291130e+06 2.577752e+06 3.626110e+06 3.904697e+06 5.340787e+06 6.324719e+06 7.252410e+06 9.277976e+06 1.038428e+07 1.181720e+07 1.319325e+07 1.412314e+07 1.472198e+07 1.474273e+07 1.517091e+07 1.518491e+07 1.521228e+07 1.521749e+07 1.521749e+07 1.521749e+07 1.521749e+07 1.523560e+07 1.524960e+07 1.524960e+07 1.524960e+07 1.524960e+07 1.525421e+07 1.525421e+07 1.525421e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-09 10687.554570 40493.961643 107530.766127 182444.008540 220427.869640 281858.991704 401320.162378 474997.449058 580030.516751 6.483130e+05 7.607993e+05 8.764157e+05 9.272012e+05 1.025061e+06 1.085101e+06 1.160856e+06 1.205555e+06 1.239366e+06 1.269912e+06 1.333183e+06 1.362820e+06 1.381512e+06 1.760330e+06 1.765017e+06 1.914478e+06 2.200586e+06 2.954984e+06 3.385725e+06 4.523652e+06 6.117601e+06 7.476769e+06 8.768101e+06 9.595513e+06 1.069479e+07 1.116634e+07 1.299227e+07 1.355993e+07 1.388544e+07 1.390444e+07 1.391636e+07 1.392838e+07 1.392838e+07 1.395072e+07 1.397497e+07 1.398231e+07 1.398231e+07 1.398231e+07 1.398885e+07 1.399585e+07 1.399585e+07 1.399585e+07 1.399585e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-10 NaN 56517.023888 121934.860215 230385.820568 291559.061505 414411.018928 498470.248885 560530.720898 638382.826040 7.153254e+05 7.696779e+05 8.406330e+05 9.355161e+05 9.580896e+05 9.845999e+05 1.080999e+06 1.100745e+06 1.143120e+06 1.352546e+06 1.362456e+06 1.391801e+06 1.440657e+06 1.742491e+06 1.778834e+06 1.997892e+06 2.339106e+06 2.485508e+06 3.463516e+06 4.507027e+06 5.293265e+06 6.041807e+06 8.087345e+06 1.024829e+07 1.079717e+07 1.205813e+07 1.311515e+07 1.315594e+07 1.350695e+07 1.367342e+07 1.367342e+07 1.369194e+07 1.370594e+07 1.372408e+07 1.373108e+07 1.373108e+07 1.373108e+07 1.373108e+07 1.373108e+07 1.373108e+07 1.373108e+07 1.373390e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-11 19467.118469 59220.730629 131605.568961 189745.447681 258020.634405 397846.152494 431512.527607 543568.333105 622624.083274 7.524033e+05 8.129641e+05 8.969616e+05 9.669505e+05 1.048428e+06 1.105289e+06 1.146104e+06 1.188850e+06 1.223182e+06 1.248723e+06 1.271233e+06 1.445564e+06 1.467719e+06 1.506616e+06 1.765110e+06 1.792671e+06 1.905313e+06 2.299841e+06 2.730592e+06 2.942758e+06 3.334736e+06 3.997850e+06 5.337660e+06 6.553309e+06 7.841943e+06 9.477381e+06 9.878962e+06 1.007033e+07 1.066606e+07 1.066606e+07 1.066606e+07 1.066606e+07 1.068818e+07 1.068818e+07 1.068818e+07 1.072055e+07 1.072055e+07 1.072055e+07 1.073385e+07 1.075270e+07 1.075270e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2013-12 16508.968579 71626.430811 93582.636678 181449.945139 249455.818169 319601.764395 375265.551469 459305.863281 539282.303606 5.814275e+05 6.381347e+05 6.750801e+05 7.914803e+05 8.357746e+05 8.441117e+05 8.935497e+05 9.722172e+05 9.902964e+05 1.010100e+06 1.050042e+06 1.289026e+06 1.346868e+06 1.550977e+06 1.937642e+06 2.054862e+06 2.098572e+06 2.623447e+06 2.843794e+06 3.522755e+06 4.171864e+06 4.881741e+06 6.400906e+06 8.164562e+06 9.060049e+06 1.034052e+07 1.063777e+07 1.106940e+07 1.107157e+07 1.108132e+07 1.110897e+07 1.111413e+07 1.111413e+07 1.113313e+07 1.114501e+07 1.114501e+07 1.114757e+07 1.114757e+07 1.115083e+07 1.115083e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-01 18193.615004 39939.231007 77062.556722 210751.198716 325414.551148 434108.663784 499231.742050 581891.540190 636104.751524 7.319155e+05 8.046067e+05 8.951043e+05 9.346007e+05 9.977177e+05 1.053679e+06 1.117600e+06 1.194226e+06 1.228147e+06 1.252255e+06 1.388046e+06 1.441130e+06 1.661880e+06 1.672859e+06 1.824146e+06 2.121501e+06 2.333618e+06 2.890290e+06 3.583215e+06 3.787643e+06 4.400919e+06 6.655304e+06 7.990654e+06 8.924634e+06 9.842833e+06 1.144033e+07 1.234487e+07 1.275077e+07 1.339421e+07 1.339421e+07 1.342754e+07 1.342900e+07 1.343678e+07 1.343828e+07 1.344528e+07 1.344528e+07 1.345228e+07 1.345448e+07 1.345448e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-02 11026.197800 56848.025440 150250.510215 262320.117199 398024.318249 508318.831934 555161.147489 650183.352998 716616.762680 8.024585e+05 9.110309e+05 9.820743e+05 1.043217e+06 1.122031e+06 1.199621e+06 1.263135e+06 1.344549e+06 1.405414e+06 1.409526e+06 1.430385e+06 1.430385e+06 1.451538e+06 1.671859e+06 1.835855e+06 2.207108e+06 2.428851e+06 2.888052e+06 3.841642e+06 4.433282e+06 4.548279e+06 6.409280e+06 7.638117e+06 8.883708e+06 1.063066e+07 1.174264e+07 1.262111e+07 1.281328e+07 1.282452e+07 1.283208e+07 1.283208e+07 1.284941e+07 1.284941e+07 1.287100e+07 1.287692e+07 1.287692e+07 1.288049e+07 1.288049e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-03 41856.032839 65869.107902 169770.881386 273966.255027 388844.521556 544543.378575 631927.708896 764488.338401 840595.367160 9.587591e+05 1.020056e+06 1.067282e+06 1.143743e+06 1.209735e+06 1.270530e+06 1.332327e+06 1.377764e+06 1.410519e+06 1.423039e+06 1.530223e+06 1.807264e+06 1.843415e+06 2.042809e+06 2.047532e+06 2.189489e+06 2.219289e+06 2.467793e+06 2.783951e+06 3.276145e+06 4.111549e+06 5.416191e+06 6.654989e+06 7.960628e+06 9.682304e+06 1.028205e+07 1.135320e+07 1.147915e+07 1.186038e+07 1.203290e+07 1.205423e+07 1.208052e+07 1.208648e+07 1.210335e+07 1.212528e+07 1.212528e+07 1.213228e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-04 25232.184583 39232.184583 99285.089409 180734.538567 279326.942076 362908.147903 450839.926027 555559.675960 639013.347413 7.379873e+05 7.965488e+05 8.549866e+05 9.312424e+05 1.032633e+06 1.102250e+06 1.142361e+06 1.181072e+06 1.206438e+06 1.268394e+06 1.293403e+06 1.325001e+06 1.379050e+06 1.390902e+06 1.532747e+06 2.057607e+06 2.750844e+06 2.881778e+06 3.197040e+06 3.705483e+06 4.692376e+06 5.799875e+06 7.428576e+06 8.374490e+06 1.036887e+07 1.161723e+07 1.162423e+07 1.191038e+07 1.191038e+07 1.191038e+07 1.191420e+07 1.192120e+07 1.192120e+07 1.192120e+07 1.192120e+07 1.192820e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-05 NaN 17227.928985 44978.750435 109899.063365 162324.368587 267111.290872 318542.834315 393684.443678 487727.468572 5.981860e+05 6.901631e+05 7.850082e+05 8.271083e+05 8.648744e+05 9.352265e+05 9.732577e+05 1.000582e+06 1.044075e+06 1.117560e+06 1.165439e+06 1.205530e+06 1.249227e+06 1.446733e+06 1.801323e+06 2.597599e+06 3.099421e+06 3.193494e+06 3.402099e+06 4.242114e+06 4.777499e+06 5.999819e+06 6.937059e+06 8.061326e+06 9.870807e+06 1.062268e+07 1.173493e+07 1.244718e+07 1.245269e+07 1.245269e+07 1.245269e+07 1.245269e+07 1.247169e+07 1.247169e+07 1.248409e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-06 NaN 32351.229848 64187.546630 130476.798057 212264.135272 319317.327515 462912.695647 522502.320981 531804.816399 6.084550e+05 7.325961e+05 8.159605e+05 8.574263e+05 9.434921e+05 9.781782e+05 1.034130e+06 1.117837e+06 1.173629e+06 1.339080e+06 1.399123e+06 1.430922e+06 1.453914e+06 1.464219e+06 1.511423e+06 1.821108e+06 2.116551e+06 3.160642e+06 3.849900e+06 4.667106e+06 6.126510e+06 6.922271e+06 8.816664e+06 1.007515e+07 1.156074e+07 1.285229e+07 1.370937e+07 1.424326e+07 1.470934e+07 1.470934e+07 1.500476e+07 1.501543e+07 1.503301e+07 1.505095e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-07 17395.126262 60930.341532 155284.490889 242306.198742 334847.112917 441349.550453 500984.388067 612300.107595 706468.194565 7.588460e+05 8.172415e+05 9.454929e+05 1.026354e+06 1.072546e+06 1.110079e+06 1.171340e+06 1.227452e+06 1.247020e+06 1.299990e+06 1.314517e+06 1.359795e+06 1.451765e+06 1.768154e+06 1.791525e+06 2.144522e+06 2.512511e+06 2.990893e+06 3.425368e+06 4.273232e+06 5.528513e+06 6.179057e+06 7.944388e+06 9.458743e+06 1.101939e+07 1.189521e+07 1.280930e+07 1.317271e+07 1.317271e+07 1.318111e+07 1.318397e+07 1.321213e+07 1.321213e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-08 17118.276431 59870.901660 144321.553723 258345.271429 411179.077501 525300.290179 568785.928348 786905.031455 873181.077655 9.354163e+05 1.022524e+06 1.127962e+06 1.196046e+06 1.279273e+06 1.290967e+06 1.331586e+06 1.425706e+06 1.511862e+06 1.697432e+06 1.756411e+06 1.781660e+06 1.942232e+06 2.229977e+06 2.377433e+06 2.556849e+06 2.943618e+06 3.991538e+06 4.572714e+06 5.243041e+06 5.983310e+06 6.973213e+06 8.318253e+06 9.366363e+06 9.883582e+06 1.045539e+07 1.119667e+07 1.167119e+07 1.222479e+07 1.222479e+07 1.222953e+07 1.223838e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-09 11766.356540 82379.941597 172610.023324 274462.623590 350528.878988 440391.670339 591567.536956 692040.961612 795249.708574 8.541849e+05 9.512645e+05 1.080947e+06 1.138492e+06 1.224503e+06 1.277070e+06 1.310421e+06 1.388076e+06 1.416799e+06 1.453331e+06 1.482251e+06 1.542327e+06 1.615166e+06 1.637143e+06 1.767856e+06 2.249258e+06 2.283728e+06 2.473577e+06 3.768045e+06 4.552870e+06 6.157817e+06 6.930869e+06 7.934573e+06 1.019861e+07 1.116766e+07 1.324323e+07 1.410602e+07 1.459375e+07 1.515375e+07 1.515998e+07 1.517437e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-10 7686.279785 54974.970759 116238.688091 175099.228934 275348.345322 335545.263866 471884.698064 576790.645225 735260.475092 8.555950e+05 9.482086e+05 1.034936e+06 1.138223e+06 1.187993e+06 1.294868e+06 1.355724e+06 1.419712e+06 1.457313e+06 1.501579e+06 1.529614e+06 1.613779e+06 1.632180e+06 1.746606e+06 1.765532e+06 1.806910e+06 1.836320e+06 2.174113e+06 2.519925e+06 3.926413e+06 5.105051e+06 5.757500e+06 7.056210e+06 8.257514e+06 8.790797e+06 8.790797e+06 9.536522e+06 1.011005e+07 1.039005e+07 1.039005e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-11 NaN 40085.299101 91636.572757 112560.477050 254301.803837 365833.657433 443699.235407 508997.690899 660867.744220 7.429288e+05 8.046260e+05 8.977490e+05 9.815526e+05 1.025466e+06 1.102743e+06 1.146192e+06 1.182286e+06 1.250814e+06 1.267165e+06 1.449628e+06 1.512876e+06 1.812493e+06 1.839472e+06 1.998785e+06 2.308626e+06 2.774328e+06 3.046437e+06 3.921712e+06 4.594792e+06 5.354966e+06 6.788126e+06 7.617161e+06 9.900574e+06 1.120646e+07 1.267127e+07 1.385002e+07 1.425480e+07 1.446091e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-12 2338.009450 45034.198587 101928.638230 197043.351557 243033.192117 341992.144323 418395.410115 528516.048037 671436.850298 7.738165e+05 8.346629e+05 8.897036e+05 9.357052e+05 1.007127e+06 1.037005e+06 1.082897e+06 1.107119e+06 1.191268e+06 1.227168e+06 1.284106e+06 1.582291e+06 1.625071e+06 1.661366e+06 1.754008e+06 1.765126e+06 1.964884e+06 2.002955e+06 2.680120e+06 3.256739e+06 4.370567e+06 6.166003e+06 7.504662e+06 8.805520e+06 9.283402e+06 1.060418e+07 1.095207e+07 1.156359e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-01 NaN 18809.131752 62452.208015 205928.573421 340537.201277 413375.741877 547711.608413 638521.225611 714895.755263 7.592136e+05 8.041775e+05 9.202704e+05 9.926603e+05 1.070192e+06 1.162752e+06 1.233544e+06 1.326967e+06 1.346332e+06 1.397467e+06 1.455774e+06 1.554331e+06 1.726160e+06 1.742518e+06 1.907098e+06 2.178388e+06 2.442297e+06 3.173395e+06 3.435192e+06 3.879653e+06 4.822684e+06 5.476739e+06 7.460054e+06 8.560791e+06 9.559271e+06 1.141259e+07 1.232403e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-02 12412.355755 50819.201311 84212.984079 152050.744763 285826.215880 415944.890973 479901.245175 583482.869079 685604.430937 8.334853e+05 9.280047e+05 9.987083e+05 1.076872e+06 1.132864e+06 1.166633e+06 1.226976e+06 1.282182e+06 1.325917e+06 1.410696e+06 1.487068e+06 1.641287e+06 1.689530e+06 1.717177e+06 1.876215e+06 1.886758e+06 2.455530e+06 3.250739e+06 3.671281e+06 4.443214e+06 5.440044e+06 6.712540e+06 8.420303e+06 1.009931e+07 1.082421e+07 1.139578e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-03 4368.139941 66144.027461 96280.032524 240903.228176 296325.617733 401235.315013 456444.075280 511718.773825 604575.351911 7.291760e+05 8.071298e+05 9.145646e+05 9.584054e+05 1.038138e+06 1.076188e+06 1.139222e+06 1.218592e+06 1.234664e+06 1.296536e+06 1.344439e+06 1.394182e+06 1.418663e+06 1.444011e+06 1.584855e+06 1.862013e+06 2.310433e+06 3.892538e+06 5.021140e+06 5.574396e+06 7.089948e+06 8.289342e+06 9.719171e+06 9.898121e+06 1.180215e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-04 4120.397718 48136.480884 90234.655582 171171.831977 282669.084286 383768.609533 502884.348900 631703.941012 733868.221551 8.307866e+05 8.814474e+05 9.543965e+05 1.028820e+06 1.056501e+06 1.138887e+06 1.171349e+06 1.201898e+06 1.289658e+06 1.348807e+06 1.410483e+06 1.460397e+06 1.619360e+06 1.641170e+06 1.651899e+06 2.601017e+06 2.879419e+06 3.479705e+06 4.070051e+06 5.741701e+06 6.964393e+06 7.863812e+06 9.498506e+06 1.071252e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-05 NaN 16343.780275 151027.715467 298625.076470 465503.938694 592999.458433 673628.446237 728497.240037 848361.103260 9.629240e+05 1.020383e+06 1.080848e+06 1.155629e+06 1.292738e+06 1.377279e+06 1.441118e+06 1.494440e+06 1.579041e+06 1.621859e+06 1.652201e+06 1.675680e+06 1.701676e+06 1.733827e+06 1.791013e+06 2.164018e+06 2.420148e+06 2.945114e+06 3.352466e+06 4.198945e+06 5.604401e+06 7.009263e+06 7.884871e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-06 9191.127816 43454.752677 71075.058928 210112.974043 323666.231316 416758.277092 484465.675242 644368.211458 747614.564550 9.378900e+05 9.988688e+05 1.104821e+06 1.196278e+06 1.247375e+06 1.307117e+06 1.348164e+06 1.396213e+06 1.461228e+06 1.516466e+06 1.570241e+06 1.644953e+06 1.710356e+06 2.227271e+06 2.566189e+06 2.598698e+06 2.623378e+06 2.896387e+06 3.339410e+06 3.835181e+06 5.095270e+06 5.569322e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-07 5872.315796 67303.607634 180825.981810 276856.460384 410123.305102 540428.343433 605333.597623 663813.723941 758798.389148 8.309819e+05 9.099655e+05 9.842065e+05 1.037116e+06 1.127842e+06 1.201599e+06 1.254823e+06 1.336352e+06 1.372892e+06 1.392408e+06 1.437006e+06 1.590514e+06 1.629455e+06 1.662239e+06 1.720897e+06 1.742079e+06 2.284836e+06 2.770599e+06 3.502643e+06 5.191910e+06 6.025245e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-08 12275.876950 85941.848117 155713.369762 312259.518824 409088.154117 531980.807639 609506.098940 730103.261808 772316.719317 8.539199e+05 9.182385e+05 1.000826e+06 1.071649e+06 1.125718e+06 1.211998e+06 1.273410e+06 1.302592e+06 1.396399e+06 1.406991e+06 1.527465e+06 1.571819e+06 1.628890e+06 1.684346e+06 1.753338e+06 1.962163e+06 2.014510e+06 2.571859e+06 3.054425e+06 4.068154e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-09 29401.764323 74923.780122 151891.820326 298731.768880 470405.448136 579834.544145 665925.439380 764439.523860 847904.408071 9.091937e+05 1.016139e+06 1.139760e+06 1.175766e+06 1.287583e+06 1.385395e+06 1.456945e+06 1.590027e+06 1.642087e+06 1.711468e+06 1.918107e+06 1.964804e+06 1.989559e+06 2.030585e+06 2.436238e+06 2.564045e+06 2.714637e+06 3.294488e+06 3.736579e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-10 14000.000000 32823.580135 97632.466614 183744.836334 217696.723697 312494.871124 353968.331368 468247.550586 563718.002848 6.590403e+05 7.436118e+05 8.308951e+05 8.877213e+05 9.341397e+05 1.036498e+06 1.073131e+06 1.128463e+06 1.190048e+06 1.232361e+06 1.251752e+06 1.310679e+06 1.415600e+06 1.434829e+06 1.591037e+06 1.645805e+06 1.952372e+06 2.549249e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-11 NaN 24865.758304 99373.210822 152317.316588 261676.877723 359812.859413 520193.403606 566704.877016 773479.044025 9.021042e+05 1.027854e+06 1.107404e+06 1.183596e+06 1.273468e+06 1.340706e+06 1.401662e+06 1.426101e+06 1.447149e+06 1.485159e+06 1.552706e+06 1.569795e+06 1.581789e+06 1.621413e+06 1.757645e+06 2.177583e+06 2.197139e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-12 14000.000000 48498.369406 134003.781715 247849.594850 366088.824576 487366.043020 666399.317643 797728.955859 928085.824535 1.017398e+06 1.067399e+06 1.199119e+06 1.313215e+06 1.393721e+06 1.441067e+06 1.548920e+06 1.600291e+06 1.662679e+06 1.685611e+06 1.734294e+06 1.898582e+06 2.055067e+06 2.089757e+06 2.260778e+06 2.649534e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-01 3213.543940 19734.228559 68339.454667 131781.563389 196111.859570 352647.385341 423183.135670 513369.096797 602016.194495 6.892939e+05 7.268795e+05 8.567179e+05 9.064933e+05 1.005971e+06 1.085454e+06 1.163163e+06 1.220078e+06 1.270053e+06 1.319866e+06 1.331589e+06 1.374022e+06 1.466467e+06 1.532124e+06 1.834273e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - 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"2016-04 14780.520614 50521.979191 102339.527767 226534.476355 302754.049580 471575.469280 589080.041076 672058.053963 771206.547665 8.838705e+05 9.312278e+05 1.023206e+06 1.082891e+06 1.201150e+06 1.239516e+06 1.297653e+06 1.351891e+06 1.430309e+06 1.501636e+06 1.560918e+06 1.646322e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-05 NaN 32920.776360 113494.422593 242171.626096 340662.893807 478898.464832 589023.795320 699911.063315 850192.047606 9.250408e+05 9.790926e+05 1.084657e+06 1.127531e+06 1.179474e+06 1.213692e+06 1.308513e+06 1.389565e+06 1.467833e+06 1.481509e+06 1.507492e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - 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"2010-08 5699.233143 26209.224424 45890.405723 69173.188463 55511.369501 80425.939928 33133.785414 47254.211352 78262.484498 95433.303584 58511.510642 54399.763546 38357.221016 14971.133539 62398.577205 22288.793384 38439.989384 71453.867046 11709.587806 80888.734320 13328.132680 4547.917779 362751.219555 11632.329370 119223.782300 4311.879333 1.957425e+05 1.881358e+05 1.144064e+06 2.032181e+06 1.797426e+06 1.527757e+06 1.223990e+06 1.319875e+06 3.947015e+05 2.256358e+06 NaN 164940.620910 7000.000000 21616.562078 10270.396630 NaN 8297.704119 NaN NaN 7000.000000 NaN 7000.000000 NaN 725.740593 NaN 2798.187880 17104.030233 NaN NaN 14000.000000 3220.950892 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5026.874645 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2010-09 18547.088350 30144.612056 63287.858606 97818.468281 61706.925367 49721.448081 114628.735167 115183.502698 90869.599744 72768.554901 79515.131173 59945.902141 35075.229500 68805.706289 30444.643237 54962.652897 69113.287732 47682.350455 56285.096434 38229.711118 27948.038901 21099.470253 47091.043858 271169.549096 34123.202937 231779.596450 2.873676e+05 1.440934e+05 1.232081e+06 1.141942e+06 1.477778e+06 1.269932e+06 1.549878e+06 1.730864e+06 7.226326e+05 4.783313e+05 4.918770e+05 228667.538200 290906.103499 28146.607206 17027.618820 4951.162504 NaN NaN 13163.771626 3558.089888 3589.664771 NaN NaN 6242.821014 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 19000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - 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"2014-04 25232.184583 14000.000000 60052.904826 81449.449158 98592.403509 83581.205827 87931.778124 104719.749933 83453.671453 98973.927374 58561.568221 58437.774627 76255.738051 101390.269022 69617.702510 40110.635040 38710.569752 25366.465042 61955.884883 25009.266000 31598.187314 54048.803677 11851.394530 141845.787481 524859.850658 693237.065059 1.309337e+05 3.152621e+05 5.084428e+05 9.868936e+05 1.107499e+06 1.628701e+06 9.459133e+05 1.994376e+06 1.248361e+06 7.000000e+03 2.861492e+05 NaN NaN 3823.738205 7000.000000 NaN NaN NaN 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-05 NaN 17227.928985 27750.821450 64920.312930 52425.305222 104786.922285 51431.543443 75141.609363 94043.024894 110458.538993 91977.050437 94845.116068 42100.162714 37766.071179 70352.134251 38031.178080 27324.267980 43493.409227 73484.344127 47879.400389 40091.072625 43696.871650 197505.830961 354590.402357 796275.221621 501822.287745 9.407318e+04 2.086054e+05 8.400150e+05 5.353851e+05 1.222320e+06 9.372390e+05 1.124267e+06 1.809481e+06 7.518762e+05 1.112245e+06 7.122555e+05 5503.027815 NaN NaN NaN 19000.000000 NaN 12407.219270 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-06 NaN 32351.229848 31836.316782 66289.251427 81787.337215 107053.192243 143595.368132 59589.625334 9302.495418 76650.181977 124141.065286 83364.436760 41465.820645 86065.819186 34686.068726 55951.600356 83707.079866 55792.122597 165451.433796 60042.224137 31799.379255 22992.429547 10304.208798 47204.539329 309685.077359 295442.552497 1.044091e+06 6.892586e+05 8.172055e+05 1.459404e+06 7.957606e+05 1.894393e+06 1.258487e+06 1.485590e+06 1.291553e+06 8.570753e+05 5.338876e+05 466078.465078 NaN 295421.578684 10671.210804 17585.537431 17936.294110 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-07 17395.126262 43535.215270 94354.149357 87021.707853 92540.914175 106502.437536 59634.837614 111315.719528 94168.086970 52377.829211 58395.495132 128251.420261 80860.585272 46192.051861 37533.072122 61260.917809 56111.977217 19568.079190 52970.665605 14526.357084 45278.152241 91969.711148 316389.200871 23371.563584 352996.439150 367989.597703 4.783819e+05 4.344751e+05 8.478634e+05 1.255281e+06 6.505439e+05 1.765331e+06 1.514355e+06 1.560650e+06 8.758149e+05 9.140881e+05 3.634125e+05 NaN 8398.633653 2863.147884 28156.702124 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-08 17118.276431 42752.625229 84450.652063 114023.717706 152833.806072 114121.212678 43485.638169 218119.103107 86276.046200 62235.259926 87107.280149 105437.946739 68083.995035 83227.247222 11694.651496 40618.768296 94119.673368 86156.536419 185570.010872 58978.612847 25249.083375 160572.315930 287744.976689 147455.676455 179415.635379 386769.056379 1.047920e+06 5.811764e+05 6.703268e+05 7.402687e+05 9.899028e+05 1.345040e+06 1.048110e+06 5.172193e+05 5.718047e+05 7.412821e+05 4.745204e+05 553602.058553 NaN 4739.020481 8852.389127 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-09 11766.356540 70613.585057 90230.081727 101852.600266 76066.255398 89862.791351 151175.866617 100473.424656 103208.746962 58935.152592 97079.614731 129682.745064 57545.028781 86011.194043 52566.195198 33350.920165 77655.827773 28722.831894 36532.136163 28919.582885 60075.572549 72839.456149 21977.481030 130712.385050 481402.426382 34469.897880 1.898491e+05 1.294468e+06 7.848245e+05 1.604948e+06 7.730516e+05 1.003704e+06 2.264034e+06 9.690580e+05 2.075566e+06 8.627908e+05 4.877316e+05 560000.000000 6225.141106 14392.718433 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-10 7686.279785 47288.690974 61263.717332 58860.540843 100249.116388 60196.918544 136339.434198 104905.947161 158469.829867 120334.556147 92613.523206 86727.441392 103286.805116 49770.280858 106875.401280 60855.470633 63987.812014 37601.528987 44265.371000 28034.890801 84165.344156 18401.552009 114425.800160 18925.289270 41378.655775 29410.028093 3.377927e+05 3.458116e+05 1.406488e+06 1.178639e+06 6.524484e+05 1.298710e+06 1.201304e+06 5.332826e+05 NaN 7.457256e+05 5.735299e+05 280000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-11 NaN 40085.299101 51551.273656 20923.904293 141741.326787 111531.853596 77865.577974 65298.455492 151870.053321 82061.009567 61697.287153 93122.910777 83803.651162 43913.672786 77276.779451 43448.623639 36094.214541 68528.384644 16350.415372 182462.893770 63248.816014 299616.780888 26978.323945 159313.694759 309841.096392 465701.774370 2.721091e+05 8.752746e+05 6.730805e+05 7.601739e+05 1.433160e+06 8.290349e+05 2.283413e+06 1.305887e+06 1.464814e+06 1.178750e+06 4.047749e+05 206112.848459 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2014-12 2338.009450 42696.189137 56894.439643 95114.713327 45989.840560 98958.952206 76403.265792 110120.637922 142920.802261 102379.619600 60846.409583 55040.740891 46001.614251 71422.187096 29877.757318 45891.358951 24222.513506 84149.338348 35899.811225 56938.134099 298184.935001 42779.811143 36294.889113 92641.894669 11117.833168 199758.341469 3.807108e+04 6.771645e+05 5.766198e+05 1.113827e+06 1.795436e+06 1.338659e+06 1.300858e+06 4.778822e+05 1.320780e+06 3.478895e+05 6.115183e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-01 NaN 18809.131752 43643.076263 143476.365405 134608.627856 72838.540601 134335.866535 90809.617198 76374.529652 44317.805600 44963.949373 116092.938968 72389.838029 77531.956013 92559.582908 70792.363429 93422.566104 19364.960856 51135.080708 58307.591149 98557.046637 171828.994625 16357.206094 164580.481733 271290.207578 263908.608181 7.310979e+05 2.617977e+05 4.444601e+05 9.430313e+05 6.540549e+05 1.983315e+06 1.100738e+06 9.984796e+05 1.853317e+06 9.114406e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-02 12412.355755 38406.845556 33393.782768 67837.760684 133775.471117 130118.675093 63956.354202 103581.623904 102121.561858 147880.848889 94519.467458 70703.530746 78163.588148 55992.518926 33768.401630 60343.512442 55206.045005 43734.718542 84779.334566 76371.753878 154218.819587 48242.969027 27646.576463 159038.475259 10543.399407 568771.844307 7.952086e+05 4.205424e+05 7.719330e+05 9.968293e+05 1.272497e+06 1.707763e+06 1.679003e+06 7.249045e+05 5.715680e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-03 4368.139941 61775.887520 30136.005063 144623.195652 55422.389557 104909.697280 55208.760267 55274.698545 92856.578086 124600.672630 77953.801804 107434.771768 43840.814860 79732.787937 38050.018025 63033.915023 79369.665209 16071.851556 61872.697863 47902.333668 49743.103874 24480.870749 25348.099244 140844.245330 277158.288574 448419.242716 1.582105e+06 1.128602e+06 5.532564e+05 1.515552e+06 1.199394e+06 1.429829e+06 1.789496e+05 1.904034e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-04 4120.397718 44016.083166 42098.174698 80937.176395 111497.252309 101099.525247 119115.739368 128819.592112 102164.280538 96918.413806 50660.778972 72949.065053 74423.421664 27681.022975 82385.876087 32462.299607 30549.388036 87759.712991 59149.196196 61675.427831 49913.956218 158963.301057 21810.137418 10729.064205 949117.308496 278402.094726 6.002861e+05 5.903465e+05 1.671649e+06 1.222692e+06 8.994191e+05 1.634694e+06 1.214017e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-05 NaN 16343.780275 134683.935192 147597.361003 166878.862224 127495.519739 80628.987804 54868.793800 119863.863223 114562.929158 57458.774398 60464.764226 74781.341514 137108.989006 84540.621394 63839.882522 53321.314742 84600.798385 42817.982258 30342.687954 23479.032326 25995.936780 32150.807773 57185.820235 373004.903124 256129.870303 5.249663e+05 4.073522e+05 8.464785e+05 1.405456e+06 1.404862e+06 8.756084e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-06 9191.127816 34263.624861 27620.306251 139037.915115 113553.257273 93092.045776 67707.398150 159902.536216 103246.353092 190275.446707 60978.815043 105951.864825 91456.906547 51097.242328 59742.359319 41047.145476 48048.962654 65014.351742 55238.028473 53775.598296 74711.669645 65403.459055 516914.261126 338918.453160 32508.514160 24680.414930 2.730094e+05 4.430222e+05 4.957713e+05 1.260089e+06 4.740520e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-07 5872.315796 61431.291838 113522.374176 96030.478574 133266.844718 130305.038331 64905.254190 58480.126318 94984.665207 72183.521869 78983.569059 74241.045574 52909.888868 90725.673217 73757.223592 53223.803895 81529.001882 36539.587446 19516.717846 44597.550143 153507.609854 38940.929237 32784.556837 58657.933191 21181.943763 542757.405428 4.857625e+05 7.320445e+05 1.689266e+06 8.333349e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-08 12275.876950 73665.971167 69771.521645 156546.149062 96828.635293 122892.653522 77525.291302 120597.162868 42213.457509 81603.139232 64318.685029 82587.536028 70822.690426 54069.221152 86280.324966 61411.904761 29181.467011 93807.472649 10591.579043 120473.835509 44354.048698 57071.138285 55456.304615 68991.650804 208825.674298 52347.075661 5.573485e+05 4.825662e+05 1.013729e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-09 29401.764323 45522.015799 76968.040204 146839.948554 171673.679256 109429.096009 86090.895235 98514.084480 83464.884211 61289.279897 106945.334949 123620.792997 36005.695177 111817.545698 97812.012630 71550.378735 133081.814697 52059.620013 69381.213861 206638.571652 46697.251204 24755.437891 41026.125804 405652.063289 127807.538226 150591.819723 5.798513e+05 4.420908e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-10 14000.000000 18823.580135 64808.886479 86112.369720 33951.887363 94798.147426 41473.460244 114279.219218 95470.452262 95322.271315 84571.513563 87283.315644 56826.229766 46418.390772 102357.785120 36633.859700 55331.328885 61585.508226 42312.614360 19390.945153 58927.385073 104921.071975 19228.360716 156208.667108 54768.101002 306567.082809 5.968770e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-11 NaN 24865.758304 74507.452518 52944.105766 109359.561135 98135.981690 160380.544193 46511.473410 206774.167009 128625.196728 125749.283046 79550.899988 76191.422212 89872.173002 67238.315529 60955.421537 24439.191831 21047.667487 38010.055279 67547.641213 17088.640029 11994.180304 39623.869759 136232.327155 419937.900185 19556.256954 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2015-12 14000.000000 34498.369406 85505.412309 113845.813135 118239.229726 121277.218444 179033.274623 131329.638216 130356.868676 89312.029471 50001.263705 131720.019853 114096.053064 80505.435581 47345.897743 107853.847684 51370.371877 62388.220925 22932.368203 48682.707999 164287.941261 156485.280890 34689.496090 171021.042677 388756.671990 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-01 3213.543940 16520.684619 48605.226108 63442.108722 64330.296181 156535.525771 70535.750329 90185.961127 88647.097698 87277.722080 37585.548112 129838.454174 49775.368916 99477.619056 79483.384567 77709.064810 56914.923494 49974.636491 49813.369829 11723.025526 42432.701569 92444.945865 65657.025561 302148.945562 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-02 13891.629340 20721.691370 112166.316749 154318.566251 93667.327677 117578.257984 114468.238241 77207.158186 79019.522681 85925.357894 62818.373369 94653.274923 98626.259639 14412.050378 71903.488396 63442.861487 52747.037286 60027.653938 40008.382480 206021.036486 46890.568780 45358.088045 166532.167813 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-03 18353.550375 48494.631841 70674.791516 115885.521080 83612.153482 45276.454051 123735.672613 114338.914912 79022.075494 96179.004264 104153.365919 78420.661052 171254.111316 64222.446520 43806.575540 34980.385712 16933.353509 36318.272021 67986.442666 32546.776357 54145.301365 13493.482671 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-04 14780.520614 35741.458577 51817.548576 124194.948588 76219.573225 168821.419700 117504.571796 82978.012887 99148.493702 112663.905688 47357.387177 91977.866046 59685.252396 118258.978619 38365.804252 58137.086830 54237.801338 78418.236388 71326.721028 59282.610260 85403.308987 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-05 NaN 32920.776360 80573.646233 128677.203503 98491.267711 138235.571025 110125.330488 110887.267995 150280.984291 74848.724902 54051.819831 105564.489585 42873.921065 51942.612413 34218.518171 94820.525215 81052.688242 78267.767639 13675.949254 25982.914677 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-06 17958.638920 9031.290904 35720.570300 163643.856247 172139.047804 157652.546874 154167.597823 120193.385494 100214.093197 62572.447623 82107.496478 108154.178521 70208.521568 37141.252390 86357.027860 69921.829085 62875.428241 20423.255196 24443.879967 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-07 36454.457248 24782.897875 105234.099377 116615.976124 64114.451842 118425.785680 107282.550162 101314.686533 136164.358434 95887.527655 160987.563259 65113.829226 110219.023402 104432.987704 50962.266199 136896.654248 80842.270086 95857.222846 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-08 45280.373036 14224.178374 93540.570848 147757.945387 88369.564695 195297.446098 117493.748560 57655.840012 132907.265603 100460.044301 84736.522917 101950.878263 81994.715213 58406.357588 55984.279463 54491.161014 87532.982072 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-09 13543.632993 56468.414754 84514.463120 106895.796922 127087.926687 89314.051675 132691.450853 153691.498737 62591.301678 133208.213752 88052.362757 64577.593705 63639.041274 97667.426442 102568.832260 92328.114290 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-10 1765.824878 61195.914947 80436.515750 123346.321677 82946.887915 158136.929363 168589.126365 110018.518191 86504.029415 162549.196934 80314.320545 131253.273865 95130.219265 22490.001498 61574.451140 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-11 NaN 55837.251699 111441.725643 110741.296392 111772.167542 44847.384719 164147.485673 180125.984382 90888.981098 71973.259517 92692.770710 204372.431433 43569.263275 35989.538721 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2016-12 NaN 52805.659963 55386.352687 116174.812373 107610.806877 109759.341040 74635.362208 145938.259214 100703.178078 114896.817293 98215.783375 74192.406168 145322.425729 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-01 8337.875863 26000.000000 121221.366085 104138.452826 113299.545815 86085.752607 139369.099191 127013.485742 66154.704652 111713.185860 83569.214341 161872.714059 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-02 NaN 50237.487443 101651.411862 103478.579796 94797.505236 94285.239288 160239.006593 141801.930962 131584.577665 94257.374842 59566.901626 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-03 4476.248773 20775.191311 74392.325782 72249.752924 143323.397539 56307.436025 210407.467883 117053.877647 101546.367791 93076.579694 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-04 NaN 30750.642034 126807.879895 154838.817832 157090.350860 72105.725147 102587.339458 91454.397901 142688.966324 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-05 3022.335184 36811.264195 54334.199788 80436.891802 81314.614006 160837.572465 180651.804749 88441.462952 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-06 NaN 31858.167598 37910.528589 81228.553673 121618.983828 85184.796073 110103.351522 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-07 28827.335193 30987.279514 126229.848204 72825.784504 80075.315891 119390.465916 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-08 NaN 14000.000000 105952.492729 81991.210907 174870.150263 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-09 18589.176575 33316.659725 86056.984325 152143.727464 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-10 NaN 35037.196888 104444.385044 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-11 4088.116947 10599.333280 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017-12 10748.025150 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_incr = prism_cum.cum_to_incr()\n", - "prism_incr[\"Paid\"].sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default (and in concert with the `pandas` philosophy), the methods associated with the `Triangle` class strive for immutability. This means that the `incr_to_cum` and `cum_to_incr` methods will return new a new triangle object that must be assigned, or it is thrown away. Many of the `chainladder.Triangle` methods have an `inplace` argument, or alternatively you can just use variable reassignment to store the transformed triangle object." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# This works\n", - "prism.incr_to_cum(inplace=True)\n", - "# So does this\n", - "prism = prism.incr_to_cum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When dealing with triangles that have an `origin` axis, `development` axis, or both at a monthly or quarterly grain, the triangle can be summarized to a higher grain using the `grain` method.\n", - "\n", - "The grain to which you want your triangle converted, specified as \"OxDy\" where \"x\" and \"y\" can take on values of \"M\", \"Q\", or \"Y\". For example:\n", - "* `grain(OYDY)` for Origin Year x Development Year.\n", - "* `grain(OQDM)` for Origin Quarter x Development Month." - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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20166,080,96218,588,057
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" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "2008 3.404254e+06 1.119109e+07 7.461301e+07 1.503428e+08 1.509829e+08 1.511527e+08 1.512289e+08 1.512648e+08 1.512772e+08 1.512842e+08\n", - "2009 3.609385e+06 1.100293e+07 8.072635e+07 1.569708e+08 1.575995e+08 1.576971e+08 1.577364e+08 1.577434e+08 1.577487e+08 NaN\n", - "2010 4.067321e+06 1.239678e+07 7.421004e+07 1.610496e+08 1.616415e+08 1.617871e+08 1.618596e+08 1.618702e+08 NaN NaN\n", - "2011 4.125232e+06 1.318314e+07 8.123977e+07 1.614129e+08 1.621876e+08 1.624175e+08 1.624907e+08 NaN NaN NaN\n", - "2012 4.584036e+06 1.400118e+07 7.779452e+07 1.521184e+08 1.525881e+08 1.528195e+08 NaN NaN NaN NaN\n", - "2013 4.889623e+06 1.460774e+07 8.441850e+07 1.611103e+08 1.616730e+08 NaN NaN NaN NaN NaN\n", - "2014 5.546158e+06 1.640813e+07 7.725679e+07 1.549699e+08 NaN NaN NaN NaN NaN NaN\n", - "2015 5.909029e+06 1.742761e+07 8.091458e+07 NaN NaN NaN NaN NaN NaN NaN\n", - "2016 6.080962e+06 1.858806e+07 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017 6.396536e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_OYDY = prism.grain(\"OYDY\")\n", - "prism_OYDY[\"Paid\"].sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Depending on the type of analysis being done, it may be more convenient to look at a triangle with its `development` axis expressed as a valuation rather than an age. This is also what the Schedule Ps look like. To do this, `Triangle` has two methods for toggling between a development triangle and a valuation triangle. The methods are `dev_to_val` and its inverse `val_to_dev`." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2008200920102011201220132014201520162017
20083,404,25411,191,08574,613,012150,342,751150,982,873151,152,726151,228,872151,264,806151,277,217151,284,217
20093,609,38511,002,92780,726,352156,970,789157,599,460157,697,094157,736,386157,743,386157,748,735
20104,067,32112,396,77774,210,043161,049,586161,641,453161,787,135161,859,565161,870,156
20114,125,23213,183,14481,239,771161,412,913162,187,629162,417,460162,490,681
20124,584,03614,001,17877,794,522152,118,384152,588,090152,819,473
20134,889,62314,607,74284,418,503161,110,312161,673,036
20145,546,15816,408,12677,256,792154,969,931
20155,909,02917,427,61180,914,580
20166,080,96218,588,057
20176,396,536
" - ], - "text/plain": [ - " 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017\n", - "2008 3.404254e+06 1.119109e+07 7.461301e+07 1.503428e+08 1.509829e+08 1.511527e+08 1.512289e+08 1.512648e+08 1.512772e+08 1.512842e+08\n", - "2009 NaN 3.609385e+06 1.100293e+07 8.072635e+07 1.569708e+08 1.575995e+08 1.576971e+08 1.577364e+08 1.577434e+08 1.577487e+08\n", - "2010 NaN NaN 4.067321e+06 1.239678e+07 7.421004e+07 1.610496e+08 1.616415e+08 1.617871e+08 1.618596e+08 1.618702e+08\n", - "2011 NaN NaN NaN 4.125232e+06 1.318314e+07 8.123977e+07 1.614129e+08 1.621876e+08 1.624175e+08 1.624907e+08\n", - "2012 NaN NaN NaN NaN 4.584036e+06 1.400118e+07 7.779452e+07 1.521184e+08 1.525881e+08 1.528195e+08\n", - "2013 NaN NaN NaN NaN NaN 4.889623e+06 1.460774e+07 8.441850e+07 1.611103e+08 1.616730e+08\n", - "2014 NaN NaN NaN NaN NaN NaN 5.546158e+06 1.640813e+07 7.725679e+07 1.549699e+08\n", - "2015 NaN NaN NaN NaN NaN NaN NaN 5.909029e+06 1.742761e+07 8.091458e+07\n", - "2016 NaN NaN NaN NaN NaN NaN NaN NaN 6.080962e+06 1.858806e+07\n", - "2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN 6.396536e+06" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_OYDY_val = prism_OYDY.dev_to_val()\n", - "prism_OYDY_val[\"Paid\"].sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When working with real-world data, the triangles can have holes, such as missing evluation(s), or no losses in certain origin(s). In these cases, it doesn't make sense to include empty accident periods or development ages. For example, in the `prism` dataset, the \"Home\" line has its latest accidents through 2016, and have no payments in development age '12'. Sometimes, dropping the non-applicable fields is usefule with the `dropna()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
20081,129,30561,658,874136,520,554136,800,554136,800,554136,800,554136,800,554136,800,554136,800,554
2009187,29267,134,599142,267,946142,547,946142,547,946142,547,946142,547,946142,547,946
2010620,60359,082,456144,757,200144,757,200144,757,200144,757,200144,757,200
2011503,29665,048,051143,971,618144,251,618144,251,618144,251,618
2012599,27760,536,642133,412,416133,412,416133,412,416
2013536,30366,443,157141,645,782141,645,782
2014965,97357,394,263133,542,848
2015371,01559,047,107
2016640,179
2017
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "2008 NaN 1.129305e+06 6.165887e+07 1.365206e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08\n", - "2009 NaN 1.872920e+05 6.713460e+07 1.422679e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 NaN\n", - "2010 NaN 6.206026e+05 5.908246e+07 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 NaN NaN\n", - "2011 NaN 5.032956e+05 6.504805e+07 1.439716e+08 1.442516e+08 1.442516e+08 1.442516e+08 NaN NaN NaN\n", - "2012 NaN 5.992768e+05 6.053664e+07 1.334124e+08 1.334124e+08 1.334124e+08 NaN NaN NaN NaN\n", - "2013 NaN 5.363029e+05 6.644316e+07 1.416458e+08 1.416458e+08 NaN NaN NaN NaN NaN\n", - "2014 NaN 9.659729e+05 5.739426e+07 1.335428e+08 NaN NaN NaN NaN NaN NaN\n", - "2015 NaN 3.710149e+05 5.904711e+07 NaN NaN NaN NaN NaN NaN NaN\n", - "2016 NaN 6.401785e+05 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_OYDY.loc[\"Home\"][\"Paid\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see what happens if we have no data for 2011." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
20081,129,30561,658,874136,520,554136,800,554136,800,554136,800,554136,800,554136,800,554136,800,554
2009187,29267,134,599142,267,946142,547,946142,547,946142,547,946142,547,946142,547,946
2010620,60359,082,456144,757,200144,757,200144,757,200144,757,200144,757,200
2011
2012599,27760,536,642133,412,416133,412,416133,412,416
2013536,30366,443,157141,645,782141,645,782
2014965,97357,394,263133,542,848
2015371,01559,047,107
2016640,179
2017
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "2008 NaN 1.129305e+06 6.165887e+07 1.365206e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08\n", - "2009 NaN 1.872920e+05 6.713460e+07 1.422679e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 NaN\n", - "2010 NaN 6.206026e+05 5.908246e+07 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 NaN NaN\n", - "2011 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012 NaN 5.992768e+05 6.053664e+07 1.334124e+08 1.334124e+08 1.334124e+08 NaN NaN NaN NaN\n", - "2013 NaN 5.363029e+05 6.644316e+07 1.416458e+08 1.416458e+08 NaN NaN NaN NaN NaN\n", - "2014 NaN 9.659729e+05 5.739426e+07 1.335428e+08 NaN NaN NaN NaN NaN NaN\n", - "2015 NaN 3.710149e+05 5.904711e+07 NaN NaN NaN NaN NaN NaN NaN\n", - "2016 NaN 6.401785e+05 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_df_2011 = prism_df.copy()\n", - "prism_df_2011.loc[\n", - " (prism_df_2011[\"AccidentDate\"] >= \"2011-01-01\")\n", - " & (prism_df_2011[\"AccidentDate\"] < \"2012-01-01\"),\n", - " \"Paid\",\n", - "] = None # Let's assume we hav no payments for losses occurred in 2011\n", - "prism_2011 = cl.Triangle(\n", - " data=prism_df_2011,\n", - " origin=\"AccidentDate\",\n", - " origin_format=\"%Y-%m-%d\",\n", - " development=\"PaymentDate\",\n", - " development_format=\"%Y-%m-%d\",\n", - " columns=[\"Paid\", \"Incurred\"],\n", - " index=[\"Line\", \"Type\"],\n", - " cumulative=False,\n", - ")\n", - "prism_2011.incr_to_cum(inplace=True)\n", - "prism_2011_OYDY = prism_2011.grain(\"OYDY\")\n", - "prism_2011_OYDY.loc[\"Home\"][\"Paid\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the `dropna()` method will retain empty periods if they are surrounded by non-empty periods with valid data." - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
24364860728496108120
20081,129,30561,658,874136,520,554136,800,554136,800,554136,800,554136,800,554136,800,554136,800,554
2009187,29267,134,599142,267,946142,547,946142,547,946142,547,946142,547,946142,547,946
2010620,60359,082,456144,757,200144,757,200144,757,200144,757,200144,757,200
2011
2012599,27760,536,642133,412,416133,412,416133,412,416
2013536,30366,443,157141,645,782141,645,782
2014965,97357,394,263133,542,848
2015371,01559,047,107
2016640,179
" - ], - "text/plain": [ - " 24 36 48 60 72 84 96 108 120\n", - "2008 1.129305e+06 6.165887e+07 1.365206e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08\n", - "2009 1.872920e+05 6.713460e+07 1.422679e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 NaN\n", - "2010 6.206026e+05 5.908246e+07 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 NaN NaN\n", - "2011 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2012 5.992768e+05 6.053664e+07 1.334124e+08 1.334124e+08 1.334124e+08 NaN NaN NaN NaN\n", - "2013 5.363029e+05 6.644316e+07 1.416458e+08 1.416458e+08 NaN NaN NaN NaN NaN\n", - "2014 9.659729e+05 5.739426e+07 1.335428e+08 NaN NaN NaN NaN NaN NaN\n", - "2015 3.710149e+05 5.904711e+07 NaN NaN NaN NaN NaN NaN NaN\n", - "2016 6.401785e+05 NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_2011_OYDY_droppedna = prism_2011_OYDY.loc[\"Home\"].dropna()\n", - "prism_2011_OYDY_droppedna.loc[\"Home\"][\"Paid\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Commutative Properties of Triangle Methods\n", - "Where it makes sense, which is in most cases, the methods described above are commutative and can be applied in any order." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Commutative? True\n", - "Commutative? True\n" - ] - } - ], - "source": [ - "print(\"Commutative?\", prism.sum().latest_diagonal == prism.latest_diagonal.sum())\n", - "print(\"Commutative?\", prism.loc[\"Home\"].link_ratio == prism.link_ratio.loc[\"Home\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Triangle Imports and Exports\n", - "To the extent the `Triangle` can be expressed as a `pandas.DataFrame`, you can use any of the pandas IO to send the data in and out. Note that converting to pandas is a one-way ticket with no inverse functions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another useful function is to copy the triangle and put it in the clipboard so we can paste it elsewhere (such as Excel):" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1224364860728496108120
20081,129,30561,658,874136,520,554136,800,554136,800,554136,800,554136,800,554136,800,554136,800,554
2009187,29267,134,599142,267,946142,547,946142,547,946142,547,946142,547,946142,547,946
2010620,60359,082,456144,757,200144,757,200144,757,200144,757,200144,757,200
2011503,29665,048,051143,971,618144,251,618144,251,618144,251,618
2012599,27760,536,642133,412,416133,412,416133,412,416
2013536,30366,443,157141,645,782141,645,782
2014965,97357,394,263133,542,848
2015371,01559,047,107
2016640,179
2017
" - ], - "text/plain": [ - " 12 24 36 48 60 72 84 96 108 120\n", - "2008 NaN 1.129305e+06 6.165887e+07 1.365206e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08\n", - "2009 NaN 1.872920e+05 6.713460e+07 1.422679e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 NaN\n", - "2010 NaN 6.206026e+05 5.908246e+07 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 NaN NaN\n", - "2011 NaN 5.032956e+05 6.504805e+07 1.439716e+08 1.442516e+08 1.442516e+08 1.442516e+08 NaN NaN NaN\n", - "2012 NaN 5.992768e+05 6.053664e+07 1.334124e+08 1.334124e+08 1.334124e+08 NaN NaN NaN NaN\n", - "2013 NaN 5.363029e+05 6.644316e+07 1.416458e+08 1.416458e+08 NaN NaN NaN NaN NaN\n", - "2014 NaN 9.659729e+05 5.739426e+07 1.335428e+08 NaN NaN NaN NaN NaN NaN\n", - "2015 NaN 3.710149e+05 5.904711e+07 NaN NaN NaN NaN NaN NaN NaN\n", - "2016 NaN 6.401785e+05 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_OYDY.loc[\"Home\", \"Paid\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "prism_OYDY.loc[\"Home\", \"Paid\"].to_clipboard()\n", - "```\n", - "\n", - "Try to paste it elsewhere." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, if you want to store the triangle elsewhere but be able to reconstitute a triangle out of it later, then you can use:\n", - "* `Triangle.to_json` and its inverse `cl.read_json` for json format
\n", - "* `Triangle.to_pickle` and its inverse `cl.read_pickle` for pickle format
\n", - "\n", - "These have the added benefit of working on multi-dimensional triangles that don't fit into a DataFrame." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercises" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(2, 2, 120, 120)
Index:[Line, Type]
Columns:[Paid, Incurred]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2017-12\n", - "Grain: OMDM\n", - "Shape: (2, 2, 120, 120)\n", - "Index: [Line, Type]\n", - "Columns: [Paid, Incurred]" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism = cl.Triangle(\n", - " data=prism_df,\n", - " origin=\"AccidentDate\",\n", - " development=\"PaymentDate\",\n", - " columns=[\"Paid\", \"Incurred\"],\n", - " index=[\"Line\", \"Type\"], # multiple indices\n", - " cumulative=False,\n", - ").incr_to_cum()\n", - "prism" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. What is the case incurred activity for calendar periods in 2015Q2 (March, April, and May in 2015) by \"Line\"?" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
development2015-042015-052015-06
Line
Auto188397617043381692254
Home103679391081382114537617
\n", - "
" - ], - "text/plain": [ - "development 2015-04 2015-05 2015-06\n", - "Line \n", - "Auto 1883976 1704338 1692254\n", - "Home 10367939 10813821 14537617" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "incr_by_line = prism.groupby(\"Line\").sum().cum_to_incr()[\"Incurred\"].dev_to_val()\n", - "incr_by_line[\n", - " (incr_by_line.valuation >= \"2015-04-01\") & (incr_by_line.valuation < \"2015-07-01\")\n", - "].sum(\"origin\").to_frame(origin_as_datetime=True).astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. For accident year 2015, what proportion of our Paid amounts come from each \"Type\" of claims?" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2017-12
Grain:OYDY
Shape:(2, 2, 10, 10)
Index:[Line, Type]
Columns:[Paid, Incurred]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2017-12\n", - "Grain: OYDY\n", - "Shape: (2, 2, 10, 10)\n", - "Index: [Line, Type]\n", - "Columns: [Paid, Incurred]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prism_OYDY = prism.grain(\"OYDY\")\n", - "prism_OYDY" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Type\n", - "Dwelling 0.729746\n", - "PD 0.270254\n", - "dtype: float64" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "by_type = (\n", - " prism_OYDY.latest_diagonal[prism_OYDY.origin == \"2015\"][\"Paid\"]\n", - " .groupby(\"Type\")\n", - " .sum()\n", - " .to_frame(origin_as_datetime=True)\n", - ")\n", - "by_type / by_type.sum()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/tutorials_intro.md b/docs/tutorials/tutorials_intro.md deleted file mode 100644 index 55389a73..00000000 --- a/docs/tutorials/tutorials_intro.md +++ /dev/null @@ -1,4 +0,0 @@ -# Onboarding Tutorials - -These tutorials serve as the basic onboarding docs on how to use -Chainladder. diff --git a/docs/utilities.ipynb b/docs/utilities.ipynb deleted file mode 100644 index 6aa89f34..00000000 --- a/docs/utilities.ipynb +++ /dev/null @@ -1,171 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7e2c2fa7-9d32-404b-9121-51d06ac57324", - "metadata": {}, - "source": [ - "# Utilities\n", - "\n", - "Utilities contains example datasets and extra functionality to facilitate a\n", - "reserving workflow.\n", - "\n", - "\n", - "## Sample Datasets\n", - "\n", - "A variety of datasets can be loaded using :func:`load_sample()`. These are\n", - "sample datasets that are used in a variety of examples within this\n", - "documentation.\n", - "\n", - "\n", - "| Dataset | Description | \n", - "|-----------|------------------------------------------------------|\n", - "| abc | ABC Data |\n", - "| auto | Auto Data |\n", - "| berqsherm | Data from the Berquist Sherman paper |\n", - "| cc_sample | Sample Insurance Data for Cape Cod Method in Struhuss|\n", - "| clrd | CAS Loss Reserving Database |\n", - "| genins | General Insurance Data used in Clark |\n", - "| ia_sample | Sample data for Incremental Additive Method in Schmidt|\n", - "| liab | more data|\n", - "| m3ir5 | more data|\n", - "| mcl | Sample insurance data for Munich Adjustment in Quarg|\n", - "| mortgage | more data|\n", - "| mw2008 | more data|\n", - "| mw2014 | more data|\n", - "| quarterly | Sample data to demonstrate changing Triangle grain|\n", - "| raa | Sample data used in Mack Chainladder|\n", - "| ukmotor | more data|\n", - "| usaa | more data|\n", - "| usauto | more data|\n", - "\n", - "\n", - "## Chainladder Persistence\n", - "\n", - "All estimators can be persisted to disk or database\n", - "using ``to_json`` or ``to_pickle``. Restoring the estimator is as simple as\n", - "``cl.read_json`` or ``cl.read_pickle``.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "41ee963e-ad14-4f23-aac6-c878c237fb28", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'{\"params\": {}, \"__class__\": \"Chainladder\"}'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chainladder as cl\n", - "model_json = cl.Chainladder().fit(cl.load_sample('raa')).to_json()\n", - "model_json" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1b8a6ff8-66bb-4316-b232-915fd3f3b36c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Chainladder()" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cl.read_json(model_json)" - ] - }, - { - "cell_type": "markdown", - "id": "81e11750-6028-409b-bb4b-a0c8e352bab3", - "metadata": {}, - "source": [ - "The saved Estimator does not retain any fitted attributes, nor does it retain\n", - "the data on which it was fit. It is simply the model definition. However,\n", - "the Triangle itself can also be saved allowing for a full rehydration of the\n", - "original model." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f9f7e1a7-225c-4c80-a204-0df7ca359a22", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "52135.228261210155" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Dumping triangle to JSON\n", - "triangle_json = cl.load_sample('raa').to_json()\n", - "\n", - "# Recalling model and Triangle and rehydrating the results\n", - "cl.read_json(model_json).fit(cl.read_json(triangle_json)).ibnr_.sum('origin')" - ] - }, - { - "cell_type": "markdown", - "id": "41c59e8d-c735-4c1b-8963-a39b9284a3f8", - "metadata": {}, - "source": [ - "```{warning}\n", - "Some features of estimators may not be json-serializable, such as a `virtual_column`\n", - "or a callable hyperparameter. In these cases, JSON serialization will fail.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35db6c48-9b17-4c69-9316-1a2c10ce92ff", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/workflow.ipynb b/docs/workflow.ipynb deleted file mode 100644 index 8b2e2a22..00000000 --- a/docs/workflow.ipynb +++ /dev/null @@ -1,1905 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c3660882-d3f0-49ee-8793-1472a60c77be", - "metadata": {}, - "source": [ - "# Workflow\n", - "``chainladder`` facilitates practical reserving workflows." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d2c17983-fe5b-4c6c-be23-aa0e6e33ea2c", - "metadata": {}, - "outputs": [], - "source": [ - "import chainladder as cl\n", - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "%config InlineBackend.figure_format = 'svg'" - ] - }, - { - "cell_type": "markdown", - "id": "b468be77-f5dd-4f8c-8440-55dd60ddf578", - "metadata": {}, - "source": [ - "(workflow:pipeline)=\n", - "## Pipeline\n", - "The :class:`Pipeline` class implements utilities to build a composite\n", - "estimator, as a chain of transforms and estimators. Said differently, a\n", - "`Pipeline` is a way to wrap multiple estimators into a single compact object.\n", - "The `Pipeline` is borrowed from scikit-learn. As an example of compactness,\n", - "we can simulate a set of triangles using bootstrap sampling, apply volume-weigted\n", - "development, exponential tail curve fitting, and get the 95%-ile IBNR estimate.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "1f11205a-81fc-4e74-8c8b-9a0a823f2a78", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('sample', BootstrapODPSample(random_state=42)),\n", - " ('dev', Development()), ('tail', TailCurve()),\n", - " ('model', Chainladder())])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe = cl.Pipeline(\n", - " steps=[\n", - " ('sample', cl.BootstrapODPSample(random_state=42)),\n", - " ('dev', cl.Development(average='volume')),\n", - " ('tail', cl.TailCurve('exponential')),\n", - " ('model', cl.Chainladder())])\n", - "\n", - "pipe.fit(cl.load_sample('genins'))" - ] - }, - { - "cell_type": "markdown", - "id": "cbc68b86-3d18-48fe-a262-01a19d8576ed", - "metadata": {}, - "source": [ - "Each estimator contained within a pipelines ``steps`` can be accessed by name\n", - "using the ``named_steps`` attribute of the `Pipeline`." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "496d5622-0c82-4b35-899b-3530cca020b5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "26063265.939845018" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe.named_steps.model.ibnr_.sum('origin').quantile(q=.95)" - ] - }, - { - "cell_type": "markdown", - "id": "b54510f8-20e2-4714-8680-a0437c6a8a2c", - "metadata": {}, - "source": [ - "As mentioned in the `Development` section, a `Pipeline` coupled with `groupby` can really streamline you reserving analysis. Here we can develop IBNR for each company using LOB level development patterns. By delegating the `groupby` operation to the `Development` estimator in the `Pipeline`, we can fully specify the entire analysis as part of our hyperparameters. This achieves a clear separation of assumption setting from data manipulation." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "45e819bb-3a42-4b28-9bc9-ba507ddcc6f4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Triangle Summary
Valuation:2261-12
Grain:OYDY
Shape:(775, 1, 10, 1)
Index:[GRNAME, LOB]
Columns:[CumPaidLoss]
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2261-12\n", - "Grain: OYDY\n", - "Shape: (775, 1, 10, 1)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [CumPaidLoss]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample('clrd')['CumPaidLoss']\n", - "\n", - "pipe = cl.Pipeline(\n", - " steps=[\n", - " ('dev', cl.Development(groupby='LOB')),\n", - " ('tail', cl.TailCurve('exponential')),\n", - " ('model', cl.Chainladder())]).fit(clrd)\n", - "\n", - "pipe.named_steps.model.ibnr_" - ] - }, - { - "cell_type": "markdown", - "id": "2e81528d-3637-4151-bac6-d6168312b5dd", - "metadata": {}, - "source": [ - "(workflow:votingchainladder)=\n", - "## VotingChainladder\n", - "\n", - "The :class:`VotingChainladder` ensemble method allows the actuary to vote between\n", - "different underlying ``ibnr_`` by way of a matrix of weights.\n", - "\n", - "For example, the actuary may choose \n", - "* the :class:`Chainladder` method for the first 4 origin periods, \n", - "* the :class:`BornhuetterFerguson` method for the next 3 origin periods,\n", - "* and the :class:`CapeCod` method for the final 3." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "748cd4db-8234-4504-a641-29cc1cda5fc1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 2261\n", - "1981 18834.000000\n", - "1982 16857.953917\n", - "1983 24083.370924\n", - "1984 28703.142163\n", - "1985 28203.700714\n", - "1986 19840.005163\n", - "1987 18840.362337\n", - "1988 23106.943030\n", - "1989 20004.502125\n", - "1990 21605.832631" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa = cl.load_sample('RAA')\n", - "cl_ult = cl.Chainladder().fit(raa).ultimate_ # Chainladder Ultimate\n", - "apriori = cl_ult*0+(cl_ult.sum()/10) # Mean Chainladder Ultimate\n", - "\n", - "bcl = cl.Chainladder()\n", - "bf = cl.BornhuetterFerguson()\n", - "cc = cl.CapeCod()\n", - "\n", - "estimators = [('bcl', bcl), ('bf', bf), ('cc', cc)]\n", - "weights = np.array([[1, 0, 0]] * 4 + [[0, 1, 0]] * 3 + [[0, 0, 1]] * 3)\n", - "vot = cl.VotingChainladder(estimators=estimators, weights=weights)\n", - "vot.fit(raa, sample_weight=apriori)\n", - "vot.ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "e219ba10-79e5-444a-b810-ce57163e1276", - "metadata": {}, - "source": [ - "Alternatively, the actuary may choose to combine all methods using weights. Omitting\n", - "the weights parameter results in the average of all predictions assuming a weight of 1.\n", - "Alternatively, a default weight can be enforced through the `default_weight` parameter.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "15074fdb-e2f1-4353-9500-d842e9c56283", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - " 2261\n", - "1981 18834.000000\n", - "1982 16887.197765\n", - "1983 24041.977401\n", - "1984 28435.540175\n", - "1985 28466.807537\n", - "1986 19770.579236\n", - "1987 18547.931167\n", - "1988 23305.361472\n", - "1989 18530.213787\n", - "1990 20331.432662" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raa = cl.load_sample('RAA')\n", - "cl_ult = cl.Chainladder().fit(raa).ultimate_ # Chainladder Ultimate\n", - "apriori = cl_ult * 0 + (float(cl_ult.sum()) / 10) # Mean Chainladder Ultimate\n", - "\n", - "bcl = cl.Chainladder()\n", - "bf = cl.BornhuetterFerguson()\n", - "cc = cl.CapeCod()\n", - "\n", - "estimators = [('bcl', bcl), ('bf', bf), ('cc', cc)]\n", - "vot = cl.VotingChainladder(estimators=estimators)\n", - "vot.fit(raa, sample_weight=apriori)\n", - "vot.ultimate_" - ] - }, - { - "cell_type": "markdown", - "id": "d4251092-6d4c-4782-9380-e42e8dd1c207", - "metadata": {}, - "source": [ - "The weights can take the form of an array (as above), a dict, or a callable:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "176da773-eedc-4183-89f2-142945c2e4d4", - "metadata": {}, - "outputs": [], - "source": [ - "callable_weight = lambda origin : np.where(origin.year < 1985, (1, 0, 0), np.where(origin.year > 1987, (0, 0, 1), (0, 1, 0)))\n", - "\n", - "dict_weight = {\n", - " '1992': (0, 1, 0),\n", - " '1993': (0, 1, 0),\n", - " '1994': (0, 1, 0),\n", - " '1995': (0, 0, 1),\n", - " '1996': (0, 0, 1),\n", - " '1997': (0, 0, 1),\n", - "} # Unmapped origins will get `default_weight`\n" - ] - }, - { - "cell_type": "markdown", - "id": "43cd39c7-e0e1-428c-ad21-cf96a94fd7a4", - "metadata": {}, - "source": [ - "(workflow:gridsearch)=\n", - "GridSearch\n", - "==========\n", - "The grid search provided by :class:`GridSearch` exhaustively generates\n", - "candidates from a grid of parameter values specified with the ``param_grid``\n", - "parameter. Like `Pipeline`, `GridSearch` borrows from its scikit-learn counterpart\n", - "``GridSearchCV``.\n", - "\n", - "Because reserving techniques are different from supervised machine learning,\n", - "`GridSearch` does not try to pick optimal hyperparameters for you. It is more of\n", - "a scenario-testing estimator.\n", - "\n", - "```{tip}\n", - "The [tryangle](https://github.com/casact/tryangle) package takes the machine learning aspect a bit farther than `chainladder` and \n", - "has built in scoring functions that make hyperparameter tuning in a reserving context possible.\n", - "```\n", - "\n", - "`GridSearch` can be applied to all other estimators, including the `Pipeline`\n", - "estimator. To use it, one must specify a ``param_grid`` as well as a ``scoring``\n", - "function which defines the estimator property(s) you wish to capture. 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pipe = cl.Pipeline(\n", - " steps=[('dev', cl.Development()),\n", - " ('model', cl.Chainladder())])\n", - "\n", - "grid = cl.GridSearch(\n", - " estimator=pipe,\n", - " param_grid={'dev__average' :['volume', 'simple', 'regression']},\n", - " scoring=lambda x : x.named_steps.model.ibnr_.sum('origin'))\n", - "\n", - "grid.fit(cl.load_sample('genins'))\n", - "ax = grid.results_.set_index('dev__average').rename(columns={'score': 'IBNR'}).plot(\n", - " kind='bar', legend=False, xlabel='', rot=0, title='GridSearch Results')\n", - "\n", - "ax.yaxis.set_major_formatter(\n", - " plt.FuncFormatter(lambda value, tick_number: \"{:,}\".format(float(value))));\n" - ] - }, - { - "cell_type": "markdown", - "id": "8ef72e0d-0d14-4f11-8838-3ed38897829e", - "metadata": {}, - "source": [ - "A couple points on the example.\n", - "\n", - "1. The syntax for GridSearch is a near replica of scikit-learn's `GridsearchCV`\n", - "2. The `param_grid` is a dictionary of hyperparamter names and possible options you may want to use.\n", - "3. Multiple different hyperparameters can be specified in a `param_grid`. Doing so will create a cartesian product of all possible assumptions.\n", - "4. When using a `Pipeline` in a `GridSearch` it is entirely possible that the `param_grid` hyperparameter names used in different steps of a the `Pipeline` can clash. The notation to disambiguate the hyperparameters is to specify both the step and the hyperparameter name in this fashion: `step__hyperparameter`\n", - "\n", - "```{note}\n", - "All of this can be done with loops, but GridSearch provides a nice shorthand for accomplishing the same as well as parallelism when your CPU has multiple cores.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "1df153e3-18df-4010-81d0-c1bfe91ae6cb", - "metadata": {}, - "source": [ - "Your `scoring` function can be any property derived off of the fitted etimator.\n", - "If capturing multiple properties is desired, multiple scoring functions can be created and\n", - "stored in a dictionary.\n", - "\n", - "Here we capture multiple properties of the `TailBondy` estimator using the\n", - "`GridSearch` routine to test the sensitivity of the model to changing hyperparameters.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "e88b07cd-b9ec-45aa-9c99-fa1de6a9c3d9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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earliest_ageTail Factor (tail_)Bondy Exponent (b_)
0121.0277630.624614
1241.0279830.625014
2361.0298560.629534
3481.0379310.651859
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" - ], - "text/plain": [ - " earliest_age Tail Factor (tail_) Bondy Exponent (b_)\n", - "0 12 1.027763 0.624614\n", - "1 24 1.027983 0.625014\n", - "2 36 1.029856 0.629534\n", - "3 48 1.037931 0.651859\n", - "4 60 1.048890 0.683528\n", - "5 72 1.046617 0.674587\n", - "6 84 1.059931 0.714418\n", - "7 96 1.072279 0.746943\n", - "8 108 1.023925 0.500000" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fit basic development to a triangle\n", - "tri = cl.load_sample('tail_sample')['paid']\n", - "dev = cl.Development(average='simple').fit_transform(tri)\n", - "\n", - "# Return both the tail factor and the Bondy exponent in the scoring function\n", - "scoring = {\n", - " 'Tail Factor (tail_)': lambda x: x.tail_.values[0,0],\n", - " 'Bondy Exponent (b_)': lambda x : x.b_.values[0,0]}\n", - "\n", - "# Vary the 'earliest_age' assumption in GridSearch\n", - "param_grid=dict(earliest_age=list(range(12, 120, 12)))\n", - "grid = cl.GridSearch(\n", - " estimator=cl.TailBondy(), \n", - " param_grid=param_grid, \n", - " scoring=scoring)\n", - "\n", - "grid.fit(dev).results_" - ] - }, - { - "cell_type": "markdown", - "id": "f7ce1f0a-0e12-4b60-9e24-b36b7a3e33a6", - "metadata": {}, - "source": [ - "Using `GridSearch` for scenario testing is entirely optional. You can write\n", - "your own looping mechanisms to achieve the same result. For example:\n", - "\n", - "```{eval-rst}\n", - ".. figure:: /auto_examples/images/sphx_glr_plot_capecod_001.png\n", - " :target: ../auto_examples/plot_capecod.html\n", - " :align: center\n", - " :scale: 50%\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "68af376a-3bf5-4f20-9aaf-decd7e31ca7b", - "metadata": {}, - "source": [ - "It is also possible to treat estimators themselves as hyperparameters. A `GridSearch` can search over `Chainladder` and `CapeCod` or any other estimator. To achieve this, you can take your `param_grid` dictionary and make it a list of dictionaries." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "1de8a3c2-6c9c-4615-99a2-91eb3bcbdf6e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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devdev__n_periodsmodelscoremodel__decay
0Development(n_periods=7)-1Chainladder()1.330331e+06NaN
1Development(n_periods=7)3Chainladder()1.254405e+06NaN
2Development(n_periods=7)7Chainladder()1.321097e+06NaN
3Development(n_periods=7)-1CapeCod()1.330331e+060.0
4Development(n_periods=7)-1CapeCod()1.278456e+060.5
5Development(n_periods=7)-1CapeCod()1.066857e+061.0
6Development(n_periods=7)3CapeCod()1.254405e+060.0
7Development(n_periods=7)3CapeCod()1.225866e+060.5
8Development(n_periods=7)3CapeCod()1.040709e+061.0
9Development(n_periods=7)7CapeCod()1.321097e+060.0
10Development(n_periods=7)7CapeCod()1.276399e+060.5
11Development(n_periods=7)7CapeCod()1.066171e+061.0
\n", - "
" - ], - "text/plain": [ - " dev dev__n_periods model score \\\n", - "0 Development(n_periods=7) -1 Chainladder() 1.330331e+06 \n", - "1 Development(n_periods=7) 3 Chainladder() 1.254405e+06 \n", - "2 Development(n_periods=7) 7 Chainladder() 1.321097e+06 \n", - "3 Development(n_periods=7) -1 CapeCod() 1.330331e+06 \n", - "4 Development(n_periods=7) -1 CapeCod() 1.278456e+06 \n", - "5 Development(n_periods=7) -1 CapeCod() 1.066857e+06 \n", - "6 Development(n_periods=7) 3 CapeCod() 1.254405e+06 \n", - "7 Development(n_periods=7) 3 CapeCod() 1.225866e+06 \n", - "8 Development(n_periods=7) 3 CapeCod() 1.040709e+06 \n", - "9 Development(n_periods=7) 7 CapeCod() 1.321097e+06 \n", - "10 Development(n_periods=7) 7 CapeCod() 1.276399e+06 \n", - "11 Development(n_periods=7) 7 CapeCod() 1.066171e+06 \n", - "\n", - " model__decay \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 0.0 \n", - "4 0.5 \n", - "5 1.0 \n", - "6 0.0 \n", - "7 0.5 \n", - "8 1.0 \n", - "9 0.0 \n", - "10 0.5 \n", - "11 1.0 " - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clrd = cl.load_sample('clrd')[['CumPaidLoss', 'EarnedPremDIR']].groupby('LOB').sum().loc['medmal']\n", - "\n", - "grid = cl.GridSearch(\n", - " estimator = cl.Pipeline(steps=[\n", - " ('dev', None),\n", - " ('model', None)]),\n", - " param_grid = [\n", - " {'dev': [cl.Development()],\n", - " 'dev__n_periods': [-1, 3, 7],\n", - " 'model': [cl.Chainladder()]},\n", - " {'dev': [cl.Development()],\n", - " 'dev__n_periods': [-1, 3, 7],\n", - " 'model': [cl.CapeCod()],\n", - " 'model__decay': [0, .5, 1]}],\n", - " scoring=lambda x : x.named_steps.model.ibnr_.sum('origin')\n", - ")\n", - "\n", - "grid.fit(\n", - " X=clrd['CumPaidLoss'], \n", - " sample_weight=clrd['EarnedPremDIR'].latest_diagonal)\n", - "\n", - "grid.results_" - ] - }, - { - "cell_type": "markdown", - "id": "140236d7-d8ab-4753-ad6c-61dce25e9797", - "metadata": {}, - "source": [ - "The workflow estimators are incredibly flexible for composing any compound reserving workflow into a single estimator. These succintly capture all \"assumptions\" and model definitions in one place." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8701877-a5e6-4718-bc6a-c38ab76bf344", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/readthedocs.yaml b/readthedocs.yaml index a7d34ac0..30fc1c53 100644 --- a/readthedocs.yaml +++ b/readthedocs.yaml @@ -7,6 +7,10 @@ build: os: "ubuntu-20.04" tools: python: "mambaforge-4.10" + jobs: + pre_build: + # Generate on-the-fly Sphinx configuration from Jupyter Book's _config.yml + - "jupyter-book 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docs/user_guide/adjustments.ipynb create mode 100644 docs/user_guide/development.ipynb create mode 100644 docs/user_guide/index.md create mode 100644 docs/user_guide/methods.ipynb create mode 100644 docs/user_guide/tails.ipynb create mode 100644 docs/user_guide/triangle.ipynb create mode 100644 docs/user_guide/utilities.ipynb create mode 100644 docs/user_guide/workflow.ipynb diff --git a/docs/gallery/index.md b/docs/gallery/index.md new file mode 100644 index 00000000..2aaa62b6 --- /dev/null +++ b/docs/gallery/index.md @@ -0,0 +1,525 @@ +# {octicon}`grabber` Example Gallery + +```{eval-rst} +:parenttoc: True + +.. title:: Gallery: contents + +.. toctree:: + :hidden: + + plot_advanced_triangle + plot_ave_analysis + plot_benktander + plot_berqsherm_case + plot_bf_apriori_from_cl + plot_bondy_sensitivity + plot_bootstrap + plot_bootstrap_comparison + plot_callable_dev_constant + plot_capecod + plot_capecod_onlevel + plot_clarkldf + plot_clarkldf_resid + plot_development_periods + plot_elrf_resid + plot_exponential_smoothing + plot_exposure_triangle + plot_extrap_period + plot_glm_ldf + plot_ibnr_runoff + plot_industry_to_company + plot_mack + plot_munich + plot_munich_resid + plot_ptf_resid + plot_stochastic_bornferg + plot_tailcurve_compare + plot_triangle_from_pandas + plot_triangle_slicing + plot_value_at_risk + plot_voting_chainladder + +``` + +You'll find a collection of gallery examples here. These notebooks syntesize many of the concepts learned in the tutorials or the user guide. + +Each of these notebooks has a corresponding badge that indicates its the level of difficulty. However, they are not ordered in any way. + +* {bdg-success}`easy` examples cover the basics of the library. +* {bdg-warning}`medium` examples cover the more advanced features of the library. +* {bdg-danger}`hard` examples have more complex workflows that are not recommended for beginners. + + + +::::{grid} +:gutter: 2 + +:::{grid-item-card} +:columns: 4 +:link: plot_benktander +:link-type: doc +**BornhutterFerguson vs Chainladder** +```{image} ../images/plot_benktander.png +--- +alt: BornhutterFerguson vs Chainladder +--- +``` ++++ +{bdg-warning}`medium` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_advanced_triangle +:link-type: doc +**Advanced Triangle Manipulation** +```{image} ../images/plot_advanced_triangle.png +--- +alt: Advanced Triangle Manipulation +--- +``` ++++ +{bdg-danger}`hard` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_ave_analysis +:link-type: doc +**Actual Vs Expected** +```{image} ../images/plot_ave_analysis.png +--- +alt: Actual Vs Expected +--- +``` ++++ +{bdg-success}`easy` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_berqsherm_case +:link-type: doc +**BerquistSherman Adjustment** +```{image} ../images/plot_berqsherm_case.png +--- +alt: BerquistSherman Adjustment +--- +``` ++++ +{bdg-success}`easy` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_bf_apriori_from_cl +:link-type: doc +**Selecting BornheutterFerguson Apriori** +```{image} ../images/plot_bf_apriori_from_cl.png +--- +alt: Selecting BornheutterFerguson Apriori +--- +``` ++++ +{bdg-success}`easy` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_bondy_sensitivity +:link-type: doc +**Bondy Tail Sensitivity** +```{image} ../images/plot_bondy_sensitivity.png +--- +alt: Bondy Tail Sensitivity +--- +``` ++++ +{bdg-warning}`medium` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_bootstrap_comparison +:link-type: doc +**BootstrapODPSample Variability** +```{image} ../images/plot_bootstrap_comparison.png +--- +alt: BootstrapODPSample Variability +--- +``` ++++ +{bdg-warning}`medium` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_bootstrap +:link-type: doc +**Basic BootstrapODPSample** +```{image} ../images/plot_bootstrap.png +--- +alt: Basic BootstrapODPSample +--- +``` ++++ +{bdg-success}`easy` + + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_callable_dev_constant +:link-type: doc +**DevelopmentConstant Callable** +```{image} ../images/plot_callable_dev_constant.png +--- +alt: DevelopmentConstant Callable +--- +``` ++++ +{bdg-danger}`hard` + + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_capecod_onlevel +:link-type: doc +**CapeCod Onleveling** +```{image} ../images/plot_capecod_onlevel.png +--- +alt: CapeCod Onleveling +--- +``` ++++ +{bdg-warning}`medium` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_capecod +:link-type: doc +**CapeCod Sensitivity** +```{image} ../images/plot_capecod.png +--- +alt: CapeCod Sensitivity +--- +``` ++++ +{bdg-danger}`hard` + + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_clarkldf_resid +:link-type: doc +**ClarkLDF Residuals** +```{image} ../images/plot_clarkldf_resid.png +--- +alt: ClarkLDF Residuals +--- +``` ++++ +{bdg-warning}`medium` + + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_clarkldf +:link-type: doc +**Clark Growth Curves** +```{image} ../images/plot_clarkldf.png +--- +alt: Clark Growth Curves +--- +``` ++++ +{bdg-success}`easy` + + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_development_periods +:link-type: doc +**Tuning Development Patterns** +```{image} ../images/plot_development_periods.png +--- +alt: Tuning Development Patterns +--- +``` ++++ +{bdg-warning}`medium` +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_elrf_resid +:link-type: doc +**ELRF Residuals** +```{image} ../images/plot_elrf_resid.png +--- +alt: ELRF Residuals +--- +``` ++++ +{bdg-success}`easy` +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_exponential_smoothing +:link-type: doc +**Attachment Age Smoothing** +```{image} ../images/plot_exponential_smoothing.png +--- +alt: Attachment Age Smoothing +--- +``` ++++ +{bdg-success}`easy` +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_exposure_triangle +:link-type: doc +**Exposure Vector** +```{image} ../images/plot_exposure_triangle.png +--- +alt: Exposure Vector +--- +``` ++++ +{bdg-success}`easy` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_extrap_period +:link-type: doc +**TailCurve Extrapolation** +```{image} ../images/plot_extrap_period.png +--- +alt: TailCurve Extrapolation +--- +``` ++++ +{bdg-warning}`medium` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_glm_ldf +:link-type: doc +**TweedieGLM Basics** +```{image} ../images/plot_glm_ldf.png +--- +alt: TweedieGLM Basics +--- +``` ++++ +{bdg-success}`easy` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_ibnr_runoff +:link-type: doc +**IBNR Runoff** +```{image} ../images/plot_ibnr_runoff.png +--- +alt: IBNR Runoff +--- +``` ++++ +{bdg-success}`easy` + + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_industry_to_company +:link-type: doc +**Making Predictions** +```{image} ../images/plot_industry_to_company.png +--- +alt: Making Predictions +--- +``` ++++ +{bdg-success}`easy` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_mack +:link-type: doc +**MackChainladder Basics** +```{image} ../images/plot_mack.png +--- +alt: MackChainladder Basics +--- +``` ++++ +{bdg-success}`easy` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_munich_resid +:link-type: doc +**MunichAdjustment Residuals** +```{image} ../images/plot_munich_resid.png +--- +alt: MunichAdjustment Residuals +--- +``` ++++ +{bdg-warning}`medium` + + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_munich +:link-type: doc +**MunichAdjustment Basics** +```{image} ../images/plot_munich.png +--- +alt: MunichAdjustment Basics +--- +``` ++++ +{bdg-success}`easy` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_ptf_resid +:link-type: doc +**PTF Residuals** +```{image} ../images/plot_ptf_resid.png +--- +alt: PTF Residuals +--- +``` ++++ +{bdg-warning}`medium` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_stochastic_bornferg +:link-type: doc +**Stochastic BornhuetterFerguson** +```{image} ../images/plot_stochastic_bornferg.png +--- +alt: Stochastic BornhuetterFerguson +--- +``` ++++ +{bdg-warning}`medium` +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_tailcurve_compare +:link-type: doc +**TailCurve Basics** +```{image} ../images/plot_tailcurve_compare.png +--- +alt: TailCurve Basics +--- +``` ++++ +{bdg-success}`easy` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_triangle_from_pandas +:link-type: doc +**Triangle Creation Basics** +```{image} ../images/plot_triangle_from_pandas.png +--- +alt: Triangle Creation Basics +--- +``` ++++ +{bdg-success}`easy` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_triangle_slicing +:link-type: doc +**Triangle Slicing** +```{image} ../images/plot_triangle_slicing.png +--- +alt: Triangle Slicing +--- +``` ++++ +{bdg-success}`easy` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_value_at_risk +:link-type: doc +**Value At Risk** +```{image} ../images/plot_value_at_risk.png +--- +alt: Value At Risk +--- +``` ++++ +{bdg-warning}`medium` + +::: + +:::{grid-item-card} +:columns: 4 +:link: plot_voting_chainladder +:link-type: doc +**VotingChainladder Basics** +```{image} ../images/plot_voting_chainladder.png +--- +alt: VotingChainladder Basics +--- +``` ++++ +{bdg-warning}`medium` + +::: +:::: \ No newline at end of file diff --git a/docs/gallery/plot_advanced_triangle.ipynb b/docs/gallery/plot_advanced_triangle.ipynb new file mode 100644 index 00000000..dab19acc --- /dev/null +++ b/docs/gallery/plot_advanced_triangle.ipynb @@ -0,0 +1,130 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Advanced Triangle Manipulation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates several advanced data manipulation features of the\n", + "Triangle class including:\n", + "\n", + " 1. Delayed calculation using `virtual_columns`\n", + " 2. Advanced `groupby` functionality\n", + " 3. Custom sorting using `loc`\n", + "\n", + "Let's suppose we want to look at the loss ratios for the top 10 commercial auto\n", + "carriers (by premium volume) as compared to the rest of the industry.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "clrd = cl.load_sample('clrd')\n", + "clrd = clrd[clrd['LOB']=='comauto']\n", + "\n", + "# Create a loss ratio virtual column\n", + "clrd['LossRatio'] = lambda clrd: clrd['IncurLoss'] / clrd['EarnedPremDIR']\n", + "\n", + "# Identify the largest companies (by premium) for 1997\n", + "top_10 = clrd['EarnedPremDIR'].groupby('GRNAME').sum().latest_diagonal\n", + "top_10 = top_10.loc[..., '1997', :].to_frame().nlargest(10)\n", + "\n", + "# Group any companies together that are not in the top 10\n", + "clrd = clrd.groupby(clrd.index['GRNAME'].map(\n", + " lambda x: x if x in top_10.index else 'Remainder')).sum()\n", + "\n", + "# Sort by company volume, but keep Remainder as last entry\n", + "clrd = clrd.loc[top_10.index.to_list() + ['Remainder']].iloc[::-1]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 437, + "width": 778 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "\n", + "ax = clrd.latest_diagonal.sum('origin')['LossRatio'].plot(\n", + " kind='barh', title='Loss Ratio');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_ave_analysis.ipynb b/docs/gallery/plot_ave_analysis.ipynb new file mode 100644 index 00000000..a919c50c --- /dev/null +++ b/docs/gallery/plot_ave_analysis.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Actual vs Expected" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how you can slice triangle objects to perform a\n", + "typical 'Actual vs Expected' analysis. We will use Medical Malpractice\n", + "payment patterns for the demonstration." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "tri_1997 = cl.load_sample('clrd')\n", + "tri_1997 = tri_1997.groupby('LOB').sum().loc['medmal']['CumPaidLoss']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Actual vs Expected analysis is an analysis of a model in the presence of new data. Let's remove the latest diagonal of our Triangle and conduct the analysis and retain the vector of losses expected in the following period, in this case, calendar year 1997." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a triangle as of the previous valuation and build IBNR model\n", + "tri_1996 = tri_1997[tri_1997.valuation < '1997']\n", + "model_1996 = cl.Chainladder().fit(cl.TailCurve().fit_transform(tri_1996))\n", + "\n", + "# Slice the expected losses from the 1997 calendar period of the model\n", + "ave = model_1996.full_triangle_.dev_to_val()\n", + "ave = ave[ave.valuation==tri_1997.valuation_date].rename('columns', 'Expected')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can compare our expectation vs the actual emerged diagonal to see where our model over/under-predicted the actual data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Slice the actual losses from the 1997 calendar period for prior AYs\n", + "ave['Actual'] = tri_1997.latest_diagonal[tri_1997.origin < '1997']\n", + "df = ave.to_frame().T.iloc[::-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 459, + "width": 583 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plotting\n", + "fig, ax = plt.subplots()\n", + "ax.grid(axis='x')\n", + "plt.hlines(\n", + " y=df.index.astype(str), \n", + " xmin=df['Actual'], \n", + " xmax=df['Expected'],\n", + " color='grey', alpha=0.4)\n", + "plt.scatter(df['Actual'], df.index.astype(str), alpha=1, label='Actual')\n", + "plt.scatter(df['Expected'], df.index.astype(str), alpha=0.8 , label='Expected')\n", + "plt.legend()\n", + "plt.title(\"Actual vs Expected results in 1997\")\n", + "plt.xlabel('Difference')\n", + "plt.ylabel('Origin');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "815088b5be8edc0041246414015ef72f7907068f95114ad939d47d8b23d533db" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_benktander.ipynb b/docs/gallery/plot_benktander.ipynb new file mode 100644 index 00000000..bc24bd20 --- /dev/null +++ b/docs/gallery/plot_benktander.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# BornhutterFerguson vs Chainladder" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates the relationship between the Chainladder and\n", + "BornhuetterFerguson methods by way fo the Benktander model. Each is a\n", + "special case of the Benktander model where ``n_iters = 1`` for BornhuetterFerguson\n", + "and as ``n_iters`` approaches infinity yields the chainladder. As ``n_iters``\n", + "increases the apriori selection becomes less relevant regardless of initial\n", + "choice." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Load Data\n", + "clrd = cl.load_sample('clrd').groupby('LOB').sum()\n", + "X = clrd.loc['medmal', 'CumPaidLoss']\n", + "sample_weight = clrd.loc['medmal', 'EarnedPremDIR'].latest_diagonal\n", + "\n", + "# Specify Model\n", + "grid = cl.GridSearch(\n", + " estimator=cl.Pipeline(steps=[\n", + " ('dev', cl.Development()),\n", + " ('tail', cl.TailCurve()),\n", + " ('model', cl.Benktander())]), \n", + " param_grid = dict(\n", + " model__n_iters=list(range(1, 100, 2)),\n", + " model__apriori=[0.50, 0.75, 1.00]), \n", + " scoring={'IBNR': lambda x: x.named_steps.model.ibnr_.sum()},\n", + " n_jobs=-1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because chainladder estimators are scikit-learn compatible, we can display them as expandable/collabsible diagrams." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GridSearch(estimator=Pipeline(steps=[('dev', Development()),\n",
+       "                                     ('tail', TailCurve()),\n",
+       "                                     ('model', Benktander())]),\n",
+       "           n_jobs=-1,\n",
+       "           param_grid={'model__apriori': [0.5, 0.75, 1.0],\n",
+       "                       'model__n_iters': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21,\n",
+       "                                          23, 25, 27, 29, 31, 33, 35, 37, 39,\n",
+       "                                          41, 43, 45, 47, 49, 51, 53, 55, 57,\n",
+       "                                          59, ...]},\n",
+       "           scoring={'IBNR': <function <lambda> at 0x000002666723AF20>})
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GridSearch(estimator=Pipeline(steps=[('dev', Development()),\n", + " ('tail', TailCurve()),\n", + " ('model', Benktander())]),\n", + " n_jobs=-1,\n", + " param_grid={'model__apriori': [0.5, 0.75, 1.0],\n", + " 'model__n_iters': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21,\n", + " 23, 25, 27, 29, 31, 33, 35, 37, 39,\n", + " 41, 43, 45, 47, 49, 51, 53, 55, 57,\n", + " 59, ...]},\n", + " scoring={'IBNR': at 0x000002666723AF20>})" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import set_config\n", + "set_config(display='diagram')\n", + "grid" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fitting this model will loop through all `param_grid` options we specified and retain the aggregate IBNR of the model we declared in our `scoring` function." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "model__apriori 0.50 0.75 1.00\n", + "model__n_iters \n", + "1 8.326572e+05 1.248986e+06 1.665314e+06\n", + "3 1.075446e+06 1.289296e+06 1.503147e+06\n", + "5 1.150192e+06 1.308018e+06 1.465843e+06\n", + "7 1.189016e+06 1.318141e+06 1.447267e+06\n", + "9 1.214161e+06 1.324979e+06 1.435798e+06" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fit Model\n", + "grid.fit(X, sample_weight=sample_weight)\n", + "\n", + "# Analyze results\n", + "output = grid.results_.pivot(\n", + " index='model__n_iters', \n", + " columns='model__apriori', \n", + " values='IBNR') \n", + "output.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the results of our output DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 459, + "width": 569 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "ax = (output / 1e6).plot(\n", + " ylabel='IBNR (Millions)',\n", + " xlabel='Number of Iterations',\n", + " title='Benktander convergence to Chainladder');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_berqsherm_case.ipynb b/docs/gallery/plot_berqsherm_case.ipynb new file mode 100644 index 00000000..9dce9b49 --- /dev/null +++ b/docs/gallery/plot_berqsherm_case.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "\n", + "# BerquistSherman Adjustment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "This example demonstrates the adjustment to case reserves using the Berquist-Sherman\n", + "method. A key assumption, and highly sensitive one at that, is the selection of\n", + "a trend factor representative of the trend in average open case reserves from\n", + "year to year.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Load data\n", + "triangle = cl.load_sample('berqsherm').loc['MedMal']\n", + "\n", + "# Specify Berquist-Sherman model\n", + "berq = cl.BerquistSherman(\n", + " paid_amount='Paid', incurred_amount='Incurred',\n", + " reported_count='Reported', closed_count='Closed',\n", + " trend=0.15)\n", + "\n", + "# Adjust our triangle data\n", + "berq_triangle = berq.fit_transform(triangle)\n", + "berq_cdf = cl.Development().fit(berq_triangle['Incurred']).cdf_\n", + "orig_cdf = cl.Development().fit(triangle['Incurred']).cdf_" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 487, + "width": 569 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plot the results\n", + "ax = (berq_cdf / orig_cdf).T.plot(\n", + " kind='bar', grid=True, legend=False,\n", + " title='Berquist Sherman CDF to Unadjusted CDF',\n", + " xlabel='Age to Ultimate', \n", + " ylabel='Case Incurred CDF Adjustment');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_bf_apriori_from_cl.ipynb b/docs/gallery/plot_bf_apriori_from_cl.ipynb new file mode 100644 index 00000000..5b36109b --- /dev/null +++ b/docs/gallery/plot_bf_apriori_from_cl.ipynb @@ -0,0 +1,127 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Selecting BornhuetterFerguson Apriori" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how you can can use the output of one method as the\n", + "apriori selection for the Bornhuetter-Ferguson Method.\n", + "\n", + "We use basic arithmetic to build up an Apriori rather than initializing it explicitely with `cl.Triangle`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Create Aprioris as the mean AY chainladder ultimate\n", + "raa = cl.load_sample('RAA')\n", + "\n", + "cl_ult = cl.Chainladder().fit(raa).ultimate_ # Chainladder Ultimate\n", + "apriori = cl_ult * 0 + (cl_ult.sum() / 10) # Mean Chainladder Ultimate\n", + "bf_ult = cl.BornhuetterFerguson(apriori=1).fit(raa, sample_weight=apriori).ultimate_\n", + "\n", + "output = pd.concat(\n", + " (cl_ult.to_frame().rename({'2261': 'Chainladder'}, axis=1),\n", + " bf_ult.to_frame().rename({'2261': 'BornhuetterFerguson'}, axis=1)),\n", + " axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 435, + "width": 609 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plot of Ultimates\n", + "\n", + "ax = output.plot(grid=True, marker='o', \n", + " xlabel='Accident Year', ylabel='Ultimate');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_bondy_sensitivity.ipynb b/docs/gallery/plot_bondy_sensitivity.ipynb new file mode 100644 index 00000000..2a321283 --- /dev/null +++ b/docs/gallery/plot_bondy_sensitivity.ipynb @@ -0,0 +1,130 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Bondy Tail Sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "This example demonstrates the usage of the `TailBondy` estimator as well as\n", + "passing multiple scoring functions to `GridSearch`. When the ``earliest_age``\n", + "is set to the last available in the Triangle, the estimator reverts to the\n", + "traditional Bondy method.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Fit basic development to a triangle\n", + "tri = cl.load_sample('tail_sample')['paid']\n", + "dev = cl.Development(average='simple').fit_transform(tri)\n", + "\n", + "# Return both the tail factor and the Bondy exponent in the scoring function\n", + "scoring = {\n", + " 'tail_factor': lambda x: x.tail_.values[0,0],\n", + " 'bondy_exponent': lambda x : x.b_.values[0,0]}\n", + "\n", + "# Vary the 'earliest_age' assumption in GridSearch\n", + "param_grid=dict(earliest_age=list(range(12, 120, 12)))\n", + "grid = cl.GridSearch(cl.TailBondy(), param_grid, scoring)\n", + "results = grid.fit(dev).results_\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 459, + "width": 597 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "ax = results.plot(x='earliest_age', y='bondy_exponent',\n", + " title='Bondy Assumption Sensitivity', marker='o')\n", + "results.plot(x='earliest_age', y='tail_factor', grid=True,\n", + " secondary_y=True, ax=ax, marker='o');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_bootstrap.ipynb b/docs/gallery/plot_bootstrap.ipynb new file mode 100644 index 00000000..bb92e7fd --- /dev/null +++ b/docs/gallery/plot_bootstrap.ipynb @@ -0,0 +1,142 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic BootstrapODPSample" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how you can can use the Overdispersed Poisson\n", + "Bootstrap sampler and get various properties about parameter uncertainty.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "\n", + "# Grab a Triangle\n", + "tri = cl.load_sample('genins')\n", + "\n", + "# Generate bootstrap samples\n", + "sims = cl.BootstrapODPSample(random_state=42).fit_transform(tri)\n", + "\n", + "# Calculate LDF for each simulation\n", + "sim_ldf = cl.Development().fit(sims).ldf_\n", + "\n", + "plot1 = tri.T / 1e6\n", + "plot2 = (sims.sum() / 1000).T / 1e6\n", + "plot3a = sim_ldf.T\n", + "plot3b = cl.Development().fit(tri).ldf_.drop_duplicates().T\n", + "plot4 = sim_ldf.T.loc['12-24']" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 859, + "width": 835 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plot the Data\n", + "fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))\n", + "\n", + "# Plot 1\n", + "plot1.plot(ax=ax00, title='Raw Data', xlabel='Development', ylabel='Incurred (Millions)')\n", + "\n", + "# Plot 2\n", + "plot2.plot(ax=ax01, title='Mean Simulation (Millions)', xlabel='Development')\n", + "\n", + "# Plot 3\n", + "plot3a.plot(legend=False, color='lightgray', ax=ax10, \n", + " title='Simulated LDF', xlabel='Development', ylabel='LDF')\n", + "plot3b.plot(legend=False, ax=ax10, grid=True)\n", + "\n", + "# Plot 4\n", + "plot4.plot(\n", + " kind='hist', bins=50, alpha=0.5, ax=ax11,\n", + " title='Age 12-24 LDF Distribution', xlabel='LDF');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_bootstrap_comparison.ipynb b/docs/gallery/plot_bootstrap_comparison.ipynb new file mode 100644 index 00000000..054accda --- /dev/null +++ b/docs/gallery/plot_bootstrap_comparison.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# BootstrapODPSample Variability" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how you can drop the outlier link ratios from the\n", + "`BootstrapODPSample`. This has a direct consquence of reducing reserve variability estimates." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Load triangle\n", + "triangle = cl.load_sample('genins')\n", + "\n", + "# Use bootstrap sampler to get resampled triangles\n", + "s1 = cl.BootstrapODPSample(\n", + " n_sims=5000, random_state=42).fit(triangle).resampled_triangles_\n", + "\n", + "## Alternatively use fit_transform() to access resampled triangles dropping\n", + "# outlier link-ratios from resampler\n", + "s2 = cl.BootstrapODPSample(\n", + " drop_high=[True] * 5+ [False] * 4, \n", + " drop_low=[True] * 5 + [False] * 4,\n", + " n_sims=5000, random_state=42).fit_transform(triangle)\n", + "\n", + "# Summarize results of first model\n", + "results = cl.Chainladder().fit(s1).ibnr_.sum('origin').rename('columns', ['Original'])\n", + "# Add another column to triangle with second set of results.\n", + "results['Dropped'] = cl.Chainladder().fit(s2).ibnr_.sum('origin')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 455, + "width": 574 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "# Plot both IBNR distributions\n", + "ax = results.to_frame().plot(\n", + " kind='hist', bins=50, alpha=0.5, \n", + " grid=True, xlabel='Ultimate',\n", + " title='Reserve Variability')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_callable_dev_constant.ipynb b/docs/gallery/plot_callable_dev_constant.ipynb new file mode 100644 index 00000000..697c5757 --- /dev/null +++ b/docs/gallery/plot_callable_dev_constant.ipynb @@ -0,0 +1,159 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# DevelopmentConstant Callable" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`DevelopmentConstant` takes a dictionary of development factors and stores them\n", + "in a development estimator. This example demonstrates how a function can be\n", + "passed into `DevelopmentConstant` rather than a static dictionary of patterns.\n", + "The function should return development patterns for each element of the Triangle's\n", + "index. When passing a function to the estimator, it behaves as if calling the\n", + "pandas ``apply`` method on the Triangle's index.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
DevelopmentConstant(callable_axis=1,\n",
+       "                    patterns=<function paid_cdfs at 0x0000017BF1FF7100>,\n",
+       "                    style='cdf')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "DevelopmentConstant(callable_axis=1,\n", + " patterns=,\n", + " style='cdf')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sample Data\n", + "agway = cl.load_sample('clrd').loc['Agway Ins Co', 'CumPaidLoss']\n", + "\n", + "def paid_cdfs(x):\n", + " \"\"\" A function that returns different CDFs depending on a specified LOB \"\"\"\n", + " cdfs = {\n", + " 'comauto': [3.832, 1.874, 1.386, 1.181, 1.085, 1.043, 1.022, 1.013, 1.007, 1],\n", + " 'medmal': [24.168, 4.127, 2.103, 1.528, 1.275, 1.161, 1.088, 1.047, 1.018, 1],\n", + " 'othliab': [10.887, 3.416, 1.957, 1.433, 1.231, 1.119, 1.06, 1.031, 1.011, 1],\n", + " 'ppauto': [2.559, 1.417, 1.181, 1.084, 1.04, 1.019, 1.009, 1.004, 1.001, 1],\n", + " 'prodliab': [13.703, 5.613, 2.92, 1.765, 1.385, 1.177, 1.072, 1.034, 1.008, 1],\n", + " 'wkcomp': [4.106, 1.865, 1.418, 1.234, 1.141, 1.09, 1.056, 1.03, 1.01, 1]}\n", + " patterns = pd.DataFrame(cdfs, index=range(12, 132, 12)).T\n", + " return patterns.loc[x.loc['LOB']].to_dict()\n", + "\n", + "# If it works with pandas apply on the triangle index...\n", + "agway.index.apply(paid_cdfs, axis=1)\n", + "# ... then it will work in DevelopmentConstant\n", + "model = cl.DevelopmentConstant(patterns=paid_cdfs, callable_axis=1, style='cdf')\n", + "\n", + "model.fit(agway)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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fK2obAAAAAABce2yWHMnLy9O5c+ckSQEBAWUe6+vrKy8vL+Xm5io1NfWKrrtv3z7L68zMTEVHRys6OlqLFy/WM888o27dul1WuxWJy8/Pz5IEcnW16aAcSVKBza9gP1Xx72cv9Jvjou8cE/3mmOg3x0S/OaZrud8k+s5R0W+OyZH7zWbJEYPBYHldkeV5vb29lZuba3XepWjUqJG6deum5s2bq3bt2iooKFBiYqI2btyoPXv26Pz58/roo480duxYderU6ZLbf/rpp8s9Zvbs2QoICJCbm5vq169/OW/jkpy0+RXspyr+/eyFfnNc9J1jot8cE/3mmOg3x3Qt95tE3zkq+s0xOXK/2XTkiOUi7uVfxnxM0fMq6tZbb9XIkSOLbW/RooXCw8O1cuVKffnllzKZTJozZ45mzpwpT0/PS74OAAAAAAC49tgsOVI0+VCR5XnNx1xO0qJ69epl7h84cKCOHj2qNWvW6OzZs9q6dav69OlzSdeYPXt2ucf4+flJkgoKCnTmzJlLah/WTp8+be8QcBnoN8dF3zkm+s0x0W+OiX5zXPSdY6LfHFNV9ltgYGCZtU0vlc2SI0Wn0lRkqoz5mIpMwbkcAwcO1Jo1ayRJ0dHRl5wcKa9uysVMJtMlHQ9r/Ps5JvrNcdF3jol+c0z0m2Oi3xwXfeeY6DfH5Mj9ZrNqKZ6enqpRo4ak8ouZZmVlKTc3V9KlJyEqqkGDBpbXaWlpNrkGAAAAAABwPDYtJWtOSJw+fVoFBaXX5E1MTCx2TmUrLCy0SbsAAAAAAMCx2TQ50rJlS0lSbm6ujh07Vupx0dHRxc6pbPHx8ZbXtWvXtsk1AAAAAACA47FpcqR79+6W12vXri3xGJPJpHXr1km6UFi1TZs2Noll1apVltetW7e2yTUAAAAAAIDjsWlypHnz5rr++uslXUiOHD58uNgxy5YtU0JCgiTplltuKbbsb0REhEaOHKmRI0dqwYIFxc4/ceJEuRVxV65caSnG6ufnZ5W0AQAAAAAAzs1mq9WYPfzww3rzzTeVl5enKVOm6F//+pfatGmjvLw8bd682TKiIygoSLfddtslt3/s2DHNmTNHbdq0UadOndSoUSP5+vrKZDIpISFBGzZs0N69eyVJrq6ueuKJJ2y2Ig4AAAAAAHA8Nk+ONGnSRC+++KJmzpypnJwc/fzzz8WOCQoK0rhx4+Tj43NZ1zCZTNq3b5/27dtX6jE1atTQU089pa5du17WNQAAAAAAwLXJ5skRSeratas+/PBDrVixQjt37lRaWprc3d1Vv3599ezZU4MHD5aXl9dltd2pUyc99dRTOnz4sOLi4pSRkaFz586psLBQvr6+aty4sTp06KB+/fqpWrVqlfzOAAAAAACAo6uS5IgkBQYG6qGHHtJDDz10Sef169dP/fr1K3V/rVq1NGDAAA0YMOAKIwQAAAAAAM7IpgVZAQAAAAAArnYkRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGokRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGokRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGokRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGokRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGokRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGokRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGokRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGokRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGokRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGokRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGruVXWhlJQUrVixQrt27VJKSorc3d1Vv359hYWF6eabb5aXl1elXGfTpk2KiIjQ8ePHdf78efn5+alVq1a6+eabFRoaWinXAAAAAAAA144qSY7s3LlTM2bMUHZ2tmVbbm6ujh49qqNHj2rNmjUaN26c6tWrd9nXyMvL08cff6ydO3dabT9z5ozOnDmjjRs3asSIERo+fPhlXwMAAAAAAFx7bJ4ciYuL0yeffKLc3Fx5e3vrjjvuUNu2bZWXl6dNmzZp9erVSkxM1HvvvaepU6fK29v7sq4zZ84cS2KkTZs2GjJkiGrXrq0TJ07ot99+U1JSkhYsWKDatWvrxhtvrMy3CAAAAAAAHJjNkyPfffedcnNz5ebmpjfeeMNqakvbtm0VFBSkH374QQkJCVq2bNlljeyIjo7Wxo0bJUldunTRmDFj5Op6oZxK8+bN1bVrV7322mtKSUnRDz/8oJ49e6p69eqV8wYBAAAAAIBDs2lB1tjYWB04cECS1L9//xJrfgwdOlQhISGSpOXLl8toNF7ydRYvXixJcnV11ejRoy2JEbOaNWvqvvvukySdP39ea9asueRrAAAAAACAa5NNkyPbtm2zvO7fv3/JAbi6Kjw8XNKFxEV0dPQlXcNgMGj//v2SpPbt2ysgIKDE43r06CEfHx9JUlRU1CVdAwAAAAAAXLtsmhyJiYmRJHl5ealp06alHte6deti51RUbGys8vPzi7VzMXd3d8vIldjY2MsaoQIAAAAAAK49Nk2OxMfHS5Lq168vNze3Uo8LDg4uds6lXuPidsq6TkFBgU6fPn1J1wEAAAAAANcmmxVkzcvL07lz5ySp1KkuZr6+vvLy8lJubq5SU1Mv6TpFjy/vOkX3p6SkqEGDBpd1ndL4+flZkkAX1z2xhQKbX8F+quLfz17oN8dF3zkm+s0x0W+OiX5zTNdyv0n0naOi3xyTI/ebzZIjBoPB8roiy/N6e3srNzfX6ryKyMnJqfB1iu6/1Os8/fTT5R7z008/SZLc3NxUv379S2r/chi/W35Z5y3zqnVZ51VX6aN/SlNgGndZ16pV6/JidASO0G/S5fXdtdxv0uX1nSP0m3Rt911Vfubot8rjCL8r6bfiHKHfJP7GXexa7jeJvrsY/WZ/3JtcnWw6csRyEffyL2M+puh5FWGuN1KR6xTdf6nXqQgXF5dKb7Ms7nXqXdZ5l3fW5XLcD4etOEa/SfRdcZfTd/Sb/TnGZ45+uxj95pgco98k+s4a/ea4uDdxTI7xmXO+frNZcsTT09PyuiLFT83HFD2vIjw8PCp8naL7L/U6s2fPLvcYRx5CVJaCggKlp6dLsp46hKsb/eaY6DfHRL85LvrOMdFvjol+c0z0m2Oi3y6dzZIjlzqFxXxMRabgFGVenrci17nUqT5FlVfP5FqWnp5umVY0e/Zsp/63cCT0m2Oi3xwT/ea46DvHRL85JvrNMdFvjol+u3Q2G+rg6empGjVqSCq/mGlWVpZyc3MlXXoSoujx5V2n6P46depc0nUAAAAAAMC1yabzQMyrwZw+fVoFBaXX5E1MTCx2zqVe4+J2yrpOVRVMBQAAAAAAVz+bJkdatmwpScrNzdWxY8dKPS46OrrYORXVrFkzS6HVou1czGg06vDhw8XOAQAAAAAAzs2myZHu3btbXq9du7bEY0wmk9atWydJql69utq0aXNJ1/Dx8VG7du0kSfv27St1as3WrVsty/4WjQsAAAAAADg3myZHmjdvruuvv17SheSIeeRGUcuWLVNCQoIk6ZZbbik2oiMiIkIjR47UyJEjtWDBghKvc9ttt0m6UJH3f//7n0wmk9X+zMxM/fjjj5IuJGAGDBhwZW8MAAAAAABcM2y+9uzDDz8sT09PFRQUaMqUKfrtt990+PBh7d+/X1988YV++OEHSVJQUJAlyXGp2rZtq169ekmStm/frrffflvbt2/X0aNHtXbtWo0fP14pKSmSpHvvvVe+vr6V8+YAAAAAAIDDs3nhjSZNmujFF1/UzJkzlZOTo59//rnYMUFBQRo3bpzVsryX6plnnlFOTo527dqlAwcO6MCBA1b7XVxcdNddd2ngwIGXfQ0AAAAAAHDtcSksLCysigudOXNGK1as0M6dO5WWliZ3d3fVr19fPXv21ODBg+Xl5VXieREREZo1a5Ykafjw4Ro5cmSZ19m4caMiIiJ0/PhxnT9/XrVq1dL111+vwYMHKzQ0tNLfFwAAAAAAcGxVlhwBAAAAAAC4Gtm85ggAAAAAAMDVjOQIAAAAAABwaiRHAAAAAACAUyM5AgAAAAAAnBrJEQAAAAAA4NRIjgAAAAAAAKdGcgQAAAAAADg1kiMAAAAAAMCpkRwBAAAAAABOjeQIAAAAAABwaiRHAAAAAACAU3O3dwAAAAAAgKtfdna2EhMT5ebmpsDAQPn6+to7JKDSkBwBrlHHjh3Tpk2bdPToUWVkZMjNzU3+/v5q1aqV+vfvr9q1a9s7RMAhnT59WjExMUpISFBqaqoMBoPy8vLk6ekpb29vBQQEKCQkRK1atVL9+vXtHS4AXDW4N7k65eTkaM2aNTp06JCMRqOCgoJ04403Kjg42HJMRkaGvv76a0VFRclkMkmSXFxcFBoaqnvuuUfXX3+9vcIHKo1LYWFhob2DgGPZvHmzpk+fLhcXF82bN8/e4TidkydPas2aNXJxcdF9990nNzc3q/0Gg0Gff/65Nm/eXGob7u7uuvvuu3XbbbfZOlyUwmAwyGg0lvrEJTMzU5s2bVJCQoLc3NxUt25dde7cWUFBQVUcKczWr1+vpUuX6sSJExU+p0GDBrr99tvVp08fubi42DA6lCU5OVnbt2/X6dOn5erqquDgYHXr1q1CX8SSk5M1e/Zsubi4aMKECVUQLS4V9yX2x72J4zpx4oTeffddnT171mq7q6urHn30UQ0cOFA5OTl64403FB8fX2Ibrq6u+ve//61evXpVRcgoQWZmprZv3255eJOSklLs4U2dOnUUEhKili1bqmvXrqpVq5a9w77qMHIEl428mn1ERkZqxYoVatmyZbGbj4KCAr3//vuKjo62bHN1dVXNmjVlMpmUmZkpSTIajfrhhx+Uk5OjkSNHVmn8zm7Lli369ddfLV+wfX19NXDgQP3rX/+Sl5eXJGnr1q2aNWuWDAaD1blz587VgAED9NhjjxXre9hOVlaWPvroI6vPVUXFx8frs88+05o1a/TKK68w/NgO5s2bpyVLlqigoMBq+3fffafBgwfr7rvvloeHR6nn5+bmXlbfo+pxX2I/3Js4JoPBoPfff79YYkSSTCaTvv76azVp0kQRERGWxEi7du3UtGlTGY1GHTx4UMeOHZPJZNLnn3+uVq1ayd/fv6rfhlM7e/as5s+fr3Xr1llG9FzMYDDIYDAoPT1dsbGxWrdunb766iv17dtXo0aNos+KIDni5FJSUi75HPMfsdLOr1OnzhXFhLIdOHBAktS9e/di+/7++2/Lzcd1112nu+++W+3atbPc+Ofk5Gjr1q2aP3++0tLStGjRInXv3l2NGzeusvid2bJlyzR37lyrbVlZWfrtt9909OhRjRs3TrGxsZoxY4aMRmOx8wsLC7V69WoZDAY9//zzVRW2UzOZTHrvvfd05MgRSVL16tUVFham1q1bKyQkRAEBAfLy8pKHh4fy8/OVm5ur1NRUJSQkKDo6WpGRkTp//rwOHjyo9957T2+99ZZcXamFXlV+/PFHLVmypMR9RqNRy5Yt0+7du/Xyyy9bDR+H/XBf4pi4N3FMq1evtnxmunfvrhEjRqhevXpKSkrSwoULtXXrVi1ZskR79+6Vp6enxo4dq7Zt21q1sWrVKn355ZcyGAxavXq1RowYYY+34pSOHTum999/X+np6VbbfXx8FBAQIG9vb7m7u8toNMpgMCg1NVU5OTmSLtzfREREaNeuXRo7dqyaNWtmh3dw9SE54uSeffbZSj2fIa22l5ycLEklTq9YtWqVJKlFixaaMGGCPD09rfb7+PioX79+6tChg15//XWlpaXpzz//1FNPPWX7wJ1cfHy8fvzxR8vPjRs3ttyAxMXFae/evYqIiNCmTZtkNBoVEhKiO++80/J0Jjo6Wr/99pvS09O1adMm3XTTTWrdurUd35FzWLNmjSUxMmjQID3wwAPFPldmnp6e8vT0VI0aNdS4cWPdcMMNeuihh/T9999r5cqVOnLkiNasWaObbrqpKt+C0zp+/LglMVKzZk3dddddatu2rfLz83XgwAH98ccfSklJUXx8vN58802NGzdOzZs3t3PU4L7EMXFv4pi2b98uSWrVqpVefvlly/ZGjRrpP//5jyZPnqytW7dKku65555iiRFJuummmxQdHa1NmzZp7969JEeqSFZWlqZNm2ZJjHTo0EH9+vVT69at5efnV+p56enpio6OVkREhPbs2aOMjAx98MEH+vDDDxndKpIjqGQMabU98xOyi4fAZWdnW4Y83nfffaV+gZOk2rVr6/bbb9c333xjedoD2/r7779lMpnk7u6ul156SV27drXs27lzpz766CMtXrxYp0+fVqNGjfT222/L29vbckyjRo3UpUsXvfbaa8rKylJERATJkSqwYcMGSVKPHj302GOPXfL5np6eGj16tDIyMhQVFaUNGzaQHKkiK1eulHRh6tqUKVNUr149y74mTZpo0KBB+uGHH/TXX38pKytLb7/9tsaMGVPizT8cF/clVYN7E8d08uRJSdLNN99c4v6BAwdaRv2UVU/khhtu0KZNm5SYmFj5QaJEK1as0NmzZ+Xm5qbnn39ePXv2rNB5fn5+6tWrl3r16qXIyEjNmDFDZ8+e1YoVK5jOJpIj+D+1atVSx44dK3RscnKyDh48KEkKDw+3YVQoiY+Pj86dO2cZFmdWdFhxRZ5+tmjRQpKKDcWDbZhvLm6++WarxIgkde7cWTfffLOWL18uSRo1apRVYsQsMDBQQ4YM0YIFCyyjGWBbCQkJkqQbb7zxitq56aabFBUVZWkPtmf+O3XrrbdaJUbMPD099eijj6pFixaaM2eODAaD3nvvPb300kvq0qVLVYeLi3Bf4li4N3FM2dnZklTi78iLt5c1PS0gIECSivU/bGfbtm2SpKFDh1Y4MXKxsLAwHTt2TEuWLNG2bdtIjojkiNPr0aOHtm7dqoyMDKWlpWn06NHlLj25efNmy03IM888UxVhoojAwECdO3dOsbGxViMHqlevbnmdn59fZoFB8zHSherwsL0zZ85Iktq3b1/i/vbt21uSI2Uth9emTRtJUlpaWiVHiJKYi+Je6VBT8+czNzf3imNCxZjn0Zs/M6Xp06eP/P39NW3aNBkMBn300Ud67rnnWHXBTrgvcUzcmzgmDw8PFRQU6Ny5cyXuL7o9JyfHqj+LMv+tLK9/UXnMU9k6d+58Re106dJFS5YssbTn7KgK5+T+85//aOzYsapTp4727dunV155RQsXLiyxGCSuDp06dZJ0YQ5v0X6qUaOG6tatK0nas2dPue3s3r1bkiznwLbMN3w1atQocX/R7T4+PqW2Yx5RYm4PtmUeIh4bG3tF7ZjPr8jSsagc5s9IWcP4zdq0aaMJEybI19dXBQUFmjlzptasWWPrEFEC7kscE/cmjikwMFCStG/fvhL3F91uTkCWxLyPVU+qjouLi71DuCaRHIE6d+6sjz76SLfccosKCgr0yy+/aMyYMdq/f7+9Q0MJBgwYIC8vLyUlJem///2v1U3ILbfcIunCkq/mkQolOXTokGWUQrdu3WwbMCRJ1apVkyQlJSWVuL/o9rJWazCPGCkrgYLKY64/sWjRost+qpKcnKxFixZZtQfbMyccS1qisiTNmjXTpEmT5OfnZ1mW8o8//rBliCgF9yWOh3sTx2Qe5fPnn39aElNmu3fv1p9//mmZWvPLL78oLy+vWBtpaWlasWKFpP8/LQq2Z05s7dy584raMZ9PQvICkiOQdOFp9MMPP6wpU6bouuuuU2Jiot5++23NnDlTGRkZ9g4PRdSpU0f33HOPJCkyMlJjxozR+vXrZTAYdMstt6hr165KTU3VmDFjNG/ePMXExCg5OVmnT5/Wnj179MUXX2jy5MnKy8uTn5+fBg8ebOd35BwaNmwo6f8XibyYuZq/JG3ZsqXUdjZv3ixJLDtaRYYMGSJ3d3elp6dr7NixWrJkSYV/J2ZkZGjx4sUaO3asMjIy5O7uriFDhtg4Ypg1aNBAkhQTE1Phcxo2bKjJkydb5tZ/++23Wrx4sU3iQ9m4L3Es3Js4pkGDBsnV1VVGo1FTp07VuHHj9Mknn2jcuHGaOnWqjEaj7rjjDrVt21ZxcXEaP368Nm/erMTERMXHx2vlypUaP368pbZMv3797PuGnIg5gbhs2TLLikKXauvWrVq6dKlVe87OpZAy3riIyWTS0qVLtXDhQuXl5alatWq69957NXDgQEkXvpxNnz5dkjR//nx7hurUfv31Vy1YsMDys6urqxo2bKg6depoz5495Q5Brlatml577TW1bNnS1qFC0vLly/X9999LulAA66677rIs5bto0SJt3rxZ3bt31759+2QymfTaa68VW41m/fr1+uyzzyRJd911F4WzqsiaNWv0xRdfWK16ERwcrJCQEPn7+8vb21vu7u4yGo0yGAxKS0tTQkKCVdV+FxcXPfHEExowYIA93oJTWrBggX799VfVr1/f8jerolJTU/X222/r1KlTVtv5m2cf3Jc4Du5NHM/SpUv1ww8/lLivffv2GjdunGJjYzV58uQy+69Xr1564YUXbBUmLpKVlaWXX37ZUry4Y8eOlqV8a9WqVep5GRkZlqV8zaOF/Pz89NFHH7GUr0iOoAzJycn68ssvtXfvXkkXqow//vjjSkxM5CbkKrF//359+eWXOn369CWd17FjRz388MMKCgqyUWS4mMFg0Msvv1zqlBlXV1e9/fbbioiI0MqVK+Xi4qJ27dqpSZMmMplMOnjwoKVuhbe3tz755BPm9lah3bt365tvvrnkz5p0odr/I488YpmTj6oRExOjiRMnSpLGjx9fajHk0mRmZmrKlCk6fvy4ZRt/8+yL+xLHwL2J49m8ebMWLVpkWdrX19dXN954o+68805LrbMtW7Zo1qxZJRYW79+/v0aPHk0h3SoWGxuradOmFRtNV61aNQUEBBR7eJOammpZocisZs2aGjt2bIVWk3IGJEdQrvXr12vu3LnKzMyUq6urmjVrZllGlJsQ+yssLNSePXu0ZcsWHTlyRAkJCbr4Y12zZk01aNBA119/vXr27KlGjRrZKVrndvz4cb377rvFlih0dXXVww8/rJtvvlkGg0Hjx49XfHx8iW24urrq2WefVe/evasgYhRlMpkUFRWlqKgoxcTEKDU1tdRjAwIC1KpVK3Xr1k3du3eXm5tbFUYKs2eeeUZnz55V8+bN9fbbb1/y+dnZ2Zo6daoOHz4sib95VwvuS65+3Js4ppycHBmNxlKLx2dkZGjTpk1KSEiQm5ub6tatq86dOzPV147S0tL0888/a+PGjTKZTBU+z8XFRb1799Y999xjWYoZJEdQQVlZWfruu++0fv16q+3chFx9CgsLlZ2drdzcXHl4eMjHx4dM/lUkOztba9asUUxMjIxGo4KDgzVgwABLfQRJSk9P1//+9z9t27bN6mYyNDRU99xzT7HpNrAP8xSanJwcyxKVPj4+lqk2uDYYjUZLUVdzATzYH/cljoV7E8C2MjIytH37dh08eFAJCQlKTU0tdn8SEBCgkJAQtWrVSl27dpWfn5+9w77qkBzBJdm/f78WL15smXNoHrIMoPKdP39eiYmJcnNzU2BgYKlPcgDAWXFfAgCoLCRHAAAAAACAU2M8GwAAlSQ5OdlSB8Hf358hqwAAh3L69GnFxMRYpmYYDAbl5eXJ09NT3t7eVlMz6tevb+9wgUpFcgRwYIWFhUpISFB+fr6CgoJKrHOwd+9ebdmyRSkpKfL29lajRo10ww03UA3eASQnJ+vgwYOSpPDwcDtHg9KcPXtWv/32mzZv3qxz585Z7atfv74GDhyowYMHM7++ii1cuFDBwcHq1q2bPDw87B0O4FSys7O1fft2RUdHKz4+XikpKcrJyVFeXp58fHxUvXp1NWjQQC1atFDPnj0p6HkVWL9+vZYuXaoTJ05U+JwGDRro9ttvV58+feTi4mLD6ICqwbQaVAqTyaS0tDRJUp06dewcjXNYtWqV5s+fr8zMTEkXVjHp37+/HnjgAfn4+EiS5syZo7Vr1xY7183NTcOHD9edd95ZpTHj0mzevFnTp0+Xi4uL5s2bZ+9wnNKWLVv0+eefq1q1apoxY0axVWcOHjyojz76qFhS5GJNmzbVa6+9plq1atkyXBQxatQoSReWNAwLC1Pfvn3VqlUrO0eFSxEXF6dDhw4pPz9fwcHBat++fbEk499//63Vq1crMTFRrq6uqlu3rrp06aKhQ4fK19fXTpE7r6ysLC1cuFArV6601IGpiPbt2+uRRx4hSWIHWVlZ+uijjxQdHX3ZbVx//fV65ZVX+MzZUVZWlpKTk+Xq6qr69etXuDB8dna2tm3bJokHcRLJEfyf3NxcLV++XFu2bFFSUpJcXFwUEhKisLAwDRo0SJ6enmWef/LkSb3yyit8iasiv//+u37++ecS97Vv316vv/66fv/993L74sEHH9Stt95qixBRCczJEYkVGOzFnGDs3bu3/v3vf1vtS0lJ0ZgxY5SdnS1J8vPzU9u2bRUQECCTyaSkpCTt3btXBoNBktS8eXNNmTKFp2tVxJwcKapu3boKDw9X3759VbduXTtEhYrIycnR9OnTtWvXLqvtdevW1fPPP68WLVpIkj799FNFRkaW2Iafn5/Gjx/P8rBVKDExUVOnTlVycnKpx7i7uysoKEhnz55VVlZWsX1PPPEEX9CqkMlk0oQJEyxLYVevXl1hYWFq3bq1QkJCFBAQIC8vL3l4eCg/P1+5ublKTU1VQkKCoqOjFRkZqfPnz0uSWrRoobfeekuurq72fEtO5/jx4/r+++8VHR1tWcrXzc1NHTt21KhRo3TdddeVeT7f4awxxhdKSUnRlClTdOrUKavtsbGxio2N1R9//KFnn322QsuHkmuzvcTERC1YsEDShT9ivXr1kpeXl7Zv367Tp09r7969ioyM1NKlS+Xr66tRo0apW7duqlmzptLS0hQZGalff/1VBoNB8+bN0w033EBdhCqwcOHCSz6n6NDWks4fPnz4FcWE8sXGxkqS2rVrV2zfwoULlZ2dLRcXF919990aNmxYsZtCg8Ggb7/9VmvXrlVsbKzWr1/PjX8V8/HxUU5OjqQLU9V++eUX/fLLL2rVqpXCw8MVFhZmGW2Hq8O0adNKfIqdnJysadOm6b333lNkZKQlMeLt7a0GDRqooKBAJ0+elNFoVHp6uj788EN9+OGH5T7gwZXLy8vT+++/b3ly3atXL3Xs2FG1atVSdna2Dh48qIiICBkMBrVu3VqPPvqo0tPTdfDgQW3cuFHbt2+X0WjU7Nmz5eHhoV69etn7LTmFNWvWWBIjgwYN0gMPPFDq58XT01Oenp6qUaOGGjdurBtuuEEPPfSQvv/+e61cuVJHjhzRmjVrdNNNN1XlW3BqBw4c0Pvvv6/c3Fyr7QUFBdqxY4d27dqlu+66S3fddVe5D2b4DncByREnZzKZ9NFHH1kSI+7u7mrUqJGMRqPi4+NlMpmUkpKit956S/fee6+GDRtm54ixatUqFRQUqFq1anrvvfcsTz9HjRqlN998U3Fxcfr666+Vn5+vd955x+qpWWBgoIYNG6bmzZvrrbfeUl5entavX0+/VoFffvml0s8nOWJ7Z8+elVR8umBhYaFlGOrQoUN1xx13lHi+t7e3nnrqKaWlpWnPnj3auHEjyZEq9tZbbyktLU3r1q3T9u3blZeXJ0mKiYlRTEyMvvnmG3Xt2lXh4eHq0KEDI3vsLDIy0pIYCQwM1K233qp69eopKSlJK1asUHJysv744w9LYuSuu+7Sv/71L0tdmaysLH3zzTfauHGjkpKStH79er6sVYG///5bp0+floeHh8aOHVssodyzZ08NHjxYkyZN0l9//aXQ0FD17t1bYWFhCgsL05EjR/Txxx8rLS1N//vf/9SuXTuWr68CGzZskCT16NFDjz322CWf7+npqdGjRysjI0NRUVHasGEDn7cqkp2drenTp1sSI02bNlWbNm2Un5+v6OhonThxQiaTSb/88ouOHTuml156ifpbFUByxMlFRkbq2LFjkqSwsDCNHj3aMl8wMzNTixcv1ooVK2QymfTjjz8qMzNT999/vz1Ddnrmm8b+/ftbDQv39PTUsGHDNGPGDJ07d06DBw8udThx69at1bNnT0VGRmr//v0kR4BSmKfEVK9e3Wp7RkaGZUj4oEGDym1n0KBB2rNnj+Li4io9RpTN1dVVHTt2VMeOHZWTk6PIyEitX7/eUuw4Ly9Pmzdv1ubNm+Xn56c+ffqob9++TMewk40bN0q6kBj54IMPrEb19OvXT6+++qr++OMPGY1G9e/fXyNHjrQ639fXV88995ySkpJ05MgRRUVF8WWtCmzZskWSNHjw4BJH2klSUFCQ7rvvPn322WdasmSJevfubdnXokULTZw4UWPGjFFWVpbWrFmj22+/vUpid2YJCQmSpBtvvPGK2rnpppsUFRVlaQ+2t3r1amVkZEiSHnnkEQ0ePNhq/86dO/W///1PKSkp2rFjh9555x2NHTuWkZLlIDni5DZt2iTpwlz4F154weqJWc2aNfXAAw+oZ8+elmz+0qVLlZ2drSeeeMJeITu9pKQkSVKbNm2K7WvZsqXldceOHctsp2vXroqMjNTJkycrNT6UrVatWrr77rsrtPzd/v379euvv0qSJk6caOvQUIJatWopNTVVqampatKkiWW7OWkiSQEBAeW2Yx55Yq5PAvvw8fHRgAEDNGDAAKWkpCgiIkIbN260jJ5MT0/X0qVLtXTpUjVu3Fjh4eHq3bu3atasaefInYf5gc3tt99e7Cbex8dHw4YN01dffSVJxb4MmLm4uGjQoEE6cuSIjh8/btuAIUmWz1Dnzp3LPM68//jx48rOzla1atUs++rXr6+bbrpJK1as0Pbt20mOVAHz37IrLaRqfoBw8fQO2M7OnTslSb169Srxd2Hnzp0VGhqqGTNmaM+ePTp48KDeeustjR8/nsK5ZaBijpP7559/JEm33HJLqUOJW7RooalTp1q+GKxevVozZsywFP1B1TL/Iatdu3axfUW3+fv7l9mO+cv5xQXRYBuvvfaa6tSpo4yMDH399deKiYlRaGioWrduXep/DRo0sJxf0n7Ynnn0wO7du622+/v7W35npqamlttOSkqKpCu/AUXlqVOnjoYPH65PP/1UU6ZM0cCBA636Jy4uTt99952efPJJvffee5Yn47At8wpspY3cadiwoeV10d+RFzPv429c1TDfm5T3VLroChrmvi7K/GDn9OnTlRccSmW+VzTX17pc5vNLujeFbZgfbvbp06fUY3x9ffXaa69ZRgYdO3ZMEydOVHp6elWE6JBIjjg58x+mkJCQMo/z8/PTpEmTLF/INm3apA8//PCSlmlD5TA/ZTHPmy+q6DKjFy85ejHzcogUYKoanTp10scff6whQ4aooKBA8+fP15gxY3TgwAF7h4Yy9OzZU5K0du1axcfHW7Z7enpaho6vWrWq3HbMxzRu3Ljyg8QVa9GihUaPHq3PP/9cL7/8srp27Wr5HWoymbRr1y59+umn9g3SSZiLGhcUFJS4v+iDmbIe0pj3UUOmaphHV5U3rSIxMdHyuuioETPzcueMsqsabdu2lSQtWrSozFWGypKcnKxFixZZtQfbM39GynsY6urqqieeeMIyEis+Pl4TJ060PLSBNZIjTs58E5Kfn1/usd7e3nr99dctQyJ37Nih999/v8Qv6bAd8w2IuVDk5TL/UjXfiMD2vLy89NBDD+mdd95R48aNlZiYqLfeekv//e9/S3yCBvvr06ePGjRoIKPRqClTpljqVEgXiiC7urpqyZIlWrZsWYmJxry8PH311VeWJUkpxnp1c3d3V/fu3TVmzBh98cUXeuSRR9SsWTNJJJKrinn1NPPI1ouZp91IKrOGj3k6DVOiqoZ5eeUVK1aUmtiSpCVLlki6MMKgpL4xLwvLKLuqMWTIELm7uys9PV1jx47VkiVLLHUsypORkaHFixdr7NixysjIkLu7u4YMGWLjiGHm5eUlqeKJxHvvvVf33HOPpAsjsyZOnMgIrRJQc8TJBQQE6NSpUzp16pRCQ0PLPd7Dw0NjxozRjBkzFBkZqb179+qdd97RfffdVwXRQpLq1aunxMRES+2Ri/33v/+VVH4m2Twcj2V8q17Tpk01depULVu2TL/88os2bNignTt36t5776Vw4FXGzc1Nzz//vN566y2dPXtWkyZNUps2bdSzZ0+FhobqkUce0Xfffae5c+dqxYoVat++vfz9/WUymZSUlKQ9e/ZYbvY7dOjA8pQOxNfXV4MHD9bgwYOVmJio9evX2zskp9C8eXMlJydr8eLF6tWrl9Uw/bNnz2rJkiXy9vaWwWDQ0qVL9fLLLxdrw2g06s8//5Qkq1pBsJ3w8HBt3bpVR48e1fvvv6/HHntM9erVs+w/d+6c5s2bZ1kdpbTfhUePHpVUfIUw2EaDBg302GOP6YsvvlB2drZ+/PFH/fjjjwoODlZISIj8/f3l7e0td3d3GY1GGQwGpaWlKSEhwWoUkIuLix577LEyp7qhctWtW1dxcXGKi4ur8FTrO+64Qz4+Pvr666+VkpKiiRMn6sEHH7RxpI6F5IiTa9y4sU6dOqX9+/dX+Immq6urXnjhBXl7e2vt2rWKiYnRJ598YuNIYdasWTPt2rWr1PmhgYGBFWpn3759krhxtBdXV1cNGzZMYWFh+vLLL7Vnzx59+eWXWrdunR5//HFWyriKXHfddZowYYI+/vhjnT59WgcOHChxOlRqaqrWrl1bYhudOnXSiy++aONIYSvBwcG6++677R2GU+jXr582b96sjIwMvfLKK+rXr59lKd+IiAhlZWVpxIgRioyMVFRUlObMmaNRo0ZZkiiJiYn69ttvdeLECUn/f2ocbKtLly7q3Lmzdu7cqT179uj5559X/fr15efnp+zsbMXHx1umOvn5+ZW6/Lk5CdmqVauqCt3pDRgwQP7+/vrmm28sIwkSExOtkh9lqVevnh555BF16tTJlmHiIk2bNlVcXJz27NlzSSN2br75Zvn4+Gj27NlKT0/XrFmzbBil4yE54uRat25tucF47LHHrApllcXFxUVPPfWUfHx8tGLFCqWlpdk4UpiZh3gfOHBAhYWFlzWfOj093TLMn/mh9hUYGKjXX39dGzdu1HfffafDhw9r7NixGjJkSLm1gFB1rrvuOn344YdasmSJVq1aVeHfeY0bN9Ydd9yhsLAwG0cIXBs6dOignj17asuWLcrKytKyZcus9gcGBmrw4MEKDAzUrFmztHbtWq1du1Z+fn4qKCjQuXPnLMc2btxYN9xwQ1W/Baf1wgsv6JNPPrEUsD59+nSxYfv+/v4aO3ZsiVNqUlJSVKdOHdWpU0c9evSoipDxfzp27KhPPvlEUVFRioqKUkxMTJnFxgMCAtSqVSt169ZN3bt3L7fOHSpfu3bttGbNGu3du1dpaWnljhgvqm/fvvLy8tKMGTOoH3kRl0Im0Tq1lJQUPfvss5Kk++67T8OGDbvkNhYsWGBZblSS5s+fX2nxoWTmpy/mmjGXatu2bdq2bZsk6cEHH2Ru71UiKytL33//vdatWyfpwpQO89xtPldXD5PJpMOHD+vw4cOKj49XVlaWcnNz5eHhIR8fHwUEBKhhw4a6/vrrVbduXXuH67R++eUXSReeklF3wrEYjUZ9++23WrVqlVWtl9DQUD399NMKDg6WJH3//fdavnx5iW0EBwdr/PjxTM+wgy1btmjdunU6evSosrKy5OXlpQYNGqhr164aNGhQuSva4OpgnkKTk5Oj/Px8y98481Qb2FdOTo5Gjx4to9GogQMHavTo0Zfcxp49e/Thhx9a6kdyr0lyBLqQ3EhLS1PNmjV17733XlYbf/zxh6KioiRJEydOrMzwAKdz4MABffHFF1ZP3PiDBcDZZGZm6vDhwzIajQoODi5xuuGOHTu0du1aJSQkyNXVVXXr1lWXLl3Ur18/y6psAHAt2r17t86dOyc3N7fLrml25MgRy2ivESNGVGJ0jonkCABchYxGo7Zu3WpZSapfv372DQgAAAC4hpEcAQAAAAAATu3yChYAcAinTp3Sc889p3//+9/2DgW45vF5c1z0HVB1+LxdezIyMhQdHa3o6Gh7hwJcESZjosJOnTqld955Ry4uLpo5c6a9w0EFGI1GnTlzxt5h4BLxWXNMfN4cF33nmPhd6Zj4vF17du3apdmzZ8vFxUXz5s2zdzjAZSM5ggrjjxlQNfisAUD5+F0JXF2o1uB4tmzZoh9++IEk8/8hOQIAAAAAgJMxGAwkmYug5ggAAAAAAHBqjBwBAAAAACfz3HPPVUo7BoOhUtoB7I3kCAAAAAA4GaZTOK6FCxdWSjtxcXGV0s61guQIcA0LCAjQ008/be8wAKfA581x0XdA1eHzdvXx9vaWr6/vZZ9vMBiUlZVViRGhPL/88ou9Q7gmkRxBhfHHzPFUq1ZN/fr1s3cYuER81hwTnzfHRd85Jn5XOiY+b1ePwMBAnTlzRqGhoRo/fvxltxMREaHZs2dXYmSAfZAcQYXxxwyoGnzWAKB8/K4ErkyzZs105swZHTt2zN6h4BJ5e3vLYDCodevWGjFixGW3s3v3bi1evLgSI3NsJEcAJ7B//379+uuvcnFx0YQJE+wdDnBNO3HihBISEuTm5qa6deuqcePG9g4JRZw5c0YJCQlKSUmRwWBQXl6ePD095e3trTp16igkJESBgYH2DhO45nFvYn/NmzfXli1blJWVpaSkJNWrV8/eIaGCmjVrpgMHDigtLU2tW7e+7HaSk5MrMSrHR3IEcAKZmZmKjo62dxiogPz8fO3bt0+xsbHKzMyUq6ur/P39df3116tly5b2Ds9ppaSkaNmyZTp06JCMRqOCgoI0cOBAtWvXznJMYmKi/vvf/+ro0aNW5/r7++u+++5T7969qzps/J/k5GQtX75c27ZtU2pqarnH+/v7q1u3bhoyZIjq169fBRECzod7E/tr3ry55XVsbCzJEQfSvHlzHThwQElJScrOzla1atXsHdI1geQIrMTFxSkpKUkuLi4KCQlRSEhIhc7LzMzU33//LUkaPny4LUMEHNK5c+d06NAhSVLnzp3l6upa7JhVq1Zp/vz5yszMLLGNRo0a6fHHH1doaKhNY4W1/fv364MPPrBaqvDEiRPaunWr7rjjDt1zzz1KT0/X22+/rbS0tGLnp6WlaebMmUpPT9fQoUOrMnRImj9/vhYvXqyCgoIKn5OWlqa//vpLq1at0rBhw3T33XfbMEKUhfsSwHaaNGliGXVwJcvxtmrVivo/VaxZs2aSpMLCQsXGxqp9+/Z2jujaQHIEki7MN/vmm290+vRpq+2NGjXSPffco86dO5d5fkZGhqVqMjchtnU5a9Ln5uaWer6Li4tmzpx5xXGhbOvWrdPcuXMVFBSkrl27Ftv//fffa/ny5WW2ceLECU2aNEkvv/yyunTpYqtQUURmZqY++eQTq5vGGjVq6Ny5c5Kk33//XW3atNG6deuUlpYmDw8P9enTR02bNlVBQYGio6MVFRWlwsJC/fzzz+ratSsjEarQl19+qVWrVll+Dg4O1vXXX6+QkBAFBATI29tb7u7uMhqNMhgMSk1NVUJCgg4ePKjExEQVFBTot99+U2Zmpp544gk7vhPnw32JY+HexDF5e3tr4sSJV9xO/fr1+dtWxYqO+jl69OhlJ0f8/PyuaFrOtYbkCBQZGakZM2bIZDIV23fixAm9//776tevnx577DF5enraIUIUdaVr0rOmvX3s379fktSzZ89i+3bu3GlJjHh5eWnQoEHq0KGDAgICZDKZlJSUpK1bt2rDhg0qKCjQjBkzNH36dPn5+VXlW3BKK1eutCxPeOutt2r48OGqVq2acnJytHDhQi1btkyLFy/WoUOH5Ovrq8mTJ6tBgwaW8wcPHqxdu3Zp2rRpMhqNWrlypR544AF7vR2nsnv3bktipGnTpnr00UfVokWLCp9/+PBhffPNNzp27JhWr16tHj16qEOHDrYKF0VwX+J4uDcBqlZAQIA+++wzFRYWysfH57Lb6dixozp27Fh5gTk4kiNOLj09XXPmzLHcgPTo0UNt2rRRfn6+oqOjtWvXLplMJkVEROjkyZMaN26catSoYeeoIUk+Pj6qXr16hY7Nzc21POmuU6eOLcNCKRISEiSpxOKcS5YskSTVrl1bkyZNKvb0pUGDBurSpYvCw8P13nvvyWAwaMWKFbr33nttHrez2717tySpS5cuevDBBy3bfXx89MADDygpKUnbtm2TJD344INWiRGzTp066cYbb9TKlSuZX1+FzImRJk2aaPLkyZf8JTo0NFSTJ0/Wm2++qbi4OK1cuZLkSBXgvsSxcW8CVB0+N5WP5IiTW7lypQwGg1xdXfXyyy9bDfcfOnSo4uLi9Pnnn+vYsWM6evSoJk6cqDfffFO1a9e2Y9TOzbwmfUFBgW688UbdfvvtcnNzK/OczZs3a/r06ZKkzz77rCrCxEUyMjIkFf9DlpeXZ6lF8sADD5Q5LLVNmzYaMmSIfv/9d+3evZvkSBUwJ7VKWy60b9++luRISdOlzLp166aVK1cWmyIA2zEXxh02bNhljy7w9PTU7bffrunTpxcrtAvb4L7EMXFvAuBaULwiIJzKnj17JEk33nhjiTf2jRs31ttvv63+/ftLuvBFYcKECSz7ZEcff/yxbr31VhmNRs2fP19jxozhabQDcHFxkaRiw8TT09Mt2zp16lRuO+ZjkpKSKjlClCQnJ0dS6U9nim4va5pTrVq1JF1ZwTtcGnNh4yudB28+v7RCyahc3Jc4Ju5Nrn2nTp3Sc889p3//+9/2DgWwGZIjTi4xMVGS1L1791KPcXd311NPPWWp1p+cnKyJEydanqiianl6eurBBx/Uu+++qyZNmighIUGTJ0/WrFmzuHm/ivn7+0uS4uPjrba7u///AXweHh7ltmN+Al7SXHxUPm9vb0kqcRUaSTp79qzltbk2SUnOnz8v6UJNGVSNmjVrStIVj9Yxn29uD7bFfYlj4t7k2mc0GnXmzBkSkQ4oMzNTCxcu1MKFC+0dylWP5IiTMz8VrchN37/+9S+NHj1aLi4uSktL08SJExUXF2fjCFGaJk2a6N1339UDDzwgLy8vrVu3Ti+99JLWrFlj79BQgjZt2kiSNmzYYLW9du3alvnZFRm2HxsbK+n/J1tgW/Xq1ZMkbd++vcT9Rbfv3bu31HbM+wIDAysxOpTFvMzh0qVLlZ+ff1lt5OXlafHixVbtwba4L3Fs3JsAVx/z6l3mFbxQOpIjTs5c3bisJ55FDRw4UM8995xcXV117tw5TZ48WYcPH7ZliCiDq6urhg4dqo8//lgdO3ZUVlaWPv/8c02YMEEnT560d3gowlyzIjo62mrJXhcXF/Xt21eS9PPPP8toNJbaRkZGhn7//XdJYj37KmL+d163bp1WrlxptW/lypWKiIhQ48aN5erqqnnz5pX4hPTEiRP6888/JUktW7a0fdCQJN10002SpGPHjmnSpEmWxGJFxcbGatKkSZYv2+b2YFvclzg+7k0AOCoKsjq5oKAgxcbG6ujRo2rbtm2Fzundu7e8vb31ySefKDs7W++8845GjRpl40hRljp16mjcuHHatGmTvvvuOx06dEhjx47VkCFDNGLECHuHB11Yj75///5au3atvv/+e508eVJ33XWXAgMDNWrUKO3cuVMxMTGaMGGC7r77brVp08ZSzC4vL0/btm3Tzz//rNTUVLm7u+vmm2+28ztyDoMGDdIff/yhvLw8ffXVV5o/f77q1q2r5ORkyyoLd9xxhzZu3Kjt27drzJgxuuWWW9SkSRMVFBQoJiZGf//9twwGg1xcXHTjjTfa+R05j44dO+rGG2/U6tWrFRsbq/HjxyskJEStW7dWcHCwAgIC5O3tLXd3dxmNRhkMBqWmpioxMVHR0dFWUzT69+/PUodVhPuSawf3JgAcjUthYWGhvYOA/Xz99df666+/1KpVK02ePPmSzt23b58++OAD5ebmWm2fP39+ZYaIS3T+/HnNnTtXa9eulXTh5qRdu3aWn+kf+zEajfrggw8sy8O6uLioWbNmatGihTw9PbV8+XLLyBF3d3f5+fnJZDJZFW2VpMcee0yDBg2yx1twShs3btRnn31WYp2XPn366LnnnlNCQoLeeOMNZWdnl9rOrbfearUcMKrGzz//rCVLllxWnR5XV1fddtttuueeeyxFlWFb3Jdcm7g3cXwnT57UK6+8Ion+cjT0XcUxrcbJmYeMx8TEWIqgVVS7du00fvx4VatWzRah4TJVr15dTz31lCZOnKigoCClpKRYbj5gX+7u7hozZozuvPNOubm5qbCwULGxsfrjjz+0ePFiqyk1RqNRKSkpSktLs3ypq1mzpl544QUSI1Wsd+/emjx5sjp37qzq1avLy8tLjRs31uOPP65nnnlGkhQSEqJXX321xOVEPT09NXLkSBIjdnLPPffok08+0cCBAytcq6d27doaOHCgPv74Y917770kRqoQ9yXXJu5NADgCRo44ufz8fI0ePVoGg0FhYWF68cUXL7mNuLg4vfPOO5a59mQkrx5Go1G//vqrlixZYvniTf9cHZKTk7Vy5Upt2bKlzMrvbm5uCg0NVY8ePdS/f3/L6im4OhmNRu3evVvx8fFyc3NT3bp11a5dO76sXUWSk5MVHx+v1NRU5eTkKD8/Xx4eHvLx8VFAQIAaNGigunXr2jtMp8V9ybWPexPHlJ2draioKEn/v44aHAMjRyqO5Ah04sQJZWdny9XVVaGhoZfVRlJSkg4ePCiJX5jApcrMzFR8fLyysrKUm5tr9UUtKCjIUnsEAJwB9yUAUHmSk5M1a9Ysubi4aOLEifYO56pGcgQAgEqUl5dneWJdp04dO0cDAACAimC1GgAAKtH27ds1ffp0ubi4aN68efYOBwCqlMlkUmJiovLz8xUUFFTidNATJ04oIiJCCQkJlimInTt3Zpn6q1R+fr727dun2NhYZWZmytXVVf7+/rr++utZoh7XFJIjAHAVys7O1q5du3TmzBl5e3urUaNGatWqlVxdqaPtKBiY6Ri2bt2qTZs26ejRo8rIyJCbm5v8/f3VqlUrDRw4UE2bNrV3iE4lOjpaQUFBJRY3xtVv6dKl+v3335WVlSXpwqpsN9xwgx588EHVqlVLkrRixQrNnTu32ApSf/zxh9q3b6+XXnqJOk1V5Ny5czp06JAkqXPnziXeY6xatUrz58+3jIi8WKNGjfT4449f9hQ44GrCtBrAgcXExGjLli1KSkqSi4uLQkJCFBYWVqGb+VOnTumdd96Ri4uLZs6cWQXRQrrwtGzhwoU6ePCg8vLyFBISoiFDhqh3796WY9atW6evv/5aBoPB6ty6devqySefVNu2bas6bKcVHR19yefs379fv/76qyRp0qRJxZIkrVu3rpTYULbDhw9ryZIlcnNz0/PPP1+sdk96ero+/vhjyxeD0tx88816+OGHSUxWkVGjRsnV1VVt27ZVeHi4unfvLk9PT3uHhQr4/vvvtXz58hL3NWnSRG+99Zb279+v999/v8x22rVrpzfeeMMWIeIiy5Yt09y5cxUUFKRPP/202P6y+rQoNzc3vfzyy+rSpYsNokR54uLidOjQIeXn5ys4OFjt27eXu7v1GIi///5bq1evVmJiolxdXVW3bl116dJFQ4cOla+vr50iv/owcgSVIiIiQrNnz2YYeRUxGo2aNWuWNm3aZLV9x44dWrJkiXr06KHRo0erZs2aZbZx5swZW4eKIvbs2aNp06ZZLdl79OhRzZw5U8ePH9d9992nnTt3as6cOcWeqEkXCmpNnTpV48aNI0FSRSZPnnxF50+aNMnqZ35HVp2oqCht27ZN7du3L5YYMRgMmjJlik6ePGnZ5u/vr4CAAJlMJiUlJVmefP/111/Kz8/Xk08+WaXxOzOTyaS9e/dq79698vb2VlhYmPr27Uti8Sp25MgRy5doNzc3de7cWfXr19fp06e1a9cu/fPPP1q1apU2btwoSWrTpo1GjRqlpk2bqqCgQAcOHNBPP/2k+Ph47du3T9u3b1fXrl3t+Zacwv79+yVJPXv2LLZv586dlj718vLSoEGD1KFDB6vfk1u3btWGDRtUUFCgGTNmaPr06fLz86vKt+DUcnJyNH36dO3atctqe926dfX888+rRYsWkqRPP/1UkZGRVsecOHFCJ06c0Nq1azV+/Hg1atSoyuK+mpEcQaViIFLVmDNnTrHESFFbt25VTEyM/vOf/6hVq1ZVGBlKk5WVpZkzZ1oSI8HBwfL29lZcXJxMJpMlqfXtt9/KZDIpLCxMXbt2Vc2aNZWWlqYtW7Zo165dMhqNmj17tj755BOepjogfkdWHfNKJSU9yVy2bJklMdKxY0c98MADatCggdUx0dHR+u677xQXF6c1a9aoT58+fDm3A4PBoLVr12rt2rWqU6eOwsPD1bdvX9WvX9/eoaGIVatWSZK8vb01ceJEqxGs//zzjyZNmqTFixcrPT1drVq10htvvGEZjeXh4aEuXbqoZcuWGjt2rFJSUrRx40aSI1UgISFBktS4ceNi+5YsWSJJql27tiZNmlTsM9egQQN16dJF4eHheu+992QwGLRixQrde++9No8bF0ybNq3EEa7JycmaNm2a3nvvPUVGRloSI97e3mrQoIEKCgp08uRJGY1Gpaen68MPP9SHH37IfaVIjgAOJyYmRhs2bJAkBQUF6cEHH1Tr1q1lNBoVHR2txYsXKzY2VhkZGZoyZYpefPFFbjCuAmvWrNG5c+fk6uqq//znP+rWrZskKT4+Xm+99ZYyMjL05ZdfKikpSc8995z69OljdX6/fv0sw19TUlIUGRmp8PBwe7wVp1SrVi0NGDCgQssqnzx5Ulu3bpUkDR8+3NahoRQpKSmSVOKX6HXr1kmSOnXqpLFjx8rFxaXYMa1bt9bkyZM1btw4JSYmauXKlSRHqtCLL76oo0ePatOmTUpLS5N0oU9//fVX/frrrwoNDVV4eLh69epFfYqrgHl62tChQ4tN7W3SpImGDh2qhQsXSrrwe7GkaWq+vr665ZZbNHfuXB09etT2QUMZGRmSiq+slpeXZ+nTBx54oMxkZJs2bTRkyBD9/vvv2r17N8mRKhIZGWlJjAQGBurWW29VvXr1lJSUpBUrVig5OVl//PGHJTFy11136V//+pc8PDwkXXho980332jjxo1KSkrS+vXrddNNN9nt/VwtSI4ADmbNmjWSLgwBnzJlitU8we7du6tbt25atmyZfvrpJ+Xn5+ujjz7SM888U+zLNqqWechjjx49LIkR6cKTl2HDhmnu3LmKi4tT9+7dS+2roUOHKioqSocOHdKOHTtIjlSB2267TcuXL1dGRoaioqI0evTocr8gb9682ZIcGTFiRFWEiRKYp8VcPMQ7KytLycnJkqSRI0eWmBgx8/b21u23367Zs2eXW5sElathw4YKCwvTfffdp3379mn9+vWKiopSbm6upAs1ZQ4fPqxvv/1WnTt3Vr9+/dSxY0dqw9iJOYFV2mjVotvNQ/1LYi7qmZ6eXnnBoVTm338XT+VNT0+3bOvUqVO57XTq1Em///67kpKSKj9IlMg8RS0wMFAffPCBfHx8LPv69eunV199VX/88YeMRqP69++vkSNHWp3v6+ur5557TklJSTpy5IiioqJIjojkiNObNWtWpbTDL8OqY75Bv+2220osoOTi4qLbbrtNzZo104cffqjz58/rs88+U05OjgYNGlTV4eL/xMfHSyp5Xm/Xrl01d+5cSVJYWFiZ7fTu3VuHDh3S8ePHKz9IFHP//ferd+/e+vzzz3Xs2DFNnjxZ/fr10/33368aNWrYOzyUoXr16srIyLAkScyK/lyROdbmY8xPWFG1XFxc1L59e7Vv314Gg0Fbt27V+vXrdeDAARUWFio/P19bt27V1q1bVbNmTfXp00d9+/YtcZoAbKegoECSrL6gFVV0OV/zk+uSmIf1l1R3C5XP399fiYmJio+Pt1ptpmgxz7L6y4x+q3rHjh2TJN1+++3FPnc+Pj4aNmyYvvrqK0nS4MGDS2zDxcVFgwYN0pEjR7iv/D8kR5yceWgxHMfZs2clqdwl08xDwt955x2dPXtW//vf/2QwGDRs2LCqCBMXOX/+vKQLNyIXK7qtvHn05i9qPFWrOo0bN9a7776rFStWaMGCBYqIiND27dt1//33q3///vYOD6WoV6+eMjIyFBMTY1XAuGhSKycnp9wkV05OjiTrL3ewD29vb4WHhys8PFxpaWlav369NmzYYEk+Z2Zmavny5Vq+fLkaNWqk8PBw9e7dmwKRVcDX11fp6elKTExU8+bNi+0/deqU5XVSUpKCg4NLbMf8sI2pUlWjTZs2SkxM1IYNGzRgwADL9tq1a6t69eo6f/68jh49Wm79utjYWEkl3+PANsxLK5eW5G/YsKHl9cU1tYoy77v4QYKzYuwh4GDMT2cqMnS4YcOGeuutt1S3bl1J0o8//qgFCxbYND6UzPxUpaR+K1oAq7xiWOanA/n5+ZUYHcrj4uKiW2+9VR9//LE6deqkrKwszZkzRxMnTrR8McPVxVxr6e+//7YkOKQLI0rMN5NRUVHltmOeIhUUFGSDKHG5/P39dccdd+ijjz7S1KlTdfPNN1ut0HbixAnNnTtXzzzzjB2jdB7XXXedJGn58uXFCk+bTCar5WAjIiJKbcf80K7oFzvYTr9+/SRdKEBdtI9cXFzUt29fSdLPP/9stcrexTIyMvT7779Lktq3b2+zWGHNfD9p/l5wsaKjeMoa0WPeV9YUU2fCyBEn5+vrq6ysLHXo0EGPP/74ZbezZcsW/fDDD5UYGUpjXr0kJSWlWNGzktStW1eTJ0/WlClTlJCQoF9//VUGg4En3lWsZs2aysnJsYz8uVwGg0GSmNJhJwEBAXrttde0efNmffvtt4qJidGrr76qoUOHavjw4VR6v4oMGDBAS5YsUUZGhqZNm6aXX37ZMhVx2LBh+u9//6sff/xR1113XYlPuqUL9WNWrlwpqeQpcbg6NG3aVE2bNtVDDz2kXbt2af369dqxY4eMRmOpXxxQuXr27Kk9e/YoLi5OEydO1B133KG6desqOTlZixcvVlxcnPr3768dO3Zo6dKlatCggeXLt3ThC9qiRYu0Y8cOSRdWkYLtNW/eXP3799fatWv1/fff6+TJk7rrrrsUGBioUaNGaefOnYqJidGECRN09913q02bNpbC5Hl5edq2bZt+/vlnpaamyt3dXTfffLOd35Hz8PPzU3Jysv75558Sa6GZp91IUlxcXKkjzs3TaYoml50ZyREn17x5c+3evVsJCQkKDAy87Hb4olZ1GjVqpLS0NB08eFDdu3ev0Dn+/v6WBElcXJyWL1/O3MIq5u/vr6SkJKWmppa4/+mnn7YcVxbzkONatWpVboC4JL169VLHjh01d+5crVmzRosXL9bmzZv16KOP2js0/J8aNWpo9OjRmj59uqKjo/XSSy9p8ODB6tmzp/r06aMjR47or7/+0ptvvqmwsDB17NhRAQEBMplMSkpK0tatW7V3715JF6a7UbPp6ufm5qauXbuqa9euOn/+vDZt2mRZ3Q221bdvX61YsUInT57UoUOH9P7771vt9/T01G233SY/Pz/99ttv+uyzz/Tbb7+pcePGKigo0JEjRyxFXWvWrGk1xQO2NXr0aJ09e1a7d+/W2rVrFRERoWbNmqlFixbq2bOnli9frqNHj+qdd96Ru7u7/Pz8ZDKZrIq2StJDDz1U5vQNVK7mzZtbko+9evVS7dq1LfvOnj2rJUuWyNvbWwaDQUuXLtXLL79crA2j0ag///xT0oVVpUByxOk1a9ZMu3fvVkpKijIzM8kaOoBWrVpp9+7d2rJlix588MEKD4OrUaOGJk6cqKlTp+rw4cPav3+/jSNFUU2bNtXBgwetMvlFmYe2licmJkaSFBISUlmh4TJVq1ZNTz75pMLDw/X5558rMTFR77//vurVq2fv0PB/wsLClJeXpy+++EKZmZlasGCBFixYoGrVqqlu3bpyd3eX0WjUpk2btGnTphLbqFOnjsaOHcuoIAdTvXp1DRo0iKRWFXF3d9e4ceM0depUnTx50mqfj4+PnnnmGYWEhGjEiBE6ePCgYmJilJiYqMTERKtjPT099cILL5RYcB624e7urjFjxujXX3/V4sWLVVBQoNjYWEsdkaKMRqNlmXSzmjVr6pFHHlGvXr2qKmTown3j5s2blZGRoVdeeUX9+vWzLOUbERGhrKwsjRgxQpGRkYqKitKcOXM0atQoSxIlMTFR3377rU6cOCGJ0ZFmJEecXNGhxLGxsercubMdo0FFdOzYUfPmzVNaWpq2bt16Sb/MqlWrpjfffFMffPCB5Ykoqob5s3bgwIHLbsO8KoN0oYgarg6tWrXSBx98oEWLFmnx4sWs3nWVCQ8PV7NmzfTNN99YksLZ2dmKi4sr8zw3NzcNGDBAI0eO5MEBUAEBAQGaNm2atm3bpkOHDsloNCooKEg33HCD5TPk5uam119/XfPnz9e6dessRSB9fHzUuXNnDR8+vNRirbAdd3d3jRo1Sv3799fKlSu1ZcsWy5LnJXFzc1NoaKh69Oih/v37U7DaDjp06KCePXtqy5YtysrK0rJly6z2BwYGavDgwQoMDNSsWbO0du1arV27Vn5+fiooKNC5c+csxzZu3Fg33HBDVb+Fq5JL4cVVk+BUMjMzLbVGhg8frhEjRlxWOzExMVqzZo0kUfysCkycOFFnz55VSEiIxo4de8nnG41Gffrpp9q2bZskaf78+ZUdIi6Sk5NjeQrTtm3byyp8FRERoV9++UXShf8HzIV2cfVITEzUsmXLLMXr+H14dUlMTFRkZKSOHDmi+Ph4ZWVlKTc3Vx4eHvLx8VFAQIAaNGig1q1bq2vXrjy9toNJkybJxcVFzzzzzBVN94VjSE9Pl5ubm3x9fSkIeZXJzMws9fdkUFCQpfYI7MdoNOrbb7/VqlWrrAohh4aG6umnn7YkGr///nurgrtFBQcHa/z48apTp06VxHy1IzkCAAAAAIADyszM1OHDh2U0GhUcHFzi8r47duzQ2rVrlZCQIFdXV9WtW1ddunRRv3795O7OZBIzkiMAAAAAAMCpkSYCAAAAACglJUXR0dGKj49XSkqKDAaD8vLy5O3tLV9fXzVo0EDNmzdXy5YtmQqFaw7JEQAAAABwYlFRUfr999919OjRCh3v7++vQYMG6bbbbmNaBq4ZTKsBAAAAACeUl5enTz/9VDt27Lis84ODg/Xiiy/quuuuq+TIcDnOnDmjhIQEq1E/np6e8vb2Vp06dRQSEkKx6zKQHAEAAAAAJzR16lTt3r1bklSnTh21a9dOfn5+ys7O1sGDB3XixAm5u7vrwQcfVOvWrXX8+HHFxMRo69atyszMlCT5+vpqwoQJJEjsJDk5WcuXL9e2bduUmppa7vH+/v7q1q2bhgwZovr161dBhI6D5AgAAAAAOJnNmzdr+vTpkqThw4frrrvukqurq9UxGzdu1KxZs1RYWKgpU6aoWbNmki4sI7tixQrNmzdPBQUFatiwoaZNm1bsfNjW/PnztXjxYhUUFFzyuW5ubho2bJjuvvtuG0TmmJggBgAAAABOZv369ZKksLAwjRgxosRjevfuraSkJC1YsEDz5s3T+PHjJUnu7u4aNmyYgoOD9cEHH+jkyZNav369+vXrV1XhO70vv/xSq1atsvwcHBys66+/XiEhIQoICJC3t7fc3d1lNBplMBiUmpqqhIQEHTx4UImJiSooKNBvv/2mzMxMPfHEE3Z8J1cPkiMAAAAA4GTi4uIkSeHh4WUeFx4ergULFmj//v2WGhZmXbt2VVhYmCIjI7VlyxaSI1Vk9+7dlsRI06ZN9eijj6pFixYVPv/w4cP65ptvdOzYMa1evVo9evRQhw4dbBWuw2DcEwAAAAA4GXPNkNq1a5d5nHm/yWTS2bNni+0PCwuTJB0/frySI0RpzImRJk2aaPLkyZeUGJGk0NBQTZ48WY0bN5YkrVy5srJDdEgkRwAAAADAyVSrVk2SSkx4FFV0v4eHR7H95tVPzMkW2J55yeVhw4ZZjeS5FJ6enrr99tut2nN2JEcAAAAAwMmYV5dZt25dmceZa5P4+PiUOMrEaDRKkry9vSs5QpTGnIi60tVmzOeT2LqA5AgAAAAAOJnu3btLkiIjI7Vw4UKVtIjp5s2btXDhQklSx44d5eLiUuyY+Ph4SeVPz0HlqVmzpiTp9OnTV9SO+Xxze86OgqwAAAAA4GQGDBigP/74Q6dOndIvv/yiiIgItW/fXrVq1VJ2drYOHjxoqSPi7u6uO++8s8R2IiMjJUnNmzevstidXbNmzZSWlqalS5eqW7duJU53Kk9eXp4WL15saQ+MHAEAAAAAp+Ph4aFXX31V/v7+kqQzZ85o9erVWrRokf78809LYsTV1VVPPvmkGjVqVKyNlJQUHT58WF5eXmrfvn2Vxu/MbrrpJknSsWPHNGnSJMXGxl7S+bGxsZo0aZJlxSJze87OpbCk8VMAAAAAgGteZmamfvnlF23cuFHZ2dmW7W5ubmrXrp1GjBjBqJCr0BdffKHVq1dbfg4JCVHr1q0VHBysgIAAeXt7y93dXUajUQaDQampqUpMTFR0dLQSEhIs5/Xv319PPfWUPd7CVYfkCAAAAAA4OZPJpNOnTysrK0teXl6qX7++vLy87B0WyvDzzz9ryZIlMplMl3yuq6urbrvtNt1zzz0l1pJxRiRHAAAAAABwQKdPn9ayZcu0Y8cOpaWllXt87dq11bVrV916660KCgqqgggdB8kRAAAAAAAcXHJysuLj45WamqqcnBzl5+fLw8NDPj4+CggIUIMGDVS3bl17h3nVIjkCAAAAAACcGqvVAAAAAAAAp0ZyBAAAAAAAODWSIwAAAAAAwKmRHAEAAAAAwMlERERo1KhRuvvuu+0dylWB5AgAAAAAAE6KNVouIDkCAAAAAACcGskRAAAAAADg1NztHQAAAAAAAKiYWbNmVUo7SUlJldLOtYLkCAAAAAAADmLdunX2DuGaxLQaAAAAAADg1Bg5AgAAAACAg/D19VVWVpY6dOigxx9//LLb2bJli3744YdKjMyxkRwBAAAAAMBBNG/eXLt371ZCQoICAwMvu50aNWpUYlSOj2k1AAAAAAA4iGbNmkmSUlJSlJmZaedorh0kRwAAAAAAcBDNmze3vI6NjbVjJNcWptUAAAAAAOAgiiZHjh49qs6dO19WO/Xr11d4eHhlheXwXAoLCwvtHQQAAAAAAIC9MK0GAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGokRwAAAAAAgFMjOQIAAAAAAJwayREAAAAAAODUSI4AAAAAAACnRnIEAAAAAAA4NXd7BwAAAP6/Z599VmfOnJEk/fe//1XdunXtHJH9jRw50vJ6wYIFdowEAABcqxg5AgAAAAAAnBojRwAAAHBVYbQQAKCqMXIEAAAAAAA4NZIjAAAAAADAqZEcAQAAAAAATo3kCAAAAAAAcGoUZAUAoAqYTCatXbtWGzdu1MmTJ5WTk6PatWurWbNmuvHGG9W+fftLbtNgMGj9+vXauXOnTpw4oczMTLm6usrPz0+tWrVS37591bZt2xLPPXbsmF577TVJUrVq1fTFF1/I09Oz3Gvm5ubq8ccfl8FgkCR9+OGHatSoUYnHFhYWatu2bYqKitKRI0eUnp4uo9GomjVrqkmTJurevbv69OkjNze3S37vZTl58qQiIiK0b98+paamKicnRzVq1FBQUJA6duyoG2+8UTVq1CizjYiICM2aNUuSFB4ermeffVYFBQXatGmT1q9fr/j4eGVmZqpGjRpq1qyZwsPD1aNHjzLbTE5O1nPPPSdJCgwM1GeffSZJ2r9/v1atWqXY2FidPXtWnp6eatasmQYPHqyuXbtatWEymbR9+3atWrVK8fHxysjIUI0aNdS6dWvdcccdpfZFSYxGozZv3qzt27fr2LFjyszMVGFhoWrWrKkWLVqoV69e6tatm1xcXEpt48CBA5o8ebIkqXXr1po0aVKJ78nLy0sNGzZUWFiYbrrpJrm7F78FLdpWUUWLsxbFUtcAgMpEcgQAABtLS0vTtGnTdOzYMavtycnJSk5OVmRkpAYMGKDRo0dXuM3IyEh98803Sk9PL7bv9OnTOn36tCIiItS5c2c9//zzqlatmtUxTZs2VUhIiBISEpSdna2dO3eqZ8+e5V5327ZtlsTIddddV+qX8ePHj+uzzz5TXFxcsX2pqalKTU3V9u3b9fvvv+uVV15RgwYNyn/T5SgoKNB3332nv//+WyaTyWrf2bNndfbsWUVHR2vx4sV66KGH1K9fvwq3nZ6ero8//lgxMTHF2t2+fbu2b9+uTp066cUXX5SPj0+F2jSZTPr222/1559/Wm3Pz8/X3r17tXfvXt155526++67JUkZGRmaNm2ajhw5YnV8WlqaNm7cqMjISL3wwgsV6scDBw5ozpw5SkpKKrbvzJkzOnPmjDZv3qwWLVro5Zdflr+/f4Xek9Fo1Ndff61Vq1YVe08HDx7UwYMHtXbtWo0fP141a9asUJsAAFQFkiMAANhQVlaW3nrrLSUmJlq2BQUFqVmzZnJ3d1dcXJzi4uK0Zs0aeXt7V6jNZcuWae7cuSosLJQk+fj4KDQ0VAEBATKZTIqPj9fRo0dVWFionTt3auLEiZoyZYq8vLys2unTp4/mzZsnSdqwYUOFvlRv2LDB6vySREdH6/3331dOTo4kyc3NTU2bNlVwcLDc3Nx05swZxcTEKD8/X4mJiXrjjTc0ZcqUK0qQmEwmffTRR9q+fbtlm6+vr1q3bi1fX1+lpqbqwIEDMhqNOn/+vGbNmqXz58/r1ltvLbftgoICffDBBzpy5IhcXV3VsmVLBQUFyWAw6ODBgzp79qwkadeuXXr33Xc1ceLEEkdGXOynn37Sn3/+KRcXF4WGhio4OFj5+fk6cOCApc1FixapQYMG6tatm6ZMmaLjx4/Ly8tLrVu3lr+/vzIzM7Vv3z4ZDAYVFBRo5syZaty4serXr1/qdSMjIzVjxgwVFBRIkjw8PBQaGqrAwEC5urrq1KlTOnz4sAoKCnTkyBGNHz9eU6dOlZ+fX7nv6YsvvlBERIRcXFzUokULBQcHq7CwUEeOHLF8Bv755x999tlnGjdunNW5/v7+uvnmmyVJf/31l2W7edvFLk74AQBwJUiOAABgQ999953lS6GHh4eeeuqpYkmFvXv3avr06VqxYkW5U0z27dtnSYy4ublpxIgRGjJkSLHESlxcnGbMmKH4+HgdP35cc+fOLTYypU+fPpo/f74KCwu1a9cuZWVlydfXt9RrZ2Zmau/evZIkFxcX9e7du9gx6enp+uSTTyyJkRtuuEEPPPBAsZEH6enp+uqrrxQVFaXs7Gx9+umnmjZtmlxdL68c2tKlS60SI8OGDdOoUaPk4eFhdc3PPvtMe/bskST98MMPCg0NVYsWLcpse8uWLTIajWrSpIleeOEFBQcHW/aZTCYtXbpUP/30kwoLC3Xo0CEtWrSo1KkgZmlpaVq6dKkaNmyoF198UQ0bNrTsy8vL0+zZs7Vp0yZJ0i+//KLDhw/r+PHjCgsL0+jRo62mBZ09e1ZTpkzRyZMnlZ+fr0WLFumZZ54p8bonT57UrFmzLImRIUOGaPjw4cX6PSkpSZ999pliYmKUmpqq2bNnF0tmXOzIkSOKjo5Ws2bN9NxzzykkJMSyr7CwUH/88Ye+/fZbSRcSSdHR0WrdurXlmKCgID322GOSrJMj5m0AANgSBVkBALCRxMRErVu3zvJzSYkRSWrfvr1effVVubi4WL60lsRkMumrr76yjBh55plndOedd5Y44qRx48aaMGGCatWqJUlavXq1UlNTrY4JDAxUy5YtJV2YDrFly5Yy38/mzZst8bVp06bEqRY///yzMjIyJEkDBgzQCy+8UOJxfn5++s9//qM2bdpIkk6cOFHu9UuTnZ2tX3/91fLz0KFDdf/991slRszXfPXVV9WsWTNJF0aE/PTTT+W2bzQaFRAQoDfeeMMqMSJJrq6uuv322zVq1CjLtsWLFysrK6vMNgsKClSzZk1NmDDBKjEiSZ6ennryySctCZBTp07pzz//VPv27fXCCy8Uq5dSu3ZtPfnkk5aft2zZUur/R998841yc3MlSXfffbcefvjhEhNi9erV0+uvv25JcOzatavYdJ6L5efnKygoSBMnTrRKjEgXkmlDhgyxGp1kTv4AAHA1IDkCAICNrFmzxvK6RYsWpU5DkaSWLVuWuV+SduzYoVOnTkmS2rVrV+7xfn5+lmkjBQUFioyMLHZM0TaKTpkpSXlTajIzM7Vx40ZJF6Y8PPzww2W25+rqqnvuuafC1y/Nxo0bLXVQatWqZanRURIPDw+rkQgHDhywmvJUmpEjR5ZZxHXYsGEKDAyUdCFJsH79+nLb/Ne//mVJXl3M29tbnTp1str2wAMPlDqyJjQ0VHXq1JF0oVBvQkJCsWPi4uK0f/9+SRdGadxxxx1lxuft7a3hw4dbfq5I/9x7771lTg/r37+/5fXRo0fLbQ8AgKpCcgQAABs5cOCA5XXfvn3LPb68Y3bt2mV53atXrwrFUHS1mouLiUpSWFiYpT5GTEyMUlJSSmzn9OnTlpEDHh4eJa7MsnfvXuXn50uSOnfuXKEaKi1atLDUQikpvoowf+GXLkzjKW/VnebNm1sVki16fkk8PDwUFhZW5jHu7u5W04zKa1NSuTVeisYYFBSk6667rszji45ASU5OLra/6P8/YWFhFZrCVPT/n0OHDpV5rIeHh7p06VLmMU2aNLG8PnPmTLnXBwCgqlBzBAAAGygsLNTx48ctP5dX10K68KXdxcXFMm3mYocPH7a83rlzZ4krwVwsOzvb8vriaTXShaKlnTp10rZt21RYWKiNGzeWOKKg6KiBrl27llgMs2h8p06d0v/+979y4yvq/PnzMhgMFS5Ma1b038E8Tag8LVu21IkTJyRdKBBalkaNGlUoptDQ0BJjKkm1atXKXQGmevXqltcXT1Mp73hzzZeiivbP4cOHK9Q/Rf9fLC1xZhYcHFxuIdqiU3iK/r8JAIC9kRwBAMAGsrOzZTQaLT+bpzyUpVq1aqpWrZrOnz9f4n7zCibShSV1L1VpdTD69OljaW/Dhg0lJkfM02XMx5cX39GjRy9r2sT58+cvOTmSmZlpeW2e2lKeosedO3euzGMDAgIq1GbR44rGVJKKrLRStDjvpR5fUs2Rov2zf//+Co1uKaq0/y/NKhJj0eRJWfV1AACoakyrAQDABsw1MMzKm+phdvFyu0Vd6ZN2k8lU4vYuXbpYRh2cPHnSasSLJMXGxlpqndSoUUMdO3a0SXzS5X1hLvpvXda/X1FFEzAljbIo6nLazMvLK/XfW7pQoPRSXOrxJbHV/z9mlREjAAD2wsgRAABs4OLRD3l5eRUaEWFeSaQkXl5eli+406ZNU+PGja8oRjNzDRFzAdkNGzZY1bcoOqWmaI2SkuIze/jhhzVkyJBKia883t7eln+Xsv79iiqaUPHx8Snz2Mtp09PT87KXJbaVov0zZswYdevWzY7RAABwdbm6/moDAHCNqFatmlUSobx6DdKFJ/tlPd0vurLJ6dOnryzAixQtBrtp0yZLrQmTyaTNmzeXeNzF/Pz8LK/NI02qQs2aNS2vK/LvfPFxZa1CI5Vcq+VK27QHe/UPAACOgOQIAAA24OLiYjX6omgxzNLExsaWWoxVsi7qunv37iuK72LXX3+9pS5KamqqoqOjJV1YgSYjI0OSVK9ePauioxdr3ry55fWePXsqNb6yFB1BU96KKiUdV3QFlZKcOHGi2DSpkphX86lIm/Zgr/4BAMARkBwBAMBG2rRpY3lddGpKadatW1fm/s6dO1teb9q0yZK0qAwuLi5WS9Ga412/fr1lW2mFWM06duxoKQp6+vRp7dixo9LiK0vR5WY3b96svLy8Mo8/duyYVV2VoueXJD8/X5GRkWUeYzQarYrWltemPRRdZnf//v2W1XquRh4eHpbXRQsbAwBgKyRHAACwkf79+1teHzlypMwEyaFDh6y+XJekZ8+eql+/vqQLdTBmzpxZ4S+OBoOh3NEPRafMbN26VVlZWVar4pSXHPH397c65ssvv1RaWlqF4jOZTOWu8FKa3r17W+qGnD17VgsXLiz1WKPRqG+++cbyc5s2bRQcHFzuNebPn1/mqjZLlizRmTNnJF34Yl/ev5U9NG/e3JKwKyws1MyZMytcpNVoNJa62pEtFJ2WVNH/hwAAuBIkRwAAsJGQkBCrhMOcOXNKTJDs27dP06ZNU2FhodVyrBdzdXXV6NGjLYU+9+7dq4kTJyo2NrbUc44fP66ffvpJTz/9tJKTk8uMt0GDBpYpKufPn9ecOXMsxUibN2+uoKCgMs+XpHvuuUe1a9eWdOFL7bhx47Rly5ZSVzpJS0vTihUr9NJLL1nVNrkU1apV05133mn5+ffff9e8efOKJY7S09P1wQcfWKbUuLm56d577y23fXd3d6WlpWnKlCnFanWYTCYtWbJE8+fPt2y77bbbrsqaI5L0yCOPWAoDHz9+XK+//rr27t1b6vGnT5/WokWL9Oyzz1Z4ylJlaNSokeV1eaN2AACoDKxWAwCADT300EM6fPiwTp8+rfz8fM2cOVMLFy5UixYt5OrqquPHj+uff/6RJA0ZMkTbtm2zjEAoSfv27TV69Gh99dVXMplMOnLkiF5//XUFBQWpcePGql69uvLy8pSRkaG4uLhLnnrTp08fxcXFSZKioqKstldE7dq1NWbMGE2dOlXnzp3T2bNn9fHHH6tWrVpq3ry5atWqpcLCQp07d04nT55UcnJymXVWKuq2225TTEyMZSrPokWLtHLlSrVp00bVq1dXamqqDhw4oPz8fMs5999/v1Udl9L07NlTSUlJOnLkiF566SVdf/31qlevngwGgw4ePKizZ89ajg0NDdVdd911xe/HVho1aqQXXnhBn376qXJzc5WYmKgpU6YoICBAzZo1U82aNWU0GpWZmanjx49XuBhtZevRo4elrs5PP/2k3bt3q2HDhlZFju+88075+vraJT4AwLWH5AgAADZUo0YNTZgwQR988IElCXLq1KliIxD69eun+++/32oaS2luuukm1a9fX19++aWlnZLaLKphw4YV+iLZu3dv/fDDD1YJCzc3N/Xq1avcc82aN2+u9957T3PmzNG+ffskSRkZGWXWIKlVq5ZlytDlcHV11SuvvKLvvvtOf//9t0wmk86dO6ctW7YUO7ZatWp6+OGH1a9fvwq17ebmpldeeUUfffSRDh8+rAMHDujAgQPFjuvQoYNeeuklq3oZV6MuXbro7bff1pw5c3Ts2DFJF4rwlpUICQwMVEBAQFWFqH79+mnjxo06cOCACgsLS/w3Hzx4MMkRAEClITkCAICN1alTR++++67Wrl2rjRs36uTJkzIYDPLz81PTpk01YMAAderU6ZLabNu2rT755BNFRUVp586dOnLkiNLT05WTkyMvLy/VqlVLwcHBatmypTp16mS1oktZateurXbt2llNtWjfvr3VMsIVERgYqDfffFOHDx9WZGSkDh48qNTUVGVlZcnNzU01atRQ/fr11axZM7Vv315t2rQpc0pRRbi5uenRRx/VwIEDtXbtWu3bt0+pqanKycmRr6+vgoOD1alTJ914442XPO2ldu3amjRpkjZs2KCNGzcqPj5e586dk6+vr5o2bap+/fqpZ8+eVxR/VWrcuLHee+897dmzR9u2bdOhQ4eUlpam7Oxsubu7q2bNmgoODlaLFi3UoUMHhYaGysXFpcric3Nz0/jx47V27Vpt3bpVJ06cUFZWFsVZAQA241JYGWNZAQAAriERERGaNWuWJCk8PFzPPvusnSMCAAC2REFWAAAAAADg1EiOAAAAAAAAp0ZyBAAAAAAAODWSIwAAAAAAwKmRHAEAAAAAAE6N5AgAAAAAAHBqLOULAAAAAACcGiNHAAAAAACAUyM5AgAAAAAAnBrJEQAAAAAA4NRIjgAAAAAAAKdGcgQAAAAAADg1kiMAAAAAAMCpkRwBAAAAAABOjeQIAAAAAABwaiRHAAAAAACAUyM5AgAAAAAAnBrJEQAAAAAA4NRIjgAAAAAAAKdGcgQAAAAAADg1kiMAAAAAAMCpkRwBAAAAAABOjeQIAAAAAABwaiRHAAAAAACAUyM5AgAAAAAAnBrJEQAAAAAA4NT+H5YlxVxhC5IAAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 503, + "width": 547 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "ax = model.ldf_.T.plot(kind='bar', title='Agway Insurance LDFs');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.11 ('cl_dev')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + }, + "vscode": { + "interpreter": { + "hash": "cc8120b3d9d10be7e89b07ff6d4d09ab1619cf80570f11e1a80e248c6b69ac77" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_capecod.ipynb b/docs/gallery/plot_capecod.ipynb new file mode 100644 index 00000000..2d270dc4 --- /dev/null +++ b/docs/gallery/plot_capecod.ipynb @@ -0,0 +1,138 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# CapeCod Apriori Sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates the usage of the deterministic CapeCod method and\n", + "shows the sensitivity of the apriori expectation to various choices of ``trend``\n", + "and ``decay``. Instead of using `GridSearch` we declare our own looping." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Grab data\n", + "ppauto_loss = cl.load_sample('clrd').groupby('LOB').sum().loc['ppauto', 'CumPaidLoss']\n", + "ppauto_prem = cl.load_sample('clrd').groupby('LOB').sum() \\\n", + " .loc['ppauto']['EarnedPremDIR'].latest_diagonal\n", + "\n", + "def get_apriori(decay, trend):\n", + " \"\"\" Function to grab apriori array from cape cod method \"\"\"\n", + " cc = cl.CapeCod(decay=decay, trend=trend)\n", + " cc.fit(ppauto_loss, sample_weight=ppauto_prem)\n", + " return cc.detrended_apriori_.to_frame()\n", + "\n", + "def get_plot_data(trend):\n", + " \"\"\" Function to grab plot data \"\"\"\n", + " # Initial apriori DataFrame\n", + " detrended_aprioris = get_apriori(0,trend)\n", + " detrended_aprioris.columns=['decay: 0%']\n", + "\n", + " # Add columns to apriori DataFrame\n", + " for item in [25, 50, 75, 100]:\n", + " detrended_aprioris[f'decay: {item}%'] = get_apriori(item/100, trend)\n", + " return detrended_aprioris\n", + "\n", + "# Plot Data\n", + "plot1 = get_plot_data(-0.05)\n", + "plot2 = get_plot_data(-.025)\n", + "plot3 = get_plot_data(0)\n", + "plot4 = get_plot_data(0.025)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 913, + "width": 835 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(\n", + " ncols=2, nrows=2, sharex=True, figsize=(10,10))\n", + "fig.suptitle(\"Private Passenger Auto Cape Cod Detrended Aprioris\")\n", + "plot1.plot(ax=ax00, title='Trend: -5.0%')\n", + "plot2.plot(ax=ax01, title='Trend: -2.5%')\n", + "plot3.plot(ax=ax10, title='Trend: 0.0%')\n", + "plot4.plot(ax=ax11, title='Trend: 2.5%');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_capecod_onlevel.ipynb b/docs/gallery/plot_capecod_onlevel.ipynb new file mode 100644 index 00000000..2d391295 --- /dev/null +++ b/docs/gallery/plot_capecod_onlevel.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# CapeCod Onleveling" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how to incorporate on-leveling into the `CapeCod`\n", + "estimator. The on-level approach emulates the approach taken by Friedland in\n", + "\"Estimating Unpaid Claims Using Basic Techniques\" Chapter 10. The `ParallelogramOLF`\n", + "estimator is new in chainladder 0.7.9 as is the ``xyz`` triangle.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "\n", + "# Grab a triangle\n", + "xyz = cl.load_sample('xyz')\n", + "\n", + "# Premium on-leveling factors\n", + "rate_history = pd.DataFrame({\n", + " 'date': ['1/1/1999', '1/1/2000', '1/1/2001', '1/1/2002', '1/1/2003',\n", + " '1/1/2004', '1/1/2005', '1/1/2006', '1/1/2007', '1/1/2008'],\n", + " 'rate_change': [.02, .02, .02, .02, .05, .075, .15, .1, -.2, -.2]\n", + "})\n", + "\n", + "# Loss on-leveling factors\n", + "tort_reform = pd.DataFrame({\n", + " 'date': ['1/1/2006', '1/1/2007'],\n", + " 'rate_change': [-0.1067, -.25]\n", + "})\n", + "\n", + "# In addition to development, include onlevel estimator in pipeline for loss\n", + "pipe = cl.Pipeline(steps=[\n", + " ('olf', cl.ParallelogramOLF(tort_reform, change_col='rate_change', date_col='date', vertical_line=True)),\n", + " ('dev', cl.Development(n_periods=2)),\n", + " ('model', cl.CapeCod(trend=0.034))\n", + "])\n", + "\n", + "# Define X\n", + "X = cl.load_sample('xyz')['Incurred']\n", + "\n", + "# Separately apply on-level factors for premium\n", + "sample_weight = cl.ParallelogramOLF(\n", + " rate_history, change_col='rate_change', date_col='date',\n", + " vertical_line=True).fit_transform(xyz['Premium'].latest_diagonal)\n", + "\n", + "# Fit Cod Estimator\n", + "pipe.fit(X, sample_weight=sample_weight).named_steps.model.ultimate_\n", + "\n", + "# Create a Cape Cod pipeline without onleveling\n", + "pipe2 = cl.Pipeline(steps=[\n", + " ('dev', cl.Development(n_periods=2)),\n", + " ('model', cl.CapeCod(trend=0.034))\n", + "])\n", + "\n", + "# Finally fit Cod Estimator without on-leveling\n", + "pipe2.fit(X, sample_weight=xyz['Premium'].latest_diagonal).named_steps.model.ultimate_\n", + "\n", + "# Plot results\n", + "results = cl.concat((\n", + " pipe.named_steps.model.ultimate_.rename('columns', ['With On-level']),\n", + " pipe2.named_steps.model.ultimate_.rename('columns', ['Without On-level'])), 1).T" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 504, + "width": 569 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "ax = results.plot(\n", + " kind='bar', title='Cape Cod sensitivity to on-leveling', \n", + " subplots=True, legend=False);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_clarkldf.ipynb b/docs/gallery/plot_clarkldf.ipynb new file mode 100644 index 00000000..5484a7ad --- /dev/null +++ b/docs/gallery/plot_clarkldf.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Clark Growth Curves" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import numpy as np\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates one of the attributes of the :class:`ClarkLDF`. We can\n", + "use the growth curve ``G_`` to estimate the percent of ultimate at any given\n", + "age.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Grab Industry triangles\n", + "clrd = cl.load_sample('clrd').groupby('LOB').sum()\n", + "\n", + "# Fit Clark Cape Cod method\n", + "model = cl.ClarkLDF(growth='loglogistic').fit(\n", + " clrd['CumPaidLoss'],\n", + " sample_weight=clrd['EarnedPremDIR'].latest_diagonal)\n", + "\n", + "# sample ages\n", + "ages = np.linspace(1, 300, 30)\n", + "\n", + "# Plot results\n", + "results = model.G_(ages).T" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 459, + "width": 569 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "ax = results.plot(\n", + " title='Loglogistic Growth Curves',\n", + " xlabel='Age', ylabel='% of Ultimate');" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_clarkldf_resid.ipynb b/docs/gallery/plot_clarkldf_resid.ipynb new file mode 100644 index 00000000..19a15453 --- /dev/null +++ b/docs/gallery/plot_clarkldf_resid.ipynb @@ -0,0 +1,127 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Clark Residual Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how to recreate the normalized residual plots in\n", + "Clarks LDF Curve-Fitting paper (2003).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Fit the basic model\n", + "genins = cl.load_sample('genins')\n", + "genins = cl.ClarkLDF().fit(genins)\n", + "\n", + "# Grab Normalized Residuals as a DataFrame\n", + "norm_resid = genins.norm_resid_.melt(\n", + " var_name='Development Age',\n", + " value_name='Normalized Residual').dropna()\n", + "\n", + "# Grab Fitted Incremental values as a DataFrame\n", + "incremental_fits = genins.incremental_fits_.melt(\n", + " var_name='Development Age',\n", + " value_name='Expected Incremental Loss').dropna()\n", + "incremental_fits = incremental_fits.merge(\n", + " norm_resid, how='inner', left_index=True, right_index=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 500, + "width": 847 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plot the residuals vs Age and vs Expected Incrementals\n", + "fig, ((ax0, ax1)) = plt.subplots(ncols=2, figsize=(10,5))\n", + "\n", + "# Left plot\n", + "norm_resid.plot(\n", + " x='Development Age', y='Normalized Residual',\n", + " kind='scatter', grid=True, ylim=(-4, 4), ax=ax0)\n", + "# Right plot\n", + "incremental_fits.plot(\n", + " x='Expected Incremental Loss', y='Normalized Residual',\n", + " kind='scatter', grid=True, ylim=(-4, 4), ax=ax1)\n", + "fig.suptitle(\"Clark LDF Normalized Residual Plots\");" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_development_periods.ipynb b/docs/gallery/plot_development_periods.ipynb new file mode 100644 index 00000000..91748fbe --- /dev/null +++ b/docs/gallery/plot_development_periods.ipynb @@ -0,0 +1,137 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Tuning Development Patterns" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates testing multiple number of periods and averages in the\n", + "development transformer to see its influence on the overall ultimate estimate.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "tri = cl.load_sample('abc')\n", + "\n", + "# Set up Pipeline\n", + "pipe = cl.Pipeline(steps=[\n", + " ('dev',cl.Development()),\n", + " ('chainladder',cl.Chainladder())])\n", + "\n", + "# Develop scoring function that returns an Ultimate/Incurred Ratio\n", + "\n", + "# Set up a GridSearch space\n", + "grid = cl.GridSearch(\n", + " estimator=pipe,\n", + " param_grid=dict(\n", + " dev__n_periods=[item for item in range(2,11)],\n", + " dev__average=['simple', 'volume', 'regression']),\n", + " scoring=lambda x: (\n", + " x.named_steps.chainladder.ultimate_.sum() /\n", + " tri.latest_diagonal.sum()))\n", + "grid.fit(tri)\n", + "\n", + "# Plot data\n", + "results = pd.pivot_table(grid.results_, index='dev__n_periods',\n", + " columns='dev__average', values='score')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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GRsjPz0d5eTnOnz+P7t27N3aoCpoUPpQ3MsjOzq5y/Ny5cygqKgIg32SiS5cuDRdgI6ipQFnXc5Rfr9evX9f49SoSiQBAUah6WNy5e/dupU0ONHlea1MkawxeXl7YsGGDxpstXL58GW+99RZ2796t8QYkDfX5oonm+J4mImruWLAjIiKiZsPMzKzGNsojhmrbvrS0VGWbs2fPon///sjMzNQgygq5ubm1aq/OvXv3Kt1evnx5rfuobez1oVxccXFxUTslWZlYLIaLi4tihOLjLEYAmr1WpFKp4raq14ry7qUuLi61Hr32uNnY2DTKOcqv16ioKLXTh6uTmZmpKNgpv54MDQ012gHWxcWl1tesjWnTpsHExASAvIiempqKq1ev4uLFiwDkhcrOnTsjPDwcwcHB1fYVFhaGUaNGafxFwEMN9fmiieb4niYiau60+7cIIiIiqtGjRYGSkpJana/8R6JyQUIbPRyB01jtVSkuLsbYsWMVBS87OzvMmzcPffv2haenJywtLWFgYKC41po1axRrnTXUunGqRnPVVllZWQNEopm8vDzF7dpMgVNu+ziLEUDDvFaUY9ZkGmhTMzAwaJRzGvr1qvx6MjQ01Oj8xp56+eGHH8Ld3b3K/REREZg6dSoSEhKQnZ2NMWPGICYmBpaWlir7SUtLw/jx4xWfw61atcK8efPQs2dPtGrVCubm5tDX11e8PhctWoQPP/wQQMN9vmiiOb6niYiaOxbsiIiImrlHRwYp/2GlCeX2DzcpoArbtm1DfHw8AMDZ2RnR0dGws7NT274x/ihV/qN39OjR2L59e4NfoyEpF6vy8/M1Pk+57cPRS82Jcsy1fR82hMe9sYg6yq/Xb7/9Fq+88kq9+lN+PRUUFGh0Tm1edw2pV69eCAsLQ1BQEPLy8nD37l28/vrrWL16tcr2v/zyi6LAGRAQgCNHjlRb7G2qoteT+p4mImpK3CWWiIiomTMxMYG+vr7i57i4OI3PFQRBUYwC6jZFrqXbv3+/4varr75abbEOABISEho8BuVrxsbGNnj/DU35dZSYmFhp0Xx1ZDIZEhMTFT9bW1s3SmyNSTlPiYmJ9R7VqDx6VpO+GmJkW0No6Ner8uupoKAA6enpNZ6j/Fp63Nq2bYuPPvpI8fO6desUu8Y+Svnz5d13361xZGZjfL5o4kl9TxMRNSUW7IiIiFqAwMBAxW3lnfxqcvXq1UojIOqyCP1DDTGlUBspr8fVvn37GtsfOXKkxja1fa6UF9C/dOkS7ty5U6vz66u28fr7+0MikQCQjzRTV6xQdv78ecVrUSKRwN/fv/aBNrGOHTsqiucFBQU4depUvfpTHpFUU5GqpKQE169fr9f1GoryphBhYWH17s/JyalSwejkyZM1nqNJm8b0wgsvKHaSlclkeO+991S2q83nS3l5OY4dO1bjtRvjs/hJfU8TETUlFuyIiIhagL59+ypu//nnnxrvMvj7778rbjs7O8PT07POMSiP8lO3eUNzpLy4ek3T8U6fPq3RAvu1fa5atWqFdu3aKX7+9ttvazynIdU2XmNjYwQFBSl+XrNmTY3nKE8Z7Ny5c6OvQdYY9PT0Kr0Xf/jhh3r197DgA8h3oK3Ojh07FDvUNrXQ0FDF6MAbN25g165d9e6zT58+itsbNmyosf3atWvrfc360NXVxcKFCxU/79ixAzExMVXa1ebz5e+//0ZycnKN126Mz+In9T1NRNSUWLAjIiJqAebMmaMY/XDz5k2NCjo3b97E0qVLFT//3//9X71iUN658e7du/XqS5t4eHgobv/zzz9q2xUUFGDOnDka9VmX5+qtt95S3F62bBn27dun0XkANPojvzrm5uaKwkJqaqpGRYC5c+cqbi9fvlxlseKhs2fPYsWKFYqf582bV49om9Zrr72muL1582Zs3ry5zn117txZcbu6AklOTg7efvvtOl+noTk5OWHKlCmKn+fNm6fx61wmk1XakfSh2bNnK25v2bIFERERavvYsmULjh49WouIG8f06dPh5uYGQL78wMcff1yljaafL2lpaXj11Vc1um5jfRY/qe9pIqKmwoIdERFRC+Dq6ooXX3xR8fObb76J9957T+0C5f/++y969+6tmK7k7u5e74Kdh4eHYgRFQkICIiMj69Wfthg+fLji9rp167BkyZIqIxhv3LiBQYMG4cyZMxqNIqnLczVlyhT069cPgHw9s2HDhuGLL75QuwB8Xl4eNm3ahH79+uGll16qsf/q6OnpwcvLS3FtTTa9mDx5smIKXElJCUJDQ3Hw4MEq7fbv34/BgwcrioCBgYGYOHFiveJtSgMGDMAzzzyj+HnKlCn46KOPVI6ekslkOHjwIMaMGaNy/blJkyYpbm/evFnliL2rV6+iX79+uHnzJvT09BroUdTfp59+CgcHBwDyolFwcDD+/PNPtRtj3L17F8uWLYO3tze2bNlS5figQYMQEhICQF78Gj16NPbu3Vul3ebNmzFz5kzo6uo24KOpG6lUinfeeUfx87Zt23DlypVKbZQ/Xz7//HOVowfPnDmDkJAQJCYmavT54uvrq7h96tQp3L59uy7hV/GkvqeJiJoKd4klIiJqIb788kvExMTg4MGDitEcS5YsQdeuXeHq6go9PT3cv38fp06dqjTqwtzcHFu3bq2y22xticVijB49Ghs3bgQgn6Y7ePBguLq6Kkb/WVpaVpom1hyEhoYiJCQEhw8fhiAIeOONN7B8+XIEBgbCzMwMsbGxOH78OMrLy+Hk5IRXXnkFCxYsqLbPujxXEokEf/zxBwYOHIizZ8+ipKQEb7/9Nj766CNFjnV1dZGZmYnr16/j8uXLij+Yx44dW+/nYezYsfjkk08AyItQa9euRevWrSGVShVtvv76a8VtXV1dbNq0CSEhIUhLS0NycjL69esHf39/dOzYEYB8muf58+cV59ja2mLTpk2V+myOfv31V0Uhtry8HB988AG+/PJL9OjRAy4uLhAEAXfv3kV0dLRibTpVi/j36tULw4YNw7///gsAeOmll7B8+XJ07doVIpEI165dw8mTJyGTyTBjxgzEx8fj8OHDj/WxquPg4IB//vkHQ4cOxf3795GUlIRnnnkGtra26NKlC+zs7CCTyZCeno6LFy8iLi6u2o0MRCIRVq1ahW7duiEtLQ0ZGRkIDQ1Fx44d0bFjR5SXlyMyMhLXrl0DIJ82Pn/+/Mf0aNWbOXMmPvnkEyQmJkImk+HTTz/F+vXrFcdnzJiBb775BtevX0dxcTGmTp2KTz/9FP7+/tDX18fFixcV65L6+/sjNDQUX375ZbXXtLOzQ48ePXDs2DEUFRXB398fgwcPhoODg2KkrKenJ55//vlaPZYn+T1NRNQkBCIiImoxSkpKhNdee03Q1dUVANT4X9euXYXr16/X2O8HH3ygOOeDDz5Q2+727duCo6Oj2uu5ublVan/w4EHFsZCQEJV9xsfHqz1fldWrVyvaT58+vcb206dPV7RfvXq1yjbJyclCYGBgtc+lj4+PcOnSJY2vX9vn6qGCggJh3rx5go6OjkY5NjAwED799NMan4eaZGdnCz4+PtVeS5Vr164JAQEBNcYZGBgo3Lhxo95xPuTm5lZjXmv7WqnNa7GgoEB47rnnBIlEUuNj19fXF3JyclT2k56eLgQFBVV7/qxZs4SioiIhJCREcd/BgwdV9qdJm0dp8h5R59atW0L//v01eq0CEOzs7IQ9e/ao7e/8+fOCh4eH2vNFIpGwcOFCQRCEGl+btaGcewBCfHy8xucuX75ccZ5EIhFiY2MrHb927Vq1jwmA0KNHD+HOnTsafxafPn1aMDU1Vdvfo5+3tXkvNNV7mojoScMpsURERC2IVCrFkiVLEBcXh88++wyhoaFwdXWFkZERpFIpbG1tERgYiJdeegn79+/HiRMn0KZNmwa7vouLC86fP4/3338fXbt2hYWFhWLx+ebMzs4Ox48fxw8//ICePXvC3Nwcurq6cHZ2Rv/+/bFy5UpERUXBx8dH4z7r+lwZGBjgp59+QmxsLBYvXoy+ffvCyckJ+vr60NXVhY2NDbp06YI5c+Zgy5YtSE5OrjQtr65MTU0RGRmJr776Cr1794aNjY1Go2a8vLwQHR2NLVu2YNy4cXB3d4ehoSEMDQ3h7u6O8ePHY+vWrYiOjq7XpifaxsDAACtXrsTFixfxzjvvoHPnzrC1tYWOjg4MDQ3h6emJMWPG4Mcff8Tdu3cr7QirzNLSEsePH8ePP/6IXr16wdLSErq6unBzc8PTTz+NvXv3YtWqVVo1HVaZm5sb9u3bh+PHj+PVV19Fp06dYGtrC6lUCgMDAzg7O6Nv37548803ER4ejjt37iA0NFRtf35+frhw4QK+/vprdO7cGebm5jAwMICnpyemTZuGo0ePKkaCaovZs2fDyckJgHyn188++6zScS8vL5w9exaffvopgoKCYGJiAj09Pbi5uWH48OH4/fffcejQIUUfmggMDERMTAxef/11BAQEwMzMTDF6t76e1Pc0EdHjJhKEasaeExERERERERER0WPFEXZERERERERERERahAU7IiIiIiIiIiIiLcKCHRERERERERERkRZhwY6IiIiIiIiIiEiLsGBHRERERERERESkRViwIyIiIiIiIiIi0iIs2BEREREREREREWkRFuyIiIiIiIiIiIi0CAt2REREREREREREWoQFOyIiIiIiIiIiIi3Cgh0REREREREREZEWYcGOiIiIiIiIiIhIi7BgR0REREREREREpEVYsCMiIiIiIiIiItIiOk0dAFFjKCoqwoULFwAANjY20NHhS52IiIiIiIiIGlZZWRnS0tIAAL6+vtDX12+QflnFoBbpwoUL6Ny5c1OHQURERERERERPiMjISAQHBzdIX5wSS0REREREREREpEU4wo5aJBsbG8Xtk/85w8GOL/WWYnZ8SFOHQI3g+mnXpg6BGphBmqipQ6BGYJAua+oQqIHp5pQ3dQjUCCSFfK+2NKIy5rQlEglNHQE1hOKSXESfXwGgci2ivljFoBZJec06BzsdODvypd5SGOQbN3UI1Ah0zM2bOgRqYNIiFuxaIl0WAVocvVIW7FoiHYF5bWlEpfz8bYlYsGt5GnL9fE6JJSIiIiIiIiIi0iIs2BEREREREREREWkRFuyIiIiIiIiIiIi0CAt2REREREREREREWoQFOyIiIiIiIiIiIi3Cgh0REREREREREZEWYcGOiIiIiIiIiIhIi7BgR0REREREREREpEVYsCMiIiIiIiIiItIiLNgRERERERERERFpERbsiIiIiIiIiIiItAgLdkRERERERERERFqEBTsiIiIiIiIiIiItwoIdERERERERERGRFmHBjoiIiIiIiIiISIuwYEdERERERERERKRFWLAjIiIiIiIiIiLSIizYERERERERERERaREW7IiIiIiIiIiIiLQIC3ZERERERERERERahAU7IiIiIiIiIiIiLcKCHRERERERERERkRZhwY6IiIiIiIiIiEiLsGBHRERERERERESkRViwIyIiIiIiIiIi0iIs2BEREREREREREWkRFuyIiIiIiIiIiIi0CAt2REREREREREREWoQFOyIiIiIiIiIiIi3Cgh0REREREREREZEWYcGOiIiIiIiIiIhIi7BgR0REREREREREpEVYsCMiIiIiIiIiItIiLNgRERERERERERFpERbsiIiIiIiIiIiItAgLdkRERERERERERFqEBTsiIiIiIiIiIiItwoIdERERERERERGRFmHBjoiIiIiIiIiISIuwYEdERERERERERKRFWLAjIiIiIiIiIiLSIizYERERERERERERaRGdpg6AqMUSWwJSf4ikfoDUF5D6QiS2BAAIhX9ByH6rQS4jMn4JIuOXNWory5gMlERW00IM6A+HSH8EIG0PiM0AWTZQdhNC0S6g8E8A5Q0Sd3NkJjVGGxN3eJm4oY2xK9qYuMFUagwA2J9yEsuub2iQ60x0HYqJbkM1arswZhkuZsdWuf8Vrynob9e1Vtf99tp6HEg9VatzWgIrQwP4OdjD38EBfvZ28LW3h6WhAQBg24VLWLA7rEGu83KPbnilRzeN2k7a9AdOJd5Reezw3NlwNjOrsY872dkI+XlVrWJsKSyNDdDB1R6+rvbo4GKH9i72sDCW5/SfyEt4d/PeBrnO86Fd8X+hmuV05vKtiL6pOqfqjO/uh3ef7q/4+d1NYfgn6nKt+mgpLEwN4OPhgPae9mjnYQ8fD3uYm8hzuuvIRSxe2TDv02ef6obnnuquUdvnP9mCM1eq5lSqI0H3jq3Q3kMeq72VCcxMDGCkr4v8whIkJGUi6lIC/j4Yg9SMvAaJu7kyNzNEOy8HtPNygHcbe3i3sYe5qSEAYPf+C/js290Ncp2ZE3tg5qQeGrV9+Z1NOHcxscr9Uh0JugZ5KGK1szGFmakBDA10kV9QgsS7GYg+n4Cde84hLf3Jzau5uSG8vR3h7e2Atm0d4N3WAWZm8pzuCYvBl1/+2yDXmT6tJ6ZP76VR21df24jz529XuV8qlaBLZ09FrLZ2ZvKcGspzeicxHafP3MKuXedw/35ug8TdHJmbG8K7nSO82zmirbcj2no7wMxcntOw3efx5We7GuQ602b2wvSZvTVq+9rL63H+XNWcAoCziyW6dG0N/46u8GhtB0tLIwgCkJmZj6tX7iF8zwWcOnmjQWJuzszNDdHWR31ev/q04fI6bZZmeX39pRry2q01/Dq6wsPTDpZWD/KakY9rV+4hPOwCTp1oeXllwY6okYhtm1nhQ2wDkflyiHQDKt8vsQEkNhDpdYVgOA5C5hxAlt40MTax9V0/b+oQGtXdwtSmDqFJRL74fFOHQA3s8EfzmjqEerMxNcIrw3o2dRhaY8+P/9fUIWjMzsoEX84fpfKYmYkB/EwM4OfliIlDOuHLNfux++iTWYQFgB0bXmzqEDRma2OCT/43RuUxM1MDmJk6oUM7J4wbFYSlP4Uj7OClxxyhdvhr2ytNHYLGbGxM8dFHY1UeMzM1gFl7Z7Rv74xnnu6Mb5eFITz84mOOUDts2/FqU4egsbcWjsCgwX4qjzk4mMPBwRx9+/kg8tRNfPzhduTnFT/mCLXHnzubT14XLByBQUPU5NXRHA6O5ujT3wdRp27i40UtK68s2D1h+vTpg8OHDyMkJASHDh1q6nCeGEL5PaDsJkR6mn0TWFey+zWMyipXN7pDDyKLXyCStgcACMXHIBRsAsoTAbEFRPqDAYNn5KMFLVZCSJ8IoKRBY29u0ooykFiYgkCLdo16nRdPf1Lt8ZQi1cXT9bd2Yvud/dWea6xjiE/8XoFEJMbdglRcy42vc5wtxb2cHNxIz0DvVu6Nep0hv62t9nhidnaNfYTH3sA3EcfUHi8pf3JHwypLysxBXEoGeni7N+p1xny5rtrjdzNqzqmyhU/1hYmBHtJz82FlYlSf0Fqc5Ps5uHUvA1393Bv1OhPfXlPt8Xtp6nOakZ2P05cTcTkuGcn3c3A/Kx9l5TLYWBqjR0cPhHb3hqG+Lt6fMxhZOQU4EXOrYYNvhlLScpCQmI7Oga0a9TrTX/it2uNJKdXkNTMfZy/cxtXYZCSnZiM9Mx/lZeWwtjJBt2BPDAxpB0MDXbwzfyiycgpw6vST/e9qSko2bt9OR3CwR6NeZ9bsX6o9npxcfU7PnUvAtWtJSEnJRnp6HsrLZLC2NkGXrp4Y0L89DAx08daC4cjOLkBkZFxDh9+spKRk43bCfQR39mzU68yevrLa48lJWSrvt7I2AQDkZBfgyOGrOHc2ASnJ2Sgvl6F1G3s8Pa4zXN2s0bmLJz75bBxefXk9BKGho29+Uh/kNaiR8/rstLrl1dpGKa+HruL82QQkJ2dD9iCvY8fL8xrcxRMffz4Or73UcvLKgh1RIxHyvodQegEojZGPSJM4QWRzqHEvWlZ1aqRGDKdUFOsK/oSQ806lw0LJMaD0HERmn0Mk9YNgOBkoWF3faJudTQn/ITbvNm7kJiCrNBe2epb4tfNHjXrN2wVJdTovoyQbGSXVFwiGOPSERCRfyvTgEzgV9qHvjp1ATHIyYpJSkF5QACdTUxyZ92yjXvP6/fqPUs0pKm6Qflqin8JO4mJiMi7dTkF6XgEcLUwR9t7sRr3mjeSGy0Xf9h4Y4NcG6bkF+O1ANN4cFdJgfTdXv/51ApfjknElLhkZOQVwsDbF398+16jXjLtTt5zeTc3CkBdWqOkUOBx9A38fiMHK9ydAqiPBvGd6PrEFu9WbjuFqbDKuxiYhM6sA9ram+GNV446Qjb99v07n3UvOwuhpy1UfjE1GxMlY7NxzDj98MRlSqQTPTun1RBbs1q47imvXknDt2j1kZhbAzs4Mm35v3BGyt27VLadJSZl4+unvVB+8loSjx67j313nsGzZVEilEsya2fuJLNitWx2Ba1fv4drVJGRm5sPO3gy//9G4o2NvxafV6bz7aTn45qv/sHdPDEpLK39xee1qEvbtvYAvvp4IX39X+Pq7ov/ADti398kcOanI65UkZD3I68at2pnXtLQcLP2y+rx+vkQpr4M6YF9Yy8grC3ZEjUTIU/MLgBYSGcineAiyfAi5akZ0FW6DYPA0RLpBEBk9B6FgDYAW8tWFhjbd/q+pQ2hQfW27AABkggwHU6tb27BlW3bsRFOHQA3sx7Dmm1NDPSkWPtUPALBk5xGIRaImjkg7/PLX8aYOQWOafKt/OS4ZUZduo7t/K3i3soOBnhSFxaWNH5yWWf27+lHC2kaTvF6JTcbpmAR07eSBtq3tYaAvRWHRk5XXtWsjmjoEjWmS06vXknDm7C106ewJLy8H6OtLUfSk5XT1kaYOQWM1radXXFyGb7/Zg1Vr5wAAevdp9+QW7H5rPnmtaT294uIyLFuyB7+ue5DXkHYtpmDHXWKfMIcOHYIgCJwOS0r0IJK2ld8sPQcI6hdKForlv4SJJDaANPgxxEaNxUHfBt6m8mlHF7NvIK04s4kjIiIAmD+sJ+wtTBB5IxE7o680dTjUiIqUCnRSqaQJI6GGpFzMYV5bBuWc6upyvEtzdys+DVlZ+QAAR0fzpg2GGoxyXh2czJs2mAbEgh3Rk06stNukrIYpBcrHdTs3Tjz0WPSzq8jfkzy6jkib+LraY1x3P5SUleHjP6tfg5KaNwtTA3TycQEAZOYUICevqIkjooZgbmaIQD9XAEBWdgFycpnX5s7c3BABHd0AAFlZBcjJKWziiKgh6OjIi+lCS1nojADId/MGWlZe+RUBUQsislgDSH0AkREgywXKb0AoPgIUbAaEHNUnCQVKHZjUcIGK4yIdzydsQmzT+KjDi/AwdoGBRA/5ZYVILEjGmczL2JN8FPlldf+lsY+tfIRkUXkxjt8/21DhkobWjhuL9na2MNLVRW5RMWLT03Ek/hY2nYtBTrFmO1sFuzjh3xlT4WpuDpEIuJ9fgJjkZOy8fBXhN2428iOgR62c+xTaOdvCSE8XOYXFiEtJx7GrCdh6IgY5hTXnVEcsxqJxAyARi7FqfxTiUznqtal999bT8Ha3haGBLnLzixF/Lx0nY25h+/7zyC2o/Q50Uh0JrC2M0Lm9G6aOCIaZsQEAYEvYmYYOnaqx5KNx8PK0g6GBLvLyi3ArMR2RZ+Lxz+5zyMuvY16tjNHJ3w2TxnaBqYk8r3/uON3QoZMaX345AW3a2MtzmleEhIT7iIqKw85d55BXh2K4VCqBlZUxOgW6Y8KEbjA1lef0r7+iGjp0UuPLJRPRxsseBoZ68pzeuo+oyJvY9c/ZOuVUWes2djA21gcA3E7gWsCP0xffVM7r7Vv3EXWq4fJq9CCviS0or402wm7RokUQiUQQPVh7JTs7G4sXL0ZAQADMzc0hEomwZs2aSueEh4djypQpaNWqFQwMDGBqagp/f38sWLAASUk1L7x+//59vPnmm/Dy8oKBgQHs7OwwcOBAbN++HQCwZs0aRUy3bt2qcr67uztEIhFmzJgBADh9+jRmzJiBVq1aQU9PT/FYlBUUFODbb79F3759YWdnB11dXdja2mLQoEFYvXo1ymvYpe/AgQOYOHGi4jEbGhrC3d0dXbt2xRtvvIEDBw6oPC8rKwuffPIJunXrBgsLC0ilUtjY2MDHxwdjxozBTz/9hNTU1Crn9enTByKRCH369Kk2rqNHj2Lq1Klwd3eHvr4+zM3NERAQgHfffRdpaeoXizx06JDiOX447faPP/5A//79YWNjAwMDA7Rt2xYLFixARkZGtTFQ7Yn0ekAktoBIpAuRxAoi3S4Qm7wJkc0BQK+/6pOEPAjlKfLbuh0BSNX3r6s0DVbi2GBxk3odLbxhKjWCVKwDc10T+Jq3wfRWo/BL0IfoYulbpz7bm7WGnb41AOBk+nkUlrecrc+bi57ubrAwMICuRAIrI0N0dXXBgpBeODR3Nga01myHLldzc3jb2sBQVwoDqRQu5mYY5t0WK54ahS2TxsPO2LiRHwUp69bWDeZGBpDqSGBlYojg1i6YP7wndv9vFvq2r3mHxBl9O8HL0QaJ97Owct+TuwmMNuni6wYzE3lOLc0M0amdC14Y3wt/LX0WvQM1e58GtnPGqQ2v49SG13F0zXz8vfQ5LHx2EFzsLAAAu49dxoZ/oxvzYdAjggPcYWZqAKlUAgtzIwT4umLu9BBs+XUuenZprVEfHTu44MjOBTiycwH2b38dW36diwUvDYazozyvew9ewqa/OHr9cQnq1KoipxZG6NjRDc891xcbN8xD9+5tNOrD398VB/a/gwP730HYngX4feP/4fXXh8LJSZ7T8H0XseUPfjY/Lp2CPWBqZliR0wA3PDe3HzZs+T907+lVr74nTemhuH34IJeeeJwezat/gBuendcP6/+of14nTq3I66EWlNfHMsIuNjYWgwYNUlkkA4D8/HxMnTpVUVh7qKioCDExMYiJicFPP/2ETZs2Yfjw4Sr7OH/+PAYOHFipmFRUVIR9+/Zh3759mDNnDrp166ZxzCtWrMBLL72EsrIytW2ioqIwZswY3L17t9L9aWlpCA8PR3h4OFasWIEdO3bAzs6uyvmvvfYali5dWuX+hIQEJCQk4NSpU1izZg3u3688TfHKlSsYMGAA7t27V+n++/fv4/79+7hy5Qr+/vtvlJeX48UXa7fTi0wmw8svv4zlyyvvglVcXIxz587h3Llz+OGHH7B161YMHDiw2r7Ky8sxefJk/P7775Xuv379Or766its374dERERsLe3r1WMVJVQehUo3gehNAYoTwFEUkDSCiKDkRDp9YJIbAaY/wAhcy5QomKB0eL9gOEkiMSWEIxmAfk/V20j7QTo9an4WWTUaI+HgFv5d3EyPQbXcxOQUZwFHbEETgZ2CLENRqBFOxhLDfG2z7NYfOlnnMm8XKu++9lWTIc9kMI/KB6nq6lpCL9xE+eTkpCalw8dsRgelhYY6dMOvVu5w0xfH8tHj8CcbX/jcPwtlX2UlssQHnsDR28l4Pr9dOQWF8NUTw8Bjg6YHOAPR1NTBDk7Ye24sXh6wybklZQ83gf5hLl+Lw0HLt7EhdvJSMvOh45EDHdbCwwL9EYPb3eYGurjmxkj8NKqf3D06i2VfbhYm2HuoK4AgE/+OoDi0uq/7KPGFXs7DUdO38Clm8m4n5UHHYkYrg6WGNy9Hbr6ucPUSB+fvTISbyzZXufdXe+lZuPz1eE4dSGhYYMntW7GpyLi1A1cuZ6E9Iw8SCRiuDpZYmAfH3QObAUTY3189M5ovLN4W513d01KycLXy/ci6uythg2eVLoZl4pjx67j6tUkpKfnQiKRwMXFEgP6t0dwsAdMTAzw4aKn8L93t9Z5d9ekpCws/XYPoqOfvB1/m0LczRQci7iOq1fuIT1d/j51cbVC/4HtEdzZEyYmBlj00Vi8+84fiDxV+9kEvULaIqRvOwDy3UWPHL7a0A+BVKiU1/t50NERw8XFCv0HtUfQg7x+sHgs3n37D0Q1QF4jDrWcvD6Wgt3TTz+Nu3fv4qWXXsLIkSNhYWGB2NhYuLm5oby8HCNGjMDBgwchEokwYcIEPPXUU2jVqhVKS0sRGRmJJUuW4Pbt2xg7diyOHz+OTp06Veo/MzMTgwcPVhTrJk+ejClTpsDGxgY3btzAsmXLsHLlSpw/f16jeKOiorBhwwa4uLjgjTfeQKdOnVBeXo6IiIpdjy5cuIC+ffsiPz8ftra2eP7559GrVy9YWVkhNTUVO3bswM8//4zIyEiMGjUKERERkEorRi7t2rVLUazz8/PD888/j3bt2sHMzAzZ2dm4evUqwsPDceJE1Z3upk6dinv37kEqleK5557DkCFDYG9vD5lMhnv37iEyMhLbtm2rdZ4A4O2331YU61q1aoW33noLgYGByM/Px44dO/DDDz8gOzsbw4cPR2RkJPz9/dX29f777+P48eMYPXo0pk2bBjc3N6SkpGD58uX4999/cePGDbz66qvYtGlTnWIlOSF/DZD3fdUDpechFP0NwWACxGaLIRLpAGafQEgbAKDyiCoh72dAfwREYhOIjF8DxFYQCjYC5XcBsTmgHwqR8RsARBCEUohEUkCk/xge3ZNpx92DKnekvZ6bgIOpkQi174EX2kyERCTBS20mYW70hyiRabZjmVSkg+7WAQCA+8WZOJ91rUFjJ/VWR5/Bdyp2pD2flIztl65gor8vPg4dCB2xGJ8OHoR+v/yGYhVfGo1Z/ztyVUybPZV4B+vPnsMPo0agdyt3tLG2wss9uuHTg4cb5fEQsOHwWfwUdrLK/RduJ2Nn9BU8080X7z8zADoSMRaNH4hhn/6mshj3/tMDoC/VQdi56zh2lQWcprR5zxn8+lfV9+mlm8nYffQyxvTzw9uzBkJHIsbCZwfh6dd/Q3Gp+i93L8clY+LbawAAujoS2FmbonegJwb38MF7cwbj5z+PYefhlrGTnTbbuiMaqzdV3ZH2yvUkhB28hJGD/fHGC6HQkYix4KXBmDjnF5SUqM/r1dhkTH/hNwDy6ZN2Nqbo0aU1BvVtj3deGYpVGyPwb/iFRns8BPy5LQpr1x2tcv/Vq/cQHn4Rw4d3xGuvDoFEIsYbrw/FlKkrqs3ptWtJmDX7FwCAVKoDO1tTdO/eBgMHdsCCN4dh9Zoj2L07ptEeDwHbtkZi3eqquwxfvXIP4WEXMHxkAF59YygkOmK8vmAYpk78sdqcPsrF1Qpvvj0CgHwjkS8+2dFgsZN6avN6WZ7XYSMD8OqbD/L61jBMm1D7vL7xTsvN62Mp2F28eBF79uypNCLrYdFtyZIlOHjwIKRSKf755x8MGTKk0rldu3bF1KlT0atXL1y6dAnz58+vVDgD5NNvk5OTAQBff/01Xn/99UrXefrppzF27Fj8888/GsV7+fJl+Pr64siRIzA3N1fc36OHfJilIAiYMmUK8vPz4e/vj3379sHa2rpSH4MGDcLw4cMxbNgwnDp1CuvWrcPs2bMVx//44w8AgJubG44dOwbjR6YuhYSEYO7cuVWmjcbFxeH0afmaGN98843KEXSjR4/GJ598gqysLI0e70MXLlzAkiVLAAAdOnRAREREpcffp08fDBo0CMOGDUNJSQnmzJmDU6fUDw0/fvw4Pv74Y/zvf/+rdP/gwYMxePBg7N27F3/++Se+++472NjY1CrWO3fuVHtckynULYaQW/3xws0QpB0gMhwPkcQegn4oUPTIB5nsHoSslwHz7yESGwNGMyEymlmlK1nulxAZ/Z98BJ+Q34APgpTll1e/Nl1Y8jG0NnZFqEMPWOmZo5uVPw6naTalqqu1P4x05GuxHEqNgsCVCB8bVUU2ZZvOX4CvvT3G+/vC3sQYoV6tseNy1W8Iq+snv6QUL/2zCwfnzIaloQEm+Pviq8MRKJXJ6h0/VZVbVH1Ot564AB8XOzzd1Rd2ZsYY4NsG/56pnNNRwT7o6uWK3MJifPH3oUaMljSRV8PadNsPxMC7lR1G9/WDraUJ+gS3Rthx9d/kFxWXIe5OxVo6V2+l4nD0Dew+dgXfvD4G7z4XClsLY6z6u2rhlxpOTWvT7dhzHm1b22NEqD9srEwQ0s0L4YfVj14vKi5F/O2KGTDXb6Yg4mQs9h68hC8+eBpvvTwE1lYmWLv5eIM9Bqosv4ac7tp1Dm29HDBsWEdYW5ugV6+22L//ktr2RUWluHWrIqexsck4euw6wvddxGefjsObbwyDtbUJ1q+vWvilhpGfV0NOd5yFV1sHDBsRAGsbE/QKaYv94epzqszKyhiffTUBRkZ6kMkEfP3FLiQk1LDZHjWImvL6746zaOvtgKEP8tozpC0O1CKvnyrldcnnu3D7VsvK62PZJXbGjBkqp0+WlpYqCkQvvvhilWLdQxYWFvjqq68AyNdWu3HjhuJYUVER1q5dCwAIDAzEa6+9VuV8iUSCn3/+Gfr6mo8IWr58eaVilbJ///0XMTHyb1jWrVtXpVj30ODBg/H0008DAFavXl3p2MMCY2BgYJVinTJLS0uV5wFA79691Z4nEolgYWGh9rgqP/30E2QP/qj75ZdfVD7+wYMHY9asWQCAyMhIREWpX3y1U6dOWLhwocrYHuaprKxM5SjCmri4uFT7X+fO3MFUmVCwWXG70jp0ykqOQkgfCaHgDwiyyoVioeQsZBmzgYIN8oIeAMiyGytc0kBYcsUvjB3MNFufBQD62nJ3WG226XzFt/ddXJzr1EdeSQl2XZWPnDTS1YWvfdUlGejx2Xq8YpRNkGflnFoYGeD1EfJ/y3/YcxxpOfwipDnYfqDifRro7VKnPqIv3VZsNjH7qW5wc7Cs4QxqbDv2VMzE8e9Qt7yeibmNbQ82m5gxoTtcnZnXprRzV8WmWv5+dcvp2bMJ2PZgs4lpU3vCxYU5bUq7dlTk1M/fTaNzTEz08cWSiXBwMAcALP9uLw7ur91yMtS4dv2j9F7tqHleP/9mIhwczQEAP7bQvD6Wgt3kyZNV3h8ZGakYCTVu3Lhq+1AuTikXeE6fPo3sbHnhYNq0aSo3hgAAOzs7hIaGahSvi4sLevXqpfb4w5F6bdu2hZ+fn0ZxR0VFVdqAwsHBAQBw5MgR3Lyp+Tzth+cBqLJpR33t27cPAODj44OuXbuqbffcc89VOUeVSZMmqc2H8rTmuLi6rSlBtVBWUeSGuJo1A8sTIeT8D0JqF8hSu0OWNhCylAAIGePka9/peKvukx672wUVo0gt9cw0OsdcaoIAC3kOY3NvI7EguYYz6HGLvV8xEqc+m0bcUO7HhJtPNKW4lIpc2JpVXvvzqa4dYGFsgJyCImTnF2FwR68q//m6Vnxm+7raK+63fLDDKD1+8XcrcmpjWff315Ez8t//JGIx+gRrttkBNZ5biRWjMmys6p7Xo6diAQASiRi9u9VvEXWqH+URVNbWJnXu5/ixipz26tW23nFR3SXcqliv3tqm5pwaGOji868nopWHLQDgt18P4e+/uNGPtqlLXj9TyuvqXw7h720tM6+PZUqsuqJWdHTFk1qbDSGUR5ldvFix7seja9s9KigoSKNpsTUV4R7Gfe3aNbUFqUeVlJQgIyNDMfVz2rRpWLduHdLT09GhQweMGjUKoaGh6NWrF1q3Vv9LW6tWrdCrVy9ERERg6dKlCAsLw9ixY9GnTx907doVhoaGGsXzqOLiYsTGyv8x6tKlS7VtAwICIJVKUVpaWun5f5S3t7faY8ojB3Nza5jSqUJiYmK1x5OSkjjKTpmGr9NKZGkAHtkRWDdIcVMo1WxNSGocItQ+pyG2wZCIJACAA6nc6Uwb1eWt2pj9UP1VlwtdHfn70dRQH59PUT3LQNn4Hv4Y30O+duzM5VuRkVf98hDUOBrq/ZWVU6C47WBt2jCdUp3V5d9VVbKyK5a1sLdlXpuSpn+n1SQru+K9amen2Zek1Dhqk1NdXR18/Pk4eLdzBABs+f0ENq7jlGZtVOu8fjEO3j5PRl4fywg7dVMzU1NT69RfQUHFh2ZmZqbitq2tbbXnabpOWk1TSRsi7v79++OHH36AgYEBioqKsGXLFsyaNQtt2rSBs7Mz5s2bp3aTjE2bNikKnJcvX8bixYvRv39/mJubIyQkBCtWrEBRUVGtYlN+HlXtaKtMKpXCysoKAKqssaesuuKhWFzx0lMeeagpZ2fnav9THolIACRKRWBZSp27EenLF/QUhEKg+EB9o6J6cDGsGHWTUazZ9OSH02FLZWU4ktoyv4Vq7lo/+GwFgNS8uk+PbKh+qP487JRywSmvLUIrp4qcpmXm1bkf5dF5BUWabRxEjcfdtSKv9zPqnldrpdF5hYXMa1Nyc6tYtig9vR45VRqdV1jIndebkpu7Uk7vqx/0IZGIsejjsegYIJ9euePv01i5gn+7aKva5PWDj8fC/0Fed/59Gr/81LLz+lhG2EkkEpX3KxdqDh06pCgC1aSmwlx9qYv3oYdx9+jRAytWrNC4X0dHx0o/v/DCC3jmmWfw+++/Izw8HMeOHUN2djbu3r2Ln3/+GStXrsTChQvx8ccfVzrPyckJx48fx/79+/HXX3/h8OHDuHz5MkpLS3HkyBEcOXIEX3/9Nf777z94edV+KL4mFW5B4EL1zYnIcILitlBSx3XL9AZDJJVvl43CnTVvdkGNarBDT8XtS9k1T092M3SEh7F8/azTGZeQW8bCgTaa2LFihPepxLqNnjLW1cUwb/mUnYKSUlxIrnuRnurvmW6+itunb1bO6U9hJ1XuMqtsVLAPPp4oX9Lj3U1h+Ceq5a3P0tyM6eevuH32at1HOfbvXDG17mZiy1okuzkaObij4va5i9XP5KhO355KeU1Iq6YlNbbhwzsqbp+PuV3nfkJCKmYNxcczp01p+MhAxe2Yc6pzKhaL8L/3R6NLV/mAhb17YrDsmz2PJT6qm2GjKvJ6vpq8LvxgNLp0k+c1fE8Mli1p+Xl9LAU7dZQLdLq6uujQoUOt+1AeDZeamlptgSotrWE+YK2srJCSkoK0tLQ6xazM1tYW8+fPx/z58yGTyXDu3Dn89ddfWL58ObKysvDJJ58gODgYo0aNqnJu//790b9/fwBAeno69u3bh5UrV+LAgQO4efMmxo8fj7Nnz1Y5TxXl51F5yrEqZWVlipF1j26KQQ3M4CmIzb4AAAh530HI+77ycR0vQCgCyqv5JcRgAkSG8jUihfJUoDhcdTuxAyBTs7uu1B8is0/lfcgyIeQtqdXDoAr9bLtgftupAIBNCf9h0+3/Kh13M3REiawESUXq/3gLte+BQfbdAQAZJdk4kV7z9OR+dhVT3bnZRMMb28EHXw4dDABYduwEvjtWeTMdL2trFJeVIaGa3bsn+vtivJ+8uJOal4e9D5YpUNa7lTtOJd5BcZnq7e6NdKX4ftRwWBrK1zfbeuEiSuowipkqF8p+DDtRpbDWxsEKRaVlSLyvfoTrM918MbarPKdpOfnYf4FrfzalYb3a4/258vfpL38dx69/VX6fejpbo7i0DHdSstT2MaafH0b1kef0flYeDkVXfZ8O6uaNY+fikF/NSJz+Xbwwup+8QJ+bX4SIM5qvZ0yVDe7fAQvnDwUArP79GFZvqjw1ysPNGsUlZbiblKW2j5GD/TF8kDwf6Rl5iDhRNa/9e7fDyeibyC9Qn9e+PdtixIPCX25eEY6d4nu+LkJDffHWguEAgLVrI7B23dFKx1u1skFxcRnu3ctUdToAebFu2NCOAOSj644evV6lTb++PjgVebPaHWdDQrwxYngAACAvrwjHj1d9bVDNQgf7YcFC+UydtauPYN3qiErHW3k8yOndanI6MgBDHxRh09PzcDTimsp2r705DCF95YMMjhy6gq8+39UAj4BUGTSkIq/rfqtbXoc9ktdjR9TkdcEjef3sychrkxbsAgICFLf37t1bq3XsHmrfvr3idnR0NHr27Km2rfKaefUREBCAy5cv4/r160hISICbm2Y7mdRELBYjMDAQgYGBeOqppxRr8v3xxx8qC3bKrKysMH78eIwfPx6jRo3Cjh07cO7cOcTGxqJNm5p3kNTT00ObNm0QGxuLU6eqX9vq7NmzKC2VD/Gvb8GyRZN2AnSUXhsipanWElfA4KnK7Qv/qsM1OkBk+glQcgpC8WGg7DogywREOoDEAyKDURDpyd8TglAGIec9QChU2ZXI4hdAyIdQtBMovQoIBYDEHiK9foDBUxCJpBCEYghZrwEy9VOhW7J2ph5w0K+YWm8qrZj24qBvg362ldd/rMs6cZ7GLnjJaxIuZMXidOZlJOTfQ05pHiQiCZwN7dDHNhgBFvJ/rMqFcvwYuxnFsuqnZ4ghQm8b+fqDOaX5iMpQv/bkk6iTkyPcLcwVP1sYVCzm72ZhjrEdfCq133ax9iOcfO1t8engQTh5OxGH4+JxLe0+soqKIBGJ4WllgVE+7dCrlTsAoEwmw7t796GwtGpRbm6XYHwzfAj2Xr+B6Dt3kZCVhYLSUpjq6SHA0QGTA/zhaCpfM+lmegaWHav9LtwtQUArR7hamyt+NjeqyKmLtTlGBVfOaV1Grfk422HRuIGIupGIo1dvITbpPrLyiyCRiNDK1hLDO7VD97byfwPKymX4aOs+FJaoLrRSzfy9nOBsZ6742dxEKad2FhjWq32l9v9GXKr1Nbxb2WHhs4Nw+nIiTsTE42bifWTnFUIiFsPN0RJDerRDF193APKcfv7bPhQVV83p6H5+eHvWQBw5fQNnr95BQlIG8gtLoK8nhZuDBfp19kKPjh4AAJlMwDcbDiInv3ZLmbQUvj5OcHKo+P3I3LQir04OFhjcv/LvmXv21/7fL6/W9ljw0mCcjbmNU6fjEJeQhpzcIkjEIrg6W2FQXx8EB7QCIM/r18v3oqi46lTWkYP98eYLoYg4FYvzFxOReDcDefnFMNCXwsXJEn16tEW3YE8A8rx+/8t+5OY9eXnt0MEZTk4VOTVTzqmjBUJDfSu1Dwu7gNry8rLHG68PxdlzCYiMvIn4+DTk5Mjfqy6uVhg4oAOCguQ5LS+XYenS3ShSMe18+PCOeO21wTh2LBYxMbeRmKiUUxdL9O7tja4PRmnJZAJ+WL4PublPYE59neHkVDFIw9S8IqeOTpYIHVx5/fewPTGoLS8vB7y+YBjOnb2FyFM3ER8nz6lYIoarqxUGDOqAoGD552Z5mQxLv/5PZU7nvdAfQ4bJR0HHxaXi9w3H4ao0NVqVW0/oqMkOvs5wVNrJ2sxM6b3qZIlBQyrnde/u2ue1TdtH8nrzwXv1QV77D+qAoM4Vef32K9V5nftCfwx+kNf4m6n4ff2Tk9cmLdj17NkTlpaWyMjIwIoVK/Dqq6/C1LR2i7MGBQXBzMwM2dnZWL9+PV555RWVUzpTUlIQFhbWIHGPHDkSGzduBAB8+eWXWL58eYP0qywwMBAWFhbIzMzE/fu1mybRv39/7NixAwBw//59jQp2ADBgwADExsbi8uXLOHnypNqdYn/99ddK55BqIsNxED1alHt4TDcIIqUNHABAVpeCHQCRSAfQ6wGRXg+1bQRZBoTshTWsOyeCSDcQIt1AlUeF8iQI2W8BJU9mAQAABtl3R3871e8LHzNP+Jh5Vrqvrhs7SEQSdLTwRkcL9Ru35JTm4fvrGxGZUfMvuh0tvGH1YCfZiLTTKBM44krZeD9fjPVtr/JYkLMTgpydKt1Xl4IdAOiIxejp7oae7uq/5MkoKMQ7e/Zi/w31u2dbGBhgvL8vxvv7qm1zKvEOXt35H7JruZ5pSzG2SweM6qw6p4EeTgj0qJzTuk4z1ZGI0a2tG7q1VZ/TzLxCfLBlLw5d4o7o9TGyTwcM7636S0L/tk7wb1s5p3Up2AHynHbxdUMXX/U5zcotxCe/hFU7Ks7IQBdDevpgSE8ftW2y8wqxZN0BhB2/WqdYW4Lhg/wwpL/qzzI/H2f4+ThXuq8uBTtAntfgAHcEB7irbZOVU4Avv9uDY5HqR8UZGuoitG97hPZV/fkCADm5hVj2836EH34yp68PHeqPwaGqN/Dz9XWBr69LpfvqUrAD5GtZBXVqhaBOrdS2yc4uwFdf/4fjJ6rLqR4GDuyAgQPVD0LIySnE9z+EY//+un2uNHdDh3dE6BB/lcd8/Vzg6/dITutQsAMAiY4YnYI90OlBYU6V7KwCfP3lvzhxTPVIx169K3539vCwxYpfZ9d43f69P6l9sC3AkBHq89rBzwUdHslrXQp2gOZ5XfJFNXlVmpbeytMWK1bVnNcBvVpGXpu0YKevr4833ngDCxcuRHJyMiZMmICtW7fCyMhIZfvc3FysXbsWL774YqU+pk2bhu+//x5nzpzBN998g9dff73SeTKZDHPnzq31RgzqjB07Fu3atcOVK1fw008/ITAwELNnq3/RXLx4EfHx8RgxYoTivi1btmDkyJEwUBrJoSw6OlqxEUSrVhX/EJ07dw4A0LFjR5XnCYKAffv2AZCvRefu7q7x43r++efx888/QyaTYc6cOYiIiICZWeWdkPbu3YtVq1YBADp37ozg4GCN+6dGUHwIsux3IJJ2BKQ+gNgaEJsDEAGyLKDsKoTiCPnoPaH6xXaF3M8BvX6AbidAbAuITR70EQehOBwo/FM+6o4a1enMS/ju+ga0NWkFD2MXmOuawFRH/pmYV1aA+Py7OJN5GftTTqGwXLPPtD62FTsmH+TusE3iUFw83todhkBHR/jY2cDa0AjmBvoQQYSsoiJcTU3Dkfhb2HbxEvJK1I+Y/PzQEXRzdUWgkwNaWVrAwsAApnp6KCorQ0peHs7fS8bOK1cRcSvhMT66J1PElXi8t3kv/N0d0M7JFlbGhjAz0odIJEJ2QRGu30vD0au38E/kZeQXc5Hy5uD4+Th8vDIMHdo4oK2bLSzNjGBmrA+IgJy8Ity4nYYTMbfwb8Slaqe7fvDjfwju4IpO7VzQ2tUGlqaGsDA1RGlZObJyC3Ez8T5OxtxC2PEryC1QPxWPGsbJ6Dh8vmw32ns7oo2HHSzNDWFqagAR5Hm9GZ+KU2fisXvfRRRUk9fFX+9CUEc3BPi5wtPdFhbmhjB/kNfsnELEJaQh8nQ8wg9fRl41Uyyp/k6duokvv/oX7X2c0Lq1HSwsjOQ5FQG5uUW4eTMVkVFxCAuLQUE1U5g//WwHAgNboWNHV3h62MLCwghmZoYoKytHdnYB4uPTEBkVh/37LyPvCRwt+TidOnkDX32+Cz7tndC6jT0sLJVymlOEmzdTEHXqJsJ2V59T0i6RJ27g6892waeDEzzb2Mvfq2ZKeb0hz+te5lUtkdBIuwcsWrQIH374IYDqNygoLy9HaGgo9u/fDwBwdXXFvHnz0K1bN5ibmyM3NxfXrl3DoUOH8Pfff0NfX7/KiLOMjAy0b99esfba5MmTMXXqVNjY2ODGjRtYtmwZjh8/js6dOyMyUr52061bt6pMZXV3d0dCQgKmT5+ONWvWVPv4Lly4gO7duyMvT14ECQ0NxaRJk9C2bVtIpVKkpqbi7Nmz2LVrF44fP47XX38dX3/9daVrZWdnY9SoUejduze8vLxgZGSE9PR0HD16FN9//z0yMjIgkUhw6tQpxfTYNWvWYObMmQgODsaIESMQGBgIe3t7lJaWIj4+HqtXr0Z4uHyNstGjR2P79u2V4u7Tpw8OHz6MkJAQHDp0qMrjWrBgAb766isAgIeHB9566y0EBASgoKAAO3fuxHfffYfS0lLo6uri1KlTVQqHhw4dQt++fQEABw8eRJ8+fdQ+hw9HQn7wwQdYtGhRtc93bd25cwcuLvJvBRJOu8PZsUlr09SARseGNnUI1AgunVD/rRs1T4bJNW9gRM2PYaqsqUOgBqaXzVHXLZFOAfPa0ohK+fnbEom4l2OLUFScjWNR8jpKYmIinJ2dazhDM01exZBIJNi5cyfmzZuHdevW4fbt21i4cKHa9qp2iLW0tMSePXswcOBApKWlYePGjYopqw/NmDEDvXr1UhTs9PX16xW3r68vjh07hqeffhqxsbEICwurdsqtqqm+WVlZWLt2LdauXavyHH19ffz888+KYp2yqKgoREVFqb1ez549FSPhauPzzz9Hfn4+fvzxR8TFxWHu3LlV2piZmeGPP/5QO8qPiIiIiIiIiIjqrskLdgBgYGCAtWvX4uWXX8aqVatw5MgR3LlzB/n5+TA2Noa7uzs6deqEIUOGYPjw4Sr78Pf3x+XLl/H5559jx44duH37NkxMTODr64vnnnsOEydOxLfffqto/+hUz7rw8/PD5cuX8fvvv2P79u04ffo00tLSIJPJYGVlhbZt26Jnz54YM2YMAgMrrwt25MgRhIeHIzw8HJcvX0ZycjIyMzNhaGiI1q1bo3///nj++ecrTYcFgEmTJsHd3R3h4eGIiIjAnTt3kJKSgrKyMtja2iIwMBATJkzA+PHjIRaLa/2YxGIxli9fjgkTJuDnn39GREQEUlJSoKenBw8PDwwdOhTz58+HjY1NzZ0REREREREREVGtNdqUWG307LPPYtWqVXB2dkZiYmJTh0ONiFNiWy5OiW2ZOCW25eGU2JaJU2JbHk6JbZk4Jbbl4ZTYlolTYluGxpoSW/shWM1UYWEh/vnnHwBQu/spERERERERERFRU2sxBbubN2+q3dyivLwczz//vGKziunTpz/O0IiIiIiIiIiIiDTWYuYJLl68GJGRkZgwYQK6dOkCW1tbFBYWIiYmBr/88gvOnDkDAOjfvz+GDRvWxNESERERERERERGp1mIKdgBw5coVfPDBB2qP9+jRA1u2bIFIxHV1iIiIiIiIiIhIO7WYgt0777wDLy8vhIeHIyEhAWlpaSgtLYWVlRWCgoIwfvx4TJgwoU47pxIRERERERERET0uLaZg17ZtWyxcuBALFy5s6lCIiIiIiIiIiIjqjMPNiIiIiIiIiIiItAgLdkRERERERERERFqEBTsiIiIiIiIiIiItwoIdERERERERERGRFmHBjoiIiIiIiIiISIuwYEdERERERERERKRFWLAjIiIiIiIiIiLSIizYERERERERERERaREW7IiIiIiIiIiIiLQIC3ZERERERERERERahAU7IiIiIiIiIiIiLcKCHRERERERERERkRZhwY6IiIiIiIiIiEiLsGBHRERERERERESkRViwIyIiIiIiIiIi0iIs2BEREREREREREWkRFuyIiIiIiIiIiIi0CAt2REREREREREREWoQFOyIiIiIiIiIiIi3Cgh0REREREREREZEWYcGOiIiIiIiIiIhIi7BgR0REREREREREpEVYsCMiIiIiIiIiItIiLNgRERERERERERFpERbsiIiIiIiIiIiItAgLdkRERERERERERFqEBTsiIiIiIiIiIiItwoIdERERERERERGRFmHBjoiIiIiIiIiISIuwYEdERERERERERKRFWLAjIiIiIiIiIiLSIizYERERERERERERaREW7IiIiIiIiIiIiLQIC3ZERERERERERERahAU7IiIiIiIiIiIiLcKCHRERERERERERkRZhwY6IiIiIiIiIiEiLsGBHRERERERERESkRViwIyIiIiIiIiIi0iIs2BEREREREREREWkRnaYOgKixlQsylAuypg6DGsjdXLOmDoEagfEdUVOHQA3MLL60qUOgRmCQkNvUIVADE9/PbOoQqBHIsrKbOgRqYLIS/rvaIsnKmzoCagAioaBR+uUIOyIiIiIiIiIiIi3Cgh0REREREREREZEWYcGOiIiIiIiIiIhIi7BgR0REREREREREpEVYsCMiIiIiIiIiItIiLNgRERERERERERFpERbsiIiIiIiIiIiItAgLdkRERERERERERFqEBTsiIiIiIiIiIiItwoIdERERERERERGRFmHBjoiIiIiIiIiISIuwYEdERERERERERKRFWLAjIiIiIiIiIiLSIizYERERERERERERaREW7IiIiIiIiIiIiLQIC3ZERERERERERERahAU7IiIiIiIiIiIiLcKCHRERERERERERkRZhwY6IiIiIiIiIiEiLsGBHRERERERERESkRViwIyIiIiIiIiIi0iIs2BEREREREREREWkRFuyIiIiIiIiIiIi0CAt2REREREREREREWoQFOyIiIiIiIiIiIi3Cgh0REREREREREZEWYcGOiIiIiIiIiIhIi7BgR0REREREREREpEVYsCMiIiIiIiIiItIiLNgRERERERERERFpERbsiIiIiIiIiIiItAgLdkRERERERERERFqEBTsiIiIiIiIiIiItwoIdERERERERERGRFmHBjoiIiIiIiIiISIuwYEdERERERERERKRFWLAjIiIiIiIiIiLSIizYERERERERERERaREW7IiIiIiIiIiIiLQIC3ZERERERERERERahAU7IiIiIiIiIiIiLcKCHRERERERERERkRZhwY6IiIiIiIiIiEiLsGBHRERERERERESkRViwIyIiIiIiIiIi0iIs2BEREREREREREWkRnaYOgKjFEltBJPWDSOov/7+uL0RiSwCArGAbyrMXNMxljF+GxOQVjdqWpU+CUHKqut4g0h8OscFIiKQdALEZIMuGUHYTssKdEAq3AihvkLibIwtdI3Qwd0Z7M2f4mDmjvbkTzHWNAAA775zBophtDXKdOW36YW6b/pq1PfkrTmfE19hOXyLFCOdA9LNrD3djG5hLDZFbVoS0ohycy0xAROpVnLx/o76hN0uWxgbo4GaPDm72aO9qh/au9rAwNgAA/HPqEt7fuLdBrjNvSFc8P6SbRm1nf7cV0Tfu1Kr/cT398L9xFa+b9zaEYUfk5Vr10VKYmxminZcD2nk5wLuNPbzb2MPc1BAAsHv/BXz27e4Guc7MiT0wc1IPjdq+/M4mnLuYWOV+qY4EXYM8FLHa2ZjCzNQAhga6yC8oQeLdDESfT8DOPeeQlp7XIHE3R2aWRvDu4IS2HZzh1cEJXu2dYGYh//zd+88ZLHlve4NcZ8rzfTH1+X4atX1z1irERN9SeczZ3RrBPdvAL6gVPNraw8LKGIIgICsjH9cu3MG+necQGXG9QWJuzsysjNE2wB1tA9zQxt8VXh3dYGZpDAAI33IS37y6oUGuM/n1oZjy+lCN2i4YuwwXTsSqPObkaYvgfu3h2601PHycYGFjCgFAVloOrp1LwP6tkYjaf6lBYm6uzGxM4R3kgbZBHvDq5AGvQA+YWZsAAPauj8CSuSsb5DpT/jcGU//3lEZt3wz9BDERV1UeMzIzRNtOFfG2DfKAlYMFAOD8kStYMPjTBom3OTO3MUXbzq3hHewJryBPtA3ygJm1KQBg79rD+Gr2Tw1ynanvP41p7z+tUdvX+3+EmMOqf8cxMjNE22BPRbzewZ6wcpT/DXb+8GW80f+jBom3uVPktXMbeV6DPSvyuuYQvpq1vEGuM/WDZzDtg3EatX297wc15LU1vDu3lue1c+uKvB66hDf6LWqQeLUNC3ZEjURqF9nUIdSO2AYSix8h1g2sfL/EBiKJDcR6XSEzHI/yzGcBWXrTxNjE9g1Y2NQh1EmQZSt84DcWjoYWle63khjDSs8Y3maOCLB0x8mjPzRRhE3r4KfzmjqEerMxNcLLI3o2dRhaY8eGF5s6BI3Z2pjgk/+NUXnMzNQAZqZO6NDOCeNGBWHpT+EIO/hkFgP+OPR2U4egsTc+fgoDRwaoPGbvpAt7JwuEDPZF9LFYfLrgD+TnFj3mCLXH5gufN3UIGnv926kYMK6LymP2rtawd7VGyMhOiD54GZ8/vxr5OYWPOULt8EdCw/yR/7j8eOJj2LvbNHUYWm1rUsMUWR+Xn6I/h0Mr26YOQ+ttTVnV1CHUyk9nvnoi88qC3WPg7u6OhIQETJ8+HWvWrGnqcOptzZo1mDlzJgAgPj4e7u7uTRtQMyCU34NQdgNivd6Nep3StCHVNyivOrpDTg86lr/KR9UBkBUfhazgdwhldyASm0OkPwRiw3EQ6/oBFr+iPH08gJIGjb25SS7MQnxeGrrZtGnU64w7sqza43cLM6s93tnKE0uDpkJfIkVeaRG2J0YhMj0OGcV5sNA1hIOBBXrYeMFSz7ghw262kjJyEJ+Sge7t3Bv1OmM/W1ft8bvp2bXq751n+sLEQA/pOfmwMjWqT2gtTkpaDhIS09E5sFWjXmf6C79VezwpRX1OMzLzcfbCbVyNTUZyajbSM/NRXlYOaysTdAv2xMCQdjA00MU784ciK6cAp07XPKq2JUtNysLtuDQE9Wjcz985T31f7fHku6o/f61t5SMUcrIKEBF+CTHR8Ui5m4Xychk8vR0wdlp3uLSyQVCPNvjo+8l4Y+ZvEAShweNvblLvZiAxNgWd+rRr1OvM6/tJtceTb6v+UtLKwQwAkJORj6P/nkXMiVikJGZAVlYOzw4uGDO3L1xa2yOorw8WrZ2LBU8te+Lzmpp4H7ev3kPQQL9Gvc6coHeqPZ58K039QVHFzYyULFw/HY+uQ1UX3AlIvX0ft6/eRdAg/0a9znP+b1R7PDlefU5FooqkZiRn4Vr0TXQb3qnBYmuJUm/fx+0rdxAU2rFRr/Oc72vVHk+OT1V7TKT8Xk3OwrWoG+g2IqihQtNaLNgRNZLy3O8glMZAKI2Rj0iTOEFse6RxL1pWt+k1YqOpFcW6gq0oz64YySAAEEqOQSg9Cx3zLyHW9YNgNAWy/Or/OG2JVsYewOXsO7iUdQcZJflwMDDHrr5vNuo1b+ap/4erJua6hvgsYDz0JVLE5abihajVSC3KqdLur8Qo6Igk9QmzWVux+yQu3U7GxdspyMgtgKOlKXYvmt2o17yR1HCjVPv4eqC/fxtk5BZg9f5ovDEmpMH6bq5WbzqGq7HJuBqbhMysAtjbmuKPVY07kjL+9v06nXcvOQujp6kZkRKbjIiTsdi55xx++GIypFIJnp3S64ks2G1YcRDXL97BtYt3kZWRDztHc6zb83qjXjPhRt0+f9NSsvHth/9g346zKC2tvIzE9Ut3ceDf8/h0xTR0CHRHh0B39Bvmh/27zjdEyM3Oxm/+w/Vzt3H9XAKy7ufC1tkSayMbd7pawrWkOp13/14WvntzE/ZtPYXSkrJKx66fv40Df0Xi499fQIcurdGhS2v0fSoIB7ZFNUTIzcqGT7fj+uk4XDsdh6zUHNi5WmPd1aWNes2Ey7VbQkLZzhX7kHwrFddOxyPtjvzf5rCC9Q0VWouwfvGfuBYdh2tRN5GVmg07NxtsuFn9Fxr1detS3XP6z49hSI5PxbXom0hLlOc0vGxzQ4XWYqz/aCuuRd2onNf4Hxv1mrcuqRs8UrN/lu+R5zXqJtIS5b9zhcu2NlRoWosFO6JGIsurfmSUNhEbyNcAEWT5KM/5WGUboXAbZIbjINYNgthoDmT5qyEv5z05fo7d39Qh1MpLbUNhrmuE4vJSvHFmo8pi3UNlwpO7NuFPu080dQh1ZqgnxTtPy9fbWvL3EYiVv358gq3+/VhTh6AxTQbgXIlNxumYBHTt5IG2re1hoC9FYVFp4wenRdb/eKCpQ9BYTevpFReV4ruPd2LlXy8BAHoNbP/EFuw2fP1fU4egsZrW0ysuLMUPb2/BioP/AwD0HB7wRBbs1n/8V1OHUCt/Lms+r8Gmsu7DP5s6hFr585tdTR1Cs7Bu0R9NHUKt/LlkZ1OH0CS4S+xjcOvWLQiC0CKmw1JLpAeRtC0AQCg9CwjqFzUXiuUjBEUSG4h0gx9LdFQ3xjr6GOwon4ISlhSDhPy6jf4h7fbKiJ6wtzBBVGwidkVdaepwqBEVKRXopNInd0RsS5FwIxVZGfkAAAcXyyaOhhpKwrUkZKfnAgAc3KybOBoiImruWLAjetKJzSpuy6ov6gjlFcdFup0bKyJqAL1tvaEv0QUA7Eu6qLjfUKILF0MrWOhynbPmztfNHs/09ENJWRk+3tK8Rn9S7ZibGSLQzxUAkJVdgJwneJOCluRh4VUme7JGq7d0OlL5BCaBeSUionrilFiiFkRiuRYiaXtAZATIciGUxUIoPgJZwSZAUDMdUiiouC0yqbZ/kbjiuEincRf7JrnlnWfC29QRRjq6yC0tQlxeKk6kxWLb7Ujklqn/o93XwkVxOybzNrpZt8Gzrfuio6Wb4v60ohyEJcVg9Y3DyCotUNUNNZIV//cU2rnYwkhPF7mFxbiZnI7jVxKw9VgMcguLazxfRyzG+xMGQCIWY1V4FG6lVr/5CDW+JR+Ng5enHQwNdJGXX4RbiemIPBOPf3afQ15+zTl9lFRHAmsrY3Tyd8OksV1gamIAAPhzx+mGDp3U+Ozn6WjdzhEGRrrIzy1Cws00RB+LxX9bo5BXz6Kpp7cDjEz0AQB34jkC+nH6ZPOLaN3BBQbGesjPKcTt68mIPnQZu9cfRV52/XZ29ezgDCNT+Xs18UZKQ4RLGvhs51to3dENBiYGyM8qQMLVu4gOj8F/qw4gL4u/3zRHn+9ZiDYBrWBgYoC8rHzcvnIXUWHn8O/K/cjLym/q8KiOPg97D20ClfJ6+Y48rz+HM69qcIRdLd27dw9vv/02AgMDYWZmBl1dXdjb28PX1xcTJ07EmjVrkJNTuTDi7u4OkUiEGTNmVOnv0KFDEIlEEIlEOHToEARBwKpVq9CzZ09YWVnB1NQUnTt3xvr1lRc/LSkpwYoVK9C1a1dYWlrCxMQEPXr0wB9/qJ+LfuvWLcW1Hk7P3bp1KwYMGABbW1sYGBjA29sbb7/9NjIz6//HnyAI+PPPPzF27Fi4uLhAX18fFhYW6Ny5MxYvXoysrKx6X4MqE+v1hEhsAZFIFyKJFcR6XSExXQAd20MQ6Q1QfZKQB6Fc/kulSNoRgFRt/5WmwUocGy5wUqurdWuY6xpCKtaBpZ4xgqw88JJ3KHb0fQMhtup30vMwlm97nltaiNEuQfih84xKxToAsNE3xZRWPbGx5wtoZWzTqI+DKuvm7QZzIwNIdSSwNDFEcBsXvDKyJ/77YBb6+HrUeP70/p3g5WSDxPtZ+HXvqccQMdUkOMAdZqYGkEolsDA3QoCvK+ZOD8GWX+eiZ5fWGvXRsYMLjuxcgCM7F2D/9tex5de5WPDSYDg7WgAA9h68hE1/RTbmwyAlgd1aw9TcEFKpDswtjeEf3Aqz5w/Cmt2voVsf73r1PeHZil3jD++9WE1LamiBvb1hamkEqa4OzK1N4Ne9DWYtHIXfTnyIrqG+9ep7/EuDFLcjdp6pb6ikocD+HWBqZSLPqa0p/Hu3w+zF47Hm0jfoNjywqcOjOug0wE+RUwtbM/iH+ODZTydhXewydBvBHV+bq04DH8lrn/Z49rPJWHfzB3Qb2fJ3fK0LjrCrhYiICAwfPrxKQS4lJQUpKSm4ePEiNm/eDGtrawwfPrzW/ZeWlmLUqFHYubPygopRUVGYNm0aoqOjsWzZMmRmZmL06NE4cqTyjqPHjx/H8ePHcePGDSxcuLDG682ePRu//VZ5p89r167hiy++wLp167Bv3z74+PjU+nEAQFpaGsaMGYNjxyov/F1cXIyoqChERUVh+fLl+Oeff9ClS5c6XYMqCKVXISsKh1B6HkJ5KkQiHUDHA2KDkRDr9YZIbAaJxXKUZ86BUHy4yvmyon2QGE2WF/mMZkOWv6JKG5G0E0R6fSt+FnFKZWOKzUnGoZTLuJR9B2lFudARieFmbI0hjh3RzaYNTKUG+DJwIl49vQHH06ruDmwqlX/DryeW4mXvUBSXl+Ln2P347+55ZJbkw9XICtM9emG4cyDsDczxTacpmHR0OQrLSx73Q32iXL+bhoMXbuJiQjJSs/MhlYjhZmuBYUHe6N7OHaaG+lgyawReXvkPjl25pbIPF2szzAntCgD4dOsBFJc+uRuGaIOb8amIOHUDV64nIT0jDxKJGK5OlhjYxwedA1vBxFgfH70zGu8s3lbn3V2TUrLw9fK9iDp7q2GDJ5XirifjxIEruHbxDtLTciHRkcDF3Rp9h/ohqEcbmJga4L1vJuD9lzci+mhsrfvvOcAHvQfJd2a/fukujoZfauiHQCrEX76LE2ExuHY2AekpWdDRkcDZ0w59nwpGpz7tYGJuiHd/eRaLpv+M6IOXa91/j6Ed0WuEvDh0/fxtHP33XAM/AnpU3IXbOLHrNK5FxyE9KRMSqQ5c2tij7/juCBroBxMLI7z3+8t4/+lvEL03pqnDJQ3ExSTg+I5oXI26ifR7GdCR6sDZywH9J/VE0CB/mFgY44Otr+G90V8has+5pg6XNBQXk4Dj/0ThamQs0u9lQkcqgXNbR/Sf1AtBoR3lef3zDbw38nPm9REs2GmouLgYEyZMQE5ODkxMTPD888+jb9++sLW1RWlpKRISEnDixAls27atztd47733cOrUKUyePBmTJk2Cvb09rl+/jkWLFuHatWv47rvvMGLECHz//fc4fvw4nn/+eYwZMwZWVlY4d+4c3nvvPdy7dw/vv/8+Ro0ahfbt26u91o8//oioqCh07twZr776Ktq0aYPU1FSsXbsWW7ZsQVJSEkJDQ3Hp0iWYmprW6nHk5+cjJCQEV65cga6uLmbOnImhQ4fCxcUF+fn5OHLkCL755hukpKRgyJAhOHv2LNzc3GrumFSS5a+GLO+7SvcJAFB6HuWF2yEYToTE7GOIRDqQmH2KstR+AIof6WMFxAYjIRKbQGzyOiC2gqxgI1B+BxCbQ6w/GGKTNwCIIAilEImkgEj/cT3EJ87v8cexMrbqjogXs+/g37vn8JRLMP7nOxo6Ygne8x2D0YeWoFhWVqmtwYP163Ql8o/5989vwb7kilEccXmp+CBmG4plZRjr2hmuRtZ42rUz1scfbcRH9mTbeOgsVuw+WeX+CwnJ2BV1BU9398V7EwZARyLGookDMXzxbyqLce+OHwB9XR2Enb2O41cSHkfopMbWHdFYvanqjrRXrich7OAljBzsjzdeCIWORIwFLw3GxDm/oKSkTEVPcldjkzH9BfkXaVKpBHY2pujRpTUG9W2Pd14ZilUbI/Bv+IVGezwEbN9wAht+Oljl/msX7mDfznMY+nQQXnl/FCQ6Erz6wWjMHL4UJcXqc/ooF3drvPbRGABAUWEJvvpf3X9vJM39/ctBbFxSdTfQa2cTsP/PSAyZ0gMvfzkREh0JXvl6Emb3+BAltdiJ2bm1HV5bOhmAPK9LXl7XYLGTatt/CMOGT6ruyHwt6ib2/X4MQ2f3xSvfz5K/V3+cjZkd3qhVTunx+2vZf1j/UdUdaa9G3sC+DREY9lx/zP/pOUh0JHjt5zmY3vYV5rQZ+Ovbf7H+w61V7r8aeQP71h/BsDkDMH/FXHlef3ke09u8hJIiDiB4iFNiNXTs2DHcu3cPAPD777/jiy++wODBgxEYGIguXbpg3LhxWLp0KeLi4tC7d+8aelPt1KlT+Pbbb7FhwwYMHToUgYGBmDBhAg4dOqQomk2aNAk7d+7E1q1b8eOPP2LgwIEIDAzErFmzsHv3bojFYpSXl2PlypXVXisqKgpDhw7FsWPHMGHCBHTq1AlDhgzB5s2b8dFHHwEA7ty5g8WLF9f6cbz99tu4cuUKzMzMcOzYMaxYsQIjR45EQEAAevbsiYULF+L06dNwcHBAZmYm3n333Vpf486dO9X+l5SUVOs+my0ht9rDsoJNkBVsAQCIJPYQ6YdWbVR+D+WZL0KQ5UIkEkNiPAtS2/2QOlyD1O4UJGYfQiQ2gSz3K0B4UOyrZjdZqp+8atamA4C/EqOw/XYUAMBW3xT97KsW55ULeDGZtysV65Qtv7YXxeXyX3ZCH+wqS42jprXp/jx+AduOy4sxtubG6O9fdZ3IkZ190LWtK3ILi/HVtkONESbVQk1r0+3Ycx47w84DAGysTBDSzava9kXFpYi/fR/xt+/j+s0URJyMxefLduON9/+AiYk+3np5CKZP6N5g8VNV+TWsTfffn9HYvS0aAGBtZ4oe/TWfiWBpY4KPf5oGI2N9yGQyLF30N27HpdUrXtJMfk71a9Pt3nAMuzfKi+/WDuboMcRf474t7cyweMPzMDQxgEwmw7evb8Tt2OR6xUs1y8+ufm26/1YdxO7V8uK7taMleowKrrY9Nb2acvrvL/vx36/yTbasnSzRcww3wGsOaszryn3479d9AB7k9SnmVRkLdhpKTq74h7e6gpyOjk6tR6Q91KVLF7zyyitV7re3t8eYMfJvY9PS0jB+/HiMHj26Sjs/Pz/07NkTgHz6bnX09PTwyy+/QEen6iDL//3vf+jQQT5VY9WqVSgu1nyx7Pv37+PXX38FAHz00UcIClI9F93NzQ3vvfceAGDLli0oKKjdgrAuLi7V/te5M9/oymQFmxS3xXqqpyALJUdRdn8EZAVbIMgyKp9fcgZlGbMgK1gPkdhY3l6mZhMLeiy2JUYpbgdatqpyvKCs4n2rasrsQ9mlhbiSfRcA0MbEHjoi/rPQlP48VjF6qpOnc6VjFsYGeG20/N+fH/89jrQcLs7bHOzYc15x27+DSzUt1TsTcxvbHmw2MWNCd7g6WzZIbFQ3/26t+Pz1C3LX6BwTUwN89vN02DvJ1yP86Yv/cGg3R0tqk93rK0bL+nbTbGMtY3NDfLLpBdi7WgMAfn5/Gw7/zY1htMW/qypGy/r1qt+6k6Qd/v1lv+K2X++6Ld1E2uffn8MVt/1C1M8SfBLxLzMNOTg4KG6vXr26Ua4xYcIEtcf8/CpGvowfP15tO39/+TeCcXFx1V5r0KBBcHRUvWmAWCzG9OnTAQCZmZk4c0bzRXPDwsJQVCT/dnrcuHHVtn1Y+CwtLcXp0/zlpjEJpUpr7Ijt1DcsT0R59kKUpQSjNKUrSlP7ozTZH+Xpz0AoPgyRTsUmB0JZ7dftoYYTn5uquG2rX/VLgpSibKXb1RdXkx+01RFLYCY1bKAIqS7iktMVt23NK68TOaZbB1gYGyCnoAhZBUUYHOhV5T9fd3tFe193e8X9lsYGj+0xUGW3Eit2ALWxMq5zP0dPyT9zJRIxetcwUo8al/KoOCvbmr+kNTDUxSc/TYN7a/m/v2u+34cdm7hZjLa5HVsxO8PK3qzG9gZGevh44//B3Vv++/TaL3Zix29V1wmmpnP7yl3FbasHm/dQ85Zw+Y7itrUTc9pSVMqrI7+UVMY17DTUs2dPeHh4IC4uDvPnz8fGjRsxZswYhISEICgoCLq6uvW+hpeX+l/Azc3Na9UuN7f6aZLBwdUPC1ceoXbx4kV069at2vYPRUdHK24rFzlrojyCUROJiYnVHk9KSuIoO2UiUe3PkaUBqDxVR6RbMWJSKD0PakI1pPRmbioGPngLSmrIv0RpVF25IKtvZFQP1aVKV0cCADA11Mdn04bU2Ne4nv4Y11P+Jc7s77Yi48adGs6gxiCq6c2qoazsiil99hoUiagR1SKluno6+PD7KWjrKx8x+8fqCGz6hUUd7aR5YnX1pVi0dh7aBrgDALb+GI7Ny8IaKS6qs7r8/ktaTcSctkjMq3os2GlIKpVi586dePrpp3HlyhXFTqcAYGBggJCQEEydOhXjx4+HRCKp0zUMDdWPbBGLxbVqJ5NV/0e3ra1ttcft7CpGYWVkZFTTsrLU1NSaG6lQ2ymxzs7ONTciBZFO64ofZHXLEQCIDUYAAAShEELR/hpaU2PyMK54D6epGEF3NqNiN0pnw+q/qXp4vKi8BNml1a/zQ43Lw95KcTstm1NeWwJ314qc3s+o+9qf1kqj8woLuch2U3LzqPj8zUhT/wWpREeM976ZCP9g+bIFu/6IxKqlexs9PqobN6+KEcrpKdlq20l0xHj3l2fh110+bfbftRH47eN/Gj0+qj23dhWziTKSMpswEmoorj5Oitvp95jTlsLVp+Jv+/QkzWsPTwIW7GrBx8cHFy5cwM6dO7Fz504cPnwYN2/eRGFhIfbs2YM9e/bgm2++wX///VdjQayp1VTFFgShTv2Wl8t3NNTV1a3VNFcW4BqX2HCi4raspG7TcET6QyCSyteKEAp31LjZBTWusa4VI0hPKxXnHjqTcQsZxXmw1DNGHzsffH9N9R+JjgYW8DKVD8U7n3kbAur23qeG8XQPX8Xt04+MiFux+6TKXWaVjezsg8VT5BvLvLchDDsiLzd8kFQrIwd3VNw+d7H60eHV6duzreL2zQRuVNCUhj5TMdo8JvqWyjZisQhvf/4MOveSz4rYt/Mcvv945+MIj+poyNSeitsXTtxQ2UYsFuGt5TMQ3F++xtK+rafwwztbHkt8VHtDZ/dT3I6JuNqEkVBDGf7cAMXtmCNXmjASakjD5w5U3I45zN9dlXENu1qSSCQYPXo0Vq1ahRs3buDevXtYtWoVOnXqBAA4ffo05s6d28RR1iwlJaXa48oj5SwtNZ9HbmUlH0lQUlICKysrdOjQQaP/lKf8UgWRwVhIHW5C6nATYuOXqzbQ8QIkbtX2ITacCLGhfN1DoTwVQpGab/fF6qcwi6T+kJh9Ju9Dlony3CWaPQCqYoRTAE4P/QSnh36COW36VTne2sSuxhFxT7kEY7SL/A/G+0W5OJhS9R82GQSsjz8KAHA3tsEMj6qb5eiIxHi7/UjFlNhttyNr/XhIbmRnH5z/7lWc/+5VzBvStcrx1g5WcLGufk2kp7v74qlu8oJdWnY+DsSo/oORHo/B/TvgyM4FOLJzAWZO7FHluIebNZwczKvtY+RgfwwfJF+DNj0jDxEnqq792b93OxgZVr+sRt+ebTHiQeEvN68Ix07xtVEXA0cGICxmMcJiFmPK832rHHdvYwdHl+o/f4c+HYQhT8k/f9PTcnHsgOo/LOZ/MAq9B8k38IoIv4Ql7/1Vz+hJnQHjumD3vR+w+94PmPz60CrH3b0d4eBuXW0fQ6b0wOBJ8h2YM1KycXy36mU/Xvl6EnqNCAQAHN11Fktf3VDP6EmVgVN6IaxgPcIK1mPK/8ZUOe7e3hmOHtUPjhg6uy+GzOgDAEhPzsKxHVwvuykNmhaC8LLNCC/bjKnvP13luHsHFzh6VrPONoBhz/XHkAdF2PSkTBz7O6ra9tT4Bk3vg3DZVoTLtmLqB89UOe7ewRWOnvYqzqwwbM4ADJndH8CDvG7n3yPKOMKunhwcHDBr1ixMnToVXbt2xZkzZ7Br1y4UFhbCwEB7F/l+OJ1Xk+MPd4zVREBAgOL23r17FZtXPIlE0k6AjnvFz2KlhVF13CAyGFupvVC4rQ7X8IXE7FMIJSchFB+GUHoNgiwLIpEE0PGE2GAUxHq95P0LZSjPfhcQVE951LFcBUHIh1C4A0LpFQhCIUQSO4j1B0Bk8BREIikEoRjlma8CsnSVfbR0HS3c4KJUTDPXrdgUwMXQEiOcAiq133n3bK2v0c7UEe/6jkF0RjyOp13HjdxkZJcUQiISw93YGkMdO6KrjXwaTpmsHJ9c/BtF5aqnx22+dQKDHHzRzswJL3mHwsPEFrvvnkNmSQFcDC0xuVUP+Fq4AgCOpl7D/uRLtY63JQjwcISLtbniZ3OlDRpcrc0xsnPlXcjqMmrNx8UOH0wciKjYRBy7cgux9+4jK78IOmIR3O0sMTy4Hbp5y4vvZeUyLN6yD4UlZXV7QARfHyc4OVR85pqbVuTUycECg/tX/ndtz/6Ltb6GV2t7LHhpMM7G3Map03GIS0hDTm4RJGIRXJ2tMKivD4ID5FMhy8pl+Hr5XhQVV32vjhzsjzdfCEXEqVicv5iIxLsZyMsvhoG+FC5OlujToy26BXsCAGQyAd//sh+5eUW1jre5ax/gCkeXiunFZhYVy4M4ulhh4MjKn7/hO2r/+dumnSNeXTQK56PiEXU0FrdiU5CTXQCJRAyXVjboN8wfnbrLl5goLyvHd4t3oFjF9OQ5rw9G6Bj5F7nxsSnY/OthuHjYVHvthBt1X66iOWvf2QMO7hXPjZllxdRvx1Y2GDCu8s72+/6o/SyB1n4umP/1JJw/HovoA5dx6+o95GTmQSKRwKW1HfqNDUZgiHxTrfKycnz31mYUF5ZU6efZ98dg0AT5ms7xV+5h8/dhcGlT/R+iCdeSqj3eErXv5lWp8GJmZaK47ehpi4FTelVqH74hotbXaBPQCq/+OBvnD19B1N7zuHXpDnIyciHRkcDFywH9JvRApwHyL8DKy8rx3Uu/obigWGVfHn6u8PRT/eW3pZ1ZlXgjtkeiKF91Xy1V+x5t4aRUdDG1VsppazsMmhZSqf3edbVfp9Mr0AOvrZyDc4cuIWrPOcRfTERO+oOctnXEgMm90Gmg/Auw8rJyLHv+VxSpyamnvxs8/d1VHrOwM6sS75FtJ5+4nAJA+x7ecGqtLq/2GDS9T6X2e9ceqvU1vDp54LVf5uHcwUuI2nMW8RduV+TV2xEDJvdGp0Hy9ZbLy8qxbN7KavLqDs+O7iqPWdibV4n3yJ8nUZTf/H9fYsGugUilUoSEhODMmTMoKytDVlaWVhfs9u7di6SkJJUbQ8hkMqxduxYAYGFhgcDAQI37HTJkCKRSKUpLS7F06VJMnjwZOjpP5stMbDgeYsOxqo/pBkGstIEDAJTWoWAHACKRDkR6PQG9nmrbCLIMlGe9A6G4unXnRBDrBgK6qvMtlCehPGsBhJLjdYqzJRjtEoQRzqqfn46W7uho6V7pvroU7AD5bq1drVujq3VrtW2ySvLxUcx2HElVP8WjRFaG+dHrsTRoKnzMnDDMKQDDHikqAvJi3TtnN9cp1pZgTLcOGNVF9RbyAZ5OCPB0qnRfXaeZ6kjE6ObtpijMqZKZV4hFm/bi8MXqd/qm6g0f5Ich/X1VHvPzcYafT+VlGOpSsAPkOQ0OcEfwg4XnVcnKKcCX3+3BsUj1o+IMDXUR2rc9Qvuqfh0CQE5uIZb9vB/hT+hUkcFPdcKgUao/fzsEuqFDYOX3VV0KdgAg0ZEgsFtrBHZT//mbnZmPpR/8jZOHVH/+9hhQUeRv1cYOy7f8X43XDfV7r/bBtgChE7tj4Piqo5IBoH1nT7Tv7FnpvroU7IAHee3tjcDe3mrbZGfk4dvXN+LU3gsqj/cc1lFxu1U7R/wQ9naN1x3i+GKtY23uBs/og0FTe6k81qF7W3To3rbSfXUp2AEPctq/AwL7qx9YkH0/F0v/71ec/Ff950H3EZ0w9X9PqTzm0tYRb6ycU+m+mCNXnrjiztBZ/TBoeojKYx16eKNDj8rvq7oU7AB5TjsN8EOnAX5q22Tfz8GS537GiV3qR0x2HxWMaSpG8QGAq7cT3vzt+Ur3nT98GUX5T95SE0Nn98egB6NQH9Whpzc69Hwkr3Uo2AEP8jrQT1FwVSX7fg6WPPsTTuyMVtum++hgTPtgnMpjrt5OeHP1C5XuO3/oEgt2T5KIiAg4ODigdWvVv8CVlJTg8GH5h5OxsTFsbKr/JrWpFRcXY+7cudi+fXuVTTI+//xzXLgg/2Vl1qxZ0NPT07hfJycnzJw5EytXrsT58+cxd+5c/Pzzz2qLdqmpqdixYweeffbZuj+YJ5hQfAhlWW/JC206PhBJrAGxOQARIMuCUHoVQvERyAq3AUL1i52X534GkV4/iHU7AWI7QGwi76MsDkLRXsgKtwJC7TYHodo7mnYdH8b8BT9zF7Q1c4SlrhHMdQ0BiJBTWojrOUk4kRaLnXfPIL+s5l8Y7xfnYsbxFRjl3Amhjn7wMLaFiVQf2SWFuJh9B7vunFE5pZYa1tHL8fjg973wc3eAt7MtrEwMYWakDxFEyCkowrW7aTh+5Rb+ibyM/KKqIztI+5yMjsPny3ajvbcj2njYwdLcEKamBhAByMkrws34VJw6E4/d+y6iQMVonYcWf70LQR3dEODnCk93W1iYG8Lc1BClZeXIzilEXEIaIk/HI/zwZeQ9YX8kPm6RR69jyfvb4ePvAk9vB1hYGcPEzAAikQi52YWIu56M6GOxCP/nLAqYi2Yjav8lLH1tA7w7tYJnBxdY2JjAxMIIIgC5WQWIv3wX0YcuY9+WUyh4AkevNkeRYeexZN4v8OnSGp7+brCwNYOJpbH8vZqZh7gLiYjeG4PwDUdQkMucNgeRu8/i62dXwKdrG7Tu6A5zO3OYWj3IaUYe4mISEBV2HnvXHkZBLjdIay4i/zuDr2f/CJ9uXmjdsRXM7cxgamVSkdfztxAVdg571xxiXtUQCXXdXeAJs2jRIixevBi9evXCsGHD4OfnBxsbGxQWFuL69etYsWIFIiPl863nz5+PpUuXKs51d3dHQkICpk+fjjVr1lTq99ChQ+jbV76OysGDB9GnTx+V11+zZg1mzpwJAIiPj4e7u7vaOD/88EMAVTeOuHXrFlq1kk/PCQoKQnR0NLp06YJXX30Vbdq0QWpqKtauXYvNm+UjbZydnXHx4kWYmVVed6mmWPLy8tCtWzdcvCgfseDj44M5c+agU6dOMDY2RlZWFi5duoR9+/bhv//+g6+vL6Kj1VfT6+LOnTtwcXEBAMRFu8LZkbXplqLr2QlNHQI1grI91a8vRM2PWTx3Mm2JDBK44VBLI77PnRZbIlmW+p1uqXmSlfDf1RZJVt7UEVADKBIKcBT/AQASExMbbFNNVjFqQSaT4fDhw4qRdKo89dRT+Oyzzx5jVHXzwgsv4PDhw1izZg0mTKhaAHFwcEBYWFiVYp0mjI2NcfjwYUyePBl79uzB5cuXMX/+fLXtTU1Na30NIiIiIiIiIqKWigU7DS1YsABdunRBeHg4Tpw4gXv37il2UrW3t0eXLl0wbdo0DB1adXcqbbV69WoMGjQIK1euxIULF5CXlwc3NzeMHj0ab7/9NiwsLGruRA1LS0vs3r0bBw4cwIYNG3D06FEkJSWhqKgIpqam8PT0ROfOnTFs2DAMGjSoAR8VEREREREREVHzximxTxDlKbGrV6/GjBkzmjagRsQpsS0Xp8S2TJwS2/JwSmzLxCmxLQ+nxLZMnBLb8nBKbAvFKbEtQmNNiRU3SC9ERERERERERETUIFiwIyIiIiIiIiIi0iIs2BEREREREREREWkRFuyIiIiIiIiIiIi0CAt2REREREREREREWoRbZz5B3N3dwU2BiYiIiIiIiIi0G0fYERERERERERERaREW7IiIiIiIiIiIiLQIC3ZERERERERERERahAU7IiIiIiIiIiIiLcKCHRERERERERERkRZhwY6IiIiIiIiIiEiLsGBHRERERERERESkRViwIyIiIiIiIiIi0iIs2BEREREREREREWkRFuyIiIiIiIiIiIi0CAt2REREREREREREWoQFOyIiIiIiIiIiIi3Cgh0REREREREREZEWYcGOiIiIiIiIiIhIi7BgR0REREREREREpEVYsCMiIiIiIiIiItIiLNgRERERERERERFpERbsiIiIiIiIiIiItAgLdkRERERERERERFqEBTsiIiIiIiIiIiItwoIdERERERERERGRFmHBjoiIiIiIiIiISIuwYEdERERERERERKRFWLAjIiIiIiIiIiLSIizYERERERERERERaREW7IiIiIiIiIiIiLQIC3ZERERERERERERahAU7IiIiIiIiIiIiLcKCHRERERERERERkRZhwY6IiIiIiIiIiEiLsGBHRERERERERESkRViwIyIiIiIiIiIi0iIs2BEREREREREREWkRFuyIiIiIiIiIiIi0CAt2REREREREREREWoQFOyIiIiIiIiIiIi3Cgh0REREREREREZEWYcGOiIiIiIiIiIhIi7BgR0REREREREREpEVYsCMiIiIiIiIiItIiOk0dAFFjk4jEkIhYm24pjPWKmzoEagQpZk0dATW0Qiv+itESSYqMmjoEamBSqaSpQ6BGIDbhe7Wl0Skta+oQqDGUy5o6AmoAOmW5wL2G75dVDCIiIiIiIiIiIi3Cgh0REREREREREZEWYcGOiIiIiIiIiIhIi7BgR0REREREREREpEVYsCMiIiIiIiIiItIiLNgRERERERERERFpERbsiIiIiIiIiIiItAgLdkRERERERERERFqEBTsiIiIiIiIiIiItwoIdERERERERERGRFmHBjoiIiIiIiIiISIuwYEdERERERERERKRFWLAjIiIiIiIiIiLSIizYERERERERERERaREW7IiIiIiIiIiIiLQIC3ZERERERERERERahAU7IiIiIiIiIiIiLcKCHRERERERERERkRZhwY6IiIiIiIiIiEiLsGBHRERERERERESkRViwIyIiIiIiIiIi0iIs2BEREREREREREWkRFuyIiIiIiIiIiIi0CAt2REREREREREREWoQFOyIiIiIiIiIiIi3Cgh0REREREREREZEWYcGOiIiIiIiIiIhIi7BgR0REREREREREpEVYsCMiIiIiIiIiItIiLNgRERERERERERFpERbsiIiIiIiIiIiItAgLdkRERERERERERFqEBTsiIiIiIiIiIiItwoIdERERERERERGRFmHBjoiI6P/bu++wps63D+DfJOy9NwoooDIc4MYN7m3dtWq1VjveWltt1Vr1V61aa7W1w1q17omral0ouC3gpCqKgoLKlhlk5/0jGqAECAgkwPdzXV7XIefJOTe9m3DOfZ5BRERERESkQliwIyIiIiIiIiIiUiEs2BEREREREREREakQFuyIiIiIiIiIiIhUCAt2REREREREREREKoQFOyIiIiIiIiIiIhXCgh0REREREREREZEKYcGOiIiIiIiIiIhIhbBgR0REREREREREpEJYsCMiIiIiIiIiIlIhLNgRERERERERERGpEDVlB0BUbwlNAPWWEKh7AuoegLoHBEITAIDk5QFI0r6oltMI9D6GQO//FGpb+GI8kBtcTgshoDUQAq1BgLobIDQECtOA/EeQZB8FXvoDKKiWuOsiI3U9NDewR3PDRmimbw9XA3sYaegCAI4/D8Hye3uq5TyTHHtjslNvhdp+cu033Ex9VOr1L5uPRj+btpU677K7u3EiNrRS76kPTHS14WlrBQ87K3jYWsLD1grGutoAgIPX72DuwVPVcp6PenTARz07KtT2nY37EPz4aaWOP7adJxYO6iX7ee6Bkzh4426ljlFfGOtrw83JCm5O1mjhaIkWjlYw0pfm9OjFO1i84WS1nOe9oR0xbahiOX1/+V5cDy+dU3U1ETp7OsLNyQotHC1haWoAIz1t6GqpIzM7F9GxKQi+G42DQbeRkJJZLXHXRUZGOmjmao3mzWzg6mKFZq7WMDTUAQCcOBWGFSuPVct5Jk7wwaR3fBRqO/Oznbh1O7rU6+rqIrRv1wTNXK3RzNUaFhYGMDTQho6OBsRZuYiJScb1G09w5NhNJCVlVEvcdZWRsS5c3W3h6mYL1xY2cHGzhaGRNK+njtzE94sOVct5Jkzrjgnvd1eo7efTNuP2tcdy99k1NkW7zs7w9HKAk7MljE31IAGQmpyJ8DvPEHDsFoIvRlRLzHWVoYkuXD3t4eppDxcPe7h42MHQWHqtdPpAKFbN3Vct53n7I1+8/bGfQm3nTPgdt4Mj5e6zczRD267N4NHOCU7NrGFspg+JRILU5Ezcvx2DM4euI/hceLXEXFcZmurBtaU9XD0bwdnTHi6ejWBo8iqn/iH4Yc7uajnP+E964+1P+ijUds7YXxH2T+nrXwCwdTRH2x7N4dGuCZyaW8PY3ECa06QM3L8VgzMHQxESeK9aYq7LpHltBNdWjeDcshFcPO1haKIHADi9Lxg/fL6rWs4zfmYfvP1pX4Xazhn9M8KulpFXJ3O07dECHu2bwKmFDYzN9SGRQJrXm9E4cyAUIWfr33UvC3ZENURo8Y+yQ6gcoTkERr9AoNG65Osic0BkDoFmB0h0RkGSMg0oTFZOjEp2uOsiZYdQo2LEicoOQSkufzld2SG8MQt9XczyU6zI0BCcWjtD2SEozNJEHyv/b7DcfUZ62jBy1oansw3G9WmDFVvP4O/LDfMm4+A+xR5MqQJzc318s2i43H2GBtowdLODu5sdRo5oizU/ncKpgH9rOULVsTdgtrJDUNjsxUPhN7CV3H1WtsawsjVG997uCLn8EN/O9Yc4M7t2A1QRe658rewQFPbZ8lHwG+Yld5+VnQms7EzQrX9LhF64j2Wf7oQ4o2HmdHfIYmWHoLDPVo6B7wj5D6yt7E1hZW+KbgNbIfRcOJb/37YGm1MA2H39G2WHoLDPVo2D71sV5HVQa4QG3cPyj7dCnF5/8sqCnYoQCAQAgIULF2LRokXKDYaqnaTgOZD/CALNLjV6nsKk/uU3KCirx44mBMZ/QKDuBgCQ5FyCJGsXUBADCI0h0OoLaI+U9hY0Xg9J8lgAudUae10Tn52CJ+IEtDN1rdHzTLr6fbn7Y1++kPv6hsjj2BN9rtz36qtrY02bGRAJhIjJSsSd9CdVjrO+eJ6ajsjEF/BxdqjR8wxau7Xc/U9T0ip1vK8G9oC+liaSMsUw09N9k9DqnbjkdEQ9f4GOHg41ep4x87eUu/9ZYtk5TU4T41p4DO5GxSM2KR1JqWLkFxTCwlgPnVs6om/H5tDR0sDCqX2RmvESl8MeV3P0dUt8Qhqio5PR1tupRs8z+b0N5e6Piys7py9SxLh58wnCH8QhPj4NL15kIj+/EGZm+ujQvgl8e7aAtrYGvpg9AKlpWQgOkd/7pyFJiEtDdFQivDs2rdHzTBv1a7n7456lyH3d1NwAAJCemoULZ+7i1rXHiH+eioKCQjR1tcaItzvC3sEMbTs1xf/WjMXn722GRCKp9vjrkoTnqYh5lACvLi41ep73B/5Q7v64p/KvlcwsX+U0RYyLJ//F7eBHiH+WgoKCQjRpboPhk7vA3skC3l1csXjdJMx++3fm9HkKYh4mwKtrzV7/Tu+7stz9cTHyc2pqZQjgVU6P38btfx4h/ukLFBYUokkLWwyb0g32TSzg3a0ZFm2Ygjljfm3wOQWAhGcpiHkYD69uzWr0PNP9VpS7X6G8/n0Lt68+lOY1vxBN3OwwbGo32De1hHf35li08T3MGfVzvckrC3ZENUSSuRaSvDAg77a0R5rIFgLzoJo9aX4Vh2HovF1UrMvyhyR9bondktxLQN5NCAyXQ6DuCYnOeCDrzzeNts7ZHHkK4ekxCM+IQUpuJqy0jLGn8/waPWeUOK5K70vKSUdSTnq5bYbYdoRIIJ3K9GQDHAr72i+BVxH2NA5hz+KRLM6CrZEBznw2pUbPGZFQfb1UezZzQu8WzkjOzMKGC6H4sl+3ajt2XfXHoSu4GxWHu1HxeJGeBWszA/z1/dQaPeejZ1XL6bPEVPT95He5++5GAUHXH+JgUBg2zB8NdTURpo/o3CALdlu2XUT4/Vjcvx+LlNQsWFoaYvf2mu1J+fhxUpXeFxubihGj1srfeT8WFy89wNFjN/HT6rehri7ClEldG2zBbtv6IDy4+xz37zxD6gsxLK2NsO3ozBo95+NHCVV6X1JCOtYsOYLTR28iL6/k9CAP7j7HmeO38e3Pb8OjdWN4tG6Mnv08cObv29URcp2y/ecAPAiLwYOwp0hNzoSlrTG2nP2yRs/5JCK+Su9LikvDjwv2I+DgtdI5DXuKs3/dwNKNU+Du7Qh3b0f0HNwKZw7fqI6Q65QdP53Cg9vReHA7BqlJmbCwNcaWC1/V6DmfPKji9W9sGn6atw8BB0KQl/ufnN6OwdlD17BkyzS4t3WCe1sn9BjSBmcPXauOkOucHWtO4sGtaDy4HS3Nq50xtlyq2R6yVc9rKn76ci8C9gfLz+vBUCzZNh3u7Zzg3s4JPYZ64ezB+nFvw4IdUQ2RZP6k7BAUJtAeBgCQFIohyVgqv9HL/ZBovwWBhjcEuu9BkrUZQP14cqGoP6OqZy4zVdHHWjoMpFBSiFNx15UcjfKsPXtF2SFUma6GOhYM7AkA+O7keQhf9dZu6NYfqjs5VeQB8N2oOITcjUYnT0c0d7CEtqY6Xubk1XxwKmTz1ovKDkFhiuQ0/H4srt94jPbtmsDFxQpaWurIzm5YOQWAbb8HKTsEhVU0n15Odh7WLjuG9Xs/AAB06dWiYRbs1p5WdggKq2g+vZzsPKxddBC/H50FAPDp49EgC3bb11TPvK+1oaL59HKy8/Dzgv1Yd0I6HN+nn2eDLdhtX31C2SEorKL59HKy8/Dz/H1Yd1o6R7xPf896U7DjKrFEDZ4mBOqvurXn3QQkZU9qLsm5AAAQiMwB9cotaECqxVbbDG6GDgCAW6mRiM+WPwSIVNssPx9YG+rjn8gYHL7ZMOc2ayiKF+g01ERKjISqS/ECnYY6c1ofPH6UgNQUMQDAxs5EydFQdXgSEY/UF9JrY2t7UyVHQ9XhyYM4pCW/ymkj5rS+KJHXxmZKjqb6sGBH1NAJDYu2CysY/lN8v0a7momHasXr3nUAcDK2YT5ZrOs87awwpp0ncvPzsejIGWWHQzXIWF8b3s0bAQBS0rOQJq4/kyk3VEZGOmjdqjEAIDU1C+kNeOLz+kb9VfG1sJ7Mn0SAurp0UFp9mROLADUN6eeUOa1f1F59/0oK609eOSSWqB4RGG8G1FsAAl2gMAMoeAhJznkgazcgKWM+M0lWsQPoV3CCov0CtSYNbECscqxqNQ3OBrbQEWkiMz8bj8XxCE4Ox1/PriIz/2WVj9vbSlqwe1mQi6CEhjdkR9k2ThwONxsL6GhoICM7B48Sk3Eh4gn2hNxGenZOhe9XEwrxzRBfiIRCrD8fgqgk9pBUtp8/HwFXBwvoamkgIysHUc+TcSXsMQ4E3kZGVsU5/S91NRHMjXTRzq0x3unfFoZ6WgCA3acb3nAsZVm5fDScna2go62BzMxsPIlORnBIJI4cu4HMzCrkVF0EU1M9eLV2wNjRHWBgoA0A2F9Phu3UFct/mYCmzayhrasJcUY2nkQmIvTKQxzbH4rMNyycNnG1gu6rz2pMFedApMr7dtNUNG1hI8tp9MN4hF54gL/3/IPM9KpfKwFAk+Y20NV/ldPIxOoIlxSwdOv7aOpmK81p+ktpTs/fx/GdV948py1soasv/f6NqeKcllQ1S7dPR1N3u6K8RsQj9Fw4ju+4/OZ5dbOFrkH9y2uD7mGXlZUFfX19CAQCvP322xW2Dw4OhkAggEAgwNq1pScTDgsLw7Rp0+Ds7AwdHR3o6+vDzc0Nn376KR4/flzlOBctWiQ7b3mCgoJk7YKCgkrt7969OwQCAbp37w4AePjwIaZPnw4nJydoa2vDwcEBU6ZMwZMnJVeK/PfffzF58mQ4OTlBS0sL9vb2mDFjBhISFPsgnD59Gm+//TYcHR2hra0NAwMDtGzZEnPmzEFsbKxCxyDFCDQ7QyA0hkCgAYHIFAKN9hDqz4bA/Cyg2Uv+mySZkBS8mqxXoxUA9bKPr1FsGKzIptriprJ5m7rAUF0X6kI1GGvoobVxE7zfdAB2d5qLzmZuVTpmSyMnWGtLh+pcSAjDy4LK33jSm+nctDGMdLShoSaCqZ4O2jna47PePjg96130bFbxypdTfLzgamWO6BepWHfun1qImCrS3r0xjPS0oa4mgomBDrya2eOjkV1waOUUdG3dRKFjtGlmh5DNsxCyeRYub/gEh7+fivmT/WBvaQQAOH75HrYdZ3Gntnh7OcLQQBvq6iIYG+uiVctGmDa1O3ZunYHOHZ0VOkZLz0YIPP0lAk9/iVN/z8aubTPw+ax+sLU1BgCcDvgXe/bxM1yb2nRoAgMjHairi2BkoouW3g6Y8rEvtvz1CTp2e7OVL8e+20W2ff70nTcNlRTUprMzDIx1oa6hBiNTPXi2b4J3P++HPwPmoEOvFm907DHTe8i2zx/nA87a0sbHpSinZvrw7NAU784ZgE3n5qGDb9Wuf18b/UHRPdGFv2+9aahUCW26uJbMa8emePfLgdh08St08HN/o2OP/tBXtn3h2M03jFR1NOgedjo6Ohg6dCi2b9+OQ4cOQSwWQ1dXt8z2O3fuBACIRCKMHj26xL5ly5bhq6++QmFhYYnX7969i7t37+K3337D+vXr8c4771T/L1IFAQEBGD58ODIyMmSvPXnyBJs2bcLRo0dx7tw5NGvWDLt27cLkyZORk1N0Q//06VOsW7cOx48fx+XLl2FjI79wIxaLMWHCBBw8eLDE69nZ2bh9+zZu376N3377Dbt27cLAgQNr5hdtICR54UBOACR5t4GCeECgDogcIdAeDIFmFwiEhoDRz5CkvA/kni99gJwzgM44CIQmkOi+C4jlrFio7gVodi/6WVD2Z4Xe3KOM57iYdAf30qKRlJsONYEIjXTM4WvVBu1MXaGvroP/ebyDebf/xD/J4ZU6donhsHG8+a9N9+MSEXDvEcKexiEhQww1kRCOZsYY5NkMPs4OMNTWwk9jBmHGjsO4EPFY7jEamRhiRvcOAID/HTmLnPwCue2odkTEJOLc9Ue4ExmLxFRpThtbGaNvx+bo6OEAA10trPhoED5bc6jKq7s+S0zD8i0BuPrvk4ob0xt7FJmAS5cjcC/8OZKTM6GmJoS9nSl8e7VAW28n6OtrYfHCYZi3wL/Kq7vGxqZi9U8nERIaVc3RU1kiI+JxOSgc9+88Q3JiBtTUhLBrbIae/Tzg3bEp9A208fV3o/H1pzsRcvlhpY/v07M5ur4qJDy4+xwXztyt7l+B/iPqfiwuB9zB/dsxeJGQDpGaCHaO5ug5qDW8urhA31AHC356GwtnbEHo+fuVPn7n3u7o0tcTAPDg36e4eDKsun8F+o+o8Oe4cupf3L8djeT4dKipiWDnZI4eQ7zg1dUV+oY6+OrXiVj03iaEnqvc9S8AdO7rgS79WwIAHoTF4CKLsLUi6t5zXDkVhvs3o5GckC79/nWyQI+hXvDq1kya13WTsGjKBoQGVSGv/TzRZUArANJVYy/Wo0Jsgy7YAcD48eOxfft2iMViHD58GOPGjZPbrqCgAHv27AEA+Pn5wcLCQrbv119/xbx58wAA5ubm+OKLL9C5c2cUFBQgICAAK1euhFgsxqRJk2BmZob+/fvX/C9WjufPn2PUqFEwMjLCt99+i3bt2iE3Nxf79+/Hjz/+iISEBEydOhWrV6/GO++8A2dnZ3z22Wfw9PSEWCzGpk2bsG3bNjx58gSzZs3C7t2lV+MpKCjAoEGDEBgYCIFAgDFjxmD48OFwdHREXl4egoODsWrVKkRHR2PEiBG4fPkyvLy85ERLFZGINwOZpXt8Iu8WJNmHINEeA6HhNxAI1ADDpZAk+gIo2aNKkvk7oDUIAqE+BHqzAKEpJFk7gIJngNAI0OoDgd7nAASQSPIgEKgDAq1a+O0aJv+Y89gsZ0Xae+nROBl3DYNsO+DzZm9BTSjC7GYjMe7KMuQW5it0bA2hGrpZSC8+E7JTce1F5W9KqGq2XLmBnwOvlnr99tM4HL55D6O9PbB4iC/UREIsGeqH3qs3yS3GLRrsCy11NRz/9wEuPmQBR5l2nbqOP+SsSHsnMg5/X76HYd09MG+SH9REQsx/tzeGz9mEnLyyP6t3I+MwZv4WANIhsVamBuja2gn9OrXA11P6YN3By/jr/L819vsQ4H8gBFu2lV6R9l54LE4F/ItBA1ph1sy+EImE+HxWP7w98Xfk5pad0/sPYjH5vQ0ApAtLWFgYolPHpujt6445n/fHn5sv4O8TvGGsaQd2XsW29UGlXg//9xkCjt3CgOFe+GT+IIjUhPh0wWBMGvoTcnMU+7sKAPYOZvhs4VAA0sVEvvv6YPlvoDd2cMtFbP85oNTr92/H4Mzh6+g/uj3+73/DIVITYeaSEXjX77tK5dTOyRyzlo0EAGS/zMX3c/ZUW+wk36FN57Hjx9LXv/dvRePMwWvoN7YD/m/pSIjURPhk2ShM6fFtJXNqgVkrxgCQ5nTVZ+WvPErV49Cmc9ghZ6Xh+zejceZAKPqN64j/WzZKmtcVozGl67fIzVF81XS7JhaYtXIsgFd5nbWj2mJXBQ2+YPe6+JaQkICdO3eWWbA7e/Ys4uLiAEiLfK8lJiZi9mzpstA2Nja4evUq7O3tZfs7d+6MwYMHo0uXLhCLxZg2bRqioqKgrl72sMOaFhERAWdnZ1y6dAnm5uay1318fKCuro6VK1fi0qVLGDBgANq3b49Tp05BR0dH1q579+7Izs7Gvn37sH//fiQmJpY4DgCsWbMGgYGBUFdXx+HDh9GvX78S+zt06IAJEyagS5cuuHPnDmbOnIkLFy4o/Ds8ffq03P0NaqitJKP8/S93Q6LuDoHOaAhEVpBo9QGy/yrZpvA5JKn/BxithUCoB+hOhkB3cqlDFWZ8B4HuB9IefBJxNf4SVFxmfvlz6Bx5dhXN9O0w0LYDzLUM0dXcAwHxis1r5WPuDj016fwOp+OuQ8KZCGtNRgVz0+0JDYObrSVGeXvA0kAPfi2ccfR2yaeMw1q3QKcmjZCRnYNlfwfVYLSkiMwK5qY7GBSGFo5WGNrNAxbGeujh3RQnrpT95Dg7Nx+PniXLfg5/koCg6w9x/PI9rJ41DAve7Q0LIz1s+Kt04Zeqh1hcfk6PHLsJFxcrDOzfCuZm+ujq44KAs2X3pMrOzsPjYnOZPYiIx8VLDxBw5g6WLRmJ2Z/1h5mZPrZuv1RtvwOVJs4s/+/qsQPX4NzCBv2HecHMwgA+PZvj7HHFelOZmOlj6U/joaunicJCCX7432FER3Gus5omrmC+wb/3/ANndzv0G9UOZpaG6NzbHYFHbip0bBMLfSz5413o6mmhsLAQa+b7I7oezYmlqirK6fFdV+HsYY9+YzrAzMoQnft6IvDwdYWObWJhgG/+nAod/Vc5/XIvoh/GV0fYVAFxegV53XkFzp726De2I8ysjNC5nwcCD1Uir1umFeV1zm5ER9SvvDboOeyAksNbT548iaQk+RPE7tghrdS+Hkb72p9//omsLOmk/atWrSpRrHutdevWmDt3LgDg2bNnOHToUDX+BlXz008/lSqyAcAHH3wg205KSsIff/xRolj32owZMwAA+fn5uHKlZO+CvLw8rFq1CgDw0UcflSrWvWZsbIyVK1cCAC5evIiHDxXv6WNvb1/uv3btuIJpcZKsol6QJeahKy73IiTJgyHJ2gtJ4YuS78+9gcIXU4Cs7dKCHgAUptVUuKSAv54V3bC3MlZsfiwA6GNVfDgsV4dVNXtDim4Q2znYldhnrKONOX26AgB+OnMZCRksmtcFBwKLek+1cbUrp2XZQu7FYPcpaVF+6tAOaGxtXC2xUdUcKTY3TkvPRlU6xvUbT2SLTbzzdmfY25tUR2j0Bo7tL/qb6NnGQaH36BtoY/kvE2D1ak7C374/jqCT7AWrKv7eU3St5NG24vlhAUDPUBvfbpwKKzvpZ3Ld0iMIOlZ/htfVdcd3Fd13erRTPKdLt0yDlb0pAOD3/x3GuSNcwEmVHN9RLK/tmyr0Hj1DHSzdPr0or4sP4dxf9S+vDb5gB0C24ER+fj727dtXan92drZsHrYhQ4ZAT09Pti8gQNoV28jICCNGjCjzHFOnTi31HmUxMjJCnz595O5zcHCAgYEBAMDT0xPNmzeX265ly5ay7cjIkvO3BAcHy3q4jRo1qtxYunbtKtv+b+GPqlF+sWKo0KrsdgUxkKTPhyShPQoTOqEw0Q+F8a0heTFKOvedWjP5x6Ra91hc9PTITNNQofcYa+jB28QFABCeHoMn4vr1BKo+eJhY1LvKwqDkPJEjvd1hrKuNtJfZSMnKRn8Pl1L/PO2KPt+edlay1010tWvtd6CSIp8X5dTcWK+cluU7f0P6nSsSCtHDS7EFD6hmPHlS9HDXzKzqOb10OQIAIBIJ0dXnzRY7oDdXvFecqYV+he21dTSwdO3bcGgqnSZn869ncXhPcI3FR5UX/bCoV5yZpUGF7bV1NbB0wxQ4uEj/lm5ZcxJ/bb9cY/FR5RXvPWVqVfH1r7auJpZsngYHV2sAwJZVx/HX1tLTHpByRUfEybYVzuvWYnn9/m/8tVnx0Xp1SYMfEgsA7dq1g7OzMyIiIrBjxw5Z77HXjhw5gvT0dAAlh8MC0hVUAWkvuvKGuVpaWsLBwQGPHz+WvUdZnJ2dy11x1tDQEOnp6XBxcSmzjZGRkWy7+MIVABAaWjSJfceOHRWO6/WQY0XExMSUuz82Npa97IqrYIVhuQoTAfxnSIeGt2xTksenjcokQOVz6mfZBmpCEQDgZCwXm1BF5WVVQyTNnaG2Fr4fKb/ncnFj27XE2HbShyvvbNyHYHH5UwlQzajCt69cKRkvZdvWphXfeFLNKe8aqjJS07Jk25YWzKmyVSarGppq+N/qcWjmbgsA2LvlEnZulLOoFylXJZKqoamGxb9NgqundLTUvg1B2PXb2RoKjKqsEt+/GppqWPTHu3BtKe0Jve/3QOz+RbkdZ6gMlcqrOhZtnArXVo0BAPvWncXutadrKjKlY8HulXHjxmHx4sW4fPkyHj9+DAcHB9m+18NhzczM0Lt37xLve/FCOnTQ0tKywnNYWVnh8ePHsvcoi7whrsUJhcIK271uA0gXmCguIaFqczy8HlqsCDu7qg0rarBExboWF1a9V5VAaxAAQCJ5CeTwIkaZHHSLvnOScxQbntz71eqweYX5OKPgnHdUu5pYmMq2OeS1fnCyLcppUmrVc1q8d97LnNw3ioneTONGxXKanFnl45iZFvXiepmt+ATbVDMaORVNFZOcWPb8wCI1Ib7+bjRaejsAAI74h2DDT/X3ZrEua9y02LVSQvk5/WrtBHi2l04xcnTXFWxcebzG46PKa+xcLKfx6WW2E6kJ8dVvk+DZQXoPdGzHZWxacbTG46OqKZnXsu9rRGpCfPX7JHh2fJXXbZewadmRGo9PmViwe2X8+PFYvHgxJBIJdu3aJZtzLiUlBcePS7+wR40aVWYvOkWetkokDWNy9+IFvKCgIJiampbTukjxlXepegl0xsi2JblVHK6h2RcC9VdDpF8eqXixC6pRg2w7yLZvpkaW01LKSdcKzvrSngBXk8ORlqd4gZxqz2hvD9l2yOOSPeJ+Drwqd5XZ4oa1boFlw6VTHsw9cBIHb5Q9GT7VjmHdPWXb18Or3svRt21Rr/eHT+XPt0u1Y9DA1rLtW7fL7/Ffnu7diobBRnKRAqUbMKJoFEHYNfkrcAuFAsxdOgLtfKTD0k8fvYW1y47VSnxUef1Ht5dth4XIv1YSCgX4ctVYtOsmnfYl4NA1/LzoUG2ER1XQb2zR6K2wfx7JbSMUCvDFmrfRtrv0viXgQCh+XrC/VuKjquk3vpNsu9y8/jQBbXu0AAAE7A/Bz1/510p8ysQ57F5xdnaWDaF83aMOAPz9/ZGbK32S/d/hsABgYiKdkFSR4Zzx8fEl3qOo4r3ZCgsLy2wnFqtGb4ziBToNDQ24u7sr9I8FOzm0h0NoFQGhVQQEeh+X3q/mAogqmPBaewwEOtK5BCUFCUBOGU+BhdZlH0O9JQSG30qPUZgCSeYqRaInOfpae+Ncr+9xrtf3mOTYu9R+J10r2GqXX+QeZNsBA22lF6HJOem4kFDxMPs+1kU3IhwOW/2GtW6B8G8+Rfg3n+KjHh1K7XexNEUjk/Ln5Bjt7YGRrwp2CRliBNzlPJHKNNCnBUI2z0LI5ll4b2jp6R2a2JnBzsKo3GMM6+6Bod2kOU1KzUTgtYhSbXq3d4Wutka5x/Ft6yIr/GVkZeP8jYqL9FRan94eCDz9JQJPf4mJE3xK7Xd0MIeNjVG5xxg0oBUG9JMONU9OzsTFSw9KtenZozl0dTTLPU73rs0waIC08JeZmY3LV0r/v0GK8RvUCqeuLcKpa4swYVr3UvsdmlrAxq78a+8Bw73Qb2gbAEByUgYuBd6T227mV4PR1dcNAHAh4C5WLT70RrGTfH7DvHDi/gqcuL8Cb3/kW2q/g4sVrBuVf63Uf3R79B0pvbd7kZCOS6flXyt9smQEuvSVfr9eOBGGH+aWns+c3pzviLY4HrkKxyNXYfwnpa9/HVytYN24/Jz2G9sBfV8VYV8kpOPyqTJyumwUuvSXfk9fPH4Lq+fsltuO3pzvW21x/MlqHH+yGuNnlp4j38HVGtaNzco9Rr9xHdF3jPTa+UVCOi6fkL9C9ycrRqPLgFYAgIt/38Lqz3e9WfB1BHvYFTN+/HgEBwfjzp07uH37Njw9PbFz504A0sUY5M3H5u7ujtjYWNy4cQN5eXll9sBLSEjAkydPZO+pDH39oiETKSkpZfZYu3//fqWOW1Naty568nzq1KlKzWNXr6h7AWqNi34WFFvVT9QI0B5esv3LA1U4hzsEBkuB3H8gyTkH5D8AClMAgRogcoJAewgEmtKbEokkH5L0BYDkpdxDCYz/ACRiSLKPAHnhgCQLEFlBoNkT0B4OgUAdEkkOJKmzgELlDutWFg9DB9jqFP3RMVQvWhTAVscMfYsVxQDgRBUKYy4GdpjdbCRupjzCP8nhiBTHIi0vCyKBEI11LOBn1QZtTaW9MvILC/B9uD+yC8sfHieEAL5W0s9lWp4YV5Lk34g0VG0a2aCxqZHsZ2OdogUaGpkaYVjrFiXaV6XXmpuNJb4Z4od/omJwIeIxHsQnITUrGyKhAE7mJhjcsjk6N5V+X+QXFGLhXwF4mZdftV+I0NLZBvaWRrKfjfSKcmpnYYSBPiVzevRi5XPa3MEC8yf3xrV7MbgcFoWHT5OQlpkNkVAIB2tj9OvUHB3cHQBIc7pscwCyc0vndHgPT8yb5IdzNx7ixv1neBL7Apkvc6CtqY7GVibo2dYZPi2lK+EVFkqwakcQ0sXZlY63rnN3s4OtbdHfUUODopza2hihT2+PEu1PnpJ/wV8eFxcrzJ7VDzduPkFwSCQioxKRnv4SIpEQjexN4evrhrZejgCAgoJC/PDjCWTLGco6aEBrfDazLy5ejsDt2zGIefoCYnEOtLTUYW9vgm5dXNHh1Sp4hYUS/PxrADIyGl5OAcCtVSPYFFsh19CoaDoWG3sT+A1qVaL96SM3K30O5+Y2mPXVYNwKjULI5YeIehiP9DRpXu0dzNCrvye8OkiHQhbkF+LHpUfl5nXap73Rd4j0b2nUw3js+vMCGjmal2pX3ONHVZsmpi5z83KATbFimoFx0bWSTWNT+A3zKtH+9MHKr1jv7GaLmUtG4NY/kQg9fx9RD+KQkSqGUCSEvZMFeg5uDS8faa/kgvwC/LTwAHJels7pe18MQJ8RbQEAUfdjsef3s7BvUn4HgicRDW/BLjdvxxJFF8PiOXUwg++r/4avBewPqfQ5mrrbY+aykbh19RFCz93D4/txSE8RSz+nTSzQc6gX2nSRXv8W5Bfgp/n+yHlZ+vp36rxB6P2qUBt1Pxa7fz0D+6blT1315IHi86jXJ27ejrB2KJZXk//k9a3/5NW/Cnn1sMPMFaNx68pDhAbdw+PwWKSnZhXldZg32nQtlte5e+Xn9avB6D1KWqyNCn+O3b8ENJi8smBXzJgxYzBr1iwUFBRgx44dMDExwfnz0glkx48fL3fYq6+vL06fPo3U1FTs378fY8aMKdUGADZu3CgbEuvrW/pJUXkcHR1l26GhoWWu8Lprl2pUmX18fGBiYoIXL15g3bp1+PTTT2UrzzYkAp1REPy3KPd6n4Y3BBolizuFVSnYARAI1ADNzhBodi6zjaTwBSRp8yqYd04AgUYbCDTayD9GQSwkaV8AuQ13Nd8BNu3Rz6at3H2eRo7wNHIs8VpVCnYAoCYUwdvUBd6mZS/8kporxnf39uJyUsWFBm8TF9lKsmfjbyJfUlDBOxqWkV7uGNbGTe4+r8a28GpsW+K1qg4zVRMJ0blpY1lhTp4U8UvMP3QKgeHsQfUmhnbzwEAf+Tlt5WKLVi4lc1qVgh0gzWl798Zo7152TlMzXuKbTadw/mbZOdXV1kD/Ti3Qv1OLMtukZWbj+x1nceJKeJViresG9G+Jvv8pyr3m4W4PD3f7Eq9VpWAHSFds9fZyhLeXY5lt0tKysHLVcVy+UnYvWB0dTfT2dUdv37If0qanv8TaX04j4GzDHbreb2gb9P5PUe4191aN4N6q5CiCqhTsAOm8R206NEGbV4U5edJSs/DD/w7j6nn5D8B9ehZ9Ph2bWuLXHe9XeN7eXosqHWtd1/ettvAb7i13n5uXI9z+89mqSsEOAERqIrTp7Iw2ncteNTstRYzV8/1x9az8B5U+vYs+n46u1vj54CcVnrev6xeVD7aO6zOqPfzekn/96+btCDfvkjmtSsEOeJVTHxe08Sn7+jfthRhrvtyDf87ckbvfp2/RNBSOrtb4+cisCs/bz+mzygdbD/QZ0wF+I+Uv0ujW1glubZ1KvFaVgh3wKq9dXGUFV3nSXmRizZw9+CegrLy2lG07NrPBz8cqzlm/xp9WPlgVxIJdMRYWFvD19cXJkyexa9cumJqayoagjhs3Tu57Jk+ejMWLFyMrKwufffYZOnfuDHv7kheNt27dwrffSocT2traYujQoZWKq3PnzlBTU0N+fj5Wr16N3r17lyoeLl++vMTqrMqkpaWFzz//HPPmzUNcXBzGjBmDffv2QVdXV277jIwMbNmyBR999FEtR1oP5AShMG0uBOqtAPUWgNAMEBoBEACFqUB+OCQ5F6S99yTlT4wtyVgOaPYENLwAoQUg1H91jEhIck4DL/2lve6oRl1NCseKu3vgZtgYTfVtYaKhDwN1HQggQHpeFh5lPkdw8n0cjw1BVkGOQsd8vdgEAJyMrdqFMb2Zcw+iMO/gKbSyt0YLawuY6unASFsLAoEAaS+zER6XiIsRj3Hgxl2IuaBAnXDpVhT+t/EkPJvawKWROUwNdGGopwUIBEgXZyMiOhFXwh7j6MU7EGeXndMF646jnVsjeDe3R1M7M5gY6sJYXxt5+QVIzXyJR0+TcCXsMU5cCUdGlmKfeaqaf4If4bvv/0aLFjZwbmoJYyNdGBhoQyAA0jOy8SgyASEhkThxKgxZWWXndOmyv+DVxgGtWjWGk6M5jI11YWSog/z8AqSlvURkVCJCQiMRcPYOMjOZ05oWcjECqxYfRnNPOzR1tYaxiS70jXQgAJCR/hKRD+IReuUhTh25iSwx81EXBJ8Lxw/z9qF5q8Zo0sIGxqZ60pwKBMhIy0JkeCyuXbiP0weuMad1REjQPaz+Yg+atW6MJm62MDbVh77xq5ymZiEq/DlCz4UjYH8Isvi9WWeEBN7D6tm70KyNA5q42cHYTA/6xroQCCDN673nCA0KR4B/MPNaBoGkoayEoKDt27djwoQJAAAjIyOkpqaidevWuH79epnv+fXXX/Hhhx8CkBb9vvzyS3Tq1AkFBQUICAjAypUrkZmZCYFAgKNHj6J///6ljvG6ALdw4UIsWrSo1P6xY8di927p+PuBAwfiww8/hKWlJaKjo7FlyxYcPHgQHTt2xJUr0t5PgYGB6N69e4ljdO/eHefOnUO3bt0QFBRU5u/j4OCAJ0+eYOLEidi8eXOZ7cqLuaCgAH369MGZM2cAAI0aNcL06dPRsWNHGBkZISMjA/fv30dQUBAOHToELS0tJCVV3yTaT58+lRVOn1xzgJ0Na9P1RY87Q5QdAtWA+CDbihtRnaIfw8uL+kjvGQvK9Y36i4Y5HLe+E2bKnwKF6i4Bp8qonwrKnqOe6o7s/AwEPd8IAIiJiYGdnV21HJdVjP8YOnQodHR0kJWVhdTUVADyF5so7oMPPkBqaioWLFiAhIQEzJpVuuutpqYm1q9fL7dYp4g1a9bg2rVriIiIwNGjR3H0aMllqUeNGoVp06ZVerhtTRGJRDhy5AimT5+OrVu3Ijo6GvPmzSuzPRecICIiIiIiIiKS4iqx/6Gnp4chQ4p68AiFwjLnpStu3rx5uHHjBt577z00adIE2tra0NXVRfPmzfHJJ58gPDwc77zzTpXjsrS0xD///IMvvvgCzs7O0NTUhImJCbp27Ypt27Zhz549EIlEVT5+TdDW1saWLVsQGhqKGTNmwM3NDYaGhlBTU4ORkRFatWqFKVOmwN/fH/fucRJ8IiIiIiIiIiKAQ2KpnuKQ2PqLQ2LrJw6JrX84JLZ+4pDY+odDYusnDomtfzgktp7ikNh6oaaGxLKHHRERERERERERkQphwY6IiIiIiIiIiEiFsGBHRERERERERESkQliwIyIiIiIiIiIiUiEs2BEREREREREREakQFuyIiIiIiIiIiIhUCAt2REREREREREREKoQFOyIiIiIiIiIiIhXCgh0REREREREREZEKYcGOiIiIiIiIiIhIhbBgR0REREREREREpEJYsCMiIiIiIiIiIlIhLNgRERERERERERGpEBbsiIiIiIiIiIiIVAgLdkRERERERERERCqEBTsiIiIiIiIiIiIVwoIdERERERERERGRCmHBjoiIiIiIiIiISIWwYEdERERERERERKRCWLAjIiIiIiIiIiJSISzYERERERERERERqRAW7IiIiIiIiIiIiFQIC3ZEREREREREREQqhAU7IiIiIiIiIiIiFcKCHRERERERERERkQphwY6IiIiIiIiIiEiFsGBHRERERERERESkQliwIyIiIiIiIiIiUiEs2BEREREREREREakQFuyIiIiIiIiIiIhUCAt2REREREREREREKoQFOyIiIiIiIiIiIhXCgh0REREREREREZEKYcGOiIiIiIiIiIhIhbBgR0REREREREREpEJYsCMiIiIiIiIiIlIhLNgRERERERERERGpEBbsiIiIiIiIiIiIVAgLdkRERERERERERCqEBTsiIiIiIiIiIiIVwoIdERERERERERGRCmHBjoiIiIiIiIiISIWwYEdERERERERERKRCWLAjIiIiIiIiIiJSISzYERERERERERERqRA1ZQdAVBPy8/Nl27Hx+eW0pLomJzFD2SFQDchLT1V2CFTNcsUSZYdANSA7O0/ZIVA1K8jNUXYIVAOEednKDoGqmSCf9zT1UkGhsiOgapBdIJZt51fjZ5UFO6qXEhMTZdsd+j9VYiRU/dYrOwAiIiIiIiKiUhITE+Hg4FAtx+KQWCIiIiIiIiIiIhUikEgkHLNC9U52djbCwsIAAObm5lBTq7+dSWNjY9GuXTsAQHBwMKytrZUcEb0p5rR+Yl7rH+a0/mFO6yfmtf5hTusf5rR+aih5zc/Pl43y8/DwgJaWVrUct/5WMahB09LSQtu2bZUdRq2ztraGnZ2dssOgasSc1k/Ma/3DnNY/zGn9xLzWP8xp/cOc1k/1Pa/VNQy2OA6JJSIiIiIiIiIiUiEs2BEREREREREREakQFuyIiIiIiIiIiIhUCAt2REREREREREREKoQFOyIiIiIiIiIiIhXCgh0REREREREREZEKYcGOiIiIiIiIiIhIhQgkEolE2UEQERERERERERGRFHvYERERERERERERqRAW7IiIiIiIiIiIiFQIC3ZEREREREREREQqhAU7IiIiIiIiIiIiFcKCHRERERERERERkQphwY6IiIiIiIiIiEiFsGBHRERERERERESkQliwIyIiIiIiIiIiUiEs2BEREREREREREakQFuyI6pjr16/j22+/Rb9+/WBvbw9NTU3o6enBxcUFkyZNwoULF5QdIlVSeno6du/ejc8++wzdunVD06ZNYWhoCA0NDVhYWKB79+747rvvkJycrOxQqRrMmTMHAoFA9i8oKEjZIZGCiuetvH/du3dXdqhURUlJSfjuu+/QuXNnWFlZQVNTEzY2Nmjfvj1mz56NK1euKDtEqkD37t0V/qzye7juyc3NxcaNG9G3b19YW1vLroNdXV3x7rvv4urVq8oOkSopOzsbv/76K3r16gVzc3NoaGjA1tYWAwYMwJ49e5QdHhWTkJCAo0eP4uuvv0a/fv1gZmYm+x6dNGlSpY934sQJDB8+HHZ2dtDU1ISdnR2GDx+OEydOVH/wdZRAIpFIlB0EESmmW7duOH/+fIXtJkyYgA0bNkBDQ6MWoqI3FRAQAD8/vwrbmZmZYfv27ejTp08tREU14datW/D29kZ+fr7stcDAQBZ46giBQKBQu27durEAUAft27cPM2bMKPfhyJAhQ3Do0KHaC4oqrXv37jh37pzC7YVCIaKjo2Fra1uDUVF1iImJwYABAxAWFlZuu08//RSrVq1S+DublOf+/fsYMmQI7t+/X2abvn37wt/fH7q6urUYGclT3mdq4sSJ2Lx5s0LHkUgkmD59OtavX19mm2nTpmHdunUN/nOspuwAiEhxz549AwDY2Nhg5MiR6NKlCxo1aoSCggJcuXIFq1atwrNnz7Bt2zbk5+dj586dSo6YFGVvb48ePXrAy8sL9vb2sLa2RmFhIZ4+fQp/f38cOHAASUlJGDx4MEJCQuDp6anskKmSCgsL8d577yE/Px8WFhZISEhQdkhURTNmzMAHH3xQ5n7eVNQ9W7duxeTJk1FYWAgLCwvMmDEDPj4+MDExQVxcHB49eoQjR45AXV1d2aFSBf7880+IxeJy29y9exejR48GAPTq1YvFujogPz+/RLHO09MTs2bNgqurKzIyMnDx4kWsWrUKYrEYq1evhrW1NWbPnq3kqKk8iYmJ8PPzQ0xMDABg5MiRmDhxImxsbPD8+XNs2bIF+/btw4kTJzB27Fj89ddfSo6YirO3t0fz5s1x6tSpSr/3q6++khXrWrdujTlz5qBJkyZ49OgRvvvuO9y4cQPr16+Hubk5lixZUt2h1y0SIqozBgwYINmzZ48kPz9f7v7ExESJi4uLBIAEgOT8+fO1HCFVRVn5LO7gwYOyvA4fPrwWoqLqtnr1agkASbNmzSRz586V5TMwMFDZoZGCXuds4cKFyg6FqtHdu3clmpqaEgCSLl26SFJTU8tsm5OTU4uRUU2ZM2eO7PO8bds2ZYdDCvD395flrGPHjnKvnUJDQyXq6uoSABJjY2NJXl6eEiIlRX344YcV/l39+uuvZW32799fuwFSKV9//bXkyJEjkri4OIlEIpFERUXJ8jNx4kSFjhERESFRU1OTAJB4e3tLsrKySuwXi8USb29vCQCJmpqa5OHDh9X9a9QpnMOOqA45evQoRo0aBZFIJHe/mZkZVq1aJfvZ39+/tkKjN1BWPosbOnQomjVrBgAKDYsm1RITE4MFCxYAAH777TcOVydSIR9//DFycnJgZmaGAwcOwNDQsMy2/OzWfYWFhdixYwcAQE9PD8OHD1dyRKSIS5cuybbnzp0r99rJy8sLAwcOBACkpKQgPDy81uKjyikoKJB9Dhs3biy7Rvqvr7/+Go0aNQIALFu2rNbiI/kWL16MgQMHwtLSssrHWL16tWxqmLVr10JbW7vEfh0dHaxduxaAtGftmjVrqnyu+oAFO6J6pvhcWI8ePVJeIFTtXg+zy87OVnIkVFkffPABMjMzMXHiRM5XR6RCwsPDcebMGQDARx99BDMzMyVHRDXtzJkzsilG3nrrLejo6Cg5IlJEbm6ubNvJyanMdk2aNJFt5+Tk1GhMVHURERFITU0FAPj5+ZX58FokEsnmeQ4NDcXjx49rKUKqCRKJBIcPHwYANGvWDB06dJDbrkOHDnB1dQUAHDp0CJIGvOwCC3ZE9UzxCxqhkB/x+uLevXu4efMmAMh62lHdsHfvXhw9ehQmJiZYuXKlssMhomL27dsn2x45cqRsOyUlBREREVydux7aunWrbPudd95RYiRUGS4uLrLtyMjIMtu9flgtEAjg7Oxc43FR1bx48UK2XVFvreL7OcqkbouKipI9MOnWrVu5bV/vf/r0aYMu1PJunqieKb4yGgs7dVtWVhYiIiLwww8/oEePHigoKAAAfPLJJ0qOjBSVmpoqy9eKFStgbm6u5IioOuzbtw+urq7Q1taGvr4+nJ2dMXHiRAQGBio7NKqkq1evAgAMDQ3RvHlz7NixAy1btoSJiQlcXFxgZmYGJycnLF68GJmZmUqOlt5UZmYmDh48CABo1KgRezzXIWPHjoWBgQEA6d/T19dExd24cQPHjh0DAIwZM0bWnlRP8cWZ0tLSym1bfP/du3drLCaqeffu3ZNtV3SfWnx/8fc1NFwllqgeKSwsxPLly2U/jxo1SonRUFVs3rwZkydPLnP/559/jvHjx9diRPQm5syZg7i4OHTq1AlTpkxRdjhUTf57w/Dw4UM8fPgQW7duxdChQ7F58+Zy50Ej1fE6lw4ODvj444/xyy+/lGoTFRWFRYsWwd/fHydPnoSNjU1th0nVZP/+/bIVZCdMmACBQKDkiEhR5ubm2Lx5M8aPH49Lly6hbdu2mDlzJlxcXJCZmYlLly5h1apVyM3NRatWrfDDDz8oO2QqR9OmTaGuro68vLwKe80V3x8dHV3ToVENer0iMADY2dmV29be3l7u+xoa9rAjqkdWr16N4OBgAMCwYcPg7e2t5IiourRq1QpXr17FypUreYNRR1y8eBEbNmyAmpoa1q1bx7zVAzo6OhgzZgz++OMPXLhwATdu3MCpU6cwf/58mJqaApDOtTJkyBDk5eUpOVpSxOthWeHh4fjll19gZGSEdevWISEhAdnZ2QgJCUG/fv0AAP/++y9GjhyJwsJCZYZMb4DDYeu2YcOGITQ0FFOmTMHNmzcxceJEdOzYEX5+fli0aBF0dHTwww8/4OLFi7CyslJ2uFQOXV1d9OrVCwBw+/Zt7Nq1S267Xbt2ISwsTPZzRkZGrcRHNaN4/vT09MptW7wXZkPu4c6CHVE9ce7cOXz55ZcAAAsLC/z2229KjoiqYujQoQgLC0NYWBiCg4Oxa9cuDBs2DDdv3sT48eNx9OhRZYdICsjNzcW0adMgkUjw6aefwsPDQ9khUTV49uwZdu3ahalTp8LHxwetWrWCn58flixZgjt37qB169YApN/H/A6uG173tsrJyYFIJMLx48fx/vvvw9zcHJqamvD29sbRo0dlRbvLly/jwIEDygyZqujp06cICgoCIJ3QvPicaFQ35OXlYefOnThy5IjcSejj4+Oxa9cuWZ5JtS1evBhqatIBfxMnTsSSJUsQHR2NvLw8REdHY8mSJZg4cWKJ1blfvnyprHCpGhRfOK+iVdc1NTVl2w057yzYEdUDd+7cwbBhw5Cfnw9NTU3s3bv3jZbbJuUxMjKCu7s73N3d0bZtW4wZMwYHDhzA1q1bERkZiSFDhmDz5s3KDpMq8O233+LevXto1KgRFi5cqOxwqJoYGRmVuc/S0hL+/v6yC9C1a9fWUlT0JrS0tGTbI0eOlLtinVAoLLFgTFk9QUi1bd++XdY7cuLEiUqOhipLLBbD19cXS5cuRXJyMubMmYN79+4hJycHaWlpOHXqFHx8fBASEoJBgwbhxx9/VHbIVIF27dph48aN0NDQQF5eHhYsWIDGjRtDQ0MDjRs3xoIFC1BYWIhVq1bJ3qOvr6/EiOlNFf+bW3yhRHmKr/Ksra1dYzGpOhbsiOq4qKgo9O7dGykpKRCJRNi1a1eFq+5Q3TNhwgTZUKyPPvoIKSkpyg6JyhAeHo5ly5YBkBZtinfpp/rNyckJfn5+AKTz2j1//lzJEVFFit/8ve5FJ4+bmxtsbW0BACEhITUeF1W/bdu2AZD22hg9erSSo6HKWrhwoWwus40bN2LFihVo1qwZNDQ0YGBgAD8/PwQGBqJHjx6QSCSYNWsWbt++reSoqSLvvPMOgoODMXLkyBLfx0KhEL169cKlS5dKLA5jbGyshCipuhTPcUXDXF/3gAcqHj5bn3HRCaI67Pnz5/D19cXz588hEAiwadMmDBs2TNlhUQ0ZMmQI9u7dC7FYjOPHj2PcuHHKDonkWL16NXJzc+Hk5ISsrCzs3r27VJt///1Xtn327FnExcUBAAYNGsQCXx3XokUL2SqFz5494wIFKs7e3l72+VNkAuxnz54hISGhNkKjahQaGipbYGTgwIG86a9jJBIJ/vzzTwCAi4tLmT0k1dTU8M0338DHxweFhYX4888/sXr16toMlaqgZcuW2Lt3LwoKChAbG4vs7GzY2NhAR0cHALBz505Z2xYtWigrTKoGxf/OPn36tNy2xReaKL4ARUPDgh1RHZWUlAQ/Pz9ERkYCkPbk4QTK9Zu5ubls+8mTJ0qMhMrzugt/ZGQkxo4dW2H7b775RrYdFRXFgl0dJ29eJVJdbm5ush5zBQUF5bZ9vf/1nEtUdxRfbILDYeue+Ph42QIxr+cKLYuXl5dsOzw8vEbjouolEonkPji5ePGibLt9+/a1GRJVs+IF14o+n8X3N2/evMZiUnUcEktUB6WlpaFPnz6yp8XLly/Hhx9+qOSoqKY9e/ZMtt2Qu4YTqbLX38sA2LuuDujatats+9GjR+W2ff2A7PXQWKob8vLyZD2dzc3Nyx36TKqpeJE8Pz+/3LbFV+hmcb3uy83Nhb+/PwDpd2+nTp2UHBG9CUdHR9m10blz58pt+3oIvK2tLRwcHGo6NJXFgh1RHZOVlYUBAwbg+vXrAID58+fjiy++UHJUVBv27dsn2+aqo6pr8+bNkEgk5f4rvhBFYGCg7PWGfEFSH0RGRuL06dMApPPZsbCj+gYPHgx1dXUAKHf113PnziE5ORkA0KVLl1qJjarH8ePHkZiYCAAYN24cizh1kImJCQwMDAAAV65cKbdoV7wI4OjoWOOxUc368ccfZZ/f6dOnQyQSKTkiehMCgQBDhgwBIO1Bd/XqVbntrl69KuthN2TIEAgEglqLUdWwYEdUh+Tm5mLYsGG4dOkSAOCTTz7BkiVLlBwVvanNmzeXWOZcntWrV+Pvv/8GADg4OMDHx6c2QiOiV44cOVLuTWJ8fDzeeustWe8O9nquG0xNTTF16lQAwOnTp+XOOZmRkYGZM2fKfn7//fdrKzyqBsWHw3LqkLpJKBRiwIABAKTzNy9dulRuu5SUlBIPsQcOHFgr8VHVRUdHl7nvyJEjmD9/PgDA2dkZn3/+eW2FRTVo5syZsgcnH3/8MV6+fFli/8uXL/Hxxx8DkPaSLf73tyESSDjZClGdMWLECFkPgJ49e2LNmjXlPnHQ0NCAi4tLbYVHVeTg4ICMjAyMGDECPj4+aNKkCfT09JCRkYGwsDDs2LFDVqTV0NDAsWPH4Ovrq+So6U0sWrQIixcvBiDtYVd8BTRSTQ4ODsjLy8OIESPQsWNHODg4QFtbG0lJSQgKCsK6detkPbB8fHwQEBAATU1NJUdNikhMTIS3tzeio6OhpqaG6dOnY/jw4TAwMEBYWBhWrFghe9I/Y8YM/Prrr0qOmBSVkpICa2tr5OTkwN3dHWFhYcoOiaooPDwcXl5eyMrKAiBdpGnixIlwcnJCdnY2rl69ijVr1sgKQL169UJAQIAyQyYFGBgYoGPHjhg5ciTc3NygoaGBx48fY9++fdizZw8A6cqwZ86cqXD+Qqp5Fy9exMOHD2U/JyUlYfbs2QCAzp07yx6AvTZp0iS5x5k7dy6WL18OQDov5RdffIEmTZrg0aNHWLFiBW7cuCFr9+2339bAb1J3sGBHVIdUtjtw48aN8fjx45oJhqqNg4ODQotI2NnZYdOmTfDz86uFqKgmsWBX9yj6OR0xYgQ2bNgAIyOjmg+Kqs29e/cwePDgEjci//Xuu+9i3bp1siG0pPrWrVuHGTNmAAC+++472Y0l1U0BAQEYO3YskpKSym3Xs2dP+Pv7czXgOkBPTw9isbjM/S1atMD27dtZrFMRkyZNwpYtWxRuX1apqbCwEO+99x42bdpU5nunTJmC9evXQyhs2INCOYkDEZGSnTlzBgEBAQgMDMS9e/cQHx+P5ORkaGlpwdLSEq1atcLAgQMxatQo2RL3RFS7tmzZgnPnzuHKlSuIjIxEUlIS0tPToaenB3t7e3Tq1AkTJ05Ex44dlR0qVUHz5s1x8+ZN/Pbbb/D390dERAQyMzNhYWGBzp074/3330ePHj2UHSZV0rZt2wBIV58cP368kqOhN+Xr64vw8HBs3LgRx48fx507d5Camgo1NTVYWVmhbdu2GDduHAYPHtyg57yqSzZs2IBTp04hODgYsbGxyMzMhLm5OTw9PfHWW29hwoQJfEhSDwmFQmzcuBEjRozA+vXrERISgqSkJJiZmaFt27Z4//33uUDQK+xhR0REREREREREpEIadv9CIiIiIiIiIiIiFcOCHRERERERERERkQphwY6IiIiIiIiIiEiFsGBHRERERERERESkQliwIyIiIiIiIiIiUiEs2BEREREREREREakQFuyIiIiIiIiIiIhUCAt2REREREREREREKoQFOyIiIiIiIiIiIhXCgh0REREREREREZEKYcGOiIiIiIiIiIhIhbBgR0REREREREREpEJYsCMiIiIiIiIiIlIhLNgRERERERERERGpEBbsiIiIiIiIiIiIVAgLdkRERERERERERCqEBTsiIiIiIiIiIiIVwoIdERERERERERGRCmHBjoiIiIiIiIiISIWwYEdERERERERERKRCWLAjIiIiIiIiIiJSISzYERERERERERERqRAW7IiIiIiIiIiIiFQIC3ZEREREREREREQqhAU7IiIiIiIiIiIiFfL/g1jUVMWLUAcAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 241, + "width": 630 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "fig, ax = plt.subplots()\n", + "im = ax.imshow(results.T)\n", + "ax.set_yticks(np.arange(len(results.columns)))\n", + "ax.set_xticks(np.arange(len(results.index)))\n", + "ax.set_yticklabels(results.columns)\n", + "ax.set_xticklabels(results.index)\n", + "for i in range(len(results.index)):\n", + " for j in range(len(results.columns)):\n", + " text = ax.text(i, j, results.round(2).values[i, j],\n", + " ha=\"center\", va=\"center\", color=\"w\")\n", + "ax.set_title(\"Ultimate to Incurred Ratio\")\n", + "fig.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_elrf_resid.ipynb b/docs/gallery/plot_elrf_resid.ipynb new file mode 100644 index 00000000..a2304ef1 --- /dev/null +++ b/docs/gallery/plot_elrf_resid.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ELRF Residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example replicates the diagnostic residuals from Barnett and Zehnwirth's\n", + "\"Best Estimates for Reserves\" paper in which they describe the Extended Link\n", + "Ratio Family (ELRF) model. This `Development` estimator is based on the ELRF\n", + "model.\n", + "\n", + "The weighted standardized residuals are contained in the ``std_residuals_``\n", + "property of the fitted estimator. Using these, we can replicate Figure 2.6\n", + "from the paper.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\jboga\\miniconda3\\envs\\cl_docs\\Lib\\site-packages\\chainladder\\core\\pandas.py:364: RuntimeWarning: Mean of empty slice\n", + " obj.values = func(obj.values, axis=axis, *args, **kwargs)\n" + ] + } + ], + "source": [ + "raa = cl.load_sample('raa')\n", + "model = cl.Development().fit(raa)\n", + "\n", + "plot1a = model.std_residuals_.T\n", + "plot1b = model.std_residuals_.mean('origin').T\n", + "plot2a = model.std_residuals_\n", + "plot2b = model.std_residuals_.mean('development')\n", + "plot3a = model.std_residuals_.dev_to_val().T\n", + "plot3b = model.std_residuals_.dev_to_val().mean('origin').T\n", + "plot4 = pd.concat((\n", + " (raa[raa.valuation < raa.valuation_date] * \n", + " model.ldf_.values).unstack().rename('Fitted Values'),\n", + " model.std_residuals_.unstack().rename('Residual')), axis=1).dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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0bt26KnXWJDU1VR999JFsNps6dOiggQMHqkOHDjp9+nS1yRJ1sdvteumll5ySRYKCghQbG6uAgADl5uYqJSVFaWlpeuGFFxQREdHgY9SlvLxcL7zwgpKSkmQ2mzVgwABFRETo/Pnz2r9/v/Ly8iRdmBXknXfe0d13393kMdSlcoJQQECA0/uUlBQ988wzRjKLt7e3+vbtq27dusnb21u5ubnav3+/LBaLjh07pj//+c968skn65W48vbbbxsJK7169VLv3r3l7e2tnJycRs080xx9F++9956++uor472/v7/i4uIUFBRkJD4dO3ZMixcvrrNfEgCApkDSCgAAtejbt6+8vb1ls9kkSQcOHKj2Ye1f//qXkbASERGhu+++u8pyJuXl5Vq1apXeeecdWSwWLVu2TIMGDaoyZenYsWP10UcfSbowg0pdD4fr1q0zZnaJj4+vdkaP1atXGwkrfn5+uvXWWzVp0qQqM6kkJydryZIlKigo0K5du/TVV1/phhtuqPX4NbFarXrllVeMhJWgoCA98MADGjx4sFO53bt3629/+5uKi4t1+vRpvfLKK3rqqafqnOWlImHl2muv1axZs+Tj42NsKygo0JIlS4xknnfffVeDBg1S9+7da63zu+++k9Vq1ZVXXqlbb71V7dq1M7YdPXpUTzzxhE6dOqXi4mJ9+umnWr9+vUwmk+655x5NnDjRqfNh8+bNevnll2Wz2ZSUlKSUlJQaO8U++ugjI2ElODhYd9xxh0aNGlUlYWjTpk167bXXVFJSoh9++EHx8fFVkh0q++STT2SxWHT99dfrF7/4hdM5nTlzRi+++KLxOb3//vsaPXq003mMHz9e48ePV2JiopE8ER0drTvvvLPW4zaF8ePH6/3335fdbtfatWurTVpZu3atpAtLetU2y1B1/vGPfxidPu3atdNvfvObKklfGRkZevnll3XixAmVlZXplVde0fPPP19l1FZl7vK5f/fdd7JYLJowYYJmz57tNFPUuXPn9Nprr2n9+vWSLiyBdtVVVzH9LwAAAACPs3//fuN1fWZHqUm/fv2MpJXc3Fzl5+crLCysxvIfffSRysvLdfPNN+uGG25w6uuoPMtqffzwww9OCSs33XSTpk+f7lRvXl6eXnnlFaWmpjYqMaYumzZtksVi0dChQ/Wb3/zGaQlsm82m//3vf0ZCxKpVq3TDDTfUudRRU3Nsb0lOxy8sLNRLL71kJKxcfvnluu2226os5V1YWKh//etf2rJli0pKSvTyyy/r2WefrXVwV35+vr7//nuFhYXpwQcf1MCBA522N6bNm7rvYu/evVq+fLnxfuzYsbrrrrucEntOnz6tJUuWaPfu3U7LagEA0FyY/xsAgFq0a9fO6aG1YlYCR/v27TO+OA8LC9OiRYuqJKxIF2ZBmTp1qu666y5JF5JYKtYMduT4xfv27durLDtTWUVnSeV9K5SWluq9994zYnjsscc0derUapNCBg0apD//+c9GAsiXX36pc+fO1Xr8mvz00086fPiwpAsjVv7v//6vSsKKJA0ePFiPP/64sT5wZmam8SV6baxWq6ZMmaLbb7/dKWFFkkJDQ/XYY4+pd+/eki50Cnz44Yf1qjMhIUF33nmnU5KBdGGWi9tvv914/+WXXyo/P1/33HOPJk2aVGW0zMiRIzVx4kTjfU3ndPLkSX322WeSLoxsWbBggcaMGVNtJ8ioUaOcpmb96KOPnKZxrY7FYtGNN95YJQlHurAE1oMPPmj8/MSJE0pPT6+1PlcKDQ1VfHy8pAudKhWjtSpUzIAjSZdcckmVDqbaJCcna+fOncb7hx56qNpZiqKiojRv3jyj8yY/P18rVqyos353+dwtFosuv/xyzZ07t8rSZu3atdO9995rdLCWlZVpx44dzRIHAAAAALQkx+fJnj17Nrqeyvvm5ubWWt5ms+nmm2/Wz3/+8yr9MJX7Mupis9mMQU6SdNVVV2nmzJlV6u3UqZP+7//+TxEREY1KkqiLxWLRwIED9Yc//KHKc7i3t7duvfVWY3kau92uDRs2NHkMtSkrKzNmTZYuzGTrOEPK+++/b/TvTZo0SQ899FC1/QkdO3bUI488ori4OEnSkSNHtGnTplqPXV5ernbt2mnevHlVElakhrd5c/RdVAwOki70yd1///1VZqIJDg7Wo48+qj59+shqtTYoZgAAGoOkFQAA6uD44Fbd+sOOoxNmzZpV7UwnjhISEowZP3bv3u20dI4kdenSRf369ZN0oSPAccmYyo4dO6aDBw9KuvAF9IgRI6qU+fHHH424ExIS6pwCt0ePHpowYYIkqbi4WLt27aq1fE1WrVplvJ4yZYr69u1bY9no6GhdccUVxvv6jOLw9/fXL3/5yxq3+/r66rbbbjPeb926tcpnXZnZbK61zuHDhzt1MERGRta6JI3jLCgZGRnVlvnmm2+MmXKuv/76OmeDGTRokJH8c/ToUWVmZtZaPigoSNOnT69xe8eOHXXppZfWGWdLqfh87Xa7fvrpJ6dtP/30k9HR0tClgRyvz8suu0zDhg2rsWznzp31s5/9zHi/cuXKOpOF3OVzN5vNmj17do3bfX19dfnllxvv3SlpCQAAAACaQklJiTGDrqQqCf0NUXnfM2fO1Fo+NDS00TPYVrZr1y6dOnVK0oVZdGfOnFljWX9/f918881NctzqzJ492xh8VJnJZHIaxOPKfoa8vDwtXrxYJ0+eNH42bdo043VRUZEx+CsgIEBz5syptT4vLy/NmjXLeF+5X6I6V155Zb2Wva6Ppu67cFzOWpLmzJlT48wxvr6+ToO3AABoTiStAABQB8elMiqmDq1gs9m0Z88eSRdGk4waNarO+kwmkzFKw263Oz0sVnCcMcVxJpXKKmZ4kS4kVFS3rIfjiIy6lpKpMGjQION15SlV66OsrMypU2LSpEl17uNYJiMjQ2VlZbWWHzZsWJWRIJXFx8cbs0jYbLY6z2XgwIEKDg6ucbuvr6/TetAjR46stb5evXoZrx07TBw5to9j8kBtGtI+l112mXx9fWstExkZabyuKc6WMnLkSOO6drzeJWnNmjWSLnTGVZewVRvHtccdO9Nq4rj806lTp3Ts2LFay7vL5x4TE1NnIp1jHHWNEgQAAACA1qZy/8LFLIlaed/K/USVjRw5ssbkjoZyfI699NJL6+wTGTFiRJ3PpY0RERFR68AkSerTp4/xuqmfM8+cOaN///vfTv+99tprWrhwoR544AHt27fPKBsTE+OUtLJnzx5j9plLL720XtdCv379jJlS69NHVt++nfpo6r6LitlqpQuzs9Q1cCouLq7W5a8AAGgqVdcFAAAAThw7N/z9/Z22HT582Fg+x8fHx1iGpy6OCR2VlzyRLjzgvvPOO7LZbEpOTlZBQUG1U5U6LjlT3dJAkpySYtauXautW7fWGV9+fn61r+vr0KFDxuwhfn5+TskbNenTp4/atWunc+fOqby8XIcPH9aAAQNqLF8xG01tTCaToqOjjXM4dOhQrckN9Zki2HE9YMfpZesqW11HVnFxsXJycoz3y5cvr7LMUHWys7ON13W1T30+e8eRYnUtR+Vq7dq108iRI7VmzRodPXpU6enpio6OVnp6uo4ePSrpQidg5SV4alNQUOC01Fdt11mFoKAgdevWzThmZmZmrZ077vK51yeODh06NHscAAAAANBSKicm1DVIpjaV963cT1RZXckdDVGxBLN0Ycbauvj6+qpnz55NPtNJffpOmvM5s7S0VN99912d5caOHau77rrLacZcxz6ynJwc/fvf/27Qsc+ePauysrIak128vb3r9RxeH83Rd3Ho0CHjdWP61QAAaC4krQAAUAfHh+vK08BWTMsqXei4qM9Dc2XVLTnUoUMHDR48WDt27JDdbtf69et13XXXOZVJTU3ViRMnJF1Ya/aSSy6pUk9ZWZlTskTlmSoaG19dHJfhCQsLq3GqUUdeXl7q1KmT8WBdXFxca/lOnTrVKxbHESF1LQ9U1yglSU4jpOoq71jWcSriCo7Xj1S/ZZEqq6t96nNOjutfVxdnS5swYYIxq8ratWsVHR3tdC03dGkgx+vA19e31tl1HIWHhxvXZ1NcS6743Bt6Tbtj+wMAAADAxQgICJC3t7fxvFPXkj61qbxvXUsNBQUFNfpYlVXua6mP0NDQJk9accfnTLPZrPbt2ysiIkL9+/fXhAkT1Lt37yrlHPthMjIyGvXZnD17tsaklcDAwCabWac5+i4c3zemXw0AgOZC0goAALUoKytzGk1QeZmNphgtUtPD+/jx47Vjxw5JF5YIqpy04riO7pgxY6p9KG7O+GrjOPKoIdPuOs6WUdcUu/WdWaO25Z0uVn1mRamNK9rnYmN0B3FxcerUqZPy8vK0fv163XLLLcYsQ506dTKW26ovx+uzITO0OJata2SeJ3zuAAAAAOApOnXqZAz8ycrKanQ9lfcNDw+vtXxTLs/TmGfZhjzz1ldLP++Gh4dr6dKljdq3ufthWrq9K5et3HdRMVu0VP9YL2Y5LQAA6oukFQAAanHw4EFjmRtJ6t+/v9N2xwfBPn366Nlnn22yYw8bNkz+/v4qLS1VZmamsrOzjeVorFarNm7caJStaWmgyg+1b7/9dr1GxFwsxwfahky76/jwXNcUu45la1Pb8k4tzfFzat++vd56660WjMZ9mUwmjRs3Tp999pmKi4v15ptvGjPxjB8/vsEdZo6fe32vo8pl6bQBAAAAgNZjwIABRtJKenp6o+s5cOCA8To8PNyls1A05lm2Ic+8bYFjP9mcOXM0bdq0Foymds3Rd+F4/ufPn69XfReznBYAAPVV91z9AAC0YRs2bDBem0ymKuvHOs68cuLECacEl4vl6+urkSNHGu8dZ1bZtWuX8aV9165da1zLuH379k5r9+bk5DRZfLVxnP42Pz9fdru9zn3Ky8uVl5dnvHdc/7g6jmXrW66uOl3NcWrXs2fP1rnkTFs2YcIE43V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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 935, + "width": 1110 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(13,10))\n", + "fig.suptitle(\"Barnett Zehnwirth\\nStandardized residuals of the Extended Link Ratio Family (ELRF)\\n(Fig 2.6)\");\n", + "\n", + "\n", + "plot1a.plot(\n", + " style='.', color='gray', legend=False, ax=ax00,\n", + " xlabel='Development Month', ylabel='Weighted Standardized Residuals')\n", + "plot1b.plot(\n", + " color='red', legend=False, ax=ax00)\n", + "plot2a.plot(\n", + " style='.', color='gray', legend=False, ax=ax01, xlabel='Origin Period')\n", + "plot2b.plot(\n", + " color='red', legend=False, ax=ax01)\n", + "plot3a.plot(\n", + " style='.', color='gray', legend=False, ax=ax10,\n", + " xlabel='Valuation Date', ylabel='Weighted Standardized Residuals')\n", + "plot3b.plot(color='red', legend=False, grid=True, ax=ax10)\n", + "plot4.plot(kind='scatter', marker='o', color='gray', \n", + " x='Fitted Values', y='Residual', ax=ax11, sharey=True);\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_exponential_smoothing.ipynb b/docs/gallery/plot_exponential_smoothing.ipynb new file mode 100644 index 00000000..013af358 --- /dev/null +++ b/docs/gallery/plot_exponential_smoothing.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Attachment Age Smoothing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This simple example demonstrates how a Tail ``attachment_age`` can be used to\n", + "smooth over development patterns within the triangle. Regardless of where\n", + "the ``attachment_age`` is set, the patterns will always extrapolate one year\n", + "past the highest known lag of the `Triangle` before applying a terminal tail\n", + "factor.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "raa = cl.load_sample('raa')\n", + "\n", + "results = pd.concat((\n", + " cl.TailCurve().fit(raa).ldf_.T.iloc[:, 0].rename('Unsmoothed'),\n", + " cl.TailCurve(attachment_age=12).fit(raa).ldf_.T.iloc[:, 0].rename('Curve Fit')\n", + "), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " 1987\n", + "1977 3000000.0\n", + "1978 3000000.0\n", + "1979 3000000.0\n", + "1980 3000000.0\n", + "1981 3000000.0\n", + "1982 3000000.0\n", + "1983 3000000.0\n", + "1984 3000000.0\n", + "1985 3000000.0\n", + "1986 3000000.0\n", + "1987 3000000.0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Raw premium data in pandas\n", + "premium_df = pd.DataFrame(\n", + " {'AccYear':[item for item in range(1977, 1988)],\n", + " 'premium': [3000000]*11})\n", + "\n", + "# Create a premium 'triangle' with no development\n", + "premium = cl.Triangle(premium_df, origin='AccYear', columns='premium')\n", + "premium" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Create some loss triangle\n", + "loss = cl.load_sample('abc')\n", + "ultimate = cl.Chainladder().fit(loss).ultimate_\n", + "\n", + "loss_ratios = (ultimate / premium).to_frame()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 413, + "width": 565 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "fig, ax = plt.subplots()\n", + "plt.stem(loss_ratios.index.astype(str), loss_ratios.iloc[:, 0])\n", + "ax.grid(axis='y')\n", + "for spine in ax.spines:\n", + " ax.spines[spine].set_visible(False)\n", + "plt.show();" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_extrap_period.ipynb b/docs/gallery/plot_extrap_period.ipynb new file mode 100644 index 00000000..4618dbcf --- /dev/null +++ b/docs/gallery/plot_extrap_period.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Extrapolation Period Sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates the ``extrap_periods`` functionality of the `TailCurve`\n", + "estimator. The estimator defaults to extrapolating out 100 periods. However,\n", + "we can see that the \"Inverse Power\" curve fit doesn't converge to its asymptotic\n", + "value.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "tri = cl.load_sample('clrd').groupby('LOB').sum().loc['medmal', 'CumPaidLoss']\n", + "\n", + "# Create a fuction to grab the scalar tail value.\n", + "def scoring(model):\n", + " \"\"\" Scoring functions must return a scalar \"\"\"\n", + " return model.tail_.iloc[0, 0]\n", + "\n", + "# Create a grid of scenarios\n", + "param_grid = dict(\n", + " extrap_periods=list(range(1, 100, 6)),\n", + " curve=['inverse_power', 'exponential'])\n", + "\n", + "# Fit Grid\n", + "model = cl.GridSearch(cl.TailCurve(), param_grid=param_grid, scoring=scoring).fit(tri)\n", + "\n", + "# Plot results\n", + "results = model.results_.pivot(columns='curve', index='extrap_periods', values='score')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 459, + "width": 583 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "ax = results.plot(\n", + " ylim=(1,None), ylabel='Tail Factor',\n", + " title='Curve Fit Sensitivity to Extrapolation Period');" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_glm_ldf.ipynb b/docs/gallery/plot_glm_ldf.ipynb new file mode 100644 index 00000000..c3120718 --- /dev/null +++ b/docs/gallery/plot_glm_ldf.ipynb @@ -0,0 +1,118 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GLM Reserving" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how you can use the `TweedieGLM` estimator to incorporate\n", + "the GLM framework into a ``chainladder`` workflow. The specific case of the Over-dispersed\n", + "poisson GLM fit to incremental paids. It is further shown to match the basic\n", + "chainladder `Development` estimator.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "genins = cl.load_sample('genins')\n", + "\n", + "# Fit an ODP GLM\n", + "dev = cl.TweedieGLM(\n", + " design_matrix='C(development) + C(origin)',\n", + " link='log', power=1).fit(genins)\n", + "\n", + "# Grab LDFs vs traditional approach\n", + "glm = dev.ldf_.iloc[..., 0, :].T.iloc[:, 0].rename('GLM')\n", + "traditional = cl.Development().fit(genins).ldf_.T.iloc[:, 0].rename('Traditional')\n", + "\n", + "# Plot data\n", + "results = pd.concat((glm, traditional), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 481, + "width": 547 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "ax = results.plot(kind='bar', title='LDF: Poisson GLM vs Traditional');" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_ibnr_runoff.ipynb b/docs/gallery/plot_ibnr_runoff.ipynb new file mode 100644 index 00000000..5ead12ef --- /dev/null +++ b/docs/gallery/plot_ibnr_runoff.ipynb @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IBNR Runoff" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All IBNR models spin off several results triangles including ``inbr_``,\n", + "``ultimate_``, ``full_expectation``, and ``full_triangle_``. These can be\n", + "manipulated into a variety of formats. This example demonstrates how to\n", + "create a calendar year runoff of IBNR.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Create a triangle\n", + "triangle = cl.load_sample('genins')\n", + "\n", + "# Fit a model\n", + "model = cl.Chainladder().fit(triangle)\n", + "\n", + "# Develop IBNR runoff triangle\n", + "runoff = (model.full_triangle_.cum_to_incr() - triangle.cum_to_incr())\n", + "\n", + "# Convert to calendar period and aggregate across all accident years\n", + "cal_yr_runoff = runoff[runoff.valuation > triangle.valuation_date]\n", + "cal_yr_runoff = cal_yr_runoff.dev_to_val().sum(axis='origin')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 556 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plot results\n", + "ax = cal_yr_runoff.dropna().T.plot(\n", + " kind='bar', legend=False,\n", + " title='GenIns: IBNR Run-off', alpha=0.7,\n", + " xlabel='Calendar Year', ylabel='IBNR');" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_industry_to_company.ipynb b/docs/gallery/plot_industry_to_company.ipynb new file mode 100644 index 00000000..39039c03 --- /dev/null +++ b/docs/gallery/plot_industry_to_company.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Making Predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how you can create development patterns at a\n", + "particular ``index`` grain and apply them to another.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Industry to Company LDF Diff
1988
1989-203
1990-4,401
1991-5,781
1992-6,286
1993-4,793
1994-6,653
1995-8,327
1996-7,170
1997-273
" + ], + "text/plain": [ + " Industry to Company LDF Diff\n", + "1988 NaN\n", + "1989 -202.830662\n", + "1990 -4400.765402\n", + "1991 -5781.419855\n", + "1992 -6286.251679\n", + "1993 -4792.896607\n", + "1994 -6652.981043\n", + "1995 -8327.246649\n", + "1996 -7169.608047\n", + "1997 -273.269090" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample('clrd')['CumPaidLoss']\n", + "clrd = clrd[clrd['LOB'] == 'wkcomp']\n", + "\n", + "industry = clrd.sum()\n", + "\n", + "allstate_industry_cl = cl.Chainladder().fit(industry).predict(clrd.loc['Allstate Ins Co Grp']).ultimate_\n", + "allstate_company_cl = cl.Chainladder().fit(clrd.loc['Allstate Ins Co Grp']).ultimate_\n", + "\n", + "diff = (allstate_industry_cl - allstate_company_cl)\n", + "\n", + "output = diff.rename('development',['Industry to Company LDF Diff'])\n", + "output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_mack.ipynb b/docs/gallery/plot_mack.ipynb new file mode 100644 index 00000000..3c582451 --- /dev/null +++ b/docs/gallery/plot_mack.ipynb @@ -0,0 +1,244 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# MackChainladder Example" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how you can can use the Mack Chainladder method." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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LatestIBNRUltimateMack Std Err
198118834.0NaN18834.000000NaN
198216704.0153.95391716857.953917142.931716
198323466.0617.37092424083.370924592.148304
198427067.01636.14216328703.142163712.853921
198526180.02746.73634328926.7363431452.090330
198615852.03649.10318419501.1031841994.987807
198712314.05435.30259017749.3025902203.838469
198813112.010907.19251024019.1925105354.340512
19895395.010649.98410116044.9841016331.543044
19902063.016339.44252918402.44252924565.775709
\n", + "
" + ], + "text/plain": [ + " Latest IBNR Ultimate Mack Std Err\n", + "1981 18834.0 NaN 18834.000000 NaN\n", + "1982 16704.0 153.953917 16857.953917 142.931716\n", + "1983 23466.0 617.370924 24083.370924 592.148304\n", + "1984 27067.0 1636.142163 28703.142163 712.853921\n", + "1985 26180.0 2746.736343 28926.736343 1452.090330\n", + "1986 15852.0 3649.103184 19501.103184 1994.987807\n", + "1987 12314.0 5435.302590 17749.302590 2203.838469\n", + "1988 13112.0 10907.192510 24019.192510 5354.340512\n", + "1989 5395.0 10649.984101 16044.984101 6331.543044\n", + "1990 2063.0 16339.442529 18402.442529 24565.775709" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the data\n", + "data = cl.load_sample('raa')\n", + "\n", + "# Compute Mack Chainladder ultimates and Std Err using 'volume' average\n", + "mack = cl.MackChainladder()\n", + "dev = cl.Development(average='volume')\n", + "mack.fit(dev.fit_transform(data))\n", + "\n", + "plot_data = mack.summary_.to_frame(origin_as_datetime=False)\n", + "plot_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 591 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "ax = plot_data[['Latest', 'IBNR']].plot(\n", + " kind='bar', stacked=True, ylim=(0, None), grid=True,\n", + " yerr=pd.DataFrame({'latest': plot_data['Mack Std Err']*0,\n", + " 'IBNR': plot_data['Mack Std Err']}),\n", + " title='Mack Chainladder Ultimate',\n", + " xlabel='Accident Year', ylabel='Loss');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_munich.ipynb b/docs/gallery/plot_munich.ipynb new file mode 100644 index 00000000..60ea5850 --- /dev/null +++ b/docs/gallery/plot_munich.ipynb @@ -0,0 +1,140 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MunichAdjustment Basics" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how to adjust LDFs by the relationship between Paid\n", + "and Incurred using the MunichAdjustment.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Load data\n", + "mcl = cl.load_sample('mcl')\n", + "\n", + "# Traditional Chainladder\n", + "cl_traditional = cl.Chainladder().fit(mcl).ultimate_\n", + "\n", + "# Munich Adjustment\n", + "dev_munich = cl.MunichAdjustment(paid_to_incurred=('paid', 'incurred')).fit_transform(mcl)\n", + "cl_munich = cl.Chainladder().fit(dev_munich).ultimate_\n", + "\n", + "plot1_data = cl_munich.to_frame().T.rename(\n", + " {'incurred':'Ultimate Incurred', 'paid': 'Ultimate Paid'}, axis=1)\n", + "\n", + "plot2_data = pd.concat(\n", + " ((cl_munich['paid'] / cl_munich['incurred']).to_frame().rename(\n", + " columns={'2261': 'Munich'}),\n", + " (cl_traditional['paid'] / cl_traditional['incurred']).to_frame().rename(\n", + " columns={'2261': 'Traditional'})), axis=1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see how the Paid / Incurred Ultimates stay close to `1.0` for all origin periods under the `MunichAdjustment` while they diverge under the traditional `Development`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 607, + "width": 862 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plot data\n", + "fig, (ax0, ax1) = plt.subplots(ncols=2, sharex=True, figsize=(10,5))\n", + "plot_kw = dict(kind='bar', alpha=0.7)\n", + "\n", + "plot1_data.plot(\n", + " title='Munich Chainladder', ax=ax0, **plot_kw).set(\n", + " ylabel='Ultimate', xlabel='Accident Year')\n", + "plot2_data.plot(\n", + " title='P/I Ratio Comparison', ax=ax1, ylim=(0,1.25), **plot_kw).set(\n", + " ylabel='Paid Ultimate / Incurred Ultimate', xlabel='Accident Year');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_munich_resid.ipynb b/docs/gallery/plot_munich_resid.ipynb new file mode 100644 index 00000000..b9eb32fd --- /dev/null +++ b/docs/gallery/plot_munich_resid.ipynb @@ -0,0 +1,141 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MunichAdjustment Correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how to recreate the the residual correlation plots\n", + "of the Munich Chainladder paper.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Fit Munich Model\n", + "mcl = cl.load_sample('mcl')\n", + "model = cl.MunichAdjustment([('paid', 'incurred')]).fit(mcl)\n", + "\n", + "# Paid lambda line\n", + "paid_lambda = pd.DataFrame(\n", + " {'(P/I)': np.linspace(-2,2,2),\n", + " 'P': np.linspace(-2,2,2)*model.lambda_.loc['paid']})\n", + "\n", + "# Paid scatter\n", + "paid_plot = pd.concat(\n", + " (model.resids_['paid'].melt(value_name='P')['P'],\n", + " model.q_resids_['paid'].melt(value_name='(P/I)')['(P/I)']),\n", + " axis=1)\n", + "\n", + "# Incurred lambda line\n", + "inc_lambda = pd.DataFrame(\n", + " {'(I/P)': np.linspace(-2,2,2),\n", + " 'I': np.linspace(-2,2,2)*model.lambda_.loc['incurred']})\n", + "\n", + "# Incurred scatter\n", + "incurred_plot = pd.concat(\n", + " (model.resids_['incurred'].melt(value_name='I')['I'],\n", + " model.q_resids_['incurred'].melt(value_name='(I/P)')['(I/P)']),\n", + " axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 500, + "width": 871 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plot Data\n", + "fig, ((ax0, ax1)) = plt.subplots(ncols=2, figsize=(10,5))\n", + "\n", + "paid_lambda.plot(x='(P/I)', y='P', legend=False, ax=ax0)\n", + "paid_plot.plot(\n", + " kind='scatter', y='P', x='(P/I)', ax=ax0,\n", + " xlim=(-2,2), ylim=(-2,2), title='Paid')\n", + "\n", + "inc_lambda.plot(x='(I/P)', y='I', ax=ax1, legend=False);\n", + "incurred_plot.plot(\n", + " kind='scatter', y='I', x='(I/P)', ax=ax1,\n", + " xlim=(-2,2), ylim=(-2,2), title='Incurred');\n", + "fig.suptitle(\"Munich Chainladder Residual Correlations\");" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_ptf_resid.ipynb b/docs/gallery/plot_ptf_resid.ipynb new file mode 100644 index 00000000..c36112fe --- /dev/null +++ b/docs/gallery/plot_ptf_resid.ipynb @@ -0,0 +1,148 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PTF Residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example replicates the diagnostic residuals from Barnett and Zehnwirth's\n", + "\"Best Estimates for Reserves\" paper in which they describe the Probabilistic\n", + "Trend Family (PTF) model. With the \"ABC\" triangle, they show that the basic\n", + "chainladder, which ignores trend along the valuation axis, fails to have iid\n", + "weighted standardized residuals along the valuation of the Triangle.\n", + "\n", + "We fit a \"diagnostic\" model that deliberately ignores modeling the valuation\n", + "vector. This is done by specifying the patsy formula ``C(origin)+C(development)``\n", + "which fits origin and development as categorical features.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "abc = cl.load_sample('abc')\n", + "model = cl.BarnettZehnwirth(formula='C(origin) + C(development)').fit(abc)\n", + "\n", + "plot1a = model.std_residuals_.T\n", + "plot1b = plot1a.T.mean()\n", + "\n", + "plot2a = model.std_residuals_\n", + "plot2b = plot2a.T.mean()\n", + "\n", + "plot3a = model.std_residuals_.dev_to_val().T\n", + "plot3b = model.std_residuals_.dev_to_val().mean('origin').T\n", + "\n", + "plot4 = pd.concat((\n", + " model.triangle_ml_[model.triangle_ml_.valuation<=abc.valuation_date].log().unstack().rename('Fitted Values'),\n", + " model.std_residuals_.unstack().rename('Residual')), axis=1).dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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oUUMPPvigZs2apUuXLtld39mzZzVhwgTdc889CggIUJEiRVSvXj2NHTtWp06dsqsORz6L2nNfCQ8PN8tUq1bNnL579249+eSTqlOnjvz8/FSiRAm1atVK77zzjq5fv27XttjDkdtrzRnPTo5+9k5z5swZvfXWW+rYsaMqVKggHx8flSxZUk2bNtXLL7+s48ePZ1tHXp/bspPZufTjjz+qb9++qlq1qnx8fFS6dGk9+OCD2rx5c7o6bt68qQULFigkJMS83qpVq6ann37a7usjzeHDhzVu3Dg1bdpUpUqVMp9HgoOD9d577ykqKipH9TnrWTssLEyvv/66WrVqpbJly6pQoUIqXbq0WrdurTfeeEPnz5/Pdd0AAABAhgwAAADgDtCzZ09DkiHJGDRoUJ7rq1q1qllfdn8TJ060WXbp0qWGj4+PXcs2adLEOHnyZJaxzJs3zyw/bNgw4+rVq8YDDzyQZb333XefER8fn+12rlu3zihbtmym9RQqVMiYPXu2cfLkSXNa1apVM61v4MCBdm23xWIxnn/+eSM5OTnL+Dp37mwus27dOuPHH380SpQoka6+4sWLp1t2/PjxhoeHR6Yx1K1b1zhy5IgxceLETI9lZsfgdtZ15OSvc+fOmW77kiVLjHLlymVbR+/evY2rV69muR8NwzC+/fZbw9/fP9N6/P39jWXLlhnr1q2zKz57DRs2zKxv3rx5xubNm43KlStnGEN0dLTNsgcOHDCaNGmS7T6oW7eucfjw4SzjmDhxouHl5WXXcXnkkUfs2paszJw50yhUqFCm6yhfvryxefPmbM+ttNizO0et2XsMW7Zsadf+8Pb2Nt57771s13v79ZqRY8eOGfXr17f7Gvnzzz+zXa89/vvf/xrFixfPcl2enp6Zfi5ZH6fs/rI7N26Xl/vN7ft88+bNRsWKFbPcxs8++8yuuBx5/dnrwoULRtu2be3eH6tWrTKXtT7v7fm7/d7rqPtXXo6nPZ8xrtxH1vOys3Tp0izPReu/2bNnp1v+9OnTRmBgoF3LlypVyli5cmW2MeUkfnslJCQYzzzzjF33F29vbyM2NjZdHdbnzMmTJ41ly5Zl+XlVuHBh45dffskyLkc/i9pzX7n9WTE1NdV44403snwWq169uvH3339nue7b909+bG8aZzw7OfrZ2zAMIyUlxZgwYYLh6+ub5bZ7eXkZr7/+upGampppXXl5brPH7fsqLi7O6N+/f6YxWywWY+7cuebyx48fN+rWrZtp+aJFixqbNm3KNo6kpCRj7NixhqenZ5b7LCAgwJg/f75d2+aM8yUhIcEYPXp0tnEWLlzYmDVrVpZ15eQ5FgAAAPASAAAAcAeoVauWOfzLL78oLCwsR92P3W7YsGGKiorSmjVrdPToUUnSvffeq3r16qUr26pVK5vxS5cu6ebNm5KkSpUqqUGDBipXrpz8/PwUFxensLAw7d27V4ZhaP/+/erYsaP279+vwMDAbONKSUlR//79tWbNGhUqVEjt2rVTzZo1lZCQoE2bNpm/VP3999/14osvavbs2ZnWtXPnTvXu3dv8NbfFYlHr1q3VoEED3bhxQ5s3b9aZM2c0ZswYzZgxw679lrZ+Ly8vNWjQQLVr11ZAQIA8PT116dIl7dq1S+fOnZNhGJoxY4Zu3rypTz75xK66t27dqkmTJikpKUmBgYHq1KmTSpUqpUuXLmnfvn02ZceNG6cPPvjAHC9WrJhCQkJUpkwZnTt3TuvWrdOxY8fUs2dPPfjgg3atPyutWrXSM888Y1fZJUuW6PLly5KUaasM06dP10svvWS2DFWsWDG1bdtWlStXVkpKio4cOaJdu3bJMAz98ssv6ty5s7Zu3So/P78M61u+fLmGDh2qlJQUSZKnp6c6duyomjVrKiYmRuvXr1dkZKQGDRqkd955J6ebb7e//vpLzz//vGJiYlSsWDF16tRJFSpUUHR0tDZu3GhTduPGjerTp4/Zuo+Xl5datGihunXrytvbW+Hh4dq8ebMSEhJ07NgxtWvXTtu2bcvwup8xY4ZNKzClSpVSmzZtVL58eVksFl25ckVHjx5VWFiYuY/y4uOPP9Zzzz1njvv4+CgkJESVKlXS5cuXtXbtWkVERKh37956/vnn87y+3Eq7Xn18fNSwYUPVqlVLxYsXl2EYioiI0I4dOxQZGamkpCS98sorkqTx48fnen3Xrl1T165ddebMGUm3uu1q2rSp6tevr6JFiyo+Pl7nzp3TgQMHctTiUnY++OADjRs3zhz38fFR586dVaVKFUVHR2vdunW6cuWKUlJSNGPGDJ06dUpLly61uT7r169vXuM7d+7Url27JEktW7ZMdw/I6b0nL/cba6GhoXrttdcUFxdnttgSGBioc+fOae3atbpx44ZSUlI0evRoNWrUSG3bts20Lkdef/ZKSUlRr169tGfPHnNao0aN1KhRIwUEBCghIUEXLlzQgQMHFBERkW75ihUrmsfo448/Nqdn9tns7+9vM+6o+5ejjmdGXL2P7DV16lSNGzfOvIdZLBbdc889atCggYoWLaorV67o0KFDOnbsmKRbLQ/d7vr162ZLHiVKlFDDhg1VtWpVFS1aVImJiTp58qS2b9+uhIQERUZGqmfPntqwYYPatWuXq5hzIy4uTt27d9e2bdvMaX5+furQoYMqVaokwzB07tw57dmzR1FRUUpKSsr2HrNmzRo9/fTTSklJUZUqVdS2bVv5+/vr5MmTWr9+vZKTk3Xjxg0NGjRIoaGhmbai6cxnUXtNnjxZb775piSpSZMmCgoKkre3t/bv36+9e/dKkk6ePKkHH3xQe/bskbe3d67X5YztdcazkzOevVNSUjR48GAtXbrUnFa+fHm1bt1aZcqUUVxcnHbs2KG///5bycnJeuedd3T58mV99tln2dadk+e23HriiSe0dOlSFSpUSB07dlT16tUVExOjNWvW6MqVKzIMQ08//bTq1q2rOnXqKCQkROfOnVNAQIA6d+5sPtuvWbNGN2/eVFxcnB566CEdO3ZMJUqUyHCdqamp6t+/v5YvX25OK1mypIKDg1WyZEmdOXNG69atU2Jioq5evarhw4crOjo6y2c2Z5wv169fV48ePbRlyxZzWvXq1dWiRQuVKFFC0dHR2rp1q86dO6cbN25o7Nixio2N1euvv25X/QAAAECWXJSgBAAAAOTI7b+SL126tDFt2jTj/Pnzeao3N7+8XL58uTFlypQsW8I4ceKE0aNHD7PuJ554ItOy1i1ZpP1a+v777zfOnj1rUy4pKcl4+eWXbX6Bm9kvphMSEox69eqZZatVq2bs2rXLpkxqaqoxffp0w8PDw6ZllKx+1fzqq68aS5YsMWJiYjKcn5qaaixfvtwoXbq0WV9Wv/q1bi3Dy8vLsFgsxltvvWUkJiam254069atMywWi7nc0KFD08Vz8eJFo3v37uYvt9PK5rZFIXvNnTvXrMfDw8P49ddf05VZvXq1+et7b29v4+233zbi4uLSldu3b5/RoEEDs74xY8ZkuM7Lly8bpUqVMss1bdrUOH78uE2ZmzdvGuPHj0+3PxzdolBaiwvPPPOMce3aNZtyiYmJRkpKimEYhhEREWHza/shQ4akO98N41bLGv369TPLBQUFpWvlIykpyaZViilTpqQ7f9JERUUZX3zxRaat59jzeXD06FGbVg26du1qRERE2JSJjY01HnvssXT7O79bFBozZozx66+/Ztr6WHJysjFv3jyjSJEi5vl44sSJTOvLrkWh6dOnm/MbNGhgHD16NMN6UlNTjZ07dxpjxowxTp8+neW2Zmfr1q02v8K/77770h2PhIQEY9y4cTb3kKlTp2ZaZ06Ph71yc7+x3uc+Pj6Gp6enMXXqVCMpKcmm3OnTp41GjRqZZUNCQjKt05HXX04sW7bMrKt8+fLG9u3bMy0bGhpqvPLKK8aOHTsynG99LO3l6PtXbo5ndsu4eh/Zs8yvv/5qcw/u0qWLERYWlmHZEydOGBMmTMiwtY7w8HBj7Nixxo4dO8x7w+1iYmJsnntq166daVl748+JwYMHm/V5enoakydPzvB+nZKSYqxdu9bo27dvhi0AWreY4+PjYxQpUsT46quv0rX6EhoaatNK04gRIzKNzdHPojltUahQoUKGxWIxatasmeE5uGTJEsPb29ssv2DBgkzXbU+LQo7eXmc8Oznr2XvChAlmuTJlyhjffvtthtfBd999Z9NS1bfffpthfbl5bssJ63MpbRs7depknDp1yqbc1atXjZCQEJv7Vt++fQ1JxtixY9Nda2FhYUaFChXM8pMmTco0hvfee8/m82D8+PE2/0sYxq17Ydr/Cmn7IrPPXGc9az/++ONmuZo1a9q0EJcmOTnZ+OSTT8xnT09PT2Pr1q0Z1keLQgAAAMgJEoUAAABwx3jwwQdtvvSVbiXL1K1b13jssceMmTNnGjt27Ej3AjUrzvxCNTEx0WjcuLEhyfD19TWuXLmSYbnbu7zp2LFjptuQmppq053Qu+++m2G5Tz/91CxTuHDhdF9mW3v33Xdt1p9d9wf22L59u1lfVl3FWb8El2S8/fbb2dZt3SVL9+7dM32JcePGjXTd6jgzUWjdunU2L8WmTZuWrkxKSopRu3Zts8zXX3+dZZ0RERFGmTJlDOlWEseZM2fSlXnttdfM+sqVK2dcvnw50/pGjx5tsz8cnSgkyXjyySezXWbkyJF2l09OTrZ5kbR48WKb+YcOHTLntW/f3mHbktnnwZAhQ8wyQUFBmSbhpKSk2HSZmNW55axEIXstXrzY5mVaZrJLFLLuWiSjl13O0KlTJ3Odbdq0MW7evJlp2X/+859mWX9//wy7CDKMgpsoJMn49NNPMy176NAhM4HDYrFkmkjryOsvJ1566SWzHutuZnLDep84mr33L2ckCrl6H2W3TFJSklGtWjWzTO/evXP0zJVb1veuFStWZFrOkefFqlWrbOpbtGhRruuyToSxWCzGb7/9lmnZX375xSxbtGjRPO9fe59Fc5ooJMkIDAw0zp07l+m6rZO87rvvvkzL2ZMoZC97t9cZz07OePY+efKkmQxbrFixTJPy0qxZs8ass379+hl2QZab57acuP3HHfXr18/0Wen06dPpuvXLKsFr4cKFZrl69eplWCYmJsYoWrSoWe7FF1/MtL6EhASb/6syS7J1xvmyceNGs0ylSpXSJTnf7osvvsj2eiJRCAAAADnhIQAAAOAO8fXXX2vgwIE20wzD0LFjx/TVV1/pueeeU+vWrVWiRAkNHTpUGzZscFGkt3h7e+uRRx6RdKvbjc2bN9u13PTp0+XllXEvwRaLRSNGjDDH07rHud3nn39uDj/33HOqXbt2put76aWXMu3aIrdat25tdlGzZs0au5apWLGi2f1RZo4cOWLTBcjMmTPl4ZHxvzW+vr6aOnWqnRHnzd9//60BAwYoKSlJkvTkk0/qhRdeSFfu559/1p9//inpVlc1aedHZsqVK2fWk5SUpCVLltjMT01N1bx588zxSZMmqVSpUpnW9+6776po0aL2bVQu+Pr66j//+U+WZS5fvqxvvvlGklS8eHFNnz49y/Kenp42XTikLZsmreskSSpdunROQ86R6Oho/fDDD+b4+++/r8KFC2dY1sPDQzNnzsy0+7mCZMCAAeZ5sXr16lzXk5/HQpLCwsJsukb5+OOPVahQoUzLv/POO+b1ERsbq4ULFzo9RkcKCgrSqFGjMp3fqFEjtWzZUtKte6N191VpHH395UR+nx+5lZv7l6MU9H20dOlShYeHS5KKFCmiefPmZfq84kjWzz15+YzKCevnh8GDB+vhhx92SL29e/fWfffdl+n8nj17qly5cpJudX2W1r1dbuX2WdQer7/+uipUqJDp/JEjR5rDmT2vOpo92+usZydnPHvPnDnT7Opq/PjxGXZxaK1Lly7q0aOHpFv3yNu77r2dPc9tefXuu+9m+qxUuXJlm+4EfXx8NGXKlEzr6tevn3x8fCRJx44d07Vr19KVWbhwoeLi4iRJZcuW1dtvv51pfT4+Pvroo4/M8bSui60563yZNm2aOTxlyhTzus/M8OHDzeP/xx9/OLQLVwAAALgn5/83DwAAADhIkSJFtGTJEq1atUrTpk3T6tWrlZycnK5cXFycFi1apEWLFumBBx7Q/PnzVaJECafEdPXqVW3fvl2HDx9WVFSU4uLilJqaas63fsGzf/9+9enTJ8v6atSooebNm2dZpmnTpuZw2gs7a9euXbN5QfzYY49lWZ+Xl5eGDh2qf//731mWu93x48e1e/du/f3334qJidHNmzdlGIY5PyYmRpIUFRWlM2fOqHLlylnW179//2xfOK5bt84cbtmyZbYvTEJCQlSpUiWdPXs2u83JtZiYGPXp00dRUVGSpODgYH3yyScZll2xYoU5bO9Lxy5dupjDmzdv1osvvmiOh4WF6cKFC5JuHcfs6ixevLj69u2bp5f9WenevXu219rq1at18+ZNSbdemNrzMqV169by8/NTfHx8upd+VapUMYfXrl2rsLAw8yW/o23dutWMvWzZsurWrVuW5WvVqqV27dppy5YtToknJw4dOqS9e/cqPDxcsbGx5nakSUtoOnTokFJTUzNNwMuK9bH45JNP9Omnn+Yt6GxYfx7cc889atasWZblixQpoiFDhmjWrFnm8k8//bRTY3Sk2xNlM9K0aVPt3LlTUsb3B0dffzlhfX58+umn6tWrV74kmWTE0fcvRylI+ygjv//+uzk8ZMiQLF+W50RSUpJ27NihAwcO6MKFC7p27ZrN8511MsD+/fsdss6s3Lx5U+vXrzfHx44d67C6s7uOLRaL7rnnHvPeHh4erkaNGmW5jKOfRe2V3bbUq1dPhQsX1o0bN8y4HJGsnNftdcazk7OevXP73PjHH39IuvXcmNW90Z7ntrwoXLiw7r///izLNGrUyEz67dSpU5ZJkr6+vqpZs6aOHDkiwzAUHh6uoKAgmzJr1641hx9++OFMk5TStGrVSkFBQTp06JCkW88GdevWNec743xJTk7WqlWrzDr79++fZZ3Src+GkJAQHT16VIZhaOvWrXrggQeyXQ4AAADITMH5tgEAAACwU7du3dStWzdFRUVpw4YN2rp1q/bu3au9e/eaL/fSLF++XB07dtS2bdtUrFgxh8Vw9uxZvfrqq/r+++/TvXDPjD2//Lz9y+6MBAYGmsO3b68kHTx40Hxh4u/vb1fSRJs2bbItk+bXX3/VhAkTsv2VsrXIyMhsX7RmlyAl2b4gbN26dbblLRaLWrdu7bREoZSUFD388MMKCwuTdCsxZOnSpfL29s6wvHVrSL/++qtdLzytj/GZM2ds5lkvX69ePRUvXjzb+tq0aeO0RCF7jqH1Pjh+/LieffZZu+pOS2SJjo7W9evXVaRIEUlSpUqV1K5dO23dulWxsbFq0aKFHnnkEfXr108dOnRw6HVvvb9btmxpVzJNmzZtXJootGDBAr3zzjs6fvy4XeWTkpIUExOTqxeHgwcP1n//+19J0meffaZdu3Zp2LBh6tGjR7ZJfblh/RnUvn17u5Zp3769mSi0d+9eh8fkTI64Pzj6+suJAQMGaOLEiUpNTdVvv/2mBg0aaMSIEbr//vvVuHHjXCWn5ZSz7l+OUhD2UVa2b99uDoeEhOS5vhs3buidd97RnDlz7G4dIz9a0di/f78SEhIkSX5+fnY9b9jLEddxGmc9i9qjePHi2V4XFotFJUqU0I0bNyTd2pa8JAo5anud8ezkjGfvqKgom3v39OnT7Wql8MiRI+bw7c+Nt7PnuS0v6tSpk+kzcRrr540GDRpkW6d1eetW2NLk9tkgLVHo9mcDZ50v169fl3Qr+WncuHF2xWndMld2xxYAAADIDolCAAAAuGMFBgbqoYce0kMPPSTpVtPwe/bs0ddff625c+eaLyYOHz6sf/3rX/rwww8dst59+/bp3nvvVXR0dI6Wy6h5/NvZ8+Wz9RfuaV1dWbt8+bI5XKlSJbteKtj7EnTSpEmaPHmyXWWt2bPt9nSzYr1t1i0vZMWZL3hffPFFs4WF4sWL6+eff1bJkiUzLX/+/Hlz+Mcff8zx+m4/5wra/rDnGFrvg127duWqO5Lo6GibRIUvvvhCISEhioiIUHx8vObOnau5c+fK09NTQUFB6tSpk+6//3517do1T61zFLT9nRXDMPTEE0/YdJdhr2vXruUqUahbt2564YUXzO6s9u3bZ76wK1WqlNq3b6/g4GD179/fIfvF+nhUrVrVrmWqVatmDt9p3XY44v7gjOvPXvXq1dPUqVP14osvyjAM/fnnn3r99df1+uuvy9/fX23btlVwcLD69etn05qDozjz/uUort5H2bl48aI5XKNGjTzVFR0drS5duuS4haD8OB7W21m5cmWHturkiOtYcu6zqD3s2Q7Jvm2xhyO31xn3cmc8e0dERNiMZ9ZaZVay21/O7uLQnvPE+vrKafns/g9yxLOBM84X63txXFycPv74Y7vqtZbTawEAAAC4nWt/igQAAAA4kIeHh1q2bKmZM2dqz549KleunDnPOnEoL27evKn+/fubX86WLVtWEydO1Pr163XmzBldv35dqampMgxDhmHYvKS37hYhM/a8WMhOXFycOezn52fXMva89F21apXNS9YOHTpo7ty52rdvnyIjI5WQkGBut2EY6ty5s1nWnm3PrmsAyXnblhufffaZmXzm6empJUuWZNtqSlYtA9jj9q72CtL+kOw7hnndB1L6/VC3bl0dOHBAL7zwgk2iVkpKivbv368PP/xQ999/v6pWraq5c+fmer0FbX9nZe7cuTafP71799ZXX32l0NBQRUdHm10tpf1Zv0yz53rNzLRp07R8+XK1a9fOZnpkZKR++uknvfDCC6patar69++vU6dO5Xo9ku3xsHc/W5fLzwQQR3DE/cEZ119OPP/889q4caN69Ohh0zpObGys/vjjD7322muqV6+e7r33XrN1B0dw9v3LkVy1j+xhfc3ktQupZ555xkwS8vHx0dNPP63ly5frzz//NLseSzseJ0+eNJfLj+PhyO28nSOuY2c/i9rDEdthL0dvrzPu5c6oMz8+r+15bsuLnJ4njv4/yBHPBnfqsQUAAACyQ4tCAAAAuCvVr19fU6dO1SOPPCJJSkhI0K5du9SpU6c81bt06VLzhVWlSpW0e/dulS1bNtPyrngRbf1SKz4+3q5l0pq/z8r7779vDo8aNUqffvppluWdse3O2racWr9+vU2XPTNmzFD37t2zXa5IkSLmy4H9+/frnnvuyVMcBWV/5IT1y5MZM2boueeec0i9pUuX1rRp0/Tee+9px44d2rhxozZv3qwtW7aYXVOcP39eo0aN0qFDh3LVwlhB2d/2vOj94IMPzOF///vfev3117Ms78jrtU+fPurTp4/OnTun9evXa9OmTdq0aZPZHYphGPrhhx/MriPr1KmTq/VYHw9797N1OUd2S3encNb1lxMdOnTQ77//rsjISG3YsME8P/bv32+e22vXrlXr1q21atUqu7uOyUpBuH/lhCv2kT2KFStmJmtYvzzPqXPnzmnx4sWSbiXarly5Msvns/w+HtafDXnZTme5E55FHcnR2+uMe7kz6rT+vA4ICKAFGTsVLVrUfNZ2xLOBs49tkyZNctQdJgAAAOAotCgEAACAu9b9999vM357E/65sWbNGnP4hRdeyPJFhaQ8t5iRG9bdCJw5c0aGYWS7zJkzZ7Kcn5KSog0bNki61XLT22+/nW2dp0+fzrZMTllvm731Z7dtOfXXX3+pf//+ZncHY8aMsUkayor1+fLnn3/mOZaCsD9yytH74Hbe3t7q0KGDXn/9da1YsUKRkZH6/fffbVoImTVrVq66XHLW/rbumsWeX4hn90v0M2fOmPu2RIkSGj9+fJblY2NjnfICsmLFinrkkUc0Z84cHT58WGfOnNFbb71lviCLiorSiy++mOv6c3M8rD+TS5Uqlet136mcff3lRKlSpdS/f3/NmDFDe/bs0cWLF/Xhhx+ax+XGjRt6+umn87yegnL/yo382kf2sj5/rFv5yam1a9eazyY9e/bMNok7v5+lrLfzzJkzBa7ljjvhWdSRHL29zriXO+PZ23o7r169atMFFjLn6GcDZ5wv1sf2xIkT+d5yHQAAACCRKAQAAIC7mK+vr824j49PujI5beL+/Pnz5nDDhg2zLb9x48Yc1e8IjRs3NrsruXbtmtmKR1a2b9+e5fzIyEglJiZKksqUKWPzpXlGjhw5osjISDsjtl+TJk3M4exilm61XLJjxw6HrT8mJkZ9+vTRlStXJEldu3bNUcs0rVu3Nof/+OOPPMdjvT+OHj1qV1cG9uw3Z3L0PsiOt7e3evTooZUrVyooKMic/vPPP+e4Luv9vWvXLrte7Nizv61/vR4VFZVt+ey6G7L+nKpbt668vLJuTHjz5s12vdTMq0qVKun//u//bLp/W7lypW7evJmr+po2bWoOb9261a5ltmzZYg43a9YsV+vNrfzsqicz+X395USpUqU0duxYLV++3Jx2+PBhnThxIk/1Ouv+5Yrj6ax9ZK82bdqYw2vXrs11PQX9WapJkybmM2R8fLxDnyMcoaDvP0dz9PY649nJGc/e5cuXV5UqVczxlStXZlsnHP9s4IzzpUmTJub/pbGxsdq2bZtdcQIAAACORKIQAAAA7lr79++3Gbf+sj2NdTJRWgsxWUl7CSBl3/z8nj17ctVqSV4VK1ZMzZs3N8e//vrrLMsnJydr4cKFWZax3u4bN25km1Qwe/ZsOyLNuZCQEHN49+7dOnr0aJbl165dq7Nnzzpk3SkpKRo8eLC5zrp16+q7777LNgnDWu/evc3hhQsX6tKlS3mKqX79+uavkpOTk82uXDITExOjn376KU/rzKsePXqY++yvv/7SL7/8ki/rLVSokLp162aOX7x4Mcd1tGvXToUKFTKXX7VqVZbl//rrL7teUlWvXt0cvv1zKyNLlizJcn5OPqck512vmbG+DpKSkszEu5zq0qWLObxv3z4dOHAgy/I3btywuUasl88POb3fOIOrrr+caNu2rUqWLGmOZ3St5mRfOuv+5crj6eh9ZC/rlhoXL16c64TgnHxGxcfH68svv8zVenLLx8fH5nnjo48+ytf1Z+dOeBZ1JEdvrzOenZzx7C1JvXr1ModnzJiRL0m9dzrre/vixYuVkJCQZfm9e/fq4MGD5rj1tS8553wpXLiwTZzTp0/PsjwAAADgDCQKAQAA4I4wbdo0rV692u7yycnJeuONN8zxsmXL2vwiNE1gYKA5fO7cuWzrrVGjhjmc1ZfA8fHxGjVqlJ3ROt4TTzxhDn/44YdZdjEzderUbLsQCQwMlL+/v6RbX4CndeOSkS1btjgt8aBBgwY2LRo8//zzmbbqkpCQoJdfftlh637++efNFjhKlCihn3/+WQEBATmqo3///qpVq5akW+fIo48+avcL3Li4OF2/ft1mmoeHh0aMGGGOT548OcsXt6+++qri4uJyFLOjVaxYUY8++qg5Pnr0aLuuPUlKTU1N1/VGdHS03V02WHcZkV2rIhkpUaKEHnroIXN83LhxunHjRoZlDcPQc889Z9dLvZYtW5qtk+zYsUNhYWGZlv3kk090+PDhLOurXr26WV9oaKj+/vvvTMt+++23DksWsTdpwPo4eHh42CQ85ES9evVsuiwaO3ZsltfThAkTzOQ8f39/DR06NFfrza2c3m+cwdHXX07Ye35ER0fbfE5ldK3mZF866/7ljOPpqn1kr4ceekhVq1aVdOueNGLEiFx1y2X9LPXrr79mWcdLL72Uq8TOvLLuFnHx4sXZJgfkpzvlWdRRHL29znp2cvSzt3Tr/Pf09JR0K0F+8uTJ2S6T5sKFC3aXvZsMHTrUbKkxIiIiy32WmJiosWPHmuMhISGqW7euTRlnnS+vvPKKObx06VLNnz8/22XSuOuxBQAAgGORKAQAAIA7ws6dO9WtWzc1b95cs2bNUkRERKZlQ0NDdf/999skFr3yyis2v0hOY90V0Y8//mh2T5IZ65YwvvzyS02dOlUpKSk2Zf766y91795de/fuVZEiRbLdNmcYNmyY6tSpI+nWi5MePXpo9+7dNmUMw9DMmTP1+uuvm62kZMbDw0M9e/Y0x0eMGKGdO3emK7dkyRL17NlTKSkpTtv2f//73+bwH3/8occff1yxsbE2ZS5duqQHH3xQ+/fvz3bb7PHpp5+aLQp4eXnp+++/V+3atXNcj6enp2bPnm2+9Fm1apU6deqU5a/fDx48qNdee01VqlTJ8KXSiy++aL4QjoiIUPfu3fXXX3/ZlElMTNSrr76qOXPmOGR/5NU777yj8uXLS7r1Artly5b6/vvvM034OXfunGbOnKl69erp22+/tZn3008/qXbt2nr//fczfemWkJCgGTNmaOnSpeY06/M5J9544w2zu4hDhw6pb9++6V7YXLt2TcOGDdOKFSvs2t/lypUzf8FuGIaGDBmSriWs5ORkTZ06Vf/85z8z7EbRWqlSpcwuplJTUzVw4EAdO3bMpkxqaqo+/vhjPfbYY/L09EzXVWNutG3bVkOGDNGKFSsy/SwNCwvT448/bo7fe++92W5PVt59913zetq0aZP69++frqWuxMRE/etf/9LUqVPNaRMnTlTRokVzvd7cyOn9xlkcef3lxKBBg9SrVy9999136ZIe05w+fVoPP/ywuW9q165tJldas96X9rSw5Yz7lzOOp6v2kb28vLz08ccfm4mIv/zyi3r06JFp637h4eF644030rUI1KVLF/n5+UmS/v77bw0fPlxXr161KRMbG6tRo0Zpzpw5LnmW6tq1qwYOHGiOP/roo3rzzTczbNEmNTVV69atU79+/ezqliiv7pRnUUdxxvY649nJ0c/eklSzZk393//9nzk+efJkDR8+PNPWMlNSUrR69Wo9/vjj+d69ZkHh7+9vs8/effddTZgwId1n9MWLF9WvXz+z5UcvLy9NmTIlwzqdcb507txZw4YNM8dHjhypcePGZdoF7c2bN/XTTz+pX79+euCBB7KtHwAAAMiO/W3kAwAAAAXA3r17tXfvXv3zn/9UtWrVFBQUpFKlSsnb21vR0dE6ePBguhfi/fr1s/m1qLX7779ffn5+io+P14EDB1S/fn0FBwcrICDAfBHWvXt3de/eXdKtbls6d+6sDRs2yDAMvfzyy/r444/VrFkzFS9eXH/++ae2bt2qlJQUVaxYUc8995zGjx/v3J2SAV9fXy1YsED33nuv4uPjdfLkSbVq1Upt2rRR/fr1lZCQoE2bNunMmTOSpPfee08vvPBClnVOmDBBP/30k27cuKHw8HC1adNGbdu2VZ06dZSYmKht27aZiRpPPfWUjh8/nmXLDbnVpUsXvfDCC2Yz/d98842WL1+uLl26qEyZMjp37pzWrl2rhIQEVatWTX379tXMmTPztM53333XHK5Ro4Z++OEH/fDDD9kuV7t2bT333HM207p27arZs2drzJgxSklJ0fbt29WqVSvVrl1bTZs2VYkSJXTjxg1duHBB+/fvz7Z7stKlS2vu3LkaOHCgUlJStG/fPtWrV0+dO3dWjRo1FBsbq3Xr1uny5cvy9vbW22+/7ZJz0lr58uX1008/qWfPnoqMjFRERIQGDhyoMmXKqHXr1ipbtqxSU1MVFRWl0NBQnThxIsuWeU6cOKHx48dr/PjxqlKliho3bqwyZcrIMAxduHBB27dvV3R0tFn+kUceUbt27XIVe/369fWf//zHPK6rVq1StWrV1KVLF1WqVEmXL1/W2rVrFRsbq4CAAD333HN2tQDwzjvvqF27dkpNTdWBAwdUp04d3XvvvapQoYKuXLmijRs36tKlSypatKimTJmS6Wdamrffflvdu3dXamqq9u3bp6CgILVv3141atRQXFycNm3aZCZc/vvf/9Znn32mU6dO5WqfpElKSjJb3ihcuLAaN26sGjVqyN/fX9HR0fr777+1Z88es3zhwoX1wQcf5Gmdbdu21bvvvqtx48ZJkn7++WdVqVJFISEhqly5sqKjo7V+/XqbX//369cv2887Z8jp/cZZHH392Ss1NVUrVqzQihUr5O3trUaNGqlOnToqXry4rl27plOnTmn79u1mwpKnp6c+/PDDDOvq37+/fv/9d0m3Wm/4/fff1bBhQ5uks3/9618qUaKEJOfcv5xxPF25j+zVq1cvTZkyRa+++qqkW118NmjQQPfcc48aNmyookWL6sqVKzbPY7d3q1OiRAm9/PLLevPNNyXduo//9ttvat26tSpWrKiIiAitX79e169fl6enpz755BObF+r55fPPP9epU6e0c+dOpaSkaOLEifrPf/6j9u3bq3LlyjIMQ+fOndPu3bvNl/v50TXUnfIs6ijO2F5nPDs549lbupXYGh4ergULFkiSFixYoK+//lpNmzZVvXr1VLRoUcXGxurUqVM6cOCA2ZqNdati7ubll1/W5s2b9fPPP0u69Uw0e/ZshYSEqESJEjpz5ozWrVunmzdvmsu8//77ZpL17Zz1rP3pp58qIiJCK1eulGEY+uCDD/Thhx+qZcuWqlmzpgoXLqyYmBj9/fffOnTokNmNmnU3dwAAAECuGQAAAMAd4LPPPjOqV69uSLL7r3Dhwsabb75pJCUlZVu3h4dHpvVMnDjRpvyFCxeMZs2aZbnuBg0aGIcPHzbmzZtnThs2bFiG67enjLWTJ0+a5atWrZpl2TVr1hhlypTJNE5vb2/j448/trvOH3/80fDz88ty20eNGmUkJCQYnTt3NqetW7cuw/rsKZOR1NRU46WXXjIsFkumcdSuXds4fPiwMXHixEyPZZrsjkHVqlVzdO6l/XXu3DnTbVi7dq1Ru3Ztu+tq2LChce7cuUzrW7hwoVG0aNFMly9WrJixdOlSY926dXbFZ69hw4aZ9c2bNy9Hy4aHhxv33nuv3fu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m9Xpw95NPPjEep6am9mof8H6DqS9fcMEFuv766/XKK6+ovr5ef/3rX/XXv/61TRt/f3+tXr3amHntTPYuhg5X9eeXXnpJ77//vqTmvvu1r31NEydOlNls1rFjx/Thhx8qIyNDL7/8svLz8/WjH/1IZrPZvR8OQ8pg7cuhoaFtZl5MnjxZqampeuutt/TKK6/ogQce0E9+8hPNmjWrrz8CeInB1Jd9fX27vJmPiorSnXfeqV/+8peSpA8//JBEIbQxmPpzZ7gHBNCRhIQE3XrrrXrmmWdktVr16quv6tVXX23Txmw266abbjLuwXtzr91dGf+0tDQVFhZq06ZNqqqq0ueff66LLrqox8dB11onX7UkfnWmdUJYb5K2/P391djY2O1xWh+L5LCe689zKnV9LQcGBuquu+7SD37wA9XW1mrjxo36r//6L6e+NMPA4Dr1TlynA+vf//63Nm3aJEmKi4vTd77znT7tj+t04Ln6nEpcpwNh9OjRbZ5PmzZNl1xyif74xz9q9+7d+ulPf6pf//rXTi0fdi6uU8/At3ToF76+vvrJT36iNWvWKC4urs0sz+HDh+uyyy7Tww8/bCy51PK6M7Zs2WI8XrJkSa/ia2pq0meffSZJCgsL03nnnder/cD7Dba+fOWVV+r+++9XcnJym1lQPj4+uuCCC/TII48oKirKeD04ONip/WJocEV/3rVrl/HlXWpqqu655x5NnTpVAQEB8vf3V2xsrNasWaOrr75akrRjxw5t2LChHz4dhhJP68tXXXWVJk+ebJTotdlsvdoPvI+n9eXExETFxMRIknJzc41y04A0+Psz94AAupKSkqLf/va3WrBgQZskRpPJpKSkJD300EOaPn268bqz9/09lZaWZjzOyclxyzGGutZJXt0td9DY2Njhdj09ljPLKrQciwlfPdef59QZQUFBRtXwxsZGo/oCBieu06GJ69R90tPT9a9//UuSFB0drZ/97Gd9vma4TgeWO86pM7hO+4e/v7/WrFmjYcOGqaKiQn//+997tR+uU89Aqh36jclkUmpqqlJTU9XQ0KDq6mr5+voqPDzcmPHZen338ePHd7tPq9Wq7du3S2qetT979uxexbZr1y6dOXNGkrR48WIqXaBLg60vT58+XdOnT5fValVVVZXsdrvCw8ONxKEdO3b0KBYMLX3tzy0zByRp9erVnR7nyiuv1AcffKCGhgZt2rRJl112mYs/CYY6T+vL559/vvLz83Xq1CkVFBRo6tSpvdoPvI+n9eVx48appKRETU1NqqurU0hISK/2A+80mPsz94AAujNp0iT993//t+x2u6qqqtTU1KSRI0cay3Ju27bNaOuue+3W+62srHTLMYa61jOkKyoqumx76tSpDrfrybFOnz7d7XFaH6s3xxnq+vOcOotr2XNwnQ5dXKeut23bNj333HOSpMjISN13330uGTPgOh047jqnzuI67R8hISGaOnWq9u7dq507d8pms8nHx6dH++A69QyMhGFABAQEaOzYsRo1apQxIGu1WlVQUCCpuQS9M39cdu3apbq6OknSRRdd1OvBXUrOo7cGU1/29fVVZGSkxowZ06a60MGDB43HCQkJPd4vho7e9Odjx45Jak5wCw8P73Tf/v7+RtWJlm0Ad/GEvtz6+OXl5b3eD7ybJ/RlwFmDrT9zDwjAWWazWRERERo7dqyRJCQ1V9NrMXnyZLccu3XVNbhH6y+cuvsbUlpa2uF2PT2WxWJRdXV1p+2qqqpUX18vqTkpGz3Tn+fUWVzLnoPrdOjiOnWtnTt3au3atXI4HBo5cqTuu+8+lyUBcJ0ODHeeU2dxnfaflvGZxsZG1dTU9Hh7rlPPQKIQBo3du3fLYrFIkhYuXOjUNq0Hd3u77FhNTY2ysrIkNc8WmzBhQq/2A7QYqL7ckZqaGu3du1dS88DlqFGjXLZvDA3d9eeWTHJnlptpWY+2p9nngCsMtr7cetYLZVXRE4OtLx89elSS5OfnxxKn6LGB6s/cAwLoK6vValTvDQ8Pd1t1yJa/s5I0cuRItxxjqBs9erTxsz1w4ECXbVveDw8PV2RkZI+PlZiYaDzuaim51u+13gbO6c9z6iyuZc/BdTp0cZ26TnZ2th577DHZbDaNGDFCv/jFLzR27FiX7Z/rtP+5+5w6i+u0//R17Jrr1DOQKIRBwWaz6fXXX5fUPLB78cUXd7tNbW2tMjMzJUkTJ07UpEmTenXsbdu2yWazSWpegx7oi4Hsyx157bXXjP596aWXumy/GBqc6c8tA2m1tbVt/qN+rrq6OpWUlEhqHrQD+tNg68t2u12ff/658ZwvqOGswdaXc3NzjWMkJiaydBN6ZCD7M/eAAPpq3bp1xszatLQ0t/0N3Lhxo/F4+vTpbjnGUGcymTRv3jxJzdVn8vLyOmyXl5dnVKeZO3euTCZTj4/VervNmzd32i4jI8OIbe7cuT0+zlDXn+fUGRaLRZ999pkkadiwYYqPj3fLceAaXKdDE9ep6xw8eFCPPPKImpqaFBgYqJ///OdG9VdX4TrtX/1xTp3Bddp/Kisrjf8/RUZGKjAwsMf74Dr1DIzkol/U1NSosbGxw/esVqueeuopFRcXS5K+/vWva8yYMd3u89NPP3XJ4O6WLVskNQ9OL168uNf7wdAwmPqy1WrtsmTfhg0btGHDBknStGnTdNFFFzm9bwwNrujPrf/z9uKLLxqz+Vuz2+164YUXjPfmzJnjivABw2Dqyxs3buyy8oXdbtdLL71kfKGdmJhI8hwMg6kvf/HFF12WdD5x4oQef/xx4/kll1zSaVsMTYOpP5+Le0AA3Tl16lSn7+3cuVP/+te/JElRUVG6/PLL27XJyMjQqlWrtGrVKr322mvt3j9y5IhOnDjRZQzp6enatGmTJCksLEwXXHBBTz4CemDFihVGRboXXnhBZ8+ebfP+2bNn9cILL0hq/tvx1a9+tcP9PPDAA8Z5Lysra/d+WFiYMTazZ88eoypVa5999pn27NkjqbnidFhYWK8/11DWX+c0Kyur3b5bq6+v12OPPaba2lpJ0tKlS+Xn59erzwTX4Dr1Plyng8fhw4f1v//7v2psbNSwYcP005/+VHFxcT3eD9fp4NFf55TrtH+UlpZq3759XbaxWCz605/+ZIyxdPa9Itepd/Ad6AAw+OXm5rYZvGi9FuGJEyeMbL8Wqamp7faRk5OjZ555RosXL1ZSUpJGjRqls2fPqqioSOnp6cbs0FmzZumaa65xKq6WpZrMZnOvB3ePHj2qQ4cOGccODQ3t1X7gGbytL1ssFt12222aO3eu5s2bp+joaEnS8ePHtWXLFuMPbGRkpO644w63zYzCwBgs/Tk1NVUffPCBjh07pj179uh//ud/dNlll2nSpEkym806evSoNmzYYGSgh4aGauXKlR3uKysrq03yW8vMPqn5pqT1ZwoICNCCBQs63A88i7f15b/85S964403tGDBAk2ePFmRkZHy9/fXmTNnVFRUpE8++cT4YjwwMFC33HJLj35eGLy8rS8/+uijGjt2rC644AIlJCQoIiJCfn5+qqys1N69e/Xxxx8bSSALFy7U/Pnze/TzwuDmbf25Ne4BATjjxz/+saZMmaIFCxYoJiZGvr6+Kisr044dO7R9+3ZJ0vDhw/WjH/1I/v7+Pd7/oUOH9PTTT2vGjBlKTk7WhAkTFBwcLLvdrmPHjmnr1q3GMuJms1nf+973WK62E674mxUdHa3LL79cb7/9tgoLC3XfffcZSawnT57UO++8o6KiIknS5ZdfrqioqF7Hu3r1amVlZammpkZ/+tOfVFhYqPPPP1+StGvXLr3//vuSpJCQEK1evbrXx/FknnRO3377bT3++OO64IILlJiYqLFjxyogIEBnzpxRXl6eNmzYoIqKCiOmVatW9eo4ns4V51RSu3aHDx82HmdlZam8vNx4Pnbs2F4vYcJ12j1POqdcp87p6zk9ceKEfvOb3+jMmTOSmq+joKAgHTlypNNjhoaG9vp+jOu0e550TrlOndPXc1pVVaVf/epXmjhxoubNm6e4uDiFhYXJx8dH1dXVOnjwoDZt2mR8RxMTE6Mrrrii1/FynQ5+JAqhWx9//LGRyHCugwcP6uDBg21e6+w/fWfOnNH69eu1fv36Dt9PTU3VLbfcIl/f7rtlaWmpCgsLJTUP7vY2y7D156LkvPfzxr5ss9n0+eeft1nCprUpU6bozjvvdOs66xgYg6U/+/r66mc/+5keeeQRFRcX68iRI/rLX/7SYdvRo0fr7rvvVkhISIfvv/32252uV7tz507t3LnTeB4ZGUmikJfwxr5cWVmpdevWdfhei3HjxukHP/gBy455EW/syydOnNC7777b4XstLrnkEt10001dtoHn8cb+3IJ7QADOsNvt2rNnjzEB51zjx4/XD37wA8XGxvbpGNnZ2crOzu60zYgRI3TrrbdShr8LrvqbtXr1ap0+fVqbN29WUVGR/t//+3/t2ixbtqzPX2KMGjVK9957r37/+9+rurpa77zzjt555502bcLCwnTPPfcoIiKiT8fyVJ52Tuvq6rRp0yajAlhHpk2bph/+8IcKDg7u07E8lavO6ZNPPtnpMc69jlJSUnqdKMR12j1PO6dcp93r6znNzc3V6dOnjecvvvhit8e85pprep3wwXXaPU87p1yn3XPV797i4mJjImtn5syZozVr1vRpsgLX6eBHohD6RWJiom644Qbt379fx44d0+nTp2UymTRy5EjNmDFDqampmjJlitP7c8Xgrt1u17Zt2yQ1zwJj0AXOGEx9efjw4br11luVnZ2toqIiVVdXq6mpSaGhoYqLi9OFF16oBQsWUEkInXJVf46MjNTvfvc7bd++XTt27FBRUZFqamrkcDgUHBxsZKgvWbKEWbBwi8HUlx999FHt27dP+/fv14kTJ1RdXS2LxSJ/f3+Fh4crNjZWF1xwgebOnetUQimGlsHUl3/yk58oLy9PBQUFKi8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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 935, + "width": 1157 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(14,10))\n", + "fig.suptitle(\"Barnett Zehnwirth\\nStandardized residuals of the statistical chainladder model\\n(Fig 3.11)\");\n", + "\n", + "plot1a.plot(\n", + " style='.', color='gray', legend=False, ax=ax00,\n", + " xlabel='Development Month', ylabel='Weighted Standardized Residuals')\n", + "plot1b.plot(color='red', legend=False, ax=ax00)\n", + "\n", + "plot2a.plot(style='.', color='gray', legend=False, ax=ax01, xlabel='Origin Period')\n", + "plot2b.plot(color='red', legend=False, ax=ax01)\n", + "plot3a.plot(\n", + " style='.', color='gray', legend=False, ax=ax10,\n", + " xlabel='Valuation Date', ylabel='Weighted Standardized Residuals')\n", + "plot3b.plot(color='red', legend=False, ax=ax10)\n", + "\n", + "plot4.plot(kind='scatter', marker='o', color='gray', \n", + " x='Fitted Values', y='Residual', ax=ax11, sharey=True);\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_stochastic_bornferg.ipynb b/docs/gallery/plot_stochastic_bornferg.ipynb new file mode 100644 index 00000000..a8d2a938 --- /dev/null +++ b/docs/gallery/plot_stochastic_bornferg.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stochastic BornhuetterFerguson" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see how to use the :class:`BootstrapODPSample` and :class:`BornhuetterFerguson`\n", + "to come up with a stochastic view of the Bornhuetter-Ferguson method. This can\n", + "be done with any deterministic IBNR method which makes bootstraping so versatile.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "\n", + "# Simulation parameters\n", + "random_state = 42\n", + "n_sims = 1000\n", + "\n", + "# Get data\n", + "loss = cl.load_sample('genins')\n", + "premium = loss.latest_diagonal * 0 + 8e6\n", + "\n", + "# Simulate loss triangles\n", + "sim = cl.BootstrapODPSample(random_state=random_state, n_sims=n_sims)\n", + "sim.fit(loss, sample_weight=premium)\n", + "\n", + "\n", + "# Fit Bornhuetter-Ferguson to stochastically generated data\n", + "model = cl.BornhuetterFerguson(0.65, apriori_sigma=0.10)\n", + "model.fit(sim.resampled_triangles_, sample_weight=premium)\n", + "\n", + "# Grab completed triangle replacing simulated known data with actual known data\n", + "full_triangle = (model.full_triangle_ - model.X_ + loss) / premium\n", + "\n", + "# Limiting to the current year for plotting\n", + "current_year = full_triangle[full_triangle.origin==full_triangle.origin.max()].to_frame().T" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 459, + "width": 569 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plot the data\n", + "ax = current_year.iloc[:-1, :300].plot(\n", + " legend=False, alpha=0.1, color='red', \n", + " xlabel='Development Age', ylabel='Loss Ratio',\n", + " title='Current Accident Year BornFerg Distribution');" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_tailcurve_compare.ipynb b/docs/gallery/plot_tailcurve_compare.ipynb new file mode 100644 index 00000000..9026e8ab --- /dev/null +++ b/docs/gallery/plot_tailcurve_compare.ipynb @@ -0,0 +1,118 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TailCurve Basics" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how the ``inverse_power`` curve generally produces more\n", + "conservative tail factors than the ``exponential`` fit.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "clrd = cl.load_sample('clrd').groupby('LOB').sum()['CumPaidLoss']\n", + "cdf_ip = cl.TailCurve(curve='inverse_power').fit(clrd)\n", + "cdf_xp = cl.TailCurve(curve='exponential').fit(clrd)\n", + "\n", + "result = pd.concat((cdf_ip.tail_.rename(\"Inverse Power\"),\n", + " cdf_xp.tail_.rename(\"Exponential\")), axis=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 506, + "width": 569 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "ax = result.plot(\n", + " kind='bar', title='Curve Fit Comparison',\n", + " xlabel='Industry', ylabel='Tail Factor');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_triangle_from_pandas.ipynb b/docs/gallery/plot_triangle_from_pandas.ipynb new file mode 100644 index 00000000..59ce91da --- /dev/null +++ b/docs/gallery/plot_triangle_from_pandas.ipynb @@ -0,0 +1,533 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Triangle Creation Basics" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates the typical way you'd ingest data into a Triangle.\n", + "Data in tabular form in a pandas DataFrame is required. At a minimum, columns\n", + "specifying origin and development, and a value must be present. Note, you can\n", + "include more than one column as a list as well as any number of indices for\n", + "creating triangle subgroups.\n", + "\n", + "In this example, we create a triangle object with triangles for each company\n", + "in the CAS Loss Reserve Database for Workers' Compensation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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486Allstate Ins Co Grp1988199253546902741562768940069959573947420281872wkcomp
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" + ], + "text/plain": [ + " GRCODE GRNAME AccidentYear DevelopmentYear DevelopmentLag \\\n", + "0 86 Allstate Ins Co Grp 1988 1988 1 \n", + "1 86 Allstate Ins Co Grp 1988 1989 2 \n", + "2 86 Allstate Ins Co Grp 1988 1990 3 \n", + "3 86 Allstate Ins Co Grp 1988 1991 4 \n", + "4 86 Allstate Ins Co Grp 1988 1992 5 \n", + "\n", + " IncurLoss CumPaidLoss BulkLoss EarnedPremDIR EarnedPremCeded \\\n", + "0 367404 70571 127737 400699 5957 \n", + "1 362988 155905 60173 400699 5957 \n", + "2 347288 220744 27763 400699 5957 \n", + "3 330648 251595 15280 400699 5957 \n", + "4 354690 274156 27689 400699 5957 \n", + "\n", + " EarnedPremNet Single PostedReserve97 LOB \n", + "0 394742 0 281872 wkcomp \n", + "1 394742 0 281872 wkcomp \n", + "2 394742 0 281872 wkcomp \n", + "3 394742 0 281872 wkcomp \n", + "4 394742 0 281872 wkcomp " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read in the data\n", + "data = pd.read_csv(r'https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/clrd.csv')\n", + "\n", + "# Output\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\jboga\\miniconda3\\envs\\cl_docs\\Lib\\site-packages\\chainladder\\core\\triangle.py:187: UserWarning: \n", + " The cumulative property of your triangle is not set. This may result in\n", + " undesirable behavior. In a future release this will result in an error.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(376, 3, 10, 10)
Index:[GRNAME]
Columns:[IncurLoss, CumPaidLoss, EarnedPremDIR]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (376, 3, 10, 10)\n", + "Index: [GRNAME]\n", + "Columns: [IncurLoss, CumPaidLoss, EarnedPremDIR]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a triangle\n", + "triangle = cl.Triangle(\n", + " data, origin='AccidentYear', development='DevelopmentYear',\n", + " index=['GRNAME'], columns=['IncurLoss','CumPaidLoss','EarnedPremDIR'])\n", + "\n", + "triangle\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
19883,577,7807,059,9668,826,1519,862,68710,474,69810,814,57610,994,01411,091,36311,171,59011,203,949
19894,090,6807,964,7029,937,52011,098,58811,766,48812,118,79012,311,62912,434,82612,492,899
19904,578,4428,808,48610,985,34712,229,00112,878,54513,238,66713,452,99313,559,557
19914,648,7568,961,75511,154,24412,409,59213,092,03713,447,48113,642,414
19925,139,1429,757,69912,027,98313,289,48513,992,82114,347,271
19935,653,37910,599,42312,953,81214,292,51615,005,138
19946,246,44711,394,96013,845,76415,249,326
19956,473,84311,612,15114,010,098
19966,591,59911,473,912
19976,451,896
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1988 3577780.0 7059966.0 8826151.0 9862687.0 10474698.0 10814576.0 10994014.0 11091363.0 11171590.0 11203949.0\n", + "1989 4090680.0 7964702.0 9937520.0 11098588.0 11766488.0 12118790.0 12311629.0 12434826.0 12492899.0 NaN\n", + "1990 4578442.0 8808486.0 10985347.0 12229001.0 12878545.0 13238667.0 13452993.0 13559557.0 NaN NaN\n", + "1991 4648756.0 8961755.0 11154244.0 12409592.0 13092037.0 13447481.0 13642414.0 NaN NaN NaN\n", + "1992 5139142.0 9757699.0 12027983.0 13289485.0 13992821.0 14347271.0 NaN NaN NaN NaN\n", + "1993 5653379.0 10599423.0 12953812.0 14292516.0 15005138.0 NaN NaN NaN NaN NaN\n", + "1994 6246447.0 11394960.0 13845764.0 15249326.0 NaN NaN NaN NaN NaN NaN\n", + "1995 6473843.0 11612151.0 14010098.0 NaN NaN NaN NaN NaN NaN NaN\n", + "1996 6591599.0 11473912.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1997 6451896.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle['CumPaidLoss'].sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 474, + "width": 583 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plot data\n", + "ax = triangle['CumPaidLoss'].sum().T.plot(\n", + " marker='.', grid=True,\n", + " title='CAS Loss Reserve Database: Workers Compensation',\n", + " xlabel='Development Period', ylabel='Cumulative Paid Loss');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_triangle_slicing.ipynb b/docs/gallery/plot_triangle_slicing.ipynb new file mode 100644 index 00000000..819c368f --- /dev/null +++ b/docs/gallery/plot_triangle_slicing.ipynb @@ -0,0 +1,340 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Triangle Slicing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates the familiarity of the pandas API applied to a\n", + ":class:`Triangle` instance.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(6, 6, 10, 10)
Index:[LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (6, 6, 10, 10)\n", + "Index: [LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load data\n", + "clrd = cl.load_sample('clrd')\n", + "\n", + "# pandas-style Aggregations\n", + "clrd = clrd.groupby('LOB').sum()\n", + "clrd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
19885,93447,25895,054131,616165,117181,937198,395206,068213,374217,239
19899,43351,85598,976149,169175,588197,590211,242217,986222,707
199011,99654,742118,964163,695190,391213,972225,199235,717
19919,51773,420146,347199,262244,987260,333275,923
199212,47978,212157,400209,959244,018267,007
199318,22990,710166,325227,891276,235
199414,95294,303186,577252,449
199517,995110,181209,222
199620,390107,474
199720,361
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1988 5934.0 47258.0 95054.0 131616.0 165117.0 181937.0 198395.0 206068.0 213374.0 217239.0\n", + "1989 9433.0 51855.0 98976.0 149169.0 175588.0 197590.0 211242.0 217986.0 222707.0 NaN\n", + "1990 11996.0 54742.0 118964.0 163695.0 190391.0 213972.0 225199.0 235717.0 NaN NaN\n", + "1991 9517.0 73420.0 146347.0 199262.0 244987.0 260333.0 275923.0 NaN NaN NaN\n", + "1992 12479.0 78212.0 157400.0 209959.0 244018.0 267007.0 NaN NaN NaN NaN\n", + "1993 18229.0 90710.0 166325.0 227891.0 276235.0 NaN NaN NaN NaN NaN\n", + "1994 14952.0 94303.0 186577.0 252449.0 NaN NaN NaN NaN NaN NaN\n", + "1995 17995.0 110181.0 209222.0 NaN NaN NaN NaN NaN NaN NaN\n", + "1996 20390.0 107474.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1997 20361.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pandas-style value/column slicing\n", + "clrd = clrd['CumPaidLoss']\n", + "# pandas loc-style index slicing\n", + "clrd = clrd.loc['medmal']\n", + "clrd" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 459, + "width": 562 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plot\n", + "ax = clrd.link_ratio.T.plot(\n", + " marker='o', \n", + " title='Medical Malpractice Link Ratios',\n", + " ylabel='Link Ratio', xlabel='Accident Year');" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_value_at_risk.ipynb b/docs/gallery/plot_value_at_risk.ipynb new file mode 100644 index 00000000..772cfe64 --- /dev/null +++ b/docs/gallery/plot_value_at_risk.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Value at Risk" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example uses the `BootstrapODPSample` to simulate new triangles that\n", + "are then used to simulate an IBNR distribution from which we can do\n", + "Value at Risk percentile lookups.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Load triangle\n", + "triangle = cl.load_sample('genins')\n", + "\n", + "# Create 1000 bootstrap samples of the triangle\n", + "resampled_triangles = cl.BootstrapODPSample(random_state=42).fit_transform(triangle)\n", + "\n", + "# Create 1000 IBNR estimates\n", + "sim_ibnr = cl.Chainladder().fit(resampled_triangles).ibnr_.sum('origin')\n", + "\n", + "# X - mu\n", + "sim_ibnr = (sim_ibnr - sim_ibnr.mean()).to_frame().sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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3vw+5PzMzU1/96le1dOlSJSUl9enYN998c1jtHn744T4dFwAAAACAnpjKMpntm6TGemu7pW1oWW2V1WBkjjR2gmwpqTGsEv1FSDRAvF6v1q5dq02bNlmpagf19fX68MMPNW7cOJ144okxqBAAAAAAgPCZPdtl9my1rnu9UkWpdfG4pbR0acwEKWukbDZbjCtFfxESDQCn06kHH3xQu3btkt1u10UXXaQzzzxTubm5crvd2r9/v/71r39p9+7d+uUvf6kbbrhB5513XtjH/+Mf/xhWO5fL1d+HAAAAAABAgCktlNmz1VrWvq5aKi20hpYlJkoTpkg5Y2SLI2I40vEdHADPPfecdu3aJUn69re/rSVLlgTui4uL09y5c3XcccfpgQce0I4dO/T444/ruOOO0+TJk8M6fnZ2dljtSkpK+lw7AAAAAADBTFODzKefWBu1VVL+fsn4pZxcacwE2ZJZiGmoYHWzCDPGaNWqVZKksWPHhgREwRwOh6688srAPitXroxShQAAAAAAhMd4vTIb10pejzX/UFG+JCNNmSFNOYqAaIghJIqw+vp6NTU1SZKmTp3aY9tp06YFrtPrBwAAAAAw6GzfGFjeXoV5kstpzT00Moe5h4YgQqIIs9s/f0p9Pl+PbYPvD94PAAAAAIBYM4cOyBTkWYsxFR+SGmqlkaOkMeMIiIYokokIS0tLU3JysiRp7969PQZFO3fuDFwfPXr0gNcGAAAAAEA4TF2NzLaN1kZVhVRZJqWmSROmymZ3xLY4DBhCogiz2+2BJe1ra2v14osvdtmuqalJTz/9dGB73rx5UakPAAAAAICeGI/bmofI75NparR6ETnipInTZEtMjHV5GECsbjYALr/8cm3cuFEul0vPP/+88vLytHjxYuXm5srj8Wjv3r164403VFVVJUmaM2eOjj/++BhXDQAAAAAY7owx1kpmLU0ybrc1D5HXbQVE6ZmxLg8DjJBoAIwfP14/+MEP9Nvf/laNjY3atGmTNm3a1GXb2bNn63vf+16UKwQAAAAAoAsHdkllRTJ+IxXnS00NUs4YafSYWFeGKCAkGiBz587Vww8/rPfff19btmxRYWGhmpub5XA4lJWVpenTp2vRokU66aSTmPALAAAAABBzpqpcZtdWa6OixJqLKD1TGj9FNhuz1QwHhEQDKD09XRdffLEuvvjiWJcCAAAAAEC3jLNFZtNHkvHL1NdJpYVSQqI0caps8fGxLg9RQhQIAAAAAMAwZvx+mU0fS65WGZdTKjoo+fzS+EmypabHujxEESERAAAAAADD2Z5tUnW5jM8vFR6UWpqk0WOl7NGxrgxRRkgEAAAAAMAwZSrLZPbtlDGSygql2iopc6Q0biLz5w5DhEQAAAAAAAxDxu2ylruXkeprpPISKSnFmocojimMhyNCIgAAAAAAhhljjMxnGyRni4zLJRUfkoyRxk+WLTkl1uUhRgiJAAAAAAAYbgrzpNICGWOkkkPWPEQ5udKI7FhXhhgiJAIAAAAAYBgxTQ0y2zZZG1XlUnWllJYpjZvEPETDHCERAAAAAADDhPH7ZDZ/JPm8Ms1NUkmhFBcnTZgiW1x8rMtDjBESAQAAAAAwXOzeJtXVyHh9UlG+5HZJY8bLlp4R68owCBASAQAAAAAwDJjKMpn9u9qWuy+SGmqtOYhGj411aRgkCIkAAAAAABjiOi13X9G23P2EKbLZHbEuD4MEIREAAAAAAENYj8vdJyXHujwMIoREAAAAAAAMZQUsd4/wEBIBAAAAADBEmaYGme0dlrtPZ7l7dC0u1gUAAAAAAID+M36/tUqZyxl0aZVcLpny4s7L3Y9nuXt0jZAIAAAAAIBBzhhjDRNrqJca6qTGOpmmhrZAyCXJdL9v8HL3E6ey3D26RUgEAAAAAMAgYlwua3n6hjqpsV6m7at83s5tjSS/T/J6rfs7ffVIzta25e5HSaPHRPvh4AhCSAQAAAAAwCBgWppkdmyRSgvVsWeQMZI87tAhZa3NktMp+b2S3//5xXToVWS3S6npLHePXhESAQAAAAAQQ8bnkw7sltm3w5o/yBippdkKgtxOqbXVGmrm8Vi9g/x+a0e7XXLEWV/j4iS7Q4pPkOLire24OOv+uHgpLUO2hITYPlAMeoREAAAAAADEiKksk9m6UWpusLabm6yeRA11ks9nNbLZPg98klOsS2KSdYmPbwuCrPtZsQyHg5AIAAAAAIAoM60t0o7NMiUF1rbXI5WXSpWlVo+hlDQpNVVKTG4LhBKtrwRBGECERAAAAAAARInx+6S8vTJ7tn0+tKy2WiorkpobrSBo4lQpJ1c2Bx/ZEV38xAEAAAAAEAWmqlxm2yapsc7abmmWSouk2iprSNmoMdLYCbIlJce2UAxbhEQAAAAAAAwws2eb1XtIkvF6pYpS6+JxS2kZ0tgJUuYIhpIhpgiJAAAAAAAYQKa8xAqJjKS6aqv3UHODlJAoTZgi5YyRLY6P54g9fgoBAAAAABggxu2S+WydtVFbJeXvl4xfys61hpYlp8S2QCAIIREAAAAAAANlx6eSs1XG5ZJKCiQZafJ0KXs0Q8sw6NhjXQAAAAAAAEORKSuSKcyzVjArLZBamq3JqQmIMEgREgEAAAAAEGHG5ZL5bIO1UVslVVdK6enWEDMCIgxShEQAAAAAAETa9k2Sq22YWXGhtcT9uMmyxcfHujKgW4REAAAAAABEkCktlCnOt4aZlRySnM1SzhgpIyvWpQE9IiQCAAAAACBCjMsps7VtmFlNpTXMLC2DYWY4IhASAQAAAAAQKds2Si6njMsplRRKdrs0frJscQwzw+BHSAQAAAAAQASY4gKZkgJrmFlxgeRskUaPldIzY10aEBZCIgAAAAAADpNxtspsaxtmVl1pDTVLz5TGjGeYGY4YhEQAAAAAABwGY4zMto2S2yXjbLWGmTkc0rhJDDPDEYWQCAAAAACAw1GcL5UWyviNVFIguRhmhiMTIREAAAAAAP1knC0y2zZbG9UVUk2VFQ7lMswMRx5CIgAAAAAA+sH4/TKfrpM8LpnWVqm0bZjZ+MmyxcXFujygzwiJAAAAAADoj52fSpWlMn6/VHJIcjml3HGyMcwMRyhCIgAAAAAA+sgc2i+Tt0fGyOpBVFMlZWRKo8fFujSg3wiJAAAAAADoA1NVLrN1o7VRXSGVFUtJKdKk6QwzwxGNkAgAAAAAgDCZ5iaZjWsl45dpqJeK8iW7XZo0VbbklFiXBxwWQiIAAAAAAMJgPB6Z9Wskt0vG2SoV5ElejzVRdeaIWJcHHDZCIgAAAAAAemH8fpnNH0mNdTJej1RwQGptknLHSTljYl0eEBGERAAAAAAA9Gb3Vqm82FrJrDBfqquVRuZI4ybJZrPFujogIgiJAAAAAADogSk6KLN/p7WSWVmxVFVurWQ2cZpsDkesywMihpAIAAAAAIBumJoqmS3rrY2aSqm0SEpMkiZNky0hIbbFARFGSAQAAAAAQBdMS7PMhg8kv0+msaFtJTObFRClpMW6PCDi4mJdAAAAAAAAg4nx+aTqCpmdWyRXq4zLKRXmSR63NHGqbFkjY10iMCCiGhIZY3TgwAHt379f+fn5qqioUF1dnVwulyQpMTFRI0aMUE5OjqZMmaIZM2Zo+vTpTAIGAAAAABhQprlRqiiVqSi15hzyea3bvV7pUJ7U3CjljpdGj41xpcDAGfCQyBijzz77TB988IG2bNmipqamHtsXFxeHbKelpemEE07Q6aefrrlz58puZ4QcAAAAAODwGK9HqqqQKtuCoebGz+8zklxO67a6GqmuWho5ShrPSmYY2gYsJGppadG7776rt99+W9XV1f0+TlNTk9auXau1a9dq5MiR+tKXvqRzzjlHKSkpEawWAAAAADAcGJ9P2rpBpjhf8vtDb29usoKh+lqptUXyeiSbglYyY8YWDG0R/wl3uVx6/fXX9eqrr6qlpSXkPpvNpgkTJmjSpEmaMGGCRo4cqfT0dCUkJMgYI4/Ho4aGBtXU1Ki4uFgFBQUqKiqSMUaSVFNTo3/84x966aWXdOGFF+r8889XYmJipB8CAAAAAGCo2rlFpjDP+pzpbOst1NQgNdRJHpcVHDnipIREaUS2lJYuZWXLFkdAhKEvoj/lH374oZ566inV1NQEbktJSdG8efN0yimn6LjjjlNqamqfjtnc3KwdO3Zo/fr12rRpk1paWtTS0qJnn31W7777rq677jotXLgwkg8DAAAAADAEmdpqmYN7reFkRfnW3ENej2SzS/HxUnqWlJ4ppaZJKWkEQxh2IvoT/8gjjwSuH3XUUfriF7+oU089VfHx8f0+Zmpqqk455RSdcsop8ng8+vjjj/Xuu+9q7969qqmp0e9+9ztCIgAAAABAj4zfL/PZeknG6jVUWSbFxUvZOVJqunVJTGLOIQxrEY9FZ8+era985Ss69thjI31oxcfH64wzztAZZ5yhnTt36oUXXtD27dsjfh4AAAAAwBBzcK/UUCvj9UmlBdawsvGTZcvOiXVlwKAR0ZDohz/8oU444YRIHrJbxx57rI499lh99tlnUTkfAAAAAODIZFqaZHZvtTYqS6XGemlkjjQyO7aFAYNMRNeTj1ZAFOz444+P+jkBAAAAAEcGY4zMtk2SzyvT0iyVl1iTUo+bKJstoh+JgSMe/yMAAAAAAENXaaFUXizjN1JJgeRxS7njZUvu26JKwHBASAQAAAAAGJKMxy2zfZO1UVMp1dVIGZlSzpjYFgYMUoM2JPJ6vaqrq5PX6411KQAAAACAI9GuzyRnq4zLZfUocjikcZNkczhiXRkwKEV8dbOelJeXS7JWKRs5cmSXbcrKyvT4449r69at8nq9stvtmjNnjq6//npNnDgxmuUCAAAAAI5QpqZKJn+/jJFUViS1tkhjJ0hpGbEuDRi0ohYSFRYW6o477pAknXvuufrmN7/ZqU1VVZXuueceNTU1BW7z+/367LPPtGvXLt1777066qijolUyAAAAAOAIZPw+ma3rJRmpoVaqrpBS06y5iGy2WJcHDFpRG24WvFT9mWee2WWbxx9/PCQgCuZ2u/W73/2O4WcAAAAAgJ7l7ZEa6mS8XmuyamOksRNkS0iIdWXAoBa1kGj79u2SpIyMDE2bNq3T/dXV1Vq/fr0kKSkpSbfeeqsef/xx/frXv9bUqVMlWcPVPv7442iVDAAAAAA4wpjmJpk91udPVZRKTQ1SVrY0Iju2hQFHgKiFRNXV1ZKkyZMnd3n/unXrAtcvvvhiLVy4UElJSZo4caK++93vBu7buHHjwBYKAAAAADgiGWNktm2QfF6Z5iapokRKTJLGTZTNNmjXbQIGjaj9L2loaJCkbies3rFjR+D6kiVLQu6bMGFCoPdRfn7+gNQHAAAAADjCFR+SKkpl/H6ppFDyeKTR42RLTol1ZcARIWoTV7fPNZTQzRjQPXv2SLICoa6CpNzcXOXl5amurm7AagQAAAAADH7G65Wa6qWGOqmhXqax7bqr1WpQXSnVV0sZWVLOmBhWChxZohYSORwOeb1eOZ3OTveVlJSosbFRkjRr1qwu909NTZVkTWANAAAAABg+TGuLVHhQpr5GaqiXmhslmc7tvF7J2WItee+Is4aZORzRLxg4QkUtJMrMzFRFRYWKi4s73bdly5bA9WOOOabL/VtbrUS4u55IAAAAAIChxfh90oE9Mnu3Sz5v0O1+ye2SXE7r4myVWpqt6z6vdRkzQUrLiGH1wJEnaiHR5MmTVVFRofz8fJWVlWnMmM+7/K1evTpw/bjjjuty//Lyckndz2kEAAAAABg6TGWZzLaN1upkslYtU12N1NJk9RbyeiWfz1re3iar55AjTkpOkdKzpLHjZbPZYvoYgCNN1EKiU045RRs2bJDf79evfvUr3XjjjcrIyNDbb78dmIz66KOP1ogRIzrt6/V6VVBQIEkaO3ZstEoGAAAAAESZaW2RdmyWKbE+Axqvx1rKvqJUcruluDjJ4ZASEqWkZOuSmGRdEpKkhATCIaCfohYSLVy4UP/+979VUlKioqIi3X///Z3aXHLJJV3uu23btsBcRDNmzBjIMgEAAAAAMWD8Pilvj8wea2iZMUaqrbbmF2putEKgCVOklNS2UChRNjvzDQGRZI/WiRwOh37wgx90O1zs/PPP14knntjlfWvWrAlcnz179oDUBwAAAACIDVNZJrP6LZmdW6yAqKVZyttjXVpbpFFjpJmzZRs3UbaskbIlpxAQAQMgaj2JJGncuHF66KGH9P7772v37t1qbW1Vdna2TjvtNM2dO7fLfRobG5WXl6dRo0YpOTlZRx11VDRLBgAAAABEmPH7peYma5n6suIOQ8vKrKFlHrc18fTYCVLmCIaQAVEQ1ZBIkpKSknTeeefpvPPOC6t9enq6fvvb3w5wVQAAAACAgWD8fmvy6fpaqb5Gpq7Guh68Wlnw0LKmRikpSZo4RRo1Rra4qH9sBYYt/rcBAAAAACLKeD3Svp0yVeVSQ11IICRZC5LJ45KcTsnVarWpq7HuzMmVxkyQLTkl2mUDwx4hEQAAAAAgYozfL7P+A6mqzNo2ktwuKwxytlpzDDU3Wrd5vW1L2Nuk1HSGlgExRkgEAAAAAIicnZ9KVWXWMLPiAqmh1ppfKDgQcjikuHgrGEpJlZJSrHCIoWVATMXsf2BdXZ3y8vJUWVmplpYW+Xy+sPe9/PLLB7AyAAAAAEB/mII8mbw9Vu+hkkKprFCKS5Di4qS0dCk5VUpMlpKSpaQk2eLiY10ygCBRD4n27dunZ555Rjt27LAmJ+sHQiIAAAAAGFxMbbXM1g3WRnWFVF5s9RCaPF1KSaOXEHAEiOr/0vfee0//93//J7/fH83TAgAAAAAGkHG2ymxYI/l9Mk0NUnG+ZLdLk6bJlpEV6/IAhClqIVFhYWGngGjGjBmaMmWK0tPT5XA4olUKAAAAACBCjM8ns+EDydkq43JJBXnWHEQTp8mWOSLW5QHog6iFRK+//nogIJo4caJuu+02TZw4MVqnBwAAAABEmDFG2r5Jqq2S8fmkwjxr5bLR46TRY2JdHoA+ilpItGPHDklSQkKCfvSjH2nkyJHROjUAAAAAYCDk75c5tN8Ki0oKpNoqKXOkNH6ybDZ7rKsD0EdR+19bW1srSZo9ezYBEQAAAAAc4UxVhcz2TdZGVblUXmItZz9pGpNUA0eoqIVEiYmJkkRABAAAAABHOH9zk/wbP5CMX6ahXio+ZC1zP2m6bEnJsS4PQD9FLSQaPXq0JKmxsTFapwQAAAAARJjxetWy5m3J5ZRxtlrzEHl91hCz9MxYlwfgMEQtJJo/f74kaffu3SErnAEAAAAAjgzG5VTrJ6vkq6mS8Xqtlcyam6TcsdKo3FiXB+AwRS0kWrp0qUaMGKH6+nq99dZb0TotAAAAAKCfjDEyjfUy+3fK/+EK+d96UZ5DB2T8Rqb4kFRfI40cJY2bJJvNFutyARymqM0mlpaWpttvv10PPvignnzySSUlJemss86K1ukBAAAAAGEwfp9UXSmVFcuUF0stTZ/fJ8nf1ChvdYVUUSqlpksTp8nmcMSuYAARE7WQaOfOnZKkq6++Wk8++aT+9Kc/6c0339SCBQs0adIkpaSkhH2sY489dqDKBAAAAIBhx3jcUnmxTGmxVFkqeT2f3+f1Sc2NUlODVF8rp89jDTVLSLRWMmtbpAjAkS9qIdFPf/rTTrcVFBSooKCgT8ex2Wx65plnIlUWAAAAAAxLxuWSyopkSgulqjKpbe5YYyS5nVJTo9RYLzXUSW6XdUdcvOwZGbJnjpA/LVNiJTNgSIlaSBQpxphYlwAAAAAARyTjbJFKi2VKC6whZaY9GDJSa4vU2GDNM9TSJHk8kk1SfIKUmSVlZMmWlqHE0WNks9vlbmri8xkwxEQtJJo1axYTmQEAAABAFBljpMY6qbLc6jFUUyVrZqG2+1qarWCorlpqbbaGmdkdUmKilJUtpWdY8w4lJslms1kXe9TWPwIQZVELiX7yk59E61QAAAAAMCwZv0+qq5FqKmWqK61QyOMKut9YvYQa69uCoRbJ55UccVJikpQzRkrPlFLTZIuLj+EjARALR9xwMwAAAACAxXg9VhDUHgrVVVuhT/v9RpLHbYVBzY3W/c7Wz4OhpGQpc0RbMJTOKmXAMEdIBAAAAABHEONum3C6pECqLA/MKyQFTTrd0iK1NllDyVpbrGFkxkhxcVJyalswZA0lY/gYgHaERAAAAAAwyAVWIispkKrKQyecdrZa8wm1NFvDyFxOa9JpSXI4pPh4KXWklJ4upWVKKakEQwC6FPOQyO12Kz8/X42NjWptbVVycrLS09M1ZcoUJSQkxLo8AAAAAIgJKxgqlCkp7BwMtTRLTQ1SbfuE021DzOLirFAoY4SUmiYlp0jJqbLFM78QgN7FJCTy+/1au3at3n33Xe3fv19+v79TG7vdrhkzZujcc8/VwoULZSfpBgAAADAMmJpKmb3bQ4aSdVqJrKW5bV4hhzXh9MhRUkp7KJQimyPm/QEAHIGi/spRVlam3/72t8rLy+uxnd/v1969e7V371698cYbuu222zRmzJgoVQkAAAAA0WVaW6RdW2SK8q3tQDDUthJZS4vk87StRJYsZTHhNIDIimpIVF5ervvuu091dXUhtycmJio7O1tJSUlyOp2qrq6Wy/X5Mo15eXm67777dP/992v06NHRLBkAAAAABpTx+aQDu2T27ZR8Xiscqq+Vyoul5qYOK5GNtYKhtHTZ7ARDACIrqiHRww8/HAiI7Ha7zj77bJ199tmaMmWKbDZboJ0xRocOHdL777+vFStWyOfzqa6uTg899JAefPDBaJYMAAAAAAPCGGNNRr3jU6mlybqtpVkqK5Zqq6zVyFJSpcyRUkamlJpGMARgQEUtJPrkk08CQ8wyMjJ09913a/r06V22tdlsmjJlir7xjW/ozDPP1M9//nM1NDQoLy9P69at0/z586NVNgAAAABEnGmoldm+2ZqQWpLxuKWKMqmyVPK4pbQMKXe8lDWCYAhA1ERtNuj169cHrt9yyy3dBkQdTZ06Vbfeemtg+5NPPol4bQAAAAAw0IwxMs5WmW0bZVa9JVWVy/j9MlXl0p7tUskhyW6XJk6VjjpOtpGjCIgARFXUehLt27dPkjRp0iTNnTu3T/vOmTNHkydP1qFDh7R///6BKA8AAAAA+s14PVJdjeRyhlxMh235fVZ7I2tC6rIiqaFWsjuknLHSmPGyJSXH9sEAGLaiFhLV19dLsnoG9ceUKVN06NChwHEAAAAAINaMs1XK2y1zcJ81wXRPbf3GauN2SZVlUk2lFRpljJDGjJfSM0PmagWAaIvqxNVS2+RsAAAAAHAEMy1N0v5dMgV5n/cOcrZKzlbJ67XCIK/Xml+o/eL1Sn6/dfF5pOQ0acw4aWSObPaozQQCAN2KWkiUmZmpiooK5efn92v/9v0yMzMjVxQAAAAA9IFpbJD275QpypeM3xo21tIkVVdYPYM8ntAdbDZrniG7XbLZJYdDSkiQssZJOWNki0+IxcMAgC5FLSSaMWOGKioqVFBQoG3btmnOnDlh77tjxw4dOnRIksKe8BoAAAAAIsXU1cjs2ymVFkoyVjjU3GCtTlZbLXk9UlKylJElxcVLcXGSo+0S1/krPYcADEZRC4nmz5+vjz76SJL0yCOP6J577tGUKVN63a+goECPPPJIYPvUU08dqBIBAAAAIMD4fdYKZHl7pYoS6zZjpMYGqarMmqja55OSU6QxE6TsHNkSEmNcNQD0X9RColNPPVVTpkxRfn6+GhoadM899+icc87RmWeeqcmTJ3dqX1BQoFWrVuntt9+W12tNADd16lRCIgAAAAADpj0YUkmBTGmx5HFZtxsj1ddZE0431EnGJyWnSjljpBHZDBsDMCREdeLq22+/Xffee68aGhrk9Xr15ptv6s0331RSUpJGjRqlxMREuVwuVVVVyel0huybkZGh22+/PZrlAgAAABgGjN8nVZZLpaHBkCQZl0tqqrfmHGqsl4yk1DRpVK40YpRscVFfCwgABkxUX9HGjh2r++67T7/97W9VUFAQuN3pdKqoqKjb/SZPnqzbbrtNY8aMiUaZAAAAAIY4Y4xUUWr1GCorslYfa7/P5bICofpa66vHbU06nZYh5eRKWdmyORwxrB4ABkbUY+8JEybowQcf1AcffKAVK1YoLy9Pfr+/Uzu73a5p06bpnHPO0aJFixRHQg8AAAAgAkxri8zGtVJt1ee3dRkM2aSERCsYyhwpZWbJZiccAjB0xSR5iYuL05lnnqkzzzxTTqdThw4dUkNDg5xOp5KSkpSRkaHJkycrKSkpFuVFVENDg1auXKmNGzeqrKxMzc3NSk9PV3Z2tmbNmqX58+dr5syZsS4TAAAAGBZMTZXMhg8kV6uMz2etTFZfY01GHRIMjbFWKkvPYL4hAMNGzLvnJCUl6eijj451GQPi448/1v/93/+psbEx5Pba2lrV1tZq//79Ki0t1Z133hmjCgEAAIDhwxTkyWxdL/n9Mi6nVJBnrVBGMAQAkgZBSDRUrV69Wn/4wx9kjFFmZqbOOeccHXPMMUpLS1NdXZ3Ky8u1adMmhtEBAAAAA8z4/dLOLTJ5u63txgapME9qbpSysqXs0QRDACBCogFRVFSkP//5zzLGaNasWbrrrruUkpLSqd2Xv/xleb3eGFQIAAAADA/G7ZLZ9JFUWSpjJNVUSEX5kscj5Y6Txk+WzcHHIgCQCIkGxGOPPSaPx6P09HT913/9V5cBUTt6EgEAAAADwzTWy6z/QGpusHoTlRZJZUWS3S5NnCqNHiubzRbrMgFg0IhoQvGHP/whcN1ms+nmm2/u8r7D0fG4g01xcbG2bdsmSfrSl76kjIyMGFcEAAAADD+mvERm04eS1yPj8UiFB6XqCikpWZo0TbbMEbEuEQAGnYiGRKtXrw7ZDg5zOt53OAZzSPTxxx8Hri9YsCBwvampSY2NjUpLS1N6enosSgMAAACGPGOMtH+XzK7PJBmZlhap4IDUUCdlZlkBUXJqjKsEgMGJsU4Rtm/fPklSSkqKxo8frw8++ECvvPKKDh06FGgzevRoLV68WBdeeKGSkpL6fI7q6uqI1QsAAAAMBcbZIhXlyxQelBrrrdvqaqweRM5WaVSuNHEKk1MDQA8iGhL11MNnMPf+iaSioiJJUk5Ojv72t7/p7bff7tSmoqJCzz//vD755BPdc889GjlyZJ/OEe5z+fDDD/fpuAAAAMCRxHi9UnmxTGGeVFEmyVi3+3xSVblUUiD5/dK4idLYibLZ7bEtGAAGuYiGREuWLOnXfUNJU1OTJKmkpESHDh1SamqqrrnmGs2fP1/JyckqKCjQc889p08//VSFhYV66KGH9NOf/lR2fmEBAAAAvTLGSLVVUuFBmZICyeP+/PbmJqm+1rq/tUVKSJQmTZNG5jBBNQCEgeFmEeZyuSRJHo9HdrtdP/zhDzVz5szA/dOnT9ddd92lX/7yl/r000+1Z88erV+/XqeeemrY5/jjH//Yp1oAAACAI4nx+SS3S3I5JbdTcrVdd7bKlJdIzQ2ft3U5pfo6qaZSammSfF4pPkEaMVIaM1G2NOYDBYBwERJFWHx8fCCcOfXUU0MConZ2u13XXXedPv30U0nS2rVr+xQSZWdnh9WupKQk7GMCAAAAsWB8Pmnvdpmq8s+DIa+n5328PqmxTqqttiak9ritZe1TUqWsbGuC6uRUeg8BQB8REkVYcnJyICT6whe+0G27iRMnauTIkaqpqdGBAweiVR4AAAAwaBhjZDZ/JJUWhtwmn8/qEeT1drjutQKhuhrJ1WrtkJgsZY+TMkZI6Rmy2R0xejQAcOQjJIqw7Oxs1dXVBa731rampkYNDQ09tgMAAACGpL3bpdJCGb+xJplurLPCIL/fupj2r9aE1LLJmps6PkHKGmUNKcvIYsUyAIiQiIZEq1evjuThurV48eKonKc/Jk6cGOgZ5Pf7e2zbfj+TVgMAAGC4MSUFMnu2WflPebFUVig54qxhY3a75IiX7A4pPl6Ki5fi4qz74+OltHQpKYXhZAAQYRENif7whz9E8nBdstlsgzokmjVrllatWiVJKisr09y5c7ttW1FRIUkaOXJkNEoDAAAABgXTUCvz6SfWRl21NdysfSWyhCQrDIpzSHYHQRAARNER14XFtHc1HaROOukkORzWOOj169d3227nzp1qbGyUZAVLAAAAwHBgXE6Z9Wskn1emuUkqPGjdMXGqbFnZsqWkypaYKJsjjoAIAKIsoj2JBnMPn2hJT0/X2WefrXfeeUdbt27Vhx9+qIULF4a0aW1t1d///vfA9tKlS6NcJQAAABB9xu+T2fih1NIs43ZLBQes1cwmTLFWJQMAxFREQ6LvfOc7kTzcEeuKK67Q5s2bVVVVpUcffVR79uzRKaecopSUFBUUFOjll19WcXGxJOncc8/VjBkzYlwxAAAAEAXbN0vV5TI+v1SYJzU2SKPHSLnj6DUEAIMAq5sNgIyMDP3oRz/S8uXLVVZWprfeektvvfVWp3Znnnmmvv71r0e/QAAAACDKTP4+62IklRZINZVS5ghpwhTZWMgFAAYFQqIBMmHCBC1fvlzvvPOO1q1bp9LSUjmdTmVmZuroo4/W0qVLNXv27FiXCQAAAAw4U10hs22TtVFVLpWXSMmp0uTpssXFx7Y4AEAAIdEASkpK0kUXXaSLLroo1qUAAAAAMWFammQ2fCAZv0xDvVScLzkc0qRpsiUlx7o8AEAQ+nUCAAAAGBDG67FWMnO7ZJytUkGe5PVJ4ybJlpEV6/IAAB1EtCdRVVVVJA/XrVGjRkXlPAAAAAD6zhgj1dXI7NkmNdTJeL1WQNTaLI0ZL+WMiXWJAIAuRDQkWrZsWSQP1yWbzaZnnnlmwM8DAAAAIHzG77PmGyotlikvkpytbbcbqShfqq+RRoyyehGxkhkADEpH3JxExphYlwAAAABAkvG4pYpSqaxIprxE8nqC7vNITY1SQ61UVSalZVjzEDkcMawYANCTiIZEDAMDAAAAhj5TVy2za6tUXS75/dZtRpLbKTU1SA31UkOd5HFJRtZKZpOmyZaQGMuyAQC9iGhI9Pvf/z6ShwMAAAAwyJjqSpmP35P8fquXf2uL1WOovkZqbpQ8HslmkxISrOFlGVlS5kjZEhJiXToAoBdH3HAzAAAAALFhPB6ZTz+2AqLGeqm4wJqM2uuR7A4pIVHKypbSM6W0DNkS6TkEAEcSQiIAAAAA4dmxWWppknG7pcKDUnOTlJIqjcptC4bSZYuLj3WVAIB+IiQCAAAA0CtTViRTcMAaYlZaaA0xGz1WmjiVyagBYIiwx7oAAAAAAIObcTllPltvbdRWWUvdp6Vby9kTEAHAkEFIBAAAAKBbxhgrIHI5ZVxOax4im80KiJiMGgCGlIgON/vud78buG6z2fS73/2uy/sOR8fjAgAAABhAhXlSWZGM30jFhyRnizRmopQ5ItaVAQAiLKIhUWVlZb/uAwAAADD4mJYmme2brY3qcqmmypqgeuwE2Wy22BYHAIg4hpsBAAAA6MT4/TKffiJ5PTItzVJJoeSIk8ZPkS2O9W8AYCiK6Kv7o48+2q/7AAAAAAwyeXuk6goZn98aZuZ2SROmyJaeEevKAAADJKIhUU5OTr/uAwAAADB4mIZamd2fWRsVJVJdjZQ50lryHgAwZDHcDAAAAECA8flkNn8s+f0yTQ1SebGUkChNmMxy9wAwxBESAQAAAPjcnm1SQ52M1ysVHZK8Xmui6pTUWFcGABhgzDgHAAAADAOmpUlqaZb8Psnvb7sEXff5rEmq9++SMZLKiqSGOik7RxqVG+vyAQBRQEgEAAAADFHG75NKi2QO7pVqKsPfsaFWqiiVklOk8ZNlszMAAQCGg4iHRP/6178ifchOLr/88gE/BwAAAHCkMs4WKX+/zKEDkqvVus3vl5qbrN5DxnS4+EO3qyskv5HGT5ItKTnGjwYAEC0RD4mef/75SB+yE0IiAAAAIJQxxuotdHCvTGmRFfxIMm63tTpZdXnbcDN/9wexSTKSbDYpZ4w0YlRUagcADA4MNwMAAACOYMbrkYoPWUPKGuqs24yk5kaptkqqqZLcTskRJ6WmSXHxks0u2W1WGGSzt30NuiQmSSNGyWazxfSxAQCia0BDogkTJmjixIkDeQoAAABg2DE+nzVnUGmBTFmx5PV8fnt9rTVcrLFe8nmlhCRp9Dhp5CgpLV02G/MLAQC6NqAhUVFRkeLi4rR48WItWrRIGRkZA3k6AAAAYMjqNhgysuYdqquRqiokZ7Mkm5SSIo3MsXoEJSbFtHYAwJEh4iHR+eefrw8//FB1dXWSpPz8fOXn5+upp57SCSecoDPOOEMnnXSS4uIY6QYAAAD0pMdgyO2UGuql+hqpqUHyuK2hZFnZVjiUmSWbg/fcAIDwRfy3xte+9jVdd9112rp1q9asWaMNGzbI7XbL5/Np06ZN2rRpk1JTU7VgwQItXrxYM2fOjHQJAAAAwKBmPG7J6ZTcLsnjsr4GXYzLZYU+DXW9BEMea06hxERp1GhroumUNOYSAgD0y4D8acFut+uEE07QCSecoNbWVn388cdas2aNdu3aJUlqbm7WihUrtGLFCo0ZM0ZnnHGGTj/9dI0ePXogygEAAABizng8UnG+tSx9fU14+/QWDI0cJaVnSekZssXFD2j9AIChb8D7nyYnJ+uss87SWWedpcrKSq1evVoffPCBysrKJEllZWV67rnn9Pzzz+uYY47R4sWLdeqppyo5OXmgSwMAAAAGlDHGCnfy98sUH7ImkpZkfH4r/PF5JZ+v7eL9/KvXY4VBHo813xDBEAAgCqI6SDknJ0eXX365Lr/8cu3du1erV6/WRx99pJaWFhljtGvXLu3atUt/+9vfdNJJJ2nx4sU6/vjj6S4LAACAI0pXvYaMkeRsleqqpZpKyeWSjF/y+zsfwGaT7G1L08fFEwwBAKIiZjPZzZw5UzNnztSNN96ojRs3atWqVfrss8/k9/vldrv10Ucf6aOPPtKDDz6oadOmxapMAAAAIIQx5vNwx5i2r23bzlap8KBMUX5or6GGOqm20vraPsF0YqIUF2ddj4uT7A7JESc52r7GtX1NTiEYAgBERcyXO4iLi9Opp56qU089VQ0NDXrqqae0evXqWJcFAAAAyLhc8h/ap8b1q+RvaZKvoVHG7+t9v469hlpbJBkpMUUaNUbKGimlpMpmtw/4YwAAIFwxD4kkqb6+Xh988IHWrFmjQ4cOxbocAAAADHOmpVk6sFum4IDk98mflibj88l43DJ+vyRjJUFGQdfbLm6XVFMlNdR+3msoc4Q1ZCxjhGzx9AoCAAxOMQuJPB6PNmzYoNWrV2vr1q3ydxiLPWLECC1atEhjxoyJUYUAAAAYbkxDnbR/lzXJtLHen5rWFrkrS+WrKpdxuWWFQgr9GvjSNvxMRkpM/rzXUCrL0gMABr+oh0S7du3S6tWrtW7dOrW0tITcl5CQoJNPPlmLFy/WnDlzZKf7LQAAAKLA1FTK7NsplRdb20ZSc6NUVS7VVcvj98uelGRNJt3OFvjHmmC6/cbERCkrW8qk1xAA4MgSlZCorKxMa9as0QcffKCKiopO9x977LE644wztGDBAiUlJUWjJAAAAAxzxhipvETmwC6puuLz2xrqpaoyqb7WWpI+OUXx4ycpbuQoeZ1Oq8OQzSbJZn21hW4zzxAA4Eg1YCFRS0uLPvzwQ61Zs0Z79+7tdP/YsWN1xhln6IwzztCoUaMGqgwAAAAghHG2SAV51nxDLc3WbX6/FQpVlkmN9dZQs+RUaVSubNk5ShgxUpJkk62tmxEAAENPxEOiTZs2afXq1dq0aZO8Xm/IfWlpaVqwYIHOOOMMzZw5M9KnBgAAALpk/H6pskzm0H6pvOTz+Ya8HqmuLRxqbpRkk9LSpFG5Ula2bHFxzCUEABg2Ih4SLV++PGTb4XDohBNO0OLFizVv3jzFxQ2KBdUAAAAwDJjWoF5DrW29hoyRmpvalqevllyt1lxDGVlSTq41l5DdEdvCAQCIgQFNbCZMmKCFCxcqIyNDjY2NWrVqVUSOu3Tp0ogcBwAAAEOP8fukijIrGAruNeTxWEPKaiqlpgbJ55USEqXs0VJ2jpSRKZuN+YQAAMPXgIZERUVFevbZZyN+XEIiAAAABDPGSHU1UlG+TMkhyeX8/PZAr6Eq63a7XUpOkUaMspanT0pmSBkAAIrS6mYAAADAQDAtTVYwVJRv9Q5qv93tlhraew01ft5raNRoKxxKz5TNwZAyAACCRTwkmjVrFn+JAQAAwIAxbpdUWmgFQ21L10uS8fqslcnqq61hZW631WsoJVUakW1NRJ2UHLvCAQAY5CIeEv3kJz+J9CEBAAAAaxLqXZ9Zw8n8bfMM+Y3U3GCFQrXV1nAyY6TERClnjDWcjF5DAACEheFmAAAAGNSMzycd2C2zb4fk88oYWSuVNdRZ8wy1NluhUXyClDVCysqWMrJkS0iMdekAABxRCIkAAAAwaJnyEpntm6TmRmu7pVkqK7J6Dnm9UlyclJoujRhpLWGflMLUBwAA9BMhEQAAAAYd09wos32zVF5sbXs8UkWpVFkqeTxSSoo0eqyUMUJKTZPNztL1AAAcLkIiAAAADBrG65H27ZQ5sEvy+605h2qrrN5DLU1SYpI0cYo0aoxscbyVBQAgkiL6m7Wurk5ZWVmRPOSgPCcAAAAiyxgjlRTK7PzUmmNIkmlqtMKhuhprlbKcMdKYCaxQBgDAAIloSHTrrbfqggsu0AUXXKCUlJRIHrqT5uZmvfrqq3rzzTf1+OOPD+i5AAAAEBnG47HmF2pulJoapeYGKwxqbpQ8bquN2y1VlEiV5ZLXI6VnSmMnWJNRM98QAAADJqIhkcvl0gsvvKDXX39dS5cu1bnnnqvc3NxInkJlZWV655139N5778npdEb02AAAAOidcbmkhlqpvkZyuyUZyW+sr6b9Isn4A/eZ1hZrqXpna9fHNLICoboaax6i1mYpKVkaN1EalcsS9gAAREFEQ6Jzzz1XK1askNPp1GuvvabXX39dxx57rE477TSddNJJ/R4WVldXpw0bNuijjz7Szp07A7fb7Xadc845EaoeAAAAHRmX0wqD6mpk6tuCoZbm/h/P55PcLitccrskj8sKjlpbrJDI45EcDil3nJQ7XrbEpAg+GgAA0JOIhkTf/OY3tXTpUj311FPaunWrjDHasWOHduzYob/85S+aMGGCjj76aE2ePFnjx4/XyJEjlZ6eroSEBNlsNrlcLjU2Nqq6ulolJSU6dOiQdu/ereLi4k7nOv7443Xddddp0qRJkXwIAAAAw5Ixxhry1VgvNdTJ1NVYy8w7Wzq39fkkl9MKd9zOtl5EausOJFk9itq323oW+Xxt7V3Wdb/XaiNZ8w05HNYla4SUO15Ky2BoGQAAURbxJSEmT56se+65R7t379ZLL72kTz/9NHBfUVGRioqKDuv4J554oi655BIdffTRh1sqAADAsGRcLqmxTmqwLqahzgqHfN7Obb0+ydVqBTzOVmuFsdZmyeu1wp7e2AL/tAVBcVJiopSUJSUkBl0SpPhEKT6ecAgAgBgZsHVDjznmGN19990qLS3VypUr9eGHH6qqqqpfxxo1apQWLVqkM888U2PGjIlwpQAAAEOL8fuk1qBAp7VFammWaWm2egt11TvIyJo42uVsu7RKzU3Wvr6gQMjhkOLipOQUKTnVCnjaQ53uvkrWPvHtYVCCbHb7wD0BAACgXwYsJGo3duxYXXPNNbrmmmtUUFCgrVu3at++fSooKFBlZaU8Hk9I+/j4eOXk5Gjy5MmaMWOG5s6dy5AyAAAwLBm3y1rhy+OS/H7J72v7Gnxpu83nk2lttuYLcrbq87FcXRy3fbhY+6W1xQqUPG4rEGofPhYIhFKllFRrIun2S0KCbDaCHgAAhpIBD4mCTZo0qVPg09LSElilLCkpSSkpKdEsCQAAYNAxHre0f5dM3p4uh4D1ur/PL3nd1iTQHnfbhNBuyeWyegi5XKG9g2w2KwxyxFlhUHKqlJhkXZKSpYREhoABADAMRDUk6kpKSgrBEAAAgCTj8Uh5e2TydluhjtS2dHxj0NLyXV3aehV5PVbPII+nbXLotl5G7Wy2zyeITkqWUlKkxJS2QCjR+uqIIxACAGCYinlIBAAAMNwZr0c6uE9m/y5raJkk42yVKsuk6gor9JGs+Z+7G0Vmk2Szf75SWHy8FJdihT/xCW2XeOtr20TRDBcDAADBCIkAAABixPh8Uv4+mf07rR5AkozLac1DVFVuLReflCyNzpbsDqsnUFcX2axwKC5OimsPg+Ilu4NeQQAAIGyERAAAAFFk3C5rcumaSisccrZat7tcVjBUVS65nVJCkjR+sjRqtGyJSTGuGgAADAeERAAAAGEw7fP/yFhDvoKvq31uoLbr7UFQS1Pb0vNNn2+3zTUUOK7bLVWXW0PLXE5rKNjYidKoXNmSkqP9MAEAwDBGSAQAAIYtY4xUU/n5vD9e62KCrlsXr/U1eBLofp1Pks/z+apjzU3W0DJXixSfKI2ZIOWMIRwCAAAxQUgEAACGHWOMVFUus2ebFRKF097ftoKYaZ852nSYRNp8/sWYz5ed97itnkUup+R0WiGRz2+tPGb81kTSueOlnFzZklMj/EgBAADCR0gEAACGja7CIeP3S3U1Vojj91lLx3u9ks9rXW//GjzcLOSg3Wz4fZI/aNvhCFp5rG2FseQUaUS2bClpA/WQAQAAwkZIBAAAhrzuw6FqqaJUamr8vIdQdyuItS8vH7JYWBcrh7WvJha01HynJejj41l+HgAADDqERAAAYMgKKxyy2aTMEVJahhUC2e3WcvOOtq/Bt9mCQyJbN9cl2e2y2R3Re6AAAAARQEgEAACGHONxSxWlMgf39h4O5YyRMrPo2QMAAIY9QiIAADAkmKYGqbxEprxYqq60JoUW4RAAAEC4CIkAAMARyfh9VhhUXiJTXiI1N3x+nzHS/2/vzsOrLu+8j3/OyUlysicsEllVNoUoBZStLYlOFa3KuFLbaatt7TUqdKbOU0e8njrquD1WHbUudB/bWse6VKWCU1cCggiURRbZAgkhQMi+nH35PX/8ck5O9oXkLOT9uq5c53fOb7tDcoecT+77e7uckqPJXN6ecAgAAKBHhEQAACDuGT6fGfg4mqTmJhmN9VLVcXOZ+dAxgUB4vxpqJZfLXG7emkQ4BAAA0AsxDYlKS0u1d+9eVVdXy+FwKBgM6vbbb49lkwAAwCAxQkvCG0EpGGx9DAbNlcVC226XOSqouUlGKPTxuDpez5Dk9ZjHNjVKjfXmMvaGIdlsUqpdyh4lZedK2TmEQwAAAD2ISUi0ceNGvfbaazp69GiHfe1Dovr6et19990KBAI699xz9ZOf/CRazQQAAP1k+HxmHaC6ahl15qO8nv5fLxAwz/d6zUefR3I0myOHfD5zZbHklJZAKFfKyJIyMlhhDAAAoA+iHhL96le/0ocfftjr43Nzc3X++edr3bp12rJli2prazVs2LBBbCEAAOgLIxiUmhuluohQqKlBktH2OL+/ddSQYZj7jXYfahlR5POagZDbJbmd5nYgIAX9rZe1WqWUVCl3mLl8fWa2lGqXxWIRAAAA+i6qIdErr7zSJiCaMWOGCgoKtH37du3evbvL8woLC7Vu3ToZhqGtW7fqa1/7WjSaCwDAkGUYhgyXU0ZDnQy3q2UUT8RIHq9HRug1R1Ob2kBSS3DkdpsBj8tphkhulznFzJDCSY8Remz3Wuh1i0VKSpKSbFJqqmTPkVLsZjiUapcys2SxJUfhXwQAAOD0F7WQ6Pjx43r77bclSenp6fo//+f/qKCgQJJUXV3dbUg0ffp02e12ud1u7dmzh5AIAIBBYjQ1KFhRpuaNtQo6mhRsbjZXCuvuHEPmyJ9QIBQ5DSzgNw9KSpJsyWbYo5aRPpb2j9bWUMhul5JTpZSUiMcUWazUFQIAABgsUQuJPvjgAwWDQUnSbbfdFg6IesNqtWrChAnat2+fysvLB6uJAAAMSYbLKVWUyThaKjXWSRaLgpmZMvx+GY5mGQG/OdUr8tHvN4Mhv88Mg3w+c9swzKAnOdkMejIypbQMyZ5mjvwJBUEWi5kVhbZlYZoYAABAjEUtJNq1a5ckKT8/X3Pnzu3z+SNGjNC+fftUW1s70E0DAGDIMXxe6Xi5GQxVn1RoqpcRCEqORnkrjylQe1KG09laR6g9a0vgY7WaI4QyslpCoTTJnm7WB2LkDwAAQMKIWkhUVVUlSZo0aVK/zk9PT5ckuVwdl8AFAAC9Y9RUyTi0T6qskIIB8zXDMKeHNdRLdTUyPC75rVZZUlOltHQzALIlm8vKh+oDtX+026kNBAAAkOCiFhJ5POayt3a7vV/nu91uSVJyMr+AAgDQV4bHLe3eJuPoYfO5IbN+UGO9uTy9y2FOI7OlSNk5SskfI2vuMPkDwZi2GwAAANETtZAoMzNT9fX1amho6Nf5FRUVkqTs7OyBbBYAAKc1wzCkIyUy9uyQfB7zeX2tVHWidVWyJJtZNyh3mJSdK0tGpmxZWZIkSy8KVwMAAOD0ELWQKD8/X/X19Tp48GCfz62trVVpaakkacKECQPcMgAATk9GY52MHZvNkUKSuZT98XKpttocSpSWJo08U8rJlTKywvWDKCANAAAwNEUtJLrgggu0d+9e1dXVaePGjZo3b16vz33llVfCK6NdcMEFg9VEAABOC4bfJ+3bZdYeMoIyAgGzOPWJo5LXbRaYzh8r5eTKkhS1XwUAAAAQ56K25EhRUVG4ntDvfvc7HT9+vFfnvf766youLpZkTllbuHDhoLURAIBEZxw/KuPj1TJKvpARDMpobJAOfiEdKZGMoDRmgjRluizDRhAQAQAAoI2o/XY4fPhwXX311frLX/6ihoYG3XPPPfrHf/xHLViwoEOtg6amJu3YsUOrV69WSUlJ+PUlS5b0u/A1AACnIyMYlBzNUmO9WZS60qzhZ3i90okKqbrSXMUsd5g0epwsGVkxbjEAAADiVVT/hLhkyRIdPXpUmzZtksvl0iuvvKJXXnmlzTHf//735XA4OpxbWFioRYsWRaupAADEHcPjNlcja/kwGuulpobwUvaSZAQNqbbKnFrmdEjp6VL+2dKwkeGaQwAAAEBnohoSWSwW3XnnnXrttdf01ltvhesMRWofEFmtVl133XW68cYbo9VMAABizvD5zILTddUy6qqlhnrJ4+p4nGFIXq/kcZsfDbXm6mVWq5Q/WsofK0tKavQ/AQAAACScqBcjsFqt+sY3vqFLLrlEq1at0vbt2zutTzRixAjNnDlTV199tUaNGhXtZgIAEDWGYUjOZnPVsdpqGXVVUmODpLbTsQ2/vzUM8rgll8McLeT3SX6/uWKZRVJmjjR6nJSVw0plAAAA6LWYVawcOXKkbrnlFklSc3Oz6urq5HQ6lZqaqtzcXOXm5saqaQAADDjDMMxgx+2U3C7zw+U0p4zVVUteT9vjg0HJ7ZbcDsnplJobzfMDfinQMr3MapWSkiSbzVyxLC3d/MgdRlFqAAAA9Flc/AaZmZmpzMzMWDcDAIB+M/w+czSQwyE5mswAKBQIuZxmwGN0nGYdPt/nM49zO81C1M1Nks9rhkJSSxiULKVlmEGQ3S6ltnyk2GWxxcV/6QAAAEhg/EYJAEAXjGDQHLUT8JmPfr8Z3DgdLYFQswxns7ntcfd8rdC0ML/P/PD5Jb/XDITcTvO1oCFZLFJyshkEZWS2BkOpdik5hSlkAAAAGBSERACA045hGOaonaBh1unx+8zpXF6PWeTZ65F8rc8Nr8cMf/w+c+SOv+UjYtWw7u8nM0TyeVs+Wq7n9Ulet3lPv8+8XjBofhgR9YZCo4SyciOmjaVJqWmsSAYAAICoISQCAMQtw+83R9i4nOboHZfDnMYVWbDZMFpDl1A4dKr3NYyWgCkYEepEbAdaRhR5veZ0Mo9L8rWEQIFAawBkkWRpqRtktZq1g6w2KSVFSk4xg6HkZMmeJtnTpeRkRgkBAAAgZqIWEi1btmxArmOxWPTss88OyLUAANFjuJ1SXY0ZpgT8rSN2QtO4An4Zode9ntY6Pt1dMxwMGaEXJBnmomDttwP+1hAnVPw5tO3ztU4FCwRaRhBFXLuzj5BQ8WhrkpSSKqXYpdRUMwRKTjaDIVtyy4dNSrIRBAEAACAuRS0kqqqqitatAABxxGhqlEr2yCgv7dMoH8OQGdb4fK1TwULb3pYl4L1ehZeJD+c2LcFQ5PLxoWCn/TSvEKvFHPFjCT3K3La2vCZL63NrUutIoJSIMCjZfI3pYQAAAEhUTDcDAAwKo6FOOrBbxrFySYaZzTiaWlf5CoZqBgXMx/Aon5aRPF5Pa12g0DSvEKvFDGvahDgt+0LPFXpo2bZaW0fz2Gzm+Uk2cxRQUsu21dr6Yelk22JhFBAAAABOW1ELiZ577rleHWcYhpxOp44cOaINGzZo27ZtSk5O1g9+8AMVFBQMcisBAKfKqK2ScWCPVFlhPjcMqalRqj4h1de1LuneXijQsbQbtdMyQic8hStUxyc5xQx3Is+R2j1a2tQFIuABAAAAuha1kGjkyJF9Ov6ss87SwoULtXXrVj311FP61a9+pX/7t3/TRRddNEgtBAD0l2EYUnWljP27pZrK1tca66WqE1JDvTkiKD1DyhjRzYidiBFCoTDIlixLUlJMPz8AAABgKIj76WazZs3SD3/4Qz3//PN64YUX9MQTT2j48OGxbhYADHmG1yM11EmN9TIqyqT6GvP1YNB8veqE1NRgTi1Lz5JGjpLyRshii/v/egAAAIAhKSF+U1+4cKH+/Oc/q7q6Wu+//75uuummWDcJAIYMwzDMpecb6qWGWrPWUGO9+VrkcYGgGRRVnZCaGyVZpMyWcCh3OKOBAAAAgDiXECGRJE2ZMkXV1dXasmULIREADDLD6ZCOl8uorDBHBfm8HY8JBMwi1G6X+VhXIzmbzOlj2TnSiHwpN08WK+EQAAAAkAgSJiRKS0uTJFVXV8e4JQBwejKczdKxchnHy6W61p+1hiFz+XmPS3K3hELOZsntNFcfCwTMA5OSpJxh0sh8KTuXpeABAACABJMwIdHJkyclScHIJZABAKfEDIaOmMvUt9QUklrqCjmazZpCDbWSx2OuShb6GZyUZC4jn5YupWVI9jQpI1PKzGYFMQAAACBBJURIdPToUe3Zs0eSKFoNAKfAXI6+Qao8JuP4Eam+tnVfMCg5msz9dTXmiKFg0AyDbDYpLdsMhexpUqrdfExJkcXCiCEAAADgdBD3IdHnn3+uX/7ylwq0TGeYMWNGjFsEAInF8HnNYtInj8s4edycJhbaFwqGGhvMkUSRwVBGlpSbZz7a08yl6BklBAAAAJy2ohYSvfDCC70+NhgMqqmpSWVlZaqrqwu/npqaqsWLFw9G8wDgtGEYhjlFLBQK1dWYy9CH9vv9ZjDU3BQRDAUkW3JrMJSVI6VnMEoIAAAAGEKiFhIVFxef0vmpqan6t3/7Nw0bNmyAWgQApw/D45Gqjksnj8moOmGuNhbaZxiSy9k6layp0VytzDDMEUOZWWbB6axsgiEAAABgCIv76WZ2u13z58/X9ddfr5EjR8a6OQAQFwzDMOsJnTzWOlpIRut+n691tFBjnbkqWcAvWSxSSoqUk2cuU59JMAQAAADAFLWQ6Pbbb+/1sTabTXa7XWeccYbGjh0rK8soA0DLaKFjrdPIvJ7WfUFDcjla6ws5GiWfr3W0kN1uTiHLzJLSs1oKTlNfCAAAAECrqIVERUVF0boVAMSMEQyY4YzPK3m95qPPYy4h72t57vXI8La87vW0hjk98fvVdrSQV2puNgOhhjpzilkgIFmtUnKKlDfCnEKWkSmlZchC4A4AAACgG3E/3QwAosXwehR0OmTUVpsBjN8vBXzmo99vBjCh5z6feYzPK/lbQiGfz5zS1df7GoYU7EVIJMMsMu1okhrrzUef18yNbMnm8vQRo4UsKSl9bgsAAACAoYuQCMCQZvh80omjCh47okZXs2QEFWxuNoObvlzHkLmCWDAgBUKPgdbHQChkatkOhVB+X4cRQt0KhVXh0UIjW0YLZUlp6YwWAgAAANBvhEQAhhwjGJBOHpcqymScqAgXdDYyMmQ4HTKaG2UEAlIw2PphBNs+DwTMgCcQGmXkjzjOMB8No/UjkkWSxWoWkbZaW7ZDO3oQHi1kTiOzJDNaCAAAAMDAICQCMCQYhiHVVktHS2UcO2LWA1JLfuN2Sk0N8jQ3KuholuH1dB7uRIoMeiwWc9tqkaxJUlLLa0lJ5jQwm83ctrY8hj9sLce3XKcnFquUkspoIQAAAACDYkBDohdeeGEgL9cpi8XSp5XSAJzejGCwtSZQuFB0u22fR0Z1peR0tJ7n9Zh1fepqpOYmGX6fgqmpstjTzCXirUmtAU5otI81ydwOf7SEPVZry2NSm9cIcwAAAAAkkgENiYqLiwfycl0iJAJOf0YwaK785XaZHx5XeNtwuyJed6u39XwMv89cHr6+1gyIfF4z4Em1SyNHKfXMMbJm5SjgcvW5JhEAAAAAJDqmm0XRSy+9pJUrV4af33fffZo+fXoMWwTED8MwpKZ6qfK4jJPHpLpqs8ZPt+eopTC0v6VYdERx6HDB6JYPn1dqapC8bkkWKSVVGpkv5eZJmTmyJicrKTMzGp8qAAAAAMSlAQ2JGOHTtdLSUq1atSrWzQDiiuH3SVWV0sljMiqPmbWBQvsMo+MKYKFHn0fyeMz9oYLR7YtFt2exSMnJUt4IKXeYlJXLEvEAAAAAEGFAQ6KioqKBvNxpIxgM6pe//KUCgYBycnLU0NAQ6yYBMWEEApKzWTp5XMbJ41JNZZvRQobfLzmapOZGqaHOnG4WXl0sIvgJrQoW+rDZWmo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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 459, + "width": 580 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Plot data\n", + "fig, ax = plt.subplots()\n", + "sim_ibnr.index = [item/1000 for item in range(1000)]\n", + "(sim_ibnr/1e6).loc[0.90:].plot(kind='area', alpha=0.5,\n", + " title='Bootstrap VaR (90% and above)', ax=ax).set(\n", + " xlabel='Percentile', xlim=(0.899, 1.0), ylabel='Value (Millions)');\n", + "ax.grid(axis='y')\n", + "for spine in ax.spines:\n", + " ax.spines[spine].set_visible(False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/gallery/plot_voting_chainladder.ipynb b/docs/gallery/plot_voting_chainladder.ipynb new file mode 100644 index 00000000..1cfa74ba --- /dev/null +++ b/docs/gallery/plot_voting_chainladder.ipynb @@ -0,0 +1,138 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# VotingChainladder Basics" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates how you can can use the Voting Chainladder method.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Load the data\n", + "raa = cl.load_sample('raa')\n", + "cl_ult = cl.Chainladder().fit(raa).ultimate_ # Chainladder Ultimate\n", + "apriori = cl_ult * 0 + (float(cl_ult.sum()) / 10) # Mean Chainladder Ultimate\n", + "\n", + "# Load estimators to vote between\n", + "bcl = cl.Chainladder()\n", + "cc = cl.CapeCod()\n", + "estimators = [('bcl', bcl), ('cc', cc)]\n", + "\n", + "# Fit VotingChainladder using CC after 1987 and a blend of BCL and CC otherwise\n", + "vot = cl.VotingChainladder(\n", + " estimators=estimators,\n", + " weights=lambda origin: (0, 1) if origin.year > 1987 else (0.5, 0.5)\n", + " )\n", + "vot.fit(raa, sample_weight=apriori)\n", + "\n", + "# Plotting\n", + "bcl_ibnr = bcl.fit(raa).ibnr_.to_frame(origin_as_datetime=False)\n", + "cc_ibnr = cc.fit(raa, sample_weight=apriori).ibnr_.to_frame(origin_as_datetime=False)\n", + "vot_ibnr = vot.ibnr_.to_frame(origin_as_datetime=False)\n", + "\n", + "plot_ibnr = pd.concat([bcl_ibnr, vot_ibnr, cc_ibnr], axis=1)\n", + "plot_ibnr.columns = ['BCL', 'Voting', 'CC']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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WExOjvn376r333tPatWv1wQcfaOvWrbrnnnvYemoRzq9hISEhdfZ1HlZ+7qoduG/w4ME6ePCg1q1bp6lTpxrhUWBgoDp37qysrCz98MMPGjVqVK3j7N69W1Ldvxyi4aKjoxUdHa1Zs2Zp586d2rRpk3bv3q2ysjKVlZVp165d2rVrl9q2bas33nijsafbZLVu3Vr5+fnKzMxUz549q7S7ht7Z2dk1/r/vDEE4KP78+fv7y263Kz8/v9Z+ru3VHWLtXE3rDLNQf+3bt1dmZqYOHDhQ7b8Ldzn/4FHdTUeaM1Y+AR7g/OKTk5Ojvn37nvd/3Hmt4Zx/iXN3hUzPnj315JNPKiQkROXl5Xr11Ve1Zs0aM6fYotS3HjVxhk6uf+nG+bHZbJo5c6YWLlyoiy++WOnp6VqwYIFeeuklfiC1AOcPou6sEnCu7PD39zd1Ts3ZhAkT5O/vr8zMTP3jH/+otFrmqquukiS9/fbbxhak6vz000/G9qNhw4aZO2EYfH19NXLkSP3+97/Xq6++qrvvvls9evQw2p0rb3F+nOdoOc/SPJfzfDpJiouLq3Ec56pyfsY9f87V4fHx8bX2c7b7+fnVGmo479iJ+nP+oe6///2vccOK+srKytJ///vfSuO1FIRPgAc4z4PIzMxUYWFhI8+mZXPe8ak+Z6BcdNFFeuqpp4xtYG+++SYr0DzE+cNPTWc93HjjjbrxxhvrPKPDuZ0yODjYsxNswXr27KnFixfrtttuk5+fn7Zs2aL/9//+X42/aMDzduzYoY0bN1b6z7mU//jx43Ve79yixxk3569jx4669dZbJUnbt2/XY489pk2bNqmoqEhXXXWVhg4dqpMnT+qxxx7Te++9p+TkZGVlZSkjI0M//PCDXnvtNT311FMqKSlRSEiIpkyZ0sjvqGUKDAzU1KlTtWjRIj3//POaNm1alUPkUT/OQ8YTExP14osv6ujRoyopKdHRo0f1t7/9Tfv27dPw4cMVEBCgDz/80Dig3NWmTZuMA5oHDBhwQeffnDjPJP3mm2+0adOmavukpKToP//5j6SKlf0+Pj5V+qSnp0tyb2UtqudcIXv69Gk9/vjjWrVqldt/uMvNzdWnn36qxx9/XLm5ufL19a10M56WgG13gAc4Vz45HA4dOHCgxsMZYb5evXpp7969io+P12233eb2daGhoXr66ae1YMECHT9+3PhhCQ1zySWXKCUlpcaDwt3dppqSkiJJdR6Qjfrx9vbW9OnTNWrUKL3++uvas2eP/vnPf2rDhg267777Gnt6zd57771XY1tiYmKtB/FL/1u2X92ZanDfVVddpcLCQr3//vtKT0/Xyy+/rGXLlumiiy5Sx44d5evrq7Nnz+rjjz/Wxx9/XO0YrVu31qOPPtrgc0DQcOHh4br99tvr9TMAqpo4caK++OILZWdna/v27dq+fXulduf3j+DgYH399dd6+umnNWDAAF1yySUqLy9XUlKS8TXKZrO1qDt6edqVV16pNWvW6PTp03r55Zf1zTffaNCgQQoODlZhYaESExP1/fffq7y8XF5eXvrFL35R7Tg7d+6U9L9zNFF/ERERuueee/Taa6+psLBQ7777rt59912FhYUpPDxc7du3l81mM46dKCoqUk5OjtLS0ozwT6q4mc4999xj/NG8pSB8AjzA9U44qamp5x0+hYSEqG/fvp6aVos0cOBAffTRR8rIyNCePXvqVYsOHTro6aef1sKFC3X48GETZ9lyOIPZ6v4i6q7y8nJjKXldB8nj/HTu3Flz587Vpk2b9Pbbb+vAgQOaM2dOg84zQMMkJCTol7/8Za3n1yUkJEj6353WcP5uuOEG9erVS6+//royMjJUXl6uw4cPu/W9YNCgQZo5c6a6du16AWYKd3l5eTX2FJo0m82m3//+9/rzn/9c5S6O3t7emjlzpiIjIxUREaGkpCQdO3ZMe/bs0Z49e6r0ve+++1iJ1gABAQH63e9+p0WLFqmwsFDJyck1rvCfMWOGevfuXeX506dPa8eOHZJYhdZQEyZMUPv27bVixQrj7pvp6emVwqXadOnSRbNmzdLgwYPNnKYleTkcDkdjTwJoDrKzs+VwOBQQEMBfPhvZgw8+qFOnTikyMlILFiyo9/WFhYVatGiRsdpm5cqVnp5ii5GXl2f8pW38+PHVHoBZl2+++UavvfaaJOnZZ581zqGAOQoKCvTWW29VWdrPvwPPqe38IFft27evduuEVLFl79lnn5UkPf7444qJifHY/Foyh8OhH374QXFxcdq/f7/S0tJ07o/KQUFBioiIUJ8+fTRy5Eh169atkWbbvDz00EOSpHnz5rHK1UIKCwu1fv16JScny263KywsTBMmTKi0YuP06dN64403tGPHjkr/XqKjo3Xrrbfyh1UPSU9P17/+9S99//33VdrCw8M1Y8YMDR8+vNpry8rKjKNB2rRpc14/j6Gy8vJyJSQkKCEhQcnJycZW+Op06NBBvXv31rBhwzR8+PAav7c3d4RPAFANu91uHJLtvHUt0JLs3btXn376qXEA8/z58xt5RsCF53A4VFhYqOLiYrVq1UoBAQHGHfEAVHbmzBmlp6fLx8dHnTp1Mm46As/Kz89XamqqCgoKZLPZFB4ezspLC3BusTt79qxKS0uN7xnOrXggfAIAAAAAAICJWG8HAAAAAAAA0xA+AQAAAAAAwDRsWgc87Oeff9ZPP/2k0tJShYWFaeDAgVXOh/jqq6/0zTffKD09Xd7e3urcubOGDBmia665hsPKPYhaWAv1sA5qYR3UwjqohXVQC2uhHtZBLaylsLBQO3fuVGJioo4dO6bs7GydPXtWJSUlCggIUJs2bRQREaGoqCiNHDlSYWFhjT3lRsWZT4CHnD17Vn/729+q3IGic+fOevjhh41bYb/44ovavn17tWOEhIRo7ty53DmngaiFtVAP66AW1kEtrINaWAe1sBbqYR3UwloKCgr04Ycf6uuvvzZuzOKOgQMHatasWS02hCJ8AjzkqaeeUmJiYrVtQUFBWrx4sbZv3663335bkmSz2RQREaGysjIdPXrU+MLVpUsXPfvss/Lz87tgc29uqIW1UA/roBbWQS2sg1pYB7WwFuphHdTCOtLT07Vo0SJlZWXV2MfX11ddu3bVqVOnVFBQUKXtV7/6lWJjY82equWw7Q7wgO3btxvfEDp16qSrr75aXbp0UWZmpr744gtlZWVpzZo1xl8ibrjhBl133XVq1aqVpIr0fMWKFdqyZYsyMzO1adMmTZo0qdHeT1NGLayFelgHtbAOamEd1MI6qIW1UA/roBbWUVJSoiVLligrK0ve3t4aPXq0Bg0apODgYBUWFiopKUkbNmxQUVGR+vbtq1/+8pc6ffq0kpKStGXLFu3cuVN2u13Lli1Tq1atNHr06MZ+SxcU4RPgAVu2bJFU8Q3hr3/9qwICAoy28ePH6/e//73WrFkju92uyy+/XDfffHOl69u2bav/+7//U2Zmpvbv36+EhAS+KZwnamEt1MM6qIV1UAvroBbWQS2shXpYB7Wwjq+++koZGRlq1aqVHn/8cQ0YMKBS+8iRIzVlyhQ9+eSTWrt2raKjozVmzBiNGjVKo0aN0v79+/X8888rJydHb7zxhgYMGKDAwMBGejcXHne7Azzg4MGDkqTp06dX+oYgSQEBAZo2bZqx3HXKlCnVjuHl5aXJkydLkg4fPmzibJs3amEt1MM6qIV1UAvroBbWQS2shXpYB7Wwjri4OEkVn+dzgyenrl276vbbb5ckrVq1qlJbVFSU5s+fLz8/PxUUFGj9+vXmTthiCJ8AD8jLy5OkGg/wu+iii4zHERERNY7jbDt3bzDcRy2shXpYB7WwDmphHdTCOqiFtVAP66AW1nH8+HFJUkxMTK39nO2HDx9WYWFhpbbQ0FBj5dnOnTtNmKV1ET4BHuDtXfFPqaysrNr28vLyah/X1M/Ly8uDs2tZqIW1UA/roBbWQS2sg1pYB7WwFuphHdTCOoqKiiSpygq0c9lsNuOxMzx0NWjQIElSRkaG5ybXBBA+AR4QEhIiSTp06FC17c7lspL0888/1ziOcxlsUFCQx+bW0lALa6Ee1kEtrINaWAe1sA5qYS3UwzqohXU4P3dpaWm19ktPTzcet27dukp7cHCwJFVZFdXcET4BHhAZGSlJ+vTTT3Xq1KlKbadOndKqVauMBHz16tXVjmG32/Xll19Kki655BITZ9u8UQtroR7WQS2sg1pYB7WwDmphLdTDOqiFdURFRUmSvvjiixpXokn/O+upXbt21YZ9Z86ckVRxGHxLwt3uAA8YP368tm3bptzcXM2ePVvjx483boG6YcMGFRQU6KabbtL27duVkJCgV155RbfccovatWsnqSIdf/PNN3XkyBFJFXdKwPmhFtZCPayDWlgHtbAOamEd1MJaqId1UAvriI2NVXx8vFJTU7VkyRLdc8896tKli9Gen5+v9957T5s3b5YkjR49utpxUlNTJUkdO3Y0f9IW4uVwOByNPQmgOXjhhReMOyCcq1OnTlq8eLF27dqlpUuXGs+HhISorKxM+fn5xnPdu3fXokWLjP3dqD9qYS3UwzqohXVQC+ugFtZBLayFelgHtbCOJUuW6LvvvjM+Dg0NVUhIiAoLC3Xs2DHjbK2QkBD99a9/rXbl0+zZs3X06FFdc801uvPOOy/Y3Bsb/9cBHvKb3/xGV1xxRZVD/KKjo/WHP/xBbdu2VWxsrK6++mqj7fTp05W+IYSFhemxxx7jG0IDUQtroR7WQS2sg1pYB7WwDmphLdTDOqiFdTzyyCPGgeFSxaHhycnJOnLkiBE8tW/fXnPmzKk2eMrOzlbHjh01ePBgjRgx4kJN2xJY+QR4WF5enlJSUmS32xUWFlbtbVF37dqlb7/9VmlpafL29lbnzp01ZMgQjR8/Xr6+7Ib1FGphLdTDOqiFdVAL66AW1kEtrIV6WAe1sI64uDht3LhRqampKigokL+/vyI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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 591 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "\n", + "ax = plot_ibnr.plot(\n", + " kind='bar', ylim=(0, None), \n", + " title='Voting Chainladder IBNR',\n", + " xlabel='Accident Year', ylabel='Loss');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/getting_started/index.md b/docs/getting_started/index.md new file mode 100644 index 00000000..3c83ac78 --- /dev/null +++ b/docs/getting_started/index.md @@ -0,0 +1,19 @@ +## {octicon}`rocket` Getting Started + +::::{grid} +:gutter: 1 1 1 2 + +:::{grid-item-card} {octicon}`desktop-download;2.5em;sd-mr-1` Installation +:link: install +:link-type: doc + +How to install chainladder +::: + +:::{grid-item-card} {octicon}`beaker;2.5em;sd-mr-1` Tutorials +:link: tutorials/index +:link-type: doc + +Our beginner tutorials. +::: +:::: diff --git a/docs/getting_started/install.md b/docs/getting_started/install.md new file mode 100644 index 00000000..5a3c77e6 --- /dev/null +++ b/docs/getting_started/install.md @@ -0,0 +1,41 @@ +(installation-instructions)= +# {octicon}`desktop-download` Installation +We strongly encourage users to install chainladder in a dedicated environment, like a conda environment. + +## General Installation +There are two ways to install the chainladder package, using `pip` or `conda`: + +````{tab-set} +```{tab-item} pip + +[![](https://badge.fury.io/py/chainladder.svg)](https://anaconda.org/conda-forge/chainladder) +[![](https://pepy.tech/badge/chainladder)](https://pepy.tech/project/chainladder) + + +Installing `chainladder` using `pip`: + +`pip install chainladder` + +Alternatively, if you have git and want to enjoy unreleased features, you can +install directly from `Github`: + +`pip install git+https://github.com/casact/chainladder-python/` +``` + +```{tab-item} conda + +[![](https://img.shields.io/conda/vn/conda-forge/chainladder.svg)](https://anaconda.org/conda-forge/chainladder) +[![](https://img.shields.io/conda/dn/conda-forge/chainladder.svg)](https://anaconda.org/conda-forge/chainladder) + + +Installing `chainladder` using `conda`: + +`conda install -c conda-forge chainladder` +``` +```` + +## Developer Installation + + +If you're interested in contributing, please refer to [Contributing](contributing) +for information on the developer environment. diff --git a/docs/getting_started/tutorials/data-tutorial.ipynb b/docs/getting_started/tutorials/data-tutorial.ipynb new file mode 100644 index 00000000..1b0a6c77 --- /dev/null +++ b/docs/getting_started/tutorials/data-tutorial.ipynb @@ -0,0 +1,1619 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Preparation\n", + "## Getting Started\n", + "This tutorial focuses on data, including a brief discussion on how to best prepare your data so it works well with the `chainladder` package. \n", + "\n", + "Be sure to make sure your packages are updated. For more info on how to update your pakages, visit [Keeping Packages Updated](https://chainladder-python.readthedocs.io/en/latest/library/install.html#keeping-packages-updated)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pandas: 1.4.2\n", + "numpy: 1.22.4\n", + "chainladder: 0.8.12\n" + ] + } + ], + "source": [ + "# Black linter, optional\n", + "%load_ext lab_black\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import chainladder as cl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "print(\"pandas: \" + pd.__version__)\n", + "print(\"numpy: \" + np.__version__)\n", + "print(\"chainladder: \" + cl.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Disclaimer\n", + "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Converting Triangle Data into Long Format\n", + "\n", + "One of the most commonly asked questions is that if the data needs to be in the tabular long format as opposed to the already processed triangle format when we are loading the data for use. \n", + "\n", + "Unfortunately, the chainladder package requires the data to be in long form. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose you have a wide triangle." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age origin values\n", + "0 12 1981-01-01 5012.0\n", + "1 12 1982-01-01 106.0\n", + "2 12 1983-01-01 3410.0\n", + "3 12 1984-01-01 5655.0\n", + "4 12 1985-01-01 1092.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns = [\"age\", \"origin\", \"values\"]\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will need a valuation column (think Schedule P style triangle)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age origin values valuation\n", + "0 12 1981-01-01 5012.0 1981\n", + "1 12 1982-01-01 106.0 1982\n", + "2 12 1983-01-01 3410.0 1983\n", + "3 12 1984-01-01 5655.0 1984\n", + "4 12 1985-01-01 1092.0 1985" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"valuation\"] = (df[\"origin\"].dt.year + df[\"age\"] / 12 - 1).astype(int)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we are finally ready to load it into the chainladder package!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(34244, 4, 120, 120)
Index:[ClaimNo, Line, Type, ClaimLiability, Limit, Deductible]
Columns:[reportedCount, closedPaidCount, Paid, Incurred]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2017-12\n", + "Grain: OMDM\n", + "Shape: (34244, 4, 120, 120)\n", + "Index: [ClaimNo, Line, Type, ClaimLiability, Limit, D...\n", + "Columns: [reportedCount, closedPaidCount, Paid, Incurred]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism = cl.load_sample(\"prism\")\n", + "prism" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also look at the array representation of the Triangle and notice how it is no longer a numpy array, but instead a sparse array." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The sparse array consumes about 4.6Mb of memory. We can also see its density is very low, this is because individual claims will at most exist in only one origin period. Let's approximate the size of this Triangle assuming we used a dense array representation. Approximation can be done by assuming 8 bytes (for float64) of memory are used for each cell in the array." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dense array size: 15779.6352 MB.\n", + "Sparse array size: 4.84712 MB.\n", + "Dense array is 3255.5 times larger!\n" + ] + } + ], + "source": [ + "print(\"Dense array size:\", np.prod(prism.shape) / 1e6 * 8, \"MB.\")\n", + "print(\"Sparse array size:\", prism.values.nbytes / 1e6, \"MB.\")\n", + "print(\n", + " \"Dense array is\",\n", + " round((np.prod(prism.shape) / 1e6 * 8) / (prism.values.nbytes / 1e6), 1),\n", + " \"times larger!\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Incremental vs Cumulative Triangles\n", + "Cumulative triangles are naturally denser than those stored in an incremental fashion. While almost all actuarial techniques rely on cumulative triangles, it may be worthwhile to maintain and manipulate triangles as incremental triangles until you are ready to apply a model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Formatcoo
Data Typefloat64
Shape(34244, 4, 120, 120)
nnz121178
Density6.143513381095148e-05
Read-onlyTrue
Size4.6M
Storage ratio0.0
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism.values" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Formatcoo
Data Typefloat64
Shape(34244, 4, 120, 120)
nnz5750047
Density0.00291517360299939
Read-onlyTrue
Size219.3M
Storage ratio0.0
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism = prism.incr_to_cum()\n", + "prism.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our incremental triangle is under 5MB, but when we convert to a cumulative triangle it becomes an astonishingly large 219MB and this is despite still maintaining a sparsity of under 0.3%!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Claim-Level Data\n", + "The sparse representation of triangles allows for substantially more data to be pushed through chainladder. This gives us some nice capabilities that we would not otherwise be able to do with aggregate data.\n", + "\n", + "For example, we can now drill into the individual claim makeup of any cell in our Triangle. Let's look at January 2017 claim details at age 12." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ClaimNoLineTypeClaimLiabilityLimitDeductiblereportedCountclosedPaidCountPaidIncurred
038339AutoPDFalse8000.010001.00.00.0000000.000000
138436AutoPDTrue15000.010001.01.08337.8758638337.875863
238142AutoPDTrue8000.010001.01.07000.0000007000.000000
338195AutoPDTrue20000.010001.01.019000.00000019000.000000
438158AutoPDTrue20000.010001.01.010686.22942010686.229420
.................................
15538393AutoPDTrue8000.010001.01.07000.0000007000.000000
15638396AutoPDTrue8000.010001.01.07000.0000007000.000000
15738455AutoPDTrue20000.010001.01.09927.3512249927.351224
15838457AutoPDTrue15000.010001.01.07874.8790707874.879070
15938460AutoPDTrue15000.010001.01.03125.8406723125.840672
\n", + "

160 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " ClaimNo Line Type ClaimLiability Limit Deductible reportedCount \\\n", + "0 38339 Auto PD False 8000.0 1000 1.0 \n", + "1 38436 Auto PD True 15000.0 1000 1.0 \n", + "2 38142 Auto PD True 8000.0 1000 1.0 \n", + "3 38195 Auto PD True 20000.0 1000 1.0 \n", + "4 38158 Auto PD True 20000.0 1000 1.0 \n", + ".. ... ... ... ... ... ... ... \n", + "155 38393 Auto PD True 8000.0 1000 1.0 \n", + "156 38396 Auto PD True 8000.0 1000 1.0 \n", + "157 38455 Auto PD True 20000.0 1000 1.0 \n", + "158 38457 Auto PD True 15000.0 1000 1.0 \n", + "159 38460 Auto PD True 15000.0 1000 1.0 \n", + "\n", + " closedPaidCount Paid Incurred \n", + "0 0.0 0.000000 0.000000 \n", + "1 1.0 8337.875863 8337.875863 \n", + "2 1.0 7000.000000 7000.000000 \n", + "3 1.0 19000.000000 19000.000000 \n", + "4 1.0 10686.229420 10686.229420 \n", + ".. ... ... ... \n", + "155 1.0 7000.000000 7000.000000 \n", + "156 1.0 7000.000000 7000.000000 \n", + "157 1.0 9927.351224 9927.351224 \n", + "158 1.0 7874.879070 7874.879070 \n", + "159 1.0 3125.840672 3125.840672 \n", + "\n", + "[160 rows x 10 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "claims = prism[prism.origin == \"2017-01\"][prism.development == 12].to_frame(\n", + " origin_as_datetime=True\n", + ")\n", + "claims[abs(claims).sum(axis=\"columns\") != 0].reset_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also examine the data as the usual aggregated Triangle, by applying `sum()`. We'll also apply `grain()` so we can better visualize the data." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(prism[\"Paid\"].sum().grain(\"OYDM\").to_frame(origin_as_datetime=True).T)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With claim level data, we can set a claim large loss cap or create an excess Triangle on the fly." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "prism[\"Capped 100k Paid\"] = cl.minimum(prism[\"Paid\"], 100000)\n", + "prism[\"Excess 100k Paid\"] = prism[\"Paid\"] - prism[\"Capped 100k Paid\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " prism[\"Excess 100k Paid\"].sum().grain(\"OYDM\").to_frame(origin_as_datetime=True).T\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Claim-Level IBNR Estimates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see how we can use the API to create claim-level IBNR estimates. When using aggregate actuarial techniques, it really makes sense to perform the model fitting at an aggregate level. \n", + "\n", + "We use aggregate data to fit the model to generate reasonable development patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/dunders.py:321: RuntimeWarning: divide by zero encountered in true_divide\n", + " obj.values = obj.values / other\n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/slice.py:250: RuntimeWarning: invalid value encountered in multiply\n", + " obj.values = num_to_nan(obj.values * obj.get_array_module().array(key))\n" + ] + } + ], + "source": [ + "agg_data = prism.sum()[[\"Paid\", \"reportedCount\"]]\n", + "model_cl = cl.Chainladder().fit(agg_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the fitted model, we are not limited to predicting ultimates at the aggregated grain. Let's predict chainladder ultimates at a claim level. Here, we are using `model_cl`, which was built using `agg_data` to make prediction on `prism`, which is claim-level data." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2261-12
Grain:OMDM
Shape:(34244, 2, 120, 1)
Index:[ClaimNo, Line, Type, ClaimLiability, Limit, Deductible]
Columns:[Paid, reportedCount]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2261-12\n", + "Grain: OMDM\n", + "Shape: (34244, 2, 120, 1)\n", + "Index: [ClaimNo, Line, Type, ClaimLiability, Limit, D...\n", + "Columns: [Paid, reportedCount]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl_ults = model_cl.predict(prism[[\"Paid\", \"reportedCount\"]]).ultimate_\n", + "cl_ults" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could stop here, but let's try a Bornhuetter-Ferguson method as well. We will infer an a-priori severity from our chainladder model, `model_cl` above." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Pnd4sFsvsyKuF1wzOWaJVhIFSyiMMUjM+73PHx9jbO8pTx4aLxr3RRKWawUjSERSjk7Wje35yYJD/emWA3YeHa+43HXFtJrI+A4vl7CCbV/gbVaWOJS4MjJmoNLw0nc27tcNnYybqHUkCMFCiVZiFtynk9RkYYeAIgdFkbbPNwYE4AIeHEjO+Li8mmsj6DCyWs4NcTuFv4Iq9xIVBZc3A+7Q8GzPR8ZFJoLIwaA4H8PuESKDYgWyEwHTC4NCAIwSODiZnfF1ektZnYLGcVeSUaljFUrDCACh3IMc9dvSBWZiJjmvNYDBebPKJp6doDjvaiDETpbPFmsHYNGYioxkcOUPNwNxj3PoMLJazglxeNayXASx5YWDMRCXCQGsGInOrGcTTTpE6wHUgz0QzyOUVR7RGcHRo9pqBUsq955n4DJ44NMRdPzk863ktFsvsyeWtZtAwYmFjJipeECfSzoK8piM6K5/BdGYiKGgGqakcmWzefUIfnawuDI6PJMnk8qxdFuX0eGrWLTvT2bxb52Qm0UT/+uQxPvf9V2Y1p8ViOTNy+QVOOjuXiQUr+wyMZrCpq5nBeNrtMFQPiXSW4YRj6hmIlwuDFqMZeBzI3giiWpqB8Re86QKnDNSx4dlpBwmPAJiJz+DUWIp4OrtgxfUslqVMNp9f+HIU5yoBv4+Q31cuDPRiuam7iWxeMTyDYm4nRh2tYOvyZoZLylmYLmfg9RnkiwRALZ+B8Re88QKnmtDhwdn5Dbz3O5NootNjKZSCxDxWerVYLA65fOPKV8MSFwbg5BqUNrgpCINmYGbhpcZ5vH1tOwBDHidy3GMmCvl9iDi1iUYShX1GEtWf1A8OJOiIBd1zH52lE9lbFqNen4FSitNjjjN9JgJkKJ6e05IeFstSJZfPE/BbYdAwmkL+sidds0Bu7moCZpZ41jvsaAaXr3P69nj9BhOpKVoiQQBEhHDARyqbdxPOmkL+mklnhwbibOpupi0aZFlTiCOzdCIbM9GK1kjd0UTDiQwZreXMxLT0W197mt+/d++Mr9FisRSTzSt81kzUOBzNoNxMFPL7WNPhlJKoFVF0amySP/zGs+4Ce3wkSTjg44KVLQAMxB1BkssrEpmcqxkAbrczYxpa39lU02dwcCDB5u4mvW9s1pqBEX4r2hxhkKvDJ3JqrCAQ63U6D8bT7D46zEltOrNYLLPHlqNoMLFQeYObeCpLU9jP8lan9k9pVJCX7+/v49+fPs5/vTIAOJFEazqiLG8pPtZrmjFEAo4wMJrBxq4mxqpEE41NTjEYT7umqw2dTbMOLzWCq6c14nyuow/0aa8wqNNM9MOXB1AKxiZtLoPFcqZkczaaqKFUan1p8gEiQT8tkQD949XNRIe0E/enBwYBIwxibhE5IwyO6YV7ZVvUPTYc9JHO5hlJZgj5faxsi1TVDA5p5/FmLQzWd8Y4OTY5qz7KXjMR1Le4n/L8Dur1GfzgpT4AxienbASSxXKG5Be6n8G5TlMFYeDkAzi2/eUt4ZpmIhPu+djBIcAxE63piBIJ+mmNBFxh8HLfBADnr2hxjzWawWhiivZYkI6mEJNTuYoLvJnHmIk2dDahVMFhPRNczaDNEQb1LO6nxwqmnni6ILCeOjrC79+7tyz8NpPN86NXBgn4hEwuX7GBkMViqZ+szTNoLBXNROkpWrRtf3lLpKYwODyYwCfOz1f7JhhJTrF2meNr6G4Ju7kGL58eJxTwscFT0joS9JGacjSDjliItqgjgMYrmIoODsQJ+MQ993p9nsOzqFHk+gxczWB6h/CpsZRr4vJqEv/1cj/ffOZEWcTQk4eHiaez/Mz5Tke6auYvi8VSHzbprMFUcyCbshHLW8NVo4nS2RzHR5K8+UInCeze3b2Ak7kMWhi4mkGcrcubCXjKDoa1A3k06WgG7TFHGFTKQj40kGB9Z4ygPn5Dp6MhzMaJnEhnCfiEzuYQUJ9D+PRYyjVReYWBWeRPl5jSHnmpj3DAx03bVgLTF+CzWCy1ccpRWGHQMGIVQku9+QDLW8L0j6cr2ryPDSXJK9i5bQVdzSH+4xmndbOJQupuibjF6l4+Pc75PS1Fx0eCftdn0BEL0R51FudKC+ehwbjrPAZojwVpjQRmVbAukc7SFA7QWuFJvxqnx1Ks7ojSHA5UFgYeB7NSikde7OfaLV2s1KYoqxlYLGeG1QwaTCwUqKkZ9LRGSGfzbjN7L8Z5vLm7mddt7nLLUBjNoKs5xMBEmtFkhr7xdJG/AHDyDHQ0UUeTRzMoyXhWSnF8ZJJ1ywomJhFhQ9fsIooSmRxNIb/rF5nOZ6CU4tRYipWtEZrDgSKfgdFi+jyawcGBBMeGk9xwwXJao+aerDCwWM6EReMzEBG/iDwjIt/Rn78iIodFZK9+bdfjIiJ3iMgBEXlORK7wnONWEXlVv271jF8pIs/rY+4QaWBmRQmxkJ9MLk/WUzZiIpV1fQbdbohouanIlIPY2N3EtVs6AccP0NkUco+Np7M80zsKwHkrKmsGo8kM7R6fQamZaDQ5RTKTY1V7tGh83bLYrOoTGc3A+AC8i3slxiezTE7lWNEWoTlSRTPwCIOnj40AcO2WLlfAVfKDWCyW+skvok5nvwu8WDL2x0qp7fq1V4/dBGzVr9uALwCIyDLg48BrgauAj4tIhz7mC8Cve47bOfNbmR1ugxsd7ZLJ5kln8x4zkWPmqFSS4tBAnK7mMK2RIK/b3AU4JiIjy7p1eOlPX3XCTi8oFQYBH4PxNNm8osPjMxgreYo29Y5Wt0eKxle0RnS9oJmFbcbTWWLhALGQH59MbyY6Ne7Mv7ItSkskUJS1XDATFX4/x0cm8Qmsbo96BFz99Z0sFks52cXgMxCRNcDbgS/Vsfsu4KvK4XGgXURWAm8FHlZKDSulRoCHgZ16W6tS6nHlrGpfBW6exb3MCrcPsu4JbMIuvQ5kqJyFfHgwwSZdsmLtshibuprYqD9DQav4yYFBWiIBN3rHEAn63YW4PRZyu6CVLpwmg7dUM1jRpk1YM0zqSmZyNIf9iEiZD6ASJvt4RVuY5nCgyGRmBJfXTHRiZJKe1gihgM+9p9n4DE6MTvLgvtMzPs5iORfJLZJyFH8H/AmQLxn/jDYFfU5EwnpsNdDr2ee4Hqs1frzC+LzQVNIH2W1a73EgQ+X6RIcHE0WL/5c/8Bo+tWub+9kIg5dOT3DBihZKrV/hQOHX3xELISK0R4Nl9vWTrmZQLAyWa+FSGskzHYl01r3vlkhwWmFw2hUGUVojQeI6FFUp5S7yXmFwfCTpXquI0Fbhnurhzh8f5re//nRd5TIslnOdBY8mEpF3AP1KqadKNn0UuAB4DbAM+PDcX17ZtdwmIntEZM/AwMCcnDNa0gfZLIzGnt4cDhAN+svMRE55iAybugvCYH1nEyvaCk//RhgAZc5jcDQDQ4c2EbXFgmU+g5NjKcIBH8u0L8JgNI2+GQqDuPYZmPubzmdwaiyFiCMYvZpEIpMjm1eIFAukE6OTrhMdoD0anJVmcGw4SS6v3JagFstSJptX+BbYTHQt8E4ROQLcA9wgIl9TSp3SpqA08GUcPwDACWCt5/g1eqzW+JoK42Uopb6olNqhlNrR3d1dx6VPj9sHWfsMCppBobro8tYwfSVmItd57NEMSulsCmO+u9KwUij0NADHTAR64azgM1jdHi3TLHq0CWummkEyk6NJd3mrp4z16bFJupvDBP2+Ip+BWeA3dDYxkcqSzGTJ5vKc0mGohtZZCgOTXT0Ut8LAYlnwQnVKqY8qpdYopTYAtwA/UEr9srb1oyN/bgZe0IfcD7xfRxVdDYwppU4BDwI3ikiHdhzfCDyot42LyNX6XO8HvjW3t1kd0wfZ+ApKfQZgcg2KF9zDg06tIK9mUIrfJyxrchbs81e0lm2vpBm0x0IVfQal/gIoFJqrVTupEkWaQYlDuBKnxlJuvkBzJEAykyOby7tC67weJ//h9FiKvok0ubxycy0A2mYhDJRS9OpIKdsPwWKBbC6/OEJLK/B1EXkeeB7oAj6txx8ADgEHgH8GfhtAKTUMfArYrV+f1GPofb6kjzkIfPcMrmtGuJqBMROV+AzAiSgqrVx6aMApQ7FuWXVhAAVTUSXNIOwRBibqpprPYFVJJBE4wqQtGpyRZjCVy5PJ5mfsMzDmL9OPIZEutOs093Z6PMVxvYB7/RvtsZkLg5HklJsMeLYJg1f6Jnjdnz9Ss9qtxTJTGp10Fph+lwJKqR8CP9Tvb6iyjwJur7LtLuCuCuN7gG3lRzSeWInPIF7iMwAnoui/XikRBoMJ1i6LEQrUlqfdLWFGEhHa9JO/F+NAbokE3DIVbbFiM1Emm6d/Il1RMwDHb9A3g05sJmrK6zOoRxhcu8UJnTX5FxPpKTd3wORP9I07bTGBIp/BbBzI3gJ8XjPRcCLD3z78Mr9y9YaKfpjFwEunJzg5luLoUKLIb2SxnAm5BpuJZiQMzkWiJXkGxplaqhnE045N3JiVDg8Uwkpr8aE3binLKDYYM1FHrOAYbo+GmEhnmcrlCfp9bt/hasJgeWt4Rg7kuI6aatL33RoJ1CxUF09nmUhnPZpBoYSFedp3NYOxtNvz2Xu97dEg46kp8jNwgJmOcQBDiYKwe/Slfr72+DG+sfs4v/vmrfzG9ZuK6j0tBkwb1dJquBbLmWDLUTQYN7RUm4fiqSwiBY0BPOGl+glcKcWRoQQb6hAGV21cxo0Xr6i4LaI1gw6P1uAmnumF9kSVsFKDoxnULwzMfXo1g3TWMR1VwpSuNpFLzW7WctZ92l/V7tQs6htPcWJkkq7mcJE/pDUaRKn6m+IA9GrNoDkcKNIMjEnshguW81cPvsz/+uYLFY9fSBJa+yqthmuxnAmLphzFuUo0WBJamnaK1Hkjd0oTzwYm0iQzuZqRRPVgFsx2r2YQK67lUy3hzNDT6vgzvOU0alGaR9HiWdwrYZ7Q1y6L6v2d65tITTE2OUXAJ8RCfnq0hlIaVuq9v5n4DY6PJGmLBlm3LOYW+wPHZNUeC/KPv3Il77pyDd957iTpbP1P4E8dHW540TwTmWaEgsUyHT89MMj3XqieYJnPK5TCCoNG4vMJkaCvEFrqqUtkMCUpzBO4aUS/vvPMhEG4gmZgHMmmL7IRBivbyh3I4DSoySsYStQXfmkWKKP5NEdqF6szVVFNyWwjRCZSWUYnp2iLBhERVrRFHAfySLIorNR7TzMpSdE7PMnaZVE6m0NFDuRTYylXS3n7JStJZHL8t24sNB1jySne80+P87XHj9Z9HbPBRKQlbUMfS5187uFX+J17nnE7IpaS0844/yLIQD6n8Ta48VYsNRSykJ1FyfQQWO+pIjobKmsGxWWsT45N0tUcKjK7eOnR1+YtIV0L0++4qUQzGK/iNzgymKAlHHAT3lpLfAbGMd7TGuHUaIqTo6kKmkGx6evrTxzlnieP1bzO3pEka3X7UK/P4PT4pOu/uGZzJ9Ggn++/2FfXve87NUYur1wB2yiMlpmso09ELfJ5Nau2ppazj5Ojk2Syef7sgdLybw4mC9/vt8KgocRCfjfKJp7OFjmPwVnMQn6fW5Li6FASv0/KnoBnSmUHcrGZ6MRoqqqJCHAXxnr9BokSn4HRgqqZiY4MJVnfVSi+5/UZjGvNAHTRvPEUmVyeNe1VNAN9T1/80SE+8e39DFUJGc3nnZLda5fF6GwKFfsMxtKuZhAJ+rn+vC6+v7+/rmJ9+0+OAzQ85DM5Rw7krz9xlOs++2jdJkDL2Uk2l+f0eIqu5hDf23eax3Q/dS9GGCx4obpznZinD/JEqpCQZRARp2uZdiAfGUqwpiPqdh2bLSYDuaOp3IFsSlKcHJ1kVVt1YdAzw5IUJna/kIFsfACVhcHRoUSROSwa9OP3CROpKUaTHmHgMWOVCsn2aEEzyGTz9A4nmZzK8ZXHjlScczCeJpPNs6YjSmdzmGQmRzKTJZPNMxhPF8315gt7OD2eYp9e6Gux/5QWBg3OWzC/4zN1IP/41UEG4+m6TYCWs5P+iTR5Bbe/cQtrl0X5xLf3lz0AZLUwWAyF6s5pelojvNw3gVKKeDpblGNgcNpfGjNR8oz9BeCUu771mvXccMFyd6wlEiQc8PHEoSGUUlWzjw1dzU7Ji1q5BulsjhdOjAGeDGtPBjJU7mkwlctzfGSyqG+zqXQa12Yis9D3eCqyerOPAbfBzdjkFMeGE+SVY576ymNHKoa1mkgix0zkaE1D8YyrmXmrv95wwXJE4OH905uK5kszmHSFwZlpBs/r76xeE6Dl7MSYLTd2NfG/3nYhL/dN8FDJ33PeagbzwzsvW8XhwQS7j4wUtbz0srzF6YXshpV2npm/AJzIgE/s2la0ePp9wu+8aSsP7e/jK48d0U1tKjuPzf7dLeGaWcj37u7lHf/7J7xwYoxE2gmdNVFUlZrcG06OTpLNqzLBZxLVxkrMRIbSMNhI0E8k6GNscopDA46/5U92XsBEKsvXHi/3HXgjmLp0T4jBeNpTPbUwV2dzmCvXdUwrDFJTOQ70xxFxhMFMe0DMBNeBfAbCYDCedkuHz7T2lOXswhs+fs0mJ7nzVMkDQNb1GSyO5jbnLG+/dCXN4QD37u7VPoPybOHlLRH6J9KMJqeYSGWLWlDONb/5hs3sWN/Bp//TcSZVyzEwTJdrsPuI03nsK48dIZHO0RQqhM56o4NKMVFTG0qEQUskwHhqivFUQRgYzaAjFiwzs4GuT5SccluF7tq+itdv7eLOnxwqc5Ka7OPV7TE6PZqBWRRXlERWveWiHvafGnf/qSpxoD9ONq+4bE076WzeLTvSCExk2pmYiYxWADOvSmtpDNlcvuxv9WuPH+Vtf//jM3q4MAv/yvaoq6mXasyuA9maiRpLLBTgndtX8Z/Pn6wYTQSOZjCanOLlvgmgfIGcS/w+4XPv3e4mpdUyE4HT16BSJzbD3l5HGNy/9yTHhpOuvwCcp/aQ31dZGOiFe0NXseBriQQ4OepkRrdp53dXc8jpblbFqd4edQrwHR5IuN3hfusNmxmMZ3jkxf6ifXuHncS1aMhPp9YMhhIFzWBla/Ec15/nVLB98nD1ENN9J53F9Q1630aaiuZCM3j++Bgizt+CFQaN5ZljI9Pmnvzk1UF+5q9/yC9/6Ymi8ccODrL/1PgZ5a6cHJ2kNRJwG0E1hwNlDatMaKk1E80D792xltSU47QpzTOAQuLZniNObb3SBXKuWbssxp/9/CWs6YjWrIwKhUieSgzF0/QOT/LeHWvJ5PI88lKfm3VtcCqXlv8xHxlKEAv53fadhpZI0H16N5pBwO+jpzXC2o7KvxdTufTQYNwt43HF+g5EnMJuXnpHkm6Sm+knPRjPcHosRSToozVafP2bupvw+8Q1QVVi/8lxmkJ+dmxwOq02UhjMhc/g+RNjbOxqYnlLuKilqGVuSU3leO8/Pc7nHz1QcXs2l+cj//4cv3znE5waS/Hs8dEi5675m/OWT5kppX7BVq15e8nltAPZCoPGc+maNrdHcUXNQJtBnjwygki5k7QR7Nq+mp98+AY34qcaPa1hxianKsak7+0dBeAXrlzD67d2oRRlZpyWSICRRLkwMI7y0j4K3taXxoEM8Lfv2c4f3nhexWts1cXqDg8mXOEWCfpZ2xHjwEC8aF+TY2D2adElKU6Np1jZVt7XIRzws25ZjIMl5/Gy/9Q4F65sdc1ZDdUMMsUtVGfDCyfGuGR1Gz0zLDey1Dk1NsmhGn8HpfQOJ8nk8jxxeLji9h++PMA9u3v5wLUb+NOfvYipnBP2DI5T1yRlegsrzpTS8PHWaLDcTGQ1g/lDRHjva5zeO9UcyABPHx1hVVu0ahLYQlArvHRv7yh+n7BtdSu/eu1GoLjuEsDla9v56cHBsvpE1Rzl3mgrbzXWazZ3smV55Uqi7bEgJ0YnGYxnisp4bFnezMH+wj9vNpfnVEnimslCPj2Wchv6lLK5u4mD/ZU1g3xesf/kOBevanW1nOmEwff39/Ffr8yum95sNIPUVM71MQxMOM7jS1a31dT6LOV87D+e53fv2Vv3/qZJ1b4TY+735uVV/bf5+285j4tWOT1JzEPHqfGUa00wAmI2nBorLlHfEqlgJso789hyFPPEu65cw3t2rOG1G5eVbTMlKeLpxjqPZ0NBGJQvcHt7Rzmvp4VYKMAbzutmy/LmMgfsz162itHkFD/1JLvk8k5zmUohtF7NqS1aW2vx7mf8El5hsLm7iUODCddBdnAgQTav2LK82d2nU2chnx5zNINKbO5u5rDnPF6ODSdJZHJctKqVtmiQoF+mzTX4i++9xB2PvFp1+1hyquJcU7k8GW1GmE4YpKZy/PkDL7Lr8z/lkj99kLf87Y+YSE25YcCOZjCzqrRLiW8+c5xnteYLTgHJ546PVexXXo2jOkgim1euFu3l0ECc7hbHx7Wpq1mPJdxtht5ZagYJXfCx2EwUZKLEbOtGE1lhMD+0RIL85bsuc01CXjqbQu4X0Wh/wUwxTxV/9/1XeOzAoBvZkNd/4NvXtgOOvfG+37yGz/zcJUXHv35rN62RAN9+7qQ7dnJ0kqmcqqgZtHrMVu11CgPvfpu6Cwv9luXNZLJ5TugnK/PPba4ZnN/9wESavvFUUT6Dl83dzWRy+Yrqukk2u2hlGz6f0NUcrqkZZHN5jg4lqjbVSU3luO4vf8B9T/WWbTMCwCfF0USHBxP82t27i8aeOTbKP/3oECjFL161jpNjk/zVgy/z/AnHeXzx6jZ62iJuS1FLMZ/+zotFArt/wknQm0nvDOMXg4I/0MvBgTibtVmzoynEsqYQh3SXQyMUuprDs9YMTumqwN7E0sqagRUGiwZnEXGcmXORcDaXbO5u5k92ns9Lpyf4xS89wbv/8b9JTeU4PJRgIpXlcs/C2h4LlZnBQgEfO7et4KF9fa7fwTwxVSrT7T2+tV7NQJuT/D4p0qyMBnBgwHEi7z0+SkskUBSt1dkc5vCgozFUK9hn/BCV/AZPHBoiEvSxVbfn7G6pLQx6RxxBOFhln+FEholU1g299WIW7c7mMJNTOTdZ6MnDQ3z/xX5e7Stcn6m59FfvvoxP7NrGrdds4F8eP8o3nznBpq4mmsMBN3/DJp6VE09n2ds76j78mKTCdLY8BLQaR4YSbO1p4fyeFnYfHSnappTi4ECi6OFlU1fBHHl4MEFTyM/l69pn7TM4Oep8r9P6DGzS2eLCmIrmIuFsLhERfvtntvDYR27gE++8mD1HR/jrB19m77FRALava5/2HD972Sri6Sw/fNmxkx8uqVbqxfgMwgFf3b4TY05a2xEt6g63Wf+jHdC22Wd7R7lsTXtR1ERXc4gpHU1RSzMAyvwGSike2t/H9Vu73WvtnkYzMOp/IpOraEc2T56VnkCNZtDVHEYpSGULZU6guIWnqblkIqb+6K3ns6I1wuHBBJesbiu635l0s1sKZHN50tk8Q4mM+1RuNECo/N1U4shgko2dMXZs6OCZoyNFpr/hRIaxySn3bwuchw6jGRwciLOpu5m1HTF6hydnlWtQKFFf4jNIZYvO55ajWAzCQET8IvKMiHxHf94oIk+IyAERuVdEQno8rD8f0Ns3eM7xUT3+soi81TO+U48dEJGPzOH9zSnGibzYNANDJOjn1tdt4JevXsedPz3Mvzx+lOZwoOiPuRrXbOqksynkmoqODiaIBH3uPXsxmkF7hVae1TDCoLQHRHssRFdziIP9CVJTOV46PcFla9uK9unyhLZW0wxKVXjDCyfGOTWWKmow1N0Srukz8GoXlUxFpnPdWIWS3KbgodEijXAYrygM0ogUKtU2hwN8apfT/fUyrc3NtPbUUsFbHvzpY84T/X5Pfap6yqWnszlOjk2yvrOJHRs6mEhnefl0Icz5oDYDbe72+riaGYxnnATKAScybk1HlMmpHMOzqCF1cnQSkeKHnNZIkFxeFfmcFls5it8FvPVVPwt8Tim1BRgBPqjHPwiM6PHP6f0QkYuAW4CLgZ3AP2gB4wc+D9wEXAS8T++76DC+hMXmQC7lozddyNqOGHt7R7l0TVtddsaA38fbLlnJw/v7+Nn//RP+5fGjbOhsqvgkYkJd63UeQ2HB21RBMG3qbubAQJx9J8fJ6SxhLyYLGcqzj71Uiih6cN9p/D7hTZ76T90tYYbi6YoOYKAoX6GSMBjRT52VEo2Mmcj0PjbCwaj9Xo1kMJFhWSxU9P28+aIe7vvNa3jfVeuAwv3aiKJikp7GQcbxu+/kmNsbpFKodCnO07zjA9yx3gka2XO04DcwGmKxZuC8339qnJNjk2zsanIj33pn4Tc4OZaipyVSVPTSmF69iaCLxoEsImuAtwNf0p8FuAG4T+9yN3Czfr9Lf0Zvf5Pefxdwj1IqrZQ6DBwArtKvA0qpQ0qpDHCP3nfR8Y5LV/KBazdULLewmGgKB/jrd1+GCFy5vqPu437xtevY0BmjPRbkltes5U/feXHF/YyZaCbCwJhCvFFChi3LmznQH3f/qS/z+DicY52F1a+dv9XY3N1c5jN4aP9prtqwjI6mgkDpbgmTV1R9kjs0kHB7RHu7rBlGtGZQy0xkQliTU4V2qqXnG4qniwSdYceGZa5JqzkcoCnktz6DEhIeh/re3lHiaceHc83mTqCy1hZPZ3nu+Kj7EGAy7Nd3Ogv6itYIe44U/AYHB+KEA76icjBGS3j05X6UcoTDWv1wOBu/gZNwVvyAU6nHSH4eylHUu6r9HfAngAki7wRGlVLmGzkOrNbvVwO9AEqprIiM6f1XA497zuk9prdk/LWVLkJEbgNuA1i3bl2dlz53XLuli2u3dM37vLPhqo3L+PaHrqurT7PhwpWtPPT7b5h2v4IwKF/IqrF2WYy7/scOXre5/Pe3pbuZsckpHn2pnxWtkTK/gDG5LG8J13wy2tzdzD2JXkYSGTqaQhweTPBKX5yP/2zx34o316C7ghns4ECcKzcs40evDNQ0E1USBmaRMkIrkS72GQyU+AyMoKtFT1tkRuGSM6VvPEVbNLiocmemwyT0bepuYt+JcZ7TDxLXbO7igedPF30333vhNH/3/Vd4pW+CvIJP37yNX756vZswtlEnVl65oaMooujgQIKNXcXa8dplMQI+cQsjbvJoBrOJKDo5OsnFq4vNoq2e1rIGoxkEFrK5jYi8A+hXSj3VsKuoE6XUF5VSO5RSO7q7uxf6chY921a3VUygO1PMOWeiGQDccEFPxQVns9YWfnpwsMxfALj1iWqZiJzzOILP+A0e2uf0lH3LRT1F+xkBYBbmp4+NuEXuxpJTDCUyXKXLVlSKKKptJtI+gxbjM3AWLRM37j3fUCJTUTMoZUVrpGGagVKKt9/xk6rlGBYrRshet6WLTC7PfU8fB+BarRmMer6b+57qpW88xYdu2MrGria+tfcE4ETMtUYCru/rqg3LODmW4lVdHuXQQNz92zQE/T7WdcbcZLWNXU20RIK0x4L0Ds9MM1BKcXIsVVaI0tUMPOGlRptZ6H4G1wLvFJEjOCacG4C/B9pFxKw0a4AT+v0JYC2A3t4GDHnHS46pNm5ZpDTPwkxUC2M6UqrcRAROjoLfJ1Wdx4bSiKIH953m4lWtZaVDXGEwkSY1leNXvvQEH/uP551jtSC5YEUrLZFAFZ+BoxnE01mmSpqQmFaXRjPwNk2CYs1gMJ6uafYyOFVpGxNNNBjPMBhP16zrVEomm+dX7nyiKOFrvjFC1mia//ncKZY1hdjY1UTI7yvSDPrG01y2tp0/eMt5/Pzlq9l9ZITTYyknw76rUG7lHZeuJBTwcfd/HyGdzXFsOMnmCpq1+Ttb0RpxTcZrOqIVNYPe4WTVMNehRIZMNs+qkr9r4zPwmokKoaULWMJaKfVRpdQapdQGHAfwD5RSvwQ8CrxL73Yr8C39/n79Gb39B8qJkbofuEVHG20EtgJPAruBrTo6KaTnuH9O7s7SEMIBP2+/dCWv3zo3JrOVrRG3v0Kp8xiccLor13dwxbra/o81HTFCfh8v903w1w++zNPHRrlp24qy/bo8ZqIfvTJAIpPjx68O0D+RcktjbF7eTHdzuKLPYMyz0IyXaAcmyqUgDLRmYHwGWjNIZ3NMpLKuL6UWy3V9onwVh/eZcGzYEQIzcVD3T6T48auD7K6QpDVfmPpPW5Y30dMaJp3Nc9HKVkSEtliwyGfQN+44acEpVw/wn8+f4khJF7/O5jDvvGwV//7UCZ4/PkZeUaYZQCGnxRsZt6Y9VuYz6B9P8ZbP/Re/+pXdFYMVTFjpymqawWJ0IFfhw8AfiMgBHJ/AnXr8TqBTj/8B8BEApdQ+4BvAfuB7wO1KqZz2O3wIeBAnWukbel/LIubzv3gFb/RE6JwJPp+4Jp5L1pSbiQC+8RvX8Guv31TzPH6fsKErxpd/epj/79EDvHfH2orHNGmn7MBEmu/tO00k6COvnBLfhwYTBP3C2g6nsU6lEFSjGUCxOQKcKBefFPpaFzQDZ7/xVJZ0thCG2FmXZhAmm1cMJ2ceujgdx7Rpo5oZKpvL84//dbDo6dbkXlTrm12NuezlnPT08jbZ6qZ2ULsuimjmHIyn3ZpWm7qbuWhlK//3mROcKOniB/CBazcwOZXjz7/7EkDFsGwz5q0mvHaZoxl4cwO+8tgRUlN5Hjs4VLG0iTE1lZqJjM/A+6Cx6DKQlVI/VEq9Q78/pJS6Sim1RSn1bqVUWo+n9Octevshz/GfUUptVkqdr5T6rmf8AaXUeXrbZ+bq5ixnDzvWL+Pyde1FpS5mw2Vr2okG/fz9Ldv57LsureoU7W4Jc3J0ku/v7+Ptl6ziktVtfPOZExwaiLO+s4mA30dXS4ihig7kKbfMeakTOZnJEQsF3J4RSY8DuVU/8Q3FM4WEs3p8Bia8tAF+A5NpXk3zeKZ3lL/47kv85NVC3arZVGX99rMn2f7Jhyu2OZ0NRhDFQgG2r3U0xou1MOiIhVyBPZTIkFcUlZh5x2Uref6E8+RfmlR58ao2rtq4jKd0NnJpXgwUIoq8YdJrOmKks3n34SGezjqNby5ZwS9csYY7fvBq0e8QnIqo7bGgWy3ZEAn6CQWKe4yYqqWLRhhYLI3i/3nHRdx72zVnfJ5P7trG4x97E7u2r665X3dLmB++0s94KstN21bwc5evZt/JcR47OOT2W+iqYiYaSWbcKK3SEMZkJkss5Ceme0YkMzkyWSdb1iweg/G064voqkMYLG9g4tkxT6G2oQqhtubp1BvKaZ7KZ6IZ/OsTx4ins3N2D0bjioX8vPnC5WzubuK1Gx3ncVusoBmY+bwRau+4ZJX7vlKdsV+9dgPgJDhWCiPftrqNX3rtOnZ6TJCm/4bpa3Dv7l7GU1l+/fWb+NTNF7Olu5nfu/cZ93c2lcvzg5f6edMFPQQqtLIs7WlgqpYulqQzi6Vh+H1SVKZitkRD/mn7P4AjDFJTeWIhP9dt7eKd21fh9wkTqay7aHc1O30ivKW983nF2OSUKwwqawZ+/D4hHPCRzGTdp2EjZAYm0p5SFPU5kKExJSmOeiJgKi3UZkHyZsMmXDNRffV/+sZTPK670J1JRzAviUyWUMBH0O9ja08Lj/zhz7gaVLtupOTM7fzOvKXP13XGuFSbIytVE3jzhT2s6YiWPbEbwgE/n/m5S4rMOyZI4fhIkmwuz10/OcxVG5Zx+boOYqEAf/ELlzIYz3DfHieKfvfhYcYmp7jx4p6Kc7RGgsWagf4TtJqBxTLHmFyDN16wnEjQT1dzmOu1Q9yYAbo8LTcN46kp8go2altz6eLmaAbO02Qs5CeZyZWV7h6Mp91z1mMm6m4JI9KYLORjw0l30atkhjLX7jUJGad4vWaibz97EmNKn0lF0Vok0zk3MbCU9mk0A4APXreR67Z0VXTgB/w+7v2Na/jsL1xa9/UYwXDfU8f5o//zLCdGJ7nt+oK/6sr1HVy+rp0vP3aEXN6plxUO+KoGYTiVS8s1AysMLJY5xoSXeqON3r3DiXC+cKVjezYL9eBEwXxicgxM1mk1zQAce3Yik3UX1A2uMHB8BqGAr648kKDfx4rWCI8fGprTiKJkJsvARJqrdP+OSsLGLEhFmoHWCOIV+mZX4tvPnnTNYXMlDBKZbNVKAO2xEJNTOVJTOfrHU/iEskV/1/bVfO3XXlvWNc+wuj1asZR9NZrCAS5Y0cKPXx3kof19XLulkxtKAiw+eN1Gjg4leeTFPh7e38frt3a7Dw6llFYuXezRRBbLWctVGzvZsb6DN55f+Ie9adsKvv8Hb2Cbzgg1moE318BkH3c2h2iNBMo0g0QmRyxc0AwmMzn3n7qrOUxLOMDARJrBeIauplDVxaiU29+4hScPD/PPPz40/c51YiKJrlzfgd8ntTWDTLlmUI/P4PBggmePj3HLa5ws8NLoq9mSSGfLenkbTP7L+OQUfeNOLkclu/xc85+/83oOfOYm9n9yJ1//tavL6nrtvHgFq9ujfPI7+zkxOlnVRASFyqWGvBUGFktjuGrjMu77rdcVPV2KSFHtJLdsRZEwcBaz9liI9ljIFQ6GyUyWmI5gioUDJDI5JvSi2RIJuBVThxLpusJKDb/02nXctG0Ff/XgyzxzbGT6A+rAOI83dDbR3RyurBkYn4HHP2C0hEQdDXfu33sSEXjfa9chAmNzFB6bzOSIhSubiUxY7+jkFH0T1RsizTV+n9QUOgG/j1tft57jI5P4hKLiiaW0RoJFZqLsIqtaarEsKUxJCa9mYEIWO2Ih2mPBcs0gXVikYkE/yXTBTNQaCToRStqBXI+/wCAi/MXPX0pPa4TfuecZ0tn6+ytXw2gG6ztj9LRFqjiQyzWDxAx8Bvc/e4KrNixjdXuUtmhwXjSDdrdyaYa+8XTVvtkLwXtfs45YyM+V6ztqPgy0RksdyIuon4HFstSIhQLEQn438gcKPoOOWLDi4jY5VfAZNIWNA9nZpyUSoKsl5DiQ4+m6Iom8tMWC/P5bzqN3eNJ9qj8TCrV5QqxoDVc0E7k+A69mUFJ8rxrxdJaDAwmuP8+pI+ZNBiulbzw1o7BTr2+mFGMmGp2con88NSPbf6Npiwa563+8hj8raT1bSks4wORUzi13YjudWSwLjJNrUOwz8InzlN8WDRaVpoDiJ9ZYKKBDS51FszkScPsvDyYydeUYlGK0ifE6nbe1ODacdEMrV7ZFK5qJKvkMzPt0Nl8zq7h/3LR0dBbjtmi5JmX4k/ue40/ue67ua09kslWd70YzcKK2Mm4pisXC1Zs62dpTOWzVUNrTwDqQLZYFpqs5VGYmaosG8fnECWEsKRmQzuaJutFEBc0gGvQT9Pvobg4znsqSyeZnZCYymCzmucjkPTacdBs19bRGmEhly0w/lfIMvFpCokaugYnxN+1i22KhqmaioUTabQ5fD15zXCmmkZLpN72YzET1Uqhc6vy+5qOfgRUGFksNHBt/sZnIOCjboyHGJqfcejQmyqZYM8gRT2fdSq9dnv4JMzUTgbfW/ZlpBrm84vhIknU6X2JFm3MtpdpBpTwDr5ZgSnNXwvRgMItxezRY1YGcTOdmFHZay2fQFPIT8Amv6FLU8+VAnktKv2erGVgsC0xniZloLDnlmiHaY06vWhNiaZ6eizWDLOOprPuk5y1ZPRvNoGWOhMGpsUmmcor1Hs0AoK/Eb1Apz6BSzkEl+rVm0K01g1JNyksy4wiDeprKZ3NOeY9qMfoijtZmhEGlBkaLndIy1rm8wifUHYo8G6wwsFhq0N0cYjiZcW3jI8mMa4Yw/7DmidYskqZIXSzsJ6+cstVmEff6CerpZVBKyxyZiYwD2piJTMkLr2aQzuZI61IcSa/PIJ0lrEuH1Mo16J9IEQn6XNOWKRNRKXEukcmSyeWZrFL734spE95UxUwEjqnI1JU6GzWDUjNRTqmG9jIAKwwslpp0tYRRCrd89KhXM9DCwDhFjSklGtRmIp1v0DeechdE71PqbDQDU/foTDUDU5OoYCYqFwZmjnDAV6QBTE7lWN5q2npWv46+8TTLWyLu02xbLIRS5VqNUsotiz1Sh6nI+Cxq9SI3343fJ3X1jFhslDqQc3nVUBMRWGFgsdTEzULWfoORZKbgM4gVl1iYnCrVDJzF6vR4qqKZaNksFikRoTkcqKoZ/PtTx/nT+/dNa2558vAwIb+PlW1OTZ1YKEBrJFBkJjIL0Yq2CJNTOTe8MZHOuQl502kGXudtW4nwNGRyedcmXprEV4lC+epamoEz1/KWcENj8xtFocGN6ctghYHFsqB4i9WlszmSmRwdHp8BlGsG3kJ1AKmpPC1hZ99I0E9LOEBLOEA4MLsG9KWlCgxKKf7ukVf4ymNH+Lcne6se/+9PHeebz5zgA9dtKFpgVrRFijQDY6IwZhYj7JKZrDtWUxhozcDQ7sb/l2ZtF7SOepzIpY76SrRFHUG7mHIMZkJzKIBIIYQ4r6wwsFgWFGNTf/roaFEpCvAmNzmL26Snxj4UL1bmSQ8c09NsTESFcwUragZ7e0fpHZ6kIxbk0/+5v2Ji2gsnxvjYN5/nmk2d/PGN5xdt62mNFCWeGc3A9J5OprPk84pkJsfylunNRP0TadecBAXhWam4n2GkDs3AmKyqhZZ65+o5C53H4GQaN4cLlUuz+XxDE87ACgOLpSYr2iJct6WLe3cfc6OKOkqFQdI0gNFmohLNAHBDS8GpiGnMM7Ohmmbw7WdPEfL7+LfbrsbvE/7o/zxb1Ht3Kpfnt7/+NB2xEP/7Fy8vq6OzorVEM9ACxziXE5mcqx2YJ+5qwiCRzhJPZ4s1g1ghM9iL1zk9V5qB0ULORuexwdvTIJdvbCkKqEMYiEhERJ4UkWdFZJ+IfEKPf0VEDovIXv3arsdFRO4QkQMi8pyIXOE5160i8qp+3eoZv1JEntfH3CGNjJ+yWGbIL752HSfHUtz/7EkA10wUCfqJBH2umWhSL1JRTwlrg7fhzp///CX85bvqr5VfSmskUOaEzeUV33nuJG84v5sLVrTypz97MU8eGeabz5xw9zk5Osmx4SS/9+atFSOZVrZFGJhIu5FTRvswzuVEOuvmGLRFg4T8PrcIXyn9E+VNZYzppjTXIFlkJqpDM8jU4UDW/pizMeHM0BoNekJLF4dmkAZuUEpdBmwHdorI1XrbHyultuvXXj12E7BVv24DvgAgIsuAjwOvBa4CPi4iHfqYLwC/7jlu5xnel8UyZ7z5wh66mkN8/fFjgFMjyNAeLVQuTVQILTV4zURrl8XcfgizobWCmWj3kWH6J9K88zKnpePPX7GaaNDPS6fG3X1OaROQ6cpVSk9bhLwqVGkdn9QOZP10nczkPJE8fpojgaqaQaWmMqWalMEbqVSPZmDmrBlaquc6W30GUNzgJptX+Br8jDytMFAOcf0xqF+1QhV2AV/Vxz0OtIvISuCtwMNKqWGl1AjwMI5gWQm0KqUeV04IxFeBm2d/SxbL3BIK+Hj3jrWus9SYiYCiyqXmCTcSKCSdGVoj1Z9iZ0pLBc3g/mdPEg36edOFTllkEaGnNew+oUOhk5l50i9llTZdnRx19ptITSFCIYw0U9AMYqEATWF/1aQzM+9yj80+FPARC/krFPcr3Es9oaWljvpKmEitFWexMPCaifJ5RcC/8JoBIuIXkb1AP86C/oTe9BltCvqciJhvfTXgDWU4rsdqjR+vMF7pOm4TkT0ismdgYKCeS7dY5oT36eYsUCwMWj2VOJPpLLGQ37XtVjMTnSktkSDxdNYNH53K5fnu86d480U9RXMubykuS238AdWEwUpdUM7UCBpPOcXgmnUkVDKdK3KSN4XKhZLBFKkrfTJvr1CszgiUUMDH2OT0ZqJkiaO+Eq/duIy//IVLed3mzmnPt1hpjQQKoaWLJc9AKZVTSm0H1gBXicg24KPABcBrgGXAhxt1kZ7r+KJSaodSakd3d3ejp7NYXNZ1xnj91i6iQb/rE4DixS05VVxW2fu+ZY41g5yO6gF47vgoI8kp3uZp4QnOE32pZtASDlSt9mmc2qe0ZjCemqI1EnTvw9EMzEIcoKWGmah/Ik044CvTiNpioTJTkBEwq9oi9WkGmSyhgI/gNI1k3vOatfPS4axRLGsKMRTPoJRyks4W2kzkRSk1CjwK7FRKndKmoDTwZRw/AMAJYK3nsDV6rNb4mgrjFsui4lO7tvH3t2wvGvM2X3c0g8LiF/T7COnFaK41AyiEfp4ecxb8jd1NRfv1tDqagdEgTo1NVtUKwHkSbQr5OWk0g0mnppJx1CbTWZIee31TOFC121nfuNNhrDQWxBGepQ5k5xyr2qN1hZYm07m6ekef7Zhkv7HJqcWRgSwi3SLSrt9HgbcAL2lbPzry52bgBX3I/cD7dVTR1cCYUuoU8CBwo4h0aMfxjcCDetu4iFytz/V+4FtzeZMWy1ywoauJGy8ufvpuj4XcPINKDVeMFjGXi1dpdupQwhEGpVVQe1rDbtVUgNPj6ZrCQERY2R51NYOJ1BStUa9mkCsKn20KB6omnTkJZ+WRPF7haTDnXNUeLesPUYmENsed66xqL/hwcvPgM6jnL3QlcLeI+HGExzeUUt8RkR+ISDcgwF7gN/X+DwBvAw4ASeADAEqpYRH5FLBb7/dJpdSwfv/bwFeAKPBd/bJYFj1t0SCpqTzJTLaiMGgK+RmbnJpzMxEUQj8HJ9KIlJe3MJE8/bpQ3umxSc5bXtu8urItUuQzWN0eIRzw4feJvsdCKYjmUIB4FZ9B30SKC1e0lo1Xqlw6mcnh9zkO71FdErxWdHkiU7189bmESfY7NTbp+AwabCaa9jeqlHoOuLzC+A1V9lfA7VW23QXcVWF8D7BtumuxWBYbV6xzoqP/5qFXKi5S0ZCfkN9HJDh3T7LGTGQSzwYTGZbFQmVmBFMUr288xfplMQYm0u4CU41VbVFeOu2Ufp5ITdEaaUFE3EY9CU+RuFqhpf3jaa7fWq4ZtEVDjCWLF/xEJkss6KcjFiKXV0yks249/0okM9Ub25xLuJrBWMqWo7BYFjvXbO7k/des586fHOaV0xPlmkE4MKdaAXi7nWlhMJGumETmagbjaQbiafLKySWoxcr2CIPxNJlsnnGPRtMUCpBM50hmsvjEqWTq+AxyZSWpTfZxpezftmiQTC5PaqrQLnNSL+5uHkKitqmoVmObc4mu5jABn3BqdJJszpawtlgWPR9724Wc19NMooKZKBbyz70wcMsbG59BpmKtI7dhzXjKTTirRzNQyok8iqez7lyxsJ+ENoU1hQK6emohyshLpRwDQ6EkRcFR7PzeAm7Irtn2rb0n+MCXnyyrwJrM5GomnJ0rOKazCKfGUuSUosGywAoDi+VMiQT9/P0tlxPy+9widoZVbVFWd8y+DlElWko1g3hlzaA57EQH9Y2nCwlnrbWvxeQaHBiYIK8o1gwyjmZQcIo7C3tp4ll/hexjQ3uFLOTJTJZo0O8KChNe+uC+0zz68kBReCw4lVKXgmYAsKo9wonRSceB3GBpsDR+oxZLg7lwZSvf/p/XlbVY/OTN24qKxc0F0aBpcKM1g3hlzQCcBbl/IjVt9rHB5BoYv4Gx3cdCfqc2UboQamqezksjio6PTOq5K/gMKlQuNU/6hf4Qjmbwim5o/8KJsSLBslR8BuB8H8/0jrCsqfF9GaxmYLHMEeevaCmL6GkOB1xb+FwhIm5JitSUEzparYVmd0uY/vE0p8dThAI+t8heNVZpzeAVLQyMs7opXNAMYiXhsqVO5Edf7qerOcSm7uay87ebYnUlZqJoKFBU4jqTzXNkMAHACyfGi86xVHwG4Ghqp8dSZHOLo1CdxWJZZBhhYMpqd9XQDPomHJ/ByrbyJLBSYiFHeLmaQbRQjjuRyZJI59yF2GgIXs0gNZXj0Zf6ectFKypGv1TqaTCZydIU8rsmpJFkhsODCbf72b6TY+6+2VyedDZfsy7RucSqtihTOUXfeNpGE1kslnJawk7lUtP0vZpm0NMapm88xemxybpr+69si3BowHkqdzUDTzSRMdE0VxAGP351kEQmx00lpTEMhYZABWGQSOeIhvwE/D5aIgFGk1O80ucIoy3Lm9l3sqAZJEtai57rGIf/YDy9uMpRWCyWxYFT3jjLkNYMOqsKgwipqTyv9senjSQyrGqPktE9DUwYq4kmSmQKmkElM9F3XzhFWzTINVUKxMVCfoJ+KSpWN+mp6eRkKGd4tW8Cn8A7L1vFidFJRhKZorlq9TI4lzC5BgD+xVC11GKxLC5aIk7jk+nMRKZq6Ghyqu5yzl6hUaQZZHJuZVYoNxNlsnm+v7+PN1/YU7WInIjQ5ukBAcU+gI5YiNHJKV7pi7Ohs4kr1ztJfUY7cFteLoFyFFD8XVifgcViKaPV9RlMYybyRDdNF0lk8D6Ntng0g1xeMZzMuAux2WaEwX8fGmI8la1qIjIsawoypK87l1eks3k3XLUtGmQkOcUr/RNs7Wnm4lVOSYsXtN+gnpaX5xLLmkKEA84ybc1EFouljNao8RmkaQ4Hqpa78PYTqNdMZPYLBQplNMzim5rKE9MagalZZEw3333+FE0hP9dt7ap5/q7msKvRmJ7KXs1gYDzF0aEk5/W00B4Lsbo9ygsnHGHgagZLxGcgIu73YR3IFouljJaIUzF0YCJdNccAirOA63cgO5qBtz6Q1yzTpN+LCE0hp9vZVC7PQ/v7uOHCnmnrMDnCQFd6TRf3je6IBTk55lTp3NrTAsC21a3s12YioxkshRLWBvN9WGFgsVjKaIkEyCvoHU5WNRGBro2kF06zqEyHyTXwNqbxOmy9YZ3NYcdc9ciL/QwnMty8fdW05/dqBsmSvtFtngzurcudPIWLV7VxaDBBXNc8Kr2Gcx2TFW6FgcViKcM4dg8PJuhsqq4ZgNPxzO+TsuzoahjfQku0imbgMdGYyqXf2NPL8pYwbzhv+g6EXS0hN4HN1DWKBo2ZyJnT7xM26WY921Y7foP7957kBy/1l13DuY7pTd1oB/LSEa8WyzlEocFNlq5pFvme1giJdK7uJ8twwE9Xc6guzaApHODQYJwD/XF+8w2b62ozaTSZwYlMUU9lKCSlre+MEQ44Y9tWtQHwsW8+Dzj9jWtpQ+caRjNodDkKKwwslrMQbxvNrmk0g5u3r3arltbLG85bztplBbNSVc0gHOCZY6MAvGeHt6ttdbr1Qj4QTxc6p4WNMHDu5bzlLe7+y1sjfPrmbUSCft5wXnfdGs65gtUMLBZLVbxlsafTDN7zmvoWaS9/857Lij57QzlLfQYAV29axoau4h7M1XA1g3jaLU9tzESmJMV5PcV1jX756vUzufxzioLPYIH7GYhIRESeFJFnRWSfiHxCj28UkSdE5ICI3CsiIT0e1p8P6O0bPOf6qB5/WUTe6hnfqccOiMhHGnCfFss5hdeEU9r7uBF4QzmLtQTnOt47A4HT1eI8/Q/G02UO5HXLYkSDfl67qXIG81KkEE3U2HnqOX0auEEpdRmwHdipG91/FvicUmoLMAJ8UO//QWBEj39O74eIXATcAlwM7AT+QUT8urfy54GbgIuA9+l9LRZLFYrMRDVCS+eKaprBqrYInU0hbtq2su5zGeE1OJFxzUQmtLSzOcwLn3gr126pnauwlGiNBHjfVWu5bsv0zvkzYVphoBzi+mNQvxRwA3CfHr8buFm/36U/o7e/SZxSibuAe5RSaaXUYeAAcJV+HVBKHVJKZYB79L4Wi6UK3hyAanWJ5pJosLLP4PYbtvDwH7xhRj2eQwEfbdEgQ4k0kxUyihsdQnm2ISL8+c9fWrXe01xRl+Khn+D3Av3Aw8BBYFQpZSpUHQdW6/ergV4AvX0M6PSOlxxTbdxisVQhEvS5DsXueRAGPp+45iGvZhAO+Mt6ONRDV3OIwXjazSiOzkCYWBpDXcJAKZVTSm0H1uA8yV/QyIuqhojcJiJ7RGTPwMDAQlyCxbIoMA1ugn5xew40GiME5qJIXFdz2AktncoRCfoaHjZpmZ4ZuSSUUqPAo8A1QLuImL/CNcAJ/f4EsBZAb28DhrzjJcdUG680/xeVUjuUUju6uxtrP7NYFjstkSCdTeFpG9bMFU1hP6GAr2pF0pnQ1RLWmsHS6Vq22KknmqhbRNr1+yjwFuBFHKHwLr3brcC39Pv79Wf09h8oJ37sfuAWHW20EdgKPAnsBrbq6KQQjpP5/jm4N4vlnKYlEqhZl2iuiQb9bl2iM6W7OcxAPM1kJuc6jy0LSz0ieSVwt4768QHfUEp9R0T2A/eIyKeBZ4A79f53Av8iIgeAYZzFHaXUPhH5BrAfyAK3K6VyACLyIeBBwA/cpZTaN2d3aLGco1x/XnfDE5G8NIUDc1YTqKs5xEQqy0gyYzWDRcK034JS6jng8grjh3D8B6XjKeDdVc71GeAzFcYfAB6o43otFovmwzvn13UXC/nnrKmMSTzrHZlcUhVIFzP2W7BYLHXxzstWMZzITL9jHRhhcGw4yQ7dzcyysFhhYLFY6uLdddYeqgfj68hk80uqHPVixpawtlgs84636uhS6We82LHCwGKxzDveyqNLqTfBYsYKA4vFMu9Egn7XcWwqlloWFisMLBbLgmAK7FnNYHFghYHFYlkQjN/AJp0tDqwwsFgsC4IRBjFbpG5RYIWBxWJZEEyTm5hNOlsUWGFgsVgWBFczsGaiRYEVBhaLZUEwwsDWJlocWGFgsVgWBOtAXlxYYWCxWBaE67Z2cdv1m9i+tn2hL8WCrU1ksVgWiOZwgI+97cKFvgyLxmoGFovFYrHCwGKxWCxWGFgsFosFKwwsFovFQh3CQETWisijIrJfRPaJyO/q8T8VkRMisle/3uY55qMickBEXhaRt3rGd+qxAyLyEc/4RhF5Qo/fKyLz1+XbYrFYLHVpBlngD5VSFwFXA7eLyEV62+eUUtv16wEAve0W4GJgJ/APIuIXET/weeAm4CLgfZ7zfFafawswAnxwju7PYrFYLHUwrTBQSp1SSj2t308ALwKraxyyC7hHKZVWSh0GDgBX6dcBpdQhpVQGuAfYJSIC3ADcp4+/G7h5lvdjsVgsllkwI5+BiGwALgee0EMfEpHnROQuETFdrVcDvZ7DjuuxauOdwKhSKlsyXmn+20Rkj4jsGRgYmMmlWywWi6UGdSediUgz8O/A7ymlxkXkC8CnAKV//g3wqw25So1S6ovAF/X1DIjI0VmeqgsYnLMLOzvmXor3vJBzL8V7Xsi5l+I9z3bu9ZUG6xIGIhLEEQRfV0r9B4BSqs+z/Z+B7+iPJ4C1nsPX6DGqjA8B7SIS0NqBd/+qKKW667n2SojIHqXUjtkefyYs1NxL8Z4Xcu6leM8LOfdSvOe5nrueaCIB7gReVEr9rWd8pWe3nwNe0O/vB24RkbCIbAS2Ak8Cu4GtOnIohONkvl8ppYBHgXfp428FvnVmt2WxWCyWmVCPZnAt8CvA8yKyV499DCcaaDuOmegI8BsASql9IvINYD9OJNLtSqkcgIh8CHgQ8AN3KaX26fN9GLhHRD4NPIMjfCwWi8UyT0wrDJRSPwGkwqYHahzzGeAzFcYfqHScUuoQTrTRfPHFeZxrscy9FO95Iedeive8kHMvxXue07nFsdJYLBaLZSljy1FYLBaLBZRSZ/0LJ0rpURw/xT7gd/X4MuBh4FX9s0OPC3AHTkLcc8AVnnP9pT7Hi3ofmce5P4vjiH8BeO8cz3sB8N9AGvijknPtBF7W1/SRBvy+a819F9APvNCg77ri3NXOM09zR3CCKp7V5/nEfP2+9XY/jm/uO/P8XR8Bngf2Anvmee52nMTWl3D+t6+Zh+/5fH2v5jWOE5o/X/f8+/ocLwD/BkRqzl3PP+BifwEr0Ysq0AK8glPy4i/RixvwEeCz+v3bgO/iLMxXA0/o8dcBP9X/LH79S/6ZeZr77fpLDgBNONFXrXM473LgNTi+HO8frB84CGwCQjgL1EVzfM8V59bbrgeuoH5hMFf3XfE88zS3AM36fRAnifPq+fh96+1/APwr9QmDufyujwBdDfy/rjX33cCv6fchoH2+ft+e/7PTwPp5+htbDRwGovrzN4D/UWvuc8JMpKqXzNiF80cAxWUudgFfVQ6P4+Q5rMSJjIrg/LGEcf5R3XyKBs99EfAjpVRWKZXA0Rp2ztW8Sql+pdRuYKrkVBXLhMzlPdeYG6XUj4DhWvM1Yu4a55mPuZVSKq4/BvVLNXpeABFZg/Pg8aVa99qIuWfKXM0tIm04Dx136v0ySqnRRs9bwpuAg0qpmomyczx3AIiKSACIASdrzX1OCAMvJSUzepRSp/Sm00CPfl+xNIZS6r9xVLRT+vWgUurF+Zgb54l8p4jERKQLeCPFSXpnOm81ql1PXZzh3GfEXM1docxKw+fWxRv34pjIHlZK1TX3HNzz3wF/AuTrmW+O51bAQyLylIjcNo9zbwQGgC+LyDMi8iURaZqHeb3cgmOqqZszmVspdQL4a+AYzlo2ppR6qNYx55QwKC2Z4d2mHF2p6tOXPn4LcCFOFvRq4AYRef18zK2/qAeAx3D+aP4byDV63jPhXJi71nkaObdSKqeU2o7zt3aViGxr9Lwi8g6gXyn11HRzzfXcmuuUUlfgVC6+XUSun6e5AzimyC8opS4HEjimlkbPa84TAt4J/J969p+LuXWtuF04gnAV0CQiv1zrmHNGGFQqmQH0mUxp/bNfj1crmfFzwONKqbhW478LXDNPc6OU+oxyyoG/Bceu/MoczluNWuVDGj33rJiruaucZ17mNmhzxaPUMAnO4bzXAu8UkSM45sAbRORr013jXN2zflpFKdUPfJM6covmaO7jwHGP9nUfjnBo9LyGm4CnlaeEzzzM/WbgsFJqQCk1BfwHjk+0KueEMKhWMgOnNMat+v2tFMpc3A+8XxyuxlGhTuGoVG8QkYD+Qt6AY7Nr+NzabNCpz3kpcClQVa2bxbzVqFgmZI7vec6Yq7lrnGc+5u4WkXb9Pgq8BSfKpaHzKqU+qpRao5TagPM9/0ApVfNpcQ7vuUlEWsx74EYKJWwaOrdS6jTQKyLn66E34UTrNHReD++jThPRHM59DLham50F555rm7xVnZ79xfwCrsNRm56jEMb1Npzy2I/ghGN9H1im9xecRjsHcULddqiCx/+f9C9tP/C38zh3RM+5H3gc2D7H867AeUIaB0b1+1a97W04WshB4H814J5rzf1vODbNKT3+wfmYu9p55mnuS3FCO5/DWRD/3/n6fXvO+TPUF000V/e8CccvZsJp5/vvbDuwR5/r/6JDM+dh3iacYpxtDVrLas39CZyHjBeAfwHCtea2GcgWi8ViOTfMRBaLxWI5M6wwsFgsFosVBhaLxWKxwsBisVgsWGFgsVgsFqwwsFgsFgtWGFgsFosFKwwsFovFAvz/7HmYft1oZgYAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " (model_cl.ultimate_[\"Paid\"] / model_cl.ultimate_[\"reportedCount\"]).to_frame(\n", + " origin_as_datetime=True\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "40K seems a reasonable a-priori (at least for the last two years or so, between 2016 - 2017)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's fit an aggregate Bornhuetter-Ferguson model. Like the chainladder example, we fit the model in aggregate (summing all claims) to create a stable model from which we can generate granular predictions. We will use our Chainladder ultimate claim counts as our `sample_weight` (exposure) for the BornhuetterFerguson method." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/dunders.py:321: RuntimeWarning: divide by zero encountered in true_divide\n", + " obj.values = obj.values / other\n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/slice.py:250: RuntimeWarning: invalid value encountered in multiply\n", + " obj.values = num_to_nan(obj.values * obj.get_array_module().array(key))\n" + ] + } + ], + "source": [ + "paid_bf = cl.BornhuetterFerguson(apriori=40000).fit(\n", + " X=prism[\"Paid\"].sum().incr_to_cum(), sample_weight=cl_ults[\"reportedCount\"].sum()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar(\n", + " paid_bf.ultimate_.grain(\"OYDM\").to_frame(origin_as_datetime=False).index.year,\n", + " paid_bf.ultimate_.grain(\"OYDM\").to_frame(origin_as_datetime=False)[\"2261-12\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now create claim-level BornhuetterFerguson predictions using our claim-level Triangle. Ideally, the results should tie to the aggregate results." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "bf_ults = paid_bf.predict(\n", + " prism[\"Paid\"].incr_to_cum(), sample_weight=cl_ults[\"reportedCount\"]\n", + ").ultimate_" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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2006396,1321,333,2172,180,7152,985,7523,691,7124,074,9994,426,5464,665,0235,022,1555,111,1715,172,2315,261,947
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2009376,6861,363,2942,382,1283,471,7444,075,3134,498,4264,886,5025,149,7605,544,0005,642,2665,709,6715,808,708
2010344,0141,200,8182,098,2283,057,9843,589,6203,962,3074,304,1324,536,0154,883,2704,969,8255,029,1965,116,430
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2001357,8481,124,7881,735,3302,218,2702,745,5963,319,9943,466,3363,606,2863,833,5153,901,463...3,948,0713,948,0713,948,0713,948,0713,948,0713,948,0713,948,0713,948,0713,948,0714,016,553
2002352,1181,236,1392,170,0333,353,3223,799,0674,120,0634,647,8674,914,0395,339,085...5,498,6325,498,6325,498,6325,498,6325,498,6325,498,6325,498,6325,498,6325,498,6325,594,009
2003290,5071,292,3062,218,5253,235,1793,985,9954,132,9184,628,9104,909,315...5,378,8265,443,0845,443,0845,443,0845,443,0845,443,0845,443,0845,443,0845,443,0845,537,497
2004310,6081,418,8582,195,0473,757,4474,029,9294,381,9824,588,268...5,205,6375,297,9065,361,1975,361,1975,361,1975,361,1975,361,1975,361,1975,361,1975,454,190
2005443,1601,136,3502,128,3332,897,8213,402,6723,873,311...4,434,1334,773,5894,858,2004,916,2374,916,2374,916,2374,916,2374,916,2374,916,2375,001,513
2006396,1321,333,2172,180,7152,985,7523,691,712...4,426,5464,665,0235,022,1555,111,1715,172,2315,172,2315,172,2315,172,2315,172,2315,261,947
2007440,8321,288,4632,419,8613,483,130...4,513,1794,902,5285,166,6495,562,1825,660,7715,728,3965,728,3965,728,3965,728,3965,827,759
2008359,4801,421,1282,864,498...4,900,5455,409,3375,875,9976,192,5626,666,6356,784,7996,865,8536,865,8536,865,8536,984,945
2009376,6861,363,294...3,471,7444,075,3134,498,4264,886,5025,149,7605,544,0005,642,2665,709,6715,709,6715,808,708
2010344,014...2,098,2283,057,9843,589,6203,962,3074,304,1324,536,0154,883,2704,969,8255,029,1965,116,430
" + ], + "text/plain": [ + " 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2261\n", + "2001 357848.0 1124788.0 1735330.0 2218270.0 2745596.0 3319994.0 3466336.0 3606286.0 3833515.0 3901463.0 3.948071e+06 3.948071e+06 3.948071e+06 3.948071e+06 3.948071e+06 3.948071e+06 3.948071e+06 3.948071e+06 3.948071e+06 3.948071e+06 4.016553e+06\n", + "2002 NaN 352118.0 1236139.0 2170033.0 3353322.0 3799067.0 4120063.0 4647867.0 4914039.0 5339085.0 5.433719e+06 5.498632e+06 5.498632e+06 5.498632e+06 5.498632e+06 5.498632e+06 5.498632e+06 5.498632e+06 5.498632e+06 5.498632e+06 5.594009e+06\n", + "2003 NaN NaN 290507.0 1292306.0 2218525.0 3235179.0 3985995.0 4132918.0 4628910.0 4909315.0 5.285148e+06 5.378826e+06 5.443084e+06 5.443084e+06 5.443084e+06 5.443084e+06 5.443084e+06 5.443084e+06 5.443084e+06 5.443084e+06 5.537497e+06\n", + "2004 NaN NaN NaN 310608.0 1418858.0 2195047.0 3757447.0 4029929.0 4381982.0 4588268.0 4.835458e+06 5.205637e+06 5.297906e+06 5.361197e+06 5.361197e+06 5.361197e+06 5.361197e+06 5.361197e+06 5.361197e+06 5.361197e+06 5.454190e+06\n", + "2005 NaN NaN NaN NaN 443160.0 1136350.0 2128333.0 2897821.0 3402672.0 3873311.0 4.207459e+06 4.434133e+06 4.773589e+06 4.858200e+06 4.916237e+06 4.916237e+06 4.916237e+06 4.916237e+06 4.916237e+06 4.916237e+06 5.001513e+06\n", + "2006 NaN NaN NaN NaN NaN 396132.0 1333217.0 2180715.0 2985752.0 3691712.0 4.074999e+06 4.426546e+06 4.665023e+06 5.022155e+06 5.111171e+06 5.172231e+06 5.172231e+06 5.172231e+06 5.172231e+06 5.172231e+06 5.261947e+06\n", + "2007 NaN NaN NaN NaN NaN NaN 440832.0 1288463.0 2419861.0 3483130.0 4.088678e+06 4.513179e+06 4.902528e+06 5.166649e+06 5.562182e+06 5.660771e+06 5.728396e+06 5.728396e+06 5.728396e+06 5.728396e+06 5.827759e+06\n", + "2008 NaN NaN NaN NaN NaN NaN NaN 359480.0 1421128.0 2864498.0 4.174756e+06 4.900545e+06 5.409337e+06 5.875997e+06 6.192562e+06 6.666635e+06 6.784799e+06 6.865853e+06 6.865853e+06 6.865853e+06 6.984945e+06\n", + "2009 NaN NaN NaN NaN NaN NaN NaN NaN 376686.0 1363294.0 2.382128e+06 3.471744e+06 4.075313e+06 4.498426e+06 4.886502e+06 5.149760e+06 5.544000e+06 5.642266e+06 5.709671e+06 5.709671e+06 5.808708e+06\n", + "2010 NaN NaN NaN NaN NaN NaN NaN NaN NaN 344014.0 1.200818e+06 2.098228e+06 3.057984e+06 3.589620e+06 3.962307e+06 4.304132e+06 4.536015e+06 4.883270e+06 4.969825e+06 5.029196e+06 5.116430e+06" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genins_model.full_triangle_.dev_to_val()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice the calendar year of our ultimates. While ultimates will generally be realized before this date, the `chainladder` package picks the highest allowable date available for its `ultimate_` valuation. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2261-12-31 23:59:59.999999999')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genins_model.full_triangle_.valuation_date" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can further manipulate the \"triangle\", such as applying `cum_to_incr()`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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2001357,848766,940610,542482,940527,326574,398146,342139,950227,22967,948...68,482
2002352,118884,021933,8941,183,289445,745320,996527,804266,172425,046...64,91395,377
2003290,5071,001,799926,2191,016,654750,816146,923495,992280,405...93,67864,25794,413
2004310,6081,108,250776,1891,562,400272,482352,053206,286...370,17992,26863,29192,993
2005443,160693,190991,983769,488504,851470,639...226,674339,45684,61158,03885,275
2006396,132937,085847,498805,037705,960...351,548238,477357,13289,01661,06089,715
2007440,832847,6311,131,3981,063,269...424,501389,349264,121395,53498,58867,62699,362
2008359,4801,061,6481,443,370...725,788508,792466,660316,566474,073118,16481,054119,092
2009376,686986,608...1,089,616603,569423,113388,076263,257394,24198,26667,40599,038
2010344,014...897,410959,756531,636372,687341,826231,882347,25586,55559,37187,234
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2003372,3251,299,6412,270,9053,309,6473,885,0354,288,3934,658,3494,909,3155,285,1485,378,8265,443,0845,537,497
2004366,7241,280,0892,236,7413,259,8563,826,5874,223,8774,588,2684,835,4585,205,6375,297,9065,361,1975,454,190
2005336,2871,173,8462,051,1002,989,3003,508,9953,873,3114,207,4594,434,1334,773,5894,858,2004,916,2375,001,513
2006353,7981,234,9702,157,9033,144,9563,691,7124,074,9994,426,5464,665,0235,022,1555,111,1715,172,2315,261,947
2007391,8421,367,7652,389,9413,483,1304,088,6784,513,1794,902,5285,166,6495,562,1825,660,7715,728,3965,827,759
2008469,6481,639,3552,864,4984,174,7564,900,5455,409,3375,875,9976,192,5626,666,6356,784,7996,865,8536,984,945
2009390,5611,363,2942,382,1283,471,7444,075,3134,498,4264,886,5025,149,7605,544,0005,642,2665,709,6715,808,708
2010344,0141,200,8182,098,2283,057,9843,589,6203,962,3074,304,1324,536,0154,883,2704,969,8255,029,1965,116,430
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120 132 9999\n", + "2001 87786.584355 1.821099e+05 8.815770e+04 -1.823400e+05 -7.236421e+04 2.094632e+05 8.746170e+04 4.537702e+04 4.656613e-10 NaN NaN NaN\n", + "2002 -24007.006253 -7.676541e+04 -1.240477e+05 9.899296e+03 -1.256155e+05 -2.120939e+05 -5.802227e+04 -4.537702e+04 NaN NaN NaN NaN\n", + "2003 -81818.315504 -7.335184e+03 -5.238046e+04 -7.446777e+04 1.009605e+05 -1.554745e+05 -2.943943e+04 NaN -9.313226e-10 -9.313226e-10 NaN NaN\n", + "2004 -56115.956096 1.387690e+05 -4.169437e+04 4.975914e+05 2.033420e+05 1.581052e+05 NaN NaN -9.313226e-10 -9.313226e-10 -9.313226e-10 -9.313226e-10\n", + "2005 106872.747755 -3.749648e+04 7.723272e+04 -9.147888e+04 -1.063228e+05 NaN NaN NaN -9.313226e-10 NaN -9.313226e-10 -9.313226e-10\n", + "2006 42333.899273 9.824703e+04 2.281166e+04 -1.592040e+05 NaN -4.656613e-10 -9.313226e-10 -9.313226e-10 -9.313226e-10 -1.862645e-09 -1.862645e-09 -1.862645e-09\n", + "2007 48990.342828 -7.930205e+04 2.992047e+04 NaN NaN NaN -9.313226e-10 -9.313226e-10 NaN -2.793968e-09 -1.862645e-09 -1.862645e-09\n", + "2008 -110167.520951 -2.182267e+05 NaN NaN NaN NaN -9.313226e-10 -1.862645e-09 -1.862645e-09 -1.862645e-09 -2.793968e-09 -1.862645e-09\n", + "2009 -13874.775407 NaN NaN 4.656613e-10 NaN -9.313226e-10 NaN -9.313226e-10 -1.862645e-09 NaN -9.313226e-10 NaN\n", + "2010 NaN -2.328306e-10 -4.656613e-10 -9.313226e-10 -2.328306e-09 4.656613e-10 -2.793968e-09 1.862645e-09 1.862645e-09 2.793968e-09 -1.862645e-09 3.725290e-09" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genins_model.full_triangle_ - genins_model.full_expectation_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also filter out the lower right part of the triangle with `[genins_model.full_triangle_.valuation <= genins.valuation_date]`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
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2003-81,818-7,335-52,380-74,468100,960-155,475-29,439
2004-56,116138,769-41,694497,591203,342158,105
2005106,873-37,49677,233-91,479-106,323
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200748,990-79,30229,920
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2009-13,875
2010
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "2001 87786.584355 182109.854207 88157.704861 -182340.046069 -72364.206180 209463.211490 87461.697305 45377.021916 4.656613e-10 NaN\n", + "2002 -24007.006253 -76765.409669 -124047.730972 9899.295819 -125615.456548 -212093.852125 -58022.270396 -45377.021916 NaN NaN\n", + "2003 -81818.315504 -7335.184258 -52380.464273 -74467.773015 100960.477986 -155474.528980 -29439.426909 NaN NaN NaN\n", + "2004 -56115.956096 138768.957566 -41694.368640 497591.418491 203341.953249 158105.169615 NaN NaN NaN NaN\n", + "2005 106872.747755 -37496.484673 77232.720516 -91478.875281 -106322.768507 NaN NaN NaN NaN NaN\n", + "2006 42333.899273 98247.032955 22811.664568 -159204.019945 NaN NaN NaN NaN NaN NaN\n", + "2007 48990.342828 -79302.054276 29920.473940 NaN NaN NaN NaN NaN NaN NaN\n", + "2008 -110167.520951 -218226.711852 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009 -13874.775407 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " genins_model.full_triangle_[\n", + " genins_model.full_triangle_.valuation <= genins.valuation_date\n", + " ]\n", + " - genins_model.full_expectation_[\n", + " genins_model.full_triangle_.valuation <= genins.valuation_date\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Getting comfortable with manipulating `Triangle`s will greatly improve our ability to extract value out of the `chainladder` package. Here is another way of getting the same answer." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
200187,787182,11088,158-182,340-72,364209,46387,46245,3770
2002-24,007-76,765-124,0489,899-125,615-212,094-58,022-45,377
2003-81,818-7,335-52,380-74,468100,960-155,475-29,439
2004-56,116138,769-41,694497,591203,342158,105
2005106,873-37,49677,233-91,479-106,323
200642,33498,24722,812-159,204
200748,990-79,30229,920
2008-110,168-218,227
2009-13,875
2010
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "2001 87786.584355 182109.854207 88157.704861 -182340.046069 -72364.206180 209463.211490 87461.697305 45377.021916 4.656613e-10 NaN\n", + "2002 -24007.006253 -76765.409669 -124047.730972 9899.295819 -125615.456548 -212093.852125 -58022.270396 -45377.021916 NaN NaN\n", + "2003 -81818.315504 -7335.184258 -52380.464273 -74467.773015 100960.477986 -155474.528980 -29439.426909 NaN NaN NaN\n", + "2004 -56115.956096 138768.957566 -41694.368640 497591.418491 203341.953249 158105.169615 NaN NaN NaN NaN\n", + "2005 106872.747755 -37496.484673 77232.720516 -91478.875281 -106322.768507 NaN NaN NaN NaN NaN\n", + "2006 42333.899273 98247.032955 22811.664568 -159204.019945 NaN NaN NaN NaN NaN NaN\n", + "2007 48990.342828 -79302.054276 29920.473940 NaN NaN NaN NaN NaN NaN NaN\n", + "2008 -110167.520951 -218226.711852 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009 -13874.775407 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genins_AvE = genins - genins_model.full_expectation_\n", + "genins_AvE[genins_AvE.valuation <= genins.valuation_date]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also filter out the lower right part of the triangle with `[genins_model.full_triangle_.valuation <= genins.valuation_date]` before applying the `heatmap()`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/display.py:134: FutureWarning: this method is deprecated in favour of `Styler.to_html()`\n", + " .render()\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 1224364860728496108120
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2002-24,007-76,765-124,0489,899-125,615-212,094-58,022-45,377
2003-81,818-7,335-52,380-74,468100,960-155,475-29,439
2004-56,116138,769-41,694497,591203,342158,105
2005106,873-37,49677,233-91,479-106,323
200642,33498,24722,812-159,204
200748,990-79,30229,920
2008-110,168-218,227
2009-13,875
2010
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genins_AvE[genins_AvE.valuation <= genins.valuation_date].heatmap()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Can you figure out how to get the expected IBNR runoff in the upcoming year?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2011
200146,608
200294,634
2003375,833
2004247,190
2005334,148
2006383,287
2007605,548
20081,310,258
20091,018,834
2010856,804
" + ], + "text/plain": [ + " 2011\n", + "2001 4.660832e+04\n", + "2002 9.463381e+04\n", + "2003 3.758335e+05\n", + "2004 2.471900e+05\n", + "2005 3.341481e+05\n", + "2006 3.832866e+05\n", + "2007 6.055481e+05\n", + "2008 1.310258e+06\n", + "2009 1.018834e+06\n", + "2010 8.568035e+05" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cal_yr_ibnr = genins_model.full_triangle_.dev_to_val().cum_to_incr()\n", + "cal_yr_ibnr[cal_yr_ibnr.valuation.year == 2011]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Expected Loss Method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's talk about the expected loss method, where we know the ultimate loss already (but then why are you trying to estimate your ultimate losses?). The `ExpectedLoss` model estimator has many of the same attributes as the `Chainladder` estimator. It comes with one input assumption, the a priori (`apriori`). This is a scalar multiplier that will be applied to an exposure vector, which will produce an a priori ultimate estimate vector that we can use for the model.\n", + "\n", + "Earlier, we used the `Chainladder` method on the `genins` data. Let's use the average of the `Chainladder` ultimate to help us derive an a priori in the expected loss method. \n", + "\n", + "Below, we use `genins_model.ultimate_ * 0` and add the mean to it to preserve the index values of years 2001 - 2020." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2261
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" + ], + "text/plain": [ + " 2261\n", + "2001 5.460355e+06\n", + "2002 5.460355e+06\n", + "2003 5.460355e+06\n", + "2004 5.460355e+06\n", + "2005 5.460355e+06\n", + "2006 5.460355e+06\n", + "2007 5.460355e+06\n", + "2008 5.460355e+06\n", + "2009 5.460355e+06\n", + "2010 5.460355e+06" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expected_loss_apriori = genins_model.ultimate_ * 0 + genins_model.ultimate_.mean()\n", + "expected_loss_apriori" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's assume that we know our expected loss will be 95% of the a priori, which is common if the a priori is a function of something else, like earned premium. We set `apriori` with 0.95 inside the function, then call `fit` on the data, `genins`, and assigh the `sample_weight` with our vector of a prioris, `expected_loss_apriori`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2261
20015,187,337
20025,187,337
20035,187,337
20045,187,337
20055,187,337
20065,187,337
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20095,187,337
20105,187,337
" + ], + "text/plain": [ + " 2261\n", + "2001 5.187337e+06\n", + "2002 5.187337e+06\n", + "2003 5.187337e+06\n", + "2004 5.187337e+06\n", + "2005 5.187337e+06\n", + "2006 5.187337e+06\n", + "2007 5.187337e+06\n", + "2008 5.187337e+06\n", + "2009 5.187337e+06\n", + "2010 5.187337e+06" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "EL_model = cl.ExpectedLoss(apriori=0.95).fit(\n", + " genins, sample_weight=expected_loss_apriori\n", + ")\n", + "EL_model.ultimate_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bornhuetter-Ferguson Method\n", + "The `BornhuetterFerguson` estimator is another deterministic method having many of the same attributes as the `Chainladder` estimator. It comes with one input assumption, the a priori (`apriori`). This is a scalar multiplier that will be applied to an exposure vector, which will produce an a priori ultimate estimate vector that we can use for the model.\n", + "\n", + "Since the CAS Loss Reserve Database has premium, we will use it as an example. Let's grab the paid loss and net earned premium for the commercial auto line of business." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember that `apriori` is a scaler, which we need to apply it to a vector of exposures. Let's assume that the a priori is 0.75, for 75% loss ratio." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's set an apriori Loss Ratio estimate of 75%" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `BornhuetterFerguson` method along with all other expected loss methods like `CapeCod` and `Benktander` (discussed later), need to take in an exposure vector. The exposure vector has to be a `Triangle` itself. Remember that the `Triangle` class supports single exposure vectors." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
BornhuetterFerguson(apriori=0.75)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + ], + "text/plain": [ + "BornhuetterFerguson(apriori=0.75)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comauto = cl.load_sample(\"clrd\").groupby(\"LOB\").sum().loc[\"comauto\"]\n", + "\n", + "bf_model = cl.BornhuetterFerguson(apriori=0.75)\n", + "bf_model.fit(\n", + " comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 2261\n", + "1988 6.260970e+05\n", + "1989 6.792242e+05\n", + "1990 7.283626e+05\n", + "1991 7.299271e+05\n", + "1992 7.676100e+05\n", + "1993 8.336865e+05\n", + "1994 9.185817e+05\n", + "1995 9.543771e+05\n", + "1996 9.852804e+05\n", + "1997 1.031637e+06" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bf_model.ultimate_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having an `apriori` that takes on only a constant for all origins can be limiting. This shouldn't stop the practitioner from exploiting the fact that the `apriori` can be embedded directly in the exposure vector itself allowing full cusomization of the `apriori`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b1 = cl.BornhuetterFerguson(apriori=0.75).fit(\n", + " comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", + ")\n", + "\n", + "b2 = cl.BornhuetterFerguson(apriori=1.00).fit(\n", + " comauto[\"CumPaidLoss\"],\n", + " sample_weight=0.75 * comauto[\"EarnedPremNet\"].latest_diagonal,\n", + ")\n", + "\n", + "b1.ultimate_ == b2.ultimate_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we need to create a new colume, such as `AdjEarnedPrmNet` with varying implied loss ratios. It is recommend that we perform any data modification in `pandas` instead of `Triangle` forms.\n", + "\n", + "Let's perform the estimate using `Chainladder` and compare the results." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997], dtype='int64')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bf_model.ultimate_.to_frame(origin_as_datetime=True).index.year" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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iHegyBM7tC5XqBDa8HEOFLlJK7T2cz/tzNjNirlPk17euzf3FLXKvx7lWyqKPYMM3zsk/jS+H5Neh0eUQqWpxg9a6SClz4iJvRKPqxRiG7WAGLBkFi0Y4J/uUrwEXPuSccl+5fuDDyx9SoYuUEvuKFHlOgYfrWtXmgUuLUeReL2z+DlI+gnVfOSf8NLwErvoXNL1ax4kHERW6SJjbdzifD37czPCfTrHIs3c7R6ksHgH7UiGuKlwwGNr213VTgpQKXSRMHcwt4L1Zm34p8mtb1eaBro1oXOMPitxaSJ3jHKmyZhp4CyDxIuj6dzjnOl07Jcip0EXCUG6BhwHDFrBk2/7iFXnOXlj6MSwaDlkbnZF92t8N7QZAtSYllFrOlApdJMxYa3ls0nIWb93PO73b0q1lrd9bELb+7GyNr/4MPHlQ93y4+FFo1h2iy5ZscDljKnSRMPPmdxv5bOkOHr2y6YnL/Mh+WP6JU+SZa50rGrbrD+1uhxrNSjyv+I8KXSSMTFu+g1dnrKdH2zrc16XIF5fWOhfDSvkIVk5yhmmr3Raufwta9CiVo/uEIxW6SJhYum0/D49fxnmJlXmhR0tnIOa8Q7B8vFPku1ZAmfLQuick3w61WrsdWfxMhS4SBnbsP8JdI1OoXjGGd/u0IyYq0vmC8+snoOAw1GwJ174GLW+BmGKcQCQhqViFboy5CngDiAQ+sNa+eNz8+sAwoBqwF+hjrU33c1YROYHDeYUMHJFCbr6HMXd2IL58DKz6FKb+xRntp+s/oE7bUjHqfWkXcbIFjDGRwNtAN6AZ0MsYc/w3J68AI621rYBngBf8HVREfsvjtTw4binrdh7kzdvOdQ5NTP0JJt8NddtDr3GQ0E5lXkqctNCB9sBGa+1ma20+MA7oftwyzYDvfPe/P8F8EQmAl75ey8w1u3jquuZ0aVoddq2Gsb2c66r0GqdDD0uZ4hR6HWBbkd/TfdOKWgb08N2/EahgjIk//oGMMXcbY1KMMSmZmZmnk1dEfMYv3MZ7szfT9/z69L8gEQ6kw+iboEwc9JmkQSRKoeIUenE8AnQ2xiwBOgPbAc/xC1lrh1prk621ydWqVfPTU4uUPj9vzuKJKSu4qHFVnrqumTO02+ibID8bek+Es+q5HVFcUJwvRbcDdYv8nuCb9gtr7Q58W+jGmPLATdba/X7KKCJFpO45zL2jF1E/Po63bmtLlDcfxt4Gezc7W+Y1W7gdUVxSnC30hUBjY0wDY0wZoCfwedEFjDFVjTFHH2sIzhEvIuJnB3IKuGPEQgwwbMB5VIqJgMl3wda5cOO70OBityOKi05a6NbaQmAwMB1YA4y31q4yxjxjjLnet1gXYJ0xZj1QA3g+QHlFSq0Cj5dBYxazbW8O7/VNpn6VOPj6cVjzOVz5ArS4ye2I4rJiHYdurf0S+PK4af8ocn8iMNG/0UTkKGst//x8FT9u3MPLN7eifYMqMOdVWDAULrgfOt7ndkQJAv76UlREAmj43FQ+nr+VezsncUtyXVg6Fr59GlreCpc943Y8CRIqdJEg9/263Tw7bTVXNKvB365sChtmwueDoWEX6P42ROh/Y3HonSASxNbtPMT9Y5ZwTq2KvN6zDREZS2B8P6h+Dtw6CqLKuB1RgogKXSRI7cnOY+CIhcSVieSD/snEZW+FMbdCuXjoPQliK7odUYKMCl0kCOUWeLhn1CL2ZOfxQf9kakVmw6ge4PVAn8lQoYbbESUI6fK5IkHGWsuQyStYlLaP//VuS6tqUTDiBji0E/pPhaqN3Y4oQUqFLhJk/vfDJqYs2c4jVzTh6mZVYWxPyFgGPcdA3fPcjidBTIUuEkS+XJHBy9PXceO5dRjUJck5mmXjTLjuv9C0m9vxJMhpH7pIkFievp+Hxi+lXX3fEHLfPw9LP4YuQ5xBnEVOQoUuEgQyDhzhzhEpVC0fw3t92xG79COY8wq07Q+dH3M7noQIFbqIy3LyC7lzRAo5+R4+7H8eVbd9A188Ak2vhmte1WhDUmwqdBEXeb2Wv4xbypqMg7zZ61ya5q2EiQMh4Ty46UOI1NdcUnwqdBEXvfzNOr5ZvYsnr2nGJVX2wtg/OYNT3PaJM/KQyClQoYu4ZELKNt75YRO9O9Tj9pbRzohDUbEaPk5Omz7PibhgwZa9PDFlBZ0axfPPKxIwI66G3ANw+5fOAM8ip0GFLlLC0rIOc8+oFOpWieN/tzYnenxP2LMB+kyEWq3cjichTIUuUoIO5hYwcEQKFhjWrx2Vvh4MaT86X4A27OJ2PAlx2ocuUkIKPV4GfbyYtKzDvNu7LYkpz8HqT+GK56DlzW7HkzCgLXSREvLMtNXM2bCHl25qxfk7P4b578L5g5wh5ET8QFvoIiVgxNxURs5L4+6LG3Jrmbkw4x/OoM5XPOd2NAkjKnSRAJu1PpOnp67isnNq8FjjDPjsPki8CG54R8PHiV/p3SQSQBt2HWLwx4tpWrMib3YxRE7oC9XOhp4fQ1SM2/EkzGgfukiAZGXncceIhcSWiWT4DdUoO/46KFsFek+E2Epux5MwpEIXCYC8Qg/3jl7E7oN5TOzXhBqf3QLeAujzBVSs5XY8CVMqdBE/OzqE3MLUfbxza1NazroLDm6Hfp9DtSZux5MwpkIX8SOv1/L8l2uYvHg7D13akG5rnoAdS+BPH0O9Dm7HkzCnQhfxk0KPl8cmrWDS4nQGdKzP/Yffgg3T4drX4Oyr3Y4npYAKXcQPcgs83D92CTNW7+KvlzXhgYhPMLNHw8V/g+Q73I4npYQKXeQMHcot4M4RKczfspenr2tGf+8U+PZlOLcvXPKE2/GkFFGhi5yBPdl5DPhoAWszDvHGn1rRfedbzin9LW6Ca1/X8HFSolToIqcpfV8O/T5cwPb9R/igd0u6rP47rJoC598HVzyvs0ClxKnQRU7Dxt2H6PvhArLzChnTrxnt5t0HW2bD5c86F9vSlrm4QIUucoqWbdvPgI8WEBkRwaQ+DWkyozdkroEb34PWPd2OJ6WYCl3kFPy0cQ93j0yhcrkyjOtRlYRpPeBwljOoc6PL3I4npZwKXaSYvl6ZwQNjl9KgajnGXB1F/JQbAAMDpkKddm7HE9HVFkWK45OFW7nv48W0qFORSZdnEz/xJihTHgZ+ozKXoKFCFzmJ92Zt4rFJK7iwcTXGdthC+cl9Ib4RDJwB8UluxxP5hXa5iPwOay3//nod787axLUta/J63VlETX0aGnSGP42G2IpuRxQ5RrG20I0xVxlj1hljNhpjHj/B/HrGmO+NMUuMMcuNMbpwhYQ0j9e5YuK7szbRu30C/60ynqjvnoYWN/uuZ64yl+Bz0i10Y0wk8DZwOZAOLDTGfG6tXV1ksSeB8dbad4wxzYAvgcQA5BUJuLxCD38Zt5SvVu7kwc71+Ev2q5j5k3XCkAS94uxyaQ9stNZuBjDGjAO6A0UL3QJHN1kqATv8GVKkpBzOK+SeUYv4ceMenr6yLv23PuE7YegZuOABnTAkQa04hV4H2Fbk93Tg+As7/xP4xhhzP1AO0AG5EnL2Hc5nwPCFrNx+gDevq811K+6D3TphSEKHvz479gKGW2sTgKuBUcaY3zy2MeZuY0yKMSYlMzPTT08tcuYyDhzhlvfmsSbjICO7V+G6hf0hazP0+kRlLiGjOIW+Hahb5PcE37SiBgLjAay184BYoOrxD2StHWqtTbbWJlerVu30Eov42ebMbG5+Zx47D+Qy8boYOs26DfKznROGGuvDpoSO4hT6QqCxMaaBMaYM0BP4/LhltgKXAhhjzsEpdG2CS9Bbuf0At7w7jyMFHqZelUurmb19JwzN0AlDEnJOWujW2kJgMDAdWINzNMsqY8wzxpjrfYs9DNxljFkGjAUGWGttoEKL+MP8zVn0GvozsdGRfHXJdhrMuMM5UUgnDEmIKtaJRdbaL3EORSw67R9F7q8GOvk3mkjgzFy9i0FjFpNwVixT2qRQceZzOmFIQp7OFJVSZ/LidB6duJyWtcozLnEasT++54wwdMM7EBXjdjyR06ZCl1Jl2I9beGbaajonVeCDisOIXqQRhiR8qNClVLDW8tqM9fz3u43ccE4FXrWvELFmlk4YkrCiQpew5/Vanvp8FaN+TmNg6zie3P8ERiMMSRhSoUtYyy/08vCEZUxdtoPH20dxz9YHMYcznROGdIy5hBkVuoStnPxC/jx6MbPWZ/KfTh5uWnO/M6P/NEjQMeYSflToEpYO5BRwx4iFLNm6j+EXHaDL0kegXFXoO0XHmEvYUqFL2Nl9MJd+wxawOfMwUzpto/Wi/4Pq50DvSVChhtvxRAJGhS5hZWtWDn0+nM+e7Fymt19Cg5R/Q4OL4U8f64QhCXsqdAkLHq/l82Xbef6LtXg8hfzQcgbVl3wIzXvAje/qhCEpFVToEtKstcxcs5tXpq9j3a5DtKpZlo+rjqHCqs+gw5/hyn/phCEpNVToErLmbcri5elrWbx1Pw2qluPD66vSdf1zmI2z4bKnodODOmFIShUVuoScFekHeGn6WuZs2EPNirG8eWUlrtn/MREzPoGIKLjhXWjTy+2YIiVOhS4hY+PubF6dsY4vV+ykclw0L3WJ46bDY4mcPQEiy0D7u52t8oq13I4q4goVugS97fuP8MbM9UxclE7Z6Eie6hhNn/xPiJ4/GSJjnItrXXA/VKjpdlQRV6nQJWhlZefx9vebGP1zGgCPnAsDveOIWfIpRJeFjoOcC2uVr+5uUJEgoUKXoHMot4D352zhwzmbOVLgYVDzfP5sJhO3eipEx0GnB6Dj/VBe49KKFKVCl6CRW+Bh9M9pvP39RvblFHBXk8M8GDWF8hu/dMb5vPCv0HEwlIt3O6pIUFKhi+sKPV4mLkrnjW83kHEgl971D/C32E+plDYdYirCxY86+8njqrgdVSSoqdDFNV6v5cuVGfznm/Vs2XOYm2pl8n/VplIlfSbEVILOj8H5f4ayld2OKhISVOhS4qy1zFqfycvT17Fqx0Gujc9gfINpVMv4Ho5Ugi5PQId7oOxZbkcVCSkqdClRi9L28u+v17Fgy16urLSND+tNo+buOeA5Cy55EjrcDbGV3I4pEpJU6FIi1mQc5JXp6/h27W4uKZfKT3WmUSdrLhyqApf+A867S1dDFDlDKnQJqLSsw7w6Yz2fL9vBxTEbmVPzC+runw9H4uGyf8J5d0JMBbdjioQFFboExK6Dufz32w18snAb50eu5ftqX5B4MAUKqsHlz8J5A6FMObdjioQVFbr41f6cfN6ZtYkRc7eQ7F3FjMpf0ODwEvBUhyueh+Q7oEyc2zFFwpIKXfzicF4hH/20hfdmb6JV/jK+qDSNpCPLwdSEq16Etv1V5CIBpkIPcTsP5LI5M5t8j5dCj6XA46XAayko9B5zv9DrpeDofM+x9ws9lnzftMITzD96/9fH91JQeOz8fI+HC1jO5ArTaMxKiKoF3V6Ctv2c666ISMCp0EOU12uZ9MN8zpr1JE1s2i/TLcb3kxNM8w32YAwGnN/McdOMM8eZ7Pt5onkRBhMBpozBYChjj1A5Nx1i6sClr8C5fSE6NmCvX0R+S4Uegnbsy2HqyP/Qa+/blImwHKp/ORGRUURgiTCGCAMRxingCOObhtPdEcZgfql73097bP0XbxrHTjMGkh6Cc/to/E4Rl6jQQ4i1lq9+XkrZ6Y9wDynsrtKWCn2HEVulgdvRRCQIqNBDxN7D+Uwc9RY3Z/yH8iaPvZ2eovqlD0JEpNvRRCRIqNBDwKyl6zjy2UPcbX9kd8VmRPb5iCo1znY7logEGRV6EMvOK2TCmA+5OvVfxJtDZCY/TPVuQyAy2u1oIhKEVOhBKmVtKjsmPMztnplkxiXh7f0p1RLOdTuWiAQxFXqQyS3wMGnSWDqveYpzzV4yWtxLrRue0ZEjInJSKvQgsiptJ+s+foTe+VPZE5NA/p++oFbSBW7HEpEQoUIPAoUeL1Omfkq7JU/Qw2SQ3rgvCbf8WxevEpFTokJ32eadWSwaOYQeh8dzILo62TdNIuGcy9yOJSIhKKI4CxljrjLGrDPGbDTGPH6C+a8ZY5b6buuNMfv9njTMWGuZOn06+e904ZacT0hPvJEqjyykvMpcRE7TSbfQjTGRwNvA5UA6sNAY87m1dvXRZay1fy2y/P2ADsf4Axn7DvHj8Cfpvn8UOZGV2Hf9KOq3ud7tWCIS4oqzy6U9sNFauxnAGDMO6A6s/p3lewFP+SdeeLHW8t2PP1L9279wCxtJrXUV9fv+D1Mu3u1oIhIGilPodYBtRX5PBzqcaEFjTH2gAfDd78y/G7gboF69eqcUNNTtzc7lu+HPcG3mUAoiYtl9xTskdrzN7VgiEkb8/aVoT2CitdZzopnW2qHAUIDk5OQTXLovPM1NSSHmiwe42a4itepF1O3/ARUq1nQ7loiEmeIU+nagbpHfE3zTTqQnMOhMQ4WL7NwCvhn1b65IfxNjDNs7v0Jilzt/vQa5iIgfFafQFwKNjTENcIq8J/CbfQXGmLOBysA8vyYMUUtWriZv8n308C4htVIytfp9SJ2qiW7HEpEwdtJCt9YWGmMGA9OBSGCYtXaVMeYZIMVa+7lv0Z7AOGtPNApC6ZFXUMj0sW/SedNLxJhC0jr8k8QrH4SIYh0hKiJy2oxb/ZucnGxTUlJcee5AWbtpM5ljB3FR4VzS4lpQre8w4mo1dTuWiIQRY8wia23yiebpTFE/KPR4+Wbyh3RY+QwNTQ6bWj9CUvcnNPiEiJQoFfoZStu+g82jBnN17rdsi21EVK8PSUps43YsESmFVOinyVrLt1+Mo8XCJ7jI7Gdd0z/T9JZnIKqM29FEpJRSoZ+GvQcOsviDwVx26DO2R9dl/y1jaNq0o9uxRKSUU6GfomVLFxL72V1cZrewul5vzunzCqZMnNuxRERU6MXl8Vq+/eS/dFr7PIWmDGlXDqNZx5vcjiUi8gsVejHs3pvFmg/+zBU509kY14pad4ymfrX6bscSETmGCv0kliz8iUpf3M1FdjurGt9Ns17/wkRGux1LROQ3VOi/o7DQw/djXuKiTf/hcER5tl87lubJ3dyOJSLyu1ToJ7Br9242DRvI5bmzWVf+POoOHEl8ldpuxxIR+UMq9OOkzJ1JjW/uo73NZGWzv9Dilqd0HRYRCQkqdJ+CQg+zRj7NxWlvsS+iCjtvnEyL1pe4HUtEpNhU6MCOHdvYPvwOLstfwOpKF9HwzuHEVqzqdiwRkVNS6gt9wQ/TqP/D/bS2B1nR+v9oeeOjGoBCREJSqS30/PwCfhw+hM7bP2BXZC2ybhlDy3N0+r6IhK5SWejpW7eQNao/XQuWsbzKFTS9831iyp3ldiwRkTNS6gp9/owJNPrxYZqYI6xMfp5W1wzSLhYRCQulptBzc3OZP+xhOu8eTVpUffJ7fkaLxue6HUtExG9KRaFv3bSWQ2MG0NmzhiXVutNi4P+Iji3vdiwREb8K+0Kf/+UIzp4/hCrGy8qOr3LulQPdjiQiEhBhW+hHcnJY9MFgLtw7iU3RjanQdyQt6jdzO5aISMCEZaFvWbeMgk9u50LvJlJq9aTNgNeJiinrdiwRkYAKq0K31jL/s/doueQpCk0Uqzq/S/IlvdyOJSJSIsKm0LOzD7Li/XvoeOBL1sU0J77/KJrXSXI7lohIiQmLQt+0cgGRk++ggyedBfXuoF3/l4iM0iAUIlK6hHShW6+X+ZNeo83KFzhsyrH28hG0v7C727FERFwRsoV+8MBe1r8/kPOzv2Nl2bbUvn0EzWrUczuWiIhrQrLQNyydQ9nP7qSNdzfzGw7ivD7PEhEZ6XYsERFXhVyhL5jyJm2WPsU+cxYbrxlHh/ZXuh1JRCQohNzYahXrnM3K8hcQM3guZ6vMRUR+EXJb6Ge3vxzaX+52DBGRoBNyW+giInJiKnQRkTChQhcRCRMqdBGRMKFCFxEJEyp0EZEwoUIXEQkTKnQRkTBhrLXuPLExmUDaaf55VWCPH+OEOq2PY2l9/Err4ljhsD7qW2urnWiGa4V+JowxKdbaZLdzBAutj2NpffxK6+JY4b4+tMtFRCRMqNBFRMJEqBb6ULcDBBmtj2NpffxK6+JYYb0+QnIfuoiI/FaobqGLiMhxVOgiImEiaArdGDPMGLPbGLOyyLTWxph5xpgVxpipxpiKvunRxpgRvulrjDFDivzNX40xq4wxK40xY40xsW68njNxiuuijDHmI9/0ZcaYLkX+pp1v+kZjzH+NMabkX82Z88f6MMbEGWO+MMas9b0/XnTn1Zw5f70/ivzt50UfK5T48f+VMsaYocaY9b73yE0l/2r8wFobFDfgYqAtsLLItIVAZ9/9O4BnffdvA8b57scBqUAiUAfYApT1zRsPDHD7tQV4XQwCPvLdrw4sAiJ8vy8AzgcM8BXQze3X5tb68L1PLvFNLwPMKc3ro8jf9QDGFH2sULr58f+Vp4HnfPcjgKpuv7bTuQXNFrq1djaw97jJTYDZvvszgKP/alqgnDEmCigL5AMHffOigLK+eXHAjkDmDoRTXBfNgO98f7cb2A8kG2NqARWttT9b5106ErghsMkDwx/rw1qbY6393jc9H1gMJAQ2eWD4Y30AGGPKAw8BzwU2ceD4a13gFP8Lvnlea21Ink0aNIX+O1YB3X33bwHq+u5PBA4DGcBW4BVr7V5r7XbgFd+0DOCAtfabko0cML+3LpYB1xtjoowxDYB2vnl1gPQif5/umxYuTnV9/MIYcxZwHfBtyUQtEaezPp4F/gPklGTQEnBK68L3fgB41hiz2BgzwRhTo0QT+0mwF/odwH3GmEVABZwtcYD2gAeoDTQAHjbGNDTGVMb5D9nAN6+cMaZPyccOiN9bF8NwyjoFeB2Yi7Nuwt1prQ/fJ7exwH+ttZtLMnCAndL6MMa0AZKstVNKPmrAnep7Iwrn09pca21bYB7OhmHIiXI7wB+x1q4FrgAwxjQBrvHNug342lpbAOw2xvyE89HJAlustZm+v5kMXACMLuns/vZ768JaWwj89ehyxpi5wHpgH8fuUkgAtpdU3kA7jfVx1FBgg7X29RILWwJOY310xtk1l4rTA9WNMT9Ya7uUbHL/O411kYXzKWWyb9YEYGAJRvaboN5CN8ZU9/2MAJ4E3vXN2gp09c0rh/PF31rf9PN9RzQY4FJgTUnnDoTfWxe+11rOd/9yoNBau9pamwEcNMac71sX/YDP3Envf6e6Pny/PwdUAv7iRuZAOo33xzvW2trW2kTgQmB9OJQ5nNa6sMBUoIvvIS4FVpd0br9w+1vZozecj8EZQAHOx6KBwIM4/4KuB17k1zNby+P8K7oKZ8U/WuRxnsYp95XAKCDG7dcW4HWRCKzD+YdrJs6lNY8+TrJvPWwC3jr6N6F288f6wPmEYn3Tl/pud7r92tx8fxR5vERC9ygXf/2/Uh/ni9TlON+t1HP7tZ3OTaf+i4iEiaDe5SIiIsWnQhcRCRMqdBGRMKFCFxEJEyp0EZEwoUIXEQkTKnQRkTDx/+MDSPZCgVWwAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " bf_model.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", + " bf_model.ultimate_.to_frame(origin_as_datetime=True),\n", + " label=\"BF\",\n", + ")\n", + "\n", + "cl_model = cl.Chainladder().fit(comauto[\"CumPaidLoss\"])\n", + "plt.plot(\n", + " cl_model.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", + " cl_model.ultimate_.to_frame(origin_as_datetime=True),\n", + " label=\"CL\",\n", + ")\n", + "\n", + "plt.legend(loc=\"upper left\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benktander Method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Benktander` method is similar to the `BornhuetterFerguson` method, but allows for the specification of one additional assumption, `n_iters`, the number of iterations to recalculate the ultimates. The Benktander method generalizes both the `BornhuetterFerguson` and the `Chainladder` estimator through this assumption.\n", + "\n", + "- When `n_iters = 1`, the result is equivalent to the `BornhuetterFerguson` estimator.\n", + "- When `n_iters` is sufficiently large, the result converges to the `Chainladder` estimator." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Benktander(apriori=0.75, n_iters=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Benktander(apriori=0.75, n_iters=2)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bk_model = cl.Benktander(apriori=0.75, n_iters=2)\n", + "bk_model.fit(\n", + " comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fitting the `Benktander` method looks identical to the other methods." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Benktander(apriori=0.75, n_iters=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Benktander(apriori=0.75, n_iters=2)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bk_model.fit(\n", + " X=comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " bf_model.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", + " bf_model.ultimate_.to_frame(origin_as_datetime=True),\n", + " label=\"BF\",\n", + ")\n", + "plt.plot(\n", + " cl_model.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", + " cl_model.ultimate_.to_frame(origin_as_datetime=True),\n", + " label=\"CL\",\n", + ")\n", + "plt.plot(\n", + " bk_model.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", + " bk_model.ultimate_.to_frame(origin_as_datetime=True),\n", + " label=\"BK\",\n", + ")\n", + "plt.legend(loc=\"upper left\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cape Cod Method\n", + "The `CapeCod` method is similar to the `BornhuetterFerguson` method, except its `apriori` is computed from the `Triangle` itself. Instead of specifying an `apriori`, `decay` and `trend` need to be specified. \n", + "\n", + " - `decay` is the rate that gives weights to earlier origin periods, this parameter is required by the Generalized Cape Cod Method, as discussed in [Using Best Practices to Determine a Best Reserve Estimate](https://www.casact.org/sites/default/files/database/forum_98fforum_struhuss.pdf) by Struzzieri and Hussian. As the `decay` factor approaches 1 (the default value), the result approaches the traditional Cape Cod method. As the `decay` factor approaches 0, the result approaches the `Chainladder` method. \n", + " - `trend` is the trend rate along the origin axis to reflect systematic inflationary impacts on the a priori." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we `fit` a `CapeCod` method, we can see the `apriori` it computes with the given `decay` and `trend` assumptions. Since it is an array of estimated parameters, this `CapeCod` attribute is called the `apriori_`, with a trailing underscore." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
CapeCod()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "CapeCod()" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cc_model = cl.CapeCod()\n", + "cc_model.fit(\n", + " comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With `decay=1`, each `origin` period gets the same `apriori_` (this is the traditional Cape Cod). The `apriori_` is calculated using the latest diagonal over the used-up exposure, where the used-up exposure is the exposure vector / CDF. Let's validate the calculation of the a priori." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6856862224535671" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "latest_diagonal = comauto[\"CumPaidLoss\"].latest_diagonal\n", + "\n", + "cdf_as_origin_vector = (\n", + " cl.Chainladder().fit(comauto[\"CumPaidLoss\"]).ultimate_\n", + " / comauto[\"CumPaidLoss\"].latest_diagonal\n", + ")\n", + "\n", + "latest_diagonal.sum() / (\n", + " comauto[\"EarnedPremNet\"].latest_diagonal / cdf_as_origin_vector\n", + ").sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With `decay=0`, the `apriori_` for each `origin` period stands on its own." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2261
19880.6853
19890.7041
19900.6903
19910.6478
19920.6518
19930.6815
19940.6925
19950.7004
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" + ], + "text/plain": [ + " 2261\n", + "1988 0.685281\n", + "1989 0.704094\n", + "1990 0.690279\n", + "1991 0.647802\n", + "1992 0.651835\n", + "1993 0.681518\n", + "1994 0.692516\n", + "1995 0.700403\n", + "1996 0.703893\n", + "1997 0.761919" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cc_model = cl.CapeCod(decay=0, trend=0).fit(\n", + " X=comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", + ")\n", + "cc_model.apriori_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Doing the same on our manually calculated `apriori_` yields the same result." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1997
19880.6853
19890.7041
19900.6903
19910.6478
19920.6518
19930.6815
19940.6925
19950.7004
19960.7039
19970.7619
" + ], + "text/plain": [ + " 1997\n", + "1988 0.685281\n", + "1989 0.704094\n", + "1990 0.690279\n", + "1991 0.647802\n", + "1992 0.651835\n", + "1993 0.681518\n", + "1994 0.692516\n", + "1995 0.700403\n", + "1996 0.703893\n", + "1997 0.761919" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "latest_diagonal / (comauto[\"EarnedPremNet\"].latest_diagonal / cdf_as_origin_vector)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's verify the result of this Cape Cod model's result with the Chainladder's." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2261
1988
1989
1990
1991
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1994
19950.0000
1996
19970.0000
" + ], + "text/plain": [ + " 2261\n", + "1988 NaN\n", + "1989 NaN\n", + "1990 NaN\n", + "1991 NaN\n", + "1992 1.164153e-10\n", + "1993 1.164153e-10\n", + "1994 NaN\n", + "1995 1.164153e-10\n", + "1996 NaN\n", + "1997 1.164153e-10" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cc_model.ultimate_ - cl_model.ultimate_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can examine the `apriori_`s to see whether there exhibit any trends over time." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", + " cc_model.apriori_.to_frame(origin_as_datetime=True),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like there is a small positive trend, let's judgementally select the `trend` as 1%." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "trended_cc_model = cl.CapeCod(decay=0, trend=0.01).fit(\n", + " X=comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", + ")\n", + "\n", + "plt.plot(\n", + " cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", + " cc_model.apriori_.to_frame(origin_as_datetime=True),\n", + " label=\"Untrended\",\n", + ")\n", + "plt.plot(\n", + " trended_cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", + " trended_cc_model.apriori_.to_frame(origin_as_datetime=True),\n", + " label=\"Trended\",\n", + ")\n", + "plt.legend(loc=\"lower right\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can of course utilize both the `trend` and the `decay` parameters together. Adding `trend` to the `CapeCod` method is intended to adjust the `apriori_`s to a common level. Once at a common level, the `apriori_` can be estimated from multiple origin periods using the `decay` factor." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "trended_cc_model = cl.CapeCod(decay=0, trend=0.01).fit(\n", + " X=comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", + ")\n", + "\n", + "trended_decayed_cc_model = cl.CapeCod(decay=0.75, trend=0.01).fit(\n", + " X=comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", + ")\n", + "\n", + "plt.plot(\n", + " cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", + " cc_model.apriori_.to_frame(origin_as_datetime=True),\n", + " label=\"Untrended\",\n", + ")\n", + "plt.plot(\n", + " trended_cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", + " trended_cc_model.apriori_.to_frame(origin_as_datetime=True),\n", + " label=\"Trended\",\n", + ")\n", + "plt.plot(\n", + " trended_decayed_cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", + " trended_decayed_cc_model.apriori_.to_frame(origin_as_datetime=True),\n", + " label=\"Trended and Decayed\",\n", + ")\n", + "plt.legend(loc=\"lower right\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once estimated, it is necessary to detrend our `apriori_`s back to their untrended levels and these are contained in `detrended_apriori_`. It is the `detrended_apriori_` that gets used in the calculation of `ultimate_` losses." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " trended_cc_model.apriori_.to_frame(origin_as_datetime=True).index.year,\n", + " trended_cc_model.apriori_.to_frame(origin_as_datetime=True),\n", + " label=\"Trended\",\n", + ")\n", + "plt.plot(\n", + " trended_cc_model.detrended_apriori_.to_frame(origin_as_datetime=True).index.year,\n", + " trended_cc_model.detrended_apriori_.to_frame(origin_as_datetime=True),\n", + " label=\"Detended to Original\",\n", + ")\n", + "plt.legend(loc=\"lower right\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `detrended_apriori_` is a much smoother estimate of the initial expected `ultimate_`. With the `detrended_apriori_` in hand, the `CapeCod` method estimator behaves exactly like our the `BornhuetterFerguson` model." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bf_model = cl.BornhuetterFerguson().fit(\n", + " X=comauto[\"CumPaidLoss\"],\n", + " sample_weight=trended_cc_model.detrended_apriori_\n", + " * comauto[\"EarnedPremNet\"].latest_diagonal,\n", + ")\n", + "\n", + "bf_model.ultimate_.sum() == trended_cc_model.ultimate_.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recap\n", + "\n", + "All the deterministic estimators have `ultimate_`, `ibnr_`, `full_expecation_` and `full_triangle_` attributes that are themselves `Triangle`s. These can be manipulated in a variety of ways to gain additional insights from our model. The expected loss methods take in an exposure vector, which itself is a `Triangle` through the `sample_weight` argument of the `fit` method. The `CapeCod` method has the additional attributes `apriori_` and `detrended_apriori_` to accommodate the selection of its `trend` and `decay` assumptions.\n", + "\n", + "Finally, these estimators work very well with the transformers discussed in previous tutorials. Let's demonstrate the compositional nature of these estimators." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(1, 2, 10, 10)
Index:[LOB]
Columns:[CumPaidLoss, EarnedPremNet]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (1, 2, 10, 10)\n", + "Index: [LOB]\n", + "Columns: [CumPaidLoss, EarnedPremNet]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wkcomp = (\n", + " cl.load_sample(\"clrd\")\n", + " .groupby(\"LOB\")\n", + " .sum()\n", + " .loc[\"wkcomp\"][[\"CumPaidLoss\", \"EarnedPremNet\"]]\n", + ")\n", + "wkcomp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's calculate the age-to-age factors:\n", + "- Without the the 1995 valuation period\n", + "- Using volume weighted for the first 5 factors, and simple average for the next 4 factors (for a total of 9 age-to-age factors)\n", + "- Using no more than 7 periods (with `n_periods`)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "patterns = cl.Pipeline(\n", + " [\n", + " (\n", + " \"dev\",\n", + " cl.Development(\n", + " average=[\"volume\"] * 5 + [\"simple\"] * 4,\n", + " n_periods=7,\n", + " drop_valuation=\"1995\",\n", + " ),\n", + " ),\n", + " (\"tail\", cl.TailCurve(curve=\"inverse_power\", extrap_periods=80)),\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2261
19881,331,221
19891,416,505
19901,523,470
19911,581,962
19921,541,458
19931,484,168
19941,525,963
19951,548,534
19961,541,068
19971,507,592
" + ], + "text/plain": [ + " 2261\n", + "1988 1.331221e+06\n", + "1989 1.416505e+06\n", + "1990 1.523470e+06\n", + "1991 1.581962e+06\n", + "1992 1.541458e+06\n", + "1993 1.484168e+06\n", + "1994 1.525963e+06\n", + "1995 1.548534e+06\n", + "1996 1.541068e+06\n", + "1997 1.507592e+06" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cc = cl.CapeCod(decay=0.8, trend=0.02).fit(\n", + " X=patterns.fit_transform(wkcomp[\"CumPaidLoss\"]),\n", + " sample_weight=wkcomp[\"EarnedPremNet\"].latest_diagonal,\n", + ")\n", + "cc.ultimate_" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar(\n", + " cc.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", + " cc.ultimate_.to_frame(origin_as_datetime=True)[\"2261\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Voting Chainladder\n", + "\n", + "A `VotingChainladder` is an ensemble meta-estimator that fits several base chainladder methods, each on the whole triangle. Then it combines the individual predictions based on a matrix of weights to form a final prediction.\n", + "\n", + "Let's begin by loading the `raa` dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "raa = cl.load_sample(\"raa\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate the Chainladder's estimator." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "cl_mod = cl.Chainladder()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate the Expected Loss's estimator." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "el_mod = cl.ExpectedLoss(apriori=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate the Bornhuetter-Ferguson's estimator. Remember that the `BornhuetterFerguson` requires one argument, the `apriori`." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "bf_mod = cl.BornhuetterFerguson(apriori=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate the Cape Cod's estimator and their required arguments." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "cc_mod = cl.CapeCod(decay=1, trend=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate the Benktander's estimator and their required arguments." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "bk_mod = cl.Benktander(apriori=1, n_iters=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's prepare the `estimators` variable. The `estimators` parameter in `VotingChainladder` must be in an array of tuples, with (estimator_name, estimator) pairing." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "estimators = [\n", + " (\"cl\", cl_mod),\n", + " (\"el\", el_mod),\n", + " (\"bf\", bf_mod),\n", + " (\"cc\", cc_mod),\n", + " (\"bk\", bk_mod),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that some estimators (in this case, `BornhuetterFerguson`, `CapeCod`, and `Benktander`) also require the variable `sample_weight`, let's use the mean of `Chainladder`'s average ultimate estimate." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 2261\n", + "1981 18834.000000\n", + "1982 16875.500226\n", + "1983 24058.534810\n", + "1984 28542.580970\n", + "1985 28236.843134\n", + "1986 19905.317262\n", + "1987 18947.245455\n", + "1988 23106.943030\n", + "1989 20004.502125\n", + "1990 21605.832631" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_weights = np.array(\n", + " [[0.6, 0, 0.2, 0.2, 0]] * 4 + [[0, 0, 0.5, 0.5, 0]] * 3 + [[0, 0, 0, 1, 0]] * 3\n", + ")\n", + "\n", + "vot_mod = cl.VotingChainladder(estimators=estimators, weights=model_weights).fit(\n", + " raa, sample_weight=sample_weight\n", + ")\n", + "vot_mod.ultimate_" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " cl_mod.fit(raa).ultimate_.to_frame(origin_as_datetime=True).index.year,\n", + " cl_mod.fit(raa).ultimate_.to_frame(origin_as_datetime=True),\n", + " label=\"Chainladder\",\n", + " linestyle=\"dashed\",\n", + " marker=\"o\",\n", + ")\n", + "plt.plot(\n", + " el_mod.fit(raa, sample_weight=sample_weight)\n", + " .ultimate_.to_frame(origin_as_datetime=True)\n", + " .index.year,\n", + " el_mod.fit(raa, sample_weight=sample_weight).ultimate_.to_frame(\n", + " origin_as_datetime=True\n", + " ),\n", + " label=\"Expected Loss\",\n", + " linestyle=\"dashed\",\n", + " marker=\"o\",\n", + ")\n", + "plt.plot(\n", + " bf_mod.fit(raa, sample_weight=sample_weight)\n", + " .ultimate_.to_frame(origin_as_datetime=True)\n", + " .index.year,\n", + " bf_mod.fit(raa, sample_weight=sample_weight).ultimate_.to_frame(\n", + " origin_as_datetime=True\n", + " ),\n", + " label=\"Bornhuetter-Ferguson\",\n", + " linestyle=\"dashed\",\n", + " marker=\"o\",\n", + ")\n", + "plt.plot(\n", + " cc_mod.fit(raa, sample_weight=sample_weight)\n", + " .ultimate_.to_frame(origin_as_datetime=True)\n", + " .index.year,\n", + " cc_mod.fit(raa, sample_weight=sample_weight).ultimate_.to_frame(\n", + " origin_as_datetime=True\n", + " ),\n", + " label=\"Cape Cod\",\n", + " linestyle=\"dashed\",\n", + " marker=\"o\",\n", + ")\n", + "plt.plot(\n", + " bk_mod.fit(raa, sample_weight=sample_weight)\n", + " .ultimate_.to_frame(origin_as_datetime=True)\n", + " .index.year,\n", + " bk_mod.fit(raa, sample_weight=sample_weight).ultimate_.to_frame(\n", + " origin_as_datetime=True\n", + " ),\n", + " label=\"Benktander\",\n", + " linestyle=\"dashed\",\n", + " marker=\"o\",\n", + ")\n", + "plt.plot(\n", + " vot_mod.ultimate_.to_frame(origin_as_datetime=True).index.year,\n", + " vot_mod.ultimate_.to_frame(origin_as_datetime=True),\n", + " label=\"Selected\",\n", + ")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also call the `weights` attribute to confirm the weights being used by the `VotingChainladder` ensemble model." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.6, 0. , 0.2, 0.2, 0. ],\n", + " [0.6, 0. , 0.2, 0.2, 0. ],\n", + " [0.6, 0. , 0.2, 0.2, 0. ],\n", + " [0.6, 0. , 0.2, 0.2, 0. ],\n", + " [0. , 0. , 0.5, 0.5, 0. ],\n", + " [0. , 0. , 0.5, 0.5, 0. ],\n", + " [0. , 0. , 0.5, 0.5, 0. ],\n", + " [0. , 0. , 0. , 1. , 0. ],\n", + " [0. , 0. , 0. , 1. , 0. ],\n", + " [0. , 0. , 0. , 1. , 0. ]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vot_mod.weights" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/getting_started/tutorials/development-tutorial.ipynb b/docs/getting_started/tutorials/development-tutorial.ipynb new file mode 100644 index 00000000..228555b7 --- /dev/null +++ b/docs/getting_started/tutorials/development-tutorial.ipynb @@ -0,0 +1,3321 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Development Tutorial\n", + "## Getting Started\n", + "This tutorial focuses on selecting the development factors. \n", + "\n", + "Be sure to make sure your packages are updated. For more info on how to update your pakages, visit [Keeping Packages Updated](https://chainladder-python.readthedocs.io/en/latest/library/install.html#keeping-packages-updated)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pandas: 1.4.2\n", + "numpy: 1.22.4\n", + "chainladder: 0.8.12\n" + ] + } + ], + "source": [ + "# Black linter, optional\n", + "%load_ext lab_black\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import chainladder as cl\n", + "\n", + "print(\"pandas: \" + pd.__version__)\n", + "print(\"numpy: \" + np.__version__)\n", + "print(\"chainladder: \" + cl.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Disclaimer\n", + "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing for Violation of Chain Ladder's Assumptions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The chain ladder method is based on the strong assumptions of independence across origin periods and across valuation periods. Mack developed tests to verify if these assumptions hold, and these tests have been implemented in the `chainladder` package.\n", + "\n", + "Before the chain ladder model can be used, we should verify that the data satisfies the underlying assumptions using tests at the desired confidence interval level. If assumptions are violated, we should consider if ultimates can be estimated using other models.\n", + "\n", + "There are two main tests that we need to perform:\n", + "- The `valuation_correlation` test: \n", + " - This test tests for the assumption of independence of accident years. In fact, it tests for correlation across calendar periods (diagonals), and by extension, origin periods (rows).\n", + " - An additional parameter, `total`, can be passed, depending on if we want to calculate valuation correlation in total across all origins (`True`), or for each origin separately (`False`).\n", + " - The test uses Z-statistic.\n", + "- The `development_correlation` test:\n", + " - This test tests for the assumption of independence of the chain ladder method that assumes that subsequent development factors are not correlated (columns).\n", + " - The test uses T-statistic." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Are valuation years correlated? Or, are the origins correlated? [[False]]\n", + "Are development periods coorelated? [[False]]\n" + ] + } + ], + "source": [ + "raa = cl.load_sample(\"raa\")\n", + "print(\n", + " \"Are valuation years correlated? Or, are the origins correlated?\",\n", + " raa.valuation_correlation(p_critical=0.1, total=True).z_critical.values,\n", + ")\n", + "print(\n", + " \"Are development periods coorelated?\",\n", + " raa.development_correlation(p_critical=0.5).t_critical.values,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above tests show that the `raa` triangle is independent in both cases, suggesting that there is no evidence that the chain ladder model is not an appropriate method to develop the ultimate amounts. It is suggested to review Mack's papers to ensure a proper understanding of the methodology and the choice of `p_critical`.\n", + "\n", + "Mack also demonstrated that we can test for valuation years' correlation. To test for each valuation year's correlation individually, we set `total` to `False`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.49061.74731.45741.17391.10381.08631.05391.07661.0177
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dev.cdf_.cum_to_incr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice these attributes have a trailing underscore (`_`). This is scikit-learn's API convention, as its [documentation](https://scikit-learn.org/dev/developers/develop.html) states, \"attributes that have been estimated from the data must always have a name ending with trailing underscore, for example the coefficients of some regression estimator would be stored in a `coef_` attribute after `fit` has been called.\" In summary, the trailing underscore in class attributes is a scikit-learn's convention to denote that the attributes are estimated, or to denote that they are fitted attributes." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Assumption parameter (no underscore): volume\n", + "Estimated parameter (underscore):\n", + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725\n" + ] + } + ], + "source": [ + "print(\"Assumption parameter (no underscore):\", dev.average)\n", + "print(\"Estimated parameter (underscore):\\n\", dev.ldf_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Development Averaging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a grounding in triangle manipulation and the basics of estimators, we can start getting more creative with customizing our development factors.\n", + "\n", + "The basic `Development` estimator uses a weighted regression through the origin for estimating parameters. Mack showed that using weighted regressions allows for:\n", + "1. `volume` weighted average development patterns
\n", + "2. `simple` average development factors
\n", + "3. OLS `regression` estimate of development factor where the regression equation is Y = mX + 0
\n", + "\n", + "While he posited this framework to suggest the `MackChainladder` stochastic method, it is an elegant form even for deterministic development pattern selection." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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2001357,8481,124,7881,735,3302,218,2702,745,5963,319,9943,466,3363,606,2863,833,5153,901,463
2002352,1181,236,1392,170,0333,353,3223,799,0674,120,0634,647,8674,914,0395,339,085
2003290,5071,292,3062,218,5253,235,1793,985,9954,132,9184,628,9104,909,315
2004310,6081,418,8582,195,0473,757,4474,029,9294,381,9824,588,268
2005443,1601,136,3502,128,3332,897,8213,402,6723,873,311
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "2001 357848.0 1124788.0 1735330.0 2218270.0 2745596.0 3319994.0 3466336.0 3606286.0 3833515.0 3901463.0\n", + "2002 352118.0 1236139.0 2170033.0 3353322.0 3799067.0 4120063.0 4647867.0 4914039.0 5339085.0 NaN\n", + "2003 290507.0 1292306.0 2218525.0 3235179.0 3985995.0 4132918.0 4628910.0 4909315.0 NaN NaN\n", + "2004 310608.0 1418858.0 2195047.0 3757447.0 4029929.0 4381982.0 4588268.0 NaN NaN NaN\n", + "2005 443160.0 1136350.0 2128333.0 2897821.0 3402672.0 3873311.0 NaN NaN NaN NaN\n", + "2006 396132.0 1333217.0 2180715.0 2985752.0 3691712.0 NaN NaN NaN NaN NaN\n", + "2007 440832.0 1288463.0 2419861.0 3483130.0 NaN NaN NaN NaN NaN NaN\n", + "2008 359480.0 1421128.0 2864498.0 NaN NaN NaN NaN NaN NaN NaN\n", + "2009 376686.0 1363294.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010 344014.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genins = cl.load_sample(\"genins\")\n", + "genins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also print the `age_to_age` factors." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
20013.14321.54281.27831.23771.20921.04411.04041.06301.0177
20023.51061.75551.54531.13291.08451.12811.05731.0865
20034.44851.71671.45831.23211.03691.12001.0606
20044.56801.54711.71181.07251.08741.0471
20052.56421.87301.36151.17421.1383
20063.36561.63571.36921.2364
20072.92281.87811.4394
20083.95332.0157
20093.6192
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "2001 3.143200 1.542806 1.278299 1.237719 1.209207 1.044079 1.040374 1.063009 1.017725\n", + "2002 3.510582 1.755493 1.545286 1.132926 1.084493 1.128106 1.057268 1.086496 NaN\n", + "2003 4.448450 1.716718 1.458257 1.232079 1.036860 1.120010 1.060577 NaN NaN\n", + "2004 4.568002 1.547052 1.711784 1.072518 1.087360 1.047076 NaN NaN NaN\n", + "2005 2.564198 1.872956 1.361545 1.174217 1.138315 NaN NaN NaN NaN\n", + "2006 3.365588 1.635679 1.369162 1.236443 NaN NaN NaN NaN NaN\n", + "2007 2.922798 1.878099 1.439393 NaN NaN NaN NaN NaN NaN\n", + "2008 3.953288 2.015651 NaN NaN NaN NaN NaN NaN NaN\n", + "2009 3.619179 NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genins.age_to_age" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And colorcode with `heatmap()`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/display.py:134: FutureWarning: this method is deprecated in favour of `Styler.to_html()`\n", + " .render()\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 12-2424-3636-4848-6060-7272-8484-9696-108108-120
20013.14321.54281.27831.23771.20921.04411.04041.06301.0177
20023.51061.75551.54531.13291.08451.12811.05731.0865
20034.44851.71671.45831.23211.03691.12001.0606
20044.56801.54711.71181.07251.08741.0471
20052.56421.87301.36151.17421.1383
20063.36561.63571.36921.2364
20072.92281.87811.4394
20083.95332.0157
20093.6192
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genins.age_to_age.heatmap()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.49061.74731.45741.17391.10381.08631.05391.07661.0177
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vol = cl.Development(average=\"volume\").fit(genins).ldf_\n", + "vol" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.56611.74561.45201.18101.11121.08481.05271.07481.0177
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.566143 1.745557 1.451961 1.180984 1.111247 1.084818 1.052739 1.074753 1.017725" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = cl.Development(average=\"simple\").fit(genins).ldf_\n", + "sim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In most cases, estimator attributes are `Triangle`s themselves and can be manipulated with just like raw triangles." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LDF Type: \n", + "Difference between volume and simple average:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)-0.07550.00180.0055-0.0071-0.00740.00150.00110.0018
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) -0.075536 0.001776 0.005452 -0.007132 -0.007423 0.001452 0.001135 0.001802 NaN" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"LDF Type: \", type(vol))\n", + "print(\"Difference between volume and simple average:\")\n", + "vol - sim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can specify how the LDFs are averaged independently for each age-to-age period. For example, we can use `volume` averaging on the first pattern, `simple` the second, `regression` the third, and then repeat the cycle three times for the 9 age-to-age factors that we need. Note that the array of selected method must be of the same length as the number of age-to-age factors." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.49061.74561.46191.17391.11121.08731.05391.07481.0177
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.490607 1.745557 1.461852 1.173852 1.111247 1.087341 1.053874 1.074753 1.017725" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(average=[\"volume\", \"simple\", \"regression\"] * 3).fit(genins).ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another example, using `volume`-weighting for the first factor, `simple`-weighting for the next 5 factors, and `volume`-weighting for the last 3 factors." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.49061.74561.45201.18101.11121.08481.05391.07661.0177
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.490607 1.745557 1.451961 1.180984 1.111247 1.084818 1.053874 1.076555 1.017725" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(average=[\"volume\"] + [\"simple\"] * 5 + [\"volume\"] * 3).fit(genins).ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Averaging Period" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Development` comes with an `n_periods` parameter that allows you to select the latest `n` origin periods for fitting your development patterns. `n_periods=-1` is used to indicate the usage of all available periods, which is also the default if the parameter is not specified. The units of `n_periods` follows the `origin_grain` of the underlying triangle." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
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12-2424-3636-4848-6060-7272-8484-9696-108108-120
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" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.460401 1.846507 1.392009 1.153852 1.084915 1.097355 1.053874 1.076555 1.017725" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(n_periods=3).fit(genins).ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Much like `average`, `n_periods` can also be set for each age-to-age individually." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
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" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.532471 1.950242 1.480761 1.165122 1.103824 1.082476 1.053874 1.076555 1.017725" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(n_periods=[8, 2, 6, 5, -1, 2, -1, -1, 5]).fit(genins).ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that if we provide `n_periods` that is greater than what is available for any particular age-to-age period, all available periods will be used instead." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(n_periods=[1, 2, 3, 4, 5, 6, 7, 8, 9]).fit(\n", + " genins\n", + ").ldf_ == cl.Development(n_periods=[1, 2, 3, 4, 5, 4, 3, 2, 1]).fit(genins).ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Discarding Problematic Link Ratios\n", + "\n", + "Even with `n_periods`, there are situations where you might want to be more surgical in our selections. For example, you could have a valuation period with bad data and wish to omit the entire diagonal from your averaging." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
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" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.379677 1.751749 1.442605 1.165122 1.103824 1.086269 1.053874 1.076555 1.017725" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop_valuation=\"2004\").fit(genins).ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also do an olympic averaging (i.e. exluding high and low from each period)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/development/base.py:144: UserWarning: Some exclusions have been ignored. At least 1 (use preserve = ...) link ratio(s) is required for development estimation.\n", + " warnings.warn(warning)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
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" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.520098 1.727701 1.435147 1.193021 1.101827 1.082476 1.057268 1.076555 1.017725" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop_high=True, drop_low=True).fit(genins).ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function also accepts intergers. For example, if we want to drop the highest 3 factors from each period." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/development/base.py:144: UserWarning: Some exclusions have been ignored. At least 1 (use preserve = ...) link ratio(s) is required for development estimation.\n", + " warnings.warn(warning)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
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" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.161368 1.639211 1.368696 1.122203 1.060105 1.044079 1.053874 1.076555 1.017725" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop_high=3).fit(genins).ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There's a `preserve` that we can use, this variable allows us to specified the minimum number of LDFs required for calculation. If this minimum is not yet, the `drop_high` and `drop_low` for that age will be ignored. This is especially useful in the tail when the data is thin." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/development/base.py:144: UserWarning: Some exclusions have been ignored. At least 2 link ratio(s) is required for development estimation.\n", + " warnings.warn(warning)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.410772 1.701152 1.406103 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop_high=3, drop_low=2, preserve=2).fit(genins).ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use an array of booleans or ints." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
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" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.520098 1.768474 1.457413 1.234173 1.103824 1.086269 1.053874 1.076555 1.017725" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop_high=[True, True, False, True], drop_low=[1, 2, 0, 3]).fit(\n", + " genins\n", + ").ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or maybe there is just a single outlier link-ratio that you don't think is indicative of future development. For these, you can specify the intersection of the origin and development age of the **denominator** of the link-ratio to `drop`." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/display.py:134: FutureWarning: this method is deprecated in favour of `Styler.to_html()`\n", + " .render()\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.37971.74731.45741.17391.10381.08631.05391.07661.0177
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.379677 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop=(\"2004\", 12)).fit(genins).ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If there are more than one outliers, you can also pass an array of array to the `drop` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)3.37971.70411.45741.17391.10381.08631.05391.07661.0177
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.379677 1.704149 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop=[(\"2004\", 12), (\"2008\", 24)]).fit(genins).ldf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transformers\n", + "In `sklearn`, there are two types of estimators: transformers and predictors. A transformer transforms the input data (X) in some ways, and a predictor predicts a new value (or values, Y) by using the input data X.\n", + "\n", + "`Development` is a transformer, as the returned object is a means to create development patterns, which is used to estimate ultimates, but itself is not a reserving model (predictor). \n", + "\n", + "Transformers come with the `tranform` and `fit_transform` method. These will return a `Triangle` object, but augment it with additional information for use in a subsequent IBNR model (a predictor). `drop_high` (and `drop_low`) can take an array of boolean variables, indicating if the highest factor should be dropped for each of the LDF calculation." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "2001 357848.0 1124788.0 1735330.0 2218270.0 2745596.0 3319994.0 3466336.0 3606286.0 3833515.0 3901463.0\n", + "2002 352118.0 1236139.0 2170033.0 3353322.0 3799067.0 4120063.0 4647867.0 4914039.0 5339085.0 NaN\n", + "2003 290507.0 1292306.0 2218525.0 3235179.0 3985995.0 4132918.0 4628910.0 4909315.0 NaN NaN\n", + "2004 310608.0 1418858.0 2195047.0 3757447.0 4029929.0 4381982.0 4588268.0 NaN NaN NaN\n", + "2005 443160.0 1136350.0 2128333.0 2897821.0 3402672.0 3873311.0 NaN NaN NaN NaN\n", + "2006 396132.0 1333217.0 2180715.0 2985752.0 3691712.0 NaN NaN NaN NaN NaN\n", + "2007 440832.0 1288463.0 2419861.0 3483130.0 NaN NaN NaN NaN NaN NaN\n", + "2008 359480.0 1421128.0 2864498.0 NaN NaN NaN NaN NaN NaN NaN\n", + "2009 376686.0 1363294.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010 344014.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transformed_triangle = cl.Development(drop_high=[True] * 4 + [False] * 5).fit_transform(\n", + " genins\n", + ")\n", + "transformed_triangle" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
20013.14321.54281.27831.20921.04411.04041.06301.0177
20023.51061.75551.54531.13291.08451.12811.05731.0865
20034.44851.71671.45831.23211.03691.12001.0606
20041.54711.07251.08741.0471
20052.56421.87301.36151.17421.1383
20063.36561.63571.36921.2364
20072.92281.87811.4394
20083.9533
20093.6192
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "2001 3.143200 1.542806 1.278299 NaN 1.209207 1.044079 1.040374 1.063009 1.017725\n", + "2002 3.510582 1.755493 1.545286 1.132926 1.084493 1.128106 1.057268 1.086496 NaN\n", + "2003 4.448450 1.716718 1.458257 1.232079 1.036860 1.120010 1.060577 NaN NaN\n", + "2004 NaN 1.547052 NaN 1.072518 1.087360 1.047076 NaN NaN NaN\n", + "2005 2.564198 1.872956 1.361545 1.174217 1.138315 NaN NaN NaN NaN\n", + "2006 3.365588 1.635679 1.369162 1.236443 NaN NaN NaN NaN NaN\n", + "2007 2.922798 1.878099 1.439393 NaN NaN NaN NaN NaN NaN\n", + "2008 3.953288 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009 3.619179 NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transformed_triangle.link_ratio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our transformed triangle behaves as our original `genins` triangle. However, notice the link_ratios exclude any droppped values you specified." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/display.py:134: FutureWarning: this method is deprecated in favour of `Styler.to_html()`\n", + " .render()\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 12-2424-3636-4848-6060-7272-8484-9696-108108-120
20013.14321.54281.27831.20921.04411.04041.06301.0177
20023.51061.75551.54531.13291.08451.12811.05731.0865
20034.44851.71671.45831.23211.03691.12001.0606
20041.54711.07251.08741.0471
20052.56421.87301.36151.17421.1383
20063.36561.63571.36921.2364
20072.92281.87811.4394
20083.9533
20093.6192
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transformed_triangle.link_ratio.heatmap()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2010
20013,901,463
20025,339,085
20034,909,315
20044,588,268
20053,873,311
20063,691,712
20073,483,130
20082,864,498
20091,363,294
2010344,014
" + ], + "text/plain": [ + " 2010\n", + "2001 3901463.0\n", + "2002 5339085.0\n", + "2003 4909315.0\n", + "2004 4588268.0\n", + "2005 3873311.0\n", + "2006 3691712.0\n", + "2007 3483130.0\n", + "2008 2864498.0\n", + "2009 1363294.0\n", + "2010 344014.0" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(type(transformed_triangle))\n", + "transformed_triangle.latest_diagonal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, it has other attributes that make it IBNR model-ready." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-Ult24-Ult36-Ult48-Ult60-Ult72-Ult84-Ult96-Ult108-Ult
(All)13.13673.88702.28091.61311.38451.25431.15471.09561.0177
" + ], + "text/plain": [ + " 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult\n", + "(All) 13.136729 3.886978 2.28089 1.61311 1.384499 1.254276 1.154664 1.095637 1.017725" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transformed_triangle.cdf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`fit_transform()` is equivalent to calling `fit` and `transform` in succession on the same triangle. Again, this should feel very familiar to the `sklearn` practitioner." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development().fit_transform(genins) == cl.Development().fit(genins).transform(genins)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The reason you might want want to use `fit` and `transform` separately would be when you want to apply development patterns to a a different triangle. For example, we can:\n", + "\n", + "1. Extract the commercial auto triangles from the `clrd` dataset
\n", + "2. Summarize to an industry level and `fit` a `Development` object
\n", + "3. We can then `transform` the individual company triangles with the industry development patterns
" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(157, 1, 10, 10)
Index:[GRNAME, LOB]
Columns:[CumPaidLoss]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (157, 1, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [CumPaidLoss]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample(\"clrd\")\n", + "comauto = clrd[clrd[\"LOB\"] == \"comauto\"][\"CumPaidLoss\"]\n", + "\n", + "comauto_industry = comauto.sum()\n", + "industry_dev = cl.Development().fit(comauto_industry)\n", + "\n", + "industry_dev.transform(comauto)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Working with Multidimensional Triangles\n", + "\n", + "Several (though not all) of the estimators in `chainladder` can be fit to several triangles simultaneously. While this can be a convenient shorthand, all these estimators use the same assumptions across every triangle." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting to 6 industries simultaneously.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2261-12
Grain:OYDY
Shape:(6, 1, 1, 9)
Index:[LOB]
Columns:[CumPaidLoss]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2261-12\n", + "Grain: OYDY\n", + "Shape: (6, 1, 1, 9)\n", + "Index: [LOB]\n", + "Columns: [CumPaidLoss]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample(\"clrd\").groupby(\"LOB\").sum()[\"CumPaidLoss\"]\n", + "print(\"Fitting to \" + str(len(clrd.index)) + \" industries simultaneously.\")\n", + "cl.Development().fit_transform(clrd).cdf_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For greater control, you can slice individual triangles out and fit separate patterns to each." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Development(average='simple')\n", + "Development(n_periods=4)\n", + "Development(average='regression', n_periods=6)\n" + ] + } + ], + "source": [ + "print(cl.Development(average=\"simple\").fit(clrd.loc[\"wkcomp\"]))\n", + "print(cl.Development(n_periods=4).fit(clrd.loc[\"ppauto\"]))\n", + "print(cl.Development(average=\"regression\", n_periods=6).fit(clrd.loc[\"comauto\"]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/getting_started/tutorials/index.md b/docs/getting_started/tutorials/index.md new file mode 100644 index 00000000..cde9c5b7 --- /dev/null +++ b/docs/getting_started/tutorials/index.md @@ -0,0 +1,4 @@ +# {octicon}`beaker` Onboarding Tutorials + +These tutorials serve as the basic onboarding docs on how to use +Chainladder. diff --git a/docs/getting_started/tutorials/stochastic-tutorial.ipynb b/docs/getting_started/tutorials/stochastic-tutorial.ipynb new file mode 100644 index 00000000..e1274f4e --- /dev/null +++ b/docs/getting_started/tutorials/stochastic-tutorial.ipynb @@ -0,0 +1,2939 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying Stochastic Methods\n", + "## Getting Started\n", + "This tutorial focuses on using stochastic methods to estimate ultimates. \n", + "\n", + "Be sure to make sure your packages are updated. For more info on how to update your pakages, visit [Keeping Packages Updated](https://chainladder-python.readthedocs.io/en/latest/library/install.html#keeping-packages-updated)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pandas: 1.4.2\n", + "numpy: 1.22.4\n", + "chainladder: 0.8.12\n" + ] + } + ], + "source": [ + "# Black linter, optional\n", + "%load_ext lab_black\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import chainladder as cl\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "\n", + "print(\"pandas: \" + pd.__version__)\n", + "print(\"numpy: \" + np.__version__)\n", + "print(\"chainladder: \" + cl.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Disclaimer\n", + "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intro to MackChainladder\n", + "\n", + "Like the basic `Chainladder` method, the `MackChainladder` is entirely specified by its selected development pattern. In fact, it is the basic `Chainladder`, but with extra features." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = (\n", + " cl.load_sample(\"clrd\")\n", + " .groupby(\"LOB\")\n", + " .sum()\n", + " .loc[\"wkcomp\", [\"CumPaidLoss\", \"EarnedPremNet\"]]\n", + ")\n", + "\n", + "cl.Chainladder().fit(clrd[\"CumPaidLoss\"]).ultimate_ == cl.MackChainladder().fit(\n", + " clrd[\"CumPaidLoss\"]\n", + ").ultimate_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create a Mack's Chainladder model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "mack = cl.MackChainladder().fit(clrd[\"CumPaidLoss\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "MackChainladder has the following additional fitted features that the deterministic `Chainladder` does not:\n", + "\n", + "- `full_std_err_`: The full standard error\n", + "- `total_process_risk_`: The total process error\n", + "- `total_parameter_risk_`: The total parameter error\n", + "- `mack_std_err_`: The total prediction error by origin period\n", + "- `total_mack_std_err_`: The total prediction error across all origin periods\n", + "\n", + "Notice these are all measures of uncertainty, but where can they be applied? Let's start by examining the `link_ratios` underlying the triangle between age 12 and 24." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224
1988285,804638,532
1989307,720684,140
1990320,124757,479
1991347,417793,749
1992342,982781,402
1993342,385743,433
1994351,060750,392
1995343,841768,575
1996381,484736,040
" + ], + "text/plain": [ + " 12 24\n", + "1988 285804.0 638532.0\n", + "1989 307720.0 684140.0\n", + "1990 320124.0 757479.0\n", + "1991 347417.0 793749.0\n", + "1992 342982.0 781402.0\n", + "1993 342385.0 743433.0\n", + "1994 351060.0 750392.0\n", + "1995 343841.0 768575.0\n", + "1996 381484.0 736040.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd_first_lags = clrd[clrd.development <= 24][clrd.origin < \"1997\"][\"CumPaidLoss\"]\n", + "clrd_first_lags" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A simple average link-ratio can be directly computed." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.2066789527531494" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd_first_lags.link_ratio.to_frame(origin_as_datetime=True).mean()[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also verify that the result is the same as the `Development` object." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.2066789527531494" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(average=\"simple\").fit(clrd[\"CumPaidLoss\"]).ldf_.to_frame(\n", + " origin_as_datetime=False\n", + ").values[0, 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Linear Regression Framework\n", + "\n", + "Mack noted that the estimate for the LDF is really just a linear regression fit. In the case of using the `simple` average, it is a weighted regression where the weight is $\\left (\\frac{1}{X} \\right )^{2}$.\n", + "\n", + "Let's take a look at the fitted coefficient and verify that this ties to the direct calculations that we made earlier.\n", + "With the regression framework in hand, we can get more information about our LDF estimate than just the coefficient." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1477: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n", + " warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
WLS Regression Results
Dep. Variable: y R-squared (uncentered): 0.997
Model: WLS Adj. R-squared (uncentered): 0.997
Method: Least Squares F-statistic: 2887.
Date: Wed, 22 Jun 2022 Prob (F-statistic): 1.60e-11
Time: 22:25:53 Log-Likelihood: -107.89
No. Observations: 9 AIC: 217.8
Df Residuals: 8 BIC: 218.0
Df Model: 1
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
x1 2.2067 0.041 53.735 0.000 2.112 2.301
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 7.448 Durbin-Watson: 1.177
Prob(Omnibus): 0.024 Jarque-Bera (JB): 2.533
Skew: -1.187 Prob(JB): 0.282
Kurtosis: 4.058 Cond. No. 1.00


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " WLS Regression Results \n", + "=======================================================================================\n", + "Dep. Variable: y R-squared (uncentered): 0.997\n", + "Model: WLS Adj. R-squared (uncentered): 0.997\n", + "Method: Least Squares F-statistic: 2887.\n", + "Date: Wed, 22 Jun 2022 Prob (F-statistic): 1.60e-11\n", + "Time: 22:25:53 Log-Likelihood: -107.89\n", + "No. Observations: 9 AIC: 217.8\n", + "Df Residuals: 8 BIC: 218.0\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "x1 2.2067 0.041 53.735 0.000 2.112 2.301\n", + "==============================================================================\n", + "Omnibus: 7.448 Durbin-Watson: 1.177\n", + "Prob(Omnibus): 0.024 Jarque-Bera (JB): 2.533\n", + "Skew: -1.187 Prob(JB): 0.282\n", + "Kurtosis: 4.058 Cond. No. 1.00\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", + "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = clrd_first_lags.to_frame(origin_as_datetime=True).values[:, 1]\n", + "x = clrd_first_lags.to_frame(origin_as_datetime=True).values[:, 0]\n", + "\n", + "model = sm.WLS(y, x, weights=(1 / x) ** 2)\n", + "results = model.fit()\n", + "results.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By toggling the weights of our regression, we can handle the most common types of averaging used in picking loss development factors.\n", + "- For simple average, the weights are $\\left (\\frac{1}{X} \\right )^{2}$\n", + "- For volume-weighted average, the weights are $\\left (\\frac{1}{X} \\right )$\n", + "- For \"regression\" average, the weights are 1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simple average:\n", + "True\n", + "Volume-weighted average:\n", + "True\n", + "Regression average:\n", + "True\n" + ] + } + ], + "source": [ + "print(\"Simple average:\")\n", + "print(\n", + " round(\n", + " cl.Development(average=\"simple\")\n", + " .fit(clrd_first_lags)\n", + " .ldf_.to_frame(origin_as_datetime=False)\n", + " .values[0, 0],\n", + " 10,\n", + " )\n", + " == round(sm.WLS(y, x, weights=(1 / x) ** 2).fit().params[0], 10)\n", + ")\n", + "\n", + "print(\"Volume-weighted average:\")\n", + "print(\n", + " round(\n", + " cl.Development(average=\"volume\")\n", + " .fit(clrd_first_lags)\n", + " .ldf_.to_frame(origin_as_datetime=False)\n", + " .values[0, 0],\n", + " 10,\n", + " )\n", + " == round(sm.WLS(y, x, weights=(1 / x)).fit().params[0], 10)\n", + ")\n", + "\n", + "print(\"Regression average:\")\n", + "print(\n", + " round(\n", + " cl.Development(average=\"regression\")\n", + " .fit(clrd_first_lags)\n", + " .ldf_.to_frame(origin_as_datetime=False)\n", + " .values[0, 0],\n", + " 10,\n", + " )\n", + " == round(sm.OLS(y, x).fit().params[0], 10)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The regression framework is what the `Development` estimator uses to set development patterns. Although we discard the information in the deterministic methods, in the stochastic methods, `Development` has two useful statistics for estimating reserve variability, both of which come from the regression framework. The stastics are `sigma_` and `std_err_` , and they are used by the `MackChainladder` estimator to determine the prediction error of our reserves." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "dev = cl.Development(average=\"simple\").fit(clrd[\"CumPaidLoss\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 0.041066 0.012024 0.005101 0.003734 0.003303 0.003337 0.00419 0.006831 0.003222" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dev.std_err_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember that `std_err_` is calculated as $\\frac{\\sigma}{\\sqrt{N}}$." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.0411, 0.012 , 0.0051, 0.0037, 0.0033, 0.0033, 0.0042, 0.0068,\n", + " 0.0032])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.round(\n", + " dev.sigma_.to_frame(origin_as_datetime=False).transpose()[\"(All)\"].values\n", + " / np.sqrt(\n", + " clrd[\"CumPaidLoss\"].age_to_age.to_frame(origin_as_datetime=False).count()\n", + " ).values,\n", + " 4,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the regression framework uses the weighting method, we can easily turn \"on and off\" any observation we want using the dropping capabilities such as `drop_valuation` in the `Development` estimator. Dropping link ratios not only affects the `ldf_` and `cdf_`, but also the `std_err_` and `sigma` of the estimates.\n", + "\n", + "Can we eliminate the 1988 valuation from our triangle, which is identical to eliminating the first observation from our 12-24 regression fit? Let's calculate the `std_err` for the `ldf_` of ages 12-24, and compare it to the value calculated using the weighted least squares regression." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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12243648607284961081209999
198801,868,353,5814,494,250,6616,460,788,0437,973,402,7039,155,354,14610,211,709,42011,360,087,73013,081,563,68813,595,400,48713,595,400,487
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120 9999\n", + "1988 0.0 1.868354e+09 4.494251e+09 6.460788e+09 7.973403e+09 9.155354e+09 1.021171e+10 1.136009e+10 1.308156e+10 1.359540e+10 1.359540e+10" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mack.total_process_risk_**2" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12243648607284961081209999
19881,868,353,5814,494,250,6616,460,788,0437,973,402,7039,155,354,14610,211,709,42011,360,087,73013,081,563,68813,595,400,48713,595,400,487
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120 9999\n", + "1988 NaN 1.868354e+09 4.494251e+09 6.460788e+09 7.973403e+09 9.155354e+09 1.021171e+10 1.136009e+10 1.308156e+10 1.359540e+10 1.359540e+10" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(mack.process_risk_**2).sum(axis=\"origin\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, independence is also assumed to apply to the overall standard error of reserves, as expected." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "106117274249.93411" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(mack.parameter_risk_**2 + mack.process_risk_**2).sum(axis=2).sum(axis=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "106117274249.93411" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(mack.mack_std_err_**2).sum(axis=2).sum(axis=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "This over-reliance on independence is one of the weaknesses of the `MackChainladder` method. Nevertheless, if the data align with this assumption, then `total_mack_std_err_` is a reasonable esimator of reserve variability.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mack Reserve Variability\n", + "The `mack_std_err_` at ultimate is the reserve variability for each `origin` period." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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199325,283
199428,465
199533,172
199650,244
199799,027
" + ], + "text/plain": [ + " 9999\n", + "1988 NaN\n", + "1989 7312.634869\n", + "1990 17838.223062\n", + "1991 21813.683826\n", + "1992 23847.273221\n", + "1993 25282.602592\n", + "1994 28465.249566\n", + "1995 33171.832916\n", + "1996 50243.750958\n", + "1997 99026.911753" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mack.mack_std_err_[\n", + " mack.mack_std_err_.development == mack.mack_std_err_.development.max()\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the `summary_` method, we can more easily look at the result of the `MackChainladder` model." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LatestIBNRUltimateMack Std Err
19881,241,7151,241,715
19891,308,70613,3211,322,0277,313
19901,394,67542,2101,436,88517,838
19911,414,74779,4091,494,15621,814
19921,328,801119,7091,448,51023,847
19931,187,581167,1921,354,77325,283
19941,114,842260,4011,375,24328,465
1995962,081402,4031,364,48433,172
1996736,040636,8341,372,87450,244
1997340,1321,056,3351,396,46799,027
" + ], + "text/plain": [ + " Latest IBNR Ultimate Mack Std Err\n", + "1988 1241715.0 NaN 1.241715e+06 NaN\n", + "1989 1308706.0 1.332126e+04 1.322027e+06 7312.634869\n", + "1990 1394675.0 4.221037e+04 1.436885e+06 17838.223062\n", + "1991 1414747.0 7.940888e+04 1.494156e+06 21813.683826\n", + "1992 1328801.0 1.197087e+05 1.448510e+06 23847.273221\n", + "1993 1187581.0 1.671916e+05 1.354773e+06 25282.602592\n", + "1994 1114842.0 2.604007e+05 1.375243e+06 28465.249566\n", + "1995 962081.0 4.024025e+05 1.364484e+06 33171.832916\n", + "1996 736040.0 6.368335e+05 1.372874e+06 50243.750958\n", + "1997 340132.0 1.056335e+06 1.396467e+06 99026.911753" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mack.summary_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize the paid to date, the estimated reserves, and their standard errors with a histogram." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1800000.0)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar(\n", + " mack.summary_.to_frame(origin_as_datetime=True).index.year,\n", + " mack.summary_.to_frame(origin_as_datetime=True)[\"Latest\"],\n", + " label=\"Paid\",\n", + ")\n", + "plt.bar(\n", + " mack.summary_.to_frame(origin_as_datetime=True).index.year,\n", + " mack.summary_.to_frame(origin_as_datetime=True)[\"IBNR\"],\n", + " bottom=mack.summary_.to_frame(origin_as_datetime=True)[\"Latest\"],\n", + " yerr=mack.summary_.to_frame(origin_as_datetime=True)[\"Mack Std Err\"],\n", + " label=\"Reserves\",\n", + ")\n", + "plt.legend(loc=\"upper left\")\n", + "plt.ylim(0, 1800000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also simulate the (assumed) normally distributed IBNR with `np.random.normal()`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 1., 2., 8., 7., 13., 17., 30., 29., 41., 72., 66.,\n", + " 94., 142., 185., 195., 274., 312., 351., 392., 419., 509., 491.,\n", + " 546., 540., 577., 537., 534., 524., 483., 448., 401., 348., 300.,\n", + " 268., 210., 163., 118., 94., 68., 59., 41., 25., 19., 18.,\n", + " 7., 5., 3., 5., 5., 4.]),\n", + " array([2209836.99059939, 2233093.4303693 , 2256349.87013921,\n", + " 2279606.30990912, 2302862.74967904, 2326119.18944895,\n", + " 2349375.62921886, 2372632.06898877, 2395888.50875869,\n", + " 2419144.9485286 , 2442401.38829851, 2465657.82806842,\n", + " 2488914.26783834, 2512170.70760825, 2535427.14737816,\n", + " 2558683.58714807, 2581940.02691799, 2605196.4666879 ,\n", + " 2628452.90645781, 2651709.34622773, 2674965.78599764,\n", + " 2698222.22576755, 2721478.66553746, 2744735.10530738,\n", + " 2767991.54507729, 2791247.9848472 , 2814504.42461711,\n", + " 2837760.86438702, 2861017.30415694, 2884273.74392685,\n", + " 2907530.18369676, 2930786.62346667, 2954043.06323659,\n", + " 2977299.5030065 , 3000555.94277641, 3023812.38254632,\n", + " 3047068.82231624, 3070325.26208615, 3093581.70185606,\n", + " 3116838.14162597, 3140094.58139589, 3163351.0211658 ,\n", + " 3186607.46093571, 3209863.90070562, 3233120.34047554,\n", + " 3256376.78024545, 3279633.22001536, 3302889.65978527,\n", + " 3326146.09955519, 3349402.5393251 , 3372658.97909501]),\n", + " )" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ibnr_mean = mack.ibnr_.sum()\n", + "ibnr_sd = mack.total_mack_std_err_.values[0, 0]\n", + "n_trials = 10000\n", + "\n", + "np.random.seed(2021)\n", + "dist = np.random.normal(ibnr_mean, ibnr_sd, size=n_trials)\n", + "\n", + "plt.hist(dist, bins=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ODP Bootstrap Model\n", + "The `MackChainladder` focuses on a regression framework for determining the variability of reserve estimates. An alternative approach is to use the statistical bootstrapping, or sampling from a triangle with replacement to simulate new triangles.\n", + "\n", + "Bootstrapping imposes less model constraints than the `MackChainladder`, which allows for greater applicability in different scenarios. Sampling new triangles can be accomplished through the `BootstrapODPSample` estimator. This estimator will take a single triangle and simulate new ones from it. To simulate new triangles randomly from an existing triangle, we specify `n_sims` with how many triangles we want to simulate, and access the `resampled_triangles_` attribute to get the simulated triangles. Notice that the shape of `resampled_triangles_` matches `n_sims` at the first index." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(10000, 1, 10, 10)
Index:[LOB]
Columns:[CumPaidLoss]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (10000, 1, 10, 10)\n", + "Index: [LOB]\n", + "Columns: [CumPaidLoss]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "samples = (\n", + " cl.BootstrapODPSample(n_sims=10000).fit(clrd[\"CumPaidLoss\"]).resampled_triangles_\n", + ")\n", + "samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we could use `BootstrapODPSample` to transform our triangle into a resampled set." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The notion of the ODP Bootstrap is that as our simulations approach infinity, we should expect our mean simulation to converge on the basic `Chainladder` estimate of of reserves.\n", + "\n", + "Let's apply the basic chainladder to our original triangle and also to our simulated triangles to see whether this holds true." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chainladder's IBNR estimate: 2777812.6890986315\n", + "BootstrapODPSample's mean IBNR estimate: 2780734.7397505655\n", + "Difference $: -2922.050651933998\n", + "Difference %: 0.0010519250140232348\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:120: RuntimeWarning: overflow encountered in exp\n", + " sigma_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:124: RuntimeWarning: overflow encountered in exp\n", + " std_err_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:127: RuntimeWarning: invalid value encountered in multiply\n", + " sigma_ = sigma_ * 0\n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:128: RuntimeWarning: invalid value encountered in multiply\n", + " std_err_ = std_err_* 0\n" + ] + } + ], + "source": [ + "ibnr_cl = cl.Chainladder().fit(clrd[\"CumPaidLoss\"]).ibnr_.sum()\n", + "ibnr_bootstrap = cl.Chainladder().fit(samples).ibnr_.sum(\"origin\").mean()\n", + "\n", + "print(\n", + " \"Chainladder's IBNR estimate:\",\n", + " ibnr_cl,\n", + ")\n", + "print(\n", + " \"BootstrapODPSample's mean IBNR estimate:\",\n", + " ibnr_bootstrap,\n", + ")\n", + "print(\"Difference $:\", ibnr_cl - ibnr_bootstrap)\n", + "print(\"Difference %:\", abs(ibnr_cl - ibnr_bootstrap) / ibnr_cl)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Deterministic Methods with Bootstrapped Samples\n", + "Our `samples` is just another triangle object with all the functionality of a regular triangle. This means we can apply any functionality we want to our `samples` including any deterministic methods we learned about previously." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('dev', Development(average='simple')),\n",
+       "                ('tail', TailConstant(tail=1.05))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('dev', Development(average='simple')),\n", + " ('tail', TailConstant(tail=1.05))])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe = cl.Pipeline(\n", + " steps=[(\"dev\", cl.Development(average=\"simple\")), (\"tail\", cl.TailConstant(1.05))]\n", + ")\n", + "\n", + "pipe.fit(samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now instead of a single `ldf_` (and `cdf_`) array across developmental ages, we have 10,000 arrays of `ldf_` (and `cdf_`)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 2.247069 1.319373 1.152251 1.085909 1.051881 1.030913 1.02611 1.023726 1.004063" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.named_steps.dev.ldf_.iloc[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)2.31121.32141.15891.08091.04841.02861.02861.01481.0099
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 2.311246 1.321449 1.15891 1.080946 1.04835 1.028581 1.028582 1.014817 1.009899" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.named_steps.dev.ldf_.iloc[9999]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This allows us to look at the varibility of any fitted property used. Let's look at the distribution of the age-to-age factor between age 12 and 24, and compare it against the LDF calculated from the non-bootstrapped triangle." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "resampled_ldf = pipe.named_steps.dev.ldf_\n", + "plt.hist(pipe.named_steps.dev.ldf_.values[:, 0, 0, 0], bins=50)\n", + "\n", + "orig_dev = cl.Development(average=\"simple\").fit(clrd[\"CumPaidLoss\"])\n", + "plt.axvline(orig_dev.ldf_.values[0, 0, 0, 0], color=\"black\", linestyle=\"dashed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Bootstrap vs Mack\n", + "We can approximate some of the Mack's parameters calculated using the regression framework." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Std_BootstrapStd_Mack
12-240.0433750.041066
24-360.0122540.012024
36-480.0072700.005101
48-600.0052050.003734
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96-1080.0041380.006831
108-1200.0042310.003222
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" + ], + "text/plain": [ + " Std_Bootstrap Std_Mack\n", + "12-24 0.043375 0.041066\n", + "24-36 0.012254 0.012024\n", + "36-48 0.007270 0.005101\n", + "48-60 0.005205 0.003734\n", + "60-72 0.004130 0.003303\n", + "72-84 0.003699 0.003337\n", + "84-96 0.003752 0.004190\n", + "96-108 0.004138 0.006831\n", + "108-120 0.004231 0.003222" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bootstrap_vs_mack = resampled_ldf.std(\"index\").to_frame(origin_as_datetime=False).T\n", + "bootstrap_vs_mack.rename(columns={\"(All)\": \"Std_Bootstrap\"}, inplace=True)\n", + "bootstrap_vs_mack = bootstrap_vs_mack.merge(\n", + " orig_dev.std_err_.to_frame(origin_as_datetime=False).T,\n", + " left_index=True,\n", + " right_index=True,\n", + ")\n", + "bootstrap_vs_mack.rename(columns={\"(All)\": \"Std_Mack\"}, inplace=True)\n", + "\n", + "bootstrap_vs_mack" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " [Text(0, 0, '12-24'),\n", + " Text(1, 0, '24-36'),\n", + " Text(2, 0, '36-48'),\n", + " Text(3, 0, '48-60'),\n", + " Text(4, 0, '60-72'),\n", + " Text(5, 0, '72-84'),\n", + " Text(6, 0, '84-96'),\n", + " Text(7, 0, '96-108'),\n", + " Text(8, 0, '108-120')])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "width = 0.3\n", + "ages = np.arange(len(bootstrap_vs_mack))\n", + "\n", + "plt.bar(\n", + " ages - width / 2,\n", + " bootstrap_vs_mack[\"Std_Bootstrap\"],\n", + " width=width,\n", + " label=\"Bootstrap\",\n", + ")\n", + "plt.bar(ages + width / 2, bootstrap_vs_mack[\"Std_Mack\"], width=width, label=\"Mack\")\n", + "plt.legend(loc=\"upper right\")\n", + "plt.xticks(ages, bootstrap_vs_mack.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While the `MackChainladder` produces statistics about the mean and variance of reserve estimates, the variance or precentile of reserves would need to be fited using maximum likelihood estimation or method of moments. However, for `BootstrapODPSample` based fits, we can use the empirical distribution from the samples directly to get information about variance or the precentile of the IBNR reserves." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:120: RuntimeWarning: overflow encountered in exp\n", + " sigma_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:124: RuntimeWarning: overflow encountered in exp\n", + " std_err_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:127: RuntimeWarning: invalid value encountered in multiply\n", + " sigma_ = sigma_ * 0\n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:128: RuntimeWarning: invalid value encountered in multiply\n", + " std_err_ = std_err_* 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Standard deviation of reserve estimate: 138,546.0\n", + "99th percentile of reserve estimate: 3,111,513.0\n" + ] + } + ], + "source": [ + "ibnr = cl.Chainladder().fit(samples).ibnr_.sum(\"origin\")\n", + "\n", + "ibnr_std = ibnr.std()\n", + "print(\"Standard deviation of reserve estimate: \" + f\"{round(ibnr_std,0):,}\")\n", + "ibnr_99 = ibnr.quantile(q=0.99)\n", + "print(\"99th percentile of reserve estimate: \" + f\"{round(ibnr_99,0):,}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see how the `MackChainladder` reserve distribution compares to the `BootstrapODPSample` reserve distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(ibnr.to_frame(origin_as_datetime=True), bins=50, label=\"Bootstrap\", alpha=0.3)\n", + "plt.hist(dist, bins=50, label=\"Mack\", alpha=0.3)\n", + "plt.legend(loc=\"upper right\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Expected Loss Methods with Bootstrap\n", + "\n", + "So far, we've only applied the multiplicative methods (i.e. basic chainladder) in a stochastic context. It is possible to use an expected loss method like the `BornhuetterFerguson` method does. \n", + "\n", + "To do this, we will need an exposure vector." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "sample_weight = clrd[\"EarnedPremNet\"].latest_diagonal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Passing an `apriori_sigma` to the `BornhuetterFerguson` estimator tells it to consider the apriori selection itself as a random variable. Fitting a stochastic `BornhuetterFerguson` looks very much like the determinsitic version." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:120: RuntimeWarning: overflow encountered in exp\n", + " sigma_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:124: RuntimeWarning: overflow encountered in exp\n", + " std_err_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:127: RuntimeWarning: invalid value encountered in multiply\n", + " sigma_ = sigma_ * 0\n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/tails/base.py:128: RuntimeWarning: invalid value encountered in multiply\n", + " std_err_ = std_err_* 0\n" + ] + }, + { + "data": { + "text/html": [ + "
BornhuetterFerguson(apriori=0.65, apriori_sigma=0.1)
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" + ], + "text/plain": [ + "BornhuetterFerguson(apriori=0.65, apriori_sigma=0.1)" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fit Bornhuetter-Ferguson to stochastically generated data\n", + "bf = cl.BornhuetterFerguson(0.65, apriori_sigma=0.10)\n", + "bf.fit(samples, sample_weight=sample_weight)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use our knowledge of `Triangle` manipulation to grab most things we would want out of our model." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# Grab completed triangle replacing simulated known data with actual known data\n", + "full_triangle = bf.full_triangle_ - bf.X_ + clrd[\"CumPaidLoss\"]\n", + "# Limiting to the current year for plotting\n", + "current_year = (\n", + " full_triangle[full_triangle.origin == full_triangle.origin.max()]\n", + " .to_frame(origin_as_datetime=True)\n", + " .T\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, plotting the expected development of our full triangle over time from the Bootstrap `BornhuetterFerguson` model fans out to greater uncertainty the farther we get from our valuation date." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SW3YmJCRsNWIElpf5KEXmWYRA4q6ooFRRWWnP/T79PrTvcib3LBobeX22EBtB9t8Otg/8ZrBX61+FED5XaERShBjj+wG8HwBOnjy5i77ZhIQdgBiBlRVgackIfnnZ3heRV1ZSgpB1XlFRbJV7Il9cXFuD175k3evzeoRw5WfKBTvUsv9BAL8aWRj/VAjhDICbAHxlA/adkJCwGRCxLy8bua+smHVdUUFCamoqtsw9kS8trW3hayKorCy28HXsfJ4PjWFhodiyL2eiB+xabCE2guzPA3gjgM+FEPaCDcRTu8CEhOsJ3lLXwxN7VRVJuaKCD00Gs7NXWuVZC1//e0JfXeXn9VcWfpbQq6r4qKuzY/uVQrnieiT7QuvA+wB0hhD6AbwLQDUAxBjfB+A/APhgCOEJAAHAz8UYRzZtxAkJCVdHKSlG0kg+b2TqH0uZ4DkRd12dEbLX37NkvrBgVrpIXc5Zr+Pr2HLIyrG7sLA912q70NgINDdv6SHXE43ztmu8fwnAt23YiBISEtYPEazkmKUlvrayciWxy4IXvGVdWWn/V1WZZb26CszPAxMTxROICN3r69p/VZX97y1YPw5vuYv8AbP8yz3Ofhuwbc1LEhISXiI8sS8uUmIRsa+uGolWV/Mh0s0SuUi4IpNAn8+T0Ccnue+5OZNf5Kj11r32K9lH1vzi4pX7BWwV4FcEPsrHv679lSuOHQPuumtLD5nIPiHheoN3nmat9sVFI96qKpJ6Lke5RQTvyT1L6P4YS0u02qen+ZiZ4XOtDrylno2F9/KLnK3+de+E9e/7+HlNTvX1diydQ1WZU1N7+5YfssyvaELCdQ4fx54NdxThiyyrqkiMHR10jNbUXCmVlIJI2RO7rHdF1CguvrYWaGgwrd5r7RqHQinz+WL5xZO4f2jy8asNTUJ+n7Ls/YRWrtgGmSqRfULCViGfv5LUVzLJ5opTl95eX2+OUhF8NkFJJKnHwgIfc3PU2qen+VzErv01NgJtbTyGLHdvkXvZRaQtfb22tlgm0kNOV01imrDm5q7U/LWdNH/vyNXzcsUWO2eBRPYJCZsD6dz+4ePRlUxUWVlMrtLca2vtUVlpxDs3ZwSsh+QdWe2yjFdXbV/NzST1hgZOGJokAB57YYHbexkomzjlre/lZa4UdBwRuxzD3sr3spISqHTu3ncQo/1f7mTf0rLlh0xkn5CwERDhynr1y3TFsFdXG2lKh9d2klBkHctCn5mxSULWsrT22Vkj3OVlI9X6emrCil2X/i2CX1kpJmBZ6SEYaS8sWASOdPms1u41/eZmnoPOQ+frNX7to1QiVjYyyPsHyhE7NIM2IWF3QqQrqxgotlylUWsikJShaBMRbmWlkbggR6asbpG79rOyYquA+nqgtdXI1sNH4GhcXl6ZnwfGxmw14CUbrSxaW3kMEXh1dTGZl4qy0f49fPJWNuRT8k/WoZuwYUhkn5DwUrC4aOSbz5OkJJPU1VmC0uwsMD7O7USCkm5EdkBxXLs0/YUFyjEKf/QSS0UFtfa6OtuPj2kXGcuhKqt/dhYYGrLkJ0XMyApvaOBD+/bRPVkH6soKx1eq5o1PxpJ1rpWAJi/tQz6E3UjujY1bbt0nsk9IuBp8iKLkjBCMEGtr+d7UFHDpEslZFq2IPZfjw0ejSFKZm2NkzPQ096PPy2kpiUQrBkXJqN6MSFSW9MSEOWhlqYdgVn9zs5G6CEf6uCf0+XmTkErFu2cjbrzu7kMyS8GXOM5G/Pj/yxnbcH6J7BMSsojRLPgswdfWksxmZ4HRUZNUAL7X1ERilyNUztWlJYuOmZ01vV0SjwhXVrYPgayt5T4BC02cmuLnFxf5UPSONPiWFpK59uGzVrOEniVzH2opa1zQOLPW+HoJPOuQln9AeQR6lHvo5Z49QE/Plh4ykX1CAmAELwtekSHV1SaHjIyQsBU1U1NDUm1qsvrkIvaFBco4kmFmZ4tJTaSpypINDbYCEEEDFmXT32/7AMzSb262VYN3AvtmIrOzxefqG4fU1haXPVAlSh8S6hOd1iJwoLiJif5KlvIJYV4O0oSh/Wiy887dckRj45YfMpF9wu5FjGa9i+C91Sn5xlv2bW1G7tXVZpmOjxsZa8JYWCDBySErq7ujgwQtkhe55fPcx8SE7W952Y6dywGdnRbx4ssUAFaqwFvY0s6zhcdKxflLNvJ6fXV1cSy/JgOvt/s6OdmwUI3NW/61tcUTSLZapvZZzrgeq14mJJQVRPCSQGRx+sQkSSJ1dSRmkXJ1Nd+fmwOGh02KEblrnyK4mhpGsdTXcwXQ0mJWu4hxchI4d45/NY6KChJ7W5tZ+1mNV5a5J0sRPHBlxI23ov3n6+uLyxP42H1JVFmZJyvvlEqO0srDO2j1v19BKPzUTxjaZzlb9nK0byES2SeUP3z44swMSWx+3qxHH0HS0GDSSFWV1XQfHLT6MZJiFCUjWae2lvHtInYlMlVUWPGy8XHT7efmSG6qb6NJRSQpSDKSVOOzaH2Yo0+48p/1pO7LFHhSz1rSvtiZSNg7cP0KyJcz9jVwdAw/Kfhx+b/ZHrT+bzmioWHLD5nIPqE8kc+T0CcnjVwXForJTw5MkbJi4qenGVmjKJlSFqjkCOnmnZ0k65oai5lfXKQTV2GUCwtGoHV1wL59Fr/uHag+lt2HaSqpSjVtlpaKCdFnqMqS9q0GNbkIvviYZCT5JxTTPzvLCW6tTlIKGy1VD0c+DyWLZcseZ2Uz35O23C37VBsnIeEVYnGRJH35Mq3olRUSTkMD0N1t5K4IFTlPh4asOJjPSBXhKFmptpaThKx3kfviIpOTtHpQZqvIr66OyUlKVBKB+9j7bNYpwH3MzJjkJMIVifqyAr5VoIfPlNV2mpBE5AoDVcimCFdja2oqHrt0dx9946NwfG0c+S+WlvidyFGrEs3ZiXQ3IEbg4MEtPWQi+4SdD0kYw8O0pGdnSTzt7WZxi+gU3XLhgoUvSmqQBq6wR1msuZzp7vX1FrkzMcHjytL2lSAV0y7dXhZwltizjjq/KvDlFHwhMuDKDFUfdql9ikjlKBaRe7L3mnypsE1FBylqR9a3/BPz8yRwyWTyX2Qjb2Spa/LTakKrI/31CV3lbNm3tm75IRPZJ+xMiHCmp2mVT0yQUBoagCNHSPKVlSS0sTFa7bK4pbFXVpKIFdkix6xISISnmjEzMwy/1H4kQ4jcRVg+gcpr7aUyJtWfVQRfqt9rNq5dMpSvO7O4aCsAOXp1nWTpey2/stLCRTVmPXwW8NAQr7EmRq1ifCSPTwDzIZ1aQfkENF+a2UfjSHLKhmSWKxRau4W4JtmHED4A4C0AhmKMt66xzX0AfhPsTTsSY3zDxg0xIaEARdLMzZHAJZtUVwNdXXzkctzm3DlOAPPzRpjKRlXZYB9BIgu8ocFkiNlZavdy6sr5KTKTY1UThl7z8e5ZKClKBO+dnd5p6as/StOXw1N6ugg3+1ldK39uahKisWfDKicngfPn7VxlqQs6p9paRgl5C9z3qS3VBcuvCFZXOXH4WHvfqcr3rS1n9PYCR49u6SHXY9l/EMB7AHyo1JshhFYAvwXgTTHG8yGEPRs2uoQEwOSC6WkS/NQUX8/lgL4+yg4x0gp98UVuJ2tbcemypESusrolsQAkuKEhEp5KBXvLvaGBkpCidjy5rxU3LR1dBJ+tEQ8UV6b0xdNUdmBszOQXSUHaVoXNJPnor3eIeglIdW0kufgkMk0y8i/09tpqpaamuPIlcGWf2mxdfb9t9nPZMEwf1aP9lDO2ocH6Nck+xvhgCKHvKpv8MwB/FmM8X9h+aIPGlrCbsbJitWKmpsxKF4G3tpKExsaAF14ggUmC2bOHE4FKGwAkEskHcraq36oafEjiUWJRayulDoVEityvVsBKkkq25LHGUSoGXRr66qo5MAVJNrmcReCoN6zP9M36A6qqiiNxZmZMggFsosnl6NvQhKha974w21plFbLyjfT42lpbbfgwSp2nJr7s6kSTGbAtSUdbirm5LT/kRmj2xwFUhxA+C6AJwLtjjGutAu4HcD8AHNxiT3TCDoDCJfWYmDCSqa9nLZGGBpJcfz8JGiC5dHYWx8aLzLK12ufn6ZydnDRrVBZ+ZydlHhULW4+jUNa0iN3XlxeZi4QFXw/GyzCaBBoaTLJaXKSUJEnFtydsa7PJp7LSImrkhNbk4JuSaGXi6+f77FcdRyQtjV0F1TRpacz+/FXATeP2CWuKJPJx+L6BiU/s0gqknLENUUcbQfZVAF4F4I0A6gF8KYTwUIzx+eyGMcb3A3g/AJw8eXKXxFglXBWKbBFJzc7Skl9cNI1dTsOxMZL86qr1Ys3lih17lZVm1SusUg5aEbEKi+3bR6tWFSAVjnmt8YrEtH/fN1bWreQTRQoBJt/oPU0yIs25OcpImoRkOedyPFeRNGDHlL6uyQMw53NNDScEX56gouJKuQW4sjSyHMeqB+SjeIDiQmZAsQzjSyNoAmtqKiZ2rUZ8aQSv7Ze7Zr8N2Aiy7wcwGmOcBTAbQngQwB0AriD7hIS/g6xgEfDUVHFGaWsrSWF+ngS/vGzSisIfZanX19ukUFFBcj91in+XlszxeuBAcfkDhWNeDSJ3ySeSQ0RysoyrqoqdrYpT1+ShyB9ZrXJUyvHrq1YqOQvga95nobGIJPUZZeD6doZZIs1WsFT4pq/CKZLX6sj7JHwxNF9awodueseyjqtr4h3Smsh8OKm/VrvBSbvF2Aiy/wsA7wkhVAGoAXAvgN/YgP0mlBtkvYrgfPcln10Zozklq6steQmwkEDFg7e08P+5OTpnL17k/w0NdDB2d5ssU7WOn7smEVntiqFXkpWs38ZGc77qM0Bx5I8PIVxaIrn7Tkw+e1XW7PKyJYPJ0pUuL8tYk4GigBRV48ncW/6KtdekqueSkUS6VVVWmE3SjSZU1fzxE5jO05+3z8qV9a7kL8k2PltY19z7HoDyr2ff1LTlh1xP6OVHANwHoDOE0A/gXWCIJWKM74sxPhNC+ASAxwHkAfxujPHJzRtywo6EHK0iINVj8WGDIqCKCovRVl2buTkjfhUIy+cZLvjiiyTIEBh+efvtwN69xbHoV4MITWUNRO5y5ipuXmORVe4li6we7ckaMGvZ93P1CU3S+L3cImerj9UXcWrfXvrJllKWw9nHw2tfWgn4Ylx+Ap6YsNfr64u1/mzWrEjcSzmSfXRsfz00sfmJT5/1+yhnNDdv+SHXE43ztnVs858A/KcNGVFCeWF11TJNJybM4lxasiW8ygrkchZGKQu+ooK6elsbiSkEZso++SSt+OVlvn7TTUymamy8tlWoWHWRu4hY5K5IH19ATaGYvtm2SFcyjvRwNQr3lTVlEftyw74loRKcpLV7fV5SzsSEVchUCWQ/YahmjzJhfZ185RZoO4V1avLxVnh7u/k9tOrQKkASliZrfVfZsExP2l62kbwl/d9H4pQqu1yuqKoC7r13aw+5pUdL2F2Qs7VUaKMsu7o6I3HJJ5WVZsG3tPC92VngqadIdlNT3MfevcANN1CquVqonnRpVa308fMqY6xJQrq8SgD48ELFmssClwWvfrNZycdnk0r/liWsZKe6Okt20jmoB21/v9W212QDcLuGBq5iamvNJyFHto9X9zKS4vwlmVRV8foqKUrRNjMz1oVLJO/7zWpS8bq8roW31L3PwD/3E6a/Tlo9KPS1nDX7LU6oAhLZJ2wGVlaM4EdHLRTQV3PUTa1lvSJv2tr4V4lFFy8Cp0+zxPDqKsnp7ruZTHW1lPOZGer+KvQFmJN0zx7TvVUZ88IFc1BqW1nJ0rQFn3ylSBVNCCoT4HVrkZdIWRKIXwVMTHC84+PFVSYVZ6/cAjmoVdJAVrWSpYDipiaSVzQeTVay7FX8TZOUNHxPxD6yRo5WnzsAFGfs+mxanb8kKK0WsjJVNhy03DX761HGSUhYN2IkUU1NUWqRNp/PW7x7c3Nxar8qSDY2WhLU5CRw9qyVKqitZYXAI0doza5l8S0scHIZHzftv7HRukvV13MbNQeXD0FkqKgW7yhUkpRK/aqevYhOpZJ9+QCvcSsBy1fGnJriGFWvR1KKJguNV45lyTiKCJqc5D59pIzX8n1se7Zzlso1KExVqw3AVjoKv/Q+CU0Qktt8dUtf68Zn93pd35c+9k1KfI0jr/OXu5Rzww3AXXdt6SET2SdsDJaXaZ2OjpLQFIcu8pAGLAvOSw8ASe/cOUoXY2Mkg44O4OabGVWzlhWvsrmqk6MJZN8+TiKrq1YsTRq9wgpFbj4jVvq0mov4xuCytFUVs7GR429tJTHL8tXkcOkSj6mHJCQ/uTQ0cKxK4tJqRyStGH1ds1zOImVE5rLOJyeLLXRNSN6JqglQ5AsUW9LyAYikfVilz5T1Vns2IscXNvMtDH2TE6/n+25bcoj7MM5yRLLsE3YcYrSaNZcvG9H5ujGyehX9oRZ/iqG/fJlkvLDA92+4ATh0iCS6VjExTSyzsxxDLscM2/Z2Ph8fB557zmLTfaijtG3AyFlZn76TlYqeqVxyU5P5EpqbTYrxRcRmZixk0zuiZfErksjHwvsIH8AIr62Nz2WVa1UwMVFcbkAOYp9N6zOHfWilCFjnLjKXk1aTsydeXwTNO5V9uQf9Frxe76UdTRI+AkeTkvwC+q70fZQzVlevy0JoCQmlIat6YIAkpBtU0oISlyRHNDfzJh8dZWbmyAg/p4ibnh46W+vrrzxWjCRVEV4+z+Ps2cMVQE0NyeLsWW6jBC1p5bI2fYMPkYqPypGFKfmntdVq4ldXc/uJCYZ7Tk0VtyqU3i1C1CQnByxgMoUmF02GmnwUBjo8bFFMkmG0b01AWh0pzj7bWFxRN5KBFE7q5RtluCpm3xd5q6oyYpbzVQlevjWjVhM6ho+s8dv5zF1NBD4RTdA5lLODtqEBuO++LT1kIvuEl44YSXSDg7TKdWNLXlConyz5pibeuBcuWEcolTw4cgTYv98yZrPHmZ3l5DA5aeGR7e0keNXJGR7mOKT7imR9Oz9fv0YWuBKNqqs5zr17ud89e/hcqw+tWiYned7e6euzTL1OrxWJyH1pyQjfx9/7WvE+i1XROopS2bvXSNKXGShlLfvJRs5REbqujaQSXSOFU/qa/VrlKJJJZO7DKb317hOjdF3kiNWKKuu41opH19CvQspZxunr2/JDJrJPeGlYXCS59vebVKFIFWV3NjbSipfT9fJl6tcTE7yJOzpoLXd1mQPSY36eBD8xYY7WlhaSvFYHviTC3JxVW2xpsf3Jcpc1rsgZrTq6u0nsis6prrZaOhqvj46RZa3yDJI5RHKyXNXYxIcsqgyBj1P3ZQkkoezfb/IOYNm5vmuVfAdK9hKZKwyzrs78EVpl+SqiQ0NWqsEXnpOF7o+nc9O55nI2oXkfjC/TIMLX70L70flmLXuguHOW4vm3oVjYlqG+Hrjlli09ZCL7hPUhnyfx9ffT8pPuq6W2tGiFBjY0cPtTp0jcMZJcVXwsW5dGHaXGx4sdrfv3G4HPzFCHHxyklS3Sa2m5svLjyAj3pTIGuRyt4z17ONG0tfEYSvYaGOBnFDKqz6l/reLRZZVKBlKtHC9T6K80cmnyIjaRp1Yzkl9EtLOzxdEwvgNWU1NxwpOP/1cG7ciIrWC0WhDRyjLX6kFkrnPVvuWfaGkxgtf2GqcmD5WK9s5YL814icZXzsyWeNDrmiTKWcZRRNUWIpF9wrUxP89494sXi8MEl5fNghcpNDZy++efJ8kvL5PUurv58CSvePyxsWJHa2+vle9dXKTz89IlkphkCl/vRlZ0f78lA1VVkdQ1ubS2kkRUtmFoyNoZSnOXdNLQcGX3KenmkjO8Pp3P22TjC3jJOlaDFCVTiWAVceOzcLXqUCXOhgZzloZgE4yij3xoZbaapcYhScRLJloJSO5RKWOfjatQ1gsXiq1uX7/Gh1RqdaJJyJd1AIpDbn1Ypy+XIJR7yYT29i0/ZCL7hLWxukqCVYs/SQ4i+9ZWswAlZ5w/XxxZ09tLwlWWbD5v6f5ytNbW0upubydZrK6aVDQwQIIOgcfq6rIwzJWVYou8poZjOX6cx6ypMQv31CnuU7KPnLGKbe/sNOdkCFaPR01T5Gj0EoUevtyACFPErnMGjJBFehpvS4sleYmYVdtf41X3LE+6QDHRKmnMNzDx0TiAOV31/aphik+UEulKnlH+gfapSc1b7j5zVisdH/mjv17z95KOL7DmS1GUK3p6tvyQiewTSkNx7wqnVAINYM5RSTdVVSTSwUELu1RkTVubSRSDgyQvhTR2dFgdlhhJrM89xxXE5KStHFS5UiUVBgctkgcgUd55J49ZX2/OY+nuSu6SFdrQUFzquLraHK8qTSCHriJXFM4ImNVcKqRQjltf6Esk3NxcfExltypaxTtGVfpZxK7jy3r23ai0L1+UDSgutuYLjcn34CcI+SA0KVRUFLdT9I1ZPLlLGiqlw/sSC4ImIJ9Zq5WLr5SpMZQrFFa7hUhkn1CM1VVa1OfPkwAV0reyYpmuPlZ8YsISlqqqLBRSdVtWVy2OfnWVqwHFrAMk4WefpVQwMkJiyeX4+bY2ksDcHC14yT35PMdx442sUS+CHx7mdoODpqWrmYmXc1RqeXbWnJU+Ckax5l4m8olEqo0jK1Y6tV6Xz8InSslBqbBKrRw0TlnWPlFJDleFVmqy8hmrmmh8jLvgcwp8QhRQ3A93ebk4M9hnsnpCF9H7hCgdR/KVxujDOv14/YpIz4HivIdsUbWEDUEi+wTD+Djjx6WNi+TlGFRP1vp6EtXFiyT5GGkZtreT7NVWb2yM5Lu0xM/v38+bf2EBOHPGtPilJd74XV0m5czNcZJQT9YYLeFKEs3MjFn5o6NWqz0Ejqe3lxOPJBE5mCcnraa+yN07IX1NHGndtbVGyAqTVCE3JVmJWCX5TExYHLqkI5Gpz1IFrASBt2p9CKUSpjS+bDaqd3yKoGWNK/RUsf2y9r2FrrBNZQpnI2Ky5YwV8SOtX+PUakGavE/g8u0Js49y1udLYd++LT9kIvsE3oBnzxaTt4hOYYmKslleth6uIjslHilqZmqKRD07a01EGhpo0X7tazzO7CyPoTDMXM7CAr1/QLp/ZyfHKoJXgpVKBMTIcR46xAljZYXbnT1rdWgAklJjo0lRcnx6vVgkq7IFOqa2a2mx5C/lAoyMWNSLL9/rLfJsYw+fXSptXI5hr8WLiKWFy0nuo3+yxwWudHpq/KXaEmosIm7V/FFbQ9UM0nh8hUsfEprNns1m2/pVhj9vXxTNr1rKFd3dW37IRPa7HZcvs6qkWvjpJpPzta3NMlqHh2lBr6xY+YP2dj4Uoy5ZpLqaxNvSwudf+hKdpDHytaNHSSCLiyTjs2etP2xrq2nq8/MkW0UCKTRShFdfzxVBSws/PzRE3X942PRs1diRBa8Vi0hJurwyW+U8BswRLVkK4HjUMETWvrR5T2S+lIA0fMkycqQq2gcwC1w1dHyHKIU8yjLX615j96WYffKV5B2flKWQUoV1ZhuTAEbcmvD8KsJH4fiHL5PhSzh4fd5XEPWZskm62VQkst+tiJHke+YMb2TvvPQRIhUVJFfFbtfUULJobqa13dBgksvEBLfv7uYEMD8PPPYYyXd+nvvs6uI2k5O0ltUjVhp9XZ05SoeGjGymp62Cpiz+5mYS0tgY8MILRkiNjcCtt5q8IukFMD1aUT8qJKa4fYV1HjxoWvTsLOUoJWl5rdpXevSyiaxjxef7kgme1LUi8Hq4L4EskpasInJUsxNfi0ZykSQUwCYBhX9KdqmpKa5Cqf0qCkak77ORfXni7KTmG6N4B7H3JSixy1e21ErE+z60QilnnDhBf9MW4ppkH0L4AIC3ABiKMd56le1eDeBLAL4nxvgnGzfEhA1HPk+if+YZkqgyXSVtNDfzubJJFxYsbb+pids1N5P8Ll0iMcdIC3jPHu7/+ectoaqhga9XVNDiXl3l/uXMrajgRHH5cnHcuaJoFNOuapMAt794kdtXVvLYfX3cX329VbccHrbsWpHQ1BSlKCU+yYGrCW9mhmNRxq0I2FvNsq7n580R2dFhhFhfb5OJkrxUNx4ojnvXOUnTViKU1+B9OKKOrTh+X49H1rp3jvo4f1+tUvq7jzaSX8DLKN4n4KNwfF9aLwt5iSkblumbmPhJASj2kay3peROhb7zLcR6LPsPAngPgA+ttUEIoRLAfwTwqY0ZVsKmIZ+nFfz00yTy9nYr9KWa70tLVmtGy31lx7a28uZUoa6lJU4WXV0kiAsX6OQdHORnOzu5/fS0FS6T/j4xQQlpdpbPFYqoDlcKY1Ry09wcJw/VmWlvZzvC9nYSg9L+JyeLKzROT3NSEmn6rFsV9xoctFo5gGn59fWmmWs8dXWc7Ly+ruSmkRErQ6BzUqLWnj1GrMp2VfljxeBr1eBrzPha8iJp6eiqx1Nfbw9Z376gWKnql7oeXibS5OYjjbzk5X9HCoX1jlag2Hms7bzUowlT5+Z9Jp74yxnZOlBbcchrbRBjfDCE0HeNzX4CwJ8CePVGDCphkyCL+6mnSLRNTbRo1d9VFSmnp3mz+aqVra0Wj65oFpUgyOVIpmfPUu7I5y0yRUTe28t9zMxwspmeNgu/osIKns3MmMwiMpY8U1PD8aqNoIh6fJyfkU68vGyWtM/sFPkppl3tCWXJAlaXRv4LZfWKqEVQU1N2jMXF4qgeRSRJKlLbwv5+C/P07Qu1opC+DZjFLctczUyam3n+ssB9qKXIW+fkwxi9c9Y3ey+Vueq7UPkELG3nP6cJutQqxE84PsHKh1l6qUfb+YmjXCGDZwvxiqeXEEIPgH8A4JuQyP76RT5P7fzJJ0nWzc1MQtq7l6Q2NWVOSSVKqQyCZJGhIf6tq7NEp9FRTh5KvhJBqr5NTw/Jb3ycx15cNEtdiVsTE0bWWsKLUGpqLGKnpsaSkJScJSenrH4lT8kJWVFhjVV871nA3veO2tpaK1Wg7eQYnpw0olZhMNXZEbFPTPBx5kxx9q2PTpGsJHmlosKs8oYGy0qW1e6jYLTK8HKXCNUTqyd4L6X4aBpP+HrNO3aB4s5WWUeq9pVN5NJfb537bOJsTkB2FeAnqHKFVn5biI1YS/wmgJ+LMebDNb6cEML9AO4HgIMHD27AoRPWhXyeiUtPPEFCb2mhc6iz03RxxdP7jMqGBt7o6reqejPNzfzMo49aaQRZhHNzJML9+2mFT0/z2LLkVeJgdNTIXHKIyEplC1TPRpKCJiNZvIDJPSJxxXYrrlwrAsCiQXySlO+YJWfw3BwnIS/HaBLYu9e6DCnk8vx5ylpKzJIUJMnClzBQuKcmlNZWqybpa+cAVzpmJXX49oE+5t7HtWeLsvlwSe3DE66Prfdyji+Y5icQ/a40zmzMfylZx0tB3pIHiiN8/DHKFdvQnGUjyP4kgI8WiL4TwHeEEFZijH+e3TDG+H4A7weAkydPlvk67TpBPk99/oknSLitrYw06eqyqoi+3Z1ISDVTFA8vCWFlheStHrOSWebnrUzCgQPcTiGdi4u0ii9fJglLb1YWrrJC6+rMoo7RkoBktcqfMDtbTPAiKSUwicyVASsZR+Su/0XmOhev2dfX81opQ3dlhRPU6dMWXuo7RWmFUV/PVY+vHikZTNfYT2xysqo0sg9FlPNY10PEKH3f6+yKbvHJST5JS/v1pZZ9pI/+aqXgE528tq59iKy1gsoSt8+M9Xq8D+3MRvT43IJt0LS3FEeObPkhX/EVjTEe1v8hhA8C+Fgpok/YBuTzlE6eeIKE3NbG2PeuLmt23dJiUS4qAqamFaurVskyBFq7g4P2nmLeRfJHjvCGvnDBJJXxcX5mfp77P3DArFI5GkX4KmMga1KlDkTk4+M8L0W56HVZp5KefF33+nrTqqWdKzFJRKuJRPX1Q+D4L19mxNLEhGXMAtyf2gY2N1v5CNXbVzauIkpExiJETWAiQh/f7iUOlTv29XOycolPQhJ81yg/EerzsvJ9cTRflVP79XkDPqPYO3tlyftaO5J1soXNfFOSLMl7jb5UTZ1yw/UYjRNC+AiA+wB0hhD6AbwLQDUAxBjft6mjS3j5yOeBxx8n0c/OkogOH6b+PTlJkhbBq5+qT6cXYdbUWPMRlSEeHycZVlXR8XrkiBVDk+SjMgZzc9xPTw+Pk03e8bHXMdrEI+erauerBIFITHVkspExmkDUC3doyJKlRCgi985OO74SuxTP752Jcroqtr+lxVY6InclUHkZQw1PRHQ+Ph0wmUQTkQ+p1OQgYtR5anuRuEodK3ImG7OvycRPrj4yRucoh7De98lgeu6lFm+Je1+Et+R9PL2vuaPvz9fYz9blKXcH7atexeqsW4j1ROO8bb07izG+/RWNJmFjsLpKon/8cZJIZydryrS1mTUuTTyXK7aUq6rMMTg2RitdUo4SnWKkJn/jjSSH8XELxRwYKJZr9u83nVpWvK+7EoJNLBUVVtZ3cdEaY/hwQIVDtrfbZKSSx2rK7TNPRUq5nG2jwmoKeVxc5HuSTbq6zGehpKiGBksiU+ig17WzoZIiU70mglPXK59Q5LeXlCYrf2nJqngq/l419QVNIPIPtLQUW83eGpcVr894p6tkHFU59bH9mhgUqSQpx/fzzTZHyTqAs3JQVqOXv8ZPiOUK+X22EGUujO1CrKwwa/WJJ0hqnZ20IFpbrXxvQ4OVOFB0h48ImZqiNq1m4NPTloTU1cXs1IYGvj8wwP36omYKyVSNdsk2ftXg5RsfdunlBx8LnsvZSsTLFsq2lTXuI0UkfSimXSSrEssqYqbIF9+jVZKM4tZ1nbTi8FmnOo+qKiM/TSKSmbxjVNmo6jolUp+etm5damQ+PV0cHiq/hnILJK94K92/5rNbhbWiYfS/yFzfhQwBTbZZp2pW7lFyl17T9VFYaSlZx4dj+nDScsVODL1MuI6wsgI88gh15rk5EvPNN5McpKGrlrsae6iYWW0tb+rnn6fMoxj5Cxf4ufZ27qu93Spejo1Z9Uo1+t63zyJL5FyU07OujsSqEgSDg9YCUGGRPuJDUTKq/15Vxe18/1SF6kn3nZ01n4PeByxfQA5oOaVFni0tllegWH1Zp95J7Bt46D1NUj50McbijlB65PMmg6nWkGru+8JpihLq6CjuNQsUa+O+Jo5q93gpxU9w2bLFvgY9YM5tHUMWvTKqfeVNr7vrmD7u32fU+jIIXoLyE2ipRzljdRW4554tPWQi+3LBygrw8MMk+vl5Wg633ELCkHbe0MAJoLrauj9JHz9/niSk8MMLF0hCjY3AyZOMMFla4r4mJ5kY9eKLVn9ejbIrK400dTNLppmepi6uMgQ+ekS6rxKIfCPyxUWz/L2WrgSp2Vl7iGwaGri6aGzk+Hwce2OjNUaXZZl1gnprVQlikoBkrYsYZc2KlGVZT05S9hodtZ6409NGtNLTJRfpvD2xanwidVnJgq6xau34sWkS9PAau3fq+qJlPqFLBO6PI4vfNyj3mbR+haXvVpOg9q3jKadDpSh8SedyDr88enTLD5nIvhywsgJ88Yu0yhcW6Ey8/XaSm8IKGxv5elWVafBVVcXdo1TrZmSEN94ttzB6Z3WV1vTEBMMun3+e26ohuPRwWeKAEUouRxIaGCiu9+KlADk+pdsrKseTlyx0SSQid1nUkg5Uo0blH0TqjY3FerAnrXzeXhfZabVRqlm4t/ABbqtOXWqRqKYkPjlMpO6du/I7ZEndE7Ec1AqHlbXuoc/7FVU2Pt6vgnQ+Po9B0T+a9HzOgL6DrLNZK4rGRlvJ+CqaWtH4MFtvtfsYfL8K8Elc5YjGxi0/ZCL7nY6lJeALX2DRscVFWuB33MEbT2V4Gxv5ekWFlR+Oken7igK5fJmEFQKja/r6eCOr3sxTTzFef2rKGoOo2FdtLUkMKHa6qiaNyBkwYlMUkK89IyKT81G6u4+tV7SJj9xRxmlbG30Tvv2fCqBpUvBSia8dI4dwNpJFzleRlCaZkRFrjTg1Vbxfb6m3t3M1pc5csmh9UpG3nBUSqof2KVmottaasfvSCj7WXq0E5V/wMo6vKqlj+zo1OoZWQYo+am21ydRXzpSsI8L2x9c5aOKcmbmy/ILX57P5BOWMbajqmch+J2NhgUT/wgu8yfbtA+66izefiL6lha9LK1YDElmhav69uMjtDhwgMcm6fu45NhwZH+fNfuiQEWh1tVWzFCorrSSwyFmWtxKO1K5Q2blqvCFyFVmI4GXtyfKWta4684pzb2iw2HbJCT6M0pcC8OGeIlXvSFW45tISz3183HwB8nXIku3p4Wpizx6TyaRri2T1GUlNPmrFR8RIzlBPWK00fIbr3JzVJ9K1ytan8SUgNKkp5FUJbb4DmVpOanWmFZpCU7XKUX8BHzrpE7r00ArArwT00O8kO+Hq+uvalbNurwiyLUQi+52K+Xng858n0efzlFPuvps3+NAQiaCjg6+vrJCscjmSzfAwpZv+fovYuekmaw6+ssKVwkMPUW+uqaGln8tZPRoV4wLsplS8ukr/yipWDRmtFFQtE7CkI5VtUC9UWXgKRRSZq/WhnKyK9vENNzRhaALxr8vaFCn5yBMRmmLtVR9eMkxNDf0Ara2ctHxBMu1DDmdPVJKJlMAlwpW2r7+AxedrQtYE43MgfHSOj3jJVr+UfCU/hYhcPgFNOvJFzM1ZSevsJOId515ykrav+vr+fe8EVjSPJjifwauJVqsLXbtylnFSNE7CujA3Bzz4IIk+BEoqd9/Nm0ilejs7+boIuKGB1qCchWoNePSoSTL5PEMuv/hFK1F84ICRPGAVHZXqr4zbyUkr0KWCaEpcqq42ghcJLC8bqfqsWUkzSpJSqYH2diMUVZf0CT/S4D2heFlGBCqL1MeHy4JXvRKtWpqbKWlJkpEUo1XP6KhNSnK2esemyFhWebYI2OSkdYGS/0EylchP+1UHL2n9muwUMqrjZlcCGt/CgjWL8VKYvldNIIDp8JpMffSNro+XhuRb0USq42ajfTRh6Bilkrv08Bm45YgdWhsnYSsxM0OiP3WKN8OhQyT6hQVLZtqzhwSucsT19bTY1Jj7/Hne3MeOWUem/n62Djx/njfbvn0kuYUF3oxyeiqWfGXFYsFF8tIhczmzemdnraesyg54x6gcl55cFAopuUYtEH1dGckLvsCaJAYRv5yNPlLEW/i+FowamIg8/SpHE8TQkJUl1ph8qQHfkDyrn2vyURVMX7VSWrni+yWtSPtXCQjvO/DJSt7f4C1orVy0ovArATlxpb+LcJXNq4Yrsvp1bp7IvQXuHcsidF/jxsfS+3MAiuvs7BakhuMJV8XkJPC5zzG2vaKCVuddd5lGvrJCeaGnx8L8amt504rsz5/na8eOcVIYGgK+8hXuM5+nJd7ZaZbvvn20JEXySvRRbLlIU3V0OjqstaC2keatYmnazrfHk34sZ2suZ/HdKh+sDFSVbRDZS3aYmzOHM2BRJz7GW8QsAs3lrIyzL3MwNmYkpDHIygZM91fVTd9RSySvvyJ1lWlQ9Uxp5crMlVXua8TrvLX68YTuk5x8rRlZ+NnestqXHN4qa6FCdoo88vkCsug1KagvgJdrdK29YzVbRyebPeujsUTyXrsv9wzatrYtP2Qi+52C0VE6Y0+f5o18ww0Mr5yZsS5MPT3U6IeHeTOrXs3EBLcZGCCBHj3KH9uXvgR8/eu8udvaSDqyuNTEW3VmJictEiWbVdnYyM8qS1aNQUZH+Tl1llJ9HBGTJBt1wFLvW0WESFqRNCXrWPKPt949QUjGAYpj1iX/aBIBzPKemDAfgXIFfLkAEbf0cz38ikbkq5r8/pyUkasYcmn43rmpc9T5APaerHpFMol4s+UFVD1T2bhTU3a9JLVocgbsu/ArFMll3nmarYuTLQ0hq95LNUAxuXtS99JOdrWi31E5W/rb4HxOZL8TMDREHf38ed6EN97IGPjJSRI4QDln717r4xqCyTYDAyTMri6uBurqgE9/mmUV6upYIE03bkeHNeNQ1Uof0y5JI0YLLayqKi5LMDZGgllasqxakZISmjo7+Vl1yaqt5fFlnavBt57LUenDCn2kiaxWkZRkFmXGSmbRBDI3ZwTpE6hkheu48/PFvXABKy+hBiaaGDs6TH6Rz8FH0QDFdYh85I+vHVNRYXKVMn0VpaMJb3KSvwettDRWX744WzJBMpiutyYdPbKlD3w5A6CYpH2Ek5d1/CoqKyn5mkj66yOHgGJJp5zJvr19yw+ZyP56Rowkb2nptbWMmrn5ZpLwpUu8GdVo++JFs9wUUqnM2N5ekvryMvDJTzLTVj1hKystTr2hwaIyFB0jEtEN3tpKIlWJBfVRVXiiSjDIOav99/RwVaECbJKG5ESWJOOlovl506VlPUoy0KSmrEtJQW1t5giWbwEwchWZiVikpYvUvbWuSUUN2WWpqzWiVgleQ5cDV3Vt9F1mNW7p2yrb4GPdFdlz6ZJZ6D700/sbvNQiq9wna0nK0bXyyWOKqBJhe6KWPCYfiHfKAsWhntqHh5+8stKPfj9yBmucmqjLXcbp7d3yQyayv14RI8n7S1+i8zSXI8kfP05r/dIl3iQ33EDCuXDBJABF3Vy8SJI4fJiP0VHgs59lyQJVjmxvt7ICauThic+X21XRMIVLqhKjyD5GS3ICrLH3wYNcjXR02GflPBaBSWqYnDSy8ZZpdbWRjyxRWe2KTlFs/ORkcWKUrqeP0pGkITKTPCGyUbtBRcAoyUgJUd4qn5sr/t4EL30AxTKIXpub48rNT66+n4D/nLJRNdn6ujvyfYj0ReQar4hajlblMJQKs/Q+j6zE41spamWgSUXj8LKZL2ftSzT4iB5dT18C2WdNlyO2YTJLZH89YnXVomMGBkhkJ07Qgh8aIsnW1ZFA6+rYVESa8uQk5Zv+ft40x47Rijh/ns7dy5et8UZ7O63tfN7q5+ihjNfKSsoUamCiOHlJKzMzfF2lgEWwbW2cYE6cIFmurlrRLxG7QjZlvet4Iio5JgHTkhVL3tzM40iSkeauCcJnofq4e0FkpZBOOYcVky5NX/JKVRU/r/FkKzOWInlBctT0tE1wcjT7qBaNSXV9fAtDhVeKRPVcEpY/T4VCKvvZE7nkE7+KUNimiNpnyHrrX/VqvLNVk6gPKc2Stl73Rdgk//hELK/jlztuvXXL6+Mksr/esLJCK/2hh0jqTU3AbbeRsEdG+FpDA638EEjisk4nJ/n+pUu8MY8fJ2k89xwnjqEh7k8NvDs7ScCy9ET0AMmko8Pi70Wo2m52ljd9R4fVqK+sJMnfcAN/zHV1HJuamqgYmFYMvtaKNHU5KUU87e2W0SlpZn6e18g7B6ur7T2FN8rZJ4erGoCI0EWi2cxOoJh4sl2q/MTkK0bq2KrKKQtdJKzPyzHd1WUJUGqC4s9HxK6JRpE0ExOWEKfXRKS+ho23wH3xsbo6u3bZ+vZe3vI6ukjaF1nzTlWfJZxNltI+so5bXT99R7uB5IVk2e9y5PO00kXMra0kzZ4ePh8aIimcOMEbr7+/OOvz0iWbIPr6uO0jj7Ax+OSkRc3s3UtiGRqyMDzp3yqYVlNjYZNKzZelXVFhZQHkrFU55RMnrKbN2Jh1uVL5hPp6kxpUqkAx59KcVT1SNXzm5rgPadSKzRfpKJpGVmt1NSchtQz0maM+asUXB/PdknQcT/Bef15dtRr8U1NW00dOV5FoTY2teHw2q9+fJjo/iSg+XuGq0v6zHay8Hi4fiK6txq1xiOx1PL8PwK6dd/BKFtPfrLPZl0bw1j5g8puf0LMrAu8U9g/5E8oZra1bfshE9tcThodZAmFkhIR7002Mmx8Y4GttbYzCUT15WfRy1o6OkswPHeKN9NBD7EE7O0uiUfmEqioj4sVFa+Kxb58VSxsdtTBLxWBXVFjZA2m/e/cyBPTYMZ6D9N/BQcbuK9FL+q3Ie3HRwgfb2oobXChcUOUWfOtEWffT0+ZIlXNvzx5znLa02PEkJfgKlpJngCvDCH0tF11fX/RMSVOARcy0t5sPwbdIlOUqp7L/q2uoevaauH0SmA97VNilxuilF2nmcvT66CKtCrT/ycni8hC6LprsfG0boLgsgjKjtULRxOFXD1pBaHs/0eivn2j0HWRRzpr99RhnH0L4AIC3ABiKMd5a4v1/DuDnAAQA0wB+NMb42EYPtOyxuEgr/MIFkvWJE7SWL10i2XR28rXJSRK9SGF83FoH7tvHz87OMlFKte3VUq+3lzfy0BD3kc/zM319VihN1qRKCch52d7Om1dp3nv2MHP34EGzOhcXOZZTpywkVCn9cmQq/E8RM4BJE7JsRe6SMPRZJTBpFSCCVZVLFZdS8pcsUO/kBYpr0kg2UXSPks8mJux4Ij/p6cou1jjlHFWsv7b3lrokHpGr5B1fRkFkKKerd3L6RCnfyER+FElY8oGIzEXy3jfgk7Ck0Uvm0irEdwZTtFC2Cfla0ETldXwv7fiHthfKmeA9tqEUxHos+w8CeA+AD63x/hkAb4gxjocQ3gzg/QDu3Zjh7RLESIJ88kmLuunstGiavXv52vCwySGSLi5c4A3d20urfWSELQlfeIHbNDWRmPfts1LGFy/yBlcD8sFBkyKUQeo1eJH88jL3c/vtNnFIzz99mucwOGhyUEODWYyAlTzI560RibJn29os0kP6sByaq6vmlFR/WBGSHJQrK1yN5PNGYN7y9DfXygqv69iYNUWZmiouO6u2gd3dFs7Z2FgctaNxSkqSpS7nqC+VIKckcGX8vP73Waq6DiJF6f5KmPOOV60yBE1MInFlK2vVoWPJX+Br0MsKB4oncV97SBOHJ2yv62fJOxsz7//330t2NVPOqK/f8kNek+xjjA+GEPqu8v4X3dOHAGx9AOlOx+goJZe5OeDee3lz9veThPbvp6N1YMCIfmmJn+nvJ3GItM+epTP2zBlup7rzXV3m1BwZoV545AgJ7sknzeJTqr+IThExq6scx803038AWHz6889zYhkd5VgUqihtVxo2YFE31dVG7tJn83mS+/CwrR58WQHF0Idg+1bNHUkKkk98LL5yBmZmLF7dO1xDMBlGNWlaW+1m9A1KPNEp4kUhjJ7UfUy5L+wm61krAaDYMSlnsCpP+k5QPuPSF4PzmbQib010+l8rAy+h6Hi+SujY2JXdp/yEli1r4GUw7z/IkrXPnvW+hlKPbDmFckUZlDh+B4C/XOvNEML9AO4HgIMHD27woXcolpZYL/78eUoiPT0k8fl5Pr/hBr6njNKFBRLr5cu8gQ8dIkk99xwfctq2tfG9lhZax2fPkkh6eljJ8uxZyjkiAEXAaNkuwu3t5Rj27jVH4tQUG5m88II1Q1ERMTnzpBMLtbVcYSgJSTKRQj79JKNm5SqQJhlH9WpitIlCk5JIRhE/Wq34blHStH1dmpYWI2bAsjvlePZFzJRwpZWP5BDp1aqxo+P41noiPG+l+5IT2YkCsPMTmXtJRasWT+LSyyUh+fBHH7nje8L6RDCfvepXMErQypZPyDqK/QTmI6WyRA5c+2+27EK5QU70LcSGkX0I4ZtAsn/dWtvEGN8Pyjw4efJkmU/d60CMtMIfe4w38bFjtGzzeVreBw9SHrl0yWqeDA1xm6YmknkIwOOPk3gVXdPWxn1VV3PbF1+0UMzmZnadUvKSHH6+byzAY/f2khhl5Y6O0g/w3HMkj1yOY+joMJ1c8oysa2WZKoNTxbdUTVPWu4hS20l/luUuYvPZoCGQ0CXHjI2Z1KDSDF1dxRm/2UgPX5dGRO/zDXw5YFnkvp+tLOdswpCia7QKEZlrAvThkd7i97q5JrRsWKTG7St4ZuPdvb/Av5ctcKYJyVv/3sHqXy9F1v637I/xSp+XO3Zq6GUI4XYAvwvgzTHG0Y3Y567A5CTlm6kpazyytES5ZN8+SiSDg+Z8u3SJZNneTiKenSWRv/giSX15mcR7ww28OS9e5KpAr2kV4TstSVLJ53lTt7XRAu/uNk15cJATytmzJMWWFjqLOzutrvvEhDX5yOUsUUkx4rLgVYist9dIrqKC5yJyV7kF9VNVjH2M3ObcOR5PlvvqqiVdKSLHV5H0lqhI0odbKgM42xxEGaPKN1DjchGdd/Bma8HIqvZhh1qFSIKRA9Rnv8r618SjSV7x6t456hOjRJDeCerr7YjYfeMRX2rB1xnyY5Bz+Wrk/FLgLXy/KsiGgpY74e9Eyz6EcBDAnwH4vhjj8698SLsEKyuMfz99mtJKdzcJsaeHlu4zz5DAJYuoBr2crcPD/OyFC7ToV1b4+pEjJDDp6AcO8LULF7i9iMzXkpFEoH6pClvs7+cYL14kibS20jnb1sYJ6tw5Eu7qKvejxhqSGWZmTINvaeFqQyUTFAKolYw0ZhFqZaWVVbh0yWrDKDrG97EVGauYmteT5XdQCQiVd/ANTHxUigqZ+QbZWlko4sZnhCoL1hOlatNko1m8RONllGzIo7eipfuLjAFbKfgVgwjY698+jFTjA4pDQTWGUsjGz3tpJkvY+t/7HzRpeJlIk6mX17KP3WDdd3bSD7aFWE/o5UcA3AegM4TQD+BdAKoBIMb4PgDvBNAB4LcCf0wrMcaTmzXgssHZswy1rKkhCU5M0DI9cIAyy8iIOcvOnuWNtHcvCfn8eZL3uXPWJLyvjxPG3ByjYrRC2LOH+romBJGaatSr3s3evdaU/IUXOIbBQY5V0UANDTze44+TqEOwTlLe6p2Y4AQRI3/Ux47x/YkJnossTsXDZ0MmBwYs4kSrEEWtKBpHUSTe0hXZLSzwWCqo5mvAiwC1wtAE09RkJKh9euLxEoyg9oyKPNJqArAoIUlA8mPoPU/IvuaMt6wV2aMa/T6D1Vvj2Xh2r+WvRcpXI2xdS18rR/4FLx/5v34SzBK89ueRzXHwOr8mjXLW7L3DfYuwnmict13j/R8C8EMbNqLdgOlp4OGHSZi33GJJSocPmySjG0xF0Pbt43YvvkgyvHiRf2tqWGOjo4NW9pkzJPO77+ZN85WvWDenxkYrICaH4v79JOPlZU4KTz9N7VvVNG+6iUR07hxDOicmSLIq5ysrfnWVE8r0NPe9fz8njxj5+oULfF2fCcFq+ag5uo8A0aQkSUY6uYhM1uzqKvehKBtFx4j4pYUrFl/SkdekBRGSatnoNen07e3FoYoiJVmsiqEHjKh8Nq1fIeh9OYN93L3OzVvnWmlkI20ky1yLGL20lF0V+DpCcj77bXRdshMEUDxBetJWyKv3E+j/rNTlz9tLYeWM1dUtr3yZMmi3GqurdMg+9xyt7gMHSKBqI6iaLwsLJMGGBjpL5+cpwwwNWVmE2lpq53V1Rv4qWzAywrBKxWBLR5em3tJCHb++nhPCqVMk6ro6lmi48UbecC+8wJXE9LSNpbXVLMeFBetn29TEiaOzk9ufPs3Xm5v5mjpNKYHKE5ss0tZWs+IVuaO6N3Kkjo1ZOWVZ/4BJHiqVoBaDWnXIgSqrXVYzYPqzJBhNiL4uvZdaVOtGx1W0ih4iP++kVWkIb/FrPJoINBmJ1PV/Nh7dTzBZwsySqFaIXjbJFh/LRvTIX+Pj7v1xs+ScHYtWQ7pupTR+b8nruknu8rJTOUKVYbcQiey3GufPsztUCCTb6WmL7370URJ+jPyrGvBqED40ZI1ImpsZXVNZSVKdnrZQzWefpVyysmKWqKpESuPWKuLUKRJeaytw8iTJenGRVvzZsySn9naTcUQKyrZdWeFnb7uN5zE4yPMD+LnOTpKAkpb8DawyB/orWaW62rRskbpkmWxGay7HYyiMUiWWJQ0psmd+3uLrVRdHbQk9qSpsUUQEWHSPb/Lhi4eJ7ERspQjda951dSZf6bi+hrvOXXWL5Ij2DxGottd56VhAsfXtQye9s1aflw9DD+Uw+H7BIuts+GW2ro2ukc+j0GTmtX0fteQLp2XzCsoR2zCRJbLfSszOAl/9KglRVvXiIkn67FkSuUoGd3bSSu/vp6yjgmIjI3zv6FF+Vg3Cb7uNpPvlL5uO39xsck1FhU0eTU205s+d477uuotO3LExtj70zmDJOHJODg+TuCsqOGkcOMCb9Px5vp7LWaE1ZfnKCvQhhIqpV1atQh6HhzlxidzVlUpWb2enOYPVjNtr5WpVODVlVq6PhZdjWnXeveYvYvLJRyKurEUsIvaTh58kKivN0e2zU72Wv7RkZSDWSqACSseze23bvwcUa+3+OL4lof5XYTX/ef0vKS1L2p7oZbEDxeURfD6BXisVGqrP+TH4mP1yxfz8lh8ykf1WIZ+nrPLUUySBo0dJagcO8GY/fdpu+qNHSWinT9Oqk0Y/PW19ZicnOQE0NtLqXlkBPvc5blNVVRxlI3mkr48E9bd/SzK96Sbg1a9m1M4nPmGdr/bv50MEKofn9DTJq6eHE8H0NDV+RercdBM/ryxQEZfkANVpr621ssoKJ5UFLwtZ8oscsipXoPOqqDAyU9E0xZvreKp/ny3E5Qt2idBFwpKLNA7fRMSHHeqzssy1Xx/lovHpWpSKfNEKRdKV+gboeMCVjlJvgYu4/TFFttkwSkX3SO5SSKsnVk0k+s0qHHhm5konq97XBJBNyspWscy+puvlJ1hP+OWM5uYtP2Qi+63CxYt0yubzlF+kZasM8cQECXzfPt6Izz9vhH7hAi2k3l5atoODfG/vXk4MAwOUbubnjSRbWiz7sauL5HzmDH0F1dXAG97AG+xjHyPZ19VZpqws2fFxTkDqDtXbS9IdGWFoaEUF99vVxZte1RtlueqmVkasonTUMlEar5K6VKqgrc2ujbRjwHR2rRZEanK0ypL2jkwfw66kJE+SIiwvgfhIEK8jqxiZpAfp03IuZ52ZntjkHJec4uULEbesb19bJ6vFiwi9E9eTqy/FoKgdv1LxlrUPzSxVsEyQXJZdYXj/RDbMs1Topz9+NmLHrw683l+uuB6jcRI2APPzlG8uXbI688vLtOqVITs6albrc8/xM+fPkxwrKpipmsvx+fIyZZfubpLuhQv88TQ1mSPWZ6dWV1NHv3iRhP2qV5H4H3+ck8Ftt1kVSkkv6gdbV0eZKZezaJ+6Oo6ntZUEdflycYkBFVDbu5fEMDDAcY6MWNmClhZq+urrKjlG2boigFJliTU5iOS9M1OOY4U7KqPWE3iWzIFix6DPstWx5VzOOpUlSSmb1uvXgEkZkn2Ud6Cx6a/PeNVn/RhlCeu1bDkGba/XvI9B5OwJJruS8SUNsisfb5nrs56ss4lW2mfWMbtegltPdNFORy635YdMZL/ZiJHSzZNPkuSltff20sI9fZrkqiiZc+d4U1y4QHKurrbWgZcumQVeX88VwdAQb0RZwSp/0NrKVcDsLIl+YQG4805OMF/8IlcH3d2cNKqrTXqZmrKJ48QJvqfkp7o6OnZzOb524UJx16nmZiPwyUmuNgYGuE19PY+nyc73dFVNGKC4SYbgS/tKR1bhL5G7JI6ZGbOARXIqrwAU6/CevLzs4h2skj5kNStpzBOvnLK+TrwcwpJdRID6TWQdm3IO+8QoH1ap55rcdE00wWg15lcCPgbey0f+uvjIFx3bZ+/Kl6KxS6bx//vnGotWGNlVQNbyLxVj7/eZsGFIZL/ZuHSJZLu4aOGMKtH78MMk3fl5WsKjo3yokFdTEwl7ZoaEIYfo3BwdsePjFibouzupguOlS4y4aW5mNc25OeDjH+fNfPQo5RdF1czN8Qbr7rbmJ6OjFjff12cJU3KixmgFziT/DAxYPD7A8d9wA/ermviA+QNU+0ahjJ4sPLnJ/yCL07cBVLKY9qvkLjUv8Za8LE9PgpKSJEnIKekrVGoCmpmxssjKyFWRMW/VAsU6tF9xeD9BlvSyDbu9FCVLWhbz0pI5orMOT38MXbNS10FSVnYc8kHo4aN5/Cog+1fHz5ZXWOt/H7WUfa+codaYW4hE9puJxUWGU545Q4dnezt/xAcOUJM/d47WYFMTb7rLl/nawgK3bWggaVZU8PPS68+csbh2xZJLV21v50359NMk8cOH6cB95hmGWTY387WKCr62vMzP33ADCX1piXLL4iJ/jPv2ccyDg0ZsFRVcOaiA2dQUJSF1pWpp4TH277eSx96arqiwbFQRqZycXpKR5V9RYZq2r4UDWCtDwEI2RRa+ObgentR9SWCRmr636WmumtQ1Swlb3mL2koekFp/V60lXJOolEvkD/CrD+xB84xGRXzYj1UfkZGUWX3RN22oi9SSuiSjr29BKRxE9Xl/30k2p8fkxemTj7LNRQNnVQrliJ9bGSbgKnnqKCVQNDYxfz+cpyYyOknjHxsxyU/mD8XHLllXf2H37SORnz1o7QkWnyOJSpMrCAieSykrGzbe1AZ/5DC3gfft4/MFBqz9/222Mohkaoo9AkTV79vBmP3+e48jneYyDB/lePs/3vvIVWrlVVVwp7NvHSam93erXZDMnfYSOkph8zHuMllXqi7aJHCXLVFaa1LK6SjIWyclxqxj+bNSM4u+np/k9TE5aJq6Oq/2InHXsxsZiXT87kfkJxevqss59Runc3JUEqKgWoNjhK1L38PKHL5PgSVzn6ycBOZQXF0368q+X0tezGbRevvHyS9baz5K6UGriKBWaWY4og3r2CcKlSyT66Wlq3yK+hgZawf39VgBscJDPFRUj5+LevZRuVldJ4Jcv86bxVR1l2TY0WNJVZyezYIeG6BiuqqI2X1fHSWZhgcR8++0kyMce442o5txjY8ycnZ4mEUumqa3lZPTww9y3JKnjx/l+W5vVmRHBqHyAHLO+U5JIU+erBKIYzfL0+wrByF9WZwgWKy7JR2UNfOioGqNMTFiVTt/tSSStVYX64mYzYyVlVJW4dWTpe7nEO4d9xI/IzFv0gqx1HSdL5NnVgfbns1rn5601Y9Zhmh2zn5i8VJY9Zja2vpST9mrP/US3FrKTRLkiOWjLBEtLJPQXXySp7t/PH25vL52Wp0/Tomts5F+RNGA3w8GDvPHm52nRj43xxlMXJRG9tPrnnydx9fXxs489xs80NdGaV/XIykpOBG1tXE1UVZHMpbe/+CLH0NLCuj0tLSTLF1/k5+fmeNzubu5XKwytLKqrLbpEerwSqOSrUMbm5KSVOhC5q0yBrGCFJUq+AUxjFrk3NpqDU1m3L7xguQk+oUhWr2Lau7uL5SLp3DqOtO5S5CyLei1LVt+nJB+RsvblNXKdv4+CkZwiovYJUn6V5EncrzB0jSQt+UJp+iu5KRsGqYfX9derp2eTo0pdp1LPy53gtxmJ7DcDzzxDsldFy3yeRD88bCGIclQODlIOWVoymaKvjzfi6KgRVi5nNWm8/LG8bOGQd9zBm+YLX+C+1RlK7Qzb2+kkVjJTQwO3VxerhgYrYFZRwWM/+STJU9E2hw7ZftWYpLGx2KKU9OGLjwFWsx4wfVshiyGYjKB6N7K4Va4gq+Xn87RelX2sRuELC/ycInY6O23VkSUVkZgnY8CIX/97DT4rzfjJQIToI0x8Nykfwuj1bkUT+bILPmnJE6YmLD9BZEMzhVIELue29r8WPClnpaqrkXbCdYlE9huNy5eNII8eNemmvp6SyrlzvDFqa00nV4kEWczaz+XLVgvet89TnLmiYtraqLtL06+tJcEBJMGqKjppW1os1j2Xs//b2+mgbW62huWXL9P6zuXoUO7u5mTT2GhJT5WVlgQkUlIIqaQaJUHl81Y5UtElqruipiWygAErxqW8AUk9o6O02i9d4udUsqCmxlYxra1GyJos5Oz0oY4+gxYw4vNWsv56i94nJWWtVy9zKHw0ax37CcV/Xufsidz7BrwTVBa9/leo51rknSVnfQelrO9E2mWJRPYbieVlEuUzz5DU1Dawp4fO2mee4U3f2EiiUjLVyoqVDa6uJtGqQ1VHR3HEhohAk0ZfH8n361/nDa9MVGVhtrRQ1llZIfHX1/P/wUH+f/PN3O+5c5woFMHS1UWC7+oiiWrCqa83S3R+3salmjMiUNWoEQlrFaLUexGhSFPRRKr6qJox585x3MoBkDSkevRHjpiUpXBEEasyiDXx6HiynDUGlRvIOhNLEblknquV5fVOU2/ZZx2omni8vu2Lmim7NltiodQx/OS21tivB5SaqPz//nk5Q7LnFiKR/Ubi2Wdp1VdWWnijukrJWVtTY2GWly+TLHwTDJVGqKqy7FdZdtL4leB06BBJ/bHHuB8lKylmXuWIFfNeV0fSjJETkDJrfT/avj6OWSGTkmnq6znuxUXT0+XAlAUqKaKigtu3tloW7OXLdgNLa1dky+oqr4163A4N8RxU4VIJWIcPcxIVsal8gki7ro4rGsXZi/y1ulAsvLfYfbikiF4SDFDcsMPH0ftsW18100s2knh86KfITIlbVyNyrUCy5Qe8FLTdyOr91yLxteAnJp+QVa5Q8MAWIpH9RmFoiNb78DD1eZF3bS37zF68aEQzNGQhlBUVRqwXL9J6VWMQOQcBk11UUvjAAU4MY2O8KdrarHpkbS0JO583/4AKhjU1cRI5fZoSUk2NtTpU0bGWFiu9APAmnZsrDvGT7OBJVWUDAD5Xd6wQrKZ8dbVZ7VNTvF6Tk1YWQREhXV2WHKZroFoxCqtUWKqIYXXVyiGXqnipKqDyKfiIGe1XcpFWEJJ5ROTZRCd93lvi5UDkpVYZa5F6KWQzZ33Rs1LSUcKmI5H9RmBlhRb900+TnA4f5g+5u5uvP/mkJSmNj5OkVZZAxb/GxqxiZWurRWI0NPDv6Chviu5uHuP55y2+Xfq8ZB8lOskCnZoiOR06RKL9/OctI/emm0iYLS1WcqG2ttgSU1SKtHHvoPTJSAqfVEikJCWAk45yC+SAlRwjrb2jg695eWVuzmLcu7uLQx5nZihHiawlJykeXvH42TBIjdWHQmYTo0TEPtxQpKfzzBJdKSLPkvl2E5sn8LUIfS0LPOuwXYu4/coo4brBNck+hPABAG8BMBRjvLXE+wHAuwF8B4A5AG+PMX5towd6XUPyTYwkVBHTyIhVtMzlSHIXL5LwVlZIsF1dfH1igjeZZA3AZBuVA+jp4fNnnrFtOzpIrhUVJP0QLHpGTsnOTo5JEok6XB07xnFK/tFKwpO8lyzUTlARILLoFa8OGMGHwPM/c4Z/p6e5DxG7kq4UcSMJSISrap2COlRJVgnBMmB9eGE2/jtbE0Y+Ben8Xi7wsouiijx8TLrX3D3xbQdK6f1rEXkpeLL2Fni2hk0i8B2N9Vj2HwTwHgAfWuP9NwM4VnjcC+C3C393B0ZGaNEPDJA49+2zhhyf+Yz1XgW4zfAwCbKujoSXz/O1xcXiUro1NbRclbjU3s7PT0zw5lZ8+OIiCa+pySYGhRDqGIODwNe+xht+3z4S/YED1mxcIXs+TX9pycbhU+t9rLcyTJV4FSMnmnPnuBJRaYWmJoZ8ytEMWKy9T57yafrS2WWNK4xSPWwlx2gMas4tQvaVGrNp+DpXX2wNKLbwRealQia3ClnJZK2/a5F41oGbJe9E4rsK1yT7GOODIYS+q2zyVgAfijFGAA+FEFpDCPtijAMbNcjrFqurFn2TyzHUUo7Vr3+d762s0JIeGbHG2hUVJOHaWhL4/HxxqJ1C6UKgVV5VxbDKmRkep6eH+1heJkmGYE3FdRPv3ct9fO1rJM3WVhLusWN8r7HR4s4BK7crPbypqbj/q9LqpWvX1HBslZUk9mef5V+dn29UopBKhWdK85bDdHnZ/BGAZdru2WOhiJqEZHXPzho5y0nqJZ5svRjgSm3cE/lWkflaVnip10qFUXot3K8oSv1NSHDYCM2+B8AF97y/8NoVZB9CuB/A/QBw8ODBDTj0NuPZZ/lYXCSJNjXR4h4YYFVKyTezs4xGmZrijdzdbTHvcnzKkhT5qXH2zIwVJpOuLQu2ocFC/7QEl1P49GmuKiorWc7gllusLr1S4uXQVGapj/GOsbhvq4hVTTgmJhjvPjJiEThNTZzE6uqsCFeMFpIpp6nvR6uQy+5u207XwIdpyjrVOLPJSZ7cfJLRVlnmWilci8jXI6WowXgpEt9uzT9hx2JLHbQxxvcDeD8AnDx58ho519c5xsdJ9OfPk4BVp72qikR/9qzFditOfHGR27a1WWnhfP7KOHA1zR4ZsWJpN91kSUrSnaUrK5RRE8hjj5Gku7tZGuH4cYt1V5z83ByJVM3OJTX5eu4+gauqittLgxfBNzfTwq+utr6aMVolSh9mqH2poqdizCXBKPnKE7t8CV5q8Bp7thRvdtuNhI/PL1UzPotskbKrEXiSUhI2GRtB9hcBHHDPewuvlS9WV1m6+KmnaMUePUqy7eoi0T/6KLerrDSdXm0Iu7qsbICITZbcygrJt6KCn5uaohSyf79Z4ZJVfGRMUxPff+wxq4N/8iQLnSlRS+SrjFWtHESO09N8X8Qk63l+3kI8JdGoy1RVlTXn0CQlmWdmxmKmOzp47gqPXFjgROePV1Nj3adKEZ8Pocxmlm7G95sldJG6hw/LzK4gUkRKwnWGjSD7BwD8eAjho6BjdrLs9frnn7fCYwcPksC7uli58vOfJ0k3NFiHp8lJq9GiBtTz82bpVlfz9epqEufgII9z7BhXAZJSKiqscJis/+pqriLOnSPJ3ngjyxYfOcLtVFkSKA73lIYuAlekjaJ4Ll26kuDb2jjmuTnT+JXABFgyWH29FUiL0SY3X5lS5Zm9FAMU6/D+sdHEKULPEnu2MqTGo2QpkbpWVwkJOwTrCb38CID7AHSGEPoBvAtANQDEGN8H4ONg2OUpMPTyBzdrsNcFJias72tzM2vKqOfr3/4tX6+rI6mqMXhlpWV2ykmrKJbaWos5j5H77+mxWH1JG3K+qrF2XR3llBdfpFXe3Q3cdRcnCCUvSSpR6YKWluLMUMCKkMkBKycrYAQvCWd2luPwTbNVjripidvW1dkqZGzMNGglfXnSFmlmZZiNgiJ7SpF6ltA1FoVw+uqTCQllgPVE47ztGu9HAD+2YSO6niH55oUXSExHjpBQOzqAz32OkS+SF2QZLy1ZR6fBQWsUoSJfIh5JKXv3UmNfWLBaMCIjZbYuLDDSZ2iIx3/ta6nNi8xVeC2ft/627e12Dgr9XFqyYmoqsZDLUTaSBa/G45JbJFk0NnKFoKidhQVr0+cbfai6J2BdqETsG0Wkco4qBt+XOPAQgXsrXZZ6QkKZI2XQvhS8+CIfU1OMV1eJgbNnGVO/tESyHB6m1T07S5Lds4fEPztr8fSSOFQiQJZ3dzf17MVFs5pVLTIEHuv8eY7n2DHg1a/mGCSRNDWRwKanSfYtLcVlAGSVX77MMQEWDVNTw+NOTFiYpbR0OYAlz4RgDTLUp1bb+dh23yVqI8jdFx8Tqft4eckumpSyTT4SEnYpEtmvFxMTdMieP29tBpubSYif+hTJs7aWpD08zAmhqYlW//Q05Rzp9CoPPDdHMp2dJaF3d5M8AatX39bGv5cvk+jn5mj9nzzJbF05BdVARP1TVVFP0S4AtxscNGlI5YoBa6K9tGQlGyTNqLSxKl2Oj5vs0dxcXJbAlyyQRPRyINkla617+UVELtlKzxMSEq5AujPWg9VVNiM5dYrP+/pIgO3twCc+wfcUQjc4aGTY3s7XxsctNl7hmcounZszy1pZqe3tVjNnZoYhnhMTJLV772UGbH29Eatq6UxN2WQiqIzB+Dgt+dVVK3Y2N8dzWlw0X8ChQ5ZZm8uZI3ZkxAhVOr4IXrKNpJuXAunqWVL38eiaWBQCutkhlgkJZYhE9uvB6dOMvpmcpCRz8CAdrs8+C/z1X1udGpUzWF21LNXBQWsjp85Nyo6Vs7Ory8r8trfTQZvP85hDQxzD8ePAnXfaqqCqioTc2srPzc+bdCGHqxy+4+O02BUyubDAc5qd5crj2DGeT0sLP6+ywGpBqNBNn5mpejRyaK4HWfnFtxoELMlKsfUi9eQkTUh4xUhkfy1MTLDI2cWLJKGjR0mKS0vAAw+QSOvqKN0okqW9ndbvyIhlgEoyUcVHVZLs6CDJTUyQyDs6rNb9/Dwngjvv5AQg3bmhgSuB1VXrTSuCVK/ViQnrRNXSws8vLZHkp6b42qtfzRo5CvmcneU5a38KedRxVbfd17i5Gnw9eZUnBorDGbP9UBMSEjYFieyvBTUIX12lfKP+q3/6p8Bzz5GglpdJ+jMzJPU9e6xe+/y8ae8iPYCfU0/ZiQkSeGMjHcArK/zM3XfToq+vt1XAvn08/uQkX1OdGYDHV4345WUes7OTE8u5cxxjYyN71fb1WRauZBwldPnMT9WpWa/+rqSpxUVbXch5LAducpYmJGw5EtlfDYuLjKkfHGQ44pEjtNq//nWGWqrImeQbZYsqomV+3sIeV1as5Z+0+1zO6uc0NzMpa2WFBH/zzVa+OJ/n/7LOR0ZImCpkpoYdo6M8dksLLfaKCq5IhoZojd98M89Blrx08GzNde9gvRZkvUvbl/Wu8SncMpF7QsK2IpH91XDpEnXzykomObW10aL+8z+3rFhlyAIWOTMwwNcU1aIYekWrqP/k5KQVJrt0iaR5883APfdYqKR6yOZytMyXl42MFxZMPlpY4IQhkh8ZIdFXVzPxSzkBKgnc0GBJWmrYodLB14LKE8uCL2W9p6iYhITrCumOvBrUZvDECVr2uRzwR39ESaSykgSu3qYqCHbpknWc8ta7by/Y1ES5RU26h4ZI9MePs+DZwgIJ8/BhWvPj45xAqqs5eSwvM1NXtfEbGkjoVVXU8AcK1Sp6e+ljaGszR2guZ8RcV2et+q6GZL0nJOx4JLJfCzMz1ORDIGm2tQGf/Szwla9YffbxcUozTU2cDIaGrIyx2vypAYkqQTY0kDiVRTo8zPeOHuVjeZkEf/w4P6fetcqOvXjRCqvV11N7r6/nKuHSJWtN2NdHJ66sbtWw0XFbWq4u01zNes/lXloUTkJCwrYj3a1r4fx5JjE1NZE4z58HPvlJs8jHx2nB19ZSk5+askbX0uDn562QWC5Hsve14VVp8tAhyiwhkOSPH6cMMzXFz0guunjRQiyPHrWELL3e1saJYv9+i26prbV67361UQo+csYnYtXXv/IkqYSEhG1FIvtSiJGO2akpRq5UVDDM8sIFEqwyYisrSbAVFQyVXFoiIba2kjRF9JJMtG/Aio319pLsq6pI8p2djP6prub/k5NMfJqa4msHDpCsFxZ4zLk5rhaOH+e+VJpYETpyvCrD1pO1ql4qckaJTDU1nBQUY5+QkLDjkci+FCYm2Fe2qorO0b/5GxZAU9MP1Z1paiLRXr5sTUWamqyHKmBEX1Nj1SvVMq+3l/uvrmZp4o4OSkGdnZw4nn7aSgMr5HJ+ntvIQXzwIPejXrI+g1Wauvq7CisrXBHMzdlYZLlvVA2bhISE6wqJ7EvhxRetA9XYGMMs1YxDJNnYyCzZkRGSv9ryVVfbZCDLOJezHqqytPfvp6aukMi2NpJ4ba017M7nmVTV2spjqrhadTWP3dvL97PVJRUfn3W+LixYMTYVKVPDkISEhLJGIvss8nmWD15YIJl+6UuUb2prrYRvXR1JdmqKpKys1ro6a8EnK7ulxZydKq3b1UWib2pib1itDvJ5i7Vvb6eF72P2Q+DrXV2cLFQrxjfzli4v2Sift3GrNaB0+2TBJyTsGiSyz2JkhFmzdXUkxscft1T/mRkj08VFq1tTX0/ilU6v5CRVxZyZKSZ61c257bbi0MtLl/i5I0esMbdv+dfWRs1eNWzUPEQZr5KVACt/sLDAbVSiWJNAQkLCrkIi+yyefZbku3+/xdnX1FglxqYmErRCIhWGuLREC1pRL52d/F+1c1QyoauLpHvnnSRgJVMNDvJz7e3WSEQZurkcI4K6uqzUgG+6rRaFgFnxy8vWjEQrgISEhF2LxAAeKyuUcJaXaX0/+CCtcmWb1tfTWlfkjbJPKyut2Yes98pKThQNDXy0tHCfzc1sH1hdzQljbo7biZRVugCg9d/TQyesLymsiB6NB7BuUyqwphLFKVQyISEB6yT7EMKbALwbQCWA340x/mrm/YMAfh9Aa2Gbn48xfnxjh7oFGBhgeYSGBso5Fy5YOd5cjo5S3yxcRb3kkFWGa3U1Vwc1NSRsVbNsaWHTkcpKOoBnZ60IWn09t19askYmR45Y4pMkG3WPamnhMScnLfJHDtf11LRJSEjYVVhPw/FKAO8F8K0A+gE8HEJ4IMb4tNvs/wTwxzHG3w4hnACbkPdtwng3F089RZI/fhx4+GESqbojtbaSnCcnLQu1psbKB0iCaW6mNFNdbc87OijhnDxJgj5/nnr8woIRvRKW9u4Fbr/d2hZWVvKvLHa9Pj7O4yoKSPH1CQkJCSWwHsv+HgCnYoynASCE8FEAbwXgyT4CUHukFgCXNnKQW4KFBcbSq6SAOjgBVmJ4YoLPVRlyeZnSiQqetbZyNVBXR80+lyPRd3YCr3oVt+/vtybeknhE9B0dJHp1oQJ4XGXgApxsZN23tfFYSapJSEi4BtZD9j0ALrjn/QDuzWzziwA+FUL4CQANAL6l1I5CCPcDuB8ADh48+FLHurno7wfOnKElfuoULXxZ7FVVfB6jOVtXV621YHs7ZZczZywss66Ode27ukjgS0ssvyBtv77e6tmr1d8dd1hDcCVfySErp60if1Jma0JCwkvARgVavw3AB2OMvQC+A8D/CCFcse8Y4/tjjCdjjCe7uro26NAbhMceo+Xe1UUnreLas87X6mpKKopbb2lhduuZM3yvo8OIfu9ehlcuLlpbQzUh2bPHSgp3dZHoJdksLVltGpF+czP3J59AQkJCwkvAeiz7iwAOuOe9hdc83gHgTQAQY/xSCKEOQCeAoY0Y5KZjZoYSjrJfL1600EXp5ZWVfF9ZtEtLJPaDB5nxqiic+noSeXc3tf/ZWWr03ona3W3t+Do7mVilY83MWOZtba0layUkJCS8AqzHsn8YwLEQwuEQQg2A7wHwQGab8wDeCAAhhJsB1AEY3siBbipOn6bW3tnJfrNqNBIjLeuVFVrj6gq1tEQL+9AhEjlgGv3evXwcPUqif/FFq0GTyzEpSs09enpo+QOcXKamSO6trZwwtEpISEhIeIW4pmUfY1wJIfw4gE+CYZUfiDE+FUL4JQCPxBgfAPAzAP5bCOHfgM7at8eoYPAdgK9+lcS8bx/wwgtWkVKSjWLpVf63sZFEr8SqPXto0e/dSwLft4+S0NCQyTGNjUyMUqy8GouoI5VKJLS3p1o1CQkJG451xdkXYuY/nnntne7/pwF8w8YObYswNkaNvq6OuvvYGMk4nycBK8lpYcEKoPX1MeN1ZYUWfX09Sb63l9b49LQ5eGMkgR85wn1WVPDzBw5YSYTaWk4Yra2pXk1CQsKmIDHLs88ymaq9naS/uGjhjgD/KsSyvp4krVaEbW0k/wMHWF6hqanYol9d5WRw4422ryNHOCmowFkux5VAe3si+oSEhE3D7i6XkM+zzeDSEqWby5etv6oKjK2uWkz8wYPFUo4afO/bx5XB5CTlIO1j/37g2DFrYqLmJHNz3I8qWCbZJiEhYZOxu8l+aIghkU1NbBQyM2NlgFUyWM97e61tn5yoKk4WAqWbpSU+JNUcOULyr6xkI/GWFms+nqz5hISELcTuJvvHHyfht7dTr5czVX1ipdm3tfGvatm3tJDMOzv5GUk/y8u00g8fpvN1aopRNydOUJcfG+Pf3l6uDBISEhK2CLuX7FdWgC9/mf/395vFDZiEA5Cs6+qsQ5SIfs8evhYCrfPFRRL5jTdS7pma4gRx223c3+QkP9vTk2SbhISELcfuJfv+fpZFqK2lhT83Z+9VVFjzbVWQrKwkWR85QqKXlV5ZaU2/77iDCVNjYyT0W26h7LO8zNf37EmyTUJCwrZg95L9I4/Q2q6ooGPWQ0SvOjX5PJ2xR48ytHJ4mJE5VVXU+VW6uL3datPffDN1/MpKyjotLVt/jgkJCQkF7E6yX1xkIlVVFa37mRl7zzcHqamx/rJHj9IpOzRkJYlnZkjwr30ttxkc5KRw6BBlnPp6En2qL5+QkLDN2J1kf+oUSyQALJPgk331v7Jma2upwTc1MVFKLQAnJynNvPa13G5ggFZ/d7eFVR48mGSbhISE6wK7k+wfeojW/cgILfBSUEPvlhZGzkxM0FJvbCTR9/QAr3sd9fihIYZgtrczKufAAT5PSEhIuE6w+8h+eprljGNkETMlQHmoaXhdHcMuFxf5t7mZETh9fcA3fiMnivFxhmC2tVH26etLYZUJCQnXHXYf2T/9NKWbqSk6U0uhosLaDKrUcFMTE6aOHwde8xp+dnqa0k17O98/fDjVmk9ISLgusfvI/gtfoPRy8aLVmPcIgRZ9Lkd9vrKSlnpFBWPo77mHk4V0+c5OSja9vak9YEJCwnWL3UX2o6OsV7+wcGW4pVBZaVmz1dUk/fp6WvS3307pZ27O6tar0mVCQkLCdYzdRfaPPkrCHxykBJNFRQV1dzUBr6mhg/b4cZY8OHOG+n1PD4uc9fVZI/CEhISE6xi7h+zzeeDBB2mVX8x2VXRQSQRZ9b29JPXz50n0fX0MqTxwIOnzCQkJOwa7h+wHBljhcmSEYZSlIKu+vp6PffuYTDU4SD3+hhtYLqG7O8XPJyQk7CjsHrL/4hcZJjk0VJxEJVRUULppbWUkTlsbo2tU+fLWW0n2bW3JEZuQkLDjsC7zNITwphDCcyGEUyGEn19jm38SQng6hPBUCOEPN3aYrxArKyT7sTFa9qVQU8M4+tpaRt8cPEjiX1pi5coTJxh9k4g+ISFhB+Kaln0IoRLAewF8K4B+AA+HEB4o9J3VNscA/AKAb4gxjocQ9mzWgF8WTp+mc/XyZZJ3KeRyJPmaGso3vb2M2rn5ZhJ9Q8PWjjkhISFhA7Eey/4eAKdijKdjjEsAPgrgrZltfhjAe2OM4wAQYxza2GG+QnzmM5RwxsZKv19fTzKvq6P1fvgwLfjOTlazTESfkJCww7Eesu8BcME97y+85nEcwPEQwhdCCA+FEN5UakchhPtDCI+EEB4ZXit7daOxuMhyxgMDLHVQCrW1tOxraxltIznnNa8h4SckJCTscGxUSEkVgGMA7gPwNgD/LYTQmt0oxvj+GOPJGOPJrq0qFPbEEwy1XCuJqq6O8k19vcXPx8hGJLLwExISEnY41kP2FwEccM97C6959AN4IMa4HGM8A+B5kPy3H5/+NMsblEqiAmjBNzQwtv7wYUbiHD4M3HlniqNPSEgoG6yH7B8GcCyEcDiEUAPgewA8kNnmz0GrHiGETlDWOb1xw3yZUIXLtax6FTirrSXBNzUxtPLv/b1UuTIhIaGscE2yjzGuAPhxAJ8E8AyAP44xPhVC+KUQwncVNvskgNEQwtMAPgPgZ2OMo5s16HXjoYeAc+fWDresqzP5prubuv1rXsP/ExISEsoI60qqijF+HMDHM6+90/0fAfx04XH94NOfBs6eLf1eTQ0t+ZYWxtQ3NjJx6uabU3ZsQkJC2aF8WW1oiLXrBwdLv19XR62+r49VK3t72WKwpmZLh5mQkJCwFShfsv/MZ1gLZ3X1yveqq2nJ79tHyaarC3jDG2jlJyQkJJQhypPsVeHywoXS7zc00BHb28ua9PfeSyknISEhoUxRnmR/+jTj6+fmrnyvuppa/YEDtOxvvhm4++6k0yckJJQ1ypPhPvUp4KmnSr+XyzErtqeH4Zb33cfQy4SEhIQyRvmR/fIy8PnPl66DU11t8s2RI8A3fVNqKZiQkLArUH5k/+ijwJe/XPq9xkaWQzh2jI3Djx/f0qElJCQkbBfKj+w/8QmWM86ipobyzeHDrHvzDd+QdPqEhIRdg/Jiu7k54K//unQnKiVP3XYb8G3fxszZhISEhF2C8iL7v/1b4Otfv/L16mqGWN5+O/DGN6ZyCAkJCbsO5UX2H/tY6eqWnZ3ATTcBr389cNddWz+uhISEhG1G+ZD9yAjJPovaWsbUv/71wLd+K5uHJyQkJOwylA/Z/+//DZw/f+Xre/awZPF3fVcqW5yQkLBrUT5k/4d/eOVruRwjb976VuDQoa0fU0JCQsJ1gvIg+9OnmTWbxeHDwHd/N/C616X2ggkJCbsa5UH2H/nIla81NwNvfjPwD/8hWw0mJCQk7GLsfLLP54F3v/vK1++5B/j+72d5hISEhIRdjp1P9l/+MjA8XPxaby9w//3sPJWQkJCQsD6yDyG8KYTwXAjhVAjh56+y3T8KIcQQwsmNG+I18Cu/cuVrb3sbo2+STp+QkJAAYB1kH0KoBPBeAG8GcALA20IIJ0ps1wTgpwCsUYVsE7CwwJBLj9e/Hvipn0plixMSEhIc1mPZ3wPgVIzxdIxxCcBHAby1xHb/AcB/BLCwgeO7OrLhlg0NwLvexVr1CQkJCQl/h/WQfQ8A39+vv/Da3yGEcDeAAzHGjJldjBDC/SGER0IIjwxndfaXgx/+4eLnP/3TwDd/8yvfb0JCQkKZ4RU7aEMIFQD+C4Cfuda2Mcb3xxhPxhhPdnV1vbIDj44yEkc4cQL42Z9NOn1CQkJCCayH7C8COOCe9xZeE5oA3ArgsyGEswBeA+CBTXfSvvnNxc8//GH2lk1ISEhIuALrIfuHARwLIRwOIdQA+B4AD+jNGONkjLEzxtgXY+wD8BCA74oxPrIpI/67UT1s///czwF33rmph0tISEjYybgm2ccYVwD8OIBPAngGwB/HGJ8KIfxSCOG7NnuAJfHRjxY//+Vf3pZhJCQkJOwUhFiqq9MW4OTJk/GRR16m8e91+RdfZPPwhISEhF2AEMJXY4wvWSbf2Rm0XV2J6BMSEhLWgZ1H9t6qHxravnEkJCQk7CDsPLIXXv3q7R5BQkJCwo7Bzqv9u00+hoSEhISdjJ1r2SckJCQkrBuJ7BMSEhJ2ARLZJyQkJOwCJLJPSEhI2AVIZJ+QkJCwC5DIPiEhIWEXIJF9QkJCwi5AIvuEhISEXYBtK4QWQhgGcO5lfrwTwMgGDud6QzmfXzq3nYtyPr+ddG6HYowvufvTtpH9K0EI4ZGXU/Vtp6Cczy+d285FOZ9fOZ+bkGSchISEhF2ARPYJCQkJuwA7lezfv90D2GSU8/mlc9u5KOfzK+dzA7BDNfuEhISEhJeGnWrZJyQkJCS8BCSyT0hISNgF2HFkH0J4UwjhuRDCqRDCz2/3eDYKIYQDIYTPhBCeDiE8FUL4qe0e00YjhFAZQvh6COFj2z2WjUYIoTWE8CchhGdDCM+EEF673WPaKIQQ/k3hN/lkCOEjIYS67R7TK0EI4QMhhKEQwpPutfYQwl+FEF4o/G3bzjFuBnYU2YcQKgG8F8CbAZwA8LYQwontHdWGYQXAz8QYTwB4DYAfK6NzE34KwDPbPYhNwrsBfCLGeBOAO1Am5xlC6AHwkwBOxhhvBVAJ4Hu2d1SvGB8E8KbMaz8P4NMxxmMAPl14XlbYUWQP4B4Ap2KMp2OMSwA+CuCt2zymDUGMcSDG+LXC/9MgWfRs76g2DiGEXgB/H8DvbvdYNhohhBYA3wjg9wAgxrgUY5zY1kFtLKoA1IcQqgDkAFza5vG8IsQYHwQwlnn5rQB+v/D/7wP47q0c01Zgp5F9D4AL7nk/yogQhRBCH4C7AHx5m4eykfhNAP8OQH6bx7EZOAxgGMB/L8hUvxtCaNjuQW0EYowXAfw6gPMABgBMxhg/tb2j2hTsjTEOFP6/DGDvdg5mM7DTyL7sEUJoBPCnAP51jHFqu8ezEQghvAXAUIzxq9s9lk1CFYC7Afx2jPEuALMoExmgoF2/FZzQ9gNoCCF87/aOanMRGY9edjHpO43sLwI44J73Fl4rC4QQqkGi/3CM8c+2ezwbiG8A8F0hhLOg9PbNIYQ/2N4hbSj6AfTHGLUS+xOQ/MsB3wLgTIxxOMa4DODPAPy9bR7TZmAwhLAPAAp/h7Z5PBuOnUb2DwM4FkI4HEKoAR1FD2zzmDYEIYQAar7PxBj/y3aPZyMRY/yFGGNvjLEP/M7+JsZYNtZhjPEygAshhBsLL70RwNPbOKSNxHkArwkh5Aq/0TeiTJzPGTwA4AcK//8AgL/YxrFsCqq2ewAvBTHGlRDCjwP4JBgV8IEY41PbPKyNwjcA+D4AT4QQHi289u9jjB/fviElvAT8BIAPF4yQ0wB+cJvHsyGIMX45hPAnAL4GRox9HTu8tEAI4SMA7gPQGULoB/AuAL8K4I9DCO8AS6//k+0b4eYglUtISEhI2AXYaTJOQkJCQsLLQCL7hISEhF2ARPYJCQkJuwCJ7BMSEhJ2ARLZJyQkJOwCJLJPSEhI2AVIZJ+QkJCwC/D/A8jpvlUPorFeAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data\n", + "current_year.iloc[:, :200].reset_index(drop=True).plot(\n", + " color=\"red\",\n", + " legend=False,\n", + " alpha=0.1,\n", + " title=\"Current Accident Year Expected Development Distribution\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recap\n", + "- The Mack method approaches stochastic reserving from a regression point of view
\n", + "- Bootstrap methods approach stochastic reserving from a simulation point of view
\n", + "- Where they assumptions of each model are not violated, they produce resonably consistent estimates of reserve variability
\n", + "- Mack does impose more assumptions (i.e. constraints) on the reserve estimate making the Bootstrap approach more suitable in a broader set of applciations
\n", + "- Both methods converge to their corresponding deterministic point estimates
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/getting_started/tutorials/tail-tutorial.ipynb b/docs/getting_started/tutorials/tail-tutorial.ipynb new file mode 100644 index 00000000..d51d39dc --- /dev/null +++ b/docs/getting_started/tutorials/tail-tutorial.ipynb @@ -0,0 +1,1369 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Extending Development Patterns with Tails\n", + "## Getting Started\n", + "This tutorial focuses on extending the developent patterns beyond the tail. \n", + "\n", + "Be sure to make sure your packages are updated. For more info on how to update your pakages, visit [Keeping Packages Updated](https://chainladder-python.readthedocs.io/en/latest/library/install.html#keeping-packages-updated)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pandas: 1.4.2\n", + "numpy: 1.22.4\n", + "chainladder: 0.8.12\n" + ] + } + ], + "source": [ + "# Black linter, optional\n", + "%load_ext lab_black\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import chainladder as cl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "print(\"pandas: \" + pd.__version__)\n", + "print(\"numpy: \" + np.__version__)\n", + "print(\"chainladder: \" + cl.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Disclaimer\n", + "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Basic Tail Fitting\n", + "\n", + "Tails are another class of transformers. Similar to the `Development` estimator, they come with `fit`, `transform` and `fit_transform` methods. Also, like our `Development` estimator, you can define a tail in the absence of data or if you believe development will continue beyond your latest evaluation period." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
36912151821242730...108111114117120123126129132135
19953.0024.0065.00141.00273.00418.00550.00692.00814.00876.00...1,099.001,099.001,100.001,098.001,098.001,098.001,099.001,099.001,100.001,100.00
19961.0016.0054.00135.00260.00398.00594.00758.00871.00964.00...1,296.001,296.001,297.001,298.001,298.001,298.00
19971.0017.0055.00166.00296.00442.00587.00701.00811.00891.00...1,197.001,198.00
19981.0011.0040.0093.00185.00343.00474.00643.00744.00831.00...
19991.0014.0047.00113.00225.00379.00570.00715.00832.00955.00...
20001.006.0028.00100.00194.00297.00415.00521.00616.00697.00...
20011.007.0037.00128.00271.00427.00579.00722.00838.00937.00...
20021.0010.0045.00110.00236.00442.00668.00890.001,078.001,198.00...
20031.009.0031.0094.00192.00299.00408.00792.00873.00949.00...
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20051.007.0036.0097.00183.00...
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" + ], + "text/plain": [ + " 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99 102 105 108 111 114 117 120 123 126 129 132 135\n", + "1995 3.0 24.0 65.0 141.0 273.0 418.0 550.0 692.0 814.0 876.0 916.0 959.0 968.0 1002.0 1020.0 1031.0 1041.0 1035.0 1045.0 1054.0 1060.0 1068.0 1075.0 1079.0 1081.0 1084.0 1086.0 1089.0 1091.0 1094.0 1095.0 1096.0 1097.0 1098.0 1098.0 1099.0 1099.0 1100.0 1098.0 1098.0 1098.0 1099.0 1099.0 1100.0 1100.0\n", + "1996 1.0 16.0 54.0 135.0 260.0 398.0 594.0 758.0 871.0 964.0 1017.0 1052.0 1089.0 1108.0 1141.0 1175.0 1194.0 1208.0 1217.0 1226.0 1231.0 1243.0 1249.0 1252.0 1253.0 1256.0 1258.0 1261.0 1266.0 1267.0 1268.0 1288.0 1288.0 1289.0 1291.0 1296.0 1296.0 1297.0 1298.0 1298.0 1298.0 NaN NaN NaN NaN\n", + "1997 1.0 17.0 55.0 166.0 296.0 442.0 587.0 701.0 811.0 891.0 940.0 976.0 1000.0 1022.0 1043.0 1059.0 1068.0 1080.0 1091.0 1099.0 1104.0 1130.0 1135.0 1138.0 1141.0 1147.0 1163.0 1184.0 1185.0 1186.0 1189.0 1192.0 1194.0 1195.0 1197.0 1197.0 1198.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1998 1.0 11.0 40.0 93.0 185.0 343.0 474.0 643.0 744.0 831.0 902.0 951.0 989.0 1022.0 1053.0 1074.0 1093.0 1132.0 1145.0 1162.0 1177.0 1192.0 1197.0 1213.0 1225.0 1237.0 1265.0 1274.0 1275.0 1278.0 1286.0 1288.0 1293.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1999 1.0 14.0 47.0 113.0 225.0 379.0 570.0 715.0 832.0 955.0 1048.0 1118.0 1183.0 1214.0 1293.0 1327.0 1363.0 1383.0 1396.0 1438.0 1457.0 1474.0 1504.0 1521.0 1532.0 1547.0 1559.0 1565.0 1573.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2000 1.0 6.0 28.0 100.0 194.0 297.0 415.0 521.0 616.0 697.0 758.0 810.0 859.0 892.0 916.0 935.0 950.0 967.0 982.0 1004.0 1013.0 1028.0 1036.0 1046.0 1054.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2001 1.0 7.0 37.0 128.0 271.0 427.0 579.0 722.0 838.0 937.0 1020.0 1091.0 1160.0 1207.0 1239.0 1305.0 1323.0 1347.0 1365.0 1375.0 1387.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2002 1.0 10.0 45.0 110.0 236.0 442.0 668.0 890.0 1078.0 1198.0 1325.0 1412.0 1524.0 1615.0 1653.0 1720.0 1760.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2003 1.0 9.0 31.0 94.0 192.0 299.0 408.0 792.0 873.0 949.0 1019.0 1064.0 1100.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2004 4.0 16.0 49.0 170.0 289.0 442.0 601.0 793.0 948.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2005 1.0 7.0 36.0 97.0 183.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2006 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quarterly = cl.load_sample(\"quarterly\")\n", + "quarterly[\"paid\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Upon fitting data, we get updated `ldf_` and `cdf_` attributes that extend beyond the length of the triangle. Notice how the tail includes extra development periods (age 147) beyond the end of the triangle (age 135) at which point an age-to-ultimate tail factor is applied." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Triangle latest 135\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
3-66-99-1212-1515-1818-2121-2424-2727-3030-33...120-123123-126126-129129-132132-135135-138138-141141-144144-147147-150
(All)8.56253.55472.76591.93321.60551.40111.32701.16581.10981.0780...1.00001.00091.00001.00091.00001.00011.00011.00011.00011.0003
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" + ], + "text/plain": [ + " 135-Ult\n", + "(All) 1.00065" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp = cl.TailCurve(curve=\"exponential\").fit(quarterly[\"paid\"])\n", + "exp.tail_" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 135-Ult\n", + "(All) 1.021283" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inv = cl.TailCurve(curve=\"inverse_power\").fit(quarterly[\"paid\"])\n", + "inv.tail_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When fitting a tail, by default, all of the data will be used; however, we can specify which period of development patterns we want to begin including in the curve fitting process with `fit_period`. \n", + "\n", + "Patterns will also be generated for 100 periods beyond the end of the triangle by default, or we can specify how far beyond the triangle to project the tail factor to before dropping the age-to-age factor down to 1.0 using `extrap_periods`.\n", + "\n", + "Note that even though we can extrapolate the curve many years beyond the end of the triangle for computational purposes, the resultant development factors will compress all `ldf_` beyond one year into a single age-ultimate factor." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
36912151821242730...108111114117120123126129132135
199544.0096.00194.00420.00621.00715.00748.00906.00950.00973.00...1,098.001,099.001,103.001,100.001,098.001,100.001,100.001,098.001,101.001,100.00
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199717.0043.00135.00380.00530.00714.00813.00945.00966.001,008.00...1,203.001,200.00
199810.0043.00107.00238.00393.00574.00732.00894.00935.00967.00...
199913.0041.00109.00306.00481.00657.00821.001,007.001,021.001,141.00...
20002.0029.0088.00254.00380.00501.00615.00735.00788.00842.00...
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20022.0034.00115.00290.00472.00809.001,054.001,543.001,617.001,505.00...
20033.0019.0090.00692.00597.00929.00883.001,117.001,092.001,176.00...
20044.0038.00138.00371.00583.00756.00902.001,111.001,212.00...
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Like its inspiration `sklearn`, we can create an overall estimator known as a `Pipeline` that combines multiple transformers and optional predictors in one estimator. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "sequence = [\n", + " (\"simple_dev\", cl.Development(average=\"simple\")),\n", + " (\"inverse_power_tail\", cl.TailCurve(curve=\"inverse_power\")),\n", + "]\n", + "\n", + "pipe = cl.Pipeline(steps=sequence).fit(quarterly)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Pipeline` keeps references to each step with its `named_steps` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Development(average='simple')\n", + "TailCurve(curve='inverse_power')\n" + ] + } + ], + "source": [ + "print(pipe.named_steps.simple_dev)\n", + "print(pipe.named_steps.inverse_power_tail)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Pipeline` estimator is almost an exact replica of the `sklearn Pipeline`, and the docs for `sklearn` are very comprehensive. To learn more about `Pipeline`, [reference their docs](https://scikit-learn.org/stable/modules/compose.html#pipeline).\n", + "\n", + "With a `Triangle` transformed to include development patterns and tails, we are now ready to start fitting our suite of IBNR models." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/getting_started/tutorials/triangle-tutorial.ipynb b/docs/getting_started/tutorials/triangle-tutorial.ipynb new file mode 100644 index 00000000..b4efe067 --- /dev/null +++ b/docs/getting_started/tutorials/triangle-tutorial.ipynb @@ -0,0 +1,13403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Working with Triangles\n", + "## Getting Started\n", + "Welcome! We drafted these tutorials to help you get familiar with some of the common functionalities that most actuaries can use in their day-to-day responsibilities. The package also comes with pre-installed datasets that you can play with, which are also used in the tutorials here.\n", + "\n", + "The tutorials assume that you have the basic understanding of commonly used actuarial terms, and can independently perform an actuarial analysis in another tool, such as Microsoft Excel or another actuarial software. Furthermore, it is assumed that you already have some familiarity with Python, and that you have the basic knowledge and experience in using some common packages that are popular in the Python community, such as `pandas` and `numpy`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial is linted using [black](https://github.com/psf/black) via [nb_black](https://github.com/dnanhkhoa/nb_black). This step is optional." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext lab_black" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All tutorials and exercises rely on chainladder v0.8.12 and later. It is highly recomended that you keep your packages up-to-date.\n", + "\n", + "For more info on how to update your pakages, visit [Keeping Packages Updated](https://chainladder-python.readthedocs.io/en/latest/library/install.html#keeping-packages-updated)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pandas: 1.4.2\n", + "numpy: 1.22.4\n", + "chainladder: 0.8.12\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import chainladder as cl\n", + "\n", + "print(\"pandas: \" + pd.__version__)\n", + "print(\"numpy: \" + np.__version__)\n", + "print(\"chainladder: \" + cl.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we will be plotting for quite a bit, here's a magic function in IPython, which sets the backend of matplotlib to the 'inline' backend. With this backend, the output of plotting commands is displayed inline within frontends like the Jupyter notebook, directly below the code cell that produced it. The resulting plots will then also be stored in the notebook document." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Disclaimer\n", + "Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Working with a Triangle\n", + "Let's begin by looking at an unprocessed triangle data and load it into a `pandas.DataFrame`. We'll use the data `raa`, which is available from the repository." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " development origin values\n", + "0 1981 1981 5012.0\n", + "1 1982 1982 106.0\n", + "2 1983 1983 3410.0\n", + "3 1984 1984 5655.0\n", + "4 1985 1985 1092.0\n", + "5 1986 1986 1513.0\n", + "6 1987 1987 557.0\n", + "7 1988 1988 1351.0\n", + "8 1989 1989 3133.0\n", + "9 1990 1990 2063.0\n", + "10 1982 1981 8269.0\n", + "11 1983 1982 4285.0\n", + "12 1984 1983 8992.0\n", + "13 1985 1984 11555.0\n", + "14 1986 1985 9565.0\n", + "15 1987 1986 6445.0\n", + "16 1988 1987 4020.0\n", + "17 1989 1988 6947.0\n", + "18 1990 1989 5395.0\n", + "19 1983 1981 10907.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa_df = pd.read_csv(\n", + " \"https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/raa.csv\"\n", + ")\n", + "raa_df.head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data has three columns: \n", + "* development: or valuation time, in this case, valuation year\n", + "* origin: or accident date, in this case, accident year\n", + "* values: the values recorded for the specific accident date at the specific valuation time (such as incurred losses, paid losses, or claim counts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A table of loss experience showing total losses for a certain period (origin) at various, regular valuation dates (development), reflect the change in amounts as claims mature and emerge. Older periods in the table will have one more entry than the next youngest period, leading to the triangle shape of the data in the table or any other measure that matures over time from an origin date. Loss triangles can be used to determine loss development for a given risk." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's put our data into the triangle format." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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19845,65511,55515,76621,26623,42526,08327,067
19851,0929,56515,83622,16925,95526,180
19861,5136,44511,70212,93515,852
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1981 5012.0 8269.0 10907.0 11805.0 13539.0 16181.0 18009.0 18608.0 18662.0 18834.0\n", + "1982 106.0 4285.0 5396.0 10666.0 13782.0 15599.0 15496.0 16169.0 16704.0 NaN\n", + "1983 3410.0 8992.0 13873.0 16141.0 18735.0 22214.0 22863.0 23466.0 NaN NaN\n", + "1984 5655.0 11555.0 15766.0 21266.0 23425.0 26083.0 27067.0 NaN NaN NaN\n", + "1985 1092.0 9565.0 15836.0 22169.0 25955.0 26180.0 NaN NaN NaN NaN\n", + "1986 1513.0 6445.0 11702.0 12935.0 15852.0 NaN NaN NaN NaN NaN\n", + "1987 557.0 4020.0 10946.0 12314.0 NaN NaN NaN NaN NaN NaN\n", + "1988 1351.0 6947.0 13112.0 NaN NaN NaN NaN NaN NaN NaN\n", + "1989 3133.0 5395.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa = cl.Triangle(\n", + " raa_df,\n", + " origin=\"origin\",\n", + " development=\"development\",\n", + " columns=\"values\",\n", + " cumulative=True,\n", + ")\n", + "raa" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also load the example data directly, using `load_sample`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1981 5012.0 8269.0 10907.0 11805.0 13539.0 16181.0 18009.0 18608.0 18662.0 18834.0\n", + "1982 106.0 4285.0 5396.0 10666.0 13782.0 15599.0 15496.0 16169.0 16704.0 NaN\n", + "1983 3410.0 8992.0 13873.0 16141.0 18735.0 22214.0 22863.0 23466.0 NaN NaN\n", + "1984 5655.0 11555.0 15766.0 21266.0 23425.0 26083.0 27067.0 NaN NaN NaN\n", + "1985 1092.0 9565.0 15836.0 22169.0 25955.0 26180.0 NaN NaN NaN NaN\n", + "1986 1513.0 6445.0 11702.0 12935.0 15852.0 NaN NaN NaN NaN NaN\n", + "1987 557.0 4020.0 10946.0 12314.0 NaN NaN NaN NaN NaN NaN\n", + "1988 1351.0 6947.0 13112.0 NaN NaN NaN NaN NaN NaN NaN\n", + "1989 3133.0 5395.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa = cl.load_sample(\"raa\")\n", + "raa" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A triangle has more properties than just what is displayed. For example we can see the underlying `link_ratio`s, which represent the multiplicative change in amounts from one development period to the next." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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19858.75921.65561.39991.17081.0087
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1990
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1990
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" + ], + "text/plain": [ + " 1990\n", + "1981 18.834\n", + "1982 16.704\n", + "1983 23.466\n", + "1984 27.067\n", + "1985 26.180\n", + "1986 15.852\n", + "1987 12.314\n", + "1988 13.112\n", + "1989 5.395\n", + "1990 2.063" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa.latest_diagonal / 1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The latest diagonal also corresponds to a `valuation_date`. Note that 'valuation_date' is a datetime that is at the terminal timestamp of the period (i.e. the last split second of the year)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('1990-12-31 23:59:59.999999999')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa.valuation_date" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also tell whether our triangle:\n", + "* `is_cumulative`: returns True if the data across the development periods is cumulative, or False if it is incremental.\n", + "* `is_ultimate`: returns True if the ultimate values are contained in the triangle.\n", + "* `is_val_tri`: returns True if the development period is stated as a valuation data as opposed to an age, i.e. Schedule P style triangle (True) or the more commonly used triangle by development age (False).\n", + "* `is_full`: returns True if the triangle has been \"squared\"." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Is triangle cumulative? True\n", + "Does triangle contain ultimate projections? False\n", + "Is this a valuation triangle? False\n", + "Has the triangle been \"squared\"? False\n" + ] + } + ], + "source": [ + "print(\"Is triangle cumulative?\", raa.is_cumulative)\n", + "print(\"Does triangle contain ultimate projections?\", raa.is_ultimate)\n", + "print(\"Is this a valuation triangle?\", raa.is_val_tri)\n", + "print('Has the triangle been \"squared\"?', raa.is_full)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also inspect the triangle to understand its data granularity with `origin_grain` and `development_grain`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Origin grain: Y\n", + "Development grain: Y\n" + ] + } + ], + "source": [ + "print(\"Origin grain:\", raa.origin_grain)\n", + "print(\"Development grain:\", raa.development_grain)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The package supports monthly (\"M\"), quarterly (\"Q\"), semester (or semi-annually), (\"S\") and yearly (\"Y\") grains for both `origin_grain` and `development_grain`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Triangle Structure\n", + "The triangle described so far is a two-dimensional (accident date by valuation date) structure that spans multiple cells of data. This is a useful structure for exploring individual triangles, but becomes more problematic when working with **sets** of triangles. `Pandas` does not have a triangle `dtype`, but if it did, working with sets of triangles would be much more convenient. To facilitate working with more than one triangle at a time the `chainladder.Triangle` acts like a pandas dataframe (with an index and columns) where each cell (row x col) is an individual triangle. This structure manifests itself as a four-dimensional space. Let's take a look at another dataset `clrd`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GRCODEGRNAMEAccidentYearDevelopmentYearDevelopmentLagIncurLossCumPaidLossBulkLossEarnedPremDIREarnedPremCededEarnedPremNetSinglePostedReserve97LOB
086Allstate Ins Co Grp1988198813674047057112773740069959573947420281872wkcomp
186Allstate Ins Co Grp1988198923629881559056017340069959573947420281872wkcomp
286Allstate Ins Co Grp1988199033472882207442776340069959573947420281872wkcomp
386Allstate Ins Co Grp1988199143306482515951528040069959573947420281872wkcomp
486Allstate Ins Co Grp1988199253546902741562768940069959573947420281872wkcomp
.............................................
4284044598College Liability Ins Co Ltd RRG1995199623432498239703971630othliab
4284144598College Liability Ins Co Ltd RRG19951997383957519039703971630othliab
4284244598College Liability Ins Co Ltd RRG19961996112569825702571630othliab
4284344598College Liability Ins Co Ltd RRG19961997295172825702571630othliab
4284444598College Liability Ins Co Ltd RRG1997199718347725602561630othliab
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42845 rows × 14 columns

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" + ], + "text/plain": [ + " GRCODE GRNAME AccidentYear \\\n", + "0 86 Allstate Ins Co Grp 1988 \n", + "1 86 Allstate Ins Co Grp 1988 \n", + "2 86 Allstate Ins Co Grp 1988 \n", + "3 86 Allstate Ins Co Grp 1988 \n", + "4 86 Allstate Ins Co Grp 1988 \n", + "... ... ... ... \n", + "42840 44598 College Liability Ins Co Ltd RRG 1995 \n", + "42841 44598 College Liability Ins Co Ltd RRG 1995 \n", + "42842 44598 College Liability Ins Co Ltd RRG 1996 \n", + "42843 44598 College Liability Ins Co Ltd RRG 1996 \n", + "42844 44598 College Liability Ins Co Ltd RRG 1997 \n", + "\n", + " DevelopmentYear DevelopmentLag IncurLoss CumPaidLoss BulkLoss \\\n", + "0 1988 1 367404 70571 127737 \n", + "1 1989 2 362988 155905 60173 \n", + "2 1990 3 347288 220744 27763 \n", + "3 1991 4 330648 251595 15280 \n", + "4 1992 5 354690 274156 27689 \n", + "... ... ... ... ... ... \n", + "42840 1996 2 343 249 82 \n", + "42841 1997 3 839 575 190 \n", + "42842 1996 1 125 6 98 \n", + "42843 1997 2 95 17 28 \n", + "42844 1997 1 83 4 77 \n", + "\n", + " EarnedPremDIR EarnedPremCeded EarnedPremNet Single PostedReserve97 \\\n", + "0 400699 5957 394742 0 281872 \n", + "1 400699 5957 394742 0 281872 \n", + "2 400699 5957 394742 0 281872 \n", + "3 400699 5957 394742 0 281872 \n", + "4 400699 5957 394742 0 281872 \n", + "... ... ... ... ... ... \n", + "42840 397 0 397 1 630 \n", + "42841 397 0 397 1 630 \n", + "42842 257 0 257 1 630 \n", + "42843 257 0 257 1 630 \n", + "42844 256 0 256 1 630 \n", + "\n", + " LOB \n", + "0 wkcomp \n", + "1 wkcomp \n", + "2 wkcomp \n", + "3 wkcomp \n", + "4 wkcomp \n", + "... ... \n", + "42840 othliab \n", + "42841 othliab \n", + "42842 othliab \n", + "42843 othliab \n", + "42844 othliab \n", + "\n", + "[42845 rows x 14 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd_df = pd.read_csv(\n", + " \"https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/clrd.csv\"\n", + ")\n", + "clrd_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load the data into the sets of triangles." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (775, 6, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.Triangle(\n", + " clrd_df,\n", + " origin=\"AccidentYear\",\n", + " development=\"DevelopmentYear\",\n", + " columns=[\n", + " \"IncurLoss\",\n", + " \"CumPaidLoss\",\n", + " \"BulkLoss\",\n", + " \"EarnedPremDIR\",\n", + " \"EarnedPremCeded\",\n", + " \"EarnedPremNet\",\n", + " ],\n", + " index=[\"GRNAME\", \"LOB\"],\n", + " cumulative=True,\n", + ")\n", + "clrd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since 4D strucures do not fit nicely on 2D screens, we see a summary view instead that describes the structure rather than the underlying data itself. \n", + "\n", + "We see 5 rows of information:\n", + "* Valuation: the valuation date.\n", + "* Grain: the granularity of the data, O stands for origin, and D stands for development, `OYDY` represents triangles with accident year by development year.\n", + "* Shape: contains 4 numbers, represents the 4-D structure. This sample triangle represents a collection of 775x6 or 4,650 triangles that are themselves 10 accident years by 10 development periods.\n", + " * 775: the number of segments, which is the combination of `index`, that represents the data segments. In this case, it is each of the `GRNAME` and `LOB` unique combination.\n", + " * 6: the number of triangles for each segment, which is also the columns `[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]`. They could be paid amounts, incurred amounts, reported counts, loss ratios, closure rates, excess losses, premium, etc.\n", + " * 10: the number of accident periods.\n", + " * 10: the number of valuation periods.\n", + "* Index: the segmentation level of the triangles.\n", + "* Columns: the value types recorded in the triangles.\n", + " \n", + "To summarize the set of triangles:\n", + "* We have a total of 775 segments, which are at the `GRNAME` and `LOB` level.\n", + "* Each segment contains 6 triangles, which are `IncurLoss`, `CumPaidLoss`, `BulkLoss`, `EarnedPremDIR`, `EarnedPremCeded`, `EarnedPremNet`.\n", + "* Each triangle is 10 accident years x 10 development periods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `index.head()` allows us to see the first 5 segments in the set of triangles. Note that as the data is loaded, the triangle are sorted by `index`, in this case, `GRNAME` first, then by `LOB`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " GRNAME LOB\n", + "0 Adriatic Ins Co othliab\n", + "1 Adriatic Ins Co ppauto\n", + "2 Aegis Grp comauto\n", + "3 Aegis Grp othliab\n", + "4 Aegis Grp ppauto" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd.index.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Under the hood, the data structure is a `numpy.ndarray` with the equivalent shape. Like pandas, you can directly access the underlying numpy structure with the `values` property. By exposing the underlying `ndarray` you are free to manipulate the underlying data directly with numpy should that be an easier route to solving a problem." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data structure of clrd: \n", + "Sum of all data values: 3661713596.0\n" + ] + } + ], + "source": [ + "print(\"Data structure of clrd:\", type(clrd.values))\n", + "print(\"Sum of all data values:\", np.nansum(clrd.values))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Keep in mind though, the `chainladder.Triangle` has several methods and properties beyond the raw numpy representation and these are kept in sync by using the `chainladder.Triangle` directly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas-Style Slicing\n", + "As mentioned, the 4D structure is intended to behave like a pandas `DataFrame`. Like pandas, we can subset a dataframe by referencing individual columns by name." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 3, 10, 10)
Index:[GRNAME, LOB]
Columns:[CumPaidLoss, IncurLoss, BulkLoss]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (775, 3, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [CumPaidLoss, IncurLoss, BulkLoss]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[[\"CumPaidLoss\", \"IncurLoss\", \"BulkLoss\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also boolean-index the rows of the Triangle." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(132, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (132, 6, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[clrd[\"LOB\"] == \"wkcomp\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can even use the typical `loc`, `iloc` functionality similar to `pandas` to access subsets of data. These features can be chained together as much as you want." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
198870,571155,905220,744251,595274,156287,676298,499304,873321,808325,322
198966,547136,447179,142211,343231,430244,750254,557270,059273,873
199052,233133,370178,444204,442222,193232,940253,337256,788
199159,315128,051169,793196,685213,165234,676239,195
199239,99189,873114,117133,003154,362159,496
199319,74447,22961,90985,09987,215
199420,37946,77388,63691,077
199518,75684,71287,311
199642,60944,916
1997691
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1988 70571.0 155905.0 220744.0 251595.0 274156.0 287676.0 298499.0 304873.0 321808.0 325322.0\n", + "1989 66547.0 136447.0 179142.0 211343.0 231430.0 244750.0 254557.0 270059.0 273873.0 NaN\n", + "1990 52233.0 133370.0 178444.0 204442.0 222193.0 232940.0 253337.0 256788.0 NaN NaN\n", + "1991 59315.0 128051.0 169793.0 196685.0 213165.0 234676.0 239195.0 NaN NaN NaN\n", + "1992 39991.0 89873.0 114117.0 133003.0 154362.0 159496.0 NaN NaN NaN NaN\n", + "1993 19744.0 47229.0 61909.0 85099.0 87215.0 NaN NaN NaN NaN NaN\n", + "1994 20379.0 46773.0 88636.0 91077.0 NaN NaN NaN NaN NaN NaN\n", + "1995 18756.0 84712.0 87311.0 NaN NaN NaN NaN NaN NaN NaN\n", + "1996 42609.0 44916.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1997 691.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd.loc[\"Allstate Ins Co Grp\"].iloc[-1][\"CumPaidLoss\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas-Style Arithmetic\n", + "With complete flexibility in the ability to slice subsets of triangles, we can use basic arithmetic to derive new triangles, which is commonly used as diagnostics to explore trends." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 2, 10, 10)
Index:[GRNAME, LOB]
Columns:[CaseIncurLoss, PaidToInc]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (775, 2, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [CaseIncurLoss, PaidToInc]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[\"CaseIncurLoss\"] = clrd[\"IncurLoss\"] - clrd[\"BulkLoss\"]\n", + "clrd[\"PaidToInc\"] = clrd[\"CumPaidLoss\"] / clrd[\"CaseIncurLoss\"]\n", + "clrd[[\"CaseIncurLoss\", \"PaidToInc\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also aggregating the values across all triangles into one triangle." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
19883,577,7807,059,9668,826,1519,862,68710,474,69810,814,57610,994,01411,091,36311,171,59011,203,949
19894,090,6807,964,7029,937,52011,098,58811,766,48812,118,79012,311,62912,434,82612,492,899
19904,578,4428,808,48610,985,34712,229,00112,878,54513,238,66713,452,99313,559,557
19914,648,7568,961,75511,154,24412,409,59213,092,03713,447,48113,642,414
19925,139,1429,757,69912,027,98313,289,48513,992,82114,347,271
19935,653,37910,599,42312,953,81214,292,51615,005,138
19946,246,44711,394,96013,845,76415,249,326
19956,473,84311,612,15114,010,098
19966,591,59911,473,912
19976,451,896
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1988 3577780.0 7059966.0 8826151.0 9862687.0 10474698.0 10814576.0 10994014.0 11091363.0 11171590.0 11203949.0\n", + "1989 4090680.0 7964702.0 9937520.0 11098588.0 11766488.0 12118790.0 12311629.0 12434826.0 12492899.0 NaN\n", + "1990 4578442.0 8808486.0 10985347.0 12229001.0 12878545.0 13238667.0 13452993.0 13559557.0 NaN NaN\n", + "1991 4648756.0 8961755.0 11154244.0 12409592.0 13092037.0 13447481.0 13642414.0 NaN NaN NaN\n", + "1992 5139142.0 9757699.0 12027983.0 13289485.0 13992821.0 14347271.0 NaN NaN NaN NaN\n", + "1993 5653379.0 10599423.0 12953812.0 14292516.0 15005138.0 NaN NaN NaN NaN NaN\n", + "1994 6246447.0 11394960.0 13845764.0 15249326.0 NaN NaN NaN NaN NaN NaN\n", + "1995 6473843.0 11612151.0 14010098.0 NaN NaN NaN NaN NaN NaN NaN\n", + "1996 6591599.0 11473912.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1997 6451896.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[\"CumPaidLoss\"].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also construct a paid loss ratio triangle against `EarnedPremNet`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1988 0.258115 0.509334 0.636753 0.711533 0.755686 0.780206 0.793152 0.800175 0.805963 0.808297\n", + "1989 0.268634 0.523039 0.652594 0.728841 0.772701 0.795837 0.808501 0.816591 0.820405 NaN\n", + "1990 0.272814 0.524868 0.654580 0.728685 0.767389 0.788847 0.801618 0.807968 NaN NaN\n", + "1991 0.253951 0.489561 0.609332 0.677909 0.715189 0.734607 0.745255 NaN NaN NaN\n", + "1992 0.259518 0.492747 0.607392 0.671096 0.706613 0.724512 NaN NaN NaN NaN\n", + "1993 0.264522 0.495948 0.606111 0.668749 0.702092 NaN NaN NaN NaN NaN\n", + "1994 0.270903 0.494190 0.600480 0.661351 NaN NaN NaN NaN NaN NaN\n", + "1995 0.265095 0.475502 0.573694 NaN NaN NaN NaN NaN NaN NaN\n", + "1996 0.263451 0.458585 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1997 0.255201 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[\"CumPaidLoss\"].sum() / clrd[\"EarnedPremNet\"].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aggregating all segments together is interesting, but it is often more useful to aggregate across segments using `groupby`. For example, we may want to group the triangles by line of business and get a sum across all companies for each industry." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(6, 8, 10, 10)
Index:[LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncurLoss, PaidToInc]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (6, 8, 10, 10)\n", + "Index: [LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd.groupby(\"LOB\").sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The shape is (**6**, 8, 10, 10) because now we have 6 LOBs with 8 triangles for each LOB. We can also note that `index` is now on `[LOB]` only." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['comauto', 'medmal', 'othliab', 'ppauto', 'prodliab', 'wkcomp'],\n", + " dtype=object)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(clrd[\"LOB\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The aggregation functions, e.g. `sum`, `mean`, `std`, `min`, `max`, etc. don't have to just apply to the `index` axis. You can apply them to any of the four axes in the triangle object, which are `segments` (axis `0`, or the index axis), `columns` (axis `1`, the various financial fields), `origin` (axis `2`, across triangle rows), and `development` period (axis `3`, across triangle columns). You can also use either the axis name or number. Let's try to sum all of the segments (`[GRNAME, LOB]`) and columns (`[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]`) using axis name and number, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(1, 8, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncurLoss, PaidToInc]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (1, 8, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd.sum(axis=\"index\").sum(axis=\"segments\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
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198962,840,44866,705,57068,666,92869,702,28370,188,01370,355,67970,389,85770,453,15870,483,269
199070,064,96774,084,38875,879,27676,812,99077,179,57277,240,32177,283,93777,345,588
199174,611,61178,513,12180,235,90880,967,06181,178,87681,185,48481,278,636
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199387,896,22791,685,45693,018,48593,167,81093,473,079
199494,593,70298,130,71899,071,78199,809,122
199597,722,814101,192,332102,056,680
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199796,832,221
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1988 5.638773e+07 5.992852e+07 6.165346e+07 6.264463e+07 6.318271e+07 6.343808e+07 6.344090e+07 6.345211e+07 6.352298e+07 6.351818e+07\n", + "1989 6.284045e+07 6.670557e+07 6.866693e+07 6.970228e+07 7.018801e+07 7.035568e+07 7.038986e+07 7.045316e+07 7.048327e+07 NaN\n", + "1990 7.006497e+07 7.408439e+07 7.587928e+07 7.681299e+07 7.717957e+07 7.724032e+07 7.728394e+07 7.734559e+07 NaN NaN\n", + "1991 7.461161e+07 7.851312e+07 8.023591e+07 8.096706e+07 8.117888e+07 8.118548e+07 8.127864e+07 NaN NaN NaN\n", + "1992 8.121379e+07 8.508937e+07 8.644388e+07 8.678311e+07 8.690861e+07 8.708664e+07 NaN NaN NaN NaN\n", + "1993 8.789623e+07 9.168546e+07 9.301849e+07 9.316781e+07 9.347308e+07 NaN NaN NaN NaN NaN\n", + "1994 9.459370e+07 9.813072e+07 9.907178e+07 9.980912e+07 NaN NaN NaN NaN NaN NaN\n", + "1995 9.772281e+07 1.011923e+08 1.020567e+08 NaN NaN NaN NaN NaN NaN NaN\n", + "1996 9.849793e+07 1.009177e+08 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1997 9.683222e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd.sum(axis=0).sum(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Accessor Methods\n", + "\n", + "`Pandas` has special \"accessor\" methods for `str` and `dt`. These allow for the manipulation of data within each cell of data:\n", + "\n", + "```python\n", + "# splits lastname from first name by a comma-delimiter\n", + "df['Last_First'].str.split(',')\n", + "\n", + "# pulls the year out of each date in a dataframe column\n", + "df['Accident Date'].dt.year \n", + "```\n", + "\n", + "`chainladder` also has special \"accessor\" methods to help us manipulate the `origin`, `development` and `valuation` vectors of a triangle." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We may want to extract only the latest accident period for every triangle." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 8, 1, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncurLoss, PaidToInc]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (775, 8, 1, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[clrd.origin == clrd.origin.max()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that this triangle has only 1 row; however, all of the columns would exist, but only the youngest age would have values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We may want to extract particular diagonals from our triangles using its `valuation` vector." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12243648607284
198810,994,014
198912,118,790
199012,878,545
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199310,599,423
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84\n", + "1988 NaN NaN NaN NaN NaN NaN 10994014.0\n", + "1989 NaN NaN NaN NaN NaN 12118790.0 NaN\n", + "1990 NaN NaN NaN NaN 12878545.0 NaN NaN\n", + "1991 NaN NaN NaN 12409592.0 NaN NaN NaN\n", + "1992 NaN NaN 12027983.0 NaN NaN NaN NaN\n", + "1993 NaN 10599423.0 NaN NaN NaN NaN NaN\n", + "1994 6246447.0 NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[(clrd.valuation >= \"1994\") & (clrd.valuation < \"1995\")][\"CumPaidLoss\"].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We may even want to slice particular development periods to explore aspects of our data by development age. For example, we can look at the development factors between ages 24 and 36." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
24-36
19881.2502
19891.2477
19901.2471
19911.2446
19921.2327
19931.2221
19941.2151
19951.2065
1996
1997
" + ], + "text/plain": [ + " 24-36\n", + "1988 1.250169\n", + "1989 1.247695\n", + "1990 1.247132\n", + "1991 1.244650\n", + "1992 1.232666\n", + "1993 1.222124\n", + "1994 1.215078\n", + "1995 1.206503\n", + "1996 NaN\n", + "1997 NaN" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[(clrd.development > 12) & (clrd.development <= 36)][\"CumPaidLoss\"].sum().link_ratio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Moving Back to Pandas\n", + "When the shape of a `Triangle` object can be expressed as a 2D structure (i.e. two of its four axes have a length of 1) or less, you can use the `to_frame` method to convert your data into a `pandas.DataFrame`. Let's pick only one financial field, `CumPaidLoss` and only the latest diagonal, with `latest_diagonal`. We are now left with LOBs and origin period as our 2 axes." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(6, 1, 10, 1)
Index:[LOB]
Columns:[CumPaidLoss]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (6, 1, 10, 1)\n", + "Index: [LOB]\n", + "Columns: [CumPaidLoss]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd.groupby(\"LOB\").sum().latest_diagonal[\"CumPaidLoss\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "origin 1988 1989 1990 1991 1992 1993 1994 \\\n", + "LOB \n", + "comauto 626097 674441 718396 711762 731033 762039 768095 \n", + "medmal 217239 222707 235717 275923 267007 276235 252449 \n", + "othliab 317889 350684 361103 426085 389250 434995 402244 \n", + "ppauto 8690036 9823747 10728411 10713621 11555121 12249826 12600432 \n", + "prodliab 110973 112614 121255 100276 76059 94462 111264 \n", + "wkcomp 1241715 1308706 1394675 1414747 1328801 1187581 1114842 \n", + "\n", + "origin 1995 1996 1997 \n", + "LOB \n", + "comauto 675166 510191 272342 \n", + "medmal 209222 107474 20361 \n", + "othliab 294332 191258 54130 \n", + "ppauto 11807279 9900842 5754249 \n", + "prodliab 62018 28107 10682 \n", + "wkcomp 962081 736040 340132 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd.groupby(\"LOB\").sum().latest_diagonal[\"CumPaidLoss\"].to_frame(\n", + " origin_as_datetime=True\n", + ").astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we added `origin_as_datetime=True` inside of `to_frame(...)` as the package is under-going a deprecation cycle. You can also execute the same code without the `origin_as_datetime=...`, but you will get a warning." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/pandas.py:57: UserWarning: In an upcoming version of the package, `origin_as_datetime` will be defaulted to `True` in to_frame(...), use `origin_as_datetime=False` to preserve current setting.\n", + " warnings.warn(warning)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "origin 1988 1989 1990 1991 1992 1993 1994 \\\n", + "LOB \n", + "comauto 626097 674441 718396 711762 731033 762039 768095 \n", + "medmal 217239 222707 235717 275923 267007 276235 252449 \n", + "othliab 317889 350684 361103 426085 389250 434995 402244 \n", + "ppauto 8690036 9823747 10728411 10713621 11555121 12249826 12600432 \n", + "prodliab 110973 112614 121255 100276 76059 94462 111264 \n", + "wkcomp 1241715 1308706 1394675 1414747 1328801 1187581 1114842 \n", + "\n", + "origin 1995 1996 1997 \n", + "LOB \n", + "comauto 675166 510191 272342 \n", + "medmal 209222 107474 20361 \n", + "othliab 294332 191258 54130 \n", + "ppauto 11807279 9900842 5754249 \n", + "prodliab 62018 28107 10682 \n", + "wkcomp 962081 736040 340132 " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd.groupby(\"LOB\").sum().latest_diagonal[\"CumPaidLoss\"].to_frame().astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also aggregate process away 3 dimensions, then use `to_frame`." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LOB\n", + "comauto 718396\n", + "medmal 235717\n", + "othliab 361103\n", + "ppauto 10728411\n", + "prodliab 121255\n", + "wkcomp 1394675\n", + "dtype: int64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[clrd.origin == \"1990\"].groupby(\"LOB\").sum().latest_diagonal[\n", + " \"CumPaidLoss\"\n", + "].to_frame(origin_as_datetime=True).astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "Use the `clrd` dataset for all of the exercises." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. How do we create a new column named \"NetPaidLossRatio\" in the triangle using the existing columns?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "clrd[\"NetPaidLossRatio\"] = clrd[\"CumPaidLoss\"] / clrd[\"EarnedPremNet\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. What is the highest paid loss ratio for across all segments for origin 1997 at age 12?" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.769123134328358" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[clrd.origin == \"1997\"][clrd.development == 12][\"NetPaidLossRatio\"].max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. How do we subset the overall triangle to just include \"Alaska Nat Ins Co\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(4, 9, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncurLoss, PaidToInc, NetPaidLossRatio]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (4, 9, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[clrd[\"GRNAME\"] == \"Alaska Nat Ins Co\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. How do we create a triangle subset that includes all triangles for companies with names starting with the letter \"B\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(31, 9, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncurLoss, PaidToInc, NetPaidLossRatio]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (31, 9, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd[clrd[\"GRNAME\"].str[0] == \"B\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Which are the top 5 companies by net premium share for in 1990?" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GRNAME\n", + "State Farm Mut Grp 10532675.0\n", + "United Services Automobile Asn Grp 1378791.0\n", + "Federal Ins Co Grp 477150.0\n", + "New Jersey Manufacturers Grp 383870.0\n", + "FL Farm Bureau Grp 342036.0\n", + "dtype: float64" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd.latest_diagonal.groupby(\"GRNAME\").sum()[clrd.origin == \"1990\"][\n", + " \"EarnedPremNet\"\n", + "].to_frame(origin_as_datetime=True).sort_values(ascending=False).iloc[0:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initializing a Triangle With Your Own Data\n", + "The `chainladder.Triangle` class is designed to ingest `pandas.DataFrame` objects. However, you do not need to worry about shaping the dataframe into triangle format yourself. This happens at the time you ingest the data.\n", + "\n", + "Let's look at the initialization signature and its docstring." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mInit signature:\u001b[0m\n", + "\u001b[0mcl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTriangle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdevelopment\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0morigin_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdevelopment_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcumulative\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marray_backend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpattern\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtrailing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m \n", + "The core data structure of the chainladder package\n", + "\n", + "Parameters\n", + "----------\n", + "data: DataFrame\n", + " A single dataframe that contains columns represeting all other\n", + " arguments to the Triangle constructor\n", + "origin: str or list\n", + " A representation of the accident, reporting or more generally the\n", + " origin period of the triangle that will map to the Origin dimension\n", + "development: str or list\n", + " A representation of the development/valuation periods of the triangle\n", + " that will map to the Development dimension\n", + "columns: str or list\n", + " A representation of the numeric data of the triangle that will map to\n", + " the columns dimension. If None, then a single 'Total' key will be\n", + " generated.\n", + "index: str or list or None\n", + " A representation of the index of the triangle that will map to the\n", + " index dimension. If None, then a single 'Total' key will be generated.\n", + "origin_format: optional str\n", + " A string representation of the date format of the origin arg. If\n", + " omitted then date format will be inferred by pandas.\n", + "development_format: optional str\n", + " A string representation of the date format of the development arg. If\n", + " omitted then date format will be inferred by pandas.\n", + "cumulative: bool\n", + " Whether the triangle is cumulative or incremental. This attribute is\n", + " required to use the ``grain`` and ``dev_to_val`` methods and will be\n", + " automatically set when invoking ``cum_to_incr`` or ``incr_to_cum`` methods.\n", + "trailing: bool\n", + " When partial origin periods are present, setting trailing to True will\n", + " ensure the most recent origin period is a full period and the oldest\n", + " origin is partial. If full origin periods are present in the data, then\n", + " trailing has no effect.\n", + "\n", + "Attributes\n", + "----------\n", + "index: Series\n", + " Represents all available levels of the index dimension.\n", + "columns: Series\n", + " Represents all available levels of the value dimension.\n", + "origin: DatetimeIndex\n", + " Represents all available levels of the origin dimension.\n", + "development: Series\n", + " Represents all available levels of the development dimension.\n", + "key_labels: list\n", + " Represents the ``index`` axis labels\n", + "virtual_columns: Series\n", + " Represents the subset of columns of the triangle that are virtual.\n", + "valuation: DatetimeIndex\n", + " Represents all valuation dates of each cell in the Triangle.\n", + "origin_grain: str\n", + " The grain of the origin vector ('Y', 'S', 'Q', 'M')\n", + "development_grain: str\n", + " The grain of the development vector ('Y', 'S', 'Q', 'M')\n", + "shape: tuple\n", + " The 4D shape of the triangle instance with axes corresponding to (index, columns, origin, development)\n", + "link_ratio, age_to_age\n", + " Displays age-to-age ratios for the triangle.\n", + "valuation_date : date\n", + " The latest valuation date of the data\n", + "loc: Triangle\n", + " pandas-style ``loc`` accessor\n", + "iloc: Triangle\n", + " pandas-style ``iloc`` accessor\n", + "latest_diagonal: Triangle\n", + " The latest diagonal of the triangle\n", + "is_cumulative: bool\n", + " Whether the triangle is cumulative or not\n", + "is_ultimate: bool\n", + " Whether the triangle has an ultimate valuation\n", + "is_full: bool\n", + " Whether lower half of Triangle has been filled in\n", + "is_val_tri:\n", + " Whether the triangle development period is expressed as valuation\n", + " periods.\n", + "values: array\n", + " 4D numpy array underlying the Triangle instance\n", + "T: Triangle\n", + " Transpose index and columns of object. Only available when Triangle is\n", + " convertible to DataFrame.\n", + "\u001b[0;31mFile:\u001b[0m /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chainladder/core/triangle.py\n", + "\u001b[0;31mType:\u001b[0m type\n", + "\u001b[0;31mSubclasses:\u001b[0m \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cl.Triangle?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's use a new dataset `prism` to construct our triangles." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ClaimNoAccidentDateReportDateLineTypeClaimLiabilityLimitDeductibleTotalPaymentPaymentDateCloseDateStatusreportedCountclosedPaidCountclosedUnPaidCountopenCountPaidOutstandingIncurred
012008-01-224/19/2010HomeDwellingFalse300000.0200000.000002010-10-0810/8/2010CLOSED10100.0000000.00000
122008-01-024/20/2010HomeDwellingFalse200000.0200000.000002010-11-3011/30/2010CLOSED10100.0000000.00000
232008-01-019/23/2009HomeDwellingTrue200000.020000115744.773702010-02-172/17/2010CLOSED1100115744.773700115744.77370
342008-01-027/25/2009HomeDwellingTrue200000.02000063678.877132009-11-1811/18/2009CLOSED110063678.87713063678.87713
452008-01-1612/7/2009HomeDwellingTrue200000.020000112175.555902010-04-304/30/2010CLOSED1100112175.555900112175.55590
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" + ], + "text/plain": [ + " ClaimNo AccidentDate ReportDate Line Type ClaimLiability Limit \\\n", + "0 1 2008-01-22 4/19/2010 Home Dwelling False 300000.0 \n", + "1 2 2008-01-02 4/20/2010 Home Dwelling False 200000.0 \n", + "2 3 2008-01-01 9/23/2009 Home Dwelling True 200000.0 \n", + "3 4 2008-01-02 7/25/2009 Home Dwelling True 200000.0 \n", + "4 5 2008-01-16 12/7/2009 Home Dwelling True 200000.0 \n", + "\n", + " Deductible TotalPayment PaymentDate CloseDate Status reportedCount \\\n", + "0 20000 0.00000 2010-10-08 10/8/2010 CLOSED 1 \n", + "1 20000 0.00000 2010-11-30 11/30/2010 CLOSED 1 \n", + "2 20000 115744.77370 2010-02-17 2/17/2010 CLOSED 1 \n", + "3 20000 63678.87713 2009-11-18 11/18/2009 CLOSED 1 \n", + "4 20000 112175.55590 2010-04-30 4/30/2010 CLOSED 1 \n", + "\n", + " closedPaidCount closedUnPaidCount openCount Paid Outstanding \\\n", + "0 0 1 0 0.00000 0 \n", + "1 0 1 0 0.00000 0 \n", + "2 1 0 0 115744.77370 0 \n", + "3 1 0 0 63678.87713 0 \n", + "4 1 0 0 112175.55590 0 \n", + "\n", + " Incurred \n", + "0 0.00000 \n", + "1 0.00000 \n", + "2 115744.77370 \n", + "3 63678.87713 \n", + "4 112175.55590 " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_df = pd.read_csv(\n", + " \"https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/prism.csv\"\n", + ")\n", + "prism_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ClaimNo int64\n", + "AccidentDate object\n", + "ReportDate object\n", + "Line object\n", + "Type object\n", + "ClaimLiability bool\n", + "Limit float64\n", + "Deductible int64\n", + "TotalPayment float64\n", + "PaymentDate object\n", + "CloseDate object\n", + "Status object\n", + "reportedCount int64\n", + "closedPaidCount int64\n", + "closedUnPaidCount int64\n", + "openCount int64\n", + "Paid float64\n", + "Outstanding int64\n", + "Incurred float64\n", + "dtype: object" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_df.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We must specify the `origin`, `development`, `columns`, and `cumulative` to create a triangle object. By limiting our columns to one measure and not specifying an index, we can create a single triangle. For example, if we are only interested in the paid triangle. Because the data we have is transactional data, the `cumulative` variable should be set to `False`." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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7000.000000 NaN NaN NaN 6664.802824 NaN NaN 4945.434290 11116.651450 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-07 6782.902842 48280.849566 91311.499293 74150.344557 82262.819010 98736.828029 63087.483602 68951.825023 35752.940307 21273.949454 132177.344525 26925.746999 61660.798146 57839.394301 68521.201926 101282.440507 92303.924364 44123.390018 28977.041621 18380.124511 30097.010268 16955.457255 52771.796159 118823.692910 508910.159177 29638.996986 6.148862e+05 1.300326e+06 9.194265e+05 1.301019e+06 9.253742e+05 8.694741e+05 7.884189e+05 1.948190e+06 1.393049e+06 8.008854e+05 7.539347e+05 180000.000000 544.672072 NaN NaN NaN 5663.891540 NaN NaN 19224.174763 NaN NaN 3120.678535 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 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58139.878721 68275.186724 139825.518088 33666.375113 112055.805498 79055.750169 129779.243843 60560.767403 83997.475622 69988.899309 81477.811597 56860.360258 40814.910358 42746.511552 34332.058531 25540.598901 22509.926512 174331.804027 22154.613754 38897.008462 258494.156101 27560.994844 112641.289293 3.945288e+05 4.307503e+05 2.121664e+05 3.919781e+05 6.631136e+05 1.339811e+06 1.215649e+06 1.288634e+06 1.635438e+06 4.015812e+05 1.913656e+05 595728.700768 NaN NaN NaN 22123.015399 NaN NaN 32373.551704 NaN NaN 13294.864551 18853.725949 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-12 16508.968579 55117.462232 21956.205867 87867.308461 68005.873030 70145.946226 55663.787074 84040.311812 79976.440325 42145.200734 56707.222234 36945.403614 116400.145708 44294.325625 8337.139896 49437.910850 78667.587248 18079.165367 19803.871191 39941.860865 238984.087444 57841.919696 204109.009972 386664.454000 117220.879514 43709.801633 5.248744e+05 2.203477e+05 6.789611e+05 6.491085e+05 7.098767e+05 1.519165e+06 1.763656e+06 8.954874e+05 1.280474e+06 2.972427e+05 4.316323e+05 2173.166532 9744.078912 27655.254283 5155.768750 NaN 19000.000000 11884.290480 NaN 2561.507702 NaN 3262.315463 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-01 18193.615004 21745.616003 37123.325715 133688.641994 114663.352432 108694.112636 65123.078266 82659.798140 54213.211334 95810.771672 72691.211877 90497.611881 39496.394383 63116.935792 55961.095364 63921.094983 76625.933648 33921.288564 24108.316330 135790.918991 53083.886251 220750.025098 10978.363967 151287.418912 297354.581769 212117.796074 5.566719e+05 6.929247e+05 2.044277e+05 6.132758e+05 2.254386e+06 1.335350e+06 9.339803e+05 9.181987e+05 1.597495e+06 9.045387e+05 4.059007e+05 643443.135354 NaN 33327.603111 1457.147080 7780.537762 1505.277880 7000.000000 NaN 7000.000000 2200.973747 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-02 11026.197800 45821.827640 93402.484775 112069.606984 135704.201050 110294.513685 46842.315555 95022.205509 66433.409682 85841.689536 108572.446159 71043.368174 61142.947348 78813.624070 77590.425240 63514.033532 81413.768117 60865.062717 4112.207544 20858.982796 NaN 21153.034828 220320.183278 163996.451958 371253.474145 221742.157122 4.592018e+05 9.535892e+05 5.916407e+05 1.149968e+05 1.861001e+06 1.228837e+06 1.245592e+06 1.746955e+06 1.111973e+06 8.784765e+05 1.921667e+05 11242.330154 7554.100571 NaN 17332.509080 NaN 21594.097695 5916.661647 NaN 3571.597883 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-03 41856.032839 24013.075063 103901.773484 104195.373641 114878.266529 155698.857019 87384.330321 132560.629505 76107.028759 118163.694097 61297.159908 47225.304934 76461.714189 65991.709744 60794.858101 61796.942326 45437.707943 32754.713275 12519.623950 107184.643067 277040.807773 36150.536860 199393.909519 4723.559636 141956.497095 29800.642097 2.485032e+05 3.161585e+05 4.921938e+05 8.354045e+05 1.304642e+06 1.238798e+06 1.305639e+06 1.721676e+06 5.997509e+05 1.071144e+06 1.259519e+05 381231.752129 172514.759135 21333.624691 26290.760815 5955.249459 16877.510340 21924.855746 NaN 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-04 25232.184583 14000.000000 60052.904826 81449.449158 98592.403509 83581.205827 87931.778124 104719.749933 83453.671453 98973.927374 58561.568221 58437.774627 76255.738051 101390.269022 69617.702510 40110.635040 38710.569752 25366.465042 61955.884883 25009.266000 31598.187314 54048.803677 11851.394530 141845.787481 524859.850658 693237.065059 1.309337e+05 3.152621e+05 5.084428e+05 9.868936e+05 1.107499e+06 1.628701e+06 9.459133e+05 1.994376e+06 1.248361e+06 7.000000e+03 2.861492e+05 NaN NaN 3823.738205 7000.000000 NaN NaN NaN 7000.000000 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NaN NaN NaN NaN NaN NaN\n", + "2014-07 17395.126262 43535.215270 94354.149357 87021.707853 92540.914175 106502.437536 59634.837614 111315.719528 94168.086970 52377.829211 58395.495132 128251.420261 80860.585272 46192.051861 37533.072122 61260.917809 56111.977217 19568.079190 52970.665605 14526.357084 45278.152241 91969.711148 316389.200871 23371.563584 352996.439150 367989.597703 4.783819e+05 4.344751e+05 8.478634e+05 1.255281e+06 6.505439e+05 1.765331e+06 1.514355e+06 1.560650e+06 8.758149e+05 9.140881e+05 3.634125e+05 NaN 8398.633653 2863.147884 28156.702124 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-08 17118.276431 42752.625229 84450.652063 114023.717706 152833.806072 114121.212678 43485.638169 218119.103107 86276.046200 62235.259926 87107.280149 105437.946739 68083.995035 83227.247222 11694.651496 40618.768296 94119.673368 86156.536419 185570.010872 58978.612847 25249.083375 160572.315930 287744.976689 147455.676455 179415.635379 386769.056379 1.047920e+06 5.811764e+05 6.703268e+05 7.402687e+05 9.899028e+05 1.345040e+06 1.048110e+06 5.172193e+05 5.718047e+05 7.412821e+05 4.745204e+05 553602.058553 NaN 4739.020481 8852.389127 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-09 11766.356540 70613.585057 90230.081727 101852.600266 76066.255398 89862.791351 151175.866617 100473.424656 103208.746962 58935.152592 97079.614731 129682.745064 57545.028781 86011.194043 52566.195198 33350.920165 77655.827773 28722.831894 36532.136163 28919.582885 60075.572549 72839.456149 21977.481030 130712.385050 481402.426382 34469.897880 1.898491e+05 1.294468e+06 7.848245e+05 1.604948e+06 7.730516e+05 1.003704e+06 2.264034e+06 9.690580e+05 2.075566e+06 8.627908e+05 4.877316e+05 560000.000000 6225.141106 14392.718433 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-10 7686.279785 47288.690974 61263.717332 58860.540843 100249.116388 60196.918544 136339.434198 104905.947161 158469.829867 120334.556147 92613.523206 86727.441392 103286.805116 49770.280858 106875.401280 60855.470633 63987.812014 37601.528987 44265.371000 28034.890801 84165.344156 18401.552009 114425.800160 18925.289270 41378.655775 29410.028093 3.377927e+05 3.458116e+05 1.406488e+06 1.178639e+06 6.524484e+05 1.298710e+06 1.201304e+06 5.332826e+05 NaN 7.457256e+05 5.735299e+05 280000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-11 NaN 40085.299101 51551.273656 20923.904293 141741.326787 111531.853596 77865.577974 65298.455492 151870.053321 82061.009567 61697.287153 93122.910777 83803.651162 43913.672786 77276.779451 43448.623639 36094.214541 68528.384644 16350.415372 182462.893770 63248.816014 299616.780888 26978.323945 159313.694759 309841.096392 465701.774370 2.721091e+05 8.752746e+05 6.730805e+05 7.601739e+05 1.433160e+06 8.290349e+05 2.283413e+06 1.305887e+06 1.464814e+06 1.178750e+06 4.047749e+05 206112.848459 NaN NaN NaN NaN NaN NaN NaN 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NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-01 NaN 18809.131752 43643.076263 143476.365405 134608.627856 72838.540601 134335.866536 90809.617198 76374.529652 44317.805600 44963.949373 116092.938968 72389.838029 77531.956013 92559.582908 70792.363429 93422.566104 19364.960856 51135.080708 58307.591149 98557.046637 171828.994625 16357.206094 164580.481733 271290.207578 263908.608181 7.310979e+05 2.617977e+05 4.444601e+05 9.430313e+05 6.540549e+05 1.983315e+06 1.100738e+06 9.984796e+05 1.853317e+06 9.114406e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-02 12412.355755 38406.845556 33393.782768 67837.760684 133775.471117 130118.675093 63956.354202 103581.623904 102121.561858 147880.848889 94519.467458 70703.530746 78163.588148 55992.518926 33768.401630 60343.512442 55206.045005 43734.718542 84779.334566 76371.753878 154218.819587 48242.969027 27646.576463 159038.475259 10543.399407 568771.844307 7.952086e+05 4.205424e+05 7.719330e+05 9.968293e+05 1.272497e+06 1.707763e+06 1.679003e+06 7.249045e+05 5.715680e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-03 4368.139941 61775.887520 30136.005063 144623.195652 55422.389557 104909.697280 55208.760267 55274.698545 92856.578086 124600.672630 77953.801804 107434.771768 43840.814860 79732.787937 38050.018025 63033.915023 79369.665209 16071.851556 61872.697863 47902.333668 49743.103874 24480.870749 25348.099244 140844.245330 277158.288574 448419.242716 1.582105e+06 1.128602e+06 5.532564e+05 1.515552e+06 1.199394e+06 1.429829e+06 1.789496e+05 1.904034e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-04 4120.397718 44016.083166 42098.174698 80937.176395 111497.252309 101099.525247 119115.739368 128819.592112 102164.280538 96918.413806 50660.778972 72949.065053 74423.421664 27681.022975 82385.876087 32462.299607 30549.388036 87759.712991 59149.196196 61675.427831 49913.956218 158963.301057 21810.137418 10729.064205 949117.308496 278402.094726 6.002861e+05 5.903465e+05 1.671649e+06 1.222692e+06 8.994191e+05 1.634694e+06 1.214017e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-05 NaN 16343.780275 134683.935192 147597.361003 166878.862224 127495.519739 80628.987804 54868.793800 119863.863223 114562.929158 57458.774398 60464.764226 74781.341514 137108.989006 84540.621394 63839.882522 53321.314742 84600.798385 42817.982258 30342.687954 23479.032326 25995.936780 32150.807773 57185.820235 373004.903124 256129.870303 5.249663e+05 4.073522e+05 8.464785e+05 1.405456e+06 1.404862e+06 8.756084e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-06 9191.127816 34263.624861 27620.306251 139037.915115 113553.257273 93092.045776 67707.398150 159902.536216 103246.353092 190275.446707 60978.815043 105951.864825 91456.906547 51097.242328 59742.359319 41047.145476 48048.962654 65014.351742 55238.028473 53775.598296 74711.669645 65403.459055 516914.261126 338918.453160 32508.514160 24680.414930 2.730094e+05 4.430222e+05 4.957713e+05 1.260089e+06 4.740520e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-07 5872.315796 61431.291838 113522.374176 96030.478574 133266.844718 130305.038331 64905.254190 58480.126318 94984.665207 72183.521869 78983.569059 74241.045574 52909.888868 90725.673217 73757.223592 53223.803895 81529.001882 36539.587446 19516.717846 44597.550143 153507.609854 38940.929237 32784.556837 58657.933191 21181.943763 542757.405428 4.857625e+05 7.320445e+05 1.689266e+06 8.333349e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-08 12275.876950 73665.971167 69771.521645 156546.149062 96828.635293 122892.653522 77525.291302 120597.162868 42213.457509 81603.139232 64318.685029 82587.536028 70822.690426 54069.221152 86280.324966 61411.904761 29181.467011 93807.472649 10591.579043 120473.835509 44354.048698 57071.138285 55456.304615 68991.650804 208825.674298 52347.075661 5.573485e+05 4.825662e+05 1.013729e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-09 29401.764323 45522.015799 76968.040204 146839.948554 171673.679256 109429.096009 86090.895235 98514.084480 83464.884211 61289.279897 106945.334949 123620.792997 36005.695177 111817.545698 97812.012630 71550.378735 133081.814697 52059.620013 69381.213861 206638.571652 46697.251204 24755.437891 41026.125804 405652.063289 127807.538226 150591.819723 5.798513e+05 4.420908e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-10 14000.000000 18823.580135 64808.886479 86112.369720 33951.887363 94798.147426 41473.460244 114279.219218 95470.452262 95322.271315 84571.513563 87283.315644 56826.229766 46418.390772 102357.785120 36633.859700 55331.328885 61585.508226 42312.614360 19390.945153 58927.385073 104921.071975 19228.360716 156208.667108 54768.101002 306567.082809 5.968770e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-11 NaN 24865.758304 74507.452518 52944.105766 109359.561135 98135.981690 160380.544193 46511.473410 206774.167009 128625.196728 125749.283046 79550.899988 76191.422212 89872.173002 67238.315529 60955.421537 24439.191831 21047.667487 38010.055279 67547.641213 17088.640029 11994.180304 39623.869759 136232.327155 419937.900185 19556.256954 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-12 14000.000000 34498.369406 85505.412309 113845.813135 118239.229726 121277.218444 179033.274623 131329.638216 130356.868676 89312.029471 50001.263705 131720.019853 114096.053064 80505.435581 47345.897743 107853.847684 51370.371877 62388.220925 22932.368203 48682.707999 164287.941261 156485.280890 34689.496090 171021.042677 388756.671990 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-01 3213.543940 16520.684619 48605.226108 63442.108722 64330.296181 156535.525771 70535.750329 90185.961127 88647.097698 87277.722080 37585.548112 129838.454174 49775.368916 99477.619056 79483.384567 77709.064810 56914.923493 49974.636491 49813.369829 11723.025526 42432.701569 92444.945865 65657.025561 302148.945562 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-02 13891.629340 20721.691370 112166.316749 154318.566251 93667.327677 117578.257984 114468.238241 77207.158186 79019.522681 85925.357894 62818.373369 94653.274923 98626.259639 14412.050378 71903.488396 63442.861487 52747.037286 60027.653938 40008.382480 206021.036486 46890.568780 45358.088045 166532.167813 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-03 18353.550375 48494.631841 70674.791516 115885.521080 83612.153482 45276.454051 123735.672613 114338.914912 79022.075494 96179.004264 104153.365919 78420.661052 171254.111316 64222.446520 43806.575540 34980.385712 16933.353509 36318.272021 67986.442666 32546.776357 54145.301365 13493.482671 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-04 14780.520614 35741.458577 51817.548576 124194.948588 76219.573225 168821.419700 117504.571796 82978.012887 99148.493702 112663.905688 47357.387177 91977.866046 59685.252396 118258.978619 38365.804252 58137.086830 54237.801338 78418.236388 71326.721028 59282.610260 85403.308987 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-05 NaN 32920.776360 80573.646233 128677.203503 98491.267711 138235.571025 110125.330488 110887.267995 150280.984291 74848.724902 54051.819831 105564.489585 42873.921065 51942.612413 34218.518171 94820.525215 81052.688242 78267.767639 13675.949254 25982.914677 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-06 17958.638920 9031.290904 35720.570300 163643.856247 172139.047804 157652.546874 154167.597823 120193.385494 100214.093197 62572.447623 82107.496478 108154.178521 70208.521568 37141.252390 86357.027860 69921.829085 62875.428241 20423.255196 24443.879967 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-07 36454.457248 24782.897875 105234.099377 116615.976124 64114.451842 118425.785680 107282.550162 101314.686533 136164.358434 95887.527655 160987.563259 65113.829226 110219.023402 104432.987704 50962.266199 136896.654248 80842.270086 95857.222846 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-08 45280.373036 14224.178374 93540.570848 147757.945387 88369.564695 195297.446098 117493.748560 57655.840012 132907.265603 100460.044301 84736.522917 101950.878263 81994.715213 58406.357588 55984.279463 54491.161014 87532.982072 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-09 13543.632993 56468.414754 84514.463120 106895.796922 127087.926687 89314.051675 132691.450853 153691.498737 62591.301678 133208.213752 88052.362757 64577.593705 63639.041274 97667.426442 102568.832260 92328.114290 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-10 1765.824878 61195.914947 80436.515750 123346.321677 82946.887915 158136.929363 168589.126365 110018.518191 86504.029415 162549.196934 80314.320545 131253.273865 95130.219265 22490.001498 61574.451140 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-11 NaN 55837.251699 111441.725643 110741.296392 111772.167542 44847.384719 164147.485673 180125.984382 90888.981098 71973.259517 92692.770710 204372.431433 43569.263275 35989.538721 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-12 NaN 52805.659963 55386.352687 116174.812373 107610.806877 109759.341040 74635.362208 145938.259214 100703.178078 114896.817293 98215.783375 74192.406168 145322.425729 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-01 8337.875863 26000.000000 121221.366085 104138.452826 113299.545815 86085.752607 139369.099191 127013.485742 66154.704652 111713.185860 83569.214341 161872.714059 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-02 NaN 50237.487443 101651.411862 103478.579796 94797.505236 94285.239288 160239.006593 141801.930962 131584.577665 94257.374842 59566.901626 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-03 4476.248773 20775.191311 74392.325782 72249.752924 143323.397539 56307.436025 210407.467883 117053.877647 101546.367791 93076.579694 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-04 NaN 30750.642034 126807.879895 154838.817832 157090.350860 72105.725147 102587.339458 91454.397901 142688.966324 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-05 3022.335184 36811.264195 54334.199788 80436.891802 81314.614006 160837.572465 180651.804749 88441.462952 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-06 NaN 31858.167598 37910.528589 81228.553673 121618.983828 85184.796073 110103.351522 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-07 28827.335193 30987.279514 126229.848204 72825.784504 80075.315891 119390.465916 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-08 NaN 14000.000000 105952.492729 81991.210907 174870.150263 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-09 18589.176575 33316.659725 86056.984325 152143.727464 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-10 NaN 35037.196888 104444.385044 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-11 4088.116947 10599.333280 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017-12 10748.025150 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism = cl.Triangle(\n", + " data=prism_df,\n", + " origin=\"AccidentDate\",\n", + " development=\"PaymentDate\",\n", + " columns=\"Paid\",\n", + " cumulative=False,\n", + ")\n", + "prism" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the lowest (most-detailed) grain supported is the monthly grain, so the triangle above is aggregated to the OMDM level." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'M'" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism.origin_grain" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'M'" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism.development_grain" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to include more columns or indices we can certainly do so. Note that as we do this, we move into the 4D arena changing the display of the overall object." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(1, 2, 120, 120)
Index:[Total]
Columns:[Paid, Incurred]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2017-12\n", + "Grain: OMDM\n", + "Shape: (1, 2, 120, 120)\n", + "Index: [Total]\n", + "Columns: [Paid, Incurred]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism = cl.Triangle(\n", + " data=prism_df,\n", + " origin=\"AccidentDate\",\n", + " development=\"PaymentDate\",\n", + " columns=[\"Paid\", \"Incurred\"],\n", + " cumulative=False,\n", + ")\n", + "prism" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Pandas` has wonderful datetime inference functionality that the `Triangle` heavily uses to infer origin and development granularity. Even still, there are occassions where date format inferences can fail. It is often better to explicitly tell the triangle the date format, and is usually good pratice to explicitly state the date format instead." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ClaimNoAccidentDateReportDateLineTypeClaimLiabilityLimitDeductibleTotalPaymentPaymentDateCloseDateStatusreportedCountclosedPaidCountclosedUnPaidCountopenCountPaidOutstandingIncurred
012008-01-224/19/2010HomeDwellingFalse300000.0200000.000002010-10-0810/8/2010CLOSED10100.0000000.00000
122008-01-024/20/2010HomeDwellingFalse200000.0200000.000002010-11-3011/30/2010CLOSED10100.0000000.00000
232008-01-019/23/2009HomeDwellingTrue200000.020000115744.773702010-02-172/17/2010CLOSED1100115744.773700115744.77370
342008-01-027/25/2009HomeDwellingTrue200000.02000063678.877132009-11-1811/18/2009CLOSED110063678.87713063678.87713
452008-01-1612/7/2009HomeDwellingTrue200000.020000112175.555902010-04-304/30/2010CLOSED1100112175.555900112175.55590
\n", + "
" + ], + "text/plain": [ + " ClaimNo AccidentDate ReportDate Line Type ClaimLiability Limit \\\n", + "0 1 2008-01-22 4/19/2010 Home Dwelling False 300000.0 \n", + "1 2 2008-01-02 4/20/2010 Home Dwelling False 200000.0 \n", + "2 3 2008-01-01 9/23/2009 Home Dwelling True 200000.0 \n", + "3 4 2008-01-02 7/25/2009 Home Dwelling True 200000.0 \n", + "4 5 2008-01-16 12/7/2009 Home Dwelling True 200000.0 \n", + "\n", + " Deductible TotalPayment PaymentDate CloseDate Status reportedCount \\\n", + "0 20000 0.00000 2010-10-08 10/8/2010 CLOSED 1 \n", + "1 20000 0.00000 2010-11-30 11/30/2010 CLOSED 1 \n", + "2 20000 115744.77370 2010-02-17 2/17/2010 CLOSED 1 \n", + "3 20000 63678.87713 2009-11-18 11/18/2009 CLOSED 1 \n", + "4 20000 112175.55590 2010-04-30 4/30/2010 CLOSED 1 \n", + "\n", + " closedPaidCount closedUnPaidCount openCount Paid Outstanding \\\n", + "0 0 1 0 0.00000 0 \n", + "1 0 1 0 0.00000 0 \n", + "2 1 0 0 115744.77370 0 \n", + "3 1 0 0 63678.87713 0 \n", + "4 1 0 0 112175.55590 0 \n", + "\n", + " Incurred \n", + "0 0.00000 \n", + "1 0.00000 \n", + "2 115744.77370 \n", + "3 63678.87713 \n", + "4 112175.55590 " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(1, 2, 120, 120)
Index:[Total]
Columns:[Paid, Incurred]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2017-12\n", + "Grain: OMDM\n", + "Shape: (1, 2, 120, 120)\n", + "Index: [Total]\n", + "Columns: [Paid, Incurred]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism = cl.Triangle(\n", + " data=prism_df,\n", + " origin=\"AccidentDate\",\n", + " origin_format=\"%Y-%m-%d\",\n", + " development=\"PaymentDate\",\n", + " development_format=\"%Y-%m-%d\",\n", + " columns=[\"Paid\", \"Incurred\"],\n", + " cumulative=False,\n", + ")\n", + "prism" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Up until now, we've been playing with symmetric triangles (i.e. `orgin` and `development` periods have the same grain). However, nothing precludes us from having a different grain. Often times in practice the `development` axis is more granular than the `origin` axis. All the functionality available to symmetric triangles works equally well for asymmetric triangles." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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200875,66490,814194,956300,085347,487400,011356,795598,532...7,000
200924,32780,376136,857175,737297,690358,624325,462529,832540,217...
201022,19284,802148,299200,733208,237456,692519,691604,946484,188...
201124,93460,404107,198233,346268,904401,595530,490501,836513,133...
201218,79498,320165,689296,865335,643320,954529,733592,985686,689...
201370,95999,628238,991180,138302,213477,224585,963648,734637,203...
201418,19432,772124,801276,336344,635425,875449,242541,203587,789819,083...
201531,22186,418242,766276,598418,870575,634620,098858,771787,203...
20163,21430,41287,680238,884325,065468,785521,981560,611853,548976,906...
20178,33826,000175,935226,565324,943416,752646,836649,985811,832957,490...
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NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012 NaN 18794.078501 98319.589540 165689.365207 296864.998459 335642.536754 320954.395644 529732.563253 592985.177062 686688.855866 686447.031981 8.519169e+05 1.044212e+06 9.544724e+05 7.541892e+05 7.004901e+05 8.139560e+05 735316.703403 8.434435e+05 610979.771313 7.650731e+05 9.001753e+05 524305.875585 7.705284e+05 7.828616e+05 7.899929e+05 1.103369e+06 2.186602e+06 3.443066e+06 4.221423e+06 7.178965e+06 7.656785e+06 5.590090e+06 8.982027e+06 8.261696e+06 1.359647e+07 1.032970e+07 8.257640e+06 9.321772e+06 8.861009e+06 8.026075e+06 1.151095e+07 5.959513e+06 4.613599e+06 3.586089e+06 2.294259e+06 1.359515e+06 2.037343e+05 15753.578835 4825.817052 35572.887800 47025.437821 43406.274633 79811.940412 63600.307257 46519.756172 21303.389450 11012.139648 58720.963899 42153.387526 30589.656750 6840.626511 7168.640275 37329.362031 21325.172999 42416.968478 14000.000000 30497.072560 8423.825983 12509.598410 13281.714657 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013 NaN 70958.929207 99628.008281 238991.375169 180138.458080 302213.477793 477223.596123 585963.166386 648734.411994 637203.114079 682368.117405 9.662005e+05 7.309051e+05 7.880407e+05 9.338613e+05 8.508193e+05 9.270194e+05 711364.608560 1.126966e+06 742502.695017 5.909403e+05 8.881174e+05 706265.588894 7.213157e+05 9.861389e+05 9.881487e+05 1.225893e+06 2.064617e+06 3.226128e+06 3.337927e+06 7.487120e+06 5.812563e+06 7.699736e+06 1.429176e+07 9.297106e+06 1.339363e+07 1.017609e+07 1.108135e+07 9.348480e+06 8.782365e+06 8.332327e+06 7.631190e+06 5.444235e+06 6.958137e+06 4.240059e+06 2.547624e+06 8.930230e+05 1.256927e+06 65875.866878 49276.243262 76963.405478 95595.756248 66386.070242 44070.197828 81623.175278 13540.121014 16310.835649 25538.965303 22116.041412 5427.390291 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014 18193.615004 32771.813803 124801.186194 276336.386415 344634.732900 425874.521138 449242.485198 541203.495636 587788.640766 819083.073241 841927.820373 1.084300e+06 8.273738e+05 7.725394e+05 1.198817e+06 1.035734e+06 9.333597e+05 900446.997494 9.926431e+05 845639.783362 6.242145e+05 9.333139e+05 801369.552914 9.965165e+05 1.241265e+06 1.041576e+06 1.543091e+06 2.354836e+06 3.608809e+06 3.150000e+06 4.227372e+06 7.183240e+06 7.418863e+06 7.616530e+06 1.101130e+07 1.045179e+07 9.890481e+06 1.290310e+07 8.542991e+06 8.713766e+06 9.246068e+06 6.866563e+06 7.833314e+06 3.985606e+06 4.056370e+06 3.694519e+06 1.095143e+06 8.852198e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015 NaN 31221.487507 86418.061760 242766.433412 276598.476769 418870.290158 575633.983430 620097.757031 858771.326075 787202.598401 973846.616159 1.037602e+06 1.152322e+06 9.494363e+05 1.132752e+06 8.509203e+05 1.040219e+06 969420.903319 1.018208e+06 942013.142755 8.851064e+05 1.003449e+06 778933.792936 7.958000e+05 1.122678e+06 8.086954e+05 2.222553e+06 3.433307e+06 3.693775e+06 4.020768e+06 3.646026e+06 8.476120e+06 7.538322e+06 8.573633e+06 1.070603e+07 9.245065e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016 3213.543940 30412.313959 87680.467853 238883.577926 325065.112525 468785.338384 521980.504768 560611.271358 853548.382899 976906.136902 910871.647045 1.103004e+06 1.348982e+06 1.056142e+06 1.186347e+06 1.164726e+06 1.141416e+06 922542.939622 9.558699e+05 827461.089202 1.057683e+06 1.072619e+06 636696.163982 1.136609e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017 8337.875863 26000.000000 175935.102301 226565.055999 324943.428611 416752.154857 646836.256429 649984.979125 811832.021102 957490.473881 923912.780655 1.227946e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_df[\"AccYr\"] = prism_df[\"AccidentDate\"].str[:4]\n", + "\n", + "prism = cl.Triangle(\n", + " data=prism_df,\n", + " origin=\"AccYr\",\n", + " origin_format=\"%Y\",\n", + " development=\"PaymentDate\",\n", + " development_format=\"%Y-%m-%d\",\n", + " columns=[\"Paid\", \"Incurred\"],\n", + " cumulative=False,\n", + ")\n", + "prism[\"Paid\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While exposure triangles make sense for auditable lines such as workers' compensation lines, most other lines of business' exposures can be expressed as a 1D array (along origin period) as exposures do not develop over time. `chainladder` arithmetic requires that operations happen between a triangle and either an `int`, `float`, or another `Triangle`. To create a 1D exposure array, simply omit the `development` argument at initialization.\n", + "\n", + "The `prism` data does not consist of exposure data, but we can contrive one. Let's assume that the premium is thrice the incurred amount." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2017-12
2008-0141,872,294
2008-0243,542,684
2008-0335,920,802
2008-0430,521,592
2008-0546,807,611
2008-0635,926,236
2008-0733,305,612
2008-0835,667,952
2008-0936,773,174
2008-1036,858,772
2008-1141,939,041
2008-1234,716,883
2009-0140,435,087
2009-0251,803,670
2009-0338,273,957
2009-0444,045,041
2009-0538,573,516
2009-0634,674,998
2009-0739,372,633
2009-0841,936,689
2009-0939,120,076
2009-1033,485,416
2009-1135,884,763
2009-1235,640,359
2010-0139,187,508
2010-0236,408,113
2010-0346,100,329
2010-0439,311,907
2010-0538,617,436
2010-0638,204,654
......
2015-0718,075,734
2015-0812,204,463
2015-0911,209,737
2015-107,647,748
2015-116,591,418
2015-127,948,603
2016-015,502,819
2016-025,677,216
2016-034,541,502
2016-044,938,965
2016-054,522,476
2016-064,664,779
2016-075,134,766
2016-084,554,252
2016-094,406,520
2016-104,278,755
2016-113,955,199
2016-123,586,924
2017-013,446,326
2017-023,095,700
2017-032,680,826
2017-042,634,972
2017-052,057,550
2017-061,403,713
2017-071,375,008
2017-081,130,442
2017-09870,320
2017-10418,445
2017-1144,062
2017-1232,244
" + ], + "text/plain": [ + " 2017-12\n", + "2008-01 4.187229e+07\n", + "2008-02 4.354268e+07\n", + "2008-03 3.592080e+07\n", + "2008-04 3.052159e+07\n", + "2008-05 4.680761e+07\n", + "2008-06 3.592624e+07\n", + "2008-07 3.330561e+07\n", + "2008-08 3.566795e+07\n", + "2008-09 3.677317e+07\n", + "2008-10 3.685877e+07\n", + "2008-11 4.193904e+07\n", + "2008-12 3.471688e+07\n", + "2009-01 4.043509e+07\n", + "2009-02 5.180367e+07\n", + "2009-03 3.827396e+07\n", + "2009-04 4.404504e+07\n", + "2009-05 3.857352e+07\n", + "2009-06 3.467500e+07\n", + "2009-07 3.937263e+07\n", + "2009-08 4.193669e+07\n", + "2009-09 3.912008e+07\n", + "2009-10 3.348542e+07\n", + "2009-11 3.588476e+07\n", + "2009-12 3.564036e+07\n", + "2010-01 3.918751e+07\n", + "2010-02 3.640811e+07\n", + "2010-03 4.610033e+07\n", + "2010-04 3.931191e+07\n", + "2010-05 3.861744e+07\n", + "2010-06 3.820465e+07\n", + "2010-07 3.901837e+07\n", + "2010-08 4.156632e+07\n", + "2010-09 3.908993e+07\n", + "2010-10 4.236063e+07\n", + "2010-11 4.164149e+07\n", + "2010-12 4.410378e+07\n", + "2011-01 4.455028e+07\n", + "2011-02 4.022343e+07\n", + "2011-03 4.083951e+07\n", + "2011-04 4.290512e+07\n", + "2011-05 3.799259e+07\n", + "2011-06 4.000331e+07\n", + "2011-07 4.249876e+07\n", + "2011-08 3.861800e+07\n", + "2011-09 4.187191e+07\n", + "2011-10 4.184378e+07\n", + "2011-11 3.917826e+07\n", + "2011-12 3.694711e+07\n", + "2012-01 3.788083e+07\n", + "2012-02 4.270029e+07\n", + "2012-03 4.653762e+07\n", + "2012-04 3.785465e+07\n", + "2012-05 3.693307e+07\n", + "2012-06 3.914957e+07\n", + "2012-07 3.893848e+07\n", + "2012-08 3.586909e+07\n", + "2012-09 3.110809e+07\n", + "2012-10 4.205510e+07\n", + "2012-11 3.560778e+07\n", + "2012-12 3.382385e+07\n", + "2013-01 4.813855e+07\n", + "2013-02 3.458431e+07\n", + "2013-03 4.096955e+07\n", + "2013-04 3.508083e+07\n", + "2013-05 4.537744e+07\n", + "2013-06 4.477441e+07\n", + "2013-07 4.143155e+07\n", + "2013-08 4.576263e+07\n", + "2013-09 4.198754e+07\n", + "2013-10 4.120169e+07\n", + "2013-11 3.225811e+07\n", + "2013-12 3.345250e+07\n", + "2014-01 4.036345e+07\n", + "2014-02 3.864147e+07\n", + "2014-03 3.639684e+07\n", + "2014-04 3.578460e+07\n", + "2014-05 3.745228e+07\n", + "2014-06 4.515285e+07\n", + "2014-07 3.963638e+07\n", + "2014-08 3.671515e+07\n", + "2014-09 4.552311e+07\n", + "2014-10 3.117016e+07\n", + "2014-11 4.338274e+07\n", + "2014-12 3.469077e+07\n", + "2015-01 3.697209e+07\n", + "2015-02 3.418734e+07\n", + "2015-03 3.540646e+07\n", + "2015-04 3.213757e+07\n", + "2015-05 2.365461e+07\n", + "2015-06 1.670796e+07\n", + "2015-07 1.807573e+07\n", + "2015-08 1.220446e+07\n", + "2015-09 1.120974e+07\n", + "2015-10 7.647748e+06\n", + "2015-11 6.591418e+06\n", + "2015-12 7.948603e+06\n", + "2016-01 5.502819e+06\n", + "2016-02 5.677216e+06\n", + "2016-03 4.541502e+06\n", + "2016-04 4.938965e+06\n", + "2016-05 4.522476e+06\n", + "2016-06 4.664779e+06\n", + "2016-07 5.134766e+06\n", + "2016-08 4.554252e+06\n", + "2016-09 4.406520e+06\n", + "2016-10 4.278755e+06\n", + "2016-11 3.955199e+06\n", + "2016-12 3.586924e+06\n", + "2017-01 3.446326e+06\n", + "2017-02 3.095700e+06\n", + "2017-03 2.680826e+06\n", + "2017-04 2.634972e+06\n", + "2017-05 2.057550e+06\n", + "2017-06 1.403713e+06\n", + "2017-07 1.375008e+06\n", + "2017-08 1.130442e+06\n", + "2017-09 8.703196e+05\n", + "2017-10 4.184447e+05\n", + "2017-11 4.406235e+04\n", + "2017-12 3.224408e+04" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_df[\"Premium\"] = 3 * prism_df[\"Incurred\"]\n", + "\n", + "prism = cl.Triangle(\n", + " data=prism_df,\n", + " origin=\"AccidentDate\",\n", + " origin_format=\"%Y-%m-%d\",\n", + " columns=\"Premium\",\n", + " cumulative=False,\n", + ")\n", + "\n", + "prism" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's seperate our data by segments using `index`:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(2, 2, 120, 120)
Index:[Line]
Columns:[Paid, Incurred]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2017-12\n", + "Grain: OMDM\n", + "Shape: (2, 2, 120, 120)\n", + "Index: [Line]\n", + "Columns: [Paid, Incurred]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism = cl.Triangle(\n", + " data=prism_df,\n", + " origin=\"AccidentDate\",\n", + " origin_format=\"%Y-%m-%d\",\n", + " development=\"PaymentDate\",\n", + " development_format=\"%Y-%m-%d\",\n", + " columns=[\"Paid\", \"Incurred\"],\n", + " index=\"Line\",\n", + " cumulative=False,\n", + ")\n", + "prism" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can futher `index` by coverages, or sublines:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(2, 2, 120, 120)
Index:[Line, Type]
Columns:[Paid, Incurred]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2017-12\n", + "Grain: OMDM\n", + "Shape: (2, 2, 120, 120)\n", + "Index: [Line, Type]\n", + "Columns: [Paid, Incurred]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism = cl.Triangle(\n", + " data=prism_df,\n", + " origin=\"AccidentDate\",\n", + " origin_format=\"%Y-%m-%d\",\n", + " development=\"PaymentDate\",\n", + " development_format=\"%Y-%m-%d\",\n", + " columns=[\"Paid\", \"Incurred\"],\n", + " index=[\"Line\", \"Type\"],\n", + " cumulative=False,\n", + ")\n", + "prism" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Triangle Methods not Available in Pandas\n", + "Up until now, we've kept pretty close to the pandas API for triangle manipulation. However, there are data transformations commonly applied to triangles that don't have a nice `pandas` analogy.\n", + "\n", + "For example, we often want to convert a triangle from an incremental view into a cumulative view and vice versa. This can be accomplished with the `incr_to_cum` and `cum_to_incr` methods." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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116400.145708 44294.325625 8337.139896 49437.910850 78667.587248 18079.165367 19803.871191 39941.860865 238984.087444 57841.919696 204109.009972 386664.454000 117220.879514 43709.801633 5.248744e+05 2.203477e+05 6.789611e+05 6.491085e+05 7.098767e+05 1.519165e+06 1.763656e+06 8.954874e+05 1.280474e+06 2.972427e+05 4.316323e+05 2173.166532 9744.078912 27655.254283 5155.768750 NaN 19000.000000 11884.290480 NaN 2561.507702 NaN 3262.315463 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-01 18193.615004 21745.616003 37123.325715 133688.641994 114663.352432 108694.112636 65123.078266 82659.798140 54213.211334 95810.771672 72691.211877 90497.611881 39496.394383 63116.935792 55961.095364 63921.094983 76625.933648 33921.288564 24108.316330 135790.918991 53083.886251 220750.025098 10978.363967 151287.418912 297354.581769 212117.796074 5.566719e+05 6.929247e+05 2.044277e+05 6.132758e+05 2.254386e+06 1.335350e+06 9.339803e+05 9.181987e+05 1.597495e+06 9.045387e+05 4.059007e+05 643443.135354 NaN 33327.603111 1457.147080 7780.537762 1505.277880 7000.000000 NaN 7000.000000 2200.973747 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-02 11026.197800 45821.827640 93402.484775 112069.606984 135704.201050 110294.513685 46842.315555 95022.205509 66433.409682 85841.689536 108572.446159 71043.368174 61142.947348 78813.624070 77590.425240 63514.033532 81413.768117 60865.062717 4112.207544 20858.982796 NaN 21153.034828 220320.183278 163996.451958 371253.474145 221742.157122 4.592018e+05 9.535892e+05 5.916407e+05 1.149968e+05 1.861001e+06 1.228837e+06 1.245592e+06 1.746955e+06 1.111973e+06 8.784765e+05 1.921667e+05 11242.330154 7554.100571 NaN 17332.509080 NaN 21594.097695 5916.661647 NaN 3571.597883 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-03 41856.032839 24013.075063 103901.773484 104195.373641 114878.266529 155698.857019 87384.330321 132560.629505 76107.028759 118163.694097 61297.159908 47225.304934 76461.714189 65991.709744 60794.858101 61796.942326 45437.707943 32754.713275 12519.623950 107184.643067 277040.807773 36150.536860 199393.909519 4723.559636 141956.497095 29800.642097 2.485032e+05 3.161585e+05 4.921938e+05 8.354045e+05 1.304642e+06 1.238798e+06 1.305639e+06 1.721676e+06 5.997509e+05 1.071144e+06 1.259519e+05 381231.752129 172514.759135 21333.624691 26290.760815 5955.249459 16877.510340 21924.855746 NaN 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-04 25232.184583 14000.000000 60052.904826 81449.449158 98592.403509 83581.205827 87931.778124 104719.749933 83453.671453 98973.927374 58561.568221 58437.774627 76255.738051 101390.269022 69617.702510 40110.635040 38710.569752 25366.465042 61955.884883 25009.266000 31598.187314 54048.803677 11851.394530 141845.787481 524859.850658 693237.065059 1.309337e+05 3.152621e+05 5.084428e+05 9.868936e+05 1.107499e+06 1.628701e+06 9.459133e+05 1.994376e+06 1.248361e+06 7.000000e+03 2.861492e+05 NaN NaN 3823.738205 7000.000000 NaN NaN NaN 7000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-05 NaN 17227.928985 27750.821450 64920.312930 52425.305222 104786.922285 51431.543443 75141.609363 94043.024894 110458.538993 91977.050437 94845.116068 42100.162714 37766.071179 70352.134251 38031.178080 27324.267980 43493.409227 73484.344127 47879.400389 40091.072625 43696.871650 197505.830961 354590.402357 796275.221621 501822.287745 9.407318e+04 2.086054e+05 8.400150e+05 5.353851e+05 1.222320e+06 9.372390e+05 1.124267e+06 1.809481e+06 7.518762e+05 1.112245e+06 7.122555e+05 5503.027815 NaN NaN NaN 19000.000000 NaN 12407.219270 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-06 NaN 32351.229848 31836.316782 66289.251427 81787.337215 107053.192243 143595.368132 59589.625334 9302.495418 76650.181977 124141.065286 83364.436760 41465.820645 86065.819186 34686.068726 55951.600356 83707.079866 55792.122597 165451.433796 60042.224137 31799.379255 22992.429547 10304.208798 47204.539329 309685.077359 295442.552497 1.044091e+06 6.892586e+05 8.172055e+05 1.459404e+06 7.957606e+05 1.894393e+06 1.258487e+06 1.485590e+06 1.291553e+06 8.570753e+05 5.338876e+05 466078.465078 NaN 295421.578684 10671.210804 17585.537431 17936.294110 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-07 17395.126262 43535.215270 94354.149357 87021.707853 92540.914175 106502.437536 59634.837614 111315.719528 94168.086970 52377.829211 58395.495132 128251.420261 80860.585272 46192.051861 37533.072122 61260.917809 56111.977217 19568.079190 52970.665605 14526.357084 45278.152241 91969.711148 316389.200871 23371.563584 352996.439150 367989.597703 4.783819e+05 4.344751e+05 8.478634e+05 1.255281e+06 6.505439e+05 1.765331e+06 1.514355e+06 1.560650e+06 8.758149e+05 9.140881e+05 3.634125e+05 NaN 8398.633653 2863.147884 28156.702124 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-08 17118.276431 42752.625229 84450.652063 114023.717706 152833.806072 114121.212678 43485.638169 218119.103107 86276.046200 62235.259926 87107.280149 105437.946739 68083.995035 83227.247222 11694.651496 40618.768296 94119.673368 86156.536419 185570.010872 58978.612847 25249.083375 160572.315930 287744.976689 147455.676455 179415.635379 386769.056379 1.047920e+06 5.811764e+05 6.703268e+05 7.402687e+05 9.899028e+05 1.345040e+06 1.048110e+06 5.172193e+05 5.718047e+05 7.412821e+05 4.745204e+05 553602.058553 NaN 4739.020481 8852.389127 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-09 11766.356540 70613.585057 90230.081727 101852.600266 76066.255398 89862.791351 151175.866617 100473.424656 103208.746962 58935.152592 97079.614731 129682.745064 57545.028781 86011.194043 52566.195198 33350.920165 77655.827773 28722.831894 36532.136163 28919.582885 60075.572549 72839.456149 21977.481030 130712.385050 481402.426382 34469.897880 1.898491e+05 1.294468e+06 7.848245e+05 1.604948e+06 7.730516e+05 1.003704e+06 2.264034e+06 9.690580e+05 2.075566e+06 8.627908e+05 4.877316e+05 560000.000000 6225.141106 14392.718433 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-10 7686.279785 47288.690974 61263.717332 58860.540843 100249.116388 60196.918544 136339.434198 104905.947161 158469.829867 120334.556147 92613.523206 86727.441392 103286.805116 49770.280858 106875.401280 60855.470633 63987.812014 37601.528987 44265.371000 28034.890801 84165.344156 18401.552009 114425.800160 18925.289270 41378.655775 29410.028093 3.377927e+05 3.458116e+05 1.406488e+06 1.178639e+06 6.524484e+05 1.298710e+06 1.201304e+06 5.332826e+05 NaN 7.457256e+05 5.735299e+05 280000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-11 NaN 40085.299101 51551.273656 20923.904293 141741.326787 111531.853596 77865.577974 65298.455492 151870.053321 82061.009567 61697.287153 93122.910777 83803.651162 43913.672786 77276.779451 43448.623639 36094.214541 68528.384644 16350.415372 182462.893770 63248.816014 299616.780888 26978.323945 159313.694759 309841.096392 465701.774370 2.721091e+05 8.752746e+05 6.730805e+05 7.601739e+05 1.433160e+06 8.290349e+05 2.283413e+06 1.305887e+06 1.464814e+06 1.178750e+06 4.047749e+05 206112.848459 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-12 2338.009450 42696.189137 56894.439643 95114.713327 45989.840560 98958.952206 76403.265792 110120.637922 142920.802261 102379.619600 60846.409583 55040.740891 46001.614251 71422.187096 29877.757318 45891.358951 24222.513506 84149.338348 35899.811225 56938.134099 298184.935001 42779.811143 36294.889113 92641.894669 11117.833168 199758.341469 3.807108e+04 6.771645e+05 5.766198e+05 1.113827e+06 1.795436e+06 1.338659e+06 1.300858e+06 4.778822e+05 1.320780e+06 3.478895e+05 6.115183e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-01 NaN 18809.131752 43643.076263 143476.365405 134608.627856 72838.540601 134335.866536 90809.617198 76374.529652 44317.805600 44963.949373 116092.938968 72389.838029 77531.956013 92559.582908 70792.363429 93422.566104 19364.960856 51135.080708 58307.591149 98557.046637 171828.994625 16357.206094 164580.481733 271290.207578 263908.608181 7.310979e+05 2.617977e+05 4.444601e+05 9.430313e+05 6.540549e+05 1.983315e+06 1.100738e+06 9.984796e+05 1.853317e+06 9.114406e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-02 12412.355755 38406.845556 33393.782768 67837.760684 133775.471117 130118.675093 63956.354202 103581.623904 102121.561858 147880.848889 94519.467458 70703.530746 78163.588148 55992.518926 33768.401630 60343.512442 55206.045005 43734.718542 84779.334566 76371.753878 154218.819587 48242.969027 27646.576463 159038.475259 10543.399407 568771.844307 7.952086e+05 4.205424e+05 7.719330e+05 9.968293e+05 1.272497e+06 1.707763e+06 1.679003e+06 7.249045e+05 5.715680e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-03 4368.139941 61775.887520 30136.005063 144623.195652 55422.389557 104909.697280 55208.760267 55274.698545 92856.578086 124600.672630 77953.801804 107434.771768 43840.814860 79732.787937 38050.018025 63033.915023 79369.665209 16071.851556 61872.697863 47902.333668 49743.103874 24480.870749 25348.099244 140844.245330 277158.288574 448419.242716 1.582105e+06 1.128602e+06 5.532564e+05 1.515552e+06 1.199394e+06 1.429829e+06 1.789496e+05 1.904034e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-04 4120.397718 44016.083166 42098.174698 80937.176395 111497.252309 101099.525247 119115.739368 128819.592112 102164.280538 96918.413806 50660.778972 72949.065053 74423.421664 27681.022975 82385.876087 32462.299607 30549.388036 87759.712991 59149.196196 61675.427831 49913.956218 158963.301057 21810.137418 10729.064205 949117.308496 278402.094726 6.002861e+05 5.903465e+05 1.671649e+06 1.222692e+06 8.994191e+05 1.634694e+06 1.214017e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-05 NaN 16343.780275 134683.935192 147597.361003 166878.862224 127495.519739 80628.987804 54868.793800 119863.863223 114562.929158 57458.774398 60464.764226 74781.341514 137108.989006 84540.621394 63839.882522 53321.314742 84600.798385 42817.982258 30342.687954 23479.032326 25995.936780 32150.807773 57185.820235 373004.903124 256129.870303 5.249663e+05 4.073522e+05 8.464785e+05 1.405456e+06 1.404862e+06 8.756084e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 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61431.291838 113522.374176 96030.478574 133266.844718 130305.038331 64905.254190 58480.126318 94984.665207 72183.521869 78983.569059 74241.045574 52909.888868 90725.673217 73757.223592 53223.803895 81529.001882 36539.587446 19516.717846 44597.550143 153507.609854 38940.929237 32784.556837 58657.933191 21181.943763 542757.405428 4.857625e+05 7.320445e+05 1.689266e+06 8.333349e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-08 12275.876950 73665.971167 69771.521645 156546.149062 96828.635293 122892.653522 77525.291302 120597.162868 42213.457509 81603.139232 64318.685029 82587.536028 70822.690426 54069.221152 86280.324966 61411.904761 29181.467011 93807.472649 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2008-0146,91566,814124,030213,813232,115327,169337,502429,274...13,957,43113,957,43113,957,43113,957,43113,957,43113,957,43113,957,43113,957,43113,957,43113,957,431
2008-0228,74950,859129,891193,346253,339318,022386,523420,218491,888...14,514,22814,514,22814,514,22814,514,22814,514,22814,514,22814,514,22814,514,22814,514,228
2008-0348,80676,755167,168221,724305,232317,823389,858460,211495,434...11,973,60111,973,60111,973,60111,973,60111,973,60111,973,60111,973,60111,973,601
2008-0430,75848,521119,393149,626189,120255,849377,949398,947437,165...10,173,86410,173,86410,173,86410,173,86410,173,86410,173,86410,173,864
2008-0538,672125,646146,129204,529316,543347,897370,354424,131517,435...15,602,53715,602,53715,602,53715,602,53715,602,53715,602,537
2008-0656,789130,140227,980292,796353,879395,565482,736544,154607,251...11,975,41211,975,41211,975,41211,975,41211,975,412
2008-0727,86773,671135,167208,446280,037391,844472,093512,725545,159...11,101,87111,101,87111,101,87111,101,871
2008-084,83228,66381,174143,824189,779322,912404,645444,354556,469577,837...11,889,31711,889,31711,889,317
2008-0943,464129,621182,285272,213360,952416,569443,168512,690559,478...12,257,72512,257,725
2008-1012,48834,42787,815140,229232,315278,096350,338433,697481,291...12,286,257
2008-1145,47354,888133,855218,682258,231299,241373,241406,498442,300...
2008-123,64013,54468,336155,393226,080280,855312,267356,324415,772447,415...
2009-0114,02159,371110,579146,099227,754282,028323,566397,696445,657...
2009-0210,30545,33182,621141,394208,972266,561381,645438,786504,101559,120...
2009-0348,36088,861163,203227,741277,519363,110445,692502,141590,448...
2009-0440,94388,459152,483180,056242,690296,742360,047421,386491,791...
2009-0516,94491,422155,278214,543294,250358,107417,433506,250556,906...
2009-069,65553,37466,180145,584191,803238,671274,905310,559335,235361,875...
2009-0714,82863,434153,961208,469266,350331,255347,971394,851484,572...
2009-0842,06174,308138,483201,926267,354362,786454,595586,499653,783...
2009-0921,00048,60783,286120,808177,113219,965292,369346,735414,277485,309...
2009-1014,00076,040121,174218,190272,546293,395356,915435,409502,850524,957...
2009-1122,02786,215107,052140,355190,949270,801325,670437,262507,735...
2009-1238,59456,496156,966200,832302,883359,784393,660434,076488,811...
2010-0115,19268,531125,925182,910245,085329,709414,668517,402543,897...
2010-027,00038,46471,406109,514138,221238,312336,107434,422487,681584,158...
2010-0336,937101,487163,262202,842284,095319,846407,635448,711566,546...
2010-0421,02653,448100,108203,550293,410390,386441,502530,854581,449595,430...
2010-058,66817,587133,963195,549344,196358,484405,324456,529539,244543,483...
2010-0612,578104,735149,230262,098341,830415,573477,388551,101574,825...
..................................................................
2015-075,87267,304180,826276,856410,123540,428605,334663,814758,798830,982...
2015-0812,27685,942155,713312,260409,088531,981609,506730,103772,317853,920...
2015-0929,40274,924151,892298,732470,405579,835665,925764,440847,904909,194...
2015-1014,00032,82497,632183,745217,697312,495353,968468,248563,718659,040...
2015-1124,86699,373152,317261,677359,813520,193566,705773,479902,104...
2015-1214,00048,498134,004247,850366,089487,366666,399797,729928,0861,017,398...
2016-013,21419,73468,339131,782196,112352,647423,183513,369602,016689,294...
2016-0213,89234,613146,780301,098394,766512,344626,812704,019783,039868,964...
2016-0318,35466,848137,523253,408337,021382,297506,033620,372699,394795,573...
2016-0414,78150,522102,340226,534302,754471,575589,080672,058771,207883,870...
2016-0532,921113,494242,172340,663478,898589,024699,911850,192925,041...
2016-0617,95926,99062,711226,354398,493556,146710,314830,507930,721993,293...
2016-0736,45461,237166,471283,087347,202465,628572,910674,225810,389906,277...
2016-0845,28059,505153,045300,803389,173584,470701,964759,620892,527992,987...
2016-0913,54470,012154,527261,422388,510477,824610,516764,207826,799960,007...
2016-101,76662,962143,398266,745349,691507,828676,418786,436872,9401,035,489...
2016-1155,837167,279278,020389,792434,640598,787778,913869,802941,776...
2016-1252,806108,192224,367331,978441,737516,372662,311763,014877,911...
2017-018,33834,338155,559259,698372,997459,083598,452725,466791,620903,333...
2017-0250,237151,889255,367350,165444,450604,689746,491878,076972,333...
2017-034,47625,25199,644171,894315,217371,524581,932698,986800,532893,609...
2017-0430,751157,559312,397469,488541,593644,181735,635878,324...
2017-053,02239,83494,168174,605255,919416,757597,409685,850...
2017-0631,85869,769150,997272,616357,801467,904...
2017-0728,82759,815186,044258,870338,946458,336...
2017-0814,000119,952201,944376,814...
2017-0918,58951,906137,963290,107...
2017-1035,037139,482...
2017-114,08814,687...
2017-1210,748...
" + ], + "text/plain": [ + " 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120\n", + "2008-01 NaN NaN 46915.015123 66813.794095 124029.778031 213813.036115 232114.863761 327169.296655 337501.659808 4.292739e+05 4.796771e+05 5.055815e+05 5.259446e+05 5.799184e+05 6.227641e+05 6.956515e+05 7.408990e+05 7.827367e+05 8.017367e+05 8.224186e+05 8.298561e+05 8.964788e+05 1.174073e+06 1.195511e+06 1.227306e+06 2.045397e+06 2.422956e+06 3.046078e+06 3.542270e+06 3.915234e+06 5.528275e+06 7.038966e+06 8.502826e+06 1.120826e+07 1.246686e+07 1.308236e+07 1.344861e+07 1.373430e+07 1.392130e+07 1.392130e+07 1.393003e+07 1.393003e+07 1.393735e+07 1.393735e+07 1.393735e+07 1.393735e+07 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1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 1.451423e+07 NaN\n", + "2008-03 NaN 48805.786934 76755.240277 167167.815200 221724.495661 305231.865291 317823.348760 389858.357958 460211.248501 4.954341e+05 5.483588e+05 5.996805e+05 6.724811e+05 7.224684e+05 7.780023e+05 8.294898e+05 8.916120e+05 9.521292e+05 1.039422e+06 1.039990e+06 1.249532e+06 1.444610e+06 1.472516e+06 1.510659e+06 2.041926e+06 2.046461e+06 2.634575e+06 4.568893e+06 5.036836e+06 5.830768e+06 5.970527e+06 7.417107e+06 9.868766e+06 9.941854e+06 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1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 1.197360e+07 NaN NaN\n", + "2008-04 NaN 30758.035988 48520.929757 119393.019268 149625.626993 189119.953667 255848.995299 377949.004270 398946.805487 4.371651e+05 4.622643e+05 5.193805e+05 5.346946e+05 5.749489e+05 5.940823e+05 6.200823e+05 6.388159e+05 6.921848e+05 7.140977e+05 7.244041e+05 9.495755e+05 9.791133e+05 9.939603e+05 1.160635e+06 1.525297e+06 1.649316e+06 2.148392e+06 2.538246e+06 3.568434e+06 3.649220e+06 4.980251e+06 5.207339e+06 6.769557e+06 7.521467e+06 8.600802e+06 9.462043e+06 1.009061e+07 1.010755e+07 1.011460e+07 1.011460e+07 1.011460e+07 1.012976e+07 1.012976e+07 1.012976e+07 1.012976e+07 1.014394e+07 1.014932e+07 1.014932e+07 1.015632e+07 1.016088e+07 1.016088e+07 1.016088e+07 1.016399e+07 1.016399e+07 1.016399e+07 1.016399e+07 1.016399e+07 1.016399e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.016696e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 1.017386e+07 NaN NaN NaN\n", + "2008-05 NaN 38672.002824 125645.996032 146128.738080 204528.632057 316543.260018 347897.029551 370353.736152 424131.312183 5.174347e+05 5.845825e+05 6.407680e+05 6.982131e+05 7.457809e+05 7.878126e+05 8.385251e+05 9.312740e+05 9.682768e+05 1.023066e+06 1.038387e+06 1.095324e+06 1.099322e+06 1.288538e+06 1.577088e+06 1.945239e+06 2.371558e+06 2.703630e+06 3.645908e+06 4.562933e+06 5.408148e+06 6.726099e+06 8.690866e+06 1.022057e+07 1.069539e+07 1.325201e+07 1.447174e+07 1.476643e+07 1.524805e+07 1.554674e+07 1.554674e+07 1.555273e+07 1.555696e+07 1.556396e+07 1.556513e+07 1.556513e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.556999e+07 1.557396e+07 1.557396e+07 1.557396e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.557649e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.558354e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 1.560254e+07 NaN NaN NaN NaN\n", + "2008-06 NaN 56788.951887 130139.935043 227979.947798 292796.177958 353879.350283 395564.597226 482736.376780 544154.045979 6.072512e+05 6.272579e+05 6.743334e+05 7.266210e+05 7.408913e+05 7.636275e+05 7.800010e+05 8.201195e+05 8.438632e+05 9.314041e+05 9.476107e+05 1.049446e+06 1.057185e+06 1.136050e+06 1.160931e+06 1.170714e+06 1.359927e+06 1.529584e+06 2.390170e+06 3.678164e+06 5.134112e+06 6.334022e+06 7.050686e+06 8.452380e+06 9.900435e+06 1.073379e+07 1.153280e+07 1.173383e+07 1.173383e+07 1.191383e+07 1.191899e+07 1.191899e+07 1.193032e+07 1.193032e+07 1.193032e+07 1.193032e+07 1.194617e+07 1.195158e+07 1.195158e+07 1.195158e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.195858e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197066e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 1.197541e+07 NaN NaN NaN NaN NaN\n", + "2008-07 NaN 27867.438010 73671.186639 135166.601705 208445.856653 280036.614963 391843.736734 472092.885838 512724.773221 5.451587e+05 5.971282e+05 6.319173e+05 6.856040e+05 7.281393e+05 7.685142e+05 7.827472e+05 8.022870e+05 8.249618e+05 8.338679e+05 8.643874e+05 8.853149e+05 8.920278e+05 9.202900e+05 1.016942e+06 1.030942e+06 1.165921e+06 1.609253e+06 2.992635e+06 3.503504e+06 4.848671e+06 5.313555e+06 6.048233e+06 8.015246e+06 9.067337e+06 1.009970e+07 1.080196e+07 1.099157e+07 1.099857e+07 1.100207e+07 1.100207e+07 1.100207e+07 1.101282e+07 1.101282e+07 1.101282e+07 1.104242e+07 1.105305e+07 1.105531e+07 1.106543e+07 1.106543e+07 1.106646e+07 1.106646e+07 1.106646e+07 1.107403e+07 1.107403e+07 1.108002e+07 1.108002e+07 1.108002e+07 1.108002e+07 1.108002e+07 1.108002e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.108160e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.109487e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 1.110187e+07 NaN NaN NaN NaN NaN NaN\n", + "2008-08 4832.347658 28663.352169 81174.325149 143823.753969 189778.775105 322911.952061 404644.628455 444354.082037 556469.471645 5.778366e+05 5.972233e+05 6.339826e+05 6.655116e+05 7.068361e+05 7.171831e+05 7.421081e+05 7.475395e+05 7.781453e+05 8.136729e+05 8.352770e+05 8.501195e+05 1.000239e+06 1.163244e+06 1.173219e+06 1.209483e+06 1.473016e+06 2.240575e+06 2.787657e+06 3.333590e+06 4.434400e+06 5.584938e+06 6.704678e+06 8.343224e+06 9.018195e+06 1.062627e+07 1.111357e+07 1.167463e+07 1.183841e+07 1.183841e+07 1.185003e+07 1.185003e+07 1.185003e+07 1.185003e+07 1.186403e+07 1.186403e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188190e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 1.188932e+07 NaN NaN NaN NaN NaN NaN NaN\n", + "2008-09 NaN 43464.271322 129621.055093 182285.166689 272213.301731 360951.618287 416569.049140 443168.263249 512689.862488 5.594782e+05 6.783411e+05 7.202981e+05 7.572994e+05 7.803084e+05 8.243695e+05 8.941447e+05 9.111255e+05 9.622760e+05 9.947346e+05 9.947346e+05 1.006547e+06 1.033435e+06 1.045216e+06 1.240862e+06 1.618396e+06 2.277027e+06 2.375991e+06 3.140532e+06 3.865015e+06 5.102801e+06 5.522613e+06 7.034454e+06 8.635246e+06 1.008857e+07 1.082684e+07 1.137027e+07 1.173546e+07 1.219762e+07 1.220238e+07 1.220238e+07 1.220238e+07 1.221209e+07 1.221209e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.221755e+07 1.223034e+07 1.223416e+07 1.223416e+07 1.223672e+07 1.223672e+07 1.223672e+07 1.223672e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225072e+07 1.225772e+07 1.225772e+07 1.225772e+07 1.225772e+07 1.225772e+07 1.225772e+07 1.225772e+07 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2008-10 NaN 12487.697845 34426.791815 87814.693479 140229.326978 232315.157186 278095.749566 350337.902162 433697.325778 4.812914e+05 5.301353e+05 5.392550e+05 5.778721e+05 6.435479e+05 7.366678e+05 7.499255e+05 8.160420e+05 8.695962e+05 9.143625e+05 9.488226e+05 9.558226e+05 9.826618e+05 1.006112e+06 1.290040e+06 1.433842e+06 1.534245e+06 1.995597e+06 2.553863e+06 3.169586e+06 3.682326e+06 4.892921e+06 6.387080e+06 7.884095e+06 9.483269e+06 1.084657e+07 1.138943e+07 1.190921e+07 1.222433e+07 1.222433e+07 1.222433e+07 1.222433e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.222941e+07 1.223678e+07 1.223678e+07 1.223678e+07 1.224172e+07 1.224172e+07 1.224172e+07 1.224172e+07 1.224172e+07 1.224172e+07 1.224172e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.225167e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227067e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.227585e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 1.228626e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2008-11 NaN 45472.755295 54888.373254 133855.398409 218681.672120 258230.577504 299241.312490 373241.011129 406497.852661 4.422999e+05 4.611072e+05 4.912394e+05 5.312573e+05 5.901824e+05 6.714825e+05 6.936988e+05 7.298105e+05 7.762239e+05 8.271901e+05 8.497877e+05 9.093462e+05 9.236183e+05 9.369501e+05 1.231004e+06 1.285912e+06 1.686881e+06 1.695053e+06 2.587116e+06 3.417424e+06 4.425015e+06 6.040233e+06 8.030328e+06 9.844783e+06 1.101158e+07 1.236885e+07 1.303878e+07 1.335224e+07 1.391896e+07 1.393296e+07 1.393296e+07 1.394105e+07 1.394105e+07 1.394105e+07 1.394105e+07 1.394105e+07 1.394105e+07 1.394805e+07 1.394805e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395176e+07 1.395769e+07 1.397382e+07 1.397382e+07 1.397382e+07 1.397382e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397493e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 1.397968e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2008-12 3639.502909 13544.208461 68336.048127 155393.350775 226079.814261 280854.909998 312267.261509 356324.270548 415771.824145 4.474146e+05 4.923965e+05 5.416381e+05 5.921881e+05 6.251871e+05 6.640022e+05 6.780640e+05 7.205090e+05 7.730488e+05 7.976530e+05 8.277561e+05 8.660880e+05 1.017595e+06 1.049446e+06 1.176880e+06 1.946762e+06 1.961451e+06 2.486723e+06 2.691175e+06 3.358255e+06 3.774708e+06 5.102067e+06 5.917394e+06 8.589631e+06 9.757664e+06 1.052896e+07 1.066007e+07 1.119596e+07 1.147596e+07 1.147596e+07 1.147596e+07 1.147596e+07 1.149195e+07 1.149195e+07 1.149195e+07 1.149195e+07 1.149195e+07 1.149561e+07 1.150768e+07 1.150768e+07 1.151406e+07 1.151406e+07 1.151406e+07 1.151988e+07 1.151988e+07 1.151988e+07 1.151988e+07 1.152924e+07 1.154824e+07 1.154824e+07 1.154824e+07 1.154824e+07 1.154824e+07 1.155659e+07 1.155659e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 1.157229e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-01 NaN 14021.486015 59371.444785 110579.070674 146098.773783 227753.601115 282028.358920 323566.201835 397695.547254 4.456574e+05 5.015892e+05 5.669825e+05 6.309729e+05 6.854833e+05 7.483043e+05 7.959443e+05 7.959443e+05 8.265397e+05 8.647928e+05 8.769435e+05 9.083034e+05 9.206849e+05 9.568760e+05 9.989937e+05 1.004197e+06 1.219933e+06 1.643390e+06 2.099041e+06 3.590344e+06 5.143511e+06 6.094230e+06 7.247691e+06 8.302948e+06 1.008292e+07 1.133991e+07 1.278914e+07 1.322562e+07 1.339307e+07 1.340007e+07 1.341913e+07 1.342613e+07 1.344013e+07 1.344013e+07 1.344253e+07 1.345425e+07 1.345425e+07 1.345425e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346065e+07 1.346846e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347192e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 1.347836e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-02 10305.328650 45331.415878 82620.776579 141393.638930 208972.295172 266561.450681 381644.589580 438786.125012 504100.892148 5.591196e+05 6.059468e+05 6.904966e+05 7.320151e+05 8.393178e+05 8.666051e+05 9.384845e+05 1.008686e+06 1.021074e+06 1.041079e+06 1.071899e+06 1.119428e+06 1.139939e+06 1.144219e+06 1.169127e+06 1.447555e+06 1.670682e+06 1.796705e+06 2.648363e+06 2.872013e+06 3.960665e+06 5.653328e+06 8.767975e+06 1.066698e+07 1.354423e+07 1.551456e+07 1.592077e+07 1.610651e+07 1.666651e+07 1.694651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.722651e+07 1.723602e+07 1.723602e+07 1.725147e+07 1.725147e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726089e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 1.726789e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-03 NaN 48359.661508 88861.303637 163202.894412 227741.355370 277519.417249 363110.487751 445692.217863 502140.847640 5.904475e+05 6.277867e+05 6.650249e+05 7.299666e+05 7.495817e+05 8.013200e+05 8.337004e+05 8.460156e+05 8.579232e+05 8.830195e+05 9.162880e+05 1.084062e+06 1.115314e+06 1.286074e+06 1.506419e+06 1.506419e+06 1.752266e+06 2.216819e+06 3.082124e+06 3.552359e+06 5.131311e+06 6.244795e+06 7.479860e+06 9.032015e+06 1.044146e+07 1.142125e+07 1.212187e+07 1.272204e+07 1.272204e+07 1.272530e+07 1.272530e+07 1.272530e+07 1.273515e+07 1.273918e+07 1.274092e+07 1.274320e+07 1.274320e+07 1.274320e+07 1.274320e+07 1.274320e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.274631e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275633e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 1.275799e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-04 NaN 40942.691388 88459.201714 152483.051146 180056.368888 242690.185901 296741.643219 360047.083766 421385.604356 4.917912e+05 5.210717e+05 5.810379e+05 6.661736e+05 7.557554e+05 8.327863e+05 8.670049e+05 9.191808e+05 9.346273e+05 9.763242e+05 1.018561e+06 1.067710e+06 1.106159e+06 1.516181e+06 1.700887e+06 1.978208e+06 2.013377e+06 2.197165e+06 3.198193e+06 4.708962e+06 5.883665e+06 6.293452e+06 8.161962e+06 9.567093e+06 1.101763e+07 1.214080e+07 1.301418e+07 1.443712e+07 1.464745e+07 1.464745e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466149e+07 1.466569e+07 1.467395e+07 1.467395e+07 1.467395e+07 1.467395e+07 1.467395e+07 1.467395e+07 1.468008e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 1.468168e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-05 NaN 16943.995031 91422.237490 155277.776640 214543.369647 294249.972625 358106.808695 417432.917056 506249.809112 5.569062e+05 5.733751e+05 6.279816e+05 6.842673e+05 7.387687e+05 7.779736e+05 8.330171e+05 8.478414e+05 8.671377e+05 9.642019e+05 9.758671e+05 9.916414e+05 1.049927e+06 1.384055e+06 1.756717e+06 1.942920e+06 1.958237e+06 2.169146e+06 3.190747e+06 4.576051e+06 6.036987e+06 6.944497e+06 7.450359e+06 9.152315e+06 1.035728e+07 1.153600e+07 1.212329e+07 1.248140e+07 1.276140e+07 1.278040e+07 1.278898e+07 1.279598e+07 1.281498e+07 1.281498e+07 1.283398e+07 1.283398e+07 1.283398e+07 1.283889e+07 1.283889e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.284549e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 1.285784e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-06 9654.757434 53374.361739 66179.663536 145584.002432 191803.068685 238671.184931 274905.215675 310559.330253 335234.966257 3.618755e+05 4.823756e+05 6.131614e+05 7.019967e+05 7.710071e+05 7.898330e+05 8.034114e+05 8.283521e+05 8.473208e+05 9.199272e+05 9.441240e+05 9.865883e+05 1.069848e+06 1.203248e+06 1.217805e+06 1.902551e+06 2.562978e+06 3.154397e+06 3.346111e+06 4.668753e+06 5.474823e+06 6.452820e+06 7.975885e+06 8.894291e+06 9.876455e+06 1.021350e+07 1.099451e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.149964e+07 1.150846e+07 1.151546e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.153983e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.154374e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 1.155833e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-07 NaN 14828.433220 63433.822772 153961.033084 208468.889255 266349.529233 331254.681602 347971.496960 394851.242928 4.845718e+05 5.650205e+05 6.518535e+05 6.932173e+05 7.020563e+05 7.341949e+05 7.790193e+05 8.038361e+05 8.429726e+05 8.537775e+05 1.124488e+06 1.182916e+06 1.185770e+06 1.199899e+06 1.308624e+06 1.578925e+06 1.831957e+06 2.017518e+06 2.228647e+06 3.488772e+06 5.308304e+06 7.221543e+06 8.254964e+06 9.166061e+06 1.140133e+07 1.224734e+07 1.285849e+07 1.303281e+07 1.304001e+07 1.304001e+07 1.306155e+07 1.306650e+07 1.307522e+07 1.307522e+07 1.307522e+07 1.308029e+07 1.308029e+07 1.310235e+07 1.311146e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311460e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311682e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.311886e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 1.312421e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-08 NaN 42060.879472 74308.207021 138482.705665 201925.828181 267353.734828 362785.812915 454595.027216 586499.084445 6.537828e+05 6.779625e+05 7.591168e+05 7.908580e+05 8.139143e+05 8.948135e+05 9.215840e+05 9.530409e+05 9.703950e+05 1.013849e+06 1.051244e+06 1.051244e+06 1.080010e+06 1.260010e+06 1.484249e+06 1.490416e+06 1.846818e+06 2.504589e+06 3.556965e+06 4.336342e+06 5.516612e+06 7.522441e+06 8.157652e+06 9.327930e+06 1.113169e+07 1.203414e+07 1.311398e+07 1.376114e+07 1.389873e+07 1.391670e+07 1.391670e+07 1.392324e+07 1.392745e+07 1.392745e+07 1.392745e+07 1.392745e+07 1.392745e+07 1.392745e+07 1.392745e+07 1.393858e+07 1.393858e+07 1.393858e+07 1.394340e+07 1.395822e+07 1.395822e+07 1.396165e+07 1.396165e+07 1.396165e+07 1.396165e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 1.397890e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-09 21000.000000 48606.750645 83285.571507 120808.491963 177113.398012 219964.714007 292368.997629 346734.626390 414277.239627 4.853089e+05 5.883301e+05 6.526994e+05 6.617424e+05 7.336440e+05 7.537455e+05 8.028366e+05 8.549329e+05 8.804500e+05 9.342814e+05 9.492684e+05 9.529178e+05 9.624659e+05 1.097772e+06 1.477267e+06 1.482648e+06 1.912293e+06 2.722875e+06 2.840940e+06 3.579894e+06 4.988712e+06 6.981906e+06 9.298142e+06 1.057529e+07 1.113809e+07 1.236229e+07 1.280326e+07 1.280326e+07 1.297918e+07 1.299837e+07 1.300127e+07 1.301080e+07 1.301080e+07 1.301519e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303015e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.303689e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 1.304003e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-10 14000.000000 76039.934850 121174.024940 218190.328235 272546.475900 293394.807472 356915.447258 435408.980482 502849.808060 5.249569e+05 5.627629e+05 5.869442e+05 6.396963e+05 6.847946e+05 7.194251e+05 7.424249e+05 7.691565e+05 8.181874e+05 8.331508e+05 8.416259e+05 1.036796e+06 1.055796e+06 1.074686e+06 1.103095e+06 1.137368e+06 1.880577e+06 2.750372e+06 3.168263e+06 3.601795e+06 4.509291e+06 5.666306e+06 7.033016e+06 7.749980e+06 8.858300e+06 9.898297e+06 1.061573e+07 1.086048e+07 1.114392e+07 1.114392e+07 1.114392e+07 1.114392e+07 1.115432e+07 1.115584e+07 1.115584e+07 1.115584e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 1.116181e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-11 NaN 22026.547150 86215.491450 107051.511211 140354.912560 190948.789914 270800.504739 325670.175072 437261.737161 5.077352e+05 5.953774e+05 6.391908e+05 6.797463e+05 7.519877e+05 7.983108e+05 8.186670e+05 8.487393e+05 8.964305e+05 9.142940e+05 9.370984e+05 9.579518e+05 1.259320e+06 1.617367e+06 1.782933e+06 1.856815e+06 2.128450e+06 2.325060e+06 2.662016e+06 3.400874e+06 4.407815e+06 4.827843e+06 6.111492e+06 7.449114e+06 1.039874e+07 1.084195e+07 1.177540e+07 1.178940e+07 1.187333e+07 1.188204e+07 1.188204e+07 1.188204e+07 1.188204e+07 1.188436e+07 1.189296e+07 1.190946e+07 1.190946e+07 1.190946e+07 1.191523e+07 1.192223e+07 1.192223e+07 1.193450e+07 1.193832e+07 1.194303e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.194759e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 1.196159e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2009-12 NaN 38593.890837 56496.390306 156966.409082 200832.007436 302883.169353 359784.297023 393660.259514 434076.185488 4.888106e+05 5.751590e+05 6.697303e+05 7.106325e+05 7.530221e+05 7.882492e+05 8.325017e+05 8.982119e+05 9.239700e+05 9.276885e+05 9.664426e+05 9.833986e+05 1.012972e+06 1.030696e+06 1.058622e+06 1.146511e+06 1.313611e+06 1.835769e+06 2.872244e+06 3.934356e+06 4.475365e+06 6.030671e+06 7.634509e+06 8.899059e+06 1.002086e+07 1.077070e+07 1.150293e+07 1.152834e+07 1.152834e+07 1.181524e+07 1.181524e+07 1.181576e+07 1.182076e+07 1.182341e+07 1.182341e+07 1.183486e+07 1.183486e+07 1.184129e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.185961e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.187312e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 1.188012e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-01 NaN 15192.478687 68530.905343 125925.359623 182910.440134 245085.129610 329709.058011 414668.308627 517401.824085 5.438970e+05 5.725160e+05 6.512245e+05 7.263250e+05 7.827737e+05 8.342398e+05 8.919351e+05 9.386821e+05 1.039243e+06 1.047679e+06 1.096746e+06 1.119723e+06 1.655309e+06 1.678486e+06 1.709990e+06 1.914689e+06 2.110134e+06 2.956936e+06 3.313306e+06 4.834482e+06 5.844906e+06 7.090529e+06 8.623409e+06 1.005589e+07 1.097883e+07 1.170447e+07 1.228071e+07 1.277474e+07 1.279374e+07 1.280171e+07 1.281133e+07 1.302937e+07 1.303703e+07 1.303703e+07 1.304234e+07 1.304234e+07 1.304234e+07 1.304234e+07 1.305476e+07 1.305476e+07 1.305476e+07 1.305476e+07 1.305476e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 1.306250e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-02 7000.000000 38463.755225 71405.930126 109513.586548 138221.020801 238312.341665 336107.394308 434421.873414 487680.694486 5.841582e+05 6.116884e+05 7.064166e+05 7.558891e+05 7.904674e+05 8.338100e+05 8.772753e+05 8.987391e+05 9.137035e+05 9.706375e+05 9.934472e+05 1.021625e+06 1.089200e+06 1.089200e+06 1.662665e+06 1.908055e+06 2.171196e+06 2.251818e+06 2.957715e+06 3.608963e+06 4.745549e+06 5.903183e+06 7.466563e+06 8.333997e+06 9.548416e+06 1.040240e+07 1.148569e+07 1.203255e+07 1.203888e+07 1.203888e+07 1.203888e+07 1.205288e+07 1.206676e+07 1.206676e+07 1.208231e+07 1.208231e+07 1.208647e+07 1.209222e+07 1.209222e+07 1.209222e+07 1.209222e+07 1.209875e+07 1.209875e+07 1.211277e+07 1.211277e+07 1.211720e+07 1.211720e+07 1.211720e+07 1.211720e+07 1.211720e+07 1.211720e+07 1.211720e+07 1.212842e+07 1.212842e+07 1.212842e+07 1.212842e+07 1.212842e+07 1.212842e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213334e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 1.213604e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-03 NaN 36936.741932 101487.296477 163261.758382 202841.816717 284095.256613 319846.477099 407634.879837 448711.322288 5.665459e+05 6.511617e+05 6.945765e+05 7.652033e+05 8.336253e+05 8.700769e+05 8.796623e+05 9.498897e+05 9.928409e+05 1.065584e+06 1.078214e+06 1.102201e+06 1.146306e+06 1.205784e+06 1.603525e+06 1.817033e+06 2.398648e+06 2.967450e+06 4.071580e+06 4.638323e+06 6.011081e+06 6.167047e+06 7.408491e+06 1.008166e+07 1.137128e+07 1.360160e+07 1.441862e+07 1.527065e+07 1.527496e+07 1.528521e+07 1.529116e+07 1.529154e+07 1.529154e+07 1.529154e+07 1.529154e+07 1.530354e+07 1.530354e+07 1.530354e+07 1.530354e+07 1.531734e+07 1.531734e+07 1.531734e+07 1.531734e+07 1.531734e+07 1.531734e+07 1.531734e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.534508e+07 1.535154e+07 1.535978e+07 1.535978e+07 1.535978e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 1.536678e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-04 21025.798787 53447.650347 100108.282271 203550.357598 293410.380687 390386.256676 441502.327702 530854.031392 581448.615800 5.954295e+05 7.015736e+05 7.863745e+05 8.226035e+05 8.568538e+05 8.933597e+05 9.509980e+05 1.020576e+06 1.053592e+06 1.093243e+06 1.114472e+06 1.170713e+06 1.216180e+06 1.343722e+06 1.603057e+06 1.936123e+06 2.358016e+06 2.674655e+06 3.307984e+06 3.980773e+06 5.339263e+06 7.350477e+06 9.029510e+06 1.004691e+07 1.101464e+07 1.163080e+07 1.273287e+07 1.273475e+07 1.274091e+07 1.302091e+07 1.302091e+07 1.302091e+07 1.302091e+07 1.302091e+07 1.302399e+07 1.304299e+07 1.304299e+07 1.304299e+07 1.305085e+07 1.307520e+07 1.308920e+07 1.308920e+07 1.308920e+07 1.308920e+07 1.308920e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.309607e+07 1.310397e+07 1.310397e+07 1.310397e+07 1.310397e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-05 8667.892025 17587.403200 133963.349430 195549.431152 344196.220582 358483.861896 405324.095071 456529.481671 539243.683217 5.434832e+05 5.972616e+05 6.794807e+05 7.123604e+05 7.496064e+05 7.607013e+05 7.997535e+05 8.218416e+05 8.745161e+05 9.239205e+05 9.711416e+05 1.025058e+06 1.050590e+06 1.441485e+06 1.475722e+06 1.798494e+06 2.199549e+06 2.791450e+06 3.371107e+06 3.644318e+06 4.658849e+06 5.477320e+06 7.640377e+06 9.747816e+06 1.083215e+07 1.159991e+07 1.257350e+07 1.275350e+07 1.276050e+07 1.278591e+07 1.279906e+07 1.279906e+07 1.281510e+07 1.281510e+07 1.281510e+07 1.282533e+07 1.282533e+07 1.283175e+07 1.284047e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.284299e+07 1.285545e+07 1.285545e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.286548e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 1.287248e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-06 NaN 12578.489879 104734.811973 149229.514700 262097.759079 341830.188298 415573.168758 477387.655861 551101.021689 5.748247e+05 6.811285e+05 7.399254e+05 7.887626e+05 8.576940e+05 8.930362e+05 9.406751e+05 9.797502e+05 1.038560e+06 1.070367e+06 1.078487e+06 1.136077e+06 1.162903e+06 1.342387e+06 1.385391e+06 1.487523e+06 1.929807e+06 2.432781e+06 2.880149e+06 3.494679e+06 5.086449e+06 6.698690e+06 8.734897e+06 9.467942e+06 1.056977e+07 1.148323e+07 1.223821e+07 1.263825e+07 1.264580e+07 1.264580e+07 1.265975e+07 1.265975e+07 1.267183e+07 1.268258e+07 1.268258e+07 1.268258e+07 1.268958e+07 1.269602e+07 1.269602e+07 1.269602e+07 1.269602e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271172e+07 1.271709e+07 1.271709e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 1.273488e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-07 NaN 6381.823659 39654.582433 101993.041439 165489.117040 263829.530954 360199.553821 421473.795777 572091.437047 6.481498e+05 7.122969e+05 7.689687e+05 7.849470e+05 8.150021e+05 8.609778e+05 9.022331e+05 9.305704e+05 9.685439e+05 9.961908e+05 1.012279e+06 1.056738e+06 1.077857e+06 1.089187e+06 1.297185e+06 1.538361e+06 2.306672e+06 2.809188e+06 2.998837e+06 3.178789e+06 4.476703e+06 6.517327e+06 6.998929e+06 9.267948e+06 1.085478e+07 1.227655e+07 1.245655e+07 1.290615e+07 1.292015e+07 1.292859e+07 1.294259e+07 1.294259e+07 1.295060e+07 1.297422e+07 1.297422e+07 1.297422e+07 1.298838e+07 1.299129e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.299773e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 1.300612e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-08 5699.233143 31908.457567 77798.863290 146972.051753 202483.421254 282909.361182 316043.146596 363297.357948 441559.842446 5.369931e+05 5.955047e+05 6.499044e+05 6.882616e+05 7.032328e+05 7.656314e+05 7.879201e+05 8.263601e+05 8.978140e+05 9.095236e+05 9.904123e+05 1.003740e+06 1.008288e+06 1.371040e+06 1.382672e+06 1.501896e+06 1.506208e+06 1.701950e+06 1.890086e+06 3.034150e+06 5.066331e+06 6.863757e+06 8.391515e+06 9.615505e+06 1.093538e+07 1.133008e+07 1.358644e+07 1.358644e+07 1.375138e+07 1.375838e+07 1.378000e+07 1.379027e+07 1.379027e+07 1.379856e+07 1.379856e+07 1.379856e+07 1.380556e+07 1.380556e+07 1.381256e+07 1.381256e+07 1.381329e+07 1.381329e+07 1.381609e+07 1.383319e+07 1.383319e+07 1.383319e+07 1.384719e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385041e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 1.385544e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-09 18547.088350 48691.700406 111979.559012 209798.027293 271504.952660 321226.400741 435855.135908 551038.638606 641908.238350 7.146768e+05 7.941919e+05 8.541378e+05 8.892131e+05 9.580188e+05 9.884634e+05 1.043426e+06 1.112539e+06 1.160222e+06 1.216507e+06 1.254737e+06 1.282685e+06 1.303784e+06 1.350875e+06 1.622045e+06 1.656168e+06 1.887947e+06 2.175315e+06 2.319408e+06 3.551489e+06 4.693431e+06 6.171209e+06 7.441141e+06 8.991020e+06 1.072188e+07 1.144452e+07 1.192285e+07 1.241472e+07 1.264339e+07 1.293430e+07 1.296244e+07 1.297947e+07 1.298442e+07 1.298442e+07 1.298442e+07 1.299759e+07 1.300115e+07 1.300473e+07 1.300473e+07 1.300473e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.301098e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 1.302998e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-10 NaN 54726.513076 85405.854628 163756.093451 207564.918242 279292.938246 346942.548955 426523.966191 501958.854821 5.408842e+05 5.899772e+05 6.237525e+05 6.724372e+05 7.092039e+05 8.048044e+05 8.643060e+05 8.943391e+05 9.444745e+05 1.004661e+06 1.040223e+06 1.058083e+06 1.132911e+06 1.167192e+06 1.184052e+06 1.392052e+06 1.863881e+06 2.841982e+06 3.026758e+06 3.545800e+06 4.756933e+06 6.673352e+06 8.655088e+06 1.049613e+07 1.119918e+07 1.213215e+07 1.288093e+07 1.374539e+07 1.403413e+07 1.403413e+07 1.406010e+07 1.406010e+07 1.406010e+07 1.406010e+07 1.406010e+07 1.406010e+07 1.406776e+07 1.406776e+07 1.406776e+07 1.408381e+07 1.409620e+07 1.409620e+07 1.409620e+07 1.409620e+07 1.409620e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410336e+07 1.410850e+07 1.410850e+07 1.410850e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 1.412021e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-11 NaN 13508.711378 54321.133799 124198.585581 199640.089461 255934.626113 320190.932099 364145.776831 482359.203401 5.000009e+05 6.347390e+05 7.163326e+05 7.851899e+05 8.513736e+05 8.957894e+05 9.454921e+05 9.694748e+05 1.003383e+06 1.044711e+06 1.096672e+06 1.200355e+06 1.379582e+06 1.388305e+06 1.398910e+06 1.563274e+06 1.670687e+06 2.413681e+06 3.251451e+06 4.322566e+06 6.002265e+06 7.456257e+06 8.890760e+06 9.924880e+06 1.176561e+07 1.294822e+07 1.335507e+07 1.364310e+07 1.373043e+07 1.375643e+07 1.375643e+07 1.378330e+07 1.379665e+07 1.380365e+07 1.380733e+07 1.381635e+07 1.383485e+07 1.384090e+07 1.384090e+07 1.384090e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.385490e+07 1.386419e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.386873e+07 1.387465e+07 1.387465e+07 1.387465e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 1.388050e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2010-12 9285.681508 53110.769690 102016.178794 139705.381727 242642.755350 341557.583475 366424.012692 432159.959918 483154.032135 5.445314e+05 5.894485e+05 6.596034e+05 7.445519e+05 8.245571e+05 8.530997e+05 8.734600e+05 9.308463e+05 9.462484e+05 9.942000e+05 1.019439e+06 1.053417e+06 1.075862e+06 1.096982e+06 1.304820e+06 1.426754e+06 1.751579e+06 2.159043e+06 2.489852e+06 3.152637e+06 4.874548e+06 6.012783e+06 8.274855e+06 1.110657e+07 1.207574e+07 1.312359e+07 1.418251e+07 1.459052e+07 1.460681e+07 1.460681e+07 1.461902e+07 1.461902e+07 1.462235e+07 1.462235e+07 1.464135e+07 1.464135e+07 1.464135e+07 1.464135e+07 1.464135e+07 1.467289e+07 1.468125e+07 1.468125e+07 1.468327e+07 1.468388e+07 1.468388e+07 1.468388e+07 1.468388e+07 1.468388e+07 1.468388e+07 1.469547e+07 1.469547e+07 1.469547e+07 1.469547e+07 1.469547e+07 1.469547e+07 1.469547e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 1.470126e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-01 NaN 19101.745807 41160.494365 72959.334214 107392.349064 142114.220833 195200.244706 273995.548877 338386.632395 4.203691e+05 5.043649e+05 5.206321e+05 5.983234e+05 6.497836e+05 7.140311e+05 9.139438e+05 9.346046e+05 9.851061e+05 1.004106e+06 1.035927e+06 1.057010e+06 1.088147e+06 1.102147e+06 1.238963e+06 1.546111e+06 2.326634e+06 2.899831e+06 3.608096e+06 4.450848e+06 5.883626e+06 6.450860e+06 8.571664e+06 1.064438e+07 1.343881e+07 1.344276e+07 1.410899e+07 1.457927e+07 1.457927e+07 1.476658e+07 1.476658e+07 1.477634e+07 1.477634e+07 1.478334e+07 1.478334e+07 1.478334e+07 1.480687e+07 1.481845e+07 1.482551e+07 1.482814e+07 1.482814e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484258e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.484588e+07 1.485009e+07 1.485009e+07 1.485009e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-02 5831.889082 28053.654180 87067.238462 189119.354787 278099.280079 350083.060370 412692.469731 441418.208047 501365.092934 5.962157e+05 6.493019e+05 7.195880e+05 8.182901e+05 8.623865e+05 9.175737e+05 9.441554e+05 1.040093e+06 1.081677e+06 1.102372e+06 1.193128e+06 1.301222e+06 1.332360e+06 1.382601e+06 1.597537e+06 2.150189e+06 2.182873e+06 2.475535e+06 3.261908e+06 4.972937e+06 5.329165e+06 6.732461e+06 7.804236e+06 9.649937e+06 1.140937e+07 1.269257e+07 1.297257e+07 1.326291e+07 1.327691e+07 1.329953e+07 1.330312e+07 1.332312e+07 1.332591e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336562e+07 1.336956e+07 1.336956e+07 1.337710e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339081e+07 1.339572e+07 1.339572e+07 1.339572e+07 1.340234e+07 1.340234e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 1.340781e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-03 16123.818400 27670.995026 106255.744459 186615.744170 263401.664484 304293.715721 368852.220625 466455.573462 516190.011105 5.658252e+05 6.186063e+05 6.681244e+05 7.499626e+05 7.899559e+05 8.451226e+05 9.495912e+05 1.015705e+06 1.080412e+06 1.101935e+06 1.108935e+06 1.124154e+06 1.177935e+06 1.192716e+06 1.239913e+06 1.243670e+06 1.627715e+06 1.922758e+06 2.263876e+06 3.744920e+06 4.856131e+06 5.655887e+06 7.608230e+06 9.429245e+06 1.110058e+07 1.273057e+07 1.309075e+07 1.355075e+07 1.355775e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.356042e+07 1.357342e+07 1.357342e+07 1.357342e+07 1.357342e+07 1.357342e+07 1.357342e+07 1.358140e+07 1.358140e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.360458e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 1.361317e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-04 4838.017331 23114.032888 61987.244500 136897.118875 270364.285023 368358.836729 442964.227486 543522.835399 657518.433226 7.126609e+05 7.677243e+05 8.767519e+05 9.181269e+05 9.780924e+05 9.942741e+05 1.110282e+06 1.167157e+06 1.185615e+06 1.231003e+06 1.237013e+06 1.248898e+06 1.438419e+06 1.624281e+06 1.639675e+06 1.737094e+06 2.499125e+06 3.288566e+06 4.001112e+06 4.505067e+06 5.263091e+06 6.810471e+06 8.361025e+06 9.914343e+06 1.162237e+07 1.251148e+07 1.330832e+07 1.393403e+07 1.422957e+07 1.422957e+07 1.423773e+07 1.424492e+07 1.425192e+07 1.425192e+07 1.425192e+07 1.425192e+07 1.425192e+07 1.426218e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.427374e+07 1.428074e+07 1.428074e+07 1.428074e+07 1.428074e+07 1.429626e+07 1.429626e+07 1.429626e+07 1.429626e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.429873e+07 1.430171e+07 1.430171e+07 1.430171e+07 1.430171e+07 1.430171e+07 1.430171e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-05 NaN 25969.252116 78175.143479 154095.553299 240098.394156 288395.261595 420794.978271 465779.053434 545209.770017 5.800742e+05 6.385637e+05 7.165541e+05 7.680540e+05 8.337151e+05 8.535742e+05 8.725341e+05 9.168707e+05 9.933424e+05 1.041421e+06 1.076686e+06 1.101574e+06 1.104153e+06 1.138851e+06 1.156562e+06 1.192243e+06 1.599668e+06 2.545950e+06 3.279290e+06 4.097467e+06 5.054867e+06 5.509446e+06 7.041759e+06 8.744281e+06 9.266679e+06 1.053559e+07 1.142647e+07 1.241287e+07 1.242509e+07 1.261733e+07 1.261733e+07 1.261733e+07 1.262247e+07 1.262247e+07 1.262947e+07 1.263647e+07 1.263647e+07 1.264208e+07 1.264208e+07 1.264208e+07 1.264208e+07 1.264208e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265477e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.265906e+07 1.266420e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-06 NaN 52177.772742 157182.932638 212788.668588 256945.637034 324926.843610 412259.948897 485708.568616 583417.128856 6.484978e+05 7.639250e+05 8.179749e+05 8.480037e+05 8.823934e+05 9.093657e+05 1.020841e+06 1.069318e+06 1.123153e+06 1.179227e+06 1.207227e+06 1.237655e+06 1.247811e+06 1.260217e+06 1.625181e+06 1.814341e+06 2.157082e+06 2.472835e+06 3.718418e+06 4.584337e+06 5.210524e+06 5.730757e+06 7.135392e+06 8.230722e+06 9.800849e+06 1.201413e+07 1.279738e+07 1.308968e+07 1.325236e+07 1.325995e+07 1.327079e+07 1.327784e+07 1.327784e+07 1.327784e+07 1.328560e+07 1.329232e+07 1.330485e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.330947e+07 1.331647e+07 1.332347e+07 1.332347e+07 1.332347e+07 1.332347e+07 1.332347e+07 1.332861e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 1.333444e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-07 20445.923122 54246.419760 91025.062822 153045.982656 229529.975932 271703.480109 395459.317808 512269.726925 564181.748268 6.323235e+05 6.918065e+05 7.457673e+05 8.083736e+05 8.477697e+05 9.049143e+05 9.676758e+05 9.886758e+05 1.221099e+06 1.295405e+06 1.355246e+06 1.376466e+06 1.401756e+06 1.418567e+06 1.466177e+06 1.865005e+06 1.900775e+06 2.384575e+06 2.844575e+06 3.884598e+06 5.364595e+06 7.823422e+06 9.428651e+06 1.083024e+07 1.198015e+07 1.344805e+07 1.362948e+07 1.406833e+07 1.406833e+07 1.406833e+07 1.406833e+07 1.407533e+07 1.408933e+07 1.408933e+07 1.409633e+07 1.409759e+07 1.409759e+07 1.411159e+07 1.411159e+07 1.411159e+07 1.411159e+07 1.412256e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.412760e+07 1.414039e+07 1.414835e+07 1.414835e+07 1.414835e+07 1.414835e+07 1.414835e+07 1.415360e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 1.416625e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-08 NaN 59161.814775 59161.814775 121366.004189 234249.168070 319317.109063 416573.781419 489063.917741 571647.444056 6.030010e+05 6.813769e+05 7.673878e+05 8.340224e+05 8.915414e+05 9.457041e+05 9.803649e+05 1.039811e+06 1.071930e+06 1.115320e+06 1.171001e+06 1.180341e+06 1.394408e+06 1.691706e+06 1.780497e+06 2.105713e+06 2.813917e+06 2.824082e+06 4.178650e+06 4.780048e+06 5.652616e+06 6.469829e+06 8.255926e+06 9.438589e+06 9.725589e+06 1.098130e+07 1.196748e+07 1.246177e+07 1.266429e+07 1.278718e+07 1.278718e+07 1.278718e+07 1.278718e+07 1.279190e+07 1.279190e+07 1.279190e+07 1.279978e+07 1.279978e+07 1.279978e+07 1.282103e+07 1.282645e+07 1.282645e+07 1.283303e+07 1.283303e+07 1.283303e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.285903e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.286497e+07 1.287267e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-09 8616.989893 41195.424916 111931.583669 199014.044456 259139.404961 287016.878984 360921.699066 415577.192426 507841.344599 5.569931e+05 5.980730e+05 6.902388e+05 7.454212e+05 8.046835e+05 8.497900e+05 9.145130e+05 9.417029e+05 9.695326e+05 1.000611e+06 1.023670e+06 1.082764e+06 1.094362e+06 1.248678e+06 1.336363e+06 1.360127e+06 1.588664e+06 3.055229e+06 3.721462e+06 4.207027e+06 4.853752e+06 6.709248e+06 7.910928e+06 9.498153e+06 1.123462e+07 1.215049e+07 1.252848e+07 1.300107e+07 1.371083e+07 1.390999e+07 1.390999e+07 1.390999e+07 1.391669e+07 1.392702e+07 1.392702e+07 1.393492e+07 1.394586e+07 1.394586e+07 1.394586e+07 1.394586e+07 1.394586e+07 1.394586e+07 1.394586e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.394957e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 1.395730e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-10 11941.597417 54918.291800 121394.787021 230029.515493 348929.283946 407559.665800 433414.890401 494418.560462 533126.386993 5.909520e+05 6.285762e+05 6.968370e+05 7.481720e+05 8.050799e+05 8.671493e+05 8.944724e+05 9.443727e+05 1.000959e+06 1.028918e+06 1.072608e+06 1.103524e+06 1.237894e+06 1.308795e+06 1.735846e+06 1.753554e+06 1.962963e+06 2.964036e+06 3.702861e+06 4.331939e+06 4.955532e+06 6.478636e+06 7.965934e+06 9.695020e+06 1.115079e+07 1.174434e+07 1.220910e+07 1.301337e+07 1.329337e+07 1.383337e+07 1.384037e+07 1.385135e+07 1.385135e+07 1.386904e+07 1.387518e+07 1.387518e+07 1.387518e+07 1.387518e+07 1.388131e+07 1.389001e+07 1.389001e+07 1.389001e+07 1.389001e+07 1.389888e+07 1.389888e+07 1.389888e+07 1.391005e+07 1.392405e+07 1.392503e+07 1.392503e+07 1.393429e+07 1.393429e+07 1.393765e+07 1.394468e+07 1.394468e+07 1.394468e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 1.394793e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-11 NaN 27553.838792 105006.902823 171603.105271 204435.403562 330160.254895 368698.386252 440125.871042 512007.276897 5.894240e+05 6.729196e+05 7.550056e+05 8.085635e+05 9.313430e+05 1.046908e+06 1.099404e+06 1.130779e+06 1.185324e+06 1.196417e+06 1.200808e+06 1.220542e+06 1.254654e+06 1.308869e+06 1.637050e+06 1.643557e+06 2.336508e+06 2.642508e+06 2.822508e+06 3.728294e+06 5.424972e+06 6.925456e+06 7.362006e+06 8.382006e+06 1.048410e+07 1.120528e+07 1.166247e+07 1.268481e+07 1.269875e+07 1.299698e+07 1.299698e+07 1.299698e+07 1.299698e+07 1.299698e+07 1.300842e+07 1.301369e+07 1.301369e+07 1.302161e+07 1.303399e+07 1.303399e+07 1.303399e+07 1.303399e+07 1.303399e+07 1.304803e+07 1.304803e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305080e+07 1.305942e+07 1.305942e+07 1.305942e+07 1.305942e+07 1.305942e+07 1.305942e+07 1.305942e+07 1.305942e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2011-12 NaN 26987.348220 63712.414351 128843.426450 250481.100767 296896.131911 428193.181979 467579.675150 630883.752272 6.823751e+05 7.335912e+05 8.444677e+05 9.049189e+05 9.718150e+05 9.901548e+05 1.023835e+06 1.073395e+06 1.224679e+06 1.293617e+06 1.317231e+06 1.360700e+06 1.403723e+06 1.417410e+06 1.449591e+06 1.484123e+06 2.119267e+06 2.477225e+06 3.038492e+06 3.964172e+06 4.881915e+06 5.691535e+06 6.883943e+06 8.358723e+06 9.972595e+06 1.113098e+07 1.192067e+07 1.218352e+07 1.218352e+07 1.218761e+07 1.219264e+07 1.220009e+07 1.221148e+07 1.221148e+07 1.221457e+07 1.223919e+07 1.224905e+07 1.224905e+07 1.227089e+07 1.227089e+07 1.227089e+07 1.227089e+07 1.227089e+07 1.227089e+07 1.228463e+07 1.228463e+07 1.228463e+07 1.230336e+07 1.230336e+07 1.230805e+07 1.230805e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 1.231570e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-01 NaN 5686.987821 73042.172897 146748.277238 244447.261643 346307.665156 424226.557354 505400.057748 612559.350187 6.628856e+05 7.165620e+05 7.567891e+05 7.745374e+05 8.340645e+05 8.566990e+05 8.838164e+05 8.996067e+05 9.109208e+05 9.640703e+05 1.027045e+06 1.048597e+06 1.286409e+06 1.315259e+06 1.595805e+06 1.623071e+06 1.826616e+06 2.068501e+06 3.018092e+06 3.520224e+06 4.502966e+06 5.295910e+06 7.039475e+06 8.541053e+06 9.989760e+06 1.108067e+07 1.211276e+07 1.234259e+07 1.237549e+07 1.258568e+07 1.258568e+07 1.259262e+07 1.259262e+07 1.260446e+07 1.260626e+07 1.260626e+07 1.260626e+07 1.261208e+07 1.261667e+07 1.261667e+07 1.261667e+07 1.261667e+07 1.261924e+07 1.261924e+07 1.261924e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.261977e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 1.262694e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-02 13107.090680 25071.495144 62505.304351 163568.982079 270728.715818 344043.222508 521862.484567 590357.560986 658389.571544 7.053722e+05 7.751899e+05 9.442802e+05 1.021797e+06 1.069076e+06 1.110702e+06 1.182849e+06 1.238456e+06 1.281408e+06 1.308697e+06 1.451086e+06 1.478088e+06 1.489863e+06 1.499828e+06 1.624367e+06 1.634134e+06 1.835148e+06 2.098294e+06 2.605301e+06 3.582310e+06 6.193329e+06 7.479964e+06 8.576370e+06 1.042570e+07 1.155037e+07 1.286929e+07 1.363146e+07 1.387132e+07 1.414869e+07 1.415139e+07 1.416996e+07 1.417911e+07 1.418784e+07 1.420811e+07 1.421150e+07 1.421432e+07 1.421992e+07 1.421992e+07 1.421992e+07 1.421992e+07 1.421992e+07 1.421992e+07 1.421992e+07 1.422314e+07 1.422314e+07 1.422314e+07 1.422314e+07 1.422314e+07 1.422618e+07 1.422618e+07 1.422618e+07 1.423001e+07 1.423001e+07 1.423001e+07 1.423001e+07 1.423001e+07 1.423001e+07 1.423001e+07 1.423343e+07 1.423343e+07 1.423343e+07 1.423343e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-03 19000.000000 55631.237977 100520.304487 164333.965744 212242.702240 252184.633074 333213.452394 436909.971606 557629.911842 6.583601e+05 7.639663e+05 8.656471e+05 9.616740e+05 1.061373e+06 1.091794e+06 1.159120e+06 1.214192e+06 1.226030e+06 1.268999e+06 1.340652e+06 1.398246e+06 1.417605e+06 1.578419e+06 1.817252e+06 2.071770e+06 2.283731e+06 3.643158e+06 4.216553e+06 5.371941e+06 6.859345e+06 7.452627e+06 8.930044e+06 1.076893e+07 1.321090e+07 1.409563e+07 1.528693e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545252e+07 1.545904e+07 1.545904e+07 1.546604e+07 1.548504e+07 1.548504e+07 1.548504e+07 1.548504e+07 1.548504e+07 1.548504e+07 1.548858e+07 1.548858e+07 1.548858e+07 1.548858e+07 1.549528e+07 1.549528e+07 1.550082e+07 1.550082e+07 1.551254e+07 1.551254e+07 1.551254e+07 1.551254e+07 1.551254e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-04 17918.213682 71131.483498 130713.525758 178223.949159 245423.069125 321962.047814 399902.339759 526821.716271 622079.480539 7.544929e+05 8.520942e+05 9.208654e+05 9.842332e+05 1.060035e+06 1.093765e+06 1.135778e+06 1.185268e+06 1.226276e+06 1.255656e+06 1.269656e+06 1.321728e+06 1.340728e+06 1.358083e+06 1.372373e+06 1.611062e+06 2.075196e+06 2.674711e+06 2.914226e+06 3.780211e+06 4.427168e+06 6.114147e+06 6.303677e+06 8.734064e+06 9.269435e+06 1.076452e+07 1.132530e+07 1.197479e+07 1.197479e+07 1.253479e+07 1.255579e+07 1.256742e+07 1.257654e+07 1.257654e+07 1.258323e+07 1.259023e+07 1.259023e+07 1.259023e+07 1.259023e+07 1.259199e+07 1.259199e+07 1.259199e+07 1.259199e+07 1.260935e+07 1.260935e+07 1.261541e+07 1.261541e+07 1.261541e+07 1.261541e+07 1.261541e+07 1.261541e+07 1.261541e+07 1.261822e+07 1.261822e+07 1.261822e+07 1.261822e+07 1.261822e+07 1.261822e+07 1.261822e+07 1.261822e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-05 NaN 3226.695985 26191.588533 99994.256991 148346.443041 279589.662986 364781.858289 466238.011329 510949.980948 5.797950e+05 6.700173e+05 7.250597e+05 7.752739e+05 8.476722e+05 8.945051e+05 9.316010e+05 9.879291e+05 1.019212e+06 1.046300e+06 1.091918e+06 1.130833e+06 1.182793e+06 1.201793e+06 1.427597e+06 1.461971e+06 1.491356e+06 2.343250e+06 3.320405e+06 3.697091e+06 4.103408e+06 5.433988e+06 8.398247e+06 9.963994e+06 1.050709e+07 1.127244e+07 1.145798e+07 1.198296e+07 1.226943e+07 1.227812e+07 1.227812e+07 1.228551e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229406e+07 1.229660e+07 1.229660e+07 1.229660e+07 1.230285e+07 1.230285e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.230932e+07 1.231102e+07 1.231102e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-06 NaN 44336.944311 122316.370328 189321.587648 284404.896049 383194.460715 470511.240633 541658.196295 606660.775311 6.726998e+05 7.096006e+05 7.782762e+05 8.531763e+05 9.021134e+05 9.468536e+05 9.657455e+05 1.039464e+06 1.074760e+06 1.145933e+06 1.149063e+06 1.178384e+06 1.405470e+06 1.531945e+06 1.675000e+06 2.135000e+06 2.904485e+06 2.940886e+06 3.316617e+06 4.656003e+06 5.489658e+06 6.168191e+06 8.336974e+06 9.278123e+06 9.885747e+06 1.078799e+07 1.167835e+07 1.265670e+07 1.293670e+07 1.293670e+07 1.296108e+07 1.297199e+07 1.298447e+07 1.298447e+07 1.298447e+07 1.298447e+07 1.298783e+07 1.298783e+07 1.299528e+07 1.301089e+07 1.301789e+07 1.302604e+07 1.302604e+07 1.302604e+07 1.302604e+07 1.302604e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304349e+07 1.304986e+07 1.304986e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-07 7000.000000 17155.497667 104920.349670 162785.813919 197290.277500 338390.171046 408094.284127 478363.805707 518694.271574 5.540758e+05 6.008477e+05 6.579571e+05 7.344526e+05 8.005145e+05 8.594334e+05 9.034338e+05 9.787942e+05 1.001732e+06 1.023852e+06 1.036630e+06 1.041616e+06 1.069028e+06 1.129312e+06 1.187190e+06 1.226218e+06 2.014114e+06 2.137562e+06 2.433664e+06 2.936166e+06 4.044137e+06 4.767722e+06 6.265648e+06 7.920444e+06 9.445169e+06 1.040629e+07 1.162784e+07 1.257761e+07 1.285761e+07 1.286532e+07 1.288661e+07 1.289359e+07 1.289359e+07 1.289359e+07 1.289359e+07 1.289359e+07 1.289918e+07 1.291818e+07 1.291818e+07 1.293797e+07 1.293797e+07 1.294184e+07 1.294184e+07 1.294184e+07 1.294422e+07 1.294422e+07 1.294724e+07 1.294724e+07 1.296124e+07 1.296124e+07 1.297949e+07 1.297949e+07 1.297949e+07 1.297949e+07 1.297949e+07 1.297949e+07 1.297949e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-08 1661.157858 53519.589000 119748.109570 203374.125137 271043.138575 362065.002575 435782.172223 506023.633700 589243.789510 6.671597e+05 7.082445e+05 7.883987e+05 8.308116e+05 9.194695e+05 9.898777e+05 1.031681e+06 1.100833e+06 1.233995e+06 1.276363e+06 1.305155e+06 1.332318e+06 1.488463e+06 1.525042e+06 1.585332e+06 1.742132e+06 2.070032e+06 2.087829e+06 3.103498e+06 3.925317e+06 4.861634e+06 5.702901e+06 6.847026e+06 8.238648e+06 8.847989e+06 1.063678e+07 1.139216e+07 1.185623e+07 1.187090e+07 1.187090e+07 1.187790e+07 1.187790e+07 1.187790e+07 1.187790e+07 1.189270e+07 1.189270e+07 1.189270e+07 1.190903e+07 1.190903e+07 1.190903e+07 1.190903e+07 1.190903e+07 1.191735e+07 1.192435e+07 1.193450e+07 1.193450e+07 1.193450e+07 1.193450e+07 1.194565e+07 1.195265e+07 1.195265e+07 1.195265e+07 1.195265e+07 1.195636e+07 1.195636e+07 1.195636e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-09 4782.323680 27662.766529 39092.299225 86594.374820 163637.584146 251298.852396 317355.936609 396729.984967 535688.724185 5.735548e+05 6.912174e+05 7.423651e+05 8.279133e+05 8.736395e+05 9.201414e+05 9.363366e+05 9.791285e+05 1.037310e+06 1.052837e+06 1.077735e+06 1.146025e+06 1.548024e+06 2.061830e+06 2.223588e+06 2.478457e+06 2.493172e+06 2.772918e+06 2.902672e+06 3.517841e+06 3.805350e+06 4.913289e+06 6.357328e+06 7.857139e+06 9.518671e+06 1.016371e+07 1.025783e+07 1.026226e+07 1.027802e+07 1.027802e+07 1.028502e+07 1.028502e+07 1.028502e+07 1.028502e+07 1.028502e+07 1.028502e+07 1.028502e+07 1.030147e+07 1.030147e+07 1.030147e+07 1.030147e+07 1.030751e+07 1.032476e+07 1.032476e+07 1.032476e+07 1.032476e+07 1.032476e+07 1.033212e+07 1.033683e+07 1.035083e+07 1.036236e+07 1.036236e+07 1.036236e+07 1.036236e+07 1.036936e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-10 13392.784604 37999.786344 88414.327105 212665.812054 314474.681534 406709.342868 480783.526021 529447.755536 619717.865078 6.855869e+05 8.072495e+05 8.835255e+05 9.672502e+05 9.987685e+05 1.064904e+06 1.113668e+06 1.148777e+06 1.195815e+06 1.203935e+06 1.215583e+06 1.230136e+06 1.265179e+06 1.298115e+06 1.498842e+06 1.784161e+06 1.808229e+06 2.334130e+06 2.988355e+06 3.812712e+06 4.825244e+06 6.146147e+06 6.621202e+06 8.302078e+06 9.911238e+06 1.089825e+07 1.271749e+07 1.309617e+07 1.373716e+07 1.391716e+07 1.393224e+07 1.393224e+07 1.393224e+07 1.395654e+07 1.395900e+07 1.396907e+07 1.396907e+07 1.396907e+07 1.396907e+07 1.397403e+07 1.399294e+07 1.399294e+07 1.399294e+07 1.399294e+07 1.399294e+07 1.400956e+07 1.400956e+07 1.400956e+07 1.400956e+07 1.400956e+07 1.400956e+07 1.401837e+07 1.401837e+07 1.401837e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-11 NaN 23836.213272 72628.438203 127971.500892 161279.813503 225616.873385 300199.274272 430629.845211 567984.559855 6.249572e+05 6.883896e+05 7.746843e+05 8.615866e+05 9.334334e+05 1.017758e+06 1.047284e+06 1.060880e+06 1.085924e+06 1.141294e+06 1.212452e+06 1.240749e+06 1.256492e+06 1.312706e+06 1.470647e+06 1.483962e+06 1.619878e+06 2.061884e+06 2.414375e+06 3.313679e+06 4.277779e+06 5.528228e+06 7.874400e+06 8.505362e+06 9.684289e+06 1.007612e+07 1.150727e+07 1.179288e+07 1.179288e+07 1.179356e+07 1.179356e+07 1.180154e+07 1.180854e+07 1.180854e+07 1.181535e+07 1.183516e+07 1.183516e+07 1.183516e+07 1.183516e+07 1.185108e+07 1.185935e+07 1.186234e+07 1.186234e+07 1.186234e+07 1.186234e+07 1.186234e+07 1.186926e+07 1.186926e+07 1.186926e+07 1.186926e+07 1.186926e+07 1.186926e+07 1.186926e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012-12 26589.428550 119271.763714 214770.993496 275815.407157 316164.518512 430178.651171 493457.591885 570408.980158 609702.647390 6.788052e+05 7.779778e+05 8.455946e+05 8.911229e+05 9.691583e+05 1.030406e+06 1.066043e+06 1.124343e+06 1.205542e+06 1.222751e+06 1.305007e+06 1.409513e+06 1.445805e+06 1.447821e+06 1.465980e+06 1.474936e+06 2.286704e+06 2.297400e+06 3.213580e+06 3.689234e+06 5.478679e+06 6.456751e+06 7.495672e+06 9.071454e+06 1.037537e+07 1.080048e+07 1.118883e+07 1.119397e+07 1.119397e+07 1.119880e+07 1.120171e+07 1.120752e+07 1.121501e+07 1.122126e+07 1.122126e+07 1.124226e+07 1.125345e+07 1.125345e+07 1.125345e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.125716e+07 1.126441e+07 1.126941e+07 1.126941e+07 1.127462e+07 1.127462e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-01 NaN 42970.866185 109658.175147 269447.919867 316189.381460 346611.784658 435423.735461 536576.579542 655240.090352 7.822457e+05 8.017010e+05 8.783724e+05 9.665048e+05 1.061201e+06 1.106377e+06 1.165500e+06 1.200121e+06 1.233019e+06 1.296292e+06 1.318702e+06 1.348652e+06 1.465572e+06 1.602941e+06 1.616271e+06 1.870512e+06 2.292950e+06 2.730927e+06 2.967593e+06 3.864725e+06 5.687584e+06 8.942028e+06 1.011819e+07 1.184798e+07 1.397787e+07 1.521801e+07 1.569334e+07 1.597334e+07 1.597334e+07 1.598611e+07 1.598611e+07 1.598611e+07 1.598611e+07 1.598611e+07 1.600112e+07 1.600112e+07 1.601396e+07 1.601396e+07 1.602248e+07 1.602248e+07 1.602755e+07 1.602755e+07 1.603426e+07 1.603426e+07 1.603682e+07 1.603682e+07 1.603682e+07 1.604357e+07 1.604357e+07 1.604357e+07 1.604618e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-02 27988.063022 43617.896631 72566.065601 133608.630201 195523.863557 292490.487355 373774.127950 501460.065698 544760.351691 6.111267e+05 6.725518e+05 7.289007e+05 7.840076e+05 8.633298e+05 8.905116e+05 1.017592e+06 1.041671e+06 1.099918e+06 1.131177e+06 1.167072e+06 1.224046e+06 1.264682e+06 1.337533e+06 1.407973e+06 1.502143e+06 1.825635e+06 2.360840e+06 2.871276e+06 3.332841e+06 4.465368e+06 5.678972e+06 6.471929e+06 8.087396e+06 9.679313e+06 1.038244e+07 1.102244e+07 1.102244e+07 1.120244e+07 1.143628e+07 1.145586e+07 1.146594e+07 1.147110e+07 1.148405e+07 1.149280e+07 1.149280e+07 1.149980e+07 1.149980e+07 1.150920e+07 1.150920e+07 1.150920e+07 1.150920e+07 1.150920e+07 1.150920e+07 1.152810e+07 1.152810e+07 1.152810e+07 1.152810e+07 1.152810e+07 1.152810e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-03 17310.865710 62350.792473 78984.467469 169514.822507 251116.870701 320451.114103 361716.293417 454513.802107 556431.478462 6.185079e+05 6.350550e+05 7.256064e+05 7.711168e+05 8.266865e+05 9.034755e+05 9.718165e+05 1.310080e+06 1.365954e+06 1.388032e+06 1.439207e+06 1.499026e+06 1.509641e+06 1.564600e+06 1.582027e+06 1.597449e+06 1.827321e+06 2.344464e+06 2.885014e+06 3.274169e+06 3.809110e+06 5.983849e+06 8.805923e+06 9.648623e+06 1.081448e+07 1.210077e+07 1.295581e+07 1.295581e+07 1.322114e+07 1.358114e+07 1.358114e+07 1.358114e+07 1.358174e+07 1.358554e+07 1.360103e+07 1.360743e+07 1.361635e+07 1.363395e+07 1.363395e+07 1.363395e+07 1.363395e+07 1.364889e+07 1.364889e+07 1.364889e+07 1.364889e+07 1.364889e+07 1.365652e+07 1.365652e+07 1.365652e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-04 5213.534716 47467.983075 115619.453207 219286.922589 288557.786829 338098.594876 375043.233238 417122.540637 576373.008690 6.504329e+05 7.126937e+05 7.674279e+05 8.296378e+05 8.922551e+05 9.200228e+05 9.666319e+05 1.016964e+06 1.073423e+06 1.127420e+06 1.202770e+06 1.240333e+06 1.301322e+06 1.484444e+06 1.639827e+06 1.671938e+06 2.098844e+06 2.198512e+06 2.492866e+06 3.341801e+06 4.327619e+06 5.973460e+06 7.270875e+06 8.947007e+06 9.719981e+06 1.039949e+07 1.115061e+07 1.143061e+07 1.160240e+07 1.160746e+07 1.160746e+07 1.160746e+07 1.163546e+07 1.164543e+07 1.164543e+07 1.164543e+07 1.165924e+07 1.166682e+07 1.166682e+07 1.166682e+07 1.168582e+07 1.168938e+07 1.169361e+07 1.169361e+07 1.169361e+07 1.169361e+07 1.169361e+07 1.169361e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-05 13466.308532 57809.278441 115303.075102 243231.968147 339376.598505 463618.956908 545429.161179 640179.484699 694708.645164 7.925077e+05 8.603242e+05 9.451780e+05 1.025486e+06 1.077879e+06 1.139377e+06 1.207785e+06 1.235926e+06 1.300529e+06 1.328436e+06 1.408718e+06 1.583665e+06 1.618517e+06 1.646328e+06 2.001017e+06 2.730401e+06 2.751487e+06 3.084486e+06 4.024117e+06 4.695874e+06 6.658382e+06 7.762826e+06 1.039841e+07 1.099892e+07 1.269907e+07 1.433354e+07 1.477080e+07 1.505780e+07 1.505780e+07 1.505780e+07 1.506936e+07 1.509196e+07 1.510478e+07 1.510478e+07 1.511115e+07 1.511115e+07 1.511951e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 1.512581e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-06 6851.046160 48749.850603 115967.945182 213882.527995 262375.851270 294007.897989 414453.901237 448043.243635 511183.448431 5.827483e+05 6.581599e+05 7.390029e+05 7.938015e+05 8.735176e+05 9.029504e+05 9.489518e+05 1.019152e+06 1.062395e+06 1.164633e+06 1.217944e+06 1.272309e+06 1.303608e+06 1.536169e+06 1.542387e+06 1.559520e+06 2.074887e+06 2.228731e+06 2.389350e+06 3.188869e+06 3.701507e+06 6.894999e+06 9.027832e+06 1.148423e+07 1.286245e+07 1.426308e+07 1.474590e+07 1.487509e+07 1.487509e+07 1.487509e+07 1.487509e+07 1.489508e+07 1.490208e+07 1.490208e+07 1.490208e+07 1.490208e+07 1.490874e+07 1.490874e+07 1.490874e+07 1.491369e+07 1.492480e+07 1.492480e+07 1.492480e+07 1.492480e+07 1.492480e+07 1.492480e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-07 6782.902842 55063.752408 146375.251701 220525.596258 302788.415268 401525.243297 464612.726899 533564.551922 569317.492229 5.905914e+05 7.227688e+05 7.496945e+05 8.113553e+05 8.691947e+05 9.377159e+05 1.038998e+06 1.131302e+06 1.175426e+06 1.204403e+06 1.222783e+06 1.252880e+06 1.269835e+06 1.322607e+06 1.441431e+06 1.950341e+06 1.979980e+06 2.594866e+06 3.895193e+06 4.814619e+06 6.115638e+06 7.041012e+06 7.910486e+06 8.698905e+06 1.064710e+07 1.204014e+07 1.284103e+07 1.359496e+07 1.377496e+07 1.377551e+07 1.377551e+07 1.377551e+07 1.377551e+07 1.378117e+07 1.378117e+07 1.378117e+07 1.380040e+07 1.380040e+07 1.380040e+07 1.380352e+07 1.381052e+07 1.381052e+07 1.381052e+07 1.381052e+07 1.381052e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-08 21493.736878 37014.445919 97477.054038 211300.550680 307550.888418 378224.607183 431058.123078 566587.431435 666214.823009 7.278920e+05 8.381991e+05 9.033731e+05 9.505973e+05 1.030044e+06 1.069563e+06 1.109653e+06 1.169821e+06 1.215845e+06 1.276531e+06 1.315838e+06 1.356699e+06 1.359668e+06 1.525559e+06 1.942977e+06 2.291130e+06 2.577752e+06 3.626110e+06 3.904697e+06 5.340787e+06 6.324719e+06 7.252410e+06 9.277976e+06 1.038428e+07 1.181720e+07 1.319325e+07 1.412314e+07 1.472198e+07 1.474273e+07 1.517091e+07 1.518491e+07 1.521228e+07 1.521749e+07 1.521749e+07 1.521749e+07 1.521749e+07 1.523560e+07 1.524960e+07 1.524960e+07 1.524960e+07 1.524960e+07 1.525421e+07 1.525421e+07 1.525421e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-09 10687.554570 40493.961643 107530.766127 182444.008540 220427.869640 281858.991704 401320.162378 474997.449058 580030.516751 6.483130e+05 7.607993e+05 8.764157e+05 9.272012e+05 1.025061e+06 1.085101e+06 1.160856e+06 1.205555e+06 1.239366e+06 1.269912e+06 1.333183e+06 1.362820e+06 1.381512e+06 1.760330e+06 1.765017e+06 1.914478e+06 2.200586e+06 2.954984e+06 3.385725e+06 4.523652e+06 6.117601e+06 7.476769e+06 8.768101e+06 9.595513e+06 1.069479e+07 1.116634e+07 1.299227e+07 1.355993e+07 1.388544e+07 1.390444e+07 1.391636e+07 1.392838e+07 1.392838e+07 1.395072e+07 1.397497e+07 1.398231e+07 1.398231e+07 1.398231e+07 1.398885e+07 1.399585e+07 1.399585e+07 1.399585e+07 1.399585e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-10 NaN 56517.023888 121934.860215 230385.820568 291559.061505 414411.018928 498470.248885 560530.720898 638382.826040 7.153254e+05 7.696779e+05 8.406330e+05 9.355161e+05 9.580896e+05 9.845999e+05 1.080999e+06 1.100745e+06 1.143120e+06 1.352546e+06 1.362456e+06 1.391801e+06 1.440657e+06 1.742491e+06 1.778834e+06 1.997892e+06 2.339106e+06 2.485508e+06 3.463516e+06 4.507027e+06 5.293265e+06 6.041807e+06 8.087345e+06 1.024829e+07 1.079717e+07 1.205813e+07 1.311515e+07 1.315594e+07 1.350695e+07 1.367342e+07 1.367342e+07 1.369194e+07 1.370594e+07 1.372408e+07 1.373108e+07 1.373108e+07 1.373108e+07 1.373108e+07 1.373108e+07 1.373108e+07 1.373108e+07 1.373390e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-11 19467.118469 59220.730629 131605.568961 189745.447681 258020.634405 397846.152494 431512.527607 543568.333105 622624.083274 7.524033e+05 8.129641e+05 8.969616e+05 9.669505e+05 1.048428e+06 1.105289e+06 1.146104e+06 1.188850e+06 1.223182e+06 1.248723e+06 1.271233e+06 1.445564e+06 1.467719e+06 1.506616e+06 1.765110e+06 1.792671e+06 1.905313e+06 2.299841e+06 2.730592e+06 2.942758e+06 3.334736e+06 3.997850e+06 5.337660e+06 6.553309e+06 7.841943e+06 9.477381e+06 9.878962e+06 1.007033e+07 1.066606e+07 1.066606e+07 1.066606e+07 1.066606e+07 1.068818e+07 1.068818e+07 1.068818e+07 1.072055e+07 1.072055e+07 1.072055e+07 1.073385e+07 1.075270e+07 1.075270e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2013-12 16508.968579 71626.430811 93582.636678 181449.945139 249455.818169 319601.764395 375265.551469 459305.863281 539282.303606 5.814275e+05 6.381347e+05 6.750801e+05 7.914803e+05 8.357746e+05 8.441117e+05 8.935497e+05 9.722172e+05 9.902964e+05 1.010100e+06 1.050042e+06 1.289026e+06 1.346868e+06 1.550977e+06 1.937642e+06 2.054862e+06 2.098572e+06 2.623447e+06 2.843794e+06 3.522755e+06 4.171864e+06 4.881741e+06 6.400906e+06 8.164562e+06 9.060049e+06 1.034052e+07 1.063777e+07 1.106940e+07 1.107157e+07 1.108132e+07 1.110897e+07 1.111413e+07 1.111413e+07 1.113313e+07 1.114501e+07 1.114501e+07 1.114757e+07 1.114757e+07 1.115083e+07 1.115083e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-01 18193.615004 39939.231007 77062.556722 210751.198716 325414.551148 434108.663784 499231.742050 581891.540190 636104.751524 7.319155e+05 8.046067e+05 8.951043e+05 9.346007e+05 9.977177e+05 1.053679e+06 1.117600e+06 1.194226e+06 1.228147e+06 1.252255e+06 1.388046e+06 1.441130e+06 1.661880e+06 1.672859e+06 1.824146e+06 2.121501e+06 2.333618e+06 2.890290e+06 3.583215e+06 3.787643e+06 4.400919e+06 6.655304e+06 7.990654e+06 8.924634e+06 9.842833e+06 1.144033e+07 1.234487e+07 1.275077e+07 1.339421e+07 1.339421e+07 1.342754e+07 1.342900e+07 1.343678e+07 1.343828e+07 1.344528e+07 1.344528e+07 1.345228e+07 1.345448e+07 1.345448e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-02 11026.197800 56848.025440 150250.510215 262320.117199 398024.318249 508318.831934 555161.147489 650183.352998 716616.762680 8.024585e+05 9.110309e+05 9.820743e+05 1.043217e+06 1.122031e+06 1.199621e+06 1.263135e+06 1.344549e+06 1.405414e+06 1.409526e+06 1.430385e+06 1.430385e+06 1.451538e+06 1.671859e+06 1.835855e+06 2.207108e+06 2.428851e+06 2.888052e+06 3.841642e+06 4.433282e+06 4.548279e+06 6.409280e+06 7.638117e+06 8.883708e+06 1.063066e+07 1.174264e+07 1.262111e+07 1.281328e+07 1.282452e+07 1.283208e+07 1.283208e+07 1.284941e+07 1.284941e+07 1.287100e+07 1.287692e+07 1.287692e+07 1.288049e+07 1.288049e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-03 41856.032839 65869.107902 169770.881386 273966.255027 388844.521556 544543.378575 631927.708896 764488.338401 840595.367160 9.587591e+05 1.020056e+06 1.067282e+06 1.143743e+06 1.209735e+06 1.270530e+06 1.332327e+06 1.377764e+06 1.410519e+06 1.423039e+06 1.530223e+06 1.807264e+06 1.843415e+06 2.042809e+06 2.047532e+06 2.189489e+06 2.219289e+06 2.467793e+06 2.783951e+06 3.276145e+06 4.111549e+06 5.416191e+06 6.654989e+06 7.960628e+06 9.682304e+06 1.028205e+07 1.135320e+07 1.147915e+07 1.186038e+07 1.203290e+07 1.205423e+07 1.208052e+07 1.208648e+07 1.210335e+07 1.212528e+07 1.212528e+07 1.213228e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-04 25232.184583 39232.184583 99285.089409 180734.538567 279326.942076 362908.147903 450839.926027 555559.675960 639013.347413 7.379873e+05 7.965488e+05 8.549866e+05 9.312424e+05 1.032633e+06 1.102250e+06 1.142361e+06 1.181072e+06 1.206438e+06 1.268394e+06 1.293403e+06 1.325001e+06 1.379050e+06 1.390902e+06 1.532747e+06 2.057607e+06 2.750844e+06 2.881778e+06 3.197040e+06 3.705483e+06 4.692376e+06 5.799875e+06 7.428576e+06 8.374490e+06 1.036887e+07 1.161723e+07 1.162423e+07 1.191038e+07 1.191038e+07 1.191038e+07 1.191420e+07 1.192120e+07 1.192120e+07 1.192120e+07 1.192120e+07 1.192820e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-05 NaN 17227.928985 44978.750435 109899.063365 162324.368587 267111.290872 318542.834315 393684.443678 487727.468572 5.981860e+05 6.901631e+05 7.850082e+05 8.271083e+05 8.648744e+05 9.352265e+05 9.732577e+05 1.000582e+06 1.044075e+06 1.117560e+06 1.165439e+06 1.205530e+06 1.249227e+06 1.446733e+06 1.801323e+06 2.597599e+06 3.099421e+06 3.193494e+06 3.402099e+06 4.242114e+06 4.777499e+06 5.999819e+06 6.937059e+06 8.061326e+06 9.870807e+06 1.062268e+07 1.173493e+07 1.244718e+07 1.245269e+07 1.245269e+07 1.245269e+07 1.245269e+07 1.247169e+07 1.247169e+07 1.248409e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-06 NaN 32351.229848 64187.546630 130476.798057 212264.135272 319317.327515 462912.695647 522502.320981 531804.816399 6.084550e+05 7.325961e+05 8.159605e+05 8.574263e+05 9.434921e+05 9.781782e+05 1.034130e+06 1.117837e+06 1.173629e+06 1.339080e+06 1.399123e+06 1.430922e+06 1.453914e+06 1.464219e+06 1.511423e+06 1.821108e+06 2.116551e+06 3.160642e+06 3.849900e+06 4.667106e+06 6.126510e+06 6.922271e+06 8.816664e+06 1.007515e+07 1.156074e+07 1.285229e+07 1.370937e+07 1.424326e+07 1.470934e+07 1.470934e+07 1.500476e+07 1.501543e+07 1.503301e+07 1.505095e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-07 17395.126262 60930.341532 155284.490889 242306.198742 334847.112917 441349.550453 500984.388067 612300.107595 706468.194565 7.588460e+05 8.172415e+05 9.454929e+05 1.026354e+06 1.072546e+06 1.110079e+06 1.171340e+06 1.227452e+06 1.247020e+06 1.299990e+06 1.314517e+06 1.359795e+06 1.451765e+06 1.768154e+06 1.791525e+06 2.144522e+06 2.512511e+06 2.990893e+06 3.425368e+06 4.273232e+06 5.528513e+06 6.179057e+06 7.944388e+06 9.458743e+06 1.101939e+07 1.189521e+07 1.280930e+07 1.317271e+07 1.317271e+07 1.318111e+07 1.318397e+07 1.321213e+07 1.321213e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-08 17118.276431 59870.901660 144321.553723 258345.271429 411179.077501 525300.290179 568785.928348 786905.031455 873181.077655 9.354163e+05 1.022524e+06 1.127962e+06 1.196046e+06 1.279273e+06 1.290967e+06 1.331586e+06 1.425706e+06 1.511862e+06 1.697432e+06 1.756411e+06 1.781660e+06 1.942232e+06 2.229977e+06 2.377433e+06 2.556849e+06 2.943618e+06 3.991538e+06 4.572714e+06 5.243041e+06 5.983310e+06 6.973213e+06 8.318253e+06 9.366363e+06 9.883582e+06 1.045539e+07 1.119667e+07 1.167119e+07 1.222479e+07 1.222479e+07 1.222953e+07 1.223838e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-09 11766.356540 82379.941597 172610.023324 274462.623590 350528.878988 440391.670339 591567.536956 692040.961612 795249.708574 8.541849e+05 9.512645e+05 1.080947e+06 1.138492e+06 1.224503e+06 1.277070e+06 1.310421e+06 1.388076e+06 1.416799e+06 1.453331e+06 1.482251e+06 1.542327e+06 1.615166e+06 1.637143e+06 1.767856e+06 2.249258e+06 2.283728e+06 2.473577e+06 3.768045e+06 4.552870e+06 6.157817e+06 6.930869e+06 7.934573e+06 1.019861e+07 1.116766e+07 1.324323e+07 1.410602e+07 1.459375e+07 1.515375e+07 1.515998e+07 1.517437e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-10 7686.279785 54974.970759 116238.688091 175099.228934 275348.345322 335545.263866 471884.698064 576790.645225 735260.475092 8.555950e+05 9.482086e+05 1.034936e+06 1.138223e+06 1.187993e+06 1.294868e+06 1.355724e+06 1.419712e+06 1.457313e+06 1.501579e+06 1.529614e+06 1.613779e+06 1.632180e+06 1.746606e+06 1.765532e+06 1.806910e+06 1.836320e+06 2.174113e+06 2.519925e+06 3.926413e+06 5.105051e+06 5.757500e+06 7.056210e+06 8.257514e+06 8.790797e+06 8.790797e+06 9.536522e+06 1.011005e+07 1.039005e+07 1.039005e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-11 NaN 40085.299101 91636.572757 112560.477050 254301.803837 365833.657433 443699.235407 508997.690899 660867.744220 7.429288e+05 8.046260e+05 8.977490e+05 9.815526e+05 1.025466e+06 1.102743e+06 1.146192e+06 1.182286e+06 1.250814e+06 1.267165e+06 1.449628e+06 1.512876e+06 1.812493e+06 1.839472e+06 1.998785e+06 2.308626e+06 2.774328e+06 3.046437e+06 3.921712e+06 4.594792e+06 5.354966e+06 6.788126e+06 7.617161e+06 9.900574e+06 1.120646e+07 1.267127e+07 1.385002e+07 1.425480e+07 1.446091e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-12 2338.009450 45034.198587 101928.638230 197043.351557 243033.192117 341992.144323 418395.410115 528516.048037 671436.850298 7.738165e+05 8.346629e+05 8.897036e+05 9.357052e+05 1.007127e+06 1.037005e+06 1.082897e+06 1.107119e+06 1.191268e+06 1.227168e+06 1.284106e+06 1.582291e+06 1.625071e+06 1.661366e+06 1.754008e+06 1.765126e+06 1.964884e+06 2.002955e+06 2.680120e+06 3.256739e+06 4.370567e+06 6.166003e+06 7.504662e+06 8.805520e+06 9.283402e+06 1.060418e+07 1.095207e+07 1.156359e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-01 NaN 18809.131752 62452.208015 205928.573421 340537.201277 413375.741877 547711.608413 638521.225611 714895.755263 7.592136e+05 8.041775e+05 9.202704e+05 9.926603e+05 1.070192e+06 1.162752e+06 1.233544e+06 1.326967e+06 1.346332e+06 1.397467e+06 1.455774e+06 1.554331e+06 1.726160e+06 1.742518e+06 1.907098e+06 2.178388e+06 2.442297e+06 3.173395e+06 3.435192e+06 3.879653e+06 4.822684e+06 5.476739e+06 7.460054e+06 8.560791e+06 9.559271e+06 1.141259e+07 1.232403e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-02 12412.355755 50819.201311 84212.984079 152050.744763 285826.215880 415944.890973 479901.245175 583482.869079 685604.430937 8.334853e+05 9.280047e+05 9.987083e+05 1.076872e+06 1.132864e+06 1.166633e+06 1.226976e+06 1.282182e+06 1.325917e+06 1.410696e+06 1.487068e+06 1.641287e+06 1.689530e+06 1.717177e+06 1.876215e+06 1.886758e+06 2.455530e+06 3.250739e+06 3.671281e+06 4.443214e+06 5.440044e+06 6.712540e+06 8.420303e+06 1.009931e+07 1.082421e+07 1.139578e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-03 4368.139941 66144.027461 96280.032524 240903.228176 296325.617733 401235.315013 456444.075280 511718.773825 604575.351911 7.291760e+05 8.071298e+05 9.145646e+05 9.584054e+05 1.038138e+06 1.076188e+06 1.139222e+06 1.218592e+06 1.234664e+06 1.296536e+06 1.344439e+06 1.394182e+06 1.418663e+06 1.444011e+06 1.584855e+06 1.862013e+06 2.310433e+06 3.892538e+06 5.021140e+06 5.574396e+06 7.089948e+06 8.289342e+06 9.719171e+06 9.898121e+06 1.180215e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-04 4120.397718 48136.480884 90234.655582 171171.831977 282669.084286 383768.609533 502884.348900 631703.941012 733868.221551 8.307866e+05 8.814474e+05 9.543965e+05 1.028820e+06 1.056501e+06 1.138887e+06 1.171349e+06 1.201898e+06 1.289658e+06 1.348807e+06 1.410483e+06 1.460397e+06 1.619360e+06 1.641170e+06 1.651899e+06 2.601017e+06 2.879419e+06 3.479705e+06 4.070051e+06 5.741701e+06 6.964393e+06 7.863812e+06 9.498506e+06 1.071252e+07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-05 NaN 16343.780275 151027.715467 298625.076470 465503.938694 592999.458433 673628.446237 728497.240037 848361.103260 9.629240e+05 1.020383e+06 1.080848e+06 1.155629e+06 1.292738e+06 1.377279e+06 1.441118e+06 1.494440e+06 1.579041e+06 1.621859e+06 1.652201e+06 1.675680e+06 1.701676e+06 1.733827e+06 1.791013e+06 2.164018e+06 2.420148e+06 2.945114e+06 3.352466e+06 4.198945e+06 5.604401e+06 7.009263e+06 7.884871e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-06 9191.127816 43454.752677 71075.058928 210112.974043 323666.231316 416758.277092 484465.675242 644368.211458 747614.564550 9.378900e+05 9.988688e+05 1.104821e+06 1.196278e+06 1.247375e+06 1.307117e+06 1.348164e+06 1.396213e+06 1.461228e+06 1.516466e+06 1.570241e+06 1.644953e+06 1.710356e+06 2.227271e+06 2.566189e+06 2.598698e+06 2.623378e+06 2.896387e+06 3.339410e+06 3.835181e+06 5.095270e+06 5.569322e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-07 5872.315796 67303.607634 180825.981810 276856.460384 410123.305102 540428.343433 605333.597623 663813.723941 758798.389148 8.309819e+05 9.099655e+05 9.842065e+05 1.037116e+06 1.127842e+06 1.201599e+06 1.254823e+06 1.336352e+06 1.372892e+06 1.392408e+06 1.437006e+06 1.590514e+06 1.629455e+06 1.662239e+06 1.720897e+06 1.742079e+06 2.284836e+06 2.770599e+06 3.502643e+06 5.191910e+06 6.025245e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-08 12275.876950 85941.848117 155713.369762 312259.518824 409088.154117 531980.807639 609506.098940 730103.261808 772316.719317 8.539199e+05 9.182385e+05 1.000826e+06 1.071649e+06 1.125718e+06 1.211998e+06 1.273410e+06 1.302592e+06 1.396399e+06 1.406991e+06 1.527465e+06 1.571819e+06 1.628890e+06 1.684346e+06 1.753338e+06 1.962163e+06 2.014510e+06 2.571859e+06 3.054425e+06 4.068154e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-09 29401.764323 74923.780122 151891.820326 298731.768880 470405.448136 579834.544145 665925.439380 764439.523860 847904.408071 9.091937e+05 1.016139e+06 1.139760e+06 1.175766e+06 1.287583e+06 1.385395e+06 1.456945e+06 1.590027e+06 1.642087e+06 1.711468e+06 1.918107e+06 1.964804e+06 1.989559e+06 2.030585e+06 2.436238e+06 2.564045e+06 2.714637e+06 3.294488e+06 3.736579e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-10 14000.000000 32823.580135 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2008-0146,91519,89957,21689,78318,30295,05410,33291,772...
2008-0228,74922,10979,03363,45559,99364,68368,50233,69571,670...
2008-0348,80627,94990,41354,55783,50712,59172,03570,35335,223...
2008-0430,75817,76370,87230,23339,49466,729122,10020,99838,218...
2008-0538,67286,97420,48358,400112,01531,35422,45753,77893,303...
2008-0656,78973,35197,84064,81661,08341,68587,17261,41863,097...
2008-0727,86745,80461,49573,27971,591111,80780,24940,63232,434...
2008-084,83223,83152,51162,64945,955133,13381,73339,709112,11521,367...
2008-0943,46486,15752,66489,92888,73855,61726,59969,52246,788...
2008-1012,48821,93953,38852,41592,08645,78172,24283,35947,594...
2008-1145,4739,41678,96784,82639,54941,01174,00033,25735,802...
2008-123,6409,90554,79287,05770,68654,77531,41244,05759,44831,643...
2009-0114,02145,35051,20835,52081,65554,27541,53874,12947,962...
2009-0210,30535,02637,28958,77367,57957,589115,08357,14265,31555,019...
2009-0348,36040,50274,34264,53849,77885,59182,58256,44988,307...
2009-0440,94347,51764,02427,57362,63454,05163,30561,33970,406...
2009-0516,94474,47863,85659,26679,70763,85759,32688,81750,656...
2009-069,65543,72012,80579,40446,21946,86836,23435,65424,67626,641...
2009-0714,82848,60590,52754,50857,88164,90516,71746,88089,721...
2009-0842,06132,24764,17463,44365,42895,43291,809131,90467,284...
2009-0921,00027,60734,67937,52356,30542,85172,40454,36667,54371,032...
2009-1014,00062,04045,13497,01654,35620,84863,52178,49467,44122,107...
2009-1122,02764,18920,83633,30350,59479,85254,870111,59270,473...
2009-1238,59417,902100,47043,866102,05156,90133,87640,41654,734...
2010-0115,19253,33857,39456,98562,17584,62484,959102,73426,495...
2010-027,00031,46432,94238,10828,707100,09197,79598,31453,25996,477...
2010-0336,93764,55161,77439,58081,25335,75187,78841,076117,835...
2010-0421,02632,42246,661103,44289,86096,97651,11689,35250,59513,981...
2010-058,6688,920116,37661,586148,64714,28846,84051,20582,7144,240...
2010-0612,57892,15644,495112,86879,73273,74361,81473,71323,724...
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2015-075,87261,431113,52296,030133,267130,30564,90558,48094,98572,184...
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2015-0929,40245,52276,968146,840171,674109,42986,09198,51483,46561,289...
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NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-06 NaN 32351.229848 31836.316782 66289.251427 81787.337215 107053.192243 143595.368132 59589.625334 9302.495418 76650.181977 124141.065286 83364.436760 41465.820645 86065.819186 34686.068726 55951.600356 83707.079866 55792.122597 165451.433796 60042.224137 31799.379255 22992.429547 10304.208798 47204.539329 309685.077359 295442.552497 1.044091e+06 6.892586e+05 8.172055e+05 1.459404e+06 7.957606e+05 1.894393e+06 1.258487e+06 1.485590e+06 1.291553e+06 8.570753e+05 5.338876e+05 466078.465078 NaN 295421.578684 10671.210804 17585.537431 17936.294110 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-07 17395.126262 43535.215270 94354.149357 87021.707853 92540.914175 106502.437536 59634.837614 111315.719528 94168.086970 52377.829211 58395.495132 128251.420261 80860.585272 46192.051861 37533.072122 61260.917809 56111.977217 19568.079190 52970.665605 14526.357084 45278.152241 91969.711148 316389.200871 23371.563584 352996.439150 367989.597703 4.783819e+05 4.344751e+05 8.478634e+05 1.255281e+06 6.505439e+05 1.765331e+06 1.514355e+06 1.560650e+06 8.758149e+05 9.140881e+05 3.634125e+05 NaN 8398.633653 2863.147884 28156.702124 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-08 17118.276431 42752.625229 84450.652063 114023.717706 152833.806072 114121.212678 43485.638169 218119.103107 86276.046200 62235.259926 87107.280149 105437.946739 68083.995035 83227.247222 11694.651496 40618.768296 94119.673368 86156.536419 185570.010872 58978.612847 25249.083375 160572.315930 287744.976689 147455.676455 179415.635379 386769.056379 1.047920e+06 5.811764e+05 6.703268e+05 7.402687e+05 9.899028e+05 1.345040e+06 1.048110e+06 5.172193e+05 5.718047e+05 7.412821e+05 4.745204e+05 553602.058553 NaN 4739.020481 8852.389127 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-09 11766.356540 70613.585057 90230.081727 101852.600266 76066.255398 89862.791351 151175.866617 100473.424656 103208.746962 58935.152592 97079.614731 129682.745064 57545.028781 86011.194043 52566.195198 33350.920165 77655.827773 28722.831894 36532.136163 28919.582885 60075.572549 72839.456149 21977.481030 130712.385050 481402.426382 34469.897880 1.898491e+05 1.294468e+06 7.848245e+05 1.604948e+06 7.730516e+05 1.003704e+06 2.264034e+06 9.690580e+05 2.075566e+06 8.627908e+05 4.877316e+05 560000.000000 6225.141106 14392.718433 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-10 7686.279785 47288.690974 61263.717332 58860.540843 100249.116388 60196.918544 136339.434198 104905.947161 158469.829867 120334.556147 92613.523206 86727.441392 103286.805116 49770.280858 106875.401280 60855.470633 63987.812014 37601.528987 44265.371000 28034.890801 84165.344156 18401.552009 114425.800160 18925.289270 41378.655775 29410.028093 3.377927e+05 3.458116e+05 1.406488e+06 1.178639e+06 6.524484e+05 1.298710e+06 1.201304e+06 5.332826e+05 NaN 7.457256e+05 5.735299e+05 280000.000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-11 NaN 40085.299101 51551.273656 20923.904293 141741.326787 111531.853596 77865.577974 65298.455492 151870.053321 82061.009567 61697.287153 93122.910777 83803.651162 43913.672786 77276.779451 43448.623639 36094.214541 68528.384644 16350.415372 182462.893770 63248.816014 299616.780888 26978.323945 159313.694759 309841.096392 465701.774370 2.721091e+05 8.752746e+05 6.730805e+05 7.601739e+05 1.433160e+06 8.290349e+05 2.283413e+06 1.305887e+06 1.464814e+06 1.178750e+06 4.047749e+05 206112.848459 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2014-12 2338.009450 42696.189137 56894.439643 95114.713327 45989.840560 98958.952206 76403.265792 110120.637922 142920.802261 102379.619600 60846.409583 55040.740891 46001.614251 71422.187096 29877.757318 45891.358951 24222.513506 84149.338348 35899.811225 56938.134099 298184.935001 42779.811143 36294.889113 92641.894669 11117.833168 199758.341469 3.807108e+04 6.771645e+05 5.766198e+05 1.113827e+06 1.795436e+06 1.338659e+06 1.300858e+06 4.778822e+05 1.320780e+06 3.478895e+05 6.115183e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-01 NaN 18809.131752 43643.076263 143476.365405 134608.627856 72838.540601 134335.866535 90809.617198 76374.529652 44317.805600 44963.949373 116092.938968 72389.838029 77531.956013 92559.582908 70792.363429 93422.566104 19364.960856 51135.080708 58307.591149 98557.046637 171828.994625 16357.206094 164580.481733 271290.207578 263908.608181 7.310979e+05 2.617977e+05 4.444601e+05 9.430313e+05 6.540549e+05 1.983315e+06 1.100738e+06 9.984796e+05 1.853317e+06 9.114406e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-02 12412.355755 38406.845556 33393.782768 67837.760684 133775.471117 130118.675093 63956.354202 103581.623904 102121.561858 147880.848889 94519.467458 70703.530746 78163.588148 55992.518926 33768.401630 60343.512442 55206.045005 43734.718542 84779.334566 76371.753878 154218.819587 48242.969027 27646.576463 159038.475259 10543.399407 568771.844307 7.952086e+05 4.205424e+05 7.719330e+05 9.968293e+05 1.272497e+06 1.707763e+06 1.679003e+06 7.249045e+05 5.715680e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-03 4368.139941 61775.887520 30136.005063 144623.195652 55422.389557 104909.697280 55208.760267 55274.698545 92856.578086 124600.672630 77953.801804 107434.771768 43840.814860 79732.787937 38050.018025 63033.915023 79369.665209 16071.851556 61872.697863 47902.333668 49743.103874 24480.870749 25348.099244 140844.245330 277158.288574 448419.242716 1.582105e+06 1.128602e+06 5.532564e+05 1.515552e+06 1.199394e+06 1.429829e+06 1.789496e+05 1.904034e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-04 4120.397718 44016.083166 42098.174698 80937.176395 111497.252309 101099.525247 119115.739368 128819.592112 102164.280538 96918.413806 50660.778972 72949.065053 74423.421664 27681.022975 82385.876087 32462.299607 30549.388036 87759.712991 59149.196196 61675.427831 49913.956218 158963.301057 21810.137418 10729.064205 949117.308496 278402.094726 6.002861e+05 5.903465e+05 1.671649e+06 1.222692e+06 8.994191e+05 1.634694e+06 1.214017e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-05 NaN 16343.780275 134683.935192 147597.361003 166878.862224 127495.519739 80628.987804 54868.793800 119863.863223 114562.929158 57458.774398 60464.764226 74781.341514 137108.989006 84540.621394 63839.882522 53321.314742 84600.798385 42817.982258 30342.687954 23479.032326 25995.936780 32150.807773 57185.820235 373004.903124 256129.870303 5.249663e+05 4.073522e+05 8.464785e+05 1.405456e+06 1.404862e+06 8.756084e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-06 9191.127816 34263.624861 27620.306251 139037.915115 113553.257273 93092.045776 67707.398150 159902.536216 103246.353092 190275.446707 60978.815043 105951.864825 91456.906547 51097.242328 59742.359319 41047.145476 48048.962654 65014.351742 55238.028473 53775.598296 74711.669645 65403.459055 516914.261126 338918.453160 32508.514160 24680.414930 2.730094e+05 4.430222e+05 4.957713e+05 1.260089e+06 4.740520e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-07 5872.315796 61431.291838 113522.374176 96030.478574 133266.844718 130305.038331 64905.254190 58480.126318 94984.665207 72183.521869 78983.569059 74241.045574 52909.888868 90725.673217 73757.223592 53223.803895 81529.001882 36539.587446 19516.717846 44597.550143 153507.609854 38940.929237 32784.556837 58657.933191 21181.943763 542757.405428 4.857625e+05 7.320445e+05 1.689266e+06 8.333349e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-08 12275.876950 73665.971167 69771.521645 156546.149062 96828.635293 122892.653522 77525.291302 120597.162868 42213.457509 81603.139232 64318.685029 82587.536028 70822.690426 54069.221152 86280.324966 61411.904761 29181.467011 93807.472649 10591.579043 120473.835509 44354.048698 57071.138285 55456.304615 68991.650804 208825.674298 52347.075661 5.573485e+05 4.825662e+05 1.013729e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-09 29401.764323 45522.015799 76968.040204 146839.948554 171673.679256 109429.096009 86090.895235 98514.084480 83464.884211 61289.279897 106945.334949 123620.792997 36005.695177 111817.545698 97812.012630 71550.378735 133081.814697 52059.620013 69381.213861 206638.571652 46697.251204 24755.437891 41026.125804 405652.063289 127807.538226 150591.819723 5.798513e+05 4.420908e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-10 14000.000000 18823.580135 64808.886479 86112.369720 33951.887363 94798.147426 41473.460244 114279.219218 95470.452262 95322.271315 84571.513563 87283.315644 56826.229766 46418.390772 102357.785120 36633.859700 55331.328885 61585.508226 42312.614360 19390.945153 58927.385073 104921.071975 19228.360716 156208.667108 54768.101002 306567.082809 5.968770e+05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-11 NaN 24865.758304 74507.452518 52944.105766 109359.561135 98135.981690 160380.544193 46511.473410 206774.167009 128625.196728 125749.283046 79550.899988 76191.422212 89872.173002 67238.315529 60955.421537 24439.191831 21047.667487 38010.055279 67547.641213 17088.640029 11994.180304 39623.869759 136232.327155 419937.900185 19556.256954 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2015-12 14000.000000 34498.369406 85505.412309 113845.813135 118239.229726 121277.218444 179033.274623 131329.638216 130356.868676 89312.029471 50001.263705 131720.019853 114096.053064 80505.435581 47345.897743 107853.847684 51370.371877 62388.220925 22932.368203 48682.707999 164287.941261 156485.280890 34689.496090 171021.042677 388756.671990 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-01 3213.543940 16520.684619 48605.226108 63442.108722 64330.296181 156535.525771 70535.750329 90185.961127 88647.097698 87277.722080 37585.548112 129838.454174 49775.368916 99477.619056 79483.384567 77709.064810 56914.923494 49974.636491 49813.369829 11723.025526 42432.701569 92444.945865 65657.025561 302148.945562 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-02 13891.629340 20721.691370 112166.316749 154318.566251 93667.327677 117578.257984 114468.238241 77207.158186 79019.522681 85925.357894 62818.373369 94653.274923 98626.259639 14412.050378 71903.488396 63442.861487 52747.037286 60027.653938 40008.382480 206021.036486 46890.568780 45358.088045 166532.167813 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-03 18353.550375 48494.631841 70674.791516 115885.521080 83612.153482 45276.454051 123735.672613 114338.914912 79022.075494 96179.004264 104153.365919 78420.661052 171254.111316 64222.446520 43806.575540 34980.385712 16933.353509 36318.272021 67986.442666 32546.776357 54145.301365 13493.482671 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-04 14780.520614 35741.458577 51817.548576 124194.948588 76219.573225 168821.419700 117504.571796 82978.012887 99148.493702 112663.905688 47357.387177 91977.866046 59685.252396 118258.978619 38365.804252 58137.086830 54237.801338 78418.236388 71326.721028 59282.610260 85403.308987 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-05 NaN 32920.776360 80573.646233 128677.203503 98491.267711 138235.571025 110125.330488 110887.267995 150280.984291 74848.724902 54051.819831 105564.489585 42873.921065 51942.612413 34218.518171 94820.525215 81052.688242 78267.767639 13675.949254 25982.914677 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-06 17958.638920 9031.290904 35720.570300 163643.856247 172139.047804 157652.546874 154167.597823 120193.385494 100214.093197 62572.447623 82107.496478 108154.178521 70208.521568 37141.252390 86357.027860 69921.829085 62875.428241 20423.255196 24443.879967 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2016-07 36454.457248 24782.897875 105234.099377 116615.976124 64114.451842 118425.785680 107282.550162 101314.686533 136164.358434 95887.527655 160987.563259 65113.829226 110219.023402 104432.987704 50962.266199 136896.654248 80842.270086 95857.222846 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 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NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_incr = prism_cum.cum_to_incr()\n", + "prism_incr[\"Paid\"].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default (and in concert with the `pandas` philosophy), the methods associated with the `Triangle` class strive for immutability. This means that the `incr_to_cum` and `cum_to_incr` methods will return new a new triangle object that must be assigned, or it is thrown away. Many of the `chainladder.Triangle` methods have an `inplace` argument, or alternatively you can just use variable reassignment to store the transformed triangle object." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# This works\n", + "prism.incr_to_cum(inplace=True)\n", + "# So does this\n", + "prism = prism.incr_to_cum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When dealing with triangles that have an `origin` axis, `development` axis, or both at a monthly or quarterly grain, the triangle can be summarized to a higher grain using the `grain` method.\n", + "\n", + "The grain to which you want your triangle converted, specified as \"OxDy\" where \"x\" and \"y\" can take on values of \"M\", \"Q\", or \"Y\". For example:\n", + "* `grain(OYDY)` for Origin Year x Development Year.\n", + "* `grain(OQDM)` for Origin Quarter x Development Month." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "2008 3.404254e+06 1.119109e+07 7.461301e+07 1.503428e+08 1.509829e+08 1.511527e+08 1.512289e+08 1.512648e+08 1.512772e+08 1.512842e+08\n", + "2009 3.609385e+06 1.100293e+07 8.072635e+07 1.569708e+08 1.575995e+08 1.576971e+08 1.577364e+08 1.577434e+08 1.577487e+08 NaN\n", + "2010 4.067321e+06 1.239678e+07 7.421004e+07 1.610496e+08 1.616415e+08 1.617871e+08 1.618596e+08 1.618702e+08 NaN NaN\n", + "2011 4.125232e+06 1.318314e+07 8.123977e+07 1.614129e+08 1.621876e+08 1.624175e+08 1.624907e+08 NaN NaN NaN\n", + "2012 4.584036e+06 1.400118e+07 7.779452e+07 1.521184e+08 1.525881e+08 1.528195e+08 NaN NaN NaN NaN\n", + "2013 4.889623e+06 1.460774e+07 8.441850e+07 1.611103e+08 1.616730e+08 NaN NaN NaN NaN NaN\n", + "2014 5.546158e+06 1.640813e+07 7.725679e+07 1.549699e+08 NaN NaN NaN NaN NaN NaN\n", + "2015 5.909029e+06 1.742761e+07 8.091458e+07 NaN NaN NaN NaN NaN NaN NaN\n", + "2016 6.080962e+06 1.858806e+07 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017 6.396536e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_OYDY = prism.grain(\"OYDY\")\n", + "prism_OYDY[\"Paid\"].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Depending on the type of analysis being done, it may be more convenient to look at a triangle with its `development` axis expressed as a valuation rather than an age. This is also what the Schedule Ps look like. To do this, `Triangle` has two methods for toggling between a development triangle and a valuation triangle. The methods are `dev_to_val` and its inverse `val_to_dev`." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017\n", + "2008 3.404254e+06 1.119109e+07 7.461301e+07 1.503428e+08 1.509829e+08 1.511527e+08 1.512289e+08 1.512648e+08 1.512772e+08 1.512842e+08\n", + "2009 NaN 3.609385e+06 1.100293e+07 8.072635e+07 1.569708e+08 1.575995e+08 1.576971e+08 1.577364e+08 1.577434e+08 1.577487e+08\n", + "2010 NaN NaN 4.067321e+06 1.239678e+07 7.421004e+07 1.610496e+08 1.616415e+08 1.617871e+08 1.618596e+08 1.618702e+08\n", + "2011 NaN NaN NaN 4.125232e+06 1.318314e+07 8.123977e+07 1.614129e+08 1.621876e+08 1.624175e+08 1.624907e+08\n", + "2012 NaN NaN NaN NaN 4.584036e+06 1.400118e+07 7.779452e+07 1.521184e+08 1.525881e+08 1.528195e+08\n", + "2013 NaN NaN NaN NaN NaN 4.889623e+06 1.460774e+07 8.441850e+07 1.611103e+08 1.616730e+08\n", + "2014 NaN NaN NaN NaN NaN NaN 5.546158e+06 1.640813e+07 7.725679e+07 1.549699e+08\n", + "2015 NaN NaN NaN NaN NaN NaN NaN 5.909029e+06 1.742761e+07 8.091458e+07\n", + "2016 NaN NaN NaN NaN NaN NaN NaN NaN 6.080962e+06 1.858806e+07\n", + "2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN 6.396536e+06" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_OYDY_val = prism_OYDY.dev_to_val()\n", + "prism_OYDY_val[\"Paid\"].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When working with real-world data, the triangles can have holes, such as missing evluation(s), or no losses in certain origin(s). In these cases, it doesn't make sense to include empty accident periods or development ages. For example, in the `prism` dataset, the \"Home\" line has its latest accidents through 2016, and have no payments in development age '12'. Sometimes, dropping the non-applicable fields is usefule with the `dropna()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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2011503,29665,048,051143,971,618144,251,618144,251,618144,251,618
2012599,27760,536,642133,412,416133,412,416133,412,416
2013536,30366,443,157141,645,782141,645,782
2014965,97357,394,263133,542,848
2015371,01559,047,107
2016640,179
2017
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "2008 NaN 1.129305e+06 6.165887e+07 1.365206e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08\n", + "2009 NaN 1.872920e+05 6.713460e+07 1.422679e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 NaN\n", + "2010 NaN 6.206026e+05 5.908246e+07 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 NaN NaN\n", + "2011 NaN 5.032956e+05 6.504805e+07 1.439716e+08 1.442516e+08 1.442516e+08 1.442516e+08 NaN NaN NaN\n", + "2012 NaN 5.992768e+05 6.053664e+07 1.334124e+08 1.334124e+08 1.334124e+08 NaN NaN NaN NaN\n", + "2013 NaN 5.363029e+05 6.644316e+07 1.416458e+08 1.416458e+08 NaN NaN NaN NaN NaN\n", + "2014 NaN 9.659729e+05 5.739426e+07 1.335428e+08 NaN NaN NaN NaN NaN NaN\n", + "2015 NaN 3.710149e+05 5.904711e+07 NaN NaN NaN NaN NaN NaN NaN\n", + "2016 NaN 6.401785e+05 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_OYDY.loc[\"Home\"][\"Paid\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see what happens if we have no data for 2011." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
20081,129,30561,658,874136,520,554136,800,554136,800,554136,800,554136,800,554136,800,554136,800,554
2009187,29267,134,599142,267,946142,547,946142,547,946142,547,946142,547,946142,547,946
2010620,60359,082,456144,757,200144,757,200144,757,200144,757,200144,757,200
2011
2012599,27760,536,642133,412,416133,412,416133,412,416
2013536,30366,443,157141,645,782141,645,782
2014965,97357,394,263133,542,848
2015371,01559,047,107
2016640,179
2017
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "2008 NaN 1.129305e+06 6.165887e+07 1.365206e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08\n", + "2009 NaN 1.872920e+05 6.713460e+07 1.422679e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 NaN\n", + "2010 NaN 6.206026e+05 5.908246e+07 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 NaN NaN\n", + "2011 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012 NaN 5.992768e+05 6.053664e+07 1.334124e+08 1.334124e+08 1.334124e+08 NaN NaN NaN NaN\n", + "2013 NaN 5.363029e+05 6.644316e+07 1.416458e+08 1.416458e+08 NaN NaN NaN NaN NaN\n", + "2014 NaN 9.659729e+05 5.739426e+07 1.335428e+08 NaN NaN NaN NaN NaN NaN\n", + "2015 NaN 3.710149e+05 5.904711e+07 NaN NaN NaN NaN NaN NaN NaN\n", + "2016 NaN 6.401785e+05 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_df_2011 = prism_df.copy()\n", + "prism_df_2011.loc[\n", + " (prism_df_2011[\"AccidentDate\"] >= \"2011-01-01\")\n", + " & (prism_df_2011[\"AccidentDate\"] < \"2012-01-01\"),\n", + " \"Paid\",\n", + "] = None # Let's assume we hav no payments for losses occurred in 2011\n", + "prism_2011 = cl.Triangle(\n", + " data=prism_df_2011,\n", + " origin=\"AccidentDate\",\n", + " origin_format=\"%Y-%m-%d\",\n", + " development=\"PaymentDate\",\n", + " development_format=\"%Y-%m-%d\",\n", + " columns=[\"Paid\", \"Incurred\"],\n", + " index=[\"Line\", \"Type\"],\n", + " cumulative=False,\n", + ")\n", + "prism_2011.incr_to_cum(inplace=True)\n", + "prism_2011_OYDY = prism_2011.grain(\"OYDY\")\n", + "prism_2011_OYDY.loc[\"Home\"][\"Paid\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the `dropna()` method will retain empty periods if they are surrounded by non-empty periods with valid data." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
24364860728496108120
20081,129,30561,658,874136,520,554136,800,554136,800,554136,800,554136,800,554136,800,554136,800,554
2009187,29267,134,599142,267,946142,547,946142,547,946142,547,946142,547,946142,547,946
2010620,60359,082,456144,757,200144,757,200144,757,200144,757,200144,757,200
2011
2012599,27760,536,642133,412,416133,412,416133,412,416
2013536,30366,443,157141,645,782141,645,782
2014965,97357,394,263133,542,848
2015371,01559,047,107
2016640,179
" + ], + "text/plain": [ + " 24 36 48 60 72 84 96 108 120\n", + "2008 1.129305e+06 6.165887e+07 1.365206e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08\n", + "2009 1.872920e+05 6.713460e+07 1.422679e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 NaN\n", + "2010 6.206026e+05 5.908246e+07 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 NaN NaN\n", + "2011 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2012 5.992768e+05 6.053664e+07 1.334124e+08 1.334124e+08 1.334124e+08 NaN NaN NaN NaN\n", + "2013 5.363029e+05 6.644316e+07 1.416458e+08 1.416458e+08 NaN NaN NaN NaN NaN\n", + "2014 9.659729e+05 5.739426e+07 1.335428e+08 NaN NaN NaN NaN NaN NaN\n", + "2015 3.710149e+05 5.904711e+07 NaN NaN NaN NaN NaN NaN NaN\n", + "2016 6.401785e+05 NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_2011_OYDY_droppedna = prism_2011_OYDY.loc[\"Home\"].dropna()\n", + "prism_2011_OYDY_droppedna.loc[\"Home\"][\"Paid\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Commutative Properties of Triangle Methods\n", + "Where it makes sense, which is in most cases, the methods described above are commutative and can be applied in any order." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Commutative? True\n", + "Commutative? True\n" + ] + } + ], + "source": [ + "print(\"Commutative?\", prism.sum().latest_diagonal == prism.latest_diagonal.sum())\n", + "print(\"Commutative?\", prism.loc[\"Home\"].link_ratio == prism.link_ratio.loc[\"Home\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Triangle Imports and Exports\n", + "To the extent the `Triangle` can be expressed as a `pandas.DataFrame`, you can use any of the pandas IO to send the data in and out. Note that converting to pandas is a one-way ticket with no inverse functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another useful function is to copy the triangle and put it in the clipboard so we can paste it elsewhere (such as Excel):" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
20081,129,30561,658,874136,520,554136,800,554136,800,554136,800,554136,800,554136,800,554136,800,554
2009187,29267,134,599142,267,946142,547,946142,547,946142,547,946142,547,946142,547,946
2010620,60359,082,456144,757,200144,757,200144,757,200144,757,200144,757,200
2011503,29665,048,051143,971,618144,251,618144,251,618144,251,618
2012599,27760,536,642133,412,416133,412,416133,412,416
2013536,30366,443,157141,645,782141,645,782
2014965,97357,394,263133,542,848
2015371,01559,047,107
2016640,179
2017
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "2008 NaN 1.129305e+06 6.165887e+07 1.365206e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08 1.368006e+08\n", + "2009 NaN 1.872920e+05 6.713460e+07 1.422679e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 1.425479e+08 NaN\n", + "2010 NaN 6.206026e+05 5.908246e+07 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 1.447572e+08 NaN NaN\n", + "2011 NaN 5.032956e+05 6.504805e+07 1.439716e+08 1.442516e+08 1.442516e+08 1.442516e+08 NaN NaN NaN\n", + "2012 NaN 5.992768e+05 6.053664e+07 1.334124e+08 1.334124e+08 1.334124e+08 NaN NaN NaN NaN\n", + "2013 NaN 5.363029e+05 6.644316e+07 1.416458e+08 1.416458e+08 NaN NaN NaN NaN NaN\n", + "2014 NaN 9.659729e+05 5.739426e+07 1.335428e+08 NaN NaN NaN NaN NaN NaN\n", + "2015 NaN 3.710149e+05 5.904711e+07 NaN NaN NaN NaN NaN NaN NaN\n", + "2016 NaN 6.401785e+05 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2017 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_OYDY.loc[\"Home\", \"Paid\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "prism_OYDY.loc[\"Home\", \"Paid\"].to_clipboard()\n", + "```\n", + "\n", + "Try to paste it elsewhere." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, if you want to store the triangle elsewhere but be able to reconstitute a triangle out of it later, then you can use:\n", + "* `Triangle.to_json` and its inverse `cl.read_json` for json format
\n", + "* `Triangle.to_pickle` and its inverse `cl.read_pickle` for pickle format
\n", + "\n", + "These have the added benefit of working on multi-dimensional triangles that don't fit into a DataFrame." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(2, 2, 120, 120)
Index:[Line, Type]
Columns:[Paid, Incurred]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2017-12\n", + "Grain: OMDM\n", + "Shape: (2, 2, 120, 120)\n", + "Index: [Line, Type]\n", + "Columns: [Paid, Incurred]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism = cl.Triangle(\n", + " data=prism_df,\n", + " origin=\"AccidentDate\",\n", + " development=\"PaymentDate\",\n", + " columns=[\"Paid\", \"Incurred\"],\n", + " index=[\"Line\", \"Type\"], # multiple indices\n", + " cumulative=False,\n", + ").incr_to_cum()\n", + "prism" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. What is the case incurred activity for calendar periods in 2015Q2 (March, April, and May in 2015) by \"Line\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
development2015-042015-052015-06
Line
Auto188397617043381692254
Home103679391081382114537617
\n", + "
" + ], + "text/plain": [ + "development 2015-04 2015-05 2015-06\n", + "Line \n", + "Auto 1883976 1704338 1692254\n", + "Home 10367939 10813821 14537617" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "incr_by_line = prism.groupby(\"Line\").sum().cum_to_incr()[\"Incurred\"].dev_to_val()\n", + "incr_by_line[\n", + " (incr_by_line.valuation >= \"2015-04-01\") & (incr_by_line.valuation < \"2015-07-01\")\n", + "].sum(\"origin\").to_frame(origin_as_datetime=True).astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. For accident year 2015, what proportion of our Paid amounts come from each \"Type\" of claims?" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2017-12
Grain:OYDY
Shape:(2, 2, 10, 10)
Index:[Line, Type]
Columns:[Paid, Incurred]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2017-12\n", + "Grain: OYDY\n", + "Shape: (2, 2, 10, 10)\n", + "Index: [Line, Type]\n", + "Columns: [Paid, Incurred]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism_OYDY = prism.grain(\"OYDY\")\n", + "prism_OYDY" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Type\n", + "Dwelling 0.729746\n", + "PD 0.270254\n", + "dtype: float64" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "by_type = (\n", + " prism_OYDY.latest_diagonal[prism_OYDY.origin == \"2015\"][\"Paid\"]\n", + " .groupby(\"Type\")\n", + " .sum()\n", + " .to_frame(origin_as_datetime=True)\n", + ")\n", + "by_type / by_type.sum()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": 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It consists of popular actuarial tools, such as **triangle data manipulation**, **link ratios calculation**, and **IBNR estimates** using both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications. + +This package strives to be minimalistic in needing its own API. The syntax mimics popular packages such as [pandas](https://pandas.pydata.org/) for data manipulation and [scikit-learn](https://scikit-learn.org/) for model construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work. + +::::{grid} 1 2 2 4 +:gutter: 1 1 1 2 + +:::{grid-item-card} {octicon}`rocket;2.5em;sd-mr-1` Getting Started +:link: getting_started/index +:link-type: doc + +Install chainladder and try the beginner tutorials. +::: + +:::{grid-item-card} {octicon}`book;2.5em;sd-mr-1` User Guide +:link: user_guide/index +:link-type: doc + +A more comprehensive guide on the capabilities of chainladder. +::: + +:::{grid-item-card} {octicon}`grabber;2.5em;sd-mr-1` Galllery +:link: gallery/index +:link-type: doc + +Gallery of examples highlighting different use cases for chainladder. +::: + +:::{grid-item-card} {octicon}`code-square;2.5em;sd-mr-1` API Reference +:link: library/api +:link-type: doc + +The chainladder API Reference Manual provides a comprehensive reference for all methods. +::: + +:::: \ No newline at end of file diff --git a/docs/library/about.md b/docs/library/about.md new file mode 100644 index 00000000..502b94f6 --- /dev/null +++ b/docs/library/about.md @@ -0,0 +1 @@ +# {octicon}`heart` About \ No newline at end of file diff --git a/docs/library/api.md b/docs/library/api.md new file mode 100644 index 00000000..fd8af1da --- /dev/null +++ b/docs/library/api.md @@ -0,0 +1,184 @@ +# {octicon}`code-square` API Reference +This is the class and function reference of chainladder. Please refer to +the full user guide for further details, as the class and +function raw specifications may not be enough to give full guidelines on their +uses. + +```{eval-rst} + +:mod:`chainladder.core`: Triangle +================================= + +.. automodule:: chainladder.core + :no-members: + :no-inherited-members: + + +Classes +------- +.. currentmodule:: chainladder + +.. autosummary:: + :toctree: generated/ + :template: class_inherited.rst + + Triangle + DevelopmentCorrelation + ValuationCorrelation + + +.. _development_ref: + +:mod:`chainladder.development`: Development Patterns +==================================================== + +.. automodule:: chainladder.development + :no-members: + :no-inherited-members: + + +Classes +------- +.. currentmodule:: chainladder + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + Development + DevelopmentConstant + MunichAdjustment + IncrementalAdditive + ClarkLDF + CaseOutstanding + TweedieGLM + DevelopmentML + BarnettZehnwirth + + +.. _tails_ref: + +:mod:`chainladder.tails`: Tail Factors +======================================== + +.. automodule:: chainladder.tails + :no-members: + :no-inherited-members: + + +Classes +------- +.. currentmodule:: chainladder + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + TailConstant + TailCurve + TailBondy + TailClark + + +.. _methods_ref: + +:mod:`chainladder.methods`: IBNR Methods +======================================== + +.. automodule:: chainladder.methods + :no-members: + :no-inherited-members: + + +Classes +------- +.. currentmodule:: chainladder + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + Chainladder + MackChainladder + BornhuetterFerguson + Benktander + CapeCod + +.. _adjustments_ref: + +:mod:`chainladder.workflow`: Adjustments +======================================== +.. automodule:: chainladder.workflow + :no-members: + :no-inherited-members: + +Classes +------- +.. currentmodule:: chainladder + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + BootstrapODPSample + BerquistSherman + Trend + ParallelogramOLF + +.. _workflow_ref: + +:mod:`chainladder.workflow`: Workflow +===================================== +.. automodule:: chainladder.workflow + :no-members: + :no-inherited-members: + +Classes +------- +.. currentmodule:: chainladder + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + Pipeline + VotingChainladder + GridSearch + + +.. _utils_ref: + +:mod:`chainladder.utils`: Utilities +=================================== +.. automodule:: chainladder.utils + :no-members: + :no-inherited-members: + + +Functions +--------- +.. currentmodule:: chainladder + +.. autosummary:: + :toctree: generated/ + :template: function.rst + + load_sample + read_pickle + read_json + concat + load_sample + minimum + maximum + +Classes +------- +.. currentmodule:: chainladder + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + PatsyFormula + +``` \ No newline at end of file diff --git a/docs/library/generated/chainladder.BarnettZehnwirth.rst b/docs/library/generated/chainladder.BarnettZehnwirth.rst new file mode 100644 index 00000000..7f5b6599 --- /dev/null +++ b/docs/library/generated/chainladder.BarnettZehnwirth.rst @@ -0,0 +1,6 @@ +chainladder.BarnettZehnwirth +============================ + +.. currentmodule:: chainladder + +.. autoclass:: BarnettZehnwirth \ No newline at end of file diff --git a/docs/library/generated/chainladder.Benktander.rst b/docs/library/generated/chainladder.Benktander.rst new file mode 100644 index 00000000..ac68cb79 --- /dev/null +++ b/docs/library/generated/chainladder.Benktander.rst @@ -0,0 +1,6 @@ +chainladder.Benktander +====================== + +.. currentmodule:: chainladder + +.. autoclass:: Benktander \ No newline at end of file diff --git a/docs/library/generated/chainladder.BerquistSherman.rst b/docs/library/generated/chainladder.BerquistSherman.rst new file mode 100644 index 00000000..99464424 --- /dev/null +++ b/docs/library/generated/chainladder.BerquistSherman.rst @@ -0,0 +1,6 @@ +chainladder.BerquistSherman +=========================== + +.. currentmodule:: chainladder + +.. autoclass:: BerquistSherman \ No newline at end of file diff --git a/docs/library/generated/chainladder.BootstrapODPSample.rst b/docs/library/generated/chainladder.BootstrapODPSample.rst new file mode 100644 index 00000000..fc9529ef --- /dev/null +++ b/docs/library/generated/chainladder.BootstrapODPSample.rst @@ -0,0 +1,6 @@ +chainladder.BootstrapODPSample +============================== + +.. currentmodule:: chainladder + +.. autoclass:: BootstrapODPSample \ No newline at end of file diff --git a/docs/library/generated/chainladder.BornhuetterFerguson.rst b/docs/library/generated/chainladder.BornhuetterFerguson.rst new file mode 100644 index 00000000..d2ccbbc8 --- /dev/null +++ b/docs/library/generated/chainladder.BornhuetterFerguson.rst @@ -0,0 +1,6 @@ +chainladder.BornhuetterFerguson +=============================== + +.. currentmodule:: chainladder + +.. autoclass:: BornhuetterFerguson \ No newline at end of file diff --git a/docs/library/generated/chainladder.CapeCod.rst b/docs/library/generated/chainladder.CapeCod.rst new file mode 100644 index 00000000..bdcc0539 --- /dev/null +++ b/docs/library/generated/chainladder.CapeCod.rst @@ -0,0 +1,6 @@ +chainladder.CapeCod +=================== + +.. currentmodule:: chainladder + +.. autoclass:: CapeCod \ No newline at end of file diff --git a/docs/library/generated/chainladder.CaseOutstanding.rst b/docs/library/generated/chainladder.CaseOutstanding.rst new file mode 100644 index 00000000..1797b98e --- /dev/null +++ b/docs/library/generated/chainladder.CaseOutstanding.rst @@ -0,0 +1,6 @@ +chainladder.CaseOutstanding +=========================== + +.. currentmodule:: chainladder + +.. autoclass:: CaseOutstanding \ No newline at end of file diff --git a/docs/library/generated/chainladder.Chainladder.rst b/docs/library/generated/chainladder.Chainladder.rst new file mode 100644 index 00000000..e2956836 --- /dev/null +++ b/docs/library/generated/chainladder.Chainladder.rst @@ -0,0 +1,6 @@ +chainladder.Chainladder +======================= + +.. currentmodule:: chainladder + +.. autoclass:: Chainladder \ No newline at end of file diff --git a/docs/library/generated/chainladder.ClarkLDF.rst b/docs/library/generated/chainladder.ClarkLDF.rst new file mode 100644 index 00000000..11e49a67 --- /dev/null +++ b/docs/library/generated/chainladder.ClarkLDF.rst @@ -0,0 +1,6 @@ +chainladder.ClarkLDF +==================== + +.. currentmodule:: chainladder + +.. autoclass:: ClarkLDF \ No newline at end of file diff --git a/docs/library/generated/chainladder.Development.rst b/docs/library/generated/chainladder.Development.rst new file mode 100644 index 00000000..bde06734 --- /dev/null +++ b/docs/library/generated/chainladder.Development.rst @@ -0,0 +1,6 @@ +chainladder.Development +======================= + +.. currentmodule:: chainladder + +.. autoclass:: Development \ No newline at end of file diff --git a/docs/library/generated/chainladder.DevelopmentConstant.rst b/docs/library/generated/chainladder.DevelopmentConstant.rst new file mode 100644 index 00000000..637e3b1c --- /dev/null +++ b/docs/library/generated/chainladder.DevelopmentConstant.rst @@ -0,0 +1,6 @@ +chainladder.DevelopmentConstant +=============================== + +.. currentmodule:: chainladder + +.. autoclass:: DevelopmentConstant \ No newline at end of file diff --git a/docs/library/generated/chainladder.DevelopmentCorrelation.rst b/docs/library/generated/chainladder.DevelopmentCorrelation.rst new file mode 100644 index 00000000..2abe1bad --- /dev/null +++ b/docs/library/generated/chainladder.DevelopmentCorrelation.rst @@ -0,0 +1,6 @@ +chainladder.DevelopmentCorrelation +================================== + +.. currentmodule:: chainladder + +.. autoclass:: DevelopmentCorrelation \ No newline at end of file diff --git a/docs/library/generated/chainladder.DevelopmentML.rst b/docs/library/generated/chainladder.DevelopmentML.rst new file mode 100644 index 00000000..9c9ae399 --- /dev/null +++ b/docs/library/generated/chainladder.DevelopmentML.rst @@ -0,0 +1,6 @@ +chainladder.DevelopmentML +========================= + +.. currentmodule:: chainladder + +.. autoclass:: DevelopmentML \ No newline at end of file diff --git a/docs/library/generated/chainladder.GridSearch.rst b/docs/library/generated/chainladder.GridSearch.rst new file mode 100644 index 00000000..e3cb2dd8 --- /dev/null +++ b/docs/library/generated/chainladder.GridSearch.rst @@ -0,0 +1,6 @@ +chainladder.GridSearch +====================== + +.. currentmodule:: chainladder + +.. autoclass:: GridSearch \ No newline at end of file diff --git a/docs/library/generated/chainladder.IncrementalAdditive.rst b/docs/library/generated/chainladder.IncrementalAdditive.rst new file mode 100644 index 00000000..d1258d33 --- /dev/null +++ b/docs/library/generated/chainladder.IncrementalAdditive.rst @@ -0,0 +1,6 @@ +chainladder.IncrementalAdditive +=============================== + +.. currentmodule:: chainladder + +.. autoclass:: IncrementalAdditive \ No newline at end of file diff --git a/docs/library/generated/chainladder.MackChainladder.rst b/docs/library/generated/chainladder.MackChainladder.rst new file mode 100644 index 00000000..73c68494 --- /dev/null +++ b/docs/library/generated/chainladder.MackChainladder.rst @@ -0,0 +1,6 @@ +chainladder.MackChainladder +=========================== + +.. currentmodule:: chainladder + +.. autoclass:: MackChainladder \ No newline at end of file diff --git a/docs/library/generated/chainladder.MunichAdjustment.rst b/docs/library/generated/chainladder.MunichAdjustment.rst new file mode 100644 index 00000000..1eb4d7ef --- /dev/null +++ b/docs/library/generated/chainladder.MunichAdjustment.rst @@ -0,0 +1,6 @@ +chainladder.MunichAdjustment +============================ + +.. currentmodule:: chainladder + +.. autoclass:: MunichAdjustment \ No newline at end of file diff --git a/docs/library/generated/chainladder.ParallelogramOLF.rst b/docs/library/generated/chainladder.ParallelogramOLF.rst new file mode 100644 index 00000000..13d987ff --- /dev/null +++ b/docs/library/generated/chainladder.ParallelogramOLF.rst @@ -0,0 +1,6 @@ +chainladder.ParallelogramOLF +============================ + +.. currentmodule:: chainladder + +.. autoclass:: ParallelogramOLF \ No newline at end of file diff --git a/docs/library/generated/chainladder.PatsyFormula.rst b/docs/library/generated/chainladder.PatsyFormula.rst new file mode 100644 index 00000000..60fe7df2 --- /dev/null +++ b/docs/library/generated/chainladder.PatsyFormula.rst @@ -0,0 +1,6 @@ +chainladder.PatsyFormula +======================== + +.. currentmodule:: chainladder + +.. autoclass:: PatsyFormula \ No newline at end of file diff --git a/docs/library/generated/chainladder.Pipeline.rst b/docs/library/generated/chainladder.Pipeline.rst new file mode 100644 index 00000000..6965fecf --- /dev/null +++ b/docs/library/generated/chainladder.Pipeline.rst @@ -0,0 +1,6 @@ +chainladder.Pipeline +==================== + +.. currentmodule:: chainladder + +.. autoclass:: Pipeline \ No newline at end of file diff --git a/docs/library/generated/chainladder.TailBondy.rst b/docs/library/generated/chainladder.TailBondy.rst new file mode 100644 index 00000000..f21a4899 --- /dev/null +++ b/docs/library/generated/chainladder.TailBondy.rst @@ -0,0 +1,6 @@ +chainladder.TailBondy +===================== + +.. currentmodule:: chainladder + +.. autoclass:: TailBondy \ No newline at end of file diff --git a/docs/library/generated/chainladder.TailClark.rst b/docs/library/generated/chainladder.TailClark.rst new file mode 100644 index 00000000..929329a8 --- /dev/null +++ b/docs/library/generated/chainladder.TailClark.rst @@ -0,0 +1,6 @@ +chainladder.TailClark +===================== + +.. currentmodule:: chainladder + +.. autoclass:: TailClark \ No newline at end of file diff --git a/docs/library/generated/chainladder.TailConstant.rst b/docs/library/generated/chainladder.TailConstant.rst new file mode 100644 index 00000000..bcbc5565 --- /dev/null +++ b/docs/library/generated/chainladder.TailConstant.rst @@ -0,0 +1,6 @@ +chainladder.TailConstant +======================== + +.. currentmodule:: chainladder + +.. autoclass:: TailConstant \ No newline at end of file diff --git a/docs/library/generated/chainladder.TailCurve.rst b/docs/library/generated/chainladder.TailCurve.rst new file mode 100644 index 00000000..d5309486 --- /dev/null +++ b/docs/library/generated/chainladder.TailCurve.rst @@ -0,0 +1,6 @@ +chainladder.TailCurve +===================== + +.. currentmodule:: chainladder + +.. autoclass:: TailCurve \ No newline at end of file diff --git a/docs/library/generated/chainladder.Trend.rst b/docs/library/generated/chainladder.Trend.rst new file mode 100644 index 00000000..58613b47 --- /dev/null +++ b/docs/library/generated/chainladder.Trend.rst @@ -0,0 +1,6 @@ +chainladder.Trend +================= + +.. currentmodule:: chainladder + +.. autoclass:: Trend \ No newline at end of file diff --git a/docs/library/generated/chainladder.Triangle.rst b/docs/library/generated/chainladder.Triangle.rst new file mode 100644 index 00000000..e40d541f --- /dev/null +++ b/docs/library/generated/chainladder.Triangle.rst @@ -0,0 +1,6 @@ +chainladder.Triangle +==================== + +.. currentmodule:: chainladder + +.. autoclass:: Triangle \ No newline at end of file diff --git a/docs/library/generated/chainladder.TweedieGLM.rst b/docs/library/generated/chainladder.TweedieGLM.rst new file mode 100644 index 00000000..eec1dd4c --- /dev/null +++ b/docs/library/generated/chainladder.TweedieGLM.rst @@ -0,0 +1,6 @@ +chainladder.TweedieGLM +====================== + +.. currentmodule:: chainladder + +.. autoclass:: TweedieGLM \ No newline at end of file diff --git a/docs/library/generated/chainladder.ValuationCorrelation.rst b/docs/library/generated/chainladder.ValuationCorrelation.rst new file mode 100644 index 00000000..50f0a9e3 --- /dev/null +++ b/docs/library/generated/chainladder.ValuationCorrelation.rst @@ -0,0 +1,6 @@ +chainladder.ValuationCorrelation +================================ + +.. currentmodule:: chainladder + +.. autoclass:: ValuationCorrelation \ No newline at end of file diff --git a/docs/library/generated/chainladder.VotingChainladder.rst b/docs/library/generated/chainladder.VotingChainladder.rst new file mode 100644 index 00000000..752c5e10 --- /dev/null +++ b/docs/library/generated/chainladder.VotingChainladder.rst @@ -0,0 +1,6 @@ +chainladder.VotingChainladder +============================= + +.. currentmodule:: chainladder + +.. autoclass:: VotingChainladder \ No newline at end of file diff --git a/docs/library/generated/chainladder.concat.rst b/docs/library/generated/chainladder.concat.rst new file mode 100644 index 00000000..f024f7be --- /dev/null +++ b/docs/library/generated/chainladder.concat.rst @@ -0,0 +1,6 @@ +chainladder.concat +================== + +.. currentmodule:: chainladder + +.. autofunction:: concat \ No newline at end of file diff --git a/docs/library/generated/chainladder.load_sample.rst b/docs/library/generated/chainladder.load_sample.rst new file mode 100644 index 00000000..360d742a --- /dev/null +++ b/docs/library/generated/chainladder.load_sample.rst @@ -0,0 +1,6 @@ +chainladder.load\_sample +======================== + +.. currentmodule:: chainladder + +.. autofunction:: load_sample \ No newline at end of file diff --git a/docs/library/generated/chainladder.maximum.rst b/docs/library/generated/chainladder.maximum.rst new file mode 100644 index 00000000..09dce287 --- /dev/null +++ b/docs/library/generated/chainladder.maximum.rst @@ -0,0 +1,6 @@ +chainladder.maximum +=================== + +.. currentmodule:: chainladder + +.. autofunction:: maximum \ No newline at end of file diff --git a/docs/library/generated/chainladder.minimum.rst b/docs/library/generated/chainladder.minimum.rst new file mode 100644 index 00000000..ead5399f --- /dev/null +++ b/docs/library/generated/chainladder.minimum.rst @@ -0,0 +1,6 @@ +chainladder.minimum +=================== + +.. currentmodule:: chainladder + +.. autofunction:: minimum \ No newline at end of file diff --git a/docs/library/generated/chainladder.read_json.rst b/docs/library/generated/chainladder.read_json.rst new file mode 100644 index 00000000..f28aad8e --- /dev/null +++ b/docs/library/generated/chainladder.read_json.rst @@ -0,0 +1,6 @@ +chainladder.read\_json +====================== + +.. currentmodule:: chainladder + +.. autofunction:: read_json \ No newline at end of file diff --git a/docs/library/generated/chainladder.read_pickle.rst b/docs/library/generated/chainladder.read_pickle.rst new file mode 100644 index 00000000..407b810c --- /dev/null +++ b/docs/library/generated/chainladder.read_pickle.rst @@ -0,0 +1,6 @@ +chainladder.read\_pickle +======================== + +.. currentmodule:: chainladder + +.. autofunction:: read_pickle \ No newline at end of file diff --git a/docs/user_guide/adjustments.ipynb b/docs/user_guide/adjustments.ipynb new file mode 100644 index 00000000..fd183343 --- /dev/null +++ b/docs/user_guide/adjustments.ipynb @@ -0,0 +1,1023 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fc09520f-eda2-4f15-800e-aa5421dde199", + "metadata": {}, + "source": [ + "# Data Adjustments\n", + "\n", + "There are many useful data adjustments in reserving that are not necessarily direct\n", + "IBNR models nor development factor selections. This module covers those implemented\n", + "by ``chainladder``. In all cases, these adjustments are most useful when used\n", + "as part of a larger `workflow`.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3ee2ab5b-6cc8-432a-bf25-1a1cfec00e8e", + "metadata": {}, + "source": [ + "(adjustments:bootstrapodpsample)=\n", + "## BootstrapODPSample\n", + "### Simulations\n", + "\n", + ":class:`BootstrapODPSample` is a transformer that simulates new triangles\n", + "according to the ODP Bootstrap model. That is both the ``index`` and ``column``\n", + "of the Triangle must be of unity length. Upon fitting the Estimator, the\n", + "``index`` will contain the individual simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6bb3584c-279c-4401-ba30-7af10abb516c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1990-12
Grain:OYDY
Shape:(500, 1, 10, 10)
Index:[Total]
Columns:[values]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1990-12\n", + "Grain: OYDY\n", + "Shape: (500, 1, 10, 10)\n", + "Index: [Total]\n", + "Columns: [values]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import chainladder as cl\n", + "import pandas as pd\n", + "\n", + "raa = cl.load_sample('raa')\n", + "cl.BootstrapODPSample(n_sims=500).fit_transform(raa)" + ] + }, + { + "cell_type": "markdown", + "id": "5272f97a-181f-41f8-a83e-bc983f700748", + "metadata": {}, + "source": [ + "```{note}\n", + "The `BootstrapODPSample` can only apply to single triangles as it needs the\n", + "``index`` axis to be free to hold the different triangle simluations.\n", + "```\n", + "\n", + "### Dropping\n", + "The Estimator has full dropping support consistent with the `Development` estimator.\n", + "This allows for eliminating problematic values from the sampled residuals.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eb29be5a-26f9-4dc6-9811-1623914562b8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1990-12
Grain:OYDY
Shape:(100, 1, 10, 10)
Index:[Total]
Columns:[values]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1990-12\n", + "Grain: OYDY\n", + "Shape: (100, 1, 10, 10)\n", + "Index: [Total]\n", + "Columns: [values]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.BootstrapODPSample(n_sims=100, drop=[('1982', 12)]).fit_transform(raa)" + ] + }, + { + "cell_type": "markdown", + "id": "1e9c4598-5ab5-4cdd-ab37-ff43b6248970", + "metadata": {}, + "source": [ + "### Deterministic methods\n", + "\n", + "The class only simulates new triangles from which you can generate\n", + "statistics about parameter and process uncertainty. This allows for converting\n", + "the various deterministic IBNR methods into stochastic\n", + "methods.\n", + "\n", + "\n", + "Like the `Development` estimators, The `BootstrapODPSample` allows for ommission\n", + "of certain residuals from its sampling algorithm with a suite of \"dropping\"\n", + "parameters. See :ref:`Omitting Link Ratios`.\n", + "\n", + "### Examples\n", + ":::{panels}\n", + ":column: col-lg-4 px-2 py-2\n", + "---\n", + "**[BootstrapODPSample Variability](plot_bootstrap_comparison)**\n", + "```{glue:} plot_bootstrap_comparison\n", + "```\n", + "+++\n", + "{bdg-warning}`medium`\n", + "\n", + "---\n", + "**[Basic BootstrapODPSample](plot_bootstrap)**\n", + "```{glue:} plot_bootstrap\n", + "```\n", + "+++\n", + "{bdg-success}`easy`\n", + ":::\n", + "{cite}`shapland2016`" + ] + }, + { + "cell_type": "markdown", + "id": "9e6166f7-a83a-4d19-8823-7f647119a9bb", + "metadata": {}, + "source": [ + "(adjustments:berquistsherman)=\n", + "## BerquistSherman\n", + ":class:`BerquistSherman` provides a mechanism of restating the inner diagonals of a\n", + "triangle for changes in claims practices. These adjustments can materialize in\n", + "case incurred and paid amounts as well as closed claims count development.\n", + "\n", + "In all cases, the adjustments retain the unadjusted latest diagonal of the\n", + "triangle. For the Incurred adjustment, an assumption of the trend rate in\n", + "average open case reserves must be supplied. For the adjustments to paid\n", + "amounts and closed claim counts, an estimator, such as `Chainladder` is needed\n", + "to calulate ultimate reported count so that the ``disposal_rate_`` of the\n", + "model can be calculated.\n", + "\n", + "### Adjustments\n", + "The `BerquistSherman` technique requires values for ``paid_amount``, ``incurred_amount``,\n", + "``reported_count``, and ``closed_count`` to be available in your `Triangle`. Without\n", + "these triangles, the `BerquistSherman` model cannot be used. If these conditions\n", + "are satisfied, the `BerquistSherman` technique adjusts all triangles except the\n", + "``reported_count`` triangle.\n", + "\n", + "The Estimator wraps all adjustments up in its ``adjusted_triangle_`` property." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "aaec15bf-c98e-435e-9815-14821e2d510c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496
19691.00001.00001.00001.00001.00001.00001.00001.0000
19701.00001.00001.00001.00001.00001.00001.0000
19711.00001.00001.00001.00001.00001.0000
19721.00001.00001.00001.00001.0000
19731.00001.00001.00001.0000
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19761.0000
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96\n", + "1969 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n", + "1970 1.0 1.0 1.0 1.0 1.0 1.0 1.0 NaN\n", + "1971 1.0 1.0 1.0 1.0 1.0 1.0 NaN NaN\n", + "1972 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN\n", + "1973 1.0 1.0 1.0 1.0 NaN NaN NaN NaN\n", + "1974 1.0 1.0 1.0 NaN NaN NaN NaN NaN\n", + "1975 1.0 1.0 NaN NaN NaN NaN NaN NaN\n", + "1976 1.0 NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle = cl.load_sample('berqsherm').loc['MedMal']\n", + "berq = cl.BerquistSherman(\n", + " paid_amount='Paid', incurred_amount='Incurred',\n", + " reported_count='Reported', closed_count='Closed',\n", + " trend=0.15).fit(triangle)\n", + "\n", + "# Only Reported triangle is left unadjusted\n", + "(triangle / berq.adjusted_triangle_)['Reported']" + ] + }, + { + "cell_type": "markdown", + "id": "c55c1000-7647-4e44-89bc-20bd552ac611", + "metadata": {}, + "source": [ + "### Latest Diagonal\n", + "Only the inner diagonals of the `Triangle` are adjusted. This allows the\n", + "``adjusted_triangle_`` to be a drop in surrogate for the original." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a325af12-8e57-4435-9451-e555d1368548", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496
19692.07143.36351.59681.38560.75451.21050.88621.0000
19700.76032.20841.24200.99640.87291.49941.0000
19711.76634.34641.74291.28990.76291.0000
19720.71292.35141.62281.25401.0000
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96\n", + "1969 2.071394 3.363483 1.596756 1.385616 0.754503 1.210486 0.886243 1.0\n", + "1970 0.760317 2.208368 1.242022 0.996379 0.872924 1.499417 1.000000 NaN\n", + "1971 1.766309 4.346440 1.742887 1.289923 0.762913 1.000000 NaN NaN\n", + "1972 0.712947 2.351430 1.622842 1.253956 1.000000 NaN NaN NaN\n", + "1973 1.323418 1.602855 0.718643 1.000000 NaN NaN NaN NaN\n", + "1974 1.451868 2.851770 1.000000 NaN NaN NaN NaN NaN\n", + "1975 1.360393 1.000000 NaN NaN NaN NaN NaN NaN\n", + "1976 1.000000 NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(triangle / berq.adjusted_triangle_)['Paid']" + ] + }, + { + "cell_type": "markdown", + "id": "c1cf6de5-a21b-4b4a-ba52-14509c652cb6", + "metadata": {}, + "source": [ + "### Transform\n", + "\n", + "`BerquistSherman` is strictly a data adjustment to the `Triangle` and it does\n", + "not attempt to estimate development patterns, tails, or ultimate values. Once\n", + "``transform`` is invoked on a Triangle, the ``adjusted_triangle_`` takes the\n", + "place of the existing `Triangle`.\n", + "\n", + "\n", + "### Examples\n", + ":::{panels}\n", + ":column: col-lg-4 px-2 py-2\n", + "\n", + "---\n", + "**[BerquistSherman Adjustment](plot_berqsherm_case)**\n", + "```{glue:} plot_berqsherm_case\n", + "```\n", + "+++\n", + "{bdg-success}`easy`\n", + "\n", + ":::\n", + "{cite}`friedland2010`" + ] + }, + { + "cell_type": "markdown", + "id": "3f8b8811-0983-44f5-8640-b6625bb00c1d", + "metadata": {}, + "source": [ + "(adjustments:paralellogramolf)=\n", + "## ParallelogramOLF\n", + "\n", + "The :class:`ParallelogramOLF` estimator is used to on-level a Triangle using\n", + "the parallogram technique. It requires a \"rate history\" and supports both\n", + "vertical line estimates as well as the more common effective date estimates.\n", + "\n", + "```{eval-rst}\n", + ".. image:: images/onlevel.PNG\n", + "```\n", + "\n", + "### Rate History\n", + "The Estimator requires a rate history that has, at a minimum, the rate changes\n", + "and date-like columns reflecting the corresponding effective dates of the\n", + "rate changes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "77b3d1ae-28f5-4afa-9071-ba360779b6d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

" + ], + "text/plain": [ + " EffDate RateChange\n", + "0 2016-07-15 0.02\n", + "1 2017-03-01 0.05\n", + "2 2018-01-01 -0.03\n", + "3 2019-10-31 0.10" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rate_history = pd.DataFrame({\n", + " 'EffDate': ['2016-07-15', '2017-03-01', '2018-01-01', '2019-10-31'],\n", + " 'RateChange': [0.02, 0.05, -.03, 0.1]})\n", + "rate_history" + ] + }, + { + "cell_type": "markdown", + "id": "e50ea2cc-c82b-4ee3-a782-23ae72a0380a", + "metadata": {}, + "source": [ + "The `ParallelogramOLF` maps the rate history using the ``change_col`` and ``date_col``\n", + "arguments. Once mapped, the transformer provides an ``olf_`` property\n", + "representing the on-level factors from the parallelogram on-leveling technique.\n", + "When used as a transformer, it retains your `Triangle` as it is, but then adds\n", + "the ``olf_`` property to your `Triangle`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "252ed464-a589-4a09-9bc7-937c4b0a8529", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2020
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20181.0842
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" + ], + "text/plain": [ + " 2020\n", + "2016 1.140327\n", + "2017 1.104799\n", + "2018 1.084197\n", + "2019 1.098557\n", + "2020 1.034456" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.DataFrame({\n", + " 'Year': [2016, 2017, 2018, 2019, 2020],\n", + " 'EarnedPremium': [10_000]*5})\n", + "prem_tri = cl.Triangle(data, origin='Year', columns='EarnedPremium')\n", + "prem_tri = cl.ParallelogramOLF(rate_history, change_col='RateChange', date_col='EffDate').fit_transform(prem_tri)\n", + "prem_tri.olf_" + ] + }, + { + "cell_type": "markdown", + "id": "0952cc65-fbfe-41ac-b33e-bc41c3145798", + "metadata": {}, + "source": [ + "### Input to CapeCod\n", + "\n", + "This estimator can be used within other estimators that depend on on-leveling,\n", + "such as the :class:`CapeCod` method. This is accomplished by passing a\n", + "`ParallelogramOLF` transformed `Triangle` to the `CapeCod` estimator.\n", + "\n", + "### Examples\n", + ":::{panels}\n", + ":column: col-lg-4 px-2 py-2\n", + "---\n", + "**[CapeCod Onleveling](plot_capecod_onlevel)**\n", + "```{glue:} plot_capecod_onlevel\n", + "```\n", + "+++\n", + "{bdg-warning}`medium`\n", + "\n", + ":::\n" + ] + }, + { + "cell_type": "markdown", + "id": "ebe90dc3-cd67-4a82-8bab-8fe94a131302", + "metadata": {}, + "source": [ + "(adjustments:trend)=\n", + "## Trend\n", + "\n", + "The :class:`Trend` estimator is a convenience estimator that allows for compound\n", + "trends to be used in other estimators that have a ``trend`` assumption. This\n", + "enables more complex trend assumptions to be used." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "af652141-0a29-4dcf-9807-18f252c6cd3c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ppauto_loss = cl.load_sample('clrd').groupby('LOB').sum().loc['ppauto', 'CumPaidLoss']\n", + "ppauto_prem = cl.load_sample('clrd').groupby('LOB').sum() \\\n", + " .loc['ppauto']['EarnedPremDIR'].latest_diagonal\n", + "\n", + "# Simple trend\n", + "a = cl.CapeCod(trend=0.05).fit(ppauto_loss, sample_weight=ppauto_prem).ultimate_.sum()\n", + "\n", + "# Equivalent using a Trend Estimator. This allows us to convert to more complex trends\n", + "b = cl.CapeCod().fit(cl.Trend(.05).fit_transform(ppauto_loss), sample_weight=ppauto_prem).ultimate_.sum()\n", + "a == b\n" + ] + }, + { + "cell_type": "markdown", + "id": "0cfb7d64-aa9f-4d8f-bfbf-d8d39ba99fb4", + "metadata": {}, + "source": [ + "### Multipart Trend\n", + "\n", + "A multipart trend can be achieved if passing a list of ``trends`` and corresponding\n", + "``dates``. Dates should be represented as a list of tuples (``start``, ``end``).\n", + "\n", + "The default start and end dates for a Triangle are its ``valuation_date`` and\n", + "its earliest origin date." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "dae2933e-864f-4d42-a866-e3fda52d1f2a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1988 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24\n", + "1989 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24 NaN\n", + "1990 1.24 1.24 1.24 1.24 1.24 1.24 1.24 1.24 NaN NaN\n", + "1991 1.24 1.24 1.24 1.24 1.24 1.24 1.24 NaN NaN NaN\n", + "1992 1.23 1.23 1.23 1.23 1.23 1.23 NaN NaN NaN NaN\n", + "1993 1.19 1.19 1.19 1.19 1.19 NaN NaN NaN NaN NaN\n", + "1994 1.16 1.16 1.16 1.16 NaN NaN NaN NaN NaN NaN\n", + "1995 1.10 1.10 1.10 NaN NaN NaN NaN NaN NaN NaN\n", + "1996 1.05 1.05 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1997 1.00 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ppauto_loss = cl.load_sample('clrd').groupby('LOB').sum().loc['ppauto', 'CumPaidLoss']\n", + "cl.Trend(\n", + " trends=[.05, .03],\n", + " dates=[('1997-12-31', '1995'),('1995', '1992-07')]\n", + ").fit(ppauto_loss).trend_.round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eaaf2ea2-cf4e-4549-ae83-4f1b6476e30e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.11 ('cl_dev')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + }, + "vscode": { + "interpreter": { + "hash": "cc8120b3d9d10be7e89b07ff6d4d09ab1619cf80570f11e1a80e248c6b69ac77" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user_guide/development.ipynb b/docs/user_guide/development.ipynb new file mode 100644 index 00000000..7b362177 --- /dev/null +++ b/docs/user_guide/development.ipynb @@ -0,0 +1,5732 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "colored-filter", + "metadata": {}, + "source": [ + "# Development\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "figured-cemetery", + "metadata": {}, + "source": [ + "## Basics and Commonalities\n", + "\n", + "Before stepping into fitting development patterns, its worth reviewing the basics\n", + "of Estimators. The main modeling API implemented by chainladder follows that of\n", + "the scikit-learn estimator. An estimator is any object that learns from data." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "protecting-customer", + "metadata": {}, + "source": [ + "### Scikit-Learn API\n", + "\n", + "The scikit-learn API is a common modeling interface that is used to construct and\n", + "fit a countless variety of machine learning algorithms. The common interface\n", + "allows for very quick swapping between models with minimal code changes. The\n", + "``chainladder`` package has adopted the interface to promote a standardized approach\n", + "to fitting reserving models.\n", + "\n", + "All estimator objects can optionally be configured with parameters to uniquely\n", + "specify the model being built. This is done ahead of pushing any data through\n", + "the model.\n", + "\n", + "```python\n", + "estimator = Estimator(param1=1, param2=2)\n", + "```\n", + "All estimator objects expose a `fit` method that takes a `Triangle` as input, `X`:\n", + "\n", + "```python\n", + "estimator.fit(X=data)\n", + "```\n", + "\n", + "All estimators include a `sample_weight` option to the `fit` method to specify\n", + "an exposure basis. If an exposure base is not applicable, then this argument is\n", + "ignored.\n", + "\n", + "```python\n", + "estimator.fit(X=data, sample_weight=weight)\n", + "```\n", + "\n", + "All estimators either `transform` the input Triangle or `predict` an outcome." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "compatible-secondary", + "metadata": {}, + "source": [ + "### Transformers\n", + "\n", + "All transformers include a ``transform`` method. The method is used to transform a\n", + "Triangle and it will always return a Triangle with added features based on the\n", + "specifics of the transformer.\n", + "\n", + "```python\n", + "transformed_data = estimator.transform(data)\n", + "```\n", + "\n", + "Other than final IBNR models, ``chainladder`` estimators are transformers.\n", + "That is, they return your `Triangle` back to you with additional properties.\n", + "\n", + "Transforming can be done at the time of fit.\n", + "\n", + "```python\n", + "# Fitting and Transforming\n", + "estimator.fit(data)\n", + "transformed_data = estimator.transform(data)\n", + "# One line equivalent\n", + "transformed_data = estimator.fit_transform(data)\n", + "assert isinstance(transformed_data, cl.Triangle)\n", + "```" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "common-mileage", + "metadata": {}, + "source": [ + "### Predictors\n", + "\n", + "All predictors include a ``predict`` method.\n", + "\n", + "```python\n", + "prediction = estimator.predict(new_data)\n", + "```\n", + "\n", + "Predictors are intended to create new predictions. It is not uncommon to fit a\n", + "model on a more aggregate view, say national level, of data and predict on a\n", + "more granular triangle, state or provincial." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "lyric-syracuse", + "metadata": {}, + "source": [ + "### Parameter Types\n", + "\n", + "Estimator parameters: All the parameters of an estimator can be set when it is\n", + "instantiated or by modifying the corresponding attribute. These parameters\n", + "define how you'd like to fit an estimator and are chosen before the fitting\n", + "process. These are often referred to as hyperparameters in the context of\n", + "Machine Learning, and throughout these documents. Most of the hyperparameters\n", + "of the ``chainladder`` package take on sensible defaults.\n", + "\n", + "```python\n", + "estimator = Estimator(param1=1, param2=2)\n", + "assert estimator.param1 == 1\n", + "```\n", + "\n", + "Estimated parameters: When data is fitted with an estimator, parameters are\n", + "estimated from the data at hand. All the estimated parameters are attributes\n", + "of the estimator object ending by an underscore. The use of the underscore is\n", + "a key API design style of scikit-learn that allows for the quicker recognition\n", + "of fitted parameters vs hyperparameters:\n", + "\n", + "```python\n", + "estimator.estimated_param_\n", + "```\n", + "\n", + "In many cases the estimated parameters are themselves Triangles and can be\n", + "manipulated using the same methods we learned about in the `Triangle` class.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "mysterious-translation", + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6788e883-acdc-4436-ae4a-fca113442feb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "chainladder.core.triangle.Triangle" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dev = cl.Development().fit(cl.load_sample('ukmotor'))\n", + "type(dev.cdf_)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "accessible-uganda", + "metadata": {}, + "source": [ + "### Commonalities\n", + "\n", + "All \"Development Estimators\" are transformers and reveal common a set of properties\n", + "when they are fit.\n", + "\n", + "1. ``ldf_`` represents the fitted age-to-age factors of the model.\n", + "2. ``cdf_`` represents the fitted age-to-ultimate factors of the model.\n", + "3. All \"Development estimators\" implement the ``transform`` method.\n", + "\n", + "\n", + "``cdf_`` is nothing more than the cumulative representation of the ``ldf_`` vectors.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "naughty-survivor", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dev = cl.Development().fit(cl.load_sample('raa'))\n", + "dev.ldf_.incr_to_cum() == dev.cdf_" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "tamil-terminology", + "metadata": {}, + "source": [ + "(development:development)=\n", + "## Development\n", + "\n", + "`Development` allows for the selection of loss development patterns. Many\n", + "of the typical averaging techniques are available in this class: ``simple``,\n", + "``volume`` and ``regression`` through the origin. Additionally, `Development`\n", + "includes patterns to allow for fine-tuned exclusion of link-ratios from the LDF\n", + "calculation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "timely-illinois", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Development(average='simple')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa = cl.load_sample('raa')\n", + "cl.Development(average='simple')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cosmetic-correction", + "metadata": {}, + "source": [ + "Alternatively, you can provide a list to parameterize each development period\n", + "separately. When adjusting individual development periods the list must be\n", + "the same length as your triangles ``link_ratio`` development axis." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "female-function", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(raa.link_ratio.development)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "meaningful-aviation", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Development(average=['volume', 'simple', 'simple', 'simple', 'simple', 'simple',\n", + " 'simple', 'simple', 'simple'])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(average=['volume']+['simple']*8)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "indian-bench", + "metadata": {}, + "source": [ + "This approach works for ``average``, ``n_periods``, ``drop_high`` and ``drop_low``.\n", + "\n", + "Notice where you have not specified a parameter, a sensible default\n", + "is chosen for you." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cubic-xerox", + "metadata": {}, + "source": [ + "### Omitting link ratios\n", + "\n", + "There are several arguments for dropping individual cells from the triangle as\n", + "well as excluding whole valuation periods or highs and lows. Any combination\n", + "of the 'drop' arguments is permissible.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "naughty-writing", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Development(drop_high=[True, True, True, True, True, False, False, False,\n", + " False],\n", + " drop_low=[True, True, True, True, True, False, False, False, False])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(\n", + " drop_high=[True]*5+[False]*4, \n", + " drop_low=[True]*5+[False]*4).fit(raa)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "alive-cooperative", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Development(drop_valuation='1985')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop_valuation='1985').fit(raa)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "restricted-herald", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Development(drop=[('1985', 12), ('1987', 24)])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop=[('1985', 12), ('1987', 24)]).fit(raa)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "democratic-wheel", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Development(drop=('1985', 12), drop_valuation='1988')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop=('1985', 12), drop_valuation='1988').fit(raa)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "final-alpha", + "metadata": {}, + "source": [ + "When using `drop`, the earliest age of the `link_ratio` should be referenced.\n", + "For example, use `12` to drop the `12-24` ratio.\n", + "\n", + "```{note}\n", + "`drop_high` and `drop_low` are ignored in cases where the number of link ratios available for a given development period is less than 1.\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "electrical-shell", + "metadata": {}, + "source": [ + "### Extended Link Ratio Family\n", + "\n", + "The `Development` estimator is based on the regression framework known as the\n", + "Extended Link Ratio Family (ELRF). A nice property of this family is that we\n", + "not only get estimates for our patterns (`cdf_`, and `ldf_`), but also\n", + "measures of variability of our estimates (`sigma_`, `std_err_` and `std_residuals_`\n", + "). These variability properties are used to develop the stochastic features in the\n", + "`MackChainladder` method, but even for deterministic methods these variability\n", + "estimates can be used as a diagnostic tool to validate the appropriateness of using\n", + "multiplicative link ratios.\n", + "\n", + "The `std_residuals_` in particular is described by Barnett and Zehnwirth as a diagnostic\n", + "that points to the inferiority of the chainladder method relative to the probablistic\n", + "trend family." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "045dc473-dd73-4b57-b74d-de857c3bd256", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108\n", + "1981 -0.572151 -0.831650 -0.748883 -0.344181 0.870432 1.414340 -0.000279 -0.681860 NaN\n", + "1982 2.307508 -0.716139 1.971586 1.589984 0.198229 -0.948836 1.091934 0.731482 NaN\n", + "1983 -0.126737 -0.229861 -0.481096 -0.178044 0.905637 -0.296705 -0.898711 NaN NaN\n", + "1984 -0.430543 -0.836499 0.372285 -1.307360 0.001171 -0.106393 NaN NaN NaN\n", + "1985 1.139843 0.094282 0.617466 -0.017042 -1.543656 NaN NaN NaN NaN\n", + "1986 0.293597 0.463314 -0.680936 0.782504 NaN NaN NaN NaN NaN\n", + "1987 0.596140 2.093539 -0.580547 NaN NaN NaN NaN NaN NaN\n", + "1988 0.471658 0.660667 NaN NaN NaN NaN NaN NaN NaN\n", + "1989 -0.428176 NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa = cl.load_sample('raa')\n", + "model = cl.Development().fit(raa)\n", + "model.std_residuals_" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9865a16d-f69f-4bf6-bc19-3b362b82380b", + "metadata": {}, + "source": [ + " Replicating **Fig 2.6** from their paper can be accomplished with \n", + "a bit of manipulation of the residual triangles." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "c479daf1-9209-4d0e-984e-c9ee3ceca7f4", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\r\n", + "\r\n", + "\r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " 2021-10-04T18:56:56.318708\r\n", + " image/svg+xml\r\n", + " \r\n", + " \r\n", + " Matplotlib v3.4.2, https://matplotlib.org/\r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,8))\n", + "model.std_residuals_.T.plot(\n", + " style='.', color='gray', legend=False, grid=True, ax=ax00,\n", + " xlabel='Development Month', ylabel='Weighted Standardized Residuals')\n", + "model.std_residuals_.iloc[..., :-1].mean('origin').T.plot(\n", + " color='red', legend=False, grid=True, ax=ax00)\n", + "model.std_residuals_.plot(\n", + " style='.', color='gray', legend=False, grid=True, ax=ax01, xlabel='Origin Period')\n", + "model.std_residuals_.mean('development').plot(\n", + " color='red', legend=False, grid=True, ax=ax01)\n", + "model.std_residuals_.dev_to_val().T.plot(\n", + " style='.', color='gray', legend=False, grid=True, ax=ax10,\n", + " xlabel='Valuation Date', ylabel='Weighted Standardized Residuals')\n", + "\n", + "model.std_residuals_.dev_to_val().mean('origin').T.plot(color='red', legend=False, grid=True, ax=ax10)\n", + "pd.concat((\n", + " (raa[raa.valuation\n", + " \n", + " \n", + " \n", + " 12-24\n", + " 24-36\n", + " 36-48\n", + " 48-60\n", + " 60-72\n", + " 72-84\n", + " 84-96\n", + " 96-108\n", + " 108-120\n", + " \n", + " \n", + " \n", + " \n", + " 1998\n", + " 1.7925\n", + " 1.2056\n", + " 1.0956\n", + " 1.0457\n", + " 1.0189\n", + " 1.0097\n", + " 1.0048\n", + " 1.0023\n", + " 1.0019\n", + " \n", + " \n", + " 1999\n", + " 1.7683\n", + " 1.1986\n", + " 1.0902\n", + " 1.0435\n", + " 1.0194\n", + " 1.0092\n", + " 1.0050\n", + " 1.0024\n", + " 1.0019\n", + " \n", + " \n", + " 2000\n", + " 1.7620\n", + " 1.1902\n", + " 1.0900\n", + " 1.0430\n", + " 1.0191\n", + " 1.0101\n", + " 1.0046\n", + " 1.0024\n", + " 1.0020\n", + " \n", + " \n", + " 2001\n", + " 1.7439\n", + " 1.1913\n", + " 1.0906\n", + " 1.0436\n", + " 1.0187\n", + " 1.0090\n", + " 1.0042\n", + " 1.0021\n", + " 1.0017\n", + " \n", + " \n", + " 2002\n", + " 1.7348\n", + " 1.1940\n", + " 1.0892\n", + " 1.0442\n", + " 1.0186\n", + " 1.0085\n", + " 1.0041\n", + " 1.0020\n", + " 1.0016\n", + " \n", + " \n", + " 2003\n", + " 1.7189\n", + " 1.1853\n", + " 1.0920\n", + " 1.0438\n", + " 1.0186\n", + " 1.0092\n", + " 1.0045\n", + " 1.0022\n", + " 1.0018\n", + " \n", + " \n", + " 2004\n", + " 1.7025\n", + " 1.1867\n", + " 1.0922\n", + " 1.0415\n", + " 1.0179\n", + " 1.0088\n", + " 1.0043\n", + " 1.0021\n", + " 1.0017\n", + " \n", + " \n", + " 2005\n", + " 1.7012\n", + " 1.1860\n", + " 1.0859\n", + " 1.0412\n", + " 1.0177\n", + " 1.0087\n", + " 1.0042\n", + " 1.0021\n", + " 1.0017\n", + " \n", + " \n", + " 2006\n", + " 1.7028\n", + " 1.1797\n", + " 1.0857\n", + " 1.0411\n", + " 1.0177\n", + " 1.0087\n", + " 1.0042\n", + " 1.0021\n", + " 1.0017\n", + " \n", + " \n", + " 2007\n", + " 1.6693\n", + " 1.1804\n", + " 1.0860\n", + " 1.0412\n", + " 1.0178\n", + " 1.0087\n", + " 1.0042\n", + " 1.0021\n", + " 1.0017\n", + " \n", + " \n", + "" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "1998 1.792469 1.205560 1.095603 1.045669 1.018931 1.009749 1.004777 1.002288 1.001866\n", + "1999 1.768268 1.198631 1.090150 1.043455 1.019377 1.009244 1.004998 1.002392 1.001904\n", + "2000 1.761959 1.190201 1.089958 1.042975 1.019054 1.010127 1.004585 1.002444 1.001969\n", + "2001 1.743900 1.191331 1.090565 1.043566 1.018677 1.009022 1.004171 1.002074 1.001672\n", + "2002 1.734765 1.194005 1.089153 1.044183 1.018551 1.008452 1.004106 1.002042 1.001646\n", + "2003 1.718935 1.185285 1.091988 1.043790 1.018635 1.009171 1.004452 1.002213 1.001784\n", + "2004 1.702514 1.186716 1.092167 1.041492 1.017863 1.008798 1.004272 1.002124 1.001712\n", + "2005 1.701237 1.186004 1.085897 1.041211 1.017746 1.008741 1.004245 1.002111 1.001701\n", + "2006 1.702795 1.179664 1.085678 1.041114 1.017706 1.008722 1.004236 1.002106 1.001698\n", + "2007 1.669287 1.180366 1.085961 1.041239 1.017758 1.008747 1.004248 1.002112 1.001702" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.ldf_['paid']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "recovered-english", + "metadata": {}, + "source": [ + "### Incremental patterns\n", + "\n", + "The incremental patterns of the `CaseOutstanding` method are avilable as\n", + "additional properties for review. They are the `paid_to_prior_case_` and the\n", + "`case_to_prior_case_`. These are useful to review when deciding on the appropriate\n", + "hyperparameters for `paid_n_periods` and `case_n_periods`. Once you are satisfied\n", + "with your hyperparameter tuning, you can see the fitted selections in the\n", + "`paid_ldf_` and `case_ldf_` incremental patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "sized-adoption", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
24-3636-4848-6060-7272-8484-9696-108108-120120-132
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20020.54090.55460.54060.48020.4881
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" + ], + "text/plain": [ + " 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120 120-132\n", + "1998 0.537820 0.554128 0.525253 0.498107 0.532934 0.537997 0.587702 0.697024 0.579812\n", + "1999 0.536825 0.564892 0.544220 0.496910 0.502864 0.579975 0.641971 0.650552 NaN\n", + "2000 0.546126 0.574211 0.539091 0.487208 0.537598 0.543222 0.665505 NaN NaN\n", + "2001 0.540564 0.566029 0.514819 0.501324 0.507736 0.541364 NaN NaN NaN\n", + "2002 0.540900 0.554610 0.540609 0.480216 0.488133 NaN NaN NaN NaN\n", + "2003 0.526510 0.576514 0.536276 0.476418 NaN NaN NaN NaN NaN\n", + "2004 0.529775 0.566523 0.506884 NaN NaN NaN NaN NaN NaN\n", + "2005 0.521531 0.553888 NaN NaN NaN NaN NaN NaN NaN\n", + "2006 0.526122 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2007 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.case_to_prior_case_" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "meaningful-stage", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
24-3636-4848-6060-7272-8484-9696-108108-120120-132
(All)0.53400.56380.52960.49000.51390.55060.63170.67380.5798
" + ], + "text/plain": [ + " 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120 120-132\n", + "(All) 0.534019 0.56385 0.529593 0.490031 0.513853 0.55064 0.631726 0.673788 0.579812" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.case_ldf_" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "crucial-jefferson", + "metadata": {}, + "source": [ + "{cite}`friedland2010`" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "informed-suite", + "metadata": {}, + "source": [ + "(development:tweedieglm)=\n", + "## TweedieGLM\n", + "\n", + "The `TweedieGLM` implements the GLM reserving structure discussed by Taylor and McGuire.\n", + "A nice property of the GLM framework is that it is highly flexible in terms of including\n", + "covariates that may be predictive of loss reserves while maintaining a close relationship\n", + "to traditional methods. Additionally, the framework can be extended in a straightforward\n", + "way to incorporate various approaches to measuring prediction errors. Behind the\n", + "scenes, `TweedieGLM` is using scikit-learn's `TweedieRegressor` estimator.\n", + "\n", + "### Long Format\n", + "\n", + "GLMs are fit to triangles in \"Long Format\". That is, they are converted to pandas\n", + "DataFrames behind the scenes. Each axis of the `Triangle` is included in the\n", + "dataframe. The ``origin`` and ``development`` axes are in columns of the same name.\n", + "You can inspect what your `Triangle` looks like in long format by calling `to_frame`\n", + "with ``keepdims=True``\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "bulgarian-bookmark", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GRNAMELOBorigindevelopmentIncurLossCumPaidLossBulkLossEarnedPremDIREarnedPremCededEarnedPremNet
0Adriatic Ins Coothliab1995-01-01128.0NaN8.0139.0131.08.0
1Adriatic Ins Coothliab1995-01-012411.0NaN4.0139.0131.08.0
2Adriatic Ins Coothliab1995-01-01367.03.04.0139.0131.08.0
3Adriatic Ins Coothliab1996-01-011240.0NaN40.0410.0359.051.0
4Adriatic Ins Coothliab1996-01-012440.0NaN40.0410.0359.051.0
\n", + "
" + ], + "text/plain": [ + " GRNAME LOB origin development IncurLoss CumPaidLoss \\\n", + "0 Adriatic Ins Co othliab 1995-01-01 12 8.0 NaN \n", + "1 Adriatic Ins Co othliab 1995-01-01 24 11.0 NaN \n", + "2 Adriatic Ins Co othliab 1995-01-01 36 7.0 3.0 \n", + "3 Adriatic Ins Co othliab 1996-01-01 12 40.0 NaN \n", + "4 Adriatic Ins Co othliab 1996-01-01 24 40.0 NaN \n", + "\n", + " BulkLoss EarnedPremDIR EarnedPremCeded EarnedPremNet \n", + "0 8.0 139.0 131.0 8.0 \n", + "1 4.0 139.0 131.0 8.0 \n", + "2 4.0 139.0 131.0 8.0 \n", + "3 40.0 410.0 359.0 51.0 \n", + "4 40.0 410.0 359.0 51.0 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.load_sample('clrd').to_frame(keepdims=True).reset_index().head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "social-price", + "metadata": {}, + "source": [ + "```{warning}\n", + "'origin', 'development', and 'valuation' are reserved keywords for the dataframe. Declaring columns with these names separately will result in error.\n", + "```\n", + "\n", + "While you can inspect the `Triangle` in long format, you will not directly convert\n", + "to long format yourself. The `TweedieGLM` does this for you. Additionally,\n", + "the `origin` of the design matrix is restated in years from the earliest origin\n", + "period. That is, is if the earliest origin is '1995-01-01' then it gets replaced with\n", + "0. Consequently, '1996-04-01' would be replaced with 1.25. This is done because\n", + "datetimes have limited support in scikit-learn. Finally, the `TweedieGLM`\n", + "will automatically convert the response to an incremental basis.\n", + "\n", + "### R-style formulas\n", + "\n", + "We use the `patsy` library to allow formulation of the the feature set `X`\n", + "of the GLM. Because `X` is a parameter that used extensively throughout\n", + "`chainladder`, the `TweedieGLM` refers to it as the `design_matrix`. Those\n", + "familiar with the R programming language will be familiar with the notation\n", + "used by `patsy`. For example, we can include both `origin` and `development`\n", + "as terms in a model." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "broad-stephen", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
coef_
Intercept13.516322
development-0.006251
origin0.033863
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" + ], + "text/plain": [ + " coef_\n", + "Intercept 13.516322\n", + "development -0.006251\n", + "origin 0.033863" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genins = cl.load_sample('genins')\n", + "glm = cl.TweedieGLM(design_matrix='development + origin').fit(genins)\n", + "glm.coef_" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "heated-cardiff", + "metadata": {}, + "source": [ + "### ODP Chainladder\n", + "\n", + "Replicating the results of the volume weighted chainladder development patterns\n", + "can be done by fitting a Poisson-log GLM to incremental paids. To do this, we\n", + "can specify the `power` and `link` of the estimator as well as the `design_matrix`.\n", + "The volume-weighted chainladder method can be replicated by including both\n", + "`origin` and `development` as categorical features." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "signed-realtor", + "metadata": {}, + "outputs": [], + "source": [ + "dev = cl.TweedieGLM(\n", + " design_matrix='C(development) + C(origin)',\n", + " power=1, link='log').fit(genins)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "mounted-judge", + "metadata": {}, + "source": [ + "A trivial comparison against the traditional `Development` estimator shows\n", + "a comparable set of `ldf_` patterns.\n", + "\n", + "\n", + "### Parsimonious modeling\n", + "\n", + "Having full access to all axes of the `Triangle` along with the powerful formulation\n", + "of `patsy` allows for substantial customization of the model fit. For example,\n", + "we can include 'LOB' interactions with piecewise linear coefficients to reduce\n", + "model complexity.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "improved-corps", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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coef_
Intercept12.549945
LOB[T.ppauto]3.202703
LOB[comauto]:C(np.minimum(development, 36))[T.24]0.578694
LOB[ppauto]:C(np.minimum(development, 36))[T.24]0.449832
LOB[comauto]:C(np.minimum(development, 36))[T.36]0.790516
LOB[ppauto]:C(np.minimum(development, 36))[T.36]0.321206
LOB[comauto]:development-0.044627
LOB[ppauto]:development-0.054814
LOB[comauto]:origin0.054581
LOB[ppauto]:origin0.057790
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" + ], + "text/plain": [ + " coef_\n", + "Intercept 12.549945\n", + "LOB[T.ppauto] 3.202703\n", + "LOB[comauto]:C(np.minimum(development, 36))[T.24] 0.578694\n", + "LOB[ppauto]:C(np.minimum(development, 36))[T.24] 0.449832\n", + "LOB[comauto]:C(np.minimum(development, 36))[T.36] 0.790516\n", + "LOB[ppauto]:C(np.minimum(development, 36))[T.36] 0.321206\n", + "LOB[comauto]:development -0.044627\n", + "LOB[ppauto]:development -0.054814\n", + "LOB[comauto]:origin 0.054581\n", + "LOB[ppauto]:origin 0.057790" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample('clrd')['CumPaidLoss'].groupby('LOB').sum()\n", + "clrd=clrd[clrd['LOB'].isin(['ppauto', 'comauto'])]\n", + "dev = cl.TweedieGLM(\n", + " design_matrix='LOB+LOB:C(np.minimum(development, 36))+LOB:development+LOB:origin',\n", + " max_iter=1000).fit(clrd)\n", + "dev.coef_" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "republican-candle", + "metadata": {}, + "source": [ + "This model is limited to 10 coefficients across two lines of business. The basic\n", + "chainladder model is known to be overparameterized with at least 18 parameters\n", + "requiring estimation. Despite drastically simplifying the model, the `cdf_`\n", + "patterns of the GLM are within 1% of the traditional chainladder for every lag\n", + "and for both lines of business:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "secure-committee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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development12-Ult24-Ult36-Ult48-Ult60-Ult72-Ult84-Ult96-Ult108-Ult
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" + ], + "text/plain": [ + "development 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult \\\n", + "LOB \n", + "comauto 0.002 0.003 -0.01 0.003 0.011 0.008 0.005 -0.000 \n", + "ppauto 0.006 0.003 -0.00 0.001 0.002 0.001 0.001 0.001 \n", + "\n", + "development 108-Ult \n", + "LOB \n", + "comauto -0.002 \n", + "ppauto 0.001 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "((dev.cdf_.iloc[..., 0, :] / \n", + " cl.Development().fit(clrd).cdf_) - 1\n", + ").to_frame().round(3)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "according-headset", + "metadata": {}, + "source": [ + "Like every other Development estimator, the `TweedieGLM` produces a set of `ldf_`\n", + "patterns and can be used in a larger workflow with tail extrapolation and reserve\n", + "estimation.\n", + "\n", + "::::{grid}\n", + ":gutter: 2\n", + "\n", + ":::{grid-item-card} \n", + ":columns: 4\n", + ":link: ../gallery/plot_glm_ldf\n", + ":link-type: doc\n", + "**TweedieGLM Basics**\n", + "```{image} ../images/plot_glm_ldf.png\n", + "---\n", + "alt: TweedieGLM Basics\n", + "---\n", + "```\n", + "+++\n", + "{bdg-warning}`easy`\n", + ":::\n", + "::::\n", + "\n", + "\n", + "{cite}`taylor2016`" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "killing-constitution", + "metadata": {}, + "source": [ + "(development:developmentml)=\n", + "## DevelopmentML\n", + "\n", + "`DevelopmentML` is a general development estimator that works as an interface to\n", + "scikit-learn compliant machine learning (ML) estimators. The `TweedieGLM` is\n", + "a special case of `DevelopmentML` with the ML algorithm limited to scikit-learn's\n", + "`TweedieRegressor` estimator.\n", + "\n", + "### The Interface\n", + "\n", + "ML algorithms are designed to be fit against tabular data like a pandas DataFrame\n", + "or a 2D numpy array. A `Triangle` does not meet the definition and so `DevelopmentML`\n", + "is provided to incorporate ML into a broader reserving workflow. This includes:\n", + "\n", + " 1. Automatic conversion of Triangle to a dataframe for fitting\n", + " 2. Flexibility in expressing any preprocessing as part of a scikit-learn `Pipeline`\n", + " 3. Predictions through the terminal development age of a `Triangle` to fill in the\n", + " lower half\n", + " 4. Predictions converted to `ldf_` patterns so that the results of the estimator\n", + " are compliant with the rest of `chainladder`, like tail selection and IBNR modeling.\n", + "\n", + "### Features\n", + "\n", + "Data from any axis of a `Triangle` is available to be used in the `DevelopmentML`\n", + "estimator. For example, we can use many of the scikit-learn components to\n", + "generate development patterns from both the time axes as well as the `index` of\n", + "the `Triangle`." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "middle-machinery", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2261-12
Grain:OYDY
Shape:(6, 1, 10, 9)
Index:[LOB]
Columns:[CumPaidLoss]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2261-12\n", + "Grain: OYDY\n", + "Shape: (6, 1, 10, 9)\n", + "Index: [LOB]\n", + "Columns: [CumPaidLoss]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.compose import ColumnTransformer\n", + "\n", + "clrd = cl.load_sample('clrd').groupby('LOB').sum()['CumPaidLoss']\n", + "\n", + "# Decide how to preprocess the X (ML) dataset using sklearn\n", + "design_matrix = ColumnTransformer(transformers=[\n", + " ('dummy', OneHotEncoder(drop='first'), ['LOB', 'development']),\n", + " ('passthrough', 'passthrough', ['origin'])\n", + "])\n", + "\n", + "# Wrap preprocessing and model in a larger sklearn Pipeline\n", + "estimator_ml = Pipeline(steps=[\n", + " ('design_matrix', design_matrix),\n", + " ('model', RandomForestRegressor())\n", + "])\n", + "\n", + "# Fitting DevelopmentML fits the underlying ML model and gives access to ldf_\n", + "cl.DevelopmentML(estimator_ml=estimator_ml, y_ml='CumPaidLoss').fit(clrd).ldf_" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "atlantic-venice", + "metadata": {}, + "source": [ + "### Autoregressive\n", + "\n", + "The time-series nature of loss development naturally lends to an urge for autoregressive\n", + "features. That is, features that are based on predictions, albeit on a lagged basis.\n", + "`DevelopmentML` includes an `autoregressive` parameter that can be used to\n", + "express the response as a lagged feature as well.\n", + "\n", + "```{note}\n", + "When using `autoregressive` features, you must also declare it as a column\n", + "in your `estimator_ml` Pipeline.\n", + "```\n", + "\n", + "### PatsyFormula\n", + "\n", + "While the sklearn preprocessing API is powerful, it can be tedious work with in\n", + "some instances. In particular, modeling complex interactions is much easier to do\n", + "with Patsy. The `chainladder` package includes a custom sklearn estimator\n", + "to gain access to the patsy API. This is done through the `PatsyFormula` estimator." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "civil-state", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120
(All)2.65001.41001.19001.10001.04001.02001.01001.01001.0100
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 2.65 1.41 1.19 1.1 1.04 1.02 1.01 1.01 1.01" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "estimator_ml = Pipeline(steps=[\n", + " ('design_matrix', cl.PatsyFormula('LOB:C(origin)+LOB:C(development)+development')),\n", + " ('model', RandomForestRegressor())\n", + "])\n", + "cl.DevelopmentML(\n", + " estimator_ml=estimator_ml, \n", + " y_ml='CumPaidLoss').fit(clrd).ldf_.iloc[0, 0, 0].round(2)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "prime-teacher", + "metadata": {}, + "source": [ + "```{note}\n", + "`PatsyFormula` is not an estimator designed to work with triangles. It is an sklearn transformer designed to work with pandas DataFrames allowing it to work directly in an sklearn Pipeline.\n", + "```" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "weird-stone", + "metadata": {}, + "source": [ + "(development:barnettzehnwirth)=\n", + "## BarnettZehnwirth\n", + "\n", + "The `BarnettZehnwirth` estimator solves for development patterns using the\n", + "Probabilistic Trend Family (PTF) regression framework. Unlike the ELRF framework,\n", + "which assumes no ``valuation`` covariate, the PTF framework allows for this.\n", + "\n", + "```{eval-rst}\n", + ".. figure:: images/prob_trend_family.PNG\n", + " :align: center\n", + " :scale: 40%\n", + "```\n", + "\n", + "Structurally, the PTF regression is different from the `ELRF` (ELRF) regression framework in\n", + "two distinct ways:\n", + "\n", + " 1. Where the ELRF fits independent regressions to each adjacent development lag, the PTF\n", + " regression is fit to the entire triangle\n", + " 2. Where the ELRF is fit to cumulative amounts, the PTF is fit to the log of\n", + " the incremental amounts of the `Triangle`.\n", + "\n", + "### Formulation\n", + "\n", + "The PTF framework is an ordinary least squares (OLS) model with the response, `y`\n", + "being the log of the incremental amounts of a Triangle. These are assumed to be\n", + "normally distributed which implies the incrementals themselves are log-normal\n", + "distributed.\n", + "\n", + "The framework includes coefficients for origin periods (alpha), development periods (gamma)\n", + "and calendar period (iota).\n", + "\n", + "\n", + "$y(i, j) = \\alpha _{i} + \\sum_{k=1}^{j}\\gamma _{k}+ \\sum_{\\iota =1}^{i+j}\\gamma _{\\iota}+ \\varepsilon _{i,j}$\n", + "\n", + "These coefficients can be categorical or continuous, and to support a wide range of\n", + "model forms, patsy formulas are used.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "innovative-attachment", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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InterceptC(origin)[T.1.0]C(origin)[T.2.0]C(origin)[T.3.0]C(origin)[T.4.0]C(origin)[T.5.0]C(origin)[T.6.0]C(origin)[T.7.0]C(origin)[T.8.0]C(origin)[T.9.0]...C(development)[T.24]C(development)[T.36]C(development)[T.48]C(development)[T.60]C(development)[T.72]C(development)[T.84]C(development)[T.96]C(development)[T.108]C(development)[T.120]C(development)[T.132]
coef_11.8368630.1788240.3451120.3781330.4050930.4270410.4310760.6603830.9632231.1568...0.251091-0.055824-0.448589-0.828917-1.16913-1.507561-1.798345-2.0231-2.238333-2.427672
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" + ], + "text/plain": [ + " Intercept C(origin)[T.1.0] C(origin)[T.2.0] C(origin)[T.3.0] \\\n", + "coef_ 11.836863 0.178824 0.345112 0.378133 \n", + "\n", + " C(origin)[T.4.0] C(origin)[T.5.0] C(origin)[T.6.0] C(origin)[T.7.0] \\\n", + "coef_ 0.405093 0.427041 0.431076 0.660383 \n", + "\n", + " C(origin)[T.8.0] C(origin)[T.9.0] ... C(development)[T.24] \\\n", + "coef_ 0.963223 1.1568 ... 0.251091 \n", + "\n", + " C(development)[T.36] C(development)[T.48] C(development)[T.60] \\\n", + "coef_ -0.055824 -0.448589 -0.828917 \n", + "\n", + " C(development)[T.72] C(development)[T.84] C(development)[T.96] \\\n", + "coef_ -1.16913 -1.507561 -1.798345 \n", + "\n", + " C(development)[T.108] C(development)[T.120] C(development)[T.132] \n", + "coef_ -2.0231 -2.238333 -2.427672 \n", + "\n", + "[1 rows x 21 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abc = cl.load_sample('abc')\n", + "\n", + "# Discrete origin, development, valuation\n", + "cl.BarnettZehnwirth(formula='C(origin)+C(development)').fit(abc).coef_.T" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "nominated-zimbabwe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Interceptorigindevelopmentvaluation
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" + ], + "text/plain": [ + " Intercept origin development valuation\n", + "coef_ 8.359157 4.215981 0.319288 -4.116569" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Linear coefficients for origin, development, and valuation\n", + "cl.BarnettZehnwirth(formula='origin+development+valuation').fit(abc).coef_.T" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "pending-breakdown", + "metadata": {}, + "source": [ + "The PTF framework is particularly useful when there is calendar period inflation\n", + "influences on loss development.\n", + "\n", + "::::{grid}\n", + ":gutter: 2\n", + "\n", + ":::{grid-item-card} \n", + ":columns: 4\n", + ":link: ../gallery/plot_ptf_resid\n", + ":link-type: doc\n", + "**PTF Residuals**\n", + "```{image} ../images/plot_ptf_resid.png\n", + "---\n", + "alt: PTF Residuals\n", + "---\n", + "```\n", + "+++\n", + "{bdg-warning}`medium`\n", + ":::\n", + "::::\n", + "\n", + "{cite}`barnett2000`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "blank-enemy", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.11 ('cl_dev')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + }, + "toc-showtags": true, + "vscode": { + "interpreter": { + "hash": "cc8120b3d9d10be7e89b07ff6d4d09ab1619cf80570f11e1a80e248c6b69ac77" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user_guide/index.md b/docs/user_guide/index.md new file mode 100644 index 00000000..f2d3460d --- /dev/null +++ b/docs/user_guide/index.md @@ -0,0 +1,110 @@ +# {octicon}`book` User Guide + +Here are a few examples that we have came up with, hopefully the code is simple enough so you can fit your own needs. + +`````{grid} +:gutter: 3 + +````{grid-item-card} +:columns: 6 + +**[Triangles](triangle)** +^^^ + +Data object to manage and manipulate reserving data + +**Application**: Extend pandas syntax to manipulate reserving triangles + +```{glue:} plot_triangle_from_pandas +``` ++++ +**Classes**: **[Triangle](triangle)**, ... +```` + +````{grid-item-card} +:columns: 6 + +**[Development](development)** +^^^ +Tooling to generate loss development patterns + +**Applications**: Comprehensive library for development + +```{glue:} plot_clarkldf +``` ++++ + +**Algorithms**: [Development](development:development), [ClarkLDF](development:clarkldf), … + +```` + +````{grid-item-card} +:columns: 6 + +**[Tail Estimation](tails)** +^^^ +Extrapolate development patterns beyond the known data. + +**Applications**: Long-tailed lines of business use cases + +```{glue:} plot_exponential_smoothing +``` + ++++ +**Algorithms**: [TailCurve](tails:tailcurve), [TailConstant](tails:tailconstant), … +```` + +````{grid-item-card} +:columns: 6 + +**[IBNR Models](methods)** +^^^ + +Generate IBNR estimates and associated statistics + + +**Applications**: Constructing reserve estimates + +```{glue:} plot_mack +``` + ++++ +**Algorithms**: [Chainladder](methods:chainladder), [CapeCod](methods:capecod), … +```` + +````{grid-item-card} +:columns: 6 + +**[Adjustments](adjustments)** +^^^ +Common actuarial data adjustments + + + +**Applications**: Simulation, trending, on-leveling + +```{glue:} plot_stochastic_bornferg +``` + ++++ +**Classes**: [BootstrapODPSample](adjustments:bootstrapodpsample), [Trend](adjustments:trend), … +```` + +````{grid-item-card} +:columns: 6 + +**[Workflow](workflow)** +^^^ + +Workflow tools for complex analyses + +**Application**: Scenario testing, ensembling + +```{glue:} plot_voting_chainladder +``` + ++++ +**Utilities**: [Pipeline](workflow:pipeline), [VotingChainladder](workflow:votingchainladder), … +```` + +````` diff --git a/docs/user_guide/methods.ipynb b/docs/user_guide/methods.ipynb new file mode 100644 index 00000000..6e8fefe6 --- /dev/null +++ b/docs/user_guide/methods.ipynb @@ -0,0 +1,1140 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "terminal-failure", + "metadata": {}, + "source": [ + "# IBNR Models\n", + "\n", + "The IBNR Estimators are the final stage in analyzing reserve estimates in the\n", + "`chainladder` package. These Estimators have a `predict` method as opposed\n", + "to a `transform` method.\n", + "\n", + "\n", + "## Basics and Commonalities\n", + "\n", + "### Ultimates\n", + "\n", + "All reserving methods determine some ultimate cost of insurance claims. These\n", + "ultimates are captured in the `ultimate_` property of the estimator." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "reverse-provision", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2261
198118,834
198216,858
198324,083
198428,703
198528,927
198619,501
198717,749
198824,019
198916,045
199018,402
" + ], + "text/plain": [ + " 2261\n", + "1981 18834.000000\n", + "1982 16857.953917\n", + "1983 24083.370924\n", + "1984 28703.142163\n", + "1985 28926.736343\n", + "1986 19501.103184\n", + "1987 17749.302590\n", + "1988 24019.192510\n", + "1989 16044.984101\n", + "1990 18402.442529" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import chainladder as cl\n", + "import pandas as pd\n", + "\n", + "cl.Chainladder().fit(cl.load_sample('raa')).ultimate_" + ] + }, + { + "cell_type": "markdown", + "id": "forced-mercury", + "metadata": {}, + "source": [ + "Ultimates are measured at a valuation date way into the future. The library is\n", + "extraordinarily conservative in picking this date, and sets it to December 31, 2261.\n", + "This is set globally and can be viewed by referencing the ``ULT_VAL``\n", + "constant." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "direct-parcel", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2261-12-31 23:59:59.999999999'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.options.get_option('ULT_VAL')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "egyptian-milwaukee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2050-12-31 23:59:59.999999999'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.options.set_option('ULT_VAL', '2050-12-31 23:59:59.999999999')\n", + "cl.options.get_option('ULT_VAL')" + ] + }, + { + "cell_type": "markdown", + "id": "retired-january", + "metadata": {}, + "source": [ + "The `ultimate_` along with most of the other properties of IBNR models are triangles\n", + "and can be manipulated. However, it is important to note that the model itself\n", + "is not a Triangle, it is an scikit-learn style Estimator. This distinction is\n", + "important when wanting to manipulate model attributes." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "scientific-farming", + "metadata": {}, + "outputs": [], + "source": [ + "triangle = cl.load_sample('quarterly')\n", + "model = cl.Chainladder().fit(triangle)\n", + "# This works since we're slicing the ultimate Triangle\n", + "ult = model.ultimate_['paid']" + ] + }, + { + "cell_type": "markdown", + "id": "selected-think", + "metadata": {}, + "source": [ + "This throws an error since the model itself is not sliceable:\n", + "```python \n", + "ult = model['paid'].ultimate_\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "measured-words", + "metadata": {}, + "source": [ + "### IBNR\n", + "\n", + "Any difference between an `ultimate_` and the `latest_diagonal` of a Triangle\n", + "is contained in the `ibnr_` property of an estimator. While technically, as in\n", + "the example of a paid triangle, there can be case reserves included in the `ibnr_`\n", + "estimate, the distinction is not made by the `chainladder` package and must be\n", + "managed by you." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "incorporated-round", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2431.2695585474003" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle = cl.load_sample('quarterly')\n", + "model = cl.Chainladder().fit(triangle)\n", + "\n", + "# Determine outstanding case reserves\n", + "case_reserves = (triangle['incurred']-triangle['paid']).latest_diagonal\n", + "\n", + "# Net case reserves off of paid IBNR\n", + "true_ibnr = model.ibnr_['paid'] - case_reserves\n", + "true_ibnr.sum()" + ] + }, + { + "cell_type": "markdown", + "id": "noble-moore", + "metadata": {}, + "source": [ + "### Complete Triangles\n", + "\n", + "The `full_triangle_` and `full_expectation_` attributes give a view of the\n", + "completed `Triangle`. While the `full_expectation_` is entirely based on\n", + "`ultimate_` values and development patterns, the `full_triangle_` is a\n", + "blend of the existing triangle. These are useful for conducting an analysis\n", + "of actual results vs model expectations." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "certified-firewall", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84\n", + "2007 344.492346 557.928307 348.774627 10.847889 -11.40612 NaN NaN\n", + "2008 -21.882151 -185.514153 -340.715515 -102.582899 11.40612 NaN NaN\n", + "2009 -92.224026 -233.617500 94.508419 91.735009 NaN NaN NaN\n", + "2010 -303.438287 -209.004780 -102.567531 NaN NaN NaN NaN\n", + "2011 67.162588 70.208127 NaN NaN NaN NaN NaN\n", + "2012 5.889530 NaN NaN NaN NaN NaN NaN\n", + "2013 NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = cl.Chainladder().fit(cl.load_sample('ukmotor'))\n", + "residuals = model.full_expectation_ - model.full_triangle_\n", + "residuals[residuals.valuation<=model.X_.valuation_date]" + ] + }, + { + "cell_type": "markdown", + "id": "confidential-peoples", + "metadata": {}, + "source": [ + "Another typical analysis is to forecast the IBNR run-off for future periods." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "broken-fetish", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 2014 2015 2016\n", + "2007 NaN NaN NaN\n", + "2008 350.902024 NaN NaN\n", + "2009 661.620101 375.916667 NaN\n", + "2010 1073.335187 619.525276 351.999397\n", + "2011 1502.970266 1133.999503 654.540504\n", + "2012 2724.981102 1820.419755 1373.516924\n", + "2013 5587.058983 3351.884601 2239.221748" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expected_3y_run_off = model.full_triangle_.dev_to_val().cum_to_incr().loc[..., '2014':'2016']\n", + "expected_3y_run_off" + ] + }, + { + "cell_type": "markdown", + "id": "running-lender", + "metadata": {}, + "source": [ + "(methods:chainladder)=\n", + "## Chainladder\n", + "\n", + "The distinguishing characteristic of the :class:`Chainladder` method is that ultimate claims for each\n", + "accident year are produced from recorded values assuming that future claims’ development is\n", + "similar to prior years’ development. In this method, the actuary uses the development triangles to\n", + "track the development history of a specific group of claims. The underlying assumption in the\n", + "development technique is that claims recorded to date will continue to develop in a similar manner\n", + "in the future – that the past is indicative of the future. That is, the development technique assumes\n", + "that the relative change in a given year’s claims from one evaluation point to the next is similar to\n", + "the relative change in prior years’ claims at similar evaluation points.\n", + "\n", + "An implicit assumption in the development technique is that, for an immature accident year, the\n", + "claims observed thus far tell you something about the claims yet to be observed. This is in\n", + "contrast to the assumptions underlying the expected claims technique.\n", + "\n", + "Other important assumptions of the development method include: consistent claim processing, a\n", + "stable mix of types of claims, stable policy limits, and stable reinsurance (or excess insurance)\n", + "retention limits throughout the experience period.\n", + "\n", + "Though the algorithm underling the basic chainladder is trivial, the properties\n", + "of the `Chainladder` estimator allow for a concise access to relevant information.\n", + "\n", + "As an example, we can use the estimator to determine actual vs expected run-off\n", + "of a subsequent valuation period.\n", + "\n", + "{cite}`friedland2010`" + ] + }, + { + "cell_type": "markdown", + "id": "surprising-interest", + "metadata": {}, + "source": [ + "(methods:mackchainladder)=\n", + "## MackChainladder\n", + "\n", + "The :class:`MackChainladder` model can be regarded as a special form of a\n", + "weighted linear regression through the origin for each development period. By using\n", + "a regression framework, statistics about the variability of the data and the parameter\n", + "estimates allows for the estimation of prediction errors. The Mack Chainladder\n", + "method is the most basic of stochastic methods.\n", + " \n", + "### Compatibility\n", + "\n", + "Because of the regression framework underlying the `MackChainladder`, it is not\n", + "compatible with all development and tail estimators of the library. In fact,\n", + "it really should only be used with the `Development` estimator and `TailCurve`\n", + "tail estimator.\n", + "\n", + "```{warning}\n", + "While the MackChainladder might not error with other options for development and\n", + "tail, the stochastic properties should be ignored, in which case the basic\n", + "`Chainladder` should be used.\n", + "```\n", + "\n", + "### Examples\n", + "\n", + ":::{panels}\n", + ":column: col-lg-4 px-2 py-2\n", + "\n", + "---\n", + "**[MackChainladder Basics](plot_mack)**\n", + "```{glue:} plot_mack\n", + "```\n", + "+++\n", + "{bdg-success}`easy`\n", + "\n", + ":::\n", + "{cite}`mack1993`\n", + "{cite}`mack1999`" + ] + }, + { + "cell_type": "markdown", + "id": "lonely-maker", + "metadata": {}, + "source": [ + "(methods:bornhuetterferguson)=\n", + "## BornhuetterFerguson\n", + "\n", + "The :class:`BornhuetterFerguson` technique is essentially a blend of the\n", + "development and expected claims techniques. In the development technique, we multiply actual\n", + "claims by a cumulative claim development factor. This technique can lead to erratic, unreliable\n", + "projections when the cumulative development factor is large because a relatively small swing in\n", + "reported claims or the reporting of an unusually large claim could result in a very large swing in\n", + "projected ultimate claims. In the expected claims technique, the unpaid claim estimate is equal to\n", + "the difference between a predetermined estimate of expected claims and the actual payments.\n", + "This has the advantage of stability, but it completely ignores actual results as reported. The\n", + "Bornhuetter-Ferguson technique combines the two techniques by splitting ultimate claims into\n", + "two components: actual reported (or paid) claims and expected unreported (or unpaid) claims. As\n", + "experience matures, more weight is given to the actual claims and the expected claims become\n", + "gradually less important.\n", + "\n", + "### Exposure base\n", + "\n", + "The :class:`BornhuetterFerguson` technique is the first we explore of the Expected\n", + "Loss techniques. In this family of techniques, we need some measure of exposure.\n", + "This is handled by passing a `Triangle` representing the exposure to the `sample_weight`\n", + "argument of the `fit` method of the Estimator.\n", + "\n", + "All scikit-learn style estimators optionally support a `sample_weight` argument\n", + "and this is used by the `chainladder` package to capture the exposure base\n", + "of these Expected Loss techniques." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "outside-resistance", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "75203.23550854485" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa = cl.load_sample('raa')\n", + "sample_weight = raa.latest_diagonal*0+40_000\n", + "cl.BornhuetterFerguson(apriori=0.7).fit(\n", + " X=raa, \n", + " sample_weight=sample_weight\n", + ").ibnr_.sum()\n" + ] + }, + { + "cell_type": "markdown", + "id": "green-pharmacology", + "metadata": {}, + "source": [ + "### Apriori\n", + "\n", + "We've fit a :class:`BornhuetterFerguson` model with the assumption that our\n", + "prior belief, or `apriori` is a 70% Loss Ratio. The method supports any constant\n", + "for the `apriori` hyperparameter. The ``apriori`` then gets\n", + "multiplied into our sample weight to determine our prior belief on expected losses\n", + "prior to considering that actual emerged to date.\n", + "\n", + "Because of the multiplicative nature of `apriori` and `sample_weight` we don't\n", + "have to limit ourselves to a single constant for the `apriori`. Instead, we\n", + "can exploit the model structure to make our `sample_weight` represent our\n", + "prior belief on ultimates while setting the `apriori` to 1.0.\n", + "\n", + "For example, we can use the :class:`Chainladder` ultimates as our prior belief\n", + "in the :class:`BornhuetterFerguson` method.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "third-installation", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 2050\n", + "1981 18834.000000\n", + "1982 16898.632172\n", + "1983 24012.333266\n", + "1984 28281.843524\n", + "1985 28203.700714\n", + "1986 19840.005163\n", + "1987 18840.362337\n", + "1988 22789.948877\n", + "1989 19541.155136\n", + "1990 20986.022826" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl_ult = cl.Chainladder().fit(raa).ultimate_ # Chainladder Ultimate\n", + "apriori = cl_ult*0+(cl_ult.sum()/10) # Mean Chainladder Ultimate\n", + "cl.BornhuetterFerguson(apriori=1).fit(raa, sample_weight=apriori).ultimate_" + ] + }, + { + "cell_type": "markdown", + "id": "cosmetic-circumstances", + "metadata": {}, + "source": [ + "{cite}`friedland2010`" + ] + }, + { + "cell_type": "markdown", + "id": "removable-country", + "metadata": {}, + "source": [ + "(methods:benktander)=\n", + "## Benktander\n", + "\n", + "The :class:`Benktander` method is a credibility-weighted\n", + "average of the :class:`BornhuetterFerguson` technique and the development technique.\n", + "The advantage cited by the authors is that this method will prove more\n", + "responsive than the Bornhuetter-Ferguson technique and more stable\n", + "than the development technique.\n", + "\n", + "### Iterations\n", + "\n", + "The `Benktander` method is also known as the iterated :class:`BornhuetterFerguson`\n", + "method. This is because it is a generalization of the :class:`BornhuetterFerguson`\n", + "technique.\n", + "\n", + "The generalized formula based on `n_iters`, n is:\n", + "\n", + "$Ultimate = Apriori\\times (1-\\frac{1}{CDF})^{n} + Latest\\times \\sum_{k=0}^{n-1}(1-\\frac{1}{CDF})^{k}$\n", + "\n", + "* `n=0` yields the expected loss method \n", + "* `n=1` yields the traditional :class:`BornhuetterFerguson` method\n", + "* `n>>1` converges to the traditional :class:`Chainladder` method.\n", + "\n", + "### Examples\n", + "\n", + ":::{panels}\n", + ":column: col-lg-4 px-2 py-2\n", + "\n", + "---\n", + "**[BornhutterFerguson vs Chainladder](plot_benktander)**\n", + "```{glue:} plot_benktander\n", + "```\n", + "+++\n", + "{bdg-warning}`medium`\n", + "\n", + "\n", + ":::\n", + "\n", + "### Expected Loss Method\n", + "\n", + "Setting `n_iters` to 0 will emulate that Expected Loss method. That is to say,\n", + "the actual emerged loss experience of the Triangle will be completely ignored in\n", + "determining the ultimate. While it is a trivial calculation, it allows for\n", + "run-off patterns to be developed, which is useful for new programs new lines\n", + "of businesses.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "complete-thomas", + "metadata": {}, + "outputs": [], + "source": [ + "triangle = cl.load_sample('ukmotor')\n", + "exposure = triangle.latest_diagonal*0 + 25_000\n", + "cl.Benktander(apriori=0.75, n_iters=0).fit(\n", + " X=triangle, \n", + " sample_weight=exposure\n", + ").full_triangle_.round(0)" + ] + }, + { + "cell_type": "markdown", + "id": "maritime-shopper", + "metadata": {}, + "source": [ + "Mack noted the `Benktander` method is found to have almost always a smaller mean\n", + "squared error than the other two methods and to be almost as precise as an exact\n", + "Bayesian procedure.\n", + "\n", + "{cite}`friedland2010`" + ] + }, + { + "cell_type": "markdown", + "id": "wrong-shade", + "metadata": {}, + "source": [ + "(methods:capecod)=\n", + "## CapeCod\n", + "\n", + "The :class:`CapeCod` method, also known as the Stanard-Buhlmann method, is similar to the\n", + "Bornhuetter-Ferguson technique. The primary difference between the two methods is the\n", + "derivation of the expected claim ratio. In the Cape Cod technique, the expected claim ratio\n", + "or apriori is obtained from the triangle itself instead of an independent and often judgmental\n", + "selection as in the Bornhuetter-Ferguson technique.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "turkish-renaissance", + "metadata": {}, + "outputs": [], + "source": [ + "clrd = cl.load_sample('clrd')[['CumPaidLoss', 'EarnedPremDIR']].groupby('LOB').sum().loc['wkcomp']\n", + "loss = clrd['CumPaidLoss']\n", + "sample_weight=clrd['EarnedPremDIR'].latest_diagonal\n", + "m1 = cl.CapeCod().fit(loss, sample_weight=sample_weight)\n", + "m1.ibnr_.sum()" + ] + }, + { + "cell_type": "markdown", + "id": "automatic-midwest", + "metadata": {}, + "source": [ + "### Apriori\n", + "\n", + "The default hyperparameters for the :class:`CapeCod` method can be emulated by\n", + "the :class:`BornhuetterFerguson` method. We can manually derive the `apriori`\n", + "implicit in the CapeCod estimate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "portuguese-cartoon", + "metadata": {}, + "outputs": [], + "source": [ + "cl_ult = cl.Chainladder().fit(loss).ultimate_\n", + "apriori = loss.latest_diagonal.sum() / (sample_weight/(cl_ult/loss.latest_diagonal)).sum()\n", + "m2 = cl.BornhuetterFerguson(apriori).fit(\n", + " X=clrd['CumPaidLoss'], \n", + " sample_weight=clrd['EarnedPremDIR'].latest_diagonal)\n", + "m2.ibnr_.sum()" + ] + }, + { + "cell_type": "markdown", + "id": "national-cooler", + "metadata": {}, + "source": [ + "A parameter `apriori_sigma` can also be specified to give sampling variance to the\n", + "estimated apriori. This along with `random_state` can be used in conjuction with\n", + "the `BootstrapODPSample` estimator to build a stochastic `CapeCod` estimate.\n", + "\n", + "### Trend and On-level\n", + "\n", + "When using data implicit in the Triangle to derive the apriori, it is desirable\n", + "to bring the different origin periods to a common basis. The `CapeCod` estimator\n", + "provides a `trend` hyperparameter to allow for trending everything to the latest\n", + "origin period. However, the apriori used in the actual estimation of the IBNR is\n", + "the `detrended_apriori_` detrended back to each of the specific origin periods.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "dominant-spiritual", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Detrended Apriori Apriori\n", + "1988 0.483539 0.750128\n", + "1989 0.507716 0.750128\n", + "1990 0.533102 0.750128\n", + "1991 0.559757 0.750128\n", + "1992 0.587745 0.750128\n", + "1993 0.617132 0.750128\n", + "1994 0.647989 0.750128\n", + "1995 0.680388 0.750128\n", + "1996 0.714407 0.750128\n", + "1997 0.750128 0.750128" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m1 = cl.CapeCod(trend=0.05).fit(loss, sample_weight=sample_weight)\n", + "pd.concat((\n", + " m1.detrended_apriori_.to_frame().iloc[:, 0].rename('Detrended Apriori'),\n", + " m1.apriori_.to_frame().iloc[:, 0].rename('Apriori')), axis=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "polished-profile", + "metadata": {}, + "source": [ + "Simple one-part trends are supported directly in the hyperparameter selection.\n", + "If a more complex trend assumption is required or on-leveling, then passing\n", + "Triangles transformed by the :class:`Trend` and :class:`ParallelogramOLF`\n", + "estimators will capture these finer details as in this example from the\n", + "example gallery.\n", + "\n", + "\n", + "### Examples\n", + "\n", + ":::{panels}\n", + ":column: col-lg-4 px-2 py-2\n", + "\n", + "---\n", + "**[CapeCod Onleveling](plot_capecod_onlevel)**\n", + "```{glue:} plot_capecod_onlevel\n", + "```\n", + "+++\n", + "{bdg-warning}`medium`\n", + "\n", + "---\n", + "**[CapeCod Sensitivity](plot_capecod)**\n", + "```{glue:} plot_capecod\n", + "```\n", + "+++\n", + "{bdg-danger}`hard`\n", + "\n", + ":::\n", + "\n", + "\n", + "### Decay\n", + "\n", + "The default behavior of the `CapeCod` is to include all origin periods in the\n", + "estimation of the `apriori_`. A more localized approach, giving lesser weight\n", + "to origin periods that are farther from a target origin period, can be achieved\n", + "by flexing the `decay` hyperparameter.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "headed-ceramic", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 1988 1989 1990 1991 1992 1993 1994 \\\n", + "2050 0.617945 0.613275 0.604879 0.591887 0.57637 0.559855 0.548615 \n", + "\n", + " 1995 1996 1997 \n", + "2050 0.542234 0.540979 0.541723 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.CapeCod(decay=0.8).fit(loss, sample_weight=sample_weight).apriori_.T" + ] + }, + { + "cell_type": "markdown", + "id": "built-aviation", + "metadata": {}, + "source": [ + "With a `decay` less than 1.0, we see `apriori_` estimates that vary by origin.\n", + "\n", + "{cite}`friedland2010`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "several-sherman", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user_guide/tails.ipynb b/docs/user_guide/tails.ipynb new file mode 100644 index 00000000..36f11cc7 --- /dev/null +++ b/docs/user_guide/tails.ipynb @@ -0,0 +1,1541 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0a0a49ca-8a9d-42e0-966f-283362624557", + "metadata": {}, + "source": [ + "# Tail Estimators\n", + "Unobserved loss experience beyond the edge of a Triangle can be substantial and is a necessary part of an actuary's analysis. This is particularly so for long tailed lines more common in commercial insurance or excess layers of loss.\n", + "\n", + "With all tail estimation, we are *extrapolating* beyond the known data which comes with its own challenges. It tends to be more difficult to validate assumptions when performing tail estimation. Additionally, many of the techniques carry a high degree of sensitivity to the assumptions you will choose when conducting analysis. \n", + "\n", + "Nevertheless, it is a necessary part of the actuary's toolkit in estimating reserve liabilities." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5ee2a1e0-80ae-455e-85fa-487c838fd07d", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "markdown", + "id": "2f3423ed-d0fd-4d5e-b4de-f708f832b074", + "metadata": {}, + "source": [ + "## Basics and Commonalities\n", + "As with the `Development` family of estimators, the `Tail` module provides a variety of tail **transformers** that allow for the extrapolation of development patterns beyond the end of the triangle. Being transformers, we can expect that each estimator has a `fit` and a `transform` method. It is also expected that the `transform` method will give us back our Triangle with additional estimated parameters for incorporating our tail review in our IBNR estimates. \n", + "\n", + "### Tail\n", + "Every tail estimator produced a `tail_` attribute which represents the point estimate of the tail of the Triangle.\n", + "```{tip}\n", + "Recall that the trailing `underscore_` implies that these parameters are only available once we've `fit` our estimator.\n", + "```\n", + "\n", + "We will demonstrate this property using the [TailConstant](tails:tailconstant) estimator which we explore further below." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "6bc8412b-d6bb-4083-b12f-480481ddf6ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 120-Ult\n", + "(All) 1.1" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle = cl.load_sample('genins')\n", + "tail = cl.TailConstant(1.10).fit_transform(triangle)\n", + "tail.tail_" + ] + }, + { + "cell_type": "markdown", + "id": "658908cf-c915-4c62-adb2-b62675a506a2", + "metadata": {}, + "source": [ + "### Run Off\n", + "\n", + "In addition to point estimates, tail estimators support run-off analysis. The\n", + "`ldf_` and `cdf_` attribute splits the tail point estimate into enough\n", + "development patterns to allow for tail run-off to be examined for at least another\n", + "valuation year by default. For an annual `development_grain` grain, the development pattterns\n", + "include two additional patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "e106a1a3-d18c-4467-ad8d-3db2bf46178e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-Ult24-Ult36-Ult48-Ult60-Ult72-Ult84-Ult96-Ult108-Ult120-Ult132-Ult
(All)15.89124.55262.60541.78771.52291.37971.27011.20521.11951.10001.0514
" + ], + "text/plain": [ + " 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult 120-Ult 132-Ult\n", + "(All) 15.891235 4.552571 2.60544 1.787716 1.522949 1.379703 1.27013 1.205201 1.119497 1.1 1.051394" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tail.cdf_" + ] + }, + { + "cell_type": "markdown", + "id": "2a15cc1a-1eaf-4830-be69-8b4dfaa281d9", + "metadata": {}, + "source": [ + "```{note}\n", + "When fitting a tail estimator without first specifying a `Development` estimator first, `chainladder` assumes a volume-weighted `Development`. To override this, you should declare transform your triangle with a development transformer first.\n", + "```\n", + "\n", + "For quarterly grain, there are five additional development patterns and for monthly\n", + "there are thirteen.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "09b4ef13-881f-4475-b724-987a071729a3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 135-Ult 138-Ult 141-Ult 144-Ult 147-Ult\n", + "(All) 1.00065 1.000531 1.000434 1.000355 1.00029" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle = cl.load_sample('quarterly')['paid']\n", + "tail = cl.TailCurve().fit(triangle)\n", + "# Slice just the tail entries\n", + "tail.cdf_[~tail.ldf_.development.isin(triangle.link_ratio.development)]" + ] + }, + { + "cell_type": "markdown", + "id": "60d40444-6bbb-42db-9335-f31634322224", + "metadata": {}, + "source": [ + "```{note}\n", + "The point estimate `tail_` of the tail is the CDF for 135-Ult. The remaining CDFs simply represent the run-off of this tail as specified by the Tail estimator in use.\n", + "```\n", + "\n", + "### Attachment Age\n", + "\n", + "By default, tail estimators attach to the oldest `development` age of the `Triangle`.\n", + "In practice, the last several known development factors of a `Triangle` can be\n", + "unreliable and attaching the tail earlier and using it as a smoothing mechanism\n", + "is preferred. All tail estimators have an `attachment_age` parameter that\n", + "allows you to select the development age to which the tail will attach." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "f8aa14fb-a12f-489d-aa28-548877085bff", + "metadata": {}, + "outputs": [], + "source": [ + "triangle = cl.load_sample('genins')\n", + "unsmoothed = cl.TailCurve().fit(triangle).ldf_\n", + "smoothed = cl.TailCurve(attachment_age=24).fit(triangle).ldf_" + ] + }, + { + "cell_type": "markdown", + "id": "daf318e0-f6b5-4126-876e-883910af86e3", + "metadata": {}, + "source": [ + "In this way, we can smooth over any volatile development patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "27bd974b-481b-4899-bffe-59fc7289510c", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-06T18:17:54.184267\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.concat((\n", + " unsmoothed.T.iloc[:, 0].rename('Unsmoothed'),\n", + " smoothed.T.iloc[:, 0].rename('Age 24+ Smoothed')),\n", + " axis=1).plot(marker='o', title='Selected Link Ratio', ylabel='LDF');" + ] + }, + { + "cell_type": "markdown", + "id": "6081c268-58a0-449f-83c0-0bd72e65c3d5", + "metadata": {}, + "source": [ + "### Projection period\n", + "\n", + "Regardless of where you attach a tail, there will be incremental patterns to at\n", + "least one year past the end of the `Triangle` to support run-off analysis.\n", + "\n", + "This accommodates views of run-off for the oldest origin periods in your triangle for at least another year. As actuarial reviews typically occur no less than annually, this should be sufficient for examining run-off performance between actuarial valuations.\n", + "\n", + "Cases arise where modeling run-off on a longer time horizon is desired. For these cases, it is possible to modify the `projection_period` (in months) of all Tail Esimtators by specifying the number of months you wish to extend your patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "1ffb22de-fd73-46fc-a1f1-5e61514315be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
120-132132-144144-156156-168168-180
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" + ], + "text/plain": [ + " 120-132 132-144 144-156 156-168 168-180\n", + "(All) 1.011946 1.007056 1.004167 1.002461 1.003555" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Extend tail run-off 4 years past end of Triangle.\n", + "tail = cl.TailCurve(projection_period=12*4).fit(triangle)\n", + "tail.ldf_[~tail.ldf_.development.isin(triangle.link_ratio.development)]" + ] + }, + { + "cell_type": "markdown", + "id": "d21599f9-b4e2-4b28-8429-bd71e4be6bcd", + "metadata": {}, + "source": [ + "In this example, we see a higher development pattern for the last period than the ones prior. This is because the true tail run-off exceeds four years. To be clear, the `projection_period` has no effect on the actual estimated tail, it simply provides a longer time horizon for measuring run-off." + ] + }, + { + "cell_type": "markdown", + "id": "4c8969bc-e01e-40cc-a794-37a13104b49a", + "metadata": {}, + "source": [ + "(tails:tailconstant)=\n", + "## TailConstant\n", + "\n", + "`TailConstant` allows you to input a tail factor as a constant. This is\n", + "useful when relying on tail selections from an external source like industry data.\n", + "\n", + "The tail factor supplied applies to all individual triangles contained within\n", + "the Triangle object. If this is not the desired outcome, slicing individual\n", + "triangles and applying `TailConstant` separately to each can be done.\n", + "\n", + "### Decay\n", + "\n", + "For run-off analysis, you can control the decay of your tail. An exponential\n", + "`decay` parameter is also available to facilitate the run off analysis described\n", + "above." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "531ffe4a-8c12-4c34-a519-2cbcf0fae4be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120 120-132 132-144\n", + "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725 1.002436 1.047448" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tail = cl.TailConstant(tail=1.05, decay=0.95).fit_transform(triangle)\n", + "tail.ldf_" + ] + }, + { + "cell_type": "markdown", + "id": "c20918a2-42d1-4b51-9420-98b8b0de316e", + "metadata": {}, + "source": [ + "As we can see in the example, the 5% tail in the example is split between the\n", + "amount to run-off over the subsequent calendar period **132-144**, and the\n", + "remainder, **144-Ult**. The split is controlled by the `decay` parameter. We\n", + "can always reference our `tail_` point estimate." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "5cb579be-7e25-40d1-8756-816d54ddeb95", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 120-Ult\n", + "(All) 1.05" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tail.tail_" + ] + }, + { + "cell_type": "markdown", + "id": "40e0f469-249b-4ddd-9a45-6bee5edadcc9", + "metadata": {}, + "source": [ + "### Examples\n", + "\n", + ":::{panels}\n", + ":column: col-lg-4 px-2 py-2\n", + "\n", + "---\n", + "**[DevelopmentConstant Callable](plot_callable_dev_constant)**\n", + "```{glue:} plot_callable_dev_constant\n", + "```\n", + "+++\n", + "{bdg-danger}`hard`\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(tails:tailcurve)=\n", + "## TailCurve\n", + "\n", + "`TailCurve` allows for extrapolating a tail factor using curve fitting.\n", + "Currently, exponential decay of LDFs and inverse power curve are supported." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "736e0edd-293a-4a0f-a4cb-661804518431", + "metadata": {}, + "outputs": [], + "source": [ + "clrd = cl.load_sample('clrd').groupby('LOB').sum()['CumPaidLoss']\n", + "cdf_ip = cl.TailCurve(curve='inverse_power').fit(clrd).tail_\n", + "cdf_xp = cl.TailCurve(curve='exponential').fit(clrd).tail_" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "aaba2663-25c5-45eb-ac76-99440baa1b1c", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-06T11:19:23.654974\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = pd.concat((cdf_ip.rename(\"Inverse Power\"),\n", + " cdf_xp.rename(\"Exponential\")), axis=1).plot(\n", + " kind='bar', title='Curve Fit Comparison',\n", + " xlabel='Industry', ylabel='Tail Factor')\n", + "\n", + "ax.legend(loc='center', bbox_to_anchor=(.5, -.35), facecolor='white', ncol=2);" + ] + }, + { + "cell_type": "markdown", + "id": "82043cfd-22a0-48e5-82be-b947687f65e8", + "metadata": {}, + "source": [ + "### Regression parameters\n", + "\n", + "Underlying the curve fit is an OLS regression which generates both a `slope_`\n", + "and `intercept_` term.\n", + "\n", + "For the `exponential` curve fit with slope, $\\beta$ and intercept $\\alpha$, the tail factor\n", + "is:\n", + "\n", + "\n", + "$$\n", + "\\prod_{i}^{N}1+exp(\\beta X+\\alpha )\n", + "$$ (tail_exponential_eq)\n", + "\n", + "For the `inverse_power` curve, the tail factor is:\n", + "\n", + "$$\n", + "\\prod_{i}^{N}1+exp(\\alpha) X^{\\beta}\n", + "$$ (tail_inverse_power_eq)\n", + "\n", + "Where $X$ is your selected link ratios and $N$ is the number of years you wish to extrapolate the `tail_`\n", + "\n", + "Deriving the `tail_` factor manually:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4dba8958-8c9a-4d98-8b93-050bddf35269", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 120-Ult\n", + "(All) 1.029499" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle = cl.load_sample('genins')\n", + "tail = cl.TailCurve('exponential').fit(triangle)\n", + "tail.tail_" + ] + }, + { + "cell_type": "markdown", + "id": "f3c3aef3-c653-40b0-9828-111769bd91a3", + "metadata": {}, + "source": [ + "A manual calculation using {eq}`tail_exponential_eq` above:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "22d0b670-35ac-4472-9d45-a65a636029ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0294991710529175" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.prod(\n", + " (1+np.exp(\n", + " np.arange(triangle.shape[-1],\n", + " triangle.shape[-1] + tail.extrap_periods) * \n", + " tail.slope_.values + tail.intercept_.values)\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "82b944f8-835b-4c57-991d-43ff3032ee8b", + "metadata": {}, + "source": [ + "For completeness, let's also confirm {eq}`tail_inverse_power_eq`:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4d4423e8-0311-4df4-9e0b-b25a2e91f03f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 120-Ult\n", + "(All) 1.29243" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tail = cl.TailCurve('inverse_power').fit(triangle)\n", + "tail.tail_" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8a3a1e46-e982-4971-9f85-db8fc60f06d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.292430311543694" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.prod(\n", + " 1 + np.exp(tail.intercept_.values) * \n", + " (\n", + " np.arange(\n", + " triangle.shape[-1], \n", + " triangle.shape[-1] + tail.extrap_periods\n", + " ) ** tail.slope_.values)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c798b3b0-590c-4687-9d21-9ddbad185480", + "metadata": {}, + "source": [ + "### Extrapolation Period\n", + "\n", + "From these formulas, the actuary has control over the parameter, $N$ which \n", + "represents how far out how far beyond the edge of Results for the `inverse_power`\n", + "curve are sensitive to this parameter as it tends to converge slowly to its\n", + "asymptotic value. This parameter can be controlled using the `extrap_periods`\n", + "hyperparamter.\n", + "\n", + "```{tip}\n", + "Exponential decay is generally rapid and is less sensitive to the `extrap_period` \n", + "argument than the inverse power fit.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8a41dcc1-cefd-4967-bab9-a8d8ad5d6a40", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-06T11:19:25.779404\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tri = cl.load_sample('clrd').groupby('LOB').sum().loc['medmal', 'CumPaidLoss']\n", + "\n", + "# Create a fuction to grab the scalar tail value.\n", + "def scoring(model):\n", + " \"\"\" Scoring functions must return a scalar \"\"\"\n", + " return model.tail_.iloc[0, 0]\n", + "\n", + "# Create a grid of scenarios\n", + "param_grid = dict(\n", + " extrap_periods=list(range(1, 100, 6)),\n", + " curve=['inverse_power', 'exponential'])\n", + "\n", + "# Fit Grid\n", + "model = cl.GridSearch(cl.TailCurve(), param_grid=param_grid, scoring=scoring).fit(tri)\n", + "\n", + "# Plot results\n", + "model.results_.pivot(columns='curve', index='extrap_periods', values='score').plot(\n", + " grid=True, ylim=(1,None), title='Curve Fit Sensitivity to Extrapolation Period').set(\n", + " ylabel='Tail Factor');" + ] + }, + { + "cell_type": "markdown", + "id": "72d11c89-b369-4539-bb4c-409238836974", + "metadata": {}, + "source": [ + "This example demonstrates the `extrap_periods` functionality of the TailCurve estimator. The estimator defaults to extrapolating out 100 periods. However, we can see that the \"Inverse Power\" curve fit doesn't converge to its asymptotic value even with 100 periods whereas the \"exponential\" converges within 10 periods." + ] + }, + { + "cell_type": "markdown", + "id": "89e12919-831e-4754-badd-0aef16b4255c", + "metadata": {}, + "source": [ + "### Fit period\n", + "The default behavior of `TailCurve` is to include all `cdf_` patterns from the Triangle in extrapolating the tail. Often, the data assumptions of linearity will be violated when using all \n", + "selected patterns. In those cases, you can use `fit_period=(start_age, end_age)` for fitting to a contiguous set of patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e742020e-084f-432e-aa08-98729531909b", + "metadata": {}, + "outputs": [], + "source": [ + "dev = cl.Development().fit_transform(cl.load_sample('quarterly')['paid'])\n", + "fit_all = cl.TailCurve().fit(dev)\n", + "exclude = cl.TailCurve(fit_period=(36, None)).fit(dev)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "a5c04d20-ca44-4a4f-8984-a965ee65e319", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-06T11:21:50.267521\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "obs = (dev.ldf_ - 1).T.iloc[:, 0]\n", + "obs[obs < 0] = np.nan\n", + "\n", + "ax = np.log(obs).rename('Selected LDF').plot(\n", + " style=' ', marker='o', title=f'Fit Period affect on Tail Estimate');\n", + "pd.Series(\n", + " np.arange(1, dev.ldf_.shape[-1] + 1) * exclude.slope_.sum().values + exclude.intercept_.sum().values, \n", + " index=dev.ldf_.development, \n", + " name=f'Ages after 36: {round(exclude.tail_.values[0,0], 3)}').plot(\n", + " ax=ax, style = '--', legend=True);\n", + "pd.Series(\n", + " np.arange(1, dev.ldf_.shape[-1] + 1) * fit_all.slope_.sum().values + fit_all.intercept_.sum().values, \n", + " index=dev.ldf_.development, \n", + " name=f'All Periods: {round(fit_all.tail_.values[0,0], 3)}').plot(\n", + " ax=ax, style = '-.', legend=True);" + ] + }, + { + "cell_type": "markdown", + "id": "be565e57-32f9-4b71-9b7c-cdf36b6dc6dd", + "metadata": {}, + "source": [ + "```{warning}\n", + "The nature of curve fitting with `log` and `exp` transforms is inappropriate for development factors less\n", + "than 1.0. The default behavior of `TailCurve` is to ignore these observations when determining\n", + "the OLS regression parameters. You can force errors by setting the `errors` argument to \"raise\".\n", + "```\n", + "```python\n", + "dev = cl.TailCurve(errors='raise')\n", + "# This will fail because of LDFs < 1 and our choice to 'raise' errors\n", + "dev.fit(cl.load_sample('quarterly')['paid'])\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "65a9d484-cde0-4932-967d-3c1668a7ca3c", + "metadata": {}, + "source": [ + "You can also use a list to of booleans to specify which values you want to include. This allows for finer grain control over excluding outliers from your analysis. The following two estimators are\n", + "equivalent." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "8b5ff0eb-44c0-4ab3-add6-ec4f4412f257", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(cl.TailCurve(fit_period=[False] * 11 + [True] * 33).fit(dev).cdf_ ==\n", + " cl.TailCurve(fit_period=(36, None)).fit(dev).cdf_)" + ] + }, + { + "cell_type": "markdown", + "id": "ff111142-d7a4-4e23-b4b9-7c76127f966d", + "metadata": {}, + "source": [ + "### Examples\n", + "\n", + ":::{panels}\n", + ":column: col-lg-4 px-2 py-2\n", + "\n", + "---\n", + "**[Attachment Age Smoothing](plot_exponential_smoothing)**\n", + "```{glue:} plot_exponential_smoothing\n", + "```\n", + "+++\n", + "{bdg-success}`easy`\n", + "\n", + "---\n", + "**[TailCurve Extrapolation](plot_extrap_period)**\n", + "```{glue:} plot_extrap_period\n", + "```\n", + "+++\n", + "{bdg-warning}`medium`\n", + "\n", + "---\n", + "**[TailCurve Basics](plot_tailcurve_compare)**\n", + "```{glue:} plot_tailcurve_compare\n", + "```\n", + "+++\n", + "{bdg-success}`easy`\n", + ":::\n", + "{cite}`CAS_TFWP2013`" + ] + }, + { + "cell_type": "markdown", + "id": "38788926-acdd-4cf9-a12b-39931c5041da", + "metadata": { + "tags": [] + }, + "source": [ + "(tails:tailbondy)=\n", + "## TailBondy\n", + "Most people are familiar with the Bondy tail know it as a basic method that assumes the \n", + "`tail_` is just a repeat of the last available `ldf_`.\n", + "\n", + "Indeed, without any hyperparameter selection, this is how `TailBondy` works.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "234713f3-53c7-4213-83e1-c570b4487bc3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 120-Ult\n", + "(All) 1.017725" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tail = cl.TailBondy().fit(triangle)\n", + "tail.tail_" + ] + }, + { + "cell_type": "markdown", + "id": "5f6da5e3-ca60-47a4-927e-1f1219ff23c7", + "metadata": {}, + "source": [ + "This simple relationship has a more generalized form. If we define the `ldf_` at time $n$ as $f(n-1)$\n", + "and assume that $f(n) = f(n-1)^{B}$, it follows that the tail $F(n)$ can be defined as:\n", + "\n", + "$$\n", + "F(n)=f(n-1)^{B}f(n-1)^{B^{2}}...=f(n-1)^{\\frac{B}{1-B}}\n", + "$$ (tail_bondy_eq)\n", + "\n", + "where $B$ is some constant, and if $B$ is $\\frac{1}{2}$, then we obtain Bondy's original tail formulation.\n", + "\n", + "Rather than using the only the last known development factor or setting $B=\\frac{1}{2}$, we\n", + "can stablize things using more `ldf_` using the `earliest_age` parameter. We can then minimize\n", + "the difference in the the assumed relationship $f(n) = f(n-1)^{B}$ and our data to estimate $B$.\n", + "\n", + "This is often refered to as the *Generalized Bondy* method." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "fbad0192-78ba-49ce-ba8d-25a00a534972", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 120-Ult\n", + "(All) 1.027763" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle = cl.load_sample('tail_sample')['paid']\n", + "dev = cl.Development(average='simple').fit_transform(triangle)\n", + "\n", + "# Estimate the Bondy Tail\n", + "tail = cl.TailBondy(earliest_age=12).fit(dev)\n", + "tail.tail_" + ] + }, + { + "cell_type": "markdown", + "id": "790d9977-e8d2-44f7-9d79-7160436ed211", + "metadata": {}, + "source": [ + "Using our formulation {eq}`tail_bondy_eq` above, we can manually estimate our tail using our earliest `ldf_` \n", + "and our Bondy constant `b_`." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "caaae7b4-647e-440e-9512-26251df7ac6a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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paid
Total
Total1.027756
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" + ], + "text/plain": [ + " paid\n", + "Total \n", + "Total 1.027756" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get last fitted LDF of the model\n", + "last_fitted_ldf = (tail.earliest_ldf_ ** (tail.b_ ** 8))\n", + "\n", + "# Calculate the tail using the Bondy formula above\n", + "last_fitted_ldf ** (tail.b_ / (1-tail.b_))" + ] + }, + { + "cell_type": "markdown", + "id": "5174eef9-78ee-4bca-9a1d-70e6bfcee369", + "metadata": {}, + "source": [ + "{cite}`CAS_TFWP2013`" + ] + }, + { + "cell_type": "markdown", + "id": "af580a90-ae7b-4601-a29e-c293fe57f186", + "metadata": {}, + "source": [ + "(tails:tailclark)=\n", + "## TailClark\n", + "\n", + "`TailClark` is a continuation of the `ClarkLDF` model. Familiarity\n", + "with [ClarkLDF](development:clarkldf) will aid in understanding this Estimator.\n", + "The growth curve approach used by Clark produces development patterns for any\n", + "age including ages beyond the edge of the Triangle.\n", + "\n", + "An example completing Clark's model:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c24a18be-332e-47da-bfbf-d971d0df5089", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120120-132132-144
(All)4.09491.72051.34251.20051.13061.09101.06651.05041.03931.03131.2540
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120 120-132 132-144\n", + "(All) 4.094936 1.72047 1.342452 1.200518 1.130614 1.091014 1.066512 1.050383 1.039257 1.031297 1.254023" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genins = cl.load_sample('genins')\n", + "dev = cl.ClarkLDF()\n", + "tail = cl.TailClark()\n", + "tail.fit(dev.fit_transform(genins)).ldf_" + ] + }, + { + "cell_type": "markdown", + "id": "7ade6734-fb9f-4fe6-8867-9ee2ebe1642f", + "metadata": {}, + "source": [ + "Coupled with `attachment_age` one could use any Development estimator and replicate the `ClarkLDF` estimator." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9b7e36ee-815c-42ff-9382-6eb23eb2a02a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-2424-3636-4848-6060-7272-8484-9696-108108-120120-132132-144
(All)4.09491.72051.34251.20051.13061.09101.06651.05041.03931.03131.2540
" + ], + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120 120-132 132-144\n", + "(All) 4.094936 1.72047 1.342452 1.200518 1.130614 1.091014 1.066512 1.050383 1.039257 1.031297 1.254023" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genins = cl.load_sample('genins')\n", + "tail = cl.TailClark(attachment_age=12)\n", + "tail.fit(cl.Development().fit_transform(genins)).ldf_" + ] + }, + { + "cell_type": "markdown", + "id": "a14edaef-3960-44e2-8f9e-e0cc9e46d952", + "metadata": {}, + "source": [ + "### Truncated patterns\n", + "\n", + "Clark warns of the dangers of extrapolating too far beyond the edge of a Triangle,\n", + "particularly with a heavy tailed distribution. In these cases, it is suggested that\n", + "a suitable cut-off age or ``truncation_age`` be established where losses are considered\n", + "fully developed. This is very similar to the `extrap_periods` parameter of `TailCurve`,\n", + "however, it is expressed as an age (in months) as opposed to a period length." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ef6c4d79-7e7f-4ba9-8a7c-c286df0ef9df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-Ult24-Ult36-Ult48-Ult60-Ult72-Ult84-Ult96-Ult108-Ult120-Ult132-Ult
(All)19.20934.69102.72662.03101.69181.49641.37151.28601.22431.17811.1423
" + ], + "text/plain": [ + " 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult 120-Ult 132-Ult\n", + "(All) 19.209306 4.69099 2.726575 2.031041 1.691804 1.496359 1.371531 1.285997 1.224313 1.178065 1.142313" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tail = cl.TailClark(truncation_age=245)\n", + "tail.fit(dev.fit_transform(genins)).cdf_" + ] + }, + { + "cell_type": "markdown", + "id": "99501ed3-abd3-47e6-91bd-f43d970e23c9", + "metadata": {}, + "source": [ + "{cite}`clark2003`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98a75fe5-edbc-40ea-b545-885282ddbe29", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user_guide/triangle.ipynb b/docs/user_guide/triangle.ipynb new file mode 100644 index 00000000..0ef587ae --- /dev/null +++ b/docs/user_guide/triangle.ipynb @@ -0,0 +1,4194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a3117a95", + "metadata": {}, + "source": [ + "(triangle)=\n", + "# Triangle\n", + "\n", + "## Introduction\n", + "\n", + "With this User Guide, we will be covering all of the core functionality of `chainladder`.\n", + "All important user functionality can be referenced from the top level of the library and\n", + "so importing the library as the `cl` namespace is the preferred way of importing from `chainladder`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0f1fb6f6", + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "markdown", + "id": "ecab7331", + "metadata": {}, + "source": [ + "```{epigraph}\n", + "Analysis is the intersection of data, models, and assumptions.\n", + "```\n", + "\n", + "Reserving analysis is no different and the loss Triangle is the most ubiquitous data construct used by actuaries today. The chainladder package has its own :class:`Triangle` data structure that behaves much like a pandas ``DataFrame``. \n", + "\n", + "### Why not Pandas?\n", + "This begs the question, why not just use pandas?\n", + "There are several advantages over having a dedicated Triangle object:\n", + "\n", + " * Actuaries work with sets of triangles. DataFrames, being two dimensional, support single triangles with grace but become unwieldy with multiple triangles.\n", + " * We can carry through the meaningful pandas functionality while also supporting triangle specific methods not found in pandas\n", + " * Improved memory footprint with sparse array representation in backend\n", + " * Calculated fields with \"virtual\" columns allows for lazy column evaluation of Triangles as well as improved memory footprint for larger triangles.\n", + " * Ability to support GPU-based backends.\n", + "\n", + "Ultimately, there are a lot of things pandas can do that are not relevant to reserving, and there are a lot of things a Triangle needs to do that are not handled easily with pandas." + ] + }, + { + "cell_type": "markdown", + "id": "666cc8c0", + "metadata": {}, + "source": [ + "### Structure\n", + "\n", + "The :class:`Triangle` is the data structure of the chainladder package. Just as\n", + "Scikit-learn likes to only consume numpy arrays, Chainladder only likes\n", + "Triangles. It is a 4D data structure with labeled axes. These axes are its\n", + "index, columns, origin, development.\n", + "\n", + "``index`` (axis 0):\n", + " The index is the lowest grain at which you want to manage the triangle.\n", + " These can be things like state or company. Like a ``pandas.multiIndex``, you\n", + " can throw more than one column into the index.\n", + "\n", + "``columns`` (axis 1):\n", + " Columns are where you would want to store the different numeric values of your\n", + " data. Paid, Incurred, Counts are all reasonable choices for the columns of your\n", + " triangle.\n", + "\n", + "``origin`` (axis 2):\n", + " The origin is the period of time from which your columns originate. It can\n", + " be an Accident Month, Report Year, Policy Quarter or any other period-like vector.\n", + "\n", + "``development`` (axis 3):\n", + " Development represents the development age or date of your triangle.\n", + " Valuation Month, Valuation Year, Valuation Quarter are good choices.\n", + "\n", + "Despite this structure, you interact with it in the style of pandas. You would\n", + "use ``index`` and ``columns`` in the same way you would for a pandas DataFrame.\n", + "You can think of the 4D structure as a pandas DataFrame where each cell (row,\n", + "column) is its own triangle.\n", + "\n", + "```{eval-rst}\n", + ".. image:: images/triangle_graphic.PNG\n", + "```\n", + "\n", + "Like pandas, you can access the ``values`` property of a triangle to get its numpy\n", + "representation, however the Triangle class provides many helper methods to\n", + "keep the shape of the numpy representation in sync with the other Triangle\n", + "properties.\n", + "\n", + "## Creating a Triangle\n", + "\n", + "### Basic requirements\n", + "You must have a pandas DataFrame on hand to create a triangle. While\n", + "data can come in a variety of forms those formats should be coerced to a pandas\n", + "DataFrame before creating a triangle. The DataFrame also **must** be in tabular\n", + "(long) format, not triangle (wide) format:\n", + "\n", + "```{eval-rst}\n", + ".. image:: images/triangle_bad_good.PNG\n", + "```\n", + "\n", + "At a minimum, the DataFrame must also:\n", + "\n", + " 1. have \"date-like\" columns for the ``origin`` and ``development`` period of the triangle.\n", + " 2. Have a numeric column(s) representing the amount(s) of the triangle.\n", + "\n", + "The reason for these restriction is that the :class:`Triangle` infers a lot of\n", + "useful properties from your DataFrame. For example, it will determine the ``grain``\n", + "and ``valuation_date`` of your triangle which in turn are used to derive many\n", + "other properties of your triangle without further prompting from you.\n", + "\n", + "### Date Inference\n", + "\n", + "When instantiating a :class:`Triangle`, the ``origin`` and ``development``\n", + "arguments can take a ``str`` representing the column name in your pandas DataFrame\n", + "that contains the relevant information. Alternatively, the arguments can also\n", + "take a ``list`` in the case where your DataFrame includes multiple columns that\n", + "represent the dimension, e.g. ``['accident_year','accident_quarter']`` can be\n", + "supplied to create an ``origin`` dimension at the accident quarter grain.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "a390063e", + "metadata": {}, + "source": [ + "```python\n", + "cl.Triangle(data, origin='Acc Year', development=['Cal Year', 'Cal Month'], columns=['Paid Loss'])\n", + "```\n", + " The :class:`Triangle` relies heavily on pandas date inference. In fact,\n", + " ``pd.to_datetime(date_like)`` is exactly how it works. While pandas is excellent\n", + " at inference, it is not perfect. When initializing a Triangle you can always\n", + " use the ``origin_format`` and/or ``development_format`` arguments to force\n", + " the inference. For example, ``origin_format='%Y/%m/%d'``\n", + "\n", + "### Multidimensional Triangle\n", + "So far we've seen how to create a single Triangle, but as described in the Intro\n", + "the Triangle class can hold multiple triangles at once. These triangles share the\n", + "same ``origin`` and ``development`` axes and act as individual cells would in a\n", + "pandas DataFrame. By specifying one or more ``column`` and one or more ``index``,\n", + "we can fill out the 4D triangle structure.\n", + "\n", + "```python\n", + "cl.Triangle(data, origin='Acc Year', development='Cal Year',\n", + " columns=['Paid Loss', 'Incurred Loss'],\n", + " index=['Line of Business', 'State'])\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "9ad267aa", + "metadata": {}, + "source": [ + "### Sample Data\n", + "\n", + "The ``chainladder`` package has several sample triangles. Many of these come\n", + "from existing papers and can be used to verify the results of those papers.\n", + "Additionally, They are a quick way of exploring the functionality of the package.\n", + "These triangles can be called by name using the :func:`~chainladder.load_sample` function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2737052c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (775, 6, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.load_sample('clrd')" + ] + }, + { + "cell_type": "markdown", + "id": "55948bd3", + "metadata": {}, + "source": [ + "### Other Parameters\n", + "\n", + "Whether a triangle is cumulative or incremental in nature cannot be inferred\n", + "from the \"date-like\" vectors of your DataFrame. You can optionally specify this\n", + "property with the ``cumulative`` parameter.\n", + "```python\n", + "cl.Triangle(\n", + " data, origin='Acc Year', development=['Cal Year'], \n", + " columns=['PaidLoss'], cumulative=True)\n", + "```\n", + "\n", + "```{note}\n", + "The ``cumulative`` parameter is completely optional. If it is not specified,\n", + "the Triangle will infer its cumulative/incremental status at the point you\n", + "call on the `cum_to_incr` or `incr_to_cum` methods discussed below. Some methods\n", + "may not work until the cumulative/incremental status is known.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "23bd8268", + "metadata": {}, + "source": [ + "**Backends**\n", + "\n", + "`Triangle` is built on numpy which serves as the array backend by default.\n", + "However, you can now swap array_backend between numpy, cupy, and sparse to switch\n", + "between CPU and GPU-based computations or dense and sparse backends.\n", + "\n", + "Array backends can be set globally:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "60d34839", + "metadata": {}, + "outputs": [], + "source": [ + "cl.options.set_option('array_backend', 'cupy')\n", + "cl.options.reset_option()" + ] + }, + { + "cell_type": "markdown", + "id": "be8dabf1", + "metadata": {}, + "source": [ + "Alternatively, they can be set per Triangle instance.\n", + "\n", + "```python\n", + "cl.Triangle(..., array_backend='cupy')\n", + "```\n", + "\n", + "```{note}\n", + "You must have a CUDA-enabled graphics card and CuPY installed to use the GPU\n", + "backend. These are optional dependencies of chainladder.\n", + "```\n", + "\n", + "Chainladder by default will swap between the ``numpy`` and ``sparse`` backends. This\n", + "substantially improves the memory footprint of chainladder substantially beyond\n", + "what can be achieved with pandas alone. When a Triangle becomes sufficiently large\n", + "and has a lot of 0 or null entries, the triangle will silently swap between the\n", + "``numpy`` and ``sparse`` backends." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "baf3b810", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2017-12
Grain:OMDM
Shape:(34244, 4, 120, 120)
Index:[ClaimNo, Line, Type, ClaimLiability, Limit, Deductible]
Columns:[reportedCount, closedPaidCount, Paid, Incurred]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2017-12\n", + "Grain: OMDM\n", + "Shape: (34244, 4, 120, 120)\n", + "Index: [ClaimNo, Line, Type, ClaimLiability, Limit, D...\n", + "Columns: [reportedCount, closedPaidCount, Paid, Incurred]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism = cl.load_sample('prism')\n", + "prism" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d6027a4e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'sparse'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism.array_backend" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4b42e2ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'numpy'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism.sum().array_backend" + ] + }, + { + "cell_type": "markdown", + "id": "d4922160", + "metadata": {}, + "source": [ + "You can globally disable the backend swapping by invoking ``auto_sparse(False)``.\n", + "Any triangle with a cupy backend will not invoke auto_sparse. In the future it may be supported\n", + "when there is better sparse-GPU array support.:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bc2151fc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'sparse'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.options.set_option('auto_sparse', False)\n", + "prism = cl.load_sample('prism', array_backend='sparse')\n", + "prism.array_backend" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4e9529e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'numpy'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prism.sum().array_backend" + ] + }, + { + "cell_type": "markdown", + "id": "fc6ab53c", + "metadata": {}, + "source": [ + "```{warning}\n", + "Loading 'prism' with the numpy backend will consume all of your systems memory.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "5ac3e02e", + "metadata": {}, + "source": [ + "## Basic Functionality\n", + "\n", + "### Representation\n", + "\n", + "The `Triangle` has two different representations. When only a single\n", + "``index`` AND single ``column`` is selected. The triangle is the typical 2-dimensional\n", + "representation we typically think of." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a387dd5d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 1, 7, 7)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84\n", + "2007 3511.0 6726.0 8992.0 10704.0 11763.0 12350.0 12690.0\n", + "2008 4001.0 7703.0 9981.0 11161.0 12117.0 12746.0 NaN\n", + "2009 4355.0 8287.0 10233.0 11755.0 12993.0 NaN NaN\n", + "2010 4295.0 7750.0 9773.0 11093.0 NaN NaN NaN\n", + "2011 4150.0 7897.0 10217.0 NaN NaN NaN NaN\n", + "2012 5102.0 9650.0 NaN NaN NaN NaN NaN\n", + "2013 6283.0 NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle = cl.load_sample('ukmotor')\n", + "print(triangle.shape)\n", + "triangle" + ] + }, + { + "cell_type": "markdown", + "id": "db401c5a", + "metadata": {}, + "source": [ + "If more than one ``index`` or more than one column is present, then the Triangle\n", + "takes on more of a summary view." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0b38a9f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(775, 6, 10, 10)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (775, 6, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle = cl.load_sample('CLRD')\n", + "print(triangle.shape)\n", + "triangle" + ] + }, + { + "cell_type": "markdown", + "id": "ec566948", + "metadata": {}, + "source": [ + "### Valuation vs Development\n", + "\n", + "While most Estimators that use triangles expect the development period to be\n", + "expressed as an origin age, it is possible to transform a triangle into a valuation\n", + "triangle where the development periods are converted to valuation periods. Expressing\n", + "triangles this way may provide a more convenient view of valuation slices.\n", + "Switching between a development triangle and a valuation triangle can be\n", + "accomplished with the method `dev_to_val` and its inverse `val_to_dev`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d2a9b8a9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1981198219831984198519861987198819891990
19815,0128,26910,90711,80513,53916,18118,00918,60818,66218,834
19821064,2855,39610,66613,78215,59915,49616,16916,704
19833,4108,99213,87316,14118,73522,21422,86323,466
19845,65511,55515,76621,26623,42526,08327,067
19851,0929,56515,83622,16925,95526,180
19861,5136,44511,70212,93515,852
19875574,02010,94612,314
19881,3516,94713,112
19893,1335,395
19902,063
" + ], + "text/plain": [ + " 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990\n", + "1981 5012.0 8269.0 10907.0 11805.0 13539.0 16181.0 18009.0 18608.0 18662.0 18834.0\n", + "1982 NaN 106.0 4285.0 5396.0 10666.0 13782.0 15599.0 15496.0 16169.0 16704.0\n", + "1983 NaN NaN 3410.0 8992.0 13873.0 16141.0 18735.0 22214.0 22863.0 23466.0\n", + "1984 NaN NaN NaN 5655.0 11555.0 15766.0 21266.0 23425.0 26083.0 27067.0\n", + "1985 NaN NaN NaN NaN 1092.0 9565.0 15836.0 22169.0 25955.0 26180.0\n", + "1986 NaN NaN NaN NaN NaN 1513.0 6445.0 11702.0 12935.0 15852.0\n", + "1987 NaN NaN NaN NaN NaN NaN 557.0 4020.0 10946.0 12314.0\n", + "1988 NaN NaN NaN NaN NaN NaN NaN 1351.0 6947.0 13112.0\n", + "1989 NaN NaN NaN NaN NaN NaN NaN NaN 3133.0 5395.0\n", + "1990 NaN NaN NaN NaN NaN NaN NaN NaN NaN 2063.0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.load_sample('raa').dev_to_val()" + ] + }, + { + "cell_type": "markdown", + "id": "a1f4a01d", + "metadata": {}, + "source": [ + "Triangles have the ``is_val_tri`` property that denotes whether a triangle is in valuation\n", + "mode. The latest diagonal of a Triangle is a valuation triangle.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "dd17b4de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.load_sample('raa').latest_diagonal.is_val_tri" + ] + }, + { + "cell_type": "markdown", + "id": "8db64393", + "metadata": {}, + "source": [ + "### Incremental vs Cumulative\n", + "\n", + "A triangle is either cumulative or incremental. The ``is_cumulative``\n", + "property will identify this trait. Accumulating an incremental triangle can\n", + "be acomplished with `incr_to_cum`. The inverse operation is `cum_to_incr`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "07a035ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
19815,0123,2572,6388981,7342,6421,82859954172
19821064,1791,1115,2703,1161,817-103673535
19833,4105,5824,8812,2682,5943,479649603
19845,6555,9004,2115,5002,1592,658984
19851,0928,4736,2716,3333,786225
19861,5134,9325,2571,2332,917
19875573,4636,9261,368
19881,3515,5966,165
19893,1332,262
19902,063
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1981 5012.0 3257.0 2638.0 898.0 1734.0 2642.0 1828.0 599.0 54.0 172.0\n", + "1982 106.0 4179.0 1111.0 5270.0 3116.0 1817.0 -103.0 673.0 535.0 NaN\n", + "1983 3410.0 5582.0 4881.0 2268.0 2594.0 3479.0 649.0 603.0 NaN NaN\n", + "1984 5655.0 5900.0 4211.0 5500.0 2159.0 2658.0 984.0 NaN NaN NaN\n", + "1985 1092.0 8473.0 6271.0 6333.0 3786.0 225.0 NaN NaN NaN NaN\n", + "1986 1513.0 4932.0 5257.0 1233.0 2917.0 NaN NaN NaN NaN NaN\n", + "1987 557.0 3463.0 6926.0 1368.0 NaN NaN NaN NaN NaN NaN\n", + "1988 1351.0 5596.0 6165.0 NaN NaN NaN NaN NaN NaN NaN\n", + "1989 3133.0 2262.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa = cl.load_sample('raa')\n", + "print(raa.is_cumulative)\n", + "raa.cum_to_incr()" + ] + }, + { + "cell_type": "markdown", + "id": "fb4e488d", + "metadata": {}, + "source": [ + "### Triangle Grain\n", + "\n", + "If your triangle has origin and development grains that are more frequent then\n", + "yearly, you can easily swap to a higher grain using the `grain` method of the\n", + "`Triangle`. The `grain` method recognizes Yearly (Y), Quarterly (Q), and\n", + "Monthly (M) grains for both the origin period and development period." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e94c7362", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2006-03
Grain:OYDQ
Shape:(1, 2, 12, 45)
Index:[Total]
Columns:[incurred, paid]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2006-03\n", + "Grain: OYDQ\n", + "Shape: (1, 2, 12, 45)\n", + "Index: [Total]\n", + "Columns: [incurred, paid]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.load_sample('quarterly')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "948168c7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2006-03
Grain:OYDY
Shape:(1, 2, 12, 12)
Index:[Total]
Columns:[incurred, paid]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2006-03\n", + "Grain: OYDY\n", + "Shape: (1, 2, 12, 12)\n", + "Index: [Total]\n", + "Columns: [incurred, paid]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.load_sample('quarterly').grain('OYDY')" + ] + }, + { + "cell_type": "markdown", + "id": "2f3e684f", + "metadata": {}, + "source": [ + "It is generally a good practice to bring your data in at the lowest grain available,\n", + "so that you have full flexibility in aggregating to the grain of your choosing for\n", + "analysis and separately, the grain of your choosing for reporting and communication." + ] + }, + { + "cell_type": "markdown", + "id": "a6a578af", + "metadata": {}, + "source": [ + "### Link Ratios\n", + "\n", + "The age-to-age factors or link ratios of a Triangle can be accessed with the\n", + "``link_ratio`` property. Triangles also have a `heatmap` method that can optionally\n", + "be called to apply conditional formatting to triangle values along an axis. The\n", + "heatmap method requires IPython/Jupyter notebook to render.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4614dc7f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 12-2424-3636-4848-6060-7272-8484-9696-108108-120120-132
19772.22631.39331.18351.10701.06791.04681.03041.02311.01961.0163
19782.26821.39241.18571.10691.06491.04511.03011.02671.0207
19792.23311.40141.18711.10681.06491.04181.03521.0276
19802.19881.39441.20211.10141.07021.04961.0394
19812.21151.40041.17641.10941.07061.0536
19822.22901.38721.20371.12641.0956
19832.29181.42961.21971.1337
19842.37311.46541.2283
19852.44571.4704
19862.4031
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle = cl.load_sample('abc')\n", + "triangle.link_ratio.heatmap()" + ] + }, + { + "cell_type": "markdown", + "id": "79a55776", + "metadata": {}, + "source": [ + "The Triangle ``link_ratio`` property has unique properties from regular triangles.\n", + "They are considered patterns.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "37df4e8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle.link_ratio.is_pattern" + ] + }, + { + "cell_type": "markdown", + "id": "c3e85bb3", + "metadata": {}, + "source": [ + "They are considered incremental, and accumulate in a multiplicative fashion.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cc660eee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12-Ult24-Ult36-Ult48-Ult60-Ult72-Ult84-Ult96-Ult108-Ult120-Ult
19774.96332.22931.60001.35191.22131.14361.09241.06011.03621.0163
19784.97952.19541.57671.32981.20141.12821.07951.04791.0207
19794.85282.17311.55071.30631.18021.10831.06381.0276
19804.73942.15551.54581.28591.16751.09101.0394
19814.55942.06161.47221.25141.12801.0536
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19834.53021.97671.38271.1337
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19853.59621.4704
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" + ], + "text/plain": [ + " 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult 120-Ult\n", + "1977 4.963251 2.229335 1.600020 1.351932 1.221266 1.143601 1.092423 1.060146 1.036200 1.016259\n", + "1978 4.979511 2.195398 1.576722 1.329820 1.201420 1.128249 1.079510 1.047930 1.020665 NaN\n", + "1979 4.852844 2.173120 1.550690 1.306262 1.180228 1.108299 1.063799 1.027606 NaN NaN\n", + "1980 4.739387 2.155451 1.545787 1.285875 1.167534 1.090977 1.039414 NaN NaN NaN\n", + "1981 4.559365 2.061644 1.472161 1.251394 1.128033 1.053617 NaN NaN NaN NaN\n", + "1982 4.593220 2.060676 1.485462 1.234099 1.095568 NaN NaN NaN NaN NaN\n", + "1983 4.530235 1.976722 1.382740 1.133654 NaN NaN NaN NaN NaN NaN\n", + "1984 4.271461 1.799967 1.228344 NaN NaN NaN NaN NaN NaN NaN\n", + "1985 3.596180 1.470398 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1986 2.403115 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle = cl.load_sample('abc')\n", + "triangle.link_ratio.incr_to_cum()" + ] + }, + { + "cell_type": "markdown", + "id": "039c77e5", + "metadata": {}, + "source": [ + "### Commutativity\n", + "\n", + "Where possible, the triangle methods are designed to be commutative. For example,\n", + "each of these operations is functionally equivalent." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2f1c7680", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tri = cl.load_sample('quarterly')\n", + "# Functionally equivalent transformations\n", + "tri.grain('OYDY').val_to_dev() == tri.val_to_dev().grain('OYDY')\n", + "tri.cum_to_incr().grain('OYDY').val_to_dev() == tri.val_to_dev().cum_to_incr().grain('OYDY')\n", + "tri.grain('OYDY').cum_to_incr().val_to_dev().incr_to_cum() == tri.val_to_dev().grain('OYDY')" + ] + }, + { + "cell_type": "markdown", + "id": "6ac27f4f", + "metadata": {}, + "source": [ + "### Performance Tips\n", + "\n", + "Being mindful of commutativity and computational intensity can really help improve\n", + "the performance of the package, particularly for really large triangles. Consider\n", + "these examples that produce identical outputs but with drastically different\n", + "performance. In general, aggregations reduce the number of cells in a Triangle\n", + "and should come as early in your method chain as possible.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "655571ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25.725541299999996" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import timeit\n", + "prism = cl.load_sample('prism')\n", + "# Accumulation before aggregation - BAD\n", + "timeit.timeit(lambda : prism.incr_to_cum().sum(), number=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fff6b895", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.05498839999999916" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Aggregation before accumulation - GOOD\n", + "timeit.timeit(lambda : prism.sum().incr_to_cum(), number=1)" + ] + }, + { + "cell_type": "markdown", + "id": "cc2c6c2f", + "metadata": {}, + "source": [ + "In other cases, querying the Triangle in clever ways can improve performance.\n", + "Consider that the ``latest_diagonal`` of a cumulative Triangle is equal to the\n", + "sum of its incremental values along the 'development' axis." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f041d366", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19.788624399999996" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Accumulating a large triangle to get latest_diagonal - BAD\n", + "timeit.timeit(lambda : prism.incr_to_cum().latest_diagonal, number=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cf24c186", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07332869999999758" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Summing incrementals of a large triangle to get latest_diagonal - GOOD\n", + "timeit.timeit(lambda : prism.sum('development'), number=1)" + ] + }, + { + "cell_type": "markdown", + "id": "a0566f5f", + "metadata": {}, + "source": [ + "### Trend\n", + "A uniform `trend` factor can also be applied to a Triangle. The trend can\n", + "be applied along the ``origin`` or ``valuation`` axes." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9e095a5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12243648607284
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84\n", + "2007 1.340096 1.276282 1.215506 1.157625 1.1025 1.05 1.0\n", + "2008 1.276282 1.215506 1.157625 1.102500 1.0500 1.00 NaN\n", + "2009 1.215506 1.157625 1.102500 1.050000 1.0000 NaN NaN\n", + "2010 1.157625 1.102500 1.050000 1.000000 NaN NaN NaN\n", + "2011 1.102500 1.050000 1.000000 NaN NaN NaN NaN\n", + "2012 1.050000 1.000000 NaN NaN NaN NaN NaN\n", + "2013 1.000000 NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tri = cl.load_sample('ukmotor')\n", + "# Dividing by original triangle to show the trend factor\n", + "tri.trend(0.05, axis='valuation')/tri" + ] + }, + { + "cell_type": "markdown", + "id": "8a18e43b", + "metadata": {}, + "source": [ + "While the `trend` method only allows for a single trend, you can create\n", + "compound trends using ``start`` and ``end`` arguments and chaining them together." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "db88249f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12243648607284
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84\n", + "2007 1.614170 1.467428 1.334025 1.21275 1.1025 1.05 1.0\n", + "2008 1.467428 1.334025 1.212750 1.10250 1.0500 1.00 NaN\n", + "2009 1.334025 1.212750 1.102500 1.05000 1.0000 NaN NaN\n", + "2010 1.212750 1.102500 1.050000 1.00000 NaN NaN NaN\n", + "2011 1.102500 1.050000 1.000000 NaN NaN NaN NaN\n", + "2012 1.050000 1.000000 NaN NaN NaN NaN NaN\n", + "2013 1.000000 NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tri.trend(0.05, axis='valuation', start=tri.valuation_date, end='2011-12-31') \\\n", + " .trend(0.10, axis='valuation', start='2011-12-31')/tri" + ] + }, + { + "cell_type": "markdown", + "id": "0d68323c", + "metadata": {}, + "source": [ + "### Correlation Tests\n", + "\n", + "The multiplicative chainladder method is based on the strong assumptions of\n", + "independence across origin years and across valuation years. Mack developed\n", + "tests to verify if these assumptions hold.\n", + "\n", + "These tests are included as methods on the triangle class `valuation_correlation`\n", + "and `development_correlation`. ``False`` indicates that correlation between years\n", + "is not sufficiently large." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "924a3385", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
198219831984198519861987198819891990
1981FalseFalseFalseFalseFalseFalseFalseFalseFalse
" + ], + "text/plain": [ + " 1982 1983 1984 1985 1986 1987 1988 1989 1990\n", + "1981 False False False False False False False False False" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "triangle = cl.load_sample('raa')\n", + "triangle.valuation_correlation().z_critical" + ] + }, + { + "cell_type": "markdown", + "id": "d5bf6b74", + "metadata": {}, + "source": [ + "triangle.development_correlation().t_critical" + ] + }, + { + "cell_type": "markdown", + "id": "6ee130c8", + "metadata": {}, + "source": [ + "There are many properties of these correlation tests and they've been included\n", + "as their own classes. Refer to `ValuationCorrelation` and\n", + "`DevelopmentCorrelation` for additional information.\n", + "\n", + "{cite}`mack1994`" + ] + }, + { + "cell_type": "markdown", + "id": "bad9cfbc", + "metadata": {}, + "source": [ + "## Pandas-style syntax\n", + "\n", + "We've chosen to keep as close as possible to pandas syntax for Triangle data\n", + "manipulation. Relying on the most widely used data manipulation library in Python\n", + "gives us two benefits. This not only allows for easier adoption, but also provides\n", + "stability to the ``chainladder`` API.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3ad2d431", + "metadata": {}, + "source": [ + "### Slicing and Filtering\n", + "\n", + "With a newly minted `Triangle`, individual triangles can be sliced out\n", + "of the object using pandas-style ``loc``/``iloc`` or boolean filtering." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "65edc1e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 1, 10, 10)
Index:[GRNAME, LOB]
Columns:[EarnedPremDIR]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (775, 1, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [EarnedPremDIR]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample('clrd')\n", + "clrd.iloc[0,1]\n", + "clrd[clrd['LOB']=='othliab']\n", + "clrd['EarnedPremDIR']" + ] + }, + { + "cell_type": "markdown", + "id": "ce507e3f", + "metadata": {}, + "source": [ + "```{note}\n", + "Boolean filtering on non-index columns in pandas feels natural. We've exposed\n", + "the same syntax specifically for the index column(s) of the Triangle without the\n", + "need for ``reset_index()`` or trying to boolean-filter a ``MultiIndex``. This is\n", + "a divergence from the pandas API.\n", + "```\n", + "\n", + "As of version ``0.7.6``, four-dimensional slicing is supported:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "720ffc06", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(5, 5, 5, 4)
Index:[GRNAME, LOB]
Columns:[CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (5, 5, 5, 4)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedP..." + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample('clrd')\n", + "clrd.iloc[[0, 10, 3], 1:8, :5, :]\n", + "clrd.loc[:'Aegis Grp', 'CumPaidLoss':, '1990':'1994', :48]" + ] + }, + { + "cell_type": "markdown", + "id": "caeec05e", + "metadata": {}, + "source": [ + "As of version ``0.8.3``, ``.iat`` and ``at`` functionality have been added. Similar\n", + "to pandas, one can use these for value assignment for a single cell of a `Triangle`.\n", + "When a 'sparse' backend is in use, these accessors are the only way to modify individual\n", + "cells of a triangle.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "622a6ee4", + "metadata": {}, + "outputs": [], + "source": [ + "raa = cl.load_sample('raa').set_backend('sparse')\n", + "# To modify a sparse triangle, we need to use at or iat\n", + "raa.at['Total', 'values', '1985', 12] = 10000" + ] + }, + { + "cell_type": "markdown", + "id": "f58b53d6", + "metadata": {}, + "source": [ + "### Arithmetic\n", + "\n", + "Most arithmetic operations can be used to create new triangles within your\n", + "triangle instance. Like with pandas, these can automatically be added as new\n", + "columns to your `Triangle`." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3de7969a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 7, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet, CaseIncur]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (775, 7, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample('clrd')\n", + "clrd['CaseIncur'] = clrd['IncurLoss']-clrd['BulkLoss']\n", + "clrd" + ] + }, + { + "cell_type": "markdown", + "id": "4ccc132e", + "metadata": {}, + "source": [ + "For `origin` and `development` axes, arithmetic follows `numpy broadcasting `_\n", + "rules. If broadcasting fails, arithmetic operations will rely on ``origin`` and\n", + "``development`` vectors to determine whether an operation is legal.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "38eac8d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1981 5012.0 8269.0 10907.0 11805.0 13539.0 16181.0 18009.0 18608.0 18662.0 18834.0\n", + "1982 106.0 4285.0 5396.0 10666.0 13782.0 15599.0 15496.0 16169.0 16704.0 NaN\n", + "1983 3410.0 8992.0 13873.0 16141.0 18735.0 22214.0 22863.0 23466.0 NaN NaN\n", + "1984 5655.0 11555.0 15766.0 21266.0 23425.0 26083.0 27067.0 NaN NaN NaN\n", + "1985 1092.0 9565.0 15836.0 22169.0 25955.0 26180.0 NaN NaN NaN NaN\n", + "1986 1513.0 6445.0 11702.0 12935.0 15852.0 NaN NaN NaN NaN NaN\n", + "1987 557.0 4020.0 10946.0 12314.0 NaN NaN NaN NaN NaN NaN\n", + "1988 1351.0 6947.0 13112.0 NaN NaN NaN NaN NaN NaN NaN\n", + "1989 3133.0 5395.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa = cl.load_sample('raa')\n", + "# Allow for arithmetic beyond numpy broadcasting rules\n", + "raa[raa.origin<'1985']+raa[raa.origin>='1985']" + ] + }, + { + "cell_type": "markdown", + "id": "865ba7a9", + "metadata": {}, + "source": [ + "```python\n", + "# Numpy broadcasting equivalent fails\n", + "raa[raa.origin<'1985'].values+raa[raa.origin>='1985'].values\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "b4e37742", + "metadata": {}, + "source": [ + "Arithmetic between two Triangles with different labels will align the axes\n", + "of each Triangle consistent with arithmetic of a pandas Series. Bypassing\n", + "index matching can be accomplished with arithmetic between an Triangle and an array.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "4f0a11be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s1 = cl.load_sample('clrd').iloc[:3]\n", + "s2 = s1.sort_index(ascending=False)\n", + "s1 + s2 == 2 * s1" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9d0fd807", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s1 + s2.values == 2 * s1" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "36c1149f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s3 = s1.iloc[:, ::-1]\n", + "s1 + s3 == 2 * s1" + ] + }, + { + "cell_type": "markdown", + "id": "f4602c4f", + "metadata": {}, + "source": [ + "### Virtual Columns\n", + "There are instances where we want to defer calculations, we can create \"virtual\"\n", + "columns that defer calculation to when needed. These columns can be created by wrapping a\n", + "normal column in a function. Lambda expressions work as a tidy representation of\n", + "virtual columns." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "c26c1f24", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1988 0.242399 0.478321 0.597982 0.668209 0.709673 0.732700 0.744857 0.751453 0.756888 0.759081\n", + "1989 0.251711 0.490090 0.611483 0.682927 0.724025 0.745703 0.757569 0.765150 0.768723 NaN\n", + "1990 0.254824 0.490257 0.611415 0.680634 0.716786 0.736829 0.748758 0.754689 NaN NaN\n", + "1991 0.236422 0.455768 0.567272 0.631115 0.665822 0.683899 0.693812 NaN NaN NaN\n", + "1992 0.242317 0.460087 0.567134 0.626615 0.659779 0.676491 NaN NaN NaN NaN\n", + "1993 0.246314 0.461809 0.564389 0.622715 0.653763 NaN NaN NaN NaN NaN\n", + "1994 0.252294 0.460242 0.559230 0.615920 NaN NaN NaN NaN NaN NaN\n", + "1995 0.247836 0.444543 0.536343 NaN NaN NaN NaN NaN NaN NaN\n", + "1996 0.245855 0.427956 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1997 0.238284 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample('clrd')\n", + "# A physical column with immediate evaluation\n", + "clrd['PaidLossRatio'] = clrd['CumPaidLoss'] / clrd['EarnedPremDIR']\n", + "# A virtual column with deferred evaluation\n", + "clrd['PaidLossRatio'] = lambda clrd : clrd['CumPaidLoss'] / clrd['EarnedPremDIR']\n", + "# Good - Defer loss ratio calculation until after summing premiums and losses\n", + "clrd.sum()['PaidLossRatio']" + ] + }, + { + "cell_type": "markdown", + "id": "2021199b", + "metadata": {}, + "source": [ + "Virtual column expressions should only reference other columns in the same triangle.\n", + "A Triangle without all the underlying columns will fail.\n", + "\n", + "```python\n", + "# Eliminating EarnedPremDIR will result in a calculation failure\n", + "clrd[['CumPaidLoss', 'PaidLossRatio']].sum()['PaidLossRatio']\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "eaa8edfc", + "metadata": {}, + "source": [ + "When used in tandem with the 'sparse' backend, virtual columns can also substantially\n", + "reduce the memory footprint of your Triangle. This is because the calculation\n", + "expression is the only thing in memory.\n" + ] + }, + { + "cell_type": "markdown", + "id": "0f1a957d", + "metadata": {}, + "source": [ + "### Aggregations\n", + "\n", + "It is generally good practice to bring your data into ``chainladder`` at a ganularity\n", + "that is comfortably supported by your system RAM. This provides the greatest flexibility\n", + "in analyzing your data within the ``chainladder`` framework. However, not everything\n", + "needs to be analyzed at the most granular level. Like pandas, you can aggregate\n", + "multiple triangles within a `Triangle` by using `sum()` which can\n", + "optionally be coupled with `groupby()`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "6bd8ff1a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(1, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (1, 6, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample('clrd')\n", + "clrd.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "e6e5e294", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(6, 6, 10, 10)
Index:[LOB]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (6, 6, 10, 10)\n", + "Index: [LOB]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd.groupby('LOB').sum()" + ] + }, + { + "cell_type": "markdown", + "id": "c068a6d9", + "metadata": {}, + "source": [ + "By default, the aggregation will apply to the first axis with a length greater\n", + "than 1. Alternatively, you can specify the axis using the ``axis`` argument of\n", + "the aggregate method.\n", + "\n", + "Like pandas, the `groupby` method supports any groupable list. This allows for\n", + "complex groupings that can be derived dynamically.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "416ccea3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(11, 6, 10, 10)
Index:[GRNAME]
Columns:[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (11, 6, 10, 10)\n", + "Index: [GRNAME]\n", + "Columns: [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..." + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample('clrd')\n", + "clrd = clrd[clrd['LOB']=='comauto']\n", + "# Identify the largest commercial auto carriers (by premium) for 1997\n", + "top_10 = clrd['EarnedPremDIR'].groupby('GRNAME').sum().latest_diagonal.loc[..., '1997', :].to_frame().nlargest(10)\n", + "# Group any companies together that are not in the top 10\n", + "clrd.groupby(clrd.index['GRNAME'].map(lambda x: x if x in top_10.index else 'Remainder')).sum()" + ] + }, + { + "cell_type": "markdown", + "id": "368d5a78", + "metadata": {}, + "source": [ + "### Converting to DataFrame\n", + "When a triangle is presented with a single index level and single column, it\n", + "becomes a 2D object. As such, its display format changes to that similar to a\n", + "dataframe. These 2D triangles can easily be converted to a pandas dataframe\n", + "using the `to_frame` method.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "5b11e8eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "origin 1988 1989 1990 1991 1992 \\\n", + "LOB \n", + "comauto 626097.0 674441.0 718396.0 711762.0 731033.0 \n", + "medmal 217239.0 222707.0 235717.0 275923.0 267007.0 \n", + "othliab 317889.0 350684.0 361103.0 426085.0 389250.0 \n", + "ppauto 8690036.0 9823747.0 10728411.0 10713621.0 11555121.0 \n", + "prodliab 110973.0 112614.0 121255.0 100276.0 76059.0 \n", + "wkcomp 1241715.0 1308706.0 1394675.0 1414747.0 1328801.0 \n", + "\n", + "origin 1993 1994 1995 1996 1997 \n", + "LOB \n", + "comauto 762039.0 768095.0 675166.0 510191.0 272342.0 \n", + "medmal 276235.0 252449.0 209222.0 107474.0 20361.0 \n", + "othliab 434995.0 402244.0 294332.0 191258.0 54130.0 \n", + "ppauto 12249826.0 12600432.0 11807279.0 9900842.0 5754249.0 \n", + "prodliab 94462.0 111264.0 62018.0 28107.0 10682.0 \n", + "wkcomp 1187581.0 1114842.0 962081.0 736040.0 340132.0 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd['CumPaidLoss'].groupby('LOB').sum().latest_diagonal.to_frame()" + ] + }, + { + "cell_type": "markdown", + "id": "0dfcaa8e", + "metadata": {}, + "source": [ + "The entire 4D triangle can be flattened to a DataFrame in long format. This can\n", + "be handy for moving back and forth between pandas and chainladder.\n", + "\n", + "Because ``chainladder`` only supports valuation dates when creating new triangles,\n", + "it is often helpful converting to a valuation format before moving to pandas.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "a740a308", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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originvaluationIncurLossCumPaidLossBulkLossEarnedPremDIREarnedPremCededEarnedPremNet
GRNAMELOB
Adriatic Ins Coothliab1995-01-011995-12-31 23:59:59.9999999998.0NaN8.0139.0131.08.0
othliab1995-01-011996-12-31 23:59:59.9999999993.0NaN-4.0NaNNaNNaN
othliab1995-01-011997-12-31 23:59:59.999999999-4.03.0NaNNaNNaNNaN
othliab1996-01-011996-12-31 23:59:59.99999999940.0NaN40.0410.0359.051.0
othliab1997-01-011997-12-31 23:59:59.99999999967.0NaN31.0458.0425.033.0
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" + ], + "text/plain": [ + " origin valuation IncurLoss \\\n", + "GRNAME LOB \n", + "Adriatic Ins Co othliab 1995-01-01 1995-12-31 23:59:59.999999999 8.0 \n", + " othliab 1995-01-01 1996-12-31 23:59:59.999999999 3.0 \n", + " othliab 1995-01-01 1997-12-31 23:59:59.999999999 -4.0 \n", + " othliab 1996-01-01 1996-12-31 23:59:59.999999999 40.0 \n", + " othliab 1997-01-01 1997-12-31 23:59:59.999999999 67.0 \n", + "\n", + " CumPaidLoss BulkLoss EarnedPremDIR \\\n", + "GRNAME LOB \n", + "Adriatic Ins Co othliab NaN 8.0 139.0 \n", + " othliab NaN -4.0 NaN \n", + " othliab 3.0 NaN NaN \n", + " othliab NaN 40.0 410.0 \n", + " othliab NaN 31.0 458.0 \n", + "\n", + " EarnedPremCeded EarnedPremNet \n", + "GRNAME LOB \n", + "Adriatic Ins Co othliab 131.0 8.0 \n", + " othliab NaN NaN \n", + " othliab NaN NaN \n", + " othliab 359.0 51.0 \n", + " othliab 425.0 33.0 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample('clrd')\n", + "df = clrd.dev_to_val().cum_to_incr().to_frame()\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "5f66b8b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(775, 6, 10, 10)
Index:[GRNAME, LOB]
Columns:[BulkLoss, CumPaidLoss, EarnedPremCeded, EarnedPremDIR, EarnedPremNet, IncurLoss]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (775, 6, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [BulkLoss, CumPaidLoss, EarnedPremCeded, Earne..." + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Triangle(\n", + " df.reset_index(), index=['GRNAME', 'LOB'], \n", + " origin='origin', development='valuation',\n", + " columns=['BulkLoss', 'CumPaidLoss', 'EarnedPremCeded', \n", + " 'EarnedPremDIR', 'EarnedPremNet', 'IncurLoss']\n", + ").incr_to_cum()" + ] + }, + { + "cell_type": "markdown", + "id": "bb732da6", + "metadata": {}, + "source": [ + "To enforce long format when moving to pandas the ``keepdims`` argument guarantees\n", + "that all 4 dimensions of the Triangle will be preserved when moving to pandas.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "ccefc128", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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origindevelopmentvalues
Total
Total1981-01-01125012.0
Total1981-01-01248269.0
Total1981-01-013610907.0
Total1981-01-014811805.0
Total1981-01-016013539.0
\n", + "
" + ], + "text/plain": [ + " origin development values\n", + "Total \n", + "Total 1981-01-01 12 5012.0\n", + "Total 1981-01-01 24 8269.0\n", + "Total 1981-01-01 36 10907.0\n", + "Total 1981-01-01 48 11805.0\n", + "Total 1981-01-01 60 13539.0" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.load_sample('raa').to_frame(keepdims=True).head()" + ] + }, + { + "cell_type": "markdown", + "id": "bb8b4462", + "metadata": {}, + "source": [ + "### Exposing Pandas functionality\n", + "\n", + "The ability to move from a triangle to a pandas DataFrame opens up the full\n", + "suite of pandas functionality to you. For the more commonly used\n", + "functionality, we handle the `to_frame()` for you. For example,\n", + "``triangle.to_frame().plot()`` is equivalent to ``triangle.plot()``.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "06742d63", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-10-04T07:02:22.695112\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.4.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cl.load_sample('clrd').groupby('LOB').sum().loc['wkcomp', 'CumPaidLoss'].T.plot(\n", + " marker='.',\n", + " title='CAS Loss Reserve Database: Workers Compensation').set(\n", + " xlabel='Development Period', ylabel='Cumulative Paid Loss');" + ] + }, + { + "cell_type": "markdown", + "id": "53bd3df0", + "metadata": {}, + "source": [ + "Many of the more commonly used pandas methods are passed through in this way\n", + "allowing for working with triangles as DataFrames.\n", + "\n", + "\n", + "|Aggregations | IO | Shaping | Other|\n", + "|-------------|------------------|-------------|---------------------|\n", + "|``sum`` | ``to_clipboard`` | ``unstack`` | ``plot``|\n", + "|``mean`` | ``to_csv`` | ``pivot`` | ``rename``|\n", + "|``median`` | ``to_excel`` | ``melt`` | ``pct_chg``|\n", + "|``max`` | ``to_json`` | ``T`` | ``round``|\n", + "|``min`` | ``to_html`` | ``drop`` | ``hvplot``|\n", + "|``prod`` | ``to_dict`` | ``dropna`` | ``drop_duplicates``|\n", + "|``var`` | | | ``describe``|\n", + "|``std`` | | | | \n", + "|``cumsum`` | | | | \n", + "|``quantile`` | | | | \n", + "\n", + "```{note}\n", + "While some of these methods have been rewritten to return a Triangle, Many\n", + "are pandas methods and will have return values consistent with pandas.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "0e7f85e2", + "metadata": {}, + "source": [ + "### Accessors\n", + "\n", + "Like pandas ``.str`` and ``.dt`` accessor functions, you can also perform operations\n", + "on the ``origin``, ``development`` or ``valuation`` of a triangle. For example, all\n", + "of these operations are legal." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "c54f8d9e", + "metadata": {}, + "outputs": [], + "source": [ + "raa = cl.load_sample('raa')\n", + "x = raa[raa.origin=='1986']\n", + "x = raa[(raa.development>=24)&(raa.development<=48)]\n", + "x = raa[raa.origin<='1985-JUN']\n", + "x = raa[raa.origin>'1987-01-01'][raa.development<=36]\n", + "x = raa[raa.valuation'1987-01-01')&(raa.development<=36)]\n", + "# Instead, chain the boolean filters together.\n", + "x = raa[raa.origin>'1987-01-01'][raa.development<=36]\n", + "``` \n", + "\n", + "When using the accessors to filter a triangle, you may be left with empty portions\n", + "of the triangle that need to be trimmed up. The `dropna` method will look for\n", + "any origin periods or development periods that are fully empty at the edges\n", + "of the triangle and eliminate them for you." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "c73d271e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1224364860728496108120
19881,3516,94713,112
19893,1335,395
19902,063
" + ], + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1988 1351.0 6947.0 13112.0 NaN NaN NaN NaN NaN NaN NaN\n", + "1989 3133.0 5395.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa = cl.load_sample('raa')\n", + "raa[raa.origin>'1987-01-01']" + ] + }, + { + "cell_type": "markdown", + "id": "57689670", + "metadata": {}, + "source": [ + "There are many more methods available to manipulate triangles. The complete\n", + "list of methods is available under the `Triangle` docstrings.\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "815088b5be8edc0041246414015ef72f7907068f95114ad939d47d8b23d533db" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user_guide/utilities.ipynb b/docs/user_guide/utilities.ipynb new file mode 100644 index 00000000..6aa89f34 --- /dev/null +++ b/docs/user_guide/utilities.ipynb @@ -0,0 +1,171 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7e2c2fa7-9d32-404b-9121-51d06ac57324", + "metadata": {}, + "source": [ + "# Utilities\n", + "\n", + "Utilities contains example datasets and extra functionality to facilitate a\n", + "reserving workflow.\n", + "\n", + "\n", + "## Sample Datasets\n", + "\n", + "A variety of datasets can be loaded using :func:`load_sample()`. These are\n", + "sample datasets that are used in a variety of examples within this\n", + "documentation.\n", + "\n", + "\n", + "| Dataset | Description | \n", + "|-----------|------------------------------------------------------|\n", + "| abc | ABC Data |\n", + "| auto | Auto Data |\n", + "| berqsherm | Data from the Berquist Sherman paper |\n", + "| cc_sample | Sample Insurance Data for Cape Cod Method in Struhuss|\n", + "| clrd | CAS Loss Reserving Database |\n", + "| genins | General Insurance Data used in Clark |\n", + "| ia_sample | Sample data for Incremental Additive Method in Schmidt|\n", + "| liab | more data|\n", + "| m3ir5 | more data|\n", + "| mcl | Sample insurance data for Munich Adjustment in Quarg|\n", + "| mortgage | more data|\n", + "| mw2008 | more data|\n", + "| mw2014 | more data|\n", + "| quarterly | Sample data to demonstrate changing Triangle grain|\n", + "| raa | Sample data used in Mack Chainladder|\n", + "| ukmotor | more data|\n", + "| usaa | more data|\n", + "| usauto | more data|\n", + "\n", + "\n", + "## Chainladder Persistence\n", + "\n", + "All estimators can be persisted to disk or database\n", + "using ``to_json`` or ``to_pickle``. Restoring the estimator is as simple as\n", + "``cl.read_json`` or ``cl.read_pickle``.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "41ee963e-ad14-4f23-aac6-c878c237fb28", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"params\": {}, \"__class__\": \"Chainladder\"}'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import chainladder as cl\n", + "model_json = cl.Chainladder().fit(cl.load_sample('raa')).to_json()\n", + "model_json" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1b8a6ff8-66bb-4316-b232-915fd3f3b36c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Chainladder()" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.read_json(model_json)" + ] + }, + { + "cell_type": "markdown", + "id": "81e11750-6028-409b-bb4b-a0c8e352bab3", + "metadata": {}, + "source": [ + "The saved Estimator does not retain any fitted attributes, nor does it retain\n", + "the data on which it was fit. It is simply the model definition. However,\n", + "the Triangle itself can also be saved allowing for a full rehydration of the\n", + "original model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f9f7e1a7-225c-4c80-a204-0df7ca359a22", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "52135.228261210155" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Dumping triangle to JSON\n", + "triangle_json = cl.load_sample('raa').to_json()\n", + "\n", + "# Recalling model and Triangle and rehydrating the results\n", + "cl.read_json(model_json).fit(cl.read_json(triangle_json)).ibnr_.sum('origin')" + ] + }, + { + "cell_type": "markdown", + "id": "41c59e8d-c735-4c1b-8963-a39b9284a3f8", + "metadata": {}, + "source": [ + "```{warning}\n", + "Some features of estimators may not be json-serializable, such as a `virtual_column`\n", + "or a callable hyperparameter. In these cases, JSON serialization will fail.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35db6c48-9b17-4c69-9316-1a2c10ce92ff", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user_guide/workflow.ipynb b/docs/user_guide/workflow.ipynb new file mode 100644 index 00000000..6a2bd164 --- /dev/null +++ b/docs/user_guide/workflow.ipynb @@ -0,0 +1,1905 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c3660882-d3f0-49ee-8793-1472a60c77be", + "metadata": {}, + "source": [ + "# Workflow\n", + "``chainladder`` facilitates practical reserving workflows." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d2c17983-fe5b-4c6c-be23-aa0e6e33ea2c", + "metadata": {}, + "outputs": [], + "source": [ + "import chainladder as cl\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "markdown", + "id": "b468be77-f5dd-4f8c-8440-55dd60ddf578", + "metadata": {}, + "source": [ + "(workflow:pipeline)=\n", + "## Pipeline\n", + "The :class:`Pipeline` class implements utilities to build a composite\n", + "estimator, as a chain of transforms and estimators. Said differently, a\n", + "`Pipeline` is a way to wrap multiple estimators into a single compact object.\n", + "The `Pipeline` is borrowed from scikit-learn. As an example of compactness,\n", + "we can simulate a set of triangles using bootstrap sampling, apply volume-weigted\n", + "development, exponential tail curve fitting, and get the 95%-ile IBNR estimate.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "1f11205a-81fc-4e74-8c8b-9a0a823f2a78", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('sample', BootstrapODPSample(random_state=42)),\n", + " ('dev', Development()), ('tail', TailCurve()),\n", + " ('model', Chainladder())])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe = cl.Pipeline(\n", + " steps=[\n", + " ('sample', cl.BootstrapODPSample(random_state=42)),\n", + " ('dev', cl.Development(average='volume')),\n", + " ('tail', cl.TailCurve('exponential')),\n", + " ('model', cl.Chainladder())])\n", + "\n", + "pipe.fit(cl.load_sample('genins'))" + ] + }, + { + "cell_type": "markdown", + "id": "cbc68b86-3d18-48fe-a262-01a19d8576ed", + "metadata": {}, + "source": [ + "Each estimator contained within a pipelines ``steps`` can be accessed by name\n", + "using the ``named_steps`` attribute of the `Pipeline`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "496d5622-0c82-4b35-899b-3530cca020b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "26063265.939845018" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.named_steps.model.ibnr_.sum('origin').quantile(q=.95)" + ] + }, + { + "cell_type": "markdown", + "id": "b54510f8-20e2-4714-8680-a0437c6a8a2c", + "metadata": {}, + "source": [ + "As mentioned in the `Development` section, a `Pipeline` coupled with `groupby` can really streamline you reserving analysis. Here we can develop IBNR for each company using LOB level development patterns. By delegating the `groupby` operation to the `Development` estimator in the `Pipeline`, we can fully specify the entire analysis as part of our hyperparameters. This achieves a clear separation of assumption setting from data manipulation." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "45e819bb-3a42-4b28-9bc9-ba507ddcc6f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Triangle Summary
Valuation:2261-12
Grain:OYDY
Shape:(775, 1, 10, 1)
Index:[GRNAME, LOB]
Columns:[CumPaidLoss]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2261-12\n", + "Grain: OYDY\n", + "Shape: (775, 1, 10, 1)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [CumPaidLoss]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample('clrd')['CumPaidLoss']\n", + "\n", + "pipe = cl.Pipeline(\n", + " steps=[\n", + " ('dev', cl.Development(groupby='LOB')),\n", + " ('tail', cl.TailCurve('exponential')),\n", + " ('model', cl.Chainladder())]).fit(clrd)\n", + "\n", + "pipe.named_steps.model.ibnr_" + ] + }, + { + "cell_type": "markdown", + "id": "2e81528d-3637-4151-bac6-d6168312b5dd", + "metadata": {}, + "source": [ + "(workflow:votingchainladder)=\n", + "## VotingChainladder\n", + "\n", + "The :class:`VotingChainladder` ensemble method allows the actuary to vote between\n", + "different underlying ``ibnr_`` by way of a matrix of weights.\n", + "\n", + "For example, the actuary may choose \n", + "* the :class:`Chainladder` method for the first 4 origin periods, \n", + "* the :class:`BornhuetterFerguson` method for the next 3 origin periods,\n", + "* and the :class:`CapeCod` method for the final 3." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "748cd4db-8234-4504-a641-29cc1cda5fc1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2261
198118,834
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" + ], + "text/plain": [ + " 2261\n", + "1981 18834.000000\n", + "1982 16857.953917\n", + "1983 24083.370924\n", + "1984 28703.142163\n", + "1985 28203.700714\n", + "1986 19840.005163\n", + "1987 18840.362337\n", + "1988 23106.943030\n", + "1989 20004.502125\n", + "1990 21605.832631" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa = cl.load_sample('RAA')\n", + "cl_ult = cl.Chainladder().fit(raa).ultimate_ # Chainladder Ultimate\n", + "apriori = cl_ult*0+(cl_ult.sum()/10) # Mean Chainladder Ultimate\n", + "\n", + "bcl = cl.Chainladder()\n", + "bf = cl.BornhuetterFerguson()\n", + "cc = cl.CapeCod()\n", + "\n", + "estimators = [('bcl', bcl), ('bf', bf), ('cc', cc)]\n", + "weights = np.array([[1, 0, 0]] * 4 + [[0, 1, 0]] * 3 + [[0, 0, 1]] * 3)\n", + "vot = cl.VotingChainladder(estimators=estimators, weights=weights)\n", + "vot.fit(raa, sample_weight=apriori)\n", + "vot.ultimate_" + ] + }, + { + "cell_type": "markdown", + "id": "e219ba10-79e5-444a-b810-ce57163e1276", + "metadata": {}, + "source": [ + "Alternatively, the actuary may choose to combine all methods using weights. Omitting\n", + "the weights parameter results in the average of all predictions assuming a weight of 1.\n", + "Alternatively, a default weight can be enforced through the `default_weight` parameter.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "15074fdb-e2f1-4353-9500-d842e9c56283", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2261
198118,834
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" + ], + "text/plain": [ + " 2261\n", + "1981 18834.000000\n", + "1982 16887.197765\n", + "1983 24041.977401\n", + "1984 28435.540175\n", + "1985 28466.807537\n", + "1986 19770.579236\n", + "1987 18547.931167\n", + "1988 23305.361472\n", + "1989 18530.213787\n", + "1990 20331.432662" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raa = cl.load_sample('RAA')\n", + "cl_ult = cl.Chainladder().fit(raa).ultimate_ # Chainladder Ultimate\n", + "apriori = cl_ult * 0 + (float(cl_ult.sum()) / 10) # Mean Chainladder Ultimate\n", + "\n", + "bcl = cl.Chainladder()\n", + "bf = cl.BornhuetterFerguson()\n", + "cc = cl.CapeCod()\n", + "\n", + "estimators = [('bcl', bcl), ('bf', bf), ('cc', cc)]\n", + "vot = cl.VotingChainladder(estimators=estimators)\n", + "vot.fit(raa, sample_weight=apriori)\n", + "vot.ultimate_" + ] + }, + { + "cell_type": "markdown", + "id": "d4251092-6d4c-4782-9380-e42e8dd1c207", + "metadata": {}, + "source": [ + "The weights can take the form of an array (as above), a dict, or a callable:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "176da773-eedc-4183-89f2-142945c2e4d4", + "metadata": {}, + "outputs": [], + "source": [ + "callable_weight = lambda origin : np.where(origin.year < 1985, (1, 0, 0), np.where(origin.year > 1987, (0, 0, 1), (0, 1, 0)))\n", + "\n", + "dict_weight = {\n", + " '1992': (0, 1, 0),\n", + " '1993': (0, 1, 0),\n", + " '1994': (0, 1, 0),\n", + " '1995': (0, 0, 1),\n", + " '1996': (0, 0, 1),\n", + " '1997': (0, 0, 1),\n", + "} # Unmapped origins will get `default_weight`\n" + ] + }, + { + "cell_type": "markdown", + "id": "43cd39c7-e0e1-428c-ad21-cf96a94fd7a4", + "metadata": {}, + "source": [ + "(workflow:gridsearch)=\n", + "GridSearch\n", + "==========\n", + "The grid search provided by :class:`GridSearch` exhaustively generates\n", + "candidates from a grid of parameter values specified with the ``param_grid``\n", + "parameter. Like `Pipeline`, `GridSearch` borrows from its scikit-learn counterpart\n", + "``GridSearchCV``.\n", + "\n", + "Because reserving techniques are different from supervised machine learning,\n", + "`GridSearch` does not try to pick optimal hyperparameters for you. It is more of\n", + "a scenario-testing estimator.\n", + "\n", + "```{tip}\n", + "The [tryangle](https://github.com/casact/tryangle) package takes the machine learning aspect a bit farther than `chainladder` and \n", + "has built in scoring functions that make hyperparameter tuning in a reserving context possible.\n", + "```\n", + "\n", + "`GridSearch` can be applied to all other estimators, including the `Pipeline`\n", + "estimator. To use it, one must specify a ``param_grid`` as well as a ``scoring``\n", + "function which defines the estimator property(s) you wish to capture. " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "e8027fa2-4c55-4074-b9eb-840c9bf6a76d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\r\n", + "\r\n", + "\r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " 2021-10-04T20:34:00.309653\r\n", + " image/svg+xml\r\n", + " \r\n", + " \r\n", + " Matplotlib v3.4.2, https://matplotlib.org/\r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + "\r\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pipe = cl.Pipeline(\n", + " steps=[('dev', cl.Development()),\n", + " ('model', cl.Chainladder())])\n", + "\n", + "grid = cl.GridSearch(\n", + " estimator=pipe,\n", + " param_grid={'dev__average' :['volume', 'simple', 'regression']},\n", + " scoring=lambda x : x.named_steps.model.ibnr_.sum('origin'))\n", + "\n", + "grid.fit(cl.load_sample('genins'))\n", + "ax = grid.results_.set_index('dev__average').rename(columns={'score': 'IBNR'}).plot(\n", + " kind='bar', legend=False, xlabel='', rot=0, title='GridSearch Results')\n", + "\n", + "ax.yaxis.set_major_formatter(\n", + " plt.FuncFormatter(lambda value, tick_number: \"{:,}\".format(float(value))));\n" + ] + }, + { + "cell_type": "markdown", + "id": "8ef72e0d-0d14-4f11-8838-3ed38897829e", + "metadata": {}, + "source": [ + "A couple points on the example.\n", + "\n", + "1. The syntax for GridSearch is a near replica of scikit-learn's `GridsearchCV`\n", + "2. The `param_grid` is a dictionary of hyperparamter names and possible options you may want to use.\n", + "3. Multiple different hyperparameters can be specified in a `param_grid`. Doing so will create a cartesian product of all possible assumptions.\n", + "4. When using a `Pipeline` in a `GridSearch` it is entirely possible that the `param_grid` hyperparameter names used in different steps of a the `Pipeline` can clash. The notation to disambiguate the hyperparameters is to specify both the step and the hyperparameter name in this fashion: `step__hyperparameter`\n", + "\n", + "```{note}\n", + "All of this can be done with loops, but GridSearch provides a nice shorthand for accomplishing the same as well as parallelism when your CPU has multiple cores.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "1df153e3-18df-4010-81d0-c1bfe91ae6cb", + "metadata": {}, + "source": [ + "Your `scoring` function can be any property derived off of the fitted etimator.\n", + "If capturing multiple properties is desired, multiple scoring functions can be created and\n", + "stored in a dictionary.\n", + "\n", + "Here we capture multiple properties of the `TailBondy` estimator using the\n", + "`GridSearch` routine to test the sensitivity of the model to changing hyperparameters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "e88b07cd-b9ec-45aa-9c99-fa1de6a9c3d9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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earliest_ageTail Factor (tail_)Bondy Exponent (b_)
0121.0277630.624614
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" + ], + "text/plain": [ + " earliest_age Tail Factor (tail_) Bondy Exponent (b_)\n", + "0 12 1.027763 0.624614\n", + "1 24 1.027983 0.625014\n", + "2 36 1.029856 0.629534\n", + "3 48 1.037931 0.651859\n", + "4 60 1.048890 0.683528\n", + "5 72 1.046617 0.674587\n", + "6 84 1.059931 0.714418\n", + "7 96 1.072279 0.746943\n", + "8 108 1.023925 0.500000" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fit basic development to a triangle\n", + "tri = cl.load_sample('tail_sample')['paid']\n", + "dev = cl.Development(average='simple').fit_transform(tri)\n", + "\n", + "# Return both the tail factor and the Bondy exponent in the scoring function\n", + "scoring = {\n", + " 'Tail Factor (tail_)': lambda x: x.tail_.values[0,0],\n", + " 'Bondy Exponent (b_)': lambda x : x.b_.values[0,0]}\n", + "\n", + "# Vary the 'earliest_age' assumption in GridSearch\n", + "param_grid=dict(earliest_age=list(range(12, 120, 12)))\n", + "grid = cl.GridSearch(\n", + " estimator=cl.TailBondy(), \n", + " param_grid=param_grid, \n", + " scoring=scoring)\n", + "\n", + "grid.fit(dev).results_" + ] + }, + { + "cell_type": "markdown", + "id": "f7ce1f0a-0e12-4b60-9e24-b36b7a3e33a6", + "metadata": {}, + "source": [ + "Using `GridSearch` for scenario testing is entirely optional. You can write\n", + "your own looping mechanisms to achieve the same result. For example:\n", + "\n", + "```{eval-rst}\n", + ".. figure:: /auto_examples/images/sphx_glr_plot_capecod_001.png\n", + " :target: ../auto_examples/plot_capecod.html\n", + " :align: center\n", + " :scale: 50%\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "68af376a-3bf5-4f20-9aaf-decd7e31ca7b", + "metadata": {}, + "source": [ + "It is also possible to treat estimators themselves as hyperparameters. A `GridSearch` can search over `Chainladder` and `CapeCod` or any other estimator. To achieve this, you can take your `param_grid` dictionary and make it a list of dictionaries." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "1de8a3c2-6c9c-4615-99a2-91eb3bcbdf6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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devdev__n_periodsmodelscoremodel__decay
0Development(n_periods=7)-1Chainladder()1.330331e+06NaN
1Development(n_periods=7)3Chainladder()1.254405e+06NaN
2Development(n_periods=7)7Chainladder()1.321097e+06NaN
3Development(n_periods=7)-1CapeCod()1.330331e+060.0
4Development(n_periods=7)-1CapeCod()1.278456e+060.5
5Development(n_periods=7)-1CapeCod()1.066857e+061.0
6Development(n_periods=7)3CapeCod()1.254405e+060.0
7Development(n_periods=7)3CapeCod()1.225866e+060.5
8Development(n_periods=7)3CapeCod()1.040709e+061.0
9Development(n_periods=7)7CapeCod()1.321097e+060.0
10Development(n_periods=7)7CapeCod()1.276399e+060.5
11Development(n_periods=7)7CapeCod()1.066171e+061.0
\n", + "
" + ], + "text/plain": [ + " dev dev__n_periods model score \\\n", + "0 Development(n_periods=7) -1 Chainladder() 1.330331e+06 \n", + "1 Development(n_periods=7) 3 Chainladder() 1.254405e+06 \n", + "2 Development(n_periods=7) 7 Chainladder() 1.321097e+06 \n", + "3 Development(n_periods=7) -1 CapeCod() 1.330331e+06 \n", + "4 Development(n_periods=7) -1 CapeCod() 1.278456e+06 \n", + "5 Development(n_periods=7) -1 CapeCod() 1.066857e+06 \n", + "6 Development(n_periods=7) 3 CapeCod() 1.254405e+06 \n", + "7 Development(n_periods=7) 3 CapeCod() 1.225866e+06 \n", + "8 Development(n_periods=7) 3 CapeCod() 1.040709e+06 \n", + "9 Development(n_periods=7) 7 CapeCod() 1.321097e+06 \n", + "10 Development(n_periods=7) 7 CapeCod() 1.276399e+06 \n", + "11 Development(n_periods=7) 7 CapeCod() 1.066171e+06 \n", + "\n", + " model__decay \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 0.0 \n", + "4 0.5 \n", + "5 1.0 \n", + "6 0.0 \n", + "7 0.5 \n", + "8 1.0 \n", + "9 0.0 \n", + "10 0.5 \n", + "11 1.0 " + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clrd = cl.load_sample('clrd')[['CumPaidLoss', 'EarnedPremDIR']].groupby('LOB').sum().loc['medmal']\n", + "\n", + "grid = cl.GridSearch(\n", + " estimator = cl.Pipeline(steps=[\n", + " ('dev', None),\n", + " ('model', None)]),\n", + " param_grid = [\n", + " {'dev': [cl.Development()],\n", + " 'dev__n_periods': [-1, 3, 7],\n", + " 'model': [cl.Chainladder()]},\n", + " {'dev': [cl.Development()],\n", + " 'dev__n_periods': [-1, 3, 7],\n", + " 'model': [cl.CapeCod()],\n", + " 'model__decay': [0, .5, 1]}],\n", + " scoring=lambda x : x.named_steps.model.ibnr_.sum('origin')\n", + ")\n", + "\n", + "grid.fit(\n", + " X=clrd['CumPaidLoss'], \n", + " sample_weight=clrd['EarnedPremDIR'].latest_diagonal)\n", + "\n", + "grid.results_" + ] + }, + { + "cell_type": "markdown", + "id": "140236d7-d8ab-4753-ad6c-61dce25e9797", + "metadata": {}, + "source": [ + "The workflow estimators are incredibly flexible for composing any compound reserving workflow into a single estimator. These succintly capture all \"assumptions\" and model definitions in one place." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8701877-a5e6-4718-bc6a-c38ab76bf344", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 10e45bc8426eb47744c9be3341f86291b67efe8d Mon Sep 17 00:00:00 2001 From: John S Bogaardt Date: Sun, 11 Jun 2023 14:21:34 -0600 Subject: [PATCH 13/24] docs --- environment-docs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment-docs.yaml b/environment-docs.yaml index 32911fbf..031fd654 100644 --- a/environment-docs.yaml +++ b/environment-docs.yaml @@ -24,9 +24,9 @@ dependencies: - parso>=0.8 - nbsphinx - numpydoc + - nb_black - pip: - git+https://github.com/casact/chainladder-python/ - jupyter-book - - nb_black - sphinx-togglebutton From f21e292f2be6d7ad44cf84be6f2343a7abaa505c Mon Sep 17 00:00:00 2001 From: John S Bogaardt Date: Sun, 11 Jun 2023 14:35:22 -0600 Subject: [PATCH 14/24] docs --- docs/_toc.yml | 2 +- docs/index.md | 40 ---------------------------------------- 2 files changed, 1 insertion(+), 41 deletions(-) delete mode 100644 docs/index.md diff --git a/docs/_toc.yml b/docs/_toc.yml index b20af8b3..92cc68be 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -2,7 +2,7 @@ # Learn more at https://jupyterbook.org/customize/toc.html format: jb-book -root: index.md +root: intro.md parts: - chapters: diff --git a/docs/index.md b/docs/index.md deleted file mode 100644 index 449ae621..00000000 --- a/docs/index.md +++ /dev/null @@ -1,40 +0,0 @@ -# CHAINLADDER-PYTHON - -Welcome! - -The `chainladder-python` package was built to be able to handle all of your actuarial reserving needs in python. It consists of popular actuarial tools, such as **triangle data manipulation**, **link ratios calculation**, and **IBNR estimates** using both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications. - -This package strives to be minimalistic in needing its own API. The syntax mimics popular packages such as [pandas](https://pandas.pydata.org/) for data manipulation and [scikit-learn](https://scikit-learn.org/) for model construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work. - -::::{grid} 1 2 2 4 -:gutter: 1 1 1 2 - -:::{grid-item-card} {octicon}`rocket;2.5em;sd-mr-1` Getting Started -:link: getting_started/index -:link-type: doc - -Install chainladder and try the beginner tutorials. -::: - -:::{grid-item-card} {octicon}`book;2.5em;sd-mr-1` User Guide -:link: user_guide/index -:link-type: doc - -A more comprehensive guide on the capabilities of chainladder. -::: - -:::{grid-item-card} {octicon}`grabber;2.5em;sd-mr-1` Galllery -:link: gallery/index -:link-type: doc - -Gallery of examples highlighting different use cases for chainladder. -::: - -:::{grid-item-card} {octicon}`code-square;2.5em;sd-mr-1` API Reference -:link: library/api -:link-type: doc - -The chainladder API Reference Manual provides a comprehensive reference for all methods. -::: - -:::: \ No newline at end of file From 975ab73deba88ffdd7f8e406e6eac17093bef99a Mon Sep 17 00:00:00 2001 From: John S Bogaardt Date: Sun, 11 Jun 2023 14:06:56 -0600 Subject: [PATCH 15/24] Update pytest.yml --- .github/workflows/pytest.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 1aa40d04..f3fc14dc 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -26,7 +26,8 @@ jobs: - name: Install repo and dependencies shell: bash -l {0} run: | - conda env create --file ./ci/environment-test.yaml; + conda install mamba -y -c conda-forge -n base + mamba env create --file ./ci/environment-test.yaml; conda activate cl_test; python setup.py develop pytest --cov=chainladder --cov-report=xml From e26f927fc778f15d0df41cf3c39fe9a7e4c1e3b6 Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 12:30:50 -0700 Subject: [PATCH 16/24] Added development age test --- chainladder/core/tests/test_grain.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/chainladder/core/tests/test_grain.py b/chainladder/core/tests/test_grain.py index 6c8b1bdf..95abba17 100644 --- a/chainladder/core/tests/test_grain.py +++ b/chainladder/core/tests/test_grain.py @@ -72,29 +72,42 @@ def test_different_forms_of_grain(prism_dense, grain, trailing, alt, atol): a = t.grain(grain, trailing=trailing) b = t.incr_to_cum().grain(grain, trailing=trailing).cum_to_incr() assert abs(a - b).sum().sum() < atol + assert abs(a - b).sum().sum() < atol a = t.incr_to_cum().grain(grain, trailing=trailing) b = t.grain(grain, trailing=trailing).incr_to_cum() assert abs(a - b).sum().sum() < atol + assert abs(a - b).sum().sum() < atol def test_asymmetric_origin_grain(prism_dense): x = prism_dense.iloc[..., 8:, :].incr_to_cum() x = x[x.valuation < x.valuation_date] assert x.grain("OYDM").development[0] == 1 + x = x[x.valuation < x.valuation_date] + assert x.grain("OYDM").development[0] == 1 def test_vector_triangle_grain_mismatch(prism): + tri = prism["Paid"].sum().incr_to_cum().grain("OQDM") tri = prism["Paid"].sum().incr_to_cum().grain("OQDM") exposure = tri.latest_diagonal tri = tri.grain("OQDQ") assert (tri / exposure).development_grain == "Q" +def test_development_age(): + raa_tri = cl.load_sample("raa") + assert (raa_tri.ddims == [12, 24, 36, 48, 60, 72, 84, 96, 108, 120]).all() + + def test_annual_trailing(prism): + tri = prism["Paid"].sum().incr_to_cum() tri = prism["Paid"].sum().incr_to_cum() # (limit data to November) tri = tri[tri.valuation < tri.valuation_date].incr_to_cum() tri = tri.grain("OQDQ", trailing=True).grain("OYDY") + tri = tri[tri.valuation < tri.valuation_date].incr_to_cum() + tri = tri.grain("OQDQ", trailing=True).grain("OYDY") assert np.all(tri.ddims[:4] == np.array([12, 24, 36, 48])) From d50627fd25398d758c528349e3fd47dfaa2535d5 Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 13:00:10 -0700 Subject: [PATCH 17/24] Added tests --- chainladder/core/tests/test_grain.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/chainladder/core/tests/test_grain.py b/chainladder/core/tests/test_grain.py index 95abba17..83e3f653 100644 --- a/chainladder/core/tests/test_grain.py +++ b/chainladder/core/tests/test_grain.py @@ -109,8 +109,3 @@ def test_annual_trailing(prism): tri = tri[tri.valuation < tri.valuation_date].incr_to_cum() tri = tri.grain("OQDQ", trailing=True).grain("OYDY") assert np.all(tri.ddims[:4] == np.array([12, 24, 36, 48])) - - -def test_development_age(): - raa_tri = cl.load_sample("raa") - assert (raa_tri.ddims == [12, 24, 36, 48, 60, 72, 84, 96, 108, 120]).all() From 0f735fa462aec5d84d6ea52f3695f118a273fffc Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 13:04:11 -0700 Subject: [PATCH 18/24] New tests for development_age --- chainladder/core/tests/test_grain.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/chainladder/core/tests/test_grain.py b/chainladder/core/tests/test_grain.py index 83e3f653..65f353b9 100644 --- a/chainladder/core/tests/test_grain.py +++ b/chainladder/core/tests/test_grain.py @@ -95,11 +95,6 @@ def test_vector_triangle_grain_mismatch(prism): assert (tri / exposure).development_grain == "Q" -def test_development_age(): - raa_tri = cl.load_sample("raa") - assert (raa_tri.ddims == [12, 24, 36, 48, 60, 72, 84, 96, 108, 120]).all() - - def test_annual_trailing(prism): tri = prism["Paid"].sum().incr_to_cum() tri = prism["Paid"].sum().incr_to_cum() @@ -109,3 +104,8 @@ def test_annual_trailing(prism): tri = tri[tri.valuation < tri.valuation_date].incr_to_cum() tri = tri.grain("OQDQ", trailing=True).grain("OYDY") assert np.all(tri.ddims[:4] == np.array([12, 24, 36, 48])) + + +def test_development_age(): + raa_tri = cl.load_sample("raa") + assert (raa_tri.ddims == [12, 24, 36, 48, 60, 72, 84, 96, 108, 120]).all() From 6d5f851847979550271ebfc09f939cbbd7bd4962 Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 13:07:51 -0700 Subject: [PATCH 19/24] Removed debug prints --- chainladder/core/base.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/chainladder/core/base.py b/chainladder/core/base.py index 7c18e049..67712b89 100644 --- a/chainladder/core/base.py +++ b/chainladder/core/base.py @@ -128,9 +128,6 @@ def _set_odims(data_agg, date_axes): @staticmethod def _set_ddims(data_agg, date_axes): - # print("date origin", date_axes["__origin__"]) - # print("date development", date_axes["__development__"]) - if date_axes["__development__"].nunique() > 1: dev_lag = TriangleBase._development_lag( data_agg["__origin__"], data_agg["__development__"] @@ -150,8 +147,6 @@ def _set_ddims(data_agg, date_axes): ) dev_idx = np.zeros((len(data_agg), 1)) - print("ddims", ddims) - return ddims, dev_idx @staticmethod From 3e63614d6c546584298c8d176a103242a32d3edf Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 13:24:12 -0700 Subject: [PATCH 20/24] Removed some duplicated asserts due to merge --- chainladder/core/tests/test_grain.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/chainladder/core/tests/test_grain.py b/chainladder/core/tests/test_grain.py index 15525fd9..7db0deff 100644 --- a/chainladder/core/tests/test_grain.py +++ b/chainladder/core/tests/test_grain.py @@ -72,11 +72,10 @@ def test_different_forms_of_grain(prism_dense, grain, trailing, alt, atol): a = t.grain(grain, trailing=trailing) b = t.incr_to_cum().grain(grain, trailing=trailing).cum_to_incr() assert abs(a - b).sum().sum() < atol - assert abs(a - b).sum().sum() < atol + a = t.incr_to_cum().grain(grain, trailing=trailing) b = t.grain(grain, trailing=trailing).incr_to_cum() assert abs(a - b).sum().sum() < atol - assert abs(a - b).sum().sum() < atol def test_asymmetric_origin_grain(prism_dense): @@ -84,6 +83,7 @@ def test_asymmetric_origin_grain(prism_dense): x = prism_dense.iloc[..., 8:, :].incr_to_cum() x = x[x.valuation < x.valuation_date] assert x.grain("OYDM").development[0] == 1 + x = x[x.valuation < x.valuation_date] assert x.grain("OYDM").development[0] == 1 From 9530bee1d57040721dee309f8d11b5335aea412a Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 13:25:13 -0700 Subject: [PATCH 21/24] Removed duplicated lines due to merge --- chainladder/core/tests/test_grain.py | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/chainladder/core/tests/test_grain.py b/chainladder/core/tests/test_grain.py index 7db0deff..efd18d63 100644 --- a/chainladder/core/tests/test_grain.py +++ b/chainladder/core/tests/test_grain.py @@ -69,27 +69,22 @@ def test_different_forms_of_grain(prism_dense, grain, trailing, alt, atol): if alt == 2: t = t.val_to_dev().copy() t = t[t.valuation < "2017-09"] - a = t.grain(grain, trailing=trailing) - b = t.incr_to_cum().grain(grain, trailing=trailing).cum_to_incr() - assert abs(a - b).sum().sum() < atol a = t.incr_to_cum().grain(grain, trailing=trailing) b = t.grain(grain, trailing=trailing).incr_to_cum() assert abs(a - b).sum().sum() < atol -def test_asymmetric_origin_grain(prism_dense): def test_asymmetric_origin_grain(prism_dense): x = prism_dense.iloc[..., 8:, :].incr_to_cum() x = x[x.valuation < x.valuation_date] assert x.grain("OYDM").development[0] == 1 - + x = x[x.valuation < x.valuation_date] assert x.grain("OYDM").development[0] == 1 def test_vector_triangle_grain_mismatch(prism): - tri = prism["Paid"].sum().incr_to_cum().grain("OQDM") tri = prism["Paid"].sum().incr_to_cum().grain("OQDM") exposure = tri.latest_diagonal tri = tri.grain("OQDQ") @@ -97,13 +92,10 @@ def test_vector_triangle_grain_mismatch(prism): def test_annual_trailing(prism): - tri = prism["Paid"].sum().incr_to_cum() tri = prism["Paid"].sum().incr_to_cum() # (limit data to November) tri = tri[tri.valuation < tri.valuation_date].incr_to_cum() tri = tri.grain("OQDQ", trailing=True).grain("OYDY") - tri = tri[tri.valuation < tri.valuation_date].incr_to_cum() - tri = tri.grain("OQDQ", trailing=True).grain("OYDY") assert np.all(tri.ddims[:4] == np.array([12, 24, 36, 48])) From d73be203ac7a2ff2ca5bebdbda4f29ccc32a0e6f Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 20:10:51 -0700 Subject: [PATCH 22/24] Revised development lag --- chainladder/core/base.py | 38 +++++++++++++++++++++++--------------- 1 file changed, 23 insertions(+), 15 deletions(-) diff --git a/chainladder/core/base.py b/chainladder/core/base.py index 67712b89..ff7b150b 100644 --- a/chainladder/core/base.py +++ b/chainladder/core/base.py @@ -256,21 +256,29 @@ def _to_datetime(data, fields, period_end=False, format=None): def _development_lag(origin, valuation): """For tabular format, this will convert the origin/development difference to a development lag""" - # temp = pd.DataFrame() - # temp["valuation"] = (valuation + pd.DateOffset(1)).dt.strftime("%m/%d/%Y") - # temp["origin"] = origin.dt.strftime("%m/%d/%Y") - # temp["age_old"] = ( - # ((valuation - origin) / np.timedelta64(1, "M")).round(0).astype(int) - # ) - # temp["age_temp"] = valuation - origin - # temp["age_temp_div"] = ( - # ((valuation - origin) / np.timedelta64(1, "Y")).round(0).astype(int) - # ) * 12 - - # print(temp) - # print(temp.dtypes) - - return ((valuation - origin) / np.timedelta64(1, "Y")).round(0).astype(int) * 12 + + temp = pd.DataFrame() + temp["valuation"] = (valuation + pd.DateOffset(1)).dt.strftime("%m/%d/%Y") + temp["origin"] = origin.dt.strftime("%m/%d/%Y") + + temp["age_old"] = ( + ((valuation - origin) / np.timedelta64(1, "M")).round(0).astype(int) + ) + + temp["age_temp_div"] = ( + (valuation.dt.year * 12 + valuation.dt.month) + - (origin.dt.year * 12 + origin.dt.month) + + 1 + ) + + # print(temp.tail(10)) + + # return ((valuation - origin) / np.timedelta64(1, "Y")).round(0).astype(int) * 12 + return ( + (valuation.dt.year * 12 + valuation.dt.month) + - (origin.dt.year * 12 + origin.dt.month) + + 1 + ) @staticmethod def _get_grain(dates, trailing=False, kind="origin"): From aaba58eec973f69c67080df502dbfbd4d04c5fb2 Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Thu, 15 Jun 2023 20:11:04 -0700 Subject: [PATCH 23/24] Added test for devlopment age (quarterly) --- chainladder/core/tests/test_grain.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/chainladder/core/tests/test_grain.py b/chainladder/core/tests/test_grain.py index efd18d63..39b0ea54 100644 --- a/chainladder/core/tests/test_grain.py +++ b/chainladder/core/tests/test_grain.py @@ -100,5 +100,11 @@ def test_annual_trailing(prism): def test_development_age(): - raa_tri = cl.load_sample("raa") - assert (raa_tri.ddims == [12, 24, 36, 48, 60, 72, 84, 96, 108, 120]).all() + assert (cl.load_sample("raa").ddims == [12, 24, 36, 48, 60, 72, 84, 96, 108, 120]).all() + +def test_development_age2() + + assert (cl.load_sample("quarterly").ddims == [ 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, + 42, 45, 48, 51, 54, 57, 60, 63, 66, 69, 72, 75, 78, + 81, 84, 87, 90, 93, 96, 99, 102, 105, 108, 111, 114, 117, + 120, 123, 126, 129, 132, 135]).all() \ No newline at end of file From cba398a9e00d49057248ed8d3ad21f8abb783eb4 Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Fri, 16 Jun 2023 09:27:20 -0700 Subject: [PATCH 24/24] Missed a tiny ":" --- chainladder/core/tests/test_grain.py | 64 ++++++++++++++++++++++++---- 1 file changed, 56 insertions(+), 8 deletions(-) diff --git a/chainladder/core/tests/test_grain.py b/chainladder/core/tests/test_grain.py index 39b0ea54..978aecdb 100644 --- a/chainladder/core/tests/test_grain.py +++ b/chainladder/core/tests/test_grain.py @@ -100,11 +100,59 @@ def test_annual_trailing(prism): def test_development_age(): - assert (cl.load_sample("raa").ddims == [12, 24, 36, 48, 60, 72, 84, 96, 108, 120]).all() - -def test_development_age2() - - assert (cl.load_sample("quarterly").ddims == [ 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, - 42, 45, 48, 51, 54, 57, 60, 63, 66, 69, 72, 75, 78, - 81, 84, 87, 90, 93, 96, 99, 102, 105, 108, 111, 114, 117, - 120, 123, 126, 129, 132, 135]).all() \ No newline at end of file + assert ( + cl.load_sample("raa").ddims == [12, 24, 36, 48, 60, 72, 84, 96, 108, 120] + ).all() + + +def test_development_age_quarterly(): + assert ( + cl.load_sample("quarterly").ddims + == [ + 3, + 6, + 9, + 12, + 15, + 18, + 21, + 24, + 27, + 30, + 33, + 36, + 39, + 42, + 45, + 48, + 51, + 54, + 57, + 60, + 63, + 66, + 69, + 72, + 75, + 78, + 81, + 84, + 87, + 90, + 93, + 96, + 99, + 102, + 105, + 108, + 111, + 114, + 117, + 120, + 123, + 126, + 129, + 132, + 135, + ] + ).all()

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