bug fix in valuation slice

chainladder/core/base.py

@@ -387,14 +387,15 @@ def rename(self, axis, value):
                 A value of 0 <= axis <= 4 corresponding to axes 'index',
                 'columns', 'origin', 'development' respectively.  Both the
                 int and str representation can be used.
-            value: list
+            value: list or str
                 List of new labels to be assigned to the axis. List must be of
                 same length of the specified axis.
 
         Returns
         -------
             Triangle with relabeled axis.
         """
+        value = [value] if type(value) is str else value
         if axis == 'index' or axis == 0:
             self.index = value
         if axis == 'columns' or axis == 1:
@@ -479,12 +480,12 @@ def to_frame(self, *args, **kwargs):
         elif len(axes) == 2 or len(axes) == 1:
             tri = np.squeeze(self.values)
             axes_lookup = {0: self.kdims, 1: self.vdims,
-                           2: self.odims, 3: self.ddims}
+                           2: self.origin, 3: self.ddims}
             if len(axes) == 2:
                 return pd.DataFrame(tri, index=axes_lookup[axes[0]],
-                                    columns=axes_lookup[axes[1]])
+                                    columns=axes_lookup[axes[1]]).fillna(0)
             if len(axes) == 1:
-                return pd.Series(tri, index=axes_lookup[axes[0]])
+                return pd.Series(tri, index=axes_lookup[axes[0]]).fillna(0)
         else:
             raise ValueError('len(index) and len(columns) must be 1.')
 
@@ -699,7 +700,7 @@ def _slice_origin(self, key):
 
     def _slice_valuation(self, key):
         obj = copy.deepcopy(self)
-        # obj.valuation_date = obj.valuation[key].max()
+        obj.valuation_date = min(obj.valuation[key].max().to_timestamp(how='e'), obj.valuation_date)
         key = key.reshape(self.shape[-2:], order='f')
         nan_tri = np.ones(self.shape[-2:])
         nan_tri = key*nan_tri

chainladder/development/base.py

@@ -6,28 +6,11 @@
 import pandas as pd
 import copy
 import warnings
-from sklearn.base import BaseEstimator
+from sklearn.base import BaseEstimator, TransformerMixin
 from chainladder import WeightedRegression
 from chainladder.core import IO
 
-class DevelopmentBase(BaseEstimator, IO):
-    def fit_transform(self, X, y=None, sample_weight=None):
-        """ Equivalent to fit(X).transform(X)
-
-        Parameters
-        ----------
-        X : Triangle-like
-            Set of LDFs based on the model.
-        y : Ignored
-        sample_weight : Ignored
-
-        Returns
-        -------
-            X_new : New triangle with transformed attributes.
-        """
-        self.fit(X, y, sample_weight)
-        return self.transform(X)
-
+class DevelopmentBase(BaseEstimator, TransformerMixin, IO):
     @staticmethod
     def _get_cdf(obj):
         if obj.__dict__.get('ldf_', None) is None:

chainladder/development/munich.py

@@ -3,15 +3,15 @@
 ============================
 """
 
-from sklearn.base import BaseEstimator
+from sklearn.base import BaseEstimator, TransformerMixin
 from chainladder.utils.weighted_regression import WeightedRegression
 from chainladder.development import Development
 from chainladder.core import IO
 import numpy as np
 import copy
 
 
-class MunichAdjustment(BaseEstimator, IO):
+class MunichAdjustment(BaseEstimator, TransformerMixin, IO):
     """Applies the Munich Chainladder adjustment to a set of paid/incurred
        ldfs.
 
@@ -73,23 +73,6 @@ def transform(self, X):
         X.ldf_ = self.ldf_
         return X
 
-    def fit_transform(self, X, y=None, sample_weight=None):
-        """ Equivalent to fit(X).transform(X)
-
-        Parameters
-        ----------
-        X : Triangle-like
-            Set of LDFs to which the munich adjustment will be applied.
-        y : Ignored
-        sample_weight : Ignored
-
-        Returns
-        -------
-            X_new : New triangle with transformed attributes.
-        """
-        self.fit(X, y, sample_weight)
-        return self.transform(X)
-
     def _get_p_to_i_object(self, obj):
         paid = obj[list(self.paid_to_incurred.keys())[0]]
         for item in list(self.paid_to_incurred.keys())[1:]:

chainladder/tails/base.py

@@ -1,10 +1,10 @@
 import copy
 import numpy as np
-from sklearn.base import BaseEstimator
+from sklearn.base import BaseEstimator, TransformerMixin
 from chainladder.development import DevelopmentBase, Development
 
 
-class TailBase(BaseEstimator):
+class TailBase(BaseEstimator, TransformerMixin):
     ''' Base class for all tail methods.  Tail objects are equivalent
         to development objects with an additional set of tail statistics'''
 

docs/auto_examples/index.rst

@@ -107,6 +107,26 @@ Gallery of Chainladder Functionality
 
    /auto_examples/plot_bf_apriori_from_cl
 
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can slice triangle objects to perform a typical &#x27;Actual vs Ex...">
+
+.. only:: html
+
+    .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ave_analysis_thumb.png
+
+        :ref:`sphx_glr_auto_examples_plot_ave_analysis.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /auto_examples/plot_ave_analysis
+
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">

docs/auto_examples/plot_ave_analysis.ipynb

@@ -0,0 +1,54 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Actual Vs Expected Analysis\n\n\nThis example demonstrates how you can slice triangle objects to perform a\ntypical 'Actual vs Expected' analysis.  We will use Medical Malpractice\npayment patterns for the demo.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import chainladder as cl\nimport seaborn as sns\nsns.set_style('whitegrid')\n\n# Load the data\ntri_1997 = cl.load_dataset('clrd')\ntri_1997 = tri_1997.groupby('LOB').sum().loc['medmal']['CumPaidLoss']\n\n# Create a triangle as of the previous valuation and build IBNR model\ntri_1996 = tri_1997[tri_1997.valuation < '1997']\nmodel_1996 = cl.Chainladder().fit(cl.TailCurve().fit_transform(tri_1996))\n\n# Slice the expected losses from the 1997 calendar period of the model\nave = model_1996.full_triangle_.dev_to_val()\nave = ave[ave.development == '1997'].rename('columns', 'Expected')\n\n# Slice the actual losses from the 1997 calendar period for prior AYs\nave['Actual'] = tri_1997.latest_diagonal[tri_1997.origin < '1997']\nave['Actual - Expected'] = ave['Actual'] - ave['Expected']\n\n# Plotting\nave.to_frame().T.plot(y='Actual - Expected', kind='bar', legend=False) \\\n                .set(title='Calendar Period 1997 Performance',\n                     xlabel='Accident Period', ylabel='Actual - Expected');"
+      ]
+    }
+  ],
+  "metadata": {
+    "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": "3.6.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

docs/auto_examples/plot_ave_analysis.py

@@ -0,0 +1,34 @@
+"""
+===========================
+Actual Vs Expected Analysis
+===========================
+
+This example demonstrates how you can slice triangle objects to perform a
+typical 'Actual vs Expected' analysis.  We will use Medical Malpractice
+payment patterns for the demo.
+"""
+
+import chainladder as cl
+import seaborn as sns
+sns.set_style('whitegrid')
+
+# Load the data
+tri_1997 = cl.load_dataset('clrd')
+tri_1997 = tri_1997.groupby('LOB').sum().loc['medmal']['CumPaidLoss']
+
+# Create a triangle as of the previous valuation and build IBNR model
+tri_1996 = tri_1997[tri_1997.valuation < '1997']
+model_1996 = cl.Chainladder().fit(cl.TailCurve().fit_transform(tri_1996))
+
+# Slice the expected losses from the 1997 calendar period of the model
+ave = model_1996.full_triangle_.dev_to_val()
+ave = ave[ave.development == '1997'].rename('columns', 'Expected')
+
+# Slice the actual losses from the 1997 calendar period for prior AYs
+ave['Actual'] = tri_1997.latest_diagonal[tri_1997.origin < '1997']
+ave['Actual - Expected'] = ave['Actual'] - ave['Expected']
+
+# Plotting
+ave.to_frame().T.plot(y='Actual - Expected', kind='bar', legend=False) \
+                .set(title='Calendar Period 1997 Performance',
+                     xlabel='Accident Period', ylabel='Actual - Expected');

docs/auto_examples/plot_ave_analysis.py.md5

@@ -0,0 +1 @@
+d7f8f36282dc3592d0247e73221f94ea

docs/auto_examples/plot_ave_analysis.rst

@@ -0,0 +1,84 @@
+.. note::
+    :class: sphx-glr-download-link-note
+
+    Click :ref:`here <sphx_glr_download_auto_examples_plot_ave_analysis.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_plot_ave_analysis.py:
+
+
+===========================
+Actual Vs Expected Analysis
+===========================
+
+This example demonstrates how you can slice triangle objects to perform a
+typical 'Actual vs Expected' analysis.  We will use Medical Malpractice
+payment patterns for the demo.
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_ave_analysis_001.png
+    :class: sphx-glr-single-img
+
+
+
+
+
+.. code-block:: python
+
+
+    import chainladder as cl
+    import seaborn as sns
+    sns.set_style('whitegrid')
+
+    # Load the data
+    tri_1997 = cl.load_dataset('clrd')
+    tri_1997 = tri_1997.groupby('LOB').sum().loc['medmal']['CumPaidLoss']
+
+    # Create a triangle as of the previous valuation and build IBNR model
+    tri_1996 = tri_1997[tri_1997.valuation < '1997']
+    model_1996 = cl.Chainladder().fit(cl.TailCurve().fit_transform(tri_1996))
+
+    # Slice the expected losses from the 1997 calendar period of the model
+    ave = model_1996.full_triangle_.dev_to_val()
+    ave = ave[ave.development == '1997'].rename('columns', 'Expected')
+
+    # Slice the actual losses from the 1997 calendar period for prior AYs
+    ave['Actual'] = tri_1997.latest_diagonal[tri_1997.origin < '1997']
+    ave['Actual - Expected'] = ave['Actual'] - ave['Expected']
+
+    # Plotting
+    ave.to_frame().T.plot(y='Actual - Expected', kind='bar', legend=False) \
+                    .set(title='Calendar Period 1997 Performance',
+                         xlabel='Accident Period', ylabel='Actual - Expected');
+
+**Total running time of the script:** ( 0 minutes  1.788 seconds)
+
+
+.. _sphx_glr_download_auto_examples_plot_ave_analysis.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: plot_ave_analysis.py <plot_ave_analysis.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: plot_ave_analysis.ipynb <plot_ave_analysis.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_

docs/auto_examples/plot_exhibits.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Sample Excel Exhibit functionality\n\n\nThis example demonstrates some of the flexibility of the ``Exhibits`` class. It\ncreates an Excel file called 'clrd.xlsx' that includes various statistics on\nindustry development patterns for each line of business in the CAS loss reserve\ndatabase.\n\nSee `Exhibits<exhibits>` for more detail.\n\n\n"
+        "\n# Sample Excel Exhibit functionality\n\n\nThis example demonstrates some of the flexibility of the ``Exhibits`` class. It\ncreates an Excel file called 'clrd.xlsx' that includes various statistics on\nindustry development patterns for each line of business in the CAS loss reserve\ndatabase.\n\nOutput can be viewed online in `Google Sheets <https://docs.google.com/spreadsheets/d/1fwHK1Sys6aHDhEhFO6stVJtmZVKEcXXBsmJLSLIBLJY/edit#gid=1190415861>`_.\n\nSee `Exhibits<exhibits>` for more detail.\n\n\n"
       ]
     },
     {

docs/auto_examples/plot_exhibits.py

@@ -8,6 +8,8 @@
 industry development patterns for each line of business in the CAS loss reserve
 database.
 
+Output can be viewed online in `Google Sheets <https://docs.google.com/spreadsheets/d/1fwHK1Sys6aHDhEhFO6stVJtmZVKEcXXBsmJLSLIBLJY/edit#gid=1190415861>`_.
+
 See :ref:`Exhibits<exhibits>` for more detail.
 
 .. _exhibit_example:

docs/auto_examples/plot_exhibits.py.md5

@@ -1 +1 @@
-5a6be949f7c7265f53489c50d24e5d17
+f5234948aa65a93976f2a87b9155dd61

docs/auto_examples/plot_exhibits.rst

@@ -16,6 +16,8 @@ creates an Excel file called 'clrd.xlsx' that includes various statistics on
 industry development patterns for each line of business in the CAS loss reserve
 database.
 
+Output can be viewed online in `Google Sheets <https://docs.google.com/spreadsheets/d/1fwHK1Sys6aHDhEhFO6stVJtmZVKEcXXBsmJLSLIBLJY/edit#gid=1190415861>`_.
+
 See :ref:`Exhibits<exhibits>` for more detail.
 
 .. _exhibit_example:
@@ -77,7 +79,7 @@ See :ref:`Exhibits<exhibits>` for more detail.
     # Create Excel File
     exhibits.to_excel('clrd.xlsx')
 
-**Total running time of the script:** ( 0 minutes  2.915 seconds)
+**Total running time of the script:** ( 0 minutes  4.548 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_exhibits.py:

docs/modules/generated/chainladder.Chainladder.examples

@@ -57,6 +57,24 @@ Examples using ``chainladder.Chainladder``
 
     * :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
 
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can slice triangle objects to perform a typical &#x27;Actual vs Ex...">
+
+.. only:: html
+
+    .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ave_analysis_thumb.png
+
+        :ref:`sphx_glr_auto_examples_plot_ave_analysis.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+    * :ref:`sphx_glr_auto_examples_plot_ave_analysis.py`
+
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to adjust LDFs by the relationship between Paid and Incurred usin...">