From 3824841558cddbb60f19c9c473abe4bd903ad3fd Mon Sep 17 00:00:00 2001 From: Kenneth Hsu Date: Mon, 6 Nov 2023 21:44:42 -0800 Subject: [PATCH] Some typos --- .../sandbox_workbook_blank.ipynb | 14 +- .../sandbox_workbook_filled.ipynb | 666 +++++++++++------- 2 files changed, 400 insertions(+), 280 deletions(-) diff --git a/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb b/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb index bb3b46a9..1349f238 100644 --- a/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb +++ b/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb @@ -255,7 +255,7 @@ "id": "08b8557c-66fe-4a25-a8bf-5413ca1c1fbb", "metadata": {}, "source": [ - "What if you only want the valuation at age 60?" + "What if you only want the valuation at age 60 of AY 1998?" ] }, { @@ -283,7 +283,7 @@ "metadata": {}, "outputs": [], "source": [ - "xyz_tri.iloc[__fill_in_code__, __fill_in_code__, __fill_in_code__, __fill_in_code__]" + "xyz_tri.loc[__fill_in_code__, __fill_in_code__, __fill_in_code__, __fill_in_code__]" ] }, { @@ -417,7 +417,7 @@ "metadata": {}, "outputs": [], "source": [ - "cl.Development(average=\"simple\").fit(__fill_in_code__).ldf_" + "cl.Development(average=\"volume\").fit(__fill_in_code__).ldf_" ] }, { @@ -435,7 +435,7 @@ "metadata": {}, "outputs": [], "source": [ - "cl.Development(average=\"simple\").fit(__fill_in_code__).__fill_in_code__" + "cl.Development(average=\"volume\").fit(__fill_in_code__).__fill_in_code__" ] }, { @@ -453,7 +453,7 @@ "metadata": {}, "outputs": [], "source": [ - "cl.Development(average=\"simple\", n_periods=__fill_in_code__).fit(xyz_tri[\"Incurred\"]).cdf_" + "cl.Development(average=\"volume\", n_periods=__fill_in_code__).fit(xyz_tri[\"Incurred\"]).cdf_" ] }, { @@ -678,7 +678,7 @@ "id": "36105614-e317-4a87-a42d-282f59b1d339", "metadata": {}, "source": [ - "The Mack's Chainladder model is available." + "The Mack's Chainladder model is available. Let's use it on the Incurred triangle." ] }, { @@ -837,7 +837,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/getting_started/online_sandbox/sandbox_workbook_filled.ipynb b/docs/getting_started/online_sandbox/sandbox_workbook_filled.ipynb index 737d5f2b..32808ab3 100644 --- a/docs/getting_started/online_sandbox/sandbox_workbook_filled.ipynb +++ b/docs/getting_started/online_sandbox/sandbox_workbook_filled.ipynb @@ -31,30 +31,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: chainladder in /opt/anaconda3/lib/python3.9/site-packages (0.8.17)\n", - "Requirement already satisfied: numba>0.54 in /opt/anaconda3/lib/python3.9/site-packages (from chainladder) (0.55.1)\n", - "Requirement already satisfied: dill in /opt/anaconda3/lib/python3.9/site-packages (from chainladder) (0.3.5.1)\n", - "Requirement already satisfied: scikit-learn>=0.23 in /opt/anaconda3/lib/python3.9/site-packages (from chainladder) (1.1.1)\n", - 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A possible replacement is to upgrade to a newer version of nb-black or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n", + "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" ] } ], @@ -98,6 +99,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n", + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n", "chainladder 0.8.17\n" ] } @@ -901,7 +904,7 @@ "id": "08b8557c-66fe-4a25-a8bf-5413ca1c1fbb", "metadata": {}, "source": [ - "What if you only want the valuation at age 60?" + "What if you only want the valuation at age 60 of AY 1998?" ] }, { @@ -954,7 +957,9 @@ "cell_type": "code", "execution_count": 12, "id": "b8116ded-c788-483c-b2af-fde45b72ee4a", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -963,70 +968,190 @@ " \n", " \n", " \n", + " 12\n", + " 24\n", + " 36\n", + " 48\n", " 60\n", + " 72\n", + " 84\n", + " 96\n", + " 108\n", + " 120\n", + " 132\n", " \n", " \n", " \n", " \n", " 1998\n", + " \n", + " \n", + " 11,171\n", + " 12,380\n", " 13,216\n", + " 14,067\n", + " 14,688\n", + " 16,366\n", + " 16,163\n", + " 15,835\n", + " 15,822\n", " \n", " \n", " 1999\n", + " \n", + " 13,255\n", + " 16,405\n", + " 19,639\n", " 22,473\n", + " 23,764\n", + " 25,094\n", + " 24,795\n", + " 25,071\n", + " 25,107\n", + " \n", " \n", " \n", " 2000\n", + " 15,676\n", + " 18,749\n", + " 21,900\n", + " 27,144\n", " 29,488\n", + " 34,458\n", + " 36,949\n", + " 37,505\n", + " 37,246\n", + " \n", + " \n", " \n", " \n", " 2001\n", + " 11,827\n", + " 16,004\n", + " 21,022\n", + " 26,578\n", " 34,205\n", + " 37,136\n", + " 38,541\n", + " 38,798\n", + " \n", + " \n", + " \n", " \n", " \n", " 2002\n", + " 12,811\n", + " 20,370\n", + " 26,656\n", + " 37,667\n", " 44,414\n", + " 48,701\n", + " 48,169\n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " 2003\n", + " 9,651\n", + " 16,995\n", + " 30,354\n", + " 40,594\n", " 44,231\n", + " 44,373\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " 2004\n", + " 16,995\n", + " 40,180\n", + " 58,866\n", + " 71,707\n", " 70,288\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " 2005\n", + " 28,674\n", + " 47,432\n", + " 70,340\n", + " 70,655\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " 2006\n", + " 27,066\n", + " 46,783\n", + " 48,804\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " 2007\n", + " 19,477\n", + " 31,732\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " 2008\n", + " 18,632\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", "" ], "text/plain": [ - " 60\n", - "1998 13216.0\n", - "1999 22473.0\n", - "2000 29488.0\n", - "2001 34205.0\n", - "2002 44414.0\n", - "2003 44231.0\n", - "2004 70288.0\n", - "2005 NaN\n", - "2006 NaN\n", - "2007 NaN\n", - "2008 NaN" + " 12 24 36 48 60 72 84 96 108 120 132\n", + "1998 NaN NaN 11171.0 12380.0 13216.0 14067.0 14688.0 16366.0 16163.0 15835.0 15822.0\n", + "1999 NaN 13255.0 16405.0 19639.0 22473.0 23764.0 25094.0 24795.0 25071.0 25107.0 NaN\n", + "2000 15676.0 18749.0 21900.0 27144.0 29488.0 34458.0 36949.0 37505.0 37246.0 NaN NaN\n", + "2001 11827.0 16004.0 21022.0 26578.0 34205.0 37136.0 38541.0 38798.0 NaN NaN NaN\n", + "2002 12811.0 20370.0 26656.0 37667.0 44414.0 48701.0 48169.0 NaN NaN NaN NaN\n", + "2003 9651.0 16995.0 30354.0 40594.0 44231.0 44373.0 NaN NaN NaN NaN NaN\n", + "2004 16995.0 40180.0 58866.0 71707.0 70288.0 NaN NaN NaN NaN NaN NaN\n", + "2005 28674.0 47432.0 70340.0 70655.0 NaN NaN NaN NaN NaN NaN NaN\n", + "2006 27066.0 46783.0 48804.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2007 19477.0 31732.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n", + "2008 18632.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN" ] }, "execution_count": 12, @@ -1035,7 +1160,7 @@ } ], "source": [ - "xyz_tri.iloc[:, 0, :, 4]" + "xyz_tri.loc[:, \"Incurred\", :, :]" ] }, { @@ -1584,195 +1709,195 @@ "data": { "text/html": [ "\n", - "\n", + "
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- " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2254,11 +2379,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2267,10 +2392,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2280,9 +2405,9 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2293,8 +2418,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2382,7 +2507,7 @@ } ], "source": [ - "cl.Development(average=\"simple\").fit(xyz_tri[\"Incurred\"]).ldf_" + "cl.Development(average=\"volume\").fit(xyz_tri[\"Incurred\"]).ldf_" ] }, { @@ -2446,7 +2571,7 @@ } ], "source": [ - "cl.Development(average=\"simple\").fit(xyz_tri[\"Incurred\"]).cdf_" + "cl.Development(average=\"volume\").fit(xyz_tri[\"Incurred\"]).cdf_" ] }, { @@ -2510,7 +2635,7 @@ } ], "source": [ - "cl.Development(average=\"simple\", n_periods=3).fit(xyz_tri[\"Incurred\"]).cdf_" + "cl.Development(average=\"volume\", n_periods=3).fit(xyz_tri[\"Incurred\"]).cdf_" ] }, { @@ -3471,7 +3596,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 31, @@ -3480,14 +3605,12 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -3498,7 +3621,8 @@ " .ultimate_\n", ")\n", "plt.plot(\n", - " cc_result.to_frame().index.year, cc_result.to_frame()[\"2261\"],\n", + " cc_result.to_frame().index.year,\n", + " cc_result.to_frame()[\"2261\"],\n", ")" ] }, @@ -3515,7 +3639,7 @@ "id": "36105614-e317-4a87-a42d-282f59b1d339", "metadata": {}, "source": [ - "The Mack's Chainladder model is available." + "The Mack's Chainladder model is available. Let's use it on the Incurred triangle." ] }, { @@ -3908,7 +4032,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 35, @@ -3917,14 +4041,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -4031,21 +4153,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/anaconda3/lib/python3.9/site-packages/chainladder/utils/weighted_regression.py:76: RuntimeWarning: invalid value encountered in sqrt\n", + "/Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages/chainladder/utils/weighted_regression.py:76: RuntimeWarning: invalid value encountered in sqrt\n", " residual = (y - fitted_value) * xp.sqrt(w)\n", - "/opt/anaconda3/lib/python3.9/site-packages/chainladder/utils/weighted_regression.py:81: RuntimeWarning: invalid value encountered in sqrt\n", - " std_err = xp.sqrt(mse / d)\n", - "/opt/anaconda3/lib/python3.9/site-packages/chainladder/development/development.py:159: RuntimeWarning: invalid value encountered in sqrt\n", + "/Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages/chainladder/development/development.py:159: RuntimeWarning: invalid value encountered in sqrt\n", " / xp.swapaxes(xp.sqrt(x ** (2 - exponent))[..., 0:1, :], -1, -2)\n", - "/opt/anaconda3/lib/python3.9/site-packages/chainladder/development/development.py:167: RuntimeWarning: invalid value encountered in sqrt\n", + "/Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages/chainladder/development/development.py:167: RuntimeWarning: invalid value encountered in sqrt\n", " std = xp.sqrt((1 / num_to_nan(w)) * (self.sigma_ ** 2).values)\n", - "/opt/anaconda3/lib/python3.9/site-packages/chainladder/tails/base.py:120: RuntimeWarning: overflow encountered in exp\n", + "/Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages/chainladder/tails/base.py:120: RuntimeWarning: overflow encountered in exp\n", " sigma_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", - "/opt/anaconda3/lib/python3.9/site-packages/chainladder/tails/base.py:124: RuntimeWarning: overflow encountered in exp\n", + "/Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages/chainladder/tails/base.py:124: RuntimeWarning: overflow encountered in exp\n", " std_err_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", - "/opt/anaconda3/lib/python3.9/site-packages/chainladder/tails/base.py:127: RuntimeWarning: invalid value encountered in multiply\n", + "/Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages/chainladder/tails/base.py:127: RuntimeWarning: invalid value encountered in multiply\n", " sigma_ = sigma_ * 0\n", - "/opt/anaconda3/lib/python3.9/site-packages/chainladder/tails/base.py:128: RuntimeWarning: invalid value encountered in multiply\n", + "/Users/kennethhsu/opt/anaconda3/lib/python3.9/site-packages/chainladder/tails/base.py:128: RuntimeWarning: invalid value encountered in multiply\n", " std_err_ = std_err_* 0\n" ] }, @@ -4080,7 +4200,9 @@ "cell_type": "code", "execution_count": 38, "id": "edeba1db-97e6-43df-b1c0-590c2d7cd098", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -4094,14 +4216,12 @@ }, { "data": { - "image/png": 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\n", 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199819981.10821.06751.06441.04411.11420.98760.97970.99921.10821.06751.06441.04411.11420.98760.97970.9992
199919991.23761.19711.14431.05741.05600.98811.01111.00141.23761.19711.14431.05741.05600.98811.01111.0014
20001.19601.16811.23951.08641.16851.07231.01500.993120001.19601.16811.23951.08641.16851.07231.01500.9931
20011.35321.31351.26431.28701.08571.03781.006720011.35321.31351.26431.28701.08571.03781.0067
20021.59001.30861.41311.17911.09650.989120021.59001.30861.41311.17911.09650.9891
20031.76101.78611.33741.08961.003220031.76101.78611.33741.08961.0032
20042.36421.46511.21810.980220042.36421.46511.21810.9802
20051.65421.48301.004520051.65421.48301.0045
20061.72851.043220061.72851.0432
20071.629220071.6292