diff --git a/chainladder/development/barnzehn.py b/chainladder/development/barnzehn.py index 783b3645..35a42b39 100644 --- a/chainladder/development/barnzehn.py +++ b/chainladder/development/barnzehn.py @@ -9,10 +9,7 @@ from chainladder.development.glm import TweedieGLM from sklearn.linear_model import LinearRegression from sklearn.pipeline import Pipeline -import warnings -from chainladder.utils.utility_functions import PatsyFormula -from patsy import ModelDesc - +from chainladder.utils.utility_functions import PatsyFormula, PTF_formula class BarnettZehnwirth(TweedieGLM): """ This estimator enables modeling from the Probabilistic Trend Family as @@ -31,23 +28,38 @@ class BarnettZehnwirth(TweedieGLM): response: str Column name for the reponse variable of the GLM. If ommitted, then the first column of the Triangle will be used. - + alpha: list of int + List of origin periods denoting the first indices of each group + gamma: list of int + iota: list of int """ - def __init__(self, drop=None,drop_valuation=None,formula='C(origin) + development', response=None): + def __init__(self, drop=None,drop_valuation=None,formula=None, response=None, alpha=None, gamma=None, iota=None): self.drop = drop self.drop_valuation = drop_valuation - self.formula = formula - self.response = response + self.response = response + if formula and (alpha or gamma or iota): + raise ValueError("Model can only be specified by either a formula or some combination of alpha, gamma and iota.") + if not (formula or alpha or gamma or iota): + raise ValueError("Model must be specified, either a formula or some combination of alpha, gamma and iota.") + for Greek in [alpha,gamma,iota]: + if Greek: + if not ( (type(Greek) is list) and all(type(bound) is int for bound in Greek) ): + raise ValueError("Alpha, gamma and iota must be given as lists of integers, specifying periods.") + self.formula = formula + self.alpha = alpha + self.gamma = gamma + self.iota = iota + def fit(self, X, y=None, sample_weight=None): if max(X.shape[:2]) > 1: raise ValueError("Only single index/column triangles are supported") tri = X.cum_to_incr().log() response = X.columns[0] if not self.response else self.response self.model_ = DevelopmentML(Pipeline(steps=[ - ('design_matrix', PatsyFormula(self.formula)), + ('design_matrix', PatsyFormula(self.formula,self.alpha,self.gamma,self.iota,dgrain=min(tri.development))), ('model', LinearRegression(fit_intercept=False))]), y_ml=response, fit_incrementals=True, drop=self.drop, drop_valuation = self.drop_valuation, weighted_step = 'model').fit(X = tri, sample_weight = sample_weight) resid = tri - self.model_.triangle_ml_[ diff --git a/chainladder/development/tests/test_barnzehn.py b/chainladder/development/tests/test_barnzehn.py index a1c4bba8..fa32b506 100644 --- a/chainladder/development/tests/test_barnzehn.py +++ b/chainladder/development/tests/test_barnzehn.py @@ -1,7 +1,6 @@ import numpy as np import chainladder as cl import pytest -from chainladder.utils.utility_functions import PTF_formula abc = cl.load_sample('abc') def test_basic_bz(): @@ -40,13 +39,11 @@ def test_bz_2008(): exposure=np.array([[2.2], [2.4], [2.2], [2.0], [1.9], [1.6], [1.6], [1.8], [2.2], [2.5], [2.6]]) abc_adj = abc/exposure - origin_buckets = [(0,1),(2,2),(3,4),(5,10)] - dev_buckets = [(24,36),(36,48),(48,84),(84,108),(108,144)] - val_buckets = [(1,8),(8,9),(9,12)] - - abc_formula = PTF_formula(abc_adj,alpha=origin_buckets,gamma=dev_buckets,iota=val_buckets) - - model=cl.BarnettZehnwirth(formula=abc_formula, drop=('1982',72)).fit(abc_adj) + origin_buckets = [0,2,3,5] + dev_buckets = [0,1,2,5,7,10] + val_buckets = [0,7,8,11] + + model=cl.BarnettZehnwirth(drop=('1982',72),alpha=origin_buckets,gamma=dev_buckets,iota=val_buckets).fit(abc_adj) assert np.all( np.around(model.coef_.values,4).flatten() == np.array([11.1579,0.1989,0.0703,0.0919,0.1871,-0.3771,-0.4465,-0.3727,-0.3154,0.0432,0.0858,0.1464]) diff --git a/chainladder/utils/utility_functions.py b/chainladder/utils/utility_functions.py index fea2a552..4f99b615 100644 --- a/chainladder/utils/utility_functions.py +++ b/chainladder/utils/utility_functions.py @@ -663,8 +663,11 @@ class PatsyFormula(BaseEstimator, TransformerMixin): """ - def __init__(self, formula=None): - self.formula = formula + def __init__(self, formula=None, alpha: list = None, gamma: list = None, iota: list = None, dgrain: int = 12): + if(formula): + self.formula = formula + else: + self.formula = PTF_formula(alpha,gamma,iota,dgrain) def _check_X(self, X): from chainladder.core import Triangle @@ -769,12 +772,12 @@ def model_diagnostics(model, name=None, groupby=None): return concat(triangles, 0) -def PTF_formula(tri: Triangle, alpha: ArrayLike = None, gamma: ArrayLike = None, iota: ArrayLike = None): +def PTF_formula(alpha: list = None, gamma: list = None, iota: list = None, dgrain: int = 12): """ Helper formula that builds a patsy formula string for the BarnettZehnwirth estimator. Each axis's parameters can be grouped together. Groups of origin - parameters (alpha) are set equal, and are specified by a ranges (inclusive). + parameters (alpha) are set equal, and are specified by the first period in each bin. Groups of development (gamma) and valuation (iota) parameters are fit to - separate linear trends, specified as tuples denoting ranges with shared endpoints. + separate linear trends, specified a list denoting the endpoints of the linear pieces. In other words, development and valuation trends are fit to a piecewise linear model. A triangle must be supplied to provide some critical information. """ @@ -782,14 +785,17 @@ def PTF_formula(tri: Triangle, alpha: ArrayLike = None, gamma: ArrayLike = None, if(alpha): # The intercept term takes the place of the first alpha for ind,a in enumerate(alpha): - if(a[0]==0): + if(a==0): alpha=alpha[:ind]+alpha[(ind+1):] - formula_parts += ['+'.join([f'I({x[0]} <= origin)' for x in alpha])] - if(gamma): - dgrain = min(tri.development) - formula_parts += ['+'.join([f'I((np.minimum({x[1]-dgrain},development) - np.minimum({x[0]-dgrain},development))/{dgrain})' for x in gamma])] + formula_parts += ['+'.join([f'I({x} <= origin)' for x in alpha])] + if(gamma): + # preprocess gamma to align with grain + graingamma = [(i+1)*dgrain for i in gamma] + for ind in range(1,len(graingamma)): + formula_parts += ['+'.join([f'I((np.minimum({graingamma[ind]},development) - np.minimum({graingamma[ind-1]},development))/{dgrain})'])] if(iota): - formula_parts += ['+'.join([f'I(np.minimum({x[1]-1},valuation) - np.minimum({x[0]-1},valuation))' for x in iota])] + for ind in range(1,len(iota)): + formula_parts += ['+'.join([f'I(np.minimum({iota[ind]},valuation) - np.minimum({iota[ind-1]},valuation))'])] if(formula_parts): return '+'.join(formula_parts) return '' diff --git a/docs/images/plot_ptf_coefficients.png b/docs/images/plot_ptf_coefficients.png new file mode 100644 index 00000000..b2f81afc Binary files /dev/null and b/docs/images/plot_ptf_coefficients.png differ diff --git a/docs/library/references.bib b/docs/library/references.bib index bef8e40e..aa718393 100644 --- a/docs/library/references.bib +++ b/docs/library/references.bib @@ -87,3 +87,11 @@ @article{shapland2016 year = {2016}, url = {https://live-casact.pantheonsite.io/sites/default/files/2021-02/04-shapland.pdf} } + +@article{barnett2008, + author = {Barnett, G. and Zehnwirth, B.}, + title = {Modeling with the {M}ultivariate {P}robabilistic {T}rend {F}amily}, + journal = {Casualty Actuarial Society E-Forum}, + year = {2008}, + url = {https://www.casact.org/sites/default/files/database/forum_08fforum_3barnett_zehnwirth.pdf} +} \ No newline at end of file diff --git a/docs/user_guide/development.ipynb b/docs/user_guide/development.ipynb index eb059880..4c45d5ba 100644 --- a/docs/user_guide/development.ipynb +++ b/docs/user_guide/development.ipynb @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 1, "id": "mysterious-translation", "metadata": {}, "outputs": [], @@ -161,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "id": "6788e883-acdc-4436-ae4a-fca113442feb", "metadata": {}, "outputs": [ @@ -171,7 +171,7 @@ "chainladder.core.triangle.Triangle" ] }, - "execution_count": 13, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -202,17 +202,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "id": "naughty-survivor", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "True" + "np.True_" ] }, - "execution_count": 14, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -240,17 +240,669 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "id": "timely-illinois", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
Development(average='simple')
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" + ], "text/plain": [ "Development(average='simple')" ] }, - "execution_count": 15, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -273,7 +925,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "id": "female-function", "metadata": {}, "outputs": [ @@ -283,7 +935,7 @@ "9" ] }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -294,18 +946,671 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "id": "meaningful-aviation", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
Development(average=['volume', 'simple', 'simple', 'simple', 'simple', 'simple',\n",
+       "                     'simple', 'simple', 'simple'])
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" + ], "text/plain": [ "Development(average=['volume', 'simple', 'simple', 'simple', 'simple', 'simple',\n", " 'simple', 'simple', 'simple'])" ] }, - "execution_count": 17, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -341,84 +1646,2694 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "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)" - ] + "text/html": [ + "
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])
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" + ], + "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": 7, + "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": 21, + "execution_count": 8, + "id": "alive-cooperative", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Development(drop_valuation='1985')
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" + ], + "text/plain": [ + "Development(drop_valuation='1985')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop_valuation='1985').fit(raa)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "restricted-herald", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Development(drop=[('1985', 12), ('1987', 24)])
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" + ], + "text/plain": [ + "Development(drop=[('1985', 12), ('1987', 24)])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cl.Development(drop=[('1985', 12), ('1987', 24)]).fit(raa)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "democratic-wheel", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
Development(drop=('1985', 12), drop_valuation='1988')
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" + ], "text/plain": [ "Development(drop=('1985', 12), drop_valuation='1988')" ] }, - "execution_count": 21, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -464,7 +4379,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 11, "id": "045dc473-dd73-4b57-b74d-de857c3bd256", "metadata": {}, "outputs": [ @@ -611,7 +4526,7 @@ "1989 -0.428176 NaN NaN NaN NaN NaN NaN NaN NaN" ] }, - "execution_count": 35, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -634,7 +4549,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 12, "id": "c479daf1-9209-4d0e-984e-c9ee3ceca7f4", "metadata": { "tags": [ @@ -644,2934 +4559,17 @@ "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", 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", 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19811.64981.31901.08231.14691.19511.11301.03331.0092
19821.25931.97661.29211.13180.99341.0331
19832.63701.54281.16351.16071.18571.0264
19842.04331.36441.34891.10151.0377
19858.75921.65561.39991.0087
19864.25971.81571.2255
19877.21721.1250
19881.8874
19891.7220
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Triangle Summary
Valuation:1997-12
Grain:OYDY
Shape:(132, 1, 10, 10)
Index:[GRNAME, LOB]
Columns:[CumPaidLoss]
" + ], + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (132, 1, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [CumPaidLoss]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "clrd = cl.load_sample('clrd')\n", "clrd = clrd[clrd['LOB']=='wkcomp']['CumPaidLoss']\n", @@ -3694,7 +4977,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 16, "id": "e9c272a2-7c88-434f-aa19-9b5b3ab9636b", "metadata": {}, "outputs": [ @@ -3704,7 +4987,7 @@ "((775, 1, 10, 10), (6, 1, 1, 9))" ] }, - "execution_count": 39, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -3733,7 +5016,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 17, "id": "fd102233-1e8c-4fcf-a534-b842d1922743", "metadata": {}, "outputs": [ @@ -3743,7 +5026,7 @@ "((775, 1, 10, 10), (6, 1, 1, 9))" ] }, - "execution_count": 44, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -3784,10 +5067,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "personalized-explorer", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\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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CumPaidLoss
LOB
comauto0.928924
medmal1.569652
othliab1.330076
ppauto0.831529
prodliab1.456180
wkcomp0.898278
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" + ], + "text/plain": [ + " CumPaidLoss\n", + "LOB \n", + "comauto 0.928924\n", + "medmal 1.569652\n", + "othliab 1.330076\n", + "ppauto 0.831529\n", + "prodliab 1.456180\n", + "wkcomp 0.898278" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "clrd = cl.load_sample('clrd').groupby('LOB').sum()\n", "dev = cl.ClarkLDF(growth='weibull').fit(clrd['CumPaidLoss'])\n", @@ -4132,10 +5885,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "congressional-virtue", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "LOB\n", + "comauto 1.270910\n", + "medmal 1.707217\n", + "othliab 1.619190\n", + "ppauto 1.118796\n", + "prodliab 2.126124\n", + "wkcomp 1.311590\n", + "dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "1/dev.G_(37.5).to_frame()" ] @@ -4152,10 +5923,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "clean-massage", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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CumPaidLoss
LOB
comauto0.680323
medmal0.701447
othliab0.623803
ppauto0.825928
prodliab0.671059
wkcomp0.697935
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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) 0.842814 0.709981 0.70835 0.696826 0.637589 0.622016 0.553385 0.437373 0.524347" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tri = cl.load_sample('usauto')\n", "model = cl.CaseOutstanding(paid_to_incurred=('paid', 'incurred')).fit(tri)\n", @@ -4269,7 +6157,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 29, "id": "listed-advocate", "metadata": {}, "outputs": [ @@ -4429,7 +6317,7 @@ "2007 1.669287 1.180366 1.085961 1.041239 1.017758 1.008747 1.004248 1.002112 1.001702" ] }, - "execution_count": 24, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -4456,7 +6344,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 30, "id": "sized-adoption", "metadata": {}, "outputs": [ @@ -4616,7 +6504,7 @@ "2007 NaN NaN NaN NaN NaN NaN NaN NaN NaN" ] }, - "execution_count": 25, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -4627,7 +6515,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 31, "id": "meaningful-stage", "metadata": {}, "outputs": [ @@ -4670,7 +6558,7 @@ "(All) 0.534019 0.56385 0.529593 0.490031 0.513853 0.55064 0.631726 0.673788 0.579812" ] }, - "execution_count": 26, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -4715,7 +6603,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 32, "id": "bulgarian-bookmark", "metadata": {}, "outputs": [ @@ -4838,7 +6726,7 @@ "4 40.0 410.0 359.0 51.0 " ] }, - "execution_count": 27, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -4877,7 +6765,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 33, "id": "broad-stephen", "metadata": {}, "outputs": [ @@ -4929,7 +6817,7 @@ "origin 0.033863" ] }, - "execution_count": 28, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -4957,7 +6845,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 34, "id": "signed-realtor", "metadata": {}, "outputs": [], @@ -4987,7 +6875,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 35, "id": "improved-corps", "metadata": {}, "outputs": [ @@ -5018,31 +6906,31 @@ " \n", " \n", " Intercept\n", - " 12.549945\n", + " 12.549956\n", " \n", " \n", " LOB[T.ppauto]\n", - " 3.202703\n", + " 3.202689\n", " \n", " \n", " LOB[comauto]:C(np.minimum(development, 36))[T.24]\n", - " 0.578694\n", + " 0.578649\n", " \n", " \n", " LOB[ppauto]:C(np.minimum(development, 36))[T.24]\n", - " 0.449832\n", + " 0.449841\n", " \n", " \n", " LOB[comauto]:C(np.minimum(development, 36))[T.36]\n", - " 0.790516\n", + " 0.790407\n", " \n", " \n", " LOB[ppauto]:C(np.minimum(development, 36))[T.36]\n", - " 0.321206\n", + " 0.321221\n", " \n", " \n", " LOB[comauto]:development\n", - " -0.044627\n", + " -0.044625\n", " \n", " \n", " LOB[ppauto]:development\n", @@ -5050,7 +6938,7 @@ " \n", " \n", " LOB[comauto]:origin\n", - " 0.054581\n", + " 0.054577\n", " \n", " \n", " LOB[ppauto]:origin\n", @@ -5062,19 +6950,19 @@ ], "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", + "Intercept 12.549956\n", + "LOB[T.ppauto] 3.202689\n", + "LOB[comauto]:C(np.minimum(development, 36))[T.24] 0.578649\n", + "LOB[ppauto]:C(np.minimum(development, 36))[T.24] 0.449841\n", + "LOB[comauto]:C(np.minimum(development, 36))[T.36] 0.790407\n", + "LOB[ppauto]:C(np.minimum(development, 36))[T.36] 0.321221\n", + "LOB[comauto]:development -0.044625\n", "LOB[ppauto]:development -0.054814\n", - "LOB[comauto]:origin 0.054581\n", + "LOB[comauto]:origin 0.054577\n", "LOB[ppauto]:origin 0.057790" ] }, - "execution_count": 30, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -5103,7 +6991,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 36, "id": "secure-committee", "metadata": {}, "outputs": [ @@ -5192,7 +7080,7 @@ "ppauto 0.001 " ] }, - "execution_count": 31, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -5272,7 +7160,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 37, "id": "middle-machinery", "metadata": {}, "outputs": [ @@ -5319,7 +7207,7 @@ "Columns: [CumPaidLoss]" ] }, - "execution_count": 32, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -5376,7 +7264,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 38, "id": "civil-state", "metadata": {}, "outputs": [ @@ -5401,8 +7289,8 @@ " \n", " \n", " (All)\n", - " 2.6500\n", - " 1.4100\n", + " 2.7000\n", + " 1.4200\n", " 1.1900\n", " 1.1000\n", " 1.0400\n", @@ -5416,10 +7304,10 @@ ], "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" + "(All) 2.7 1.42 1.19 1.1 1.04 1.02 1.01 1.01 1.01" ] }, - "execution_count": 33, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -5476,22 +7364,29 @@ "\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", + "normally distributed with fixed variance, which implies the incrementals \n", + "themselves are log-normal distributed.\n", "\n", "The framework includes coefficients for origin periods (alpha), development periods (gamma)\n", - "and calendar period (iota).\n", + "and calendar period (iota). Note that chainladder uses a formulation that is different from\n", + "but equivalent to the authors' formulation. Here, the first alpha denotes a baseline origin \n", + "trend (corresponding to the top-left cell). Subsequent alphas are incremental, in the same \n", + "way gammas and iotas are.\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", + "$y(i, j) = \\alpha _{0} + \\sum_{k=1}^i \\alpha_k + \\sum_{m=1}^{j}\\gamma _{m}+ \\sum_{t =1}^{i+j}\\iota _{t}+ \\varepsilon _{i,j}$\n", "\n", - "These coefficients can be categorical or continuous, and to support a wide range of\n", - "model forms, patsy formulas are used.\n" + "$\\varepsilon_{i,j} \\sim \\mathcal{N}(0,\\sigma^2)$\n", + " \n", + "To support a wider range of model forms, patsy formulas are used. The PTF framework is \n", + "particularly useful when there is calendar period inflation influences on loss development,\n", + "which other models might not account for. To illustrate this, a simple model is fit below \n", + "with all alphas and gammas independent, and no iotas.\n" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 39, "id": "innovative-attachment", "metadata": {}, "outputs": [ @@ -5591,7 +7486,7 @@ "[1 rows x 21 columns]" ] }, - "execution_count": 34, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -5599,13 +7494,77 @@ "source": [ "abc = cl.load_sample('abc')\n", "\n", - "# Discrete origin, development, valuation\n", + "# Discrete origin and development, means are equivalent to those of an ODP model\n", "cl.BarnettZehnwirth(formula='C(origin)+C(development)').fit(abc).coef_.T" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "pending-breakdown", + "metadata": {}, + "source": [ + "\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": "markdown", + "id": "9755dc92-1ca7-48a7-8508-eec7330a1253", + "metadata": {}, + "source": [ + "The general form of the PTF family includes a great number of parameters. The number of parameters \n", + "should be reduced, where reasonable, to improve parameter estimates. Origin coefficients can be set to \n", + "0, corresponding to periods of unchanging origin levels. Adjacent development and valuation coefficients\n", + "can be set equal, creating periods of constant linear trend. In other words, development and valuation\n", + "parameters will have a piecewise linear relationship. One such model from {cite}`barnett2008`(pp. 48-49) \n", + "is described below by three graphs of its parameters." + ] + }, + { + "cell_type": "markdown", + "id": "845565e7-a8af-4a0e-acec-5ff9c75d6151", + "metadata": {}, + "source": [ + "```{eval-rst}\n", + ".. image:: ../images/plot_ptf_coefficients.png\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "1feda665-060d-4756-96d3-1645bd106a5f", + "metadata": {}, + "source": [ + "Grouping parameters like this requires complex patsy formulas, so BarnettZehnwirth can be passed \n", + "lists specifying alpha, gamma and iota in lieu of a formula.\n", + "Alpha is passed as a list of periods (indexed from 0) denoting the begnning of each origin group.\n", + "Gamma and iota are passed as lists denoting the endpoints of linear segments. This leads\n", + "Fitting the model described above is easy now:" + ] + }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 43, "id": "nominated-zimbabwe", "metadata": {}, "outputs": [ @@ -5630,81 +7589,104 @@ " \n", " \n", " \n", - " Intercept\n", - " origin\n", - " development\n", - " valuation\n", + " coef_\n", " \n", " \n", " \n", " \n", - " coef_\n", - " 8.359157\n", - " 4.215981\n", - " 0.319288\n", - " -4.116569\n", + " Intercept\n", + " 11.1579\n", + " \n", + " \n", + " I(2 <= origin)[T.True]\n", + " 0.1989\n", + " \n", + " \n", + " I(3 <= origin)[T.True]\n", + " 0.0703\n", + " \n", + " \n", + " I(5 <= origin)[T.True]\n", + " 0.0919\n", + " \n", + " \n", + " I((np.minimum(24, development) - np.minimum(12, development)) / 12)\n", + " 0.1871\n", + " \n", + " \n", + " I((np.minimum(36, development) - np.minimum(24, development)) / 12)\n", + " -0.3771\n", + " \n", + " \n", + " I((np.minimum(72, development) - np.minimum(36, development)) / 12)\n", + " -0.4465\n", + " \n", + " \n", + " I((np.minimum(96, development) - np.minimum(72, development)) / 12)\n", + " -0.3727\n", + " \n", + " \n", + " I((np.minimum(132, development) - np.minimum(96, development)) / 12)\n", + " -0.3154\n", + " \n", + " \n", + " I(np.minimum(7, valuation) - np.minimum(0, valuation))\n", + " 0.0432\n", + " \n", + " \n", + " I(np.minimum(8, valuation) - np.minimum(7, valuation))\n", + " 0.0858\n", + " \n", + " \n", + " I(np.minimum(11, valuation) - np.minimum(8, valuation))\n", + " 0.1464\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Intercept origin development valuation\n", - "coef_ 8.359157 4.215981 0.319288 -4.116569" + " coef_\n", + "Intercept 11.1579\n", + "I(2 <= origin)[T.True] 0.1989\n", + "I(3 <= origin)[T.True] 0.0703\n", + "I(5 <= origin)[T.True] 0.0919\n", + "I((np.minimum(24, development) - np.minimum(12,... 0.1871\n", + "I((np.minimum(36, development) - np.minimum(24,... -0.3771\n", + "I((np.minimum(72, development) - np.minimum(36,... -0.4465\n", + "I((np.minimum(96, development) - np.minimum(72,... -0.3727\n", + "I((np.minimum(132, development) - np.minimum(96... -0.3154\n", + "I(np.minimum(7, valuation) - np.minimum(0, valu... 0.0432\n", + "I(np.minimum(8, valuation) - np.minimum(7, valu... 0.0858\n", + "I(np.minimum(11, valuation) - np.minimum(8, val... 0.1464" ] }, - "execution_count": 35, + "execution_count": 43, "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`" + "# A reasonable model. Incrementals are adjusted for exposure (see Barnett and Zehnwirth 2000, p. 280) \n", + "# and one cell is dropped \n", + "import numpy as np\n", + "from chainladder.utils.utility_functions import PTF_formula\n", + "abc = cl.load_sample('abc')\n", + "exposure=np.array([[2.2], [2.4], [2.2], [2.0], [1.9], [1.6], [1.6], [1.8], [2.2], [2.5], [2.6]])\n", + "abc_adj = abc/exposure\n", + "\n", + "origin_buckets = [0,2,3,5]\n", + "dev_buckets = [0,1,2,5,7,10]\n", + "val_buckets = [0,7,8,11]\n", + " \n", + "model=cl.BarnettZehnwirth(drop=('1982',72),alpha=origin_buckets,gamma=dev_buckets,iota=val_buckets).fit(abc_adj)\n", + "model.coef_.round(4)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "blank-enemy", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.7.11 ('cl_dev')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -5718,7 +7700,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.11" + "version": "3.12.0" }, "toc-showtags": true, "vscode": {