From 1f6b4e3531b7c180457fea239c0b47c54dc30441 Mon Sep 17 00:00:00 2001 From: John S Bogaardt Date: Sun, 1 Mar 2020 13:29:30 -0700 Subject: [PATCH] streamlined stochastic BF/Benktander methods --- chainladder/development/bootstrap.py | 5 +- chainladder/methods/benktander.py | 25 +- chainladder/methods/bornferg.py | 13 +- chainladder/methods/tests/test_predict.py | 6 + docs/auto_examples/auto_examples_jupyter.zip | Bin 40484 -> 39991 bytes docs/auto_examples/auto_examples_python.zip | Bin 23786 -> 23294 bytes .../sphx_glr_plot_stochastic_bornferg_001.png | Bin 104408 -> 106018 bytes .../sphx_glr_plot_value_at_risk_001.png | Bin 19946 -> 20350 bytes ...phx_glr_plot_stochastic_bornferg_thumb.png | Bin 50380 -> 50290 bytes .../sphx_glr_plot_value_at_risk_thumb.png | Bin 13362 -> 13406 bytes docs/auto_examples/index.rst | 40 +-- docs/auto_examples/plot_exhibits.rst | 2 +- .../plot_exhibits_codeobj.pickle | Bin 1005 -> 1005 bytes docs/auto_examples/plot_exposure_triangle.rst | 4 +- .../plot_exposure_triangle_codeobj.pickle | Bin 629 -> 629 bytes docs/auto_examples/plot_ibnr_runoff.rst | 2 +- .../plot_ibnr_runoff_codeobj.pickle | Bin 938 -> 938 bytes .../plot_industry_to_company.rst | 2 +- .../plot_industry_to_company_codeobj.pickle | Bin 801 -> 801 bytes docs/auto_examples/plot_mack.rst | 2 +- docs/auto_examples/plot_mack_codeobj.pickle | Bin 1177 -> 1177 bytes docs/auto_examples/plot_munich.rst | 2 +- docs/auto_examples/plot_munich_codeobj.pickle | Bin 2367 -> 2343 bytes .../plot_stochastic_bornferg.ipynb | 4 +- .../auto_examples/plot_stochastic_bornferg.py | 18 +- .../plot_stochastic_bornferg.py.md5 | 2 +- .../plot_stochastic_bornferg.rst | 24 +- .../plot_stochastic_bornferg_codeobj.pickle | Bin 2383 -> 1836 bytes .../plot_triangle_from_pandas.rst | 18 +- .../plot_triangle_from_pandas_codeobj.pickle | Bin 642 -> 642 bytes docs/auto_examples/plot_triangle_slicing.rst | 2 +- .../plot_triangle_slicing_codeobj.pickle | Bin 614 -> 614 bytes docs/auto_examples/plot_value_at_risk.rst | 4 +- .../plot_value_at_risk_codeobj.pickle | Bin 786 -> 786 bytes docs/auto_examples/searchindex.bak | 4 +- docs/auto_examples/searchindex.dat | Bin 55229 -> 66534 bytes docs/auto_examples/searchindex.dir | 4 +- docs/auto_examples/sg_execution_times.rst | 36 +-- .../chainladder.BootstrapODPSample.examples | 8 +- .../chainladder.Chainladder.examples | 72 ----- .../chainladder.Development.examples | 44 ++- ...hainladder.Pipeline.score_samples.examples | 0 .../generated/chainladder.Triangle.examples | 152 +++++++++- .../chainladder.Triangle.groupby.examples | 36 --- .../chainladder.Triangle.origin.examples | 18 -- .../chainladder.Triangle.rename.examples | 18 -- .../chainladder.Triangle.sum.examples | 18 -- ...der.core.triangle.Triangle.T.plot.examples | 8 +- ...core.triangle.Triangle.origin.max.examples | 18 -- .../chainladder.load_dataset.examples | 134 +++++++- ...chainladder.Chainladder.ultimate_.examples | 22 ++ docs/tutorials/stochastic-tutorial.ipynb | 287 ++++-------------- examples/plot_stochastic_bornferg.py | 18 +- 53 files changed, 519 insertions(+), 553 deletions(-) create mode 100644 docs/modules/generated/chainladder.Pipeline.score_samples.examples create mode 100644 docs/modules/generated/chainladder.methods.chainladder.Chainladder.ultimate_.examples diff --git a/chainladder/development/bootstrap.py b/chainladder/development/bootstrap.py index 3868b0d4..a4541d90 100644 --- a/chainladder/development/bootstrap.py +++ b/chainladder/development/bootstrap.py @@ -128,7 +128,6 @@ def _get_simulation(self, X, exp_incr_triangle): adj_resid_dist = adj_resid_dist[adj_resid_dist != 0] adj_resid_dist = adj_resid_dist - xp.mean(adj_resid_dist) random_state = xp.random.RandomState(self.random_state) - self.random_state = random_state resampled_residual = [xp.expand_dims(random_state.choice(adj_resid_dist, size=exp_incr_triangle.shape, replace=True)*(exp_incr_triangle*0+1), 0) @@ -208,8 +207,10 @@ def transform(self, X): def _get_process_variance(self, full_triangle): #if self.process_dist == 'od poisson': # process_triangle = np.nan_to_num(np.array([random_state.poisson(lam=abs(item))*np.sign(np.nan_to_num(item))for item in sim_exp_incr_triangle])) + xp = cp.get_array_module(full_triangle.values) lower_tri = full_triangle.cum_to_incr() - self.cum_to_incr() - lower_tri.values = self.random_state.gamma( + random_state = xp.random.RandomState(None if not self.random_state else self.random_state + 1) + lower_tri.values = random_state.gamma( shape=abs(lower_tri.values) / self.scale_, scale=self.scale_) * \ np.sign(np.nan_to_num(lower_tri.values)) return (lower_tri + self.cum_to_incr()).incr_to_cum() diff --git a/chainladder/methods/benktander.py b/chainladder/methods/benktander.py index 9e43f78d..a89bd17d 100644 --- a/chainladder/methods/benktander.py +++ b/chainladder/methods/benktander.py @@ -22,6 +22,15 @@ class Benktander(MethodBase): Multiplier for the sample_weight used in the Bornhuetter Ferguson method. If sample_weight is already an apriori measure of ultimate, then use 1.0 + apriori_sigma : float, optional (default=0.0) + Standard deviation of the apriori. When used in conjunction with the + bootstrap model, the model samples aprioris from a lognormal distribution + using this argument as a standard deviation. + random_state : int, RandomState instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by np.random. Attributes ---------- @@ -35,9 +44,11 @@ class Benktander(MethodBase): .. [2] Benktander, G. (1976) An Approach to Credibility in Calculating IBNR for Casualty Excess Reinsurance. In The Actuarial Review, April 1976, p.7 """ - def __init__(self, apriori=1.0, n_iters=1): + def __init__(self, apriori=1.0, n_iters=1, apriori_sigma=0, random_state=None): self.apriori = apriori self.n_iters = n_iters + self.apriori_sigma = apriori_sigma + self.random_state = random_state def fit(self, X, y=None, sample_weight=None): """Applies the Benktander technique to triangle **X** @@ -60,6 +71,9 @@ def fit(self, X, y=None, sample_weight=None): if sample_weight is None: raise ValueError('sample_weight is required.') super().fit(X, y, sample_weight) + if hasattr(X, '_get_process_variance'): + if X.shape[0] != sample_weight.shape[0]: + sample_weight = sample_weight.broadcast_axis('index', X.index) obj = copy.deepcopy(self) self.sample_weight_ = sample_weight self.ultimate_ = self._get_ultimate_(X, sample_weight, obj) @@ -71,7 +85,14 @@ def _get_ultimate_(self, X, sample_weight, obj): xp = cp.get_array_module(ult.values) origin, development = -2, -1 # Set axes by name latest = X.latest_diagonal.values - apriori = sample_weight.values*self.apriori + if self.apriori_sigma != 0: + random_state = xp.random.RandomState(self.random_state) + apriori = random_state.normal( + self.apriori, self.apriori_sigma, X.shape[0]) + apriori = apriori.reshape(X.shape[0],-1)[..., np.newaxis, np.newaxis] + apriori = sample_weight.values * apriori + else: + apriori = sample_weight.values*self.apriori ult.values = \ obj.cdf_.values[..., :ult.shape[development]]*(ult.values*0+1) cdf = ult.latest_diagonal.values diff --git a/chainladder/methods/bornferg.py b/chainladder/methods/bornferg.py index 1a7f0b02..33b51a50 100644 --- a/chainladder/methods/bornferg.py +++ b/chainladder/methods/bornferg.py @@ -13,6 +13,15 @@ class BornhuetterFerguson(Benktander): Multiplier for the sample_weight used in the Bornhuetter Ferguson method. If sample_weight is already an apriori measure of ultimate, then use 1.0 + apriori_sigma : float, optional (default=0.0) + Standard deviation of the apriori. When used in conjunction with the + bootstrap model, the model samples aprioris from a lognormal distribution + using this argument as a standard deviation. + random_state : int, RandomState instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by np.random. Attributes ---------- @@ -47,8 +56,10 @@ class BornhuetterFerguson(Benktander): .. [1] Bornhuetter, R. and Ferguson, R. (1972) The Actuary and IBNR. In Proceedings of the Casualty Actuarial Society, Vol. LIX, 181 - 195 """ - def __init__(self, apriori=1.0): + def __init__(self, apriori=1.0, apriori_sigma=0.0, random_state=None): self.apriori = apriori + self.apriori_sigma = apriori_sigma + self.random_state = random_state def fit(self, X, y=None, sample_weight=None): """Applies the Bornhuetter-Ferguson technique to triangle **X** diff --git a/chainladder/methods/tests/test_predict.py b/chainladder/methods/tests/test_predict.py index 6bcbf988..eec73c5b 100644 --- a/chainladder/methods/tests/test_predict.py +++ b/chainladder/methods/tests/test_predict.py @@ -19,3 +19,9 @@ def test_bf_predict(): def test_mack_predict(): mack = cl.MackChainladder().fit(raa_1989) mack.predict(raa) + +def test_bs_random_state_predict(): + tri = cl.load_dataset('clrd').groupby('LOB').sum().loc['wkcomp', ['CumPaidLoss', 'EarnedPremNet']] + X = cl.BootstrapODPSample(random_state=100).fit_transform(tri['CumPaidLoss']) + bf = cl.BornhuetterFerguson(apriori=0.6, apriori_sigma=0.1, random_state=42).fit(X, sample_weight=tri['EarnedPremNet'].latest_diagonal) + assert bf.predict(X, sample_weight=tri['EarnedPremNet'].latest_diagonal).ibnr_ == bf.ibnr_ diff --git a/docs/auto_examples/auto_examples_jupyter.zip b/docs/auto_examples/auto_examples_jupyter.zip index 79d8d71c0f86d6099d11b75187e06b7443f63e19..94795a6bf66944963ce0f1cc1bb08f4210f474b3 100644 GIT binary patch delta 316 zcmZ3ohiUr`rVZ{6%+cA2o4p)rd6|QNj8nei9AL(i(Ch5X0YH&`vAfy8A~h-5?95R> zkyqJwIQb&869X=XzO75-WMD|-+4X69g^(ZP*7Ihg~24F2wi zY+%Oci5Z+ACr*x=Z3lA3s4rKUb z$GOr>k@b_~=IVk>p4z18-VlIyYP$=NGPXN0sI|Bm KVk?;r5(EGzpKtB} delta 743 zcmZ8fO=uHA7)`U77@}SjDyaCWm!zaaL(zk1h!=}OO7RB|revq?bhC7KcDu7{8l%LV zv?xWHQxX*gJzKk=t%?^kp=JeDV3rfwI3NbtE@On1sbDG|92d2fkbiCznrC}o z0rq^$^(`i<$dW3$OnYp_359M99XULpm}NRxuqZ$Fmt^8jJ>+9*xDKMO?_fJK@W}O3 zDoUIKste?>Y_D7I#|o2->e|1L^mPpRgjKOeGE@n3qxtMbgphFZ5G6IdV6lJkbe2E7 zqVwGiv_%>6QsXASp4b(AY%FQoOEH4k=2}X7CcwePZwYNhfW)0@N{)%ow+nJYe5F01 z-oM)?<`rz7h?bb diff --git a/docs/auto_examples/auto_examples_python.zip b/docs/auto_examples/auto_examples_python.zip index 6aa05c651c608be6fed2f7f25e5a2dae388c5e2d..3bd864719107b6967965571dc8d800d1fadbe007 100644 GIT binary patch delta 315 zcmaF0lkwkH#tluX%u(5in_E>caxw=28Ik&iEX;vGMz8TRW@djNqs;s#6IiOqY63HJ zBv6Faj*X2kI6E<5MW$!*ELH}FS*)8SUD_BY3%R9l?sb!AoGjrPCug8%W~!r*SWuLi zUz8bNoSB}RXltNnXrQT=mRX`PInhgE@)S>j$qU^1CU5X;WXZ`dE}s0;-EMQ6mkT4< zzO&x1ObdlXvqKo delta 760 zcmZ8f&1(}u9Nl!91|#tz6~r8VQmaXcV?)rQ#eh(oN)cl%Hc6H2XvSn=cXr&_ZOo}e zXb_4bj5iNLK@ddz2v&+0!GFNw!Gnr;>A|ZfFXGIS7;$0X&12sO!~6X@@?$IV;`TWD z`Z(KN8{giAx(bw!Wrxu#1@7i{5PGJ-%G9qR^hN>i)P00LDG<$%jO>dUiFgjf5*=h*^IZA#?6TyE*zV92hycdB!?On? z=f_}ya!UNrf_v1l0tftFD;-X3tInWl`3(@36WGM3HLzVFs7S;WQ{`}4wGM^y%3OZ+ zQtvMy+_PNI5~_*r7)B@JJ+|ruN;ip593N1^syjrnI6L{56za}6_6V!F4rZ?B5IZ&S z$n;aHp*RD~RLBwOUbo)6Dw9;$wSS-J>jd_wXb?_QEEHyrX4A_UW97s#qfK(l693|> zX*pIf<--DwhKBM(;f`#N?(LT5IuW#`Ht_x2wB0N*F%!Y@~qyGq~$3+Bl)O&^1l_yo$@p&_hPGkDETC682TiQ!ME9dH!sgr sCUml7l>%?|t17%8D|WXe4M52n6$$tfVRg0`rGJpvx%8;4eI*^XuRj zqVtPa>L}o!H_E$k@Ep}aR@)f@!8V5fK#Rl*Ex{iJT%@#I)a=b%-WxlaLTrs)9Bk}e zY%JeWxtTgSTiV-Eak6r-axzm{xVSh7va$W&Z(y}|GH2sK|Jw(FP(fZvN~pW1?`6DC z*E2nT5Ucx@QOVPkrsxR^E}*5SdL|w`;#DPHBt{kfg8uExz`(%pEBqJn;`Fdc6hm=2 zJct6nj?yAb@Ny0$yaP~&BL zzneL_OOefVXLhIcKHJ93SkY^gDMLRI0v4U>pFXi77h39?nkL(0`TG>QZN4JN*c2ZS zu=pVok8OgI+_Jcag`b2P2lLId+%EtcJ@;G&F5Lq*|*(APHMR8);k!-mX^dd z9kw=E*n+Pa^a#6W{bz#X083qVYisL*b7{+k4lQnkT}EMXF)I9%7rjph?L^A*oH0qb zQ3^9Xt?FW`7$`iqp3Xb=lJD1=S7{T424p?lZt9+U2WD4zc|tp4et`SXl~3cvY}%_t zFKxZH+N+(jbI>O|Y(B+0XuYxfl_5l^Byz5<#O7S}S-_cYXJ^M@NMO@t*JD`Zl1!t_ z02A_w$9huZVNInk81)JJ`vX1r73TKH7FP0NMLRwab2;=q4YN_TS$x0bGi?$p;_w5B za5+E4fYEbLcj=lSH7)IUIS9Lh+iUp!+Qm;pGv-g@R*?!Ft__d z5j<^dn+_V8DBPC=+~&eCNhcA$&G5I~EF7|Xo>|nFM*aS#xpqEf*sJ7stv2tuTXMBV z`4Aq2Ey_QCIrf^Z`{(nl`I0q09vN~O zF;y)2-qZO5d@cyeo@NbqRx)cP|1Q}~N=mY=8V)?X zIr5>5S+hx6+pn22fG42i2Gj;4Yul|88HgnRao3Xyhm{u-6B7_BXj6&Q_j|v0Ug_2* zM4>8`51Aoj*_YlK1aiq~c8#c@>=(TbS#bSGdL12Zk9)%|F+1Bq#Fy;a;%Q_Eq^8Y= zGch~a+0BQ@(%&z7Y%R5ksTSioHIS9I-Ft%CyIl0bhJW_;`ED48Nl);w&t}GZ_=WNc z3XnmGdmXm$-5-UC;bkaMKK#}+{1@=XM!|{6$<_4)rJhK9rpU?jL%#>oGEn=UN_XV0 zUAiOj-H*Eo9YKw+?0hSeevFt=5D$+m+536NqLLCR>iG6g93~EbH&O`qtBiiY{`FdW z00K^IFNgP)L%Pq!Oy7nRL{JGjoGC&IZ-doi{+?LM*f0vuqE4RmQ;*xTLb2}Brh`a# z;h6ioo3(l8;qKWQ>!=G-0VmoR(CF}*g+s{4pR{fZuA1|H4|h_V!u!?Z5D2)4h5G_; 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.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ave_analysis_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png - :ref:`sphx_glr_auto_examples_plot_ave_analysis.py` + :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py` .. raw:: html @@ -229,17 +229,17 @@ Gallery of Chainladder Functionality .. toctree:: :hidden: - /auto_examples/plot_ave_analysis + /auto_examples/plot_stochastic_bornferg .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ave_analysis_thumb.png - :ref:`sphx_glr_auto_examples_plot_bootstrap.py` + :ref:`sphx_glr_auto_examples_plot_ave_analysis.py` .. raw:: html @@ -249,17 +249,17 @@ Gallery of Chainladder Functionality .. toctree:: :hidden: - /auto_examples/plot_bootstrap + /auto_examples/plot_ave_analysis .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_benktander_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png - :ref:`sphx_glr_auto_examples_plot_benktander.py` + :ref:`sphx_glr_auto_examples_plot_bootstrap.py` .. raw:: html @@ -269,17 +269,17 @@ Gallery of Chainladder Functionality .. toctree:: :hidden: - /auto_examples/plot_benktander + /auto_examples/plot_bootstrap .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_benktander_thumb.png - :ref:`sphx_glr_auto_examples_plot_munich.py` + :ref:`sphx_glr_auto_examples_plot_benktander.py` .. raw:: html @@ -289,17 +289,17 @@ Gallery of Chainladder Functionality .. toctree:: :hidden: - /auto_examples/plot_munich + /auto_examples/plot_benktander .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_thumb.png - :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py` + :ref:`sphx_glr_auto_examples_plot_munich.py` .. raw:: html @@ -309,7 +309,7 @@ Gallery of Chainladder Functionality .. toctree:: :hidden: - /auto_examples/plot_stochastic_bornferg + /auto_examples/plot_munich .. raw:: html diff --git a/docs/auto_examples/plot_exhibits.rst b/docs/auto_examples/plot_exhibits.rst index a5f3fdd5..5f7d99e2 100644 --- a/docs/auto_examples/plot_exhibits.rst +++ b/docs/auto_examples/plot_exhibits.rst @@ -80,7 +80,7 @@ See :ref:`Exhibits` for more detail. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 7.113 seconds) + **Total running time of the script:** ( 0 minutes 3.234 seconds) .. _sphx_glr_download_auto_examples_plot_exhibits.py: diff --git a/docs/auto_examples/plot_exhibits_codeobj.pickle b/docs/auto_examples/plot_exhibits_codeobj.pickle index 08b9b870efe7749280fea4e8d634d41e502cfeed..f59622045aee57ab012fb0d4b6b7bfd1a0d46d4e 100644 GIT binary patch delta 87 zcmV-d0I2`%2ki%tZj*ZhERmozll%cB0Tz=X11td=lTHIF0Unbf0(}7|lOq8z0V|Pg tGyyM@>;g0aGn2FdNRiZ0lU4yD0YQ_W0wMuLlX(F;0ZEg>0x6TH14fg=8T$YL delta 86 zcmV-c0IC1&2ki%tZj<~0GLf1zldJ+!lgj}v0UMM10we(+lS2bK0V9)S0!We0Bmpdw sO96cWHIpC%A^|y*egi21J(Dj2Gyy@8^DF^HlOO>q0ZEZ;E|aGNM(eT~WB>pF diff --git a/docs/auto_examples/plot_exposure_triangle.rst b/docs/auto_examples/plot_exposure_triangle.rst index e468c695..fe4efbf2 100644 --- a/docs/auto_examples/plot_exposure_triangle.rst +++ b/docs/auto_examples/plot_exposure_triangle.rst @@ -29,7 +29,7 @@ optional. This example instantiates a 'premium' triangle as a single vector. .. code-block:: none - + @@ -68,7 +68,7 @@ optional. This example instantiates a 'premium' triangle as a single vector. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 1.169 seconds) + **Total running time of the script:** ( 0 minutes 0.502 seconds) .. _sphx_glr_download_auto_examples_plot_exposure_triangle.py: diff --git a/docs/auto_examples/plot_exposure_triangle_codeobj.pickle b/docs/auto_examples/plot_exposure_triangle_codeobj.pickle index 098cc34849ada65424aade7e7210062c9c48a786..309a9ce3400f5f1e1743560083eebd98032dbc84 100644 GIT binary patch delta 176 zcmey$@|9(RUO0Dhj$TMnW@27?PU@7}DH=U2d5O8HQ+hbyf<0`x`6;D9*&goXjKs{m zoWzur)S@XpJP_gd;*9*Fk|`OSr9E5)MX9-&rMdAbX%pv*Y6=!4=A|SS>m}zGrRt>> yfsD=I>cJv9@qov~NxF;@lh-hMPcCINV^o~%$Y{u@I$49sdonwtC5v8ZsU85O&Ol`V delta 176 zcmey$@|9(Ro*!31QEF~xX>NQ<+LYQU8a*s|iMgp$dN^GYOA_6Rz-+eM{FKrhprBwu zVqQvOv0ie1QL0`VM3M(08DE@{UsN(>;(D>k+>E-6{1Xp*OtxgQV3e4=o6(R_W^xmw z1*5`bOGa-)?&KW3kfO}QymX+EKpQjIdN|ar8C)5flixCW IGnMKA0KuF;jQ{`u diff --git a/docs/auto_examples/plot_ibnr_runoff.rst b/docs/auto_examples/plot_ibnr_runoff.rst index cd3d7bf9..63fa92cc 100644 --- a/docs/auto_examples/plot_ibnr_runoff.rst +++ b/docs/auto_examples/plot_ibnr_runoff.rst @@ -65,7 +65,7 @@ create a calendar year runoff of IBNR. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.960 seconds) + **Total running time of the script:** ( 0 minutes 0.382 seconds) .. _sphx_glr_download_auto_examples_plot_ibnr_runoff.py: diff --git a/docs/auto_examples/plot_ibnr_runoff_codeobj.pickle b/docs/auto_examples/plot_ibnr_runoff_codeobj.pickle index a576ca28c298d6f580ac049549dd0d01aab4b000..8e6ce966abed07a3b195eebed80b3419da946634 100644 GIT binary patch delta 160 zcmZ3*zKVT+LYQU8a*s|iMgp$dN@LgG86OCbAU{?-29YMAY*bOlMH_k z4}=$AoRMEtGG*ePC5$4ICo=|3PG;0+l%1^2wAqqBIY%!iKQSdfC9x#2IJE?1YzA8o u4~kd@M-O*$Mq*}OPGU+*YSEMot_Me7b98c%K3M+WRsl&LXn+GkuDgMp#nXVECDzHF_YW^Mem&&7XSbN delta 54 zcmV-60LlNM2B8L!E|X3I7?DR-lLP`vk?}^8qXIYq8I!yLNRu}KHUT1&ZUIn{om7!7 M8IxuLJ(J!7L+9)f!2kdN diff --git a/docs/auto_examples/plot_mack.rst b/docs/auto_examples/plot_mack.rst index 974c1b00..dc1ca955 100644 --- a/docs/auto_examples/plot_mack.rst +++ b/docs/auto_examples/plot_mack.rst @@ -64,7 +64,7 @@ This example demonstrates how you can can use the Mack Chainladder method. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 1.419 seconds) + **Total running time of the script:** ( 0 minutes 0.558 seconds) .. _sphx_glr_download_auto_examples_plot_mack.py: diff --git a/docs/auto_examples/plot_mack_codeobj.pickle b/docs/auto_examples/plot_mack_codeobj.pickle index f33a140daaa6994ed2ef86b52ca35f22e83e1cb8..f7809d74ab0380a7f6fc8ac7afebfe3295920614 100644 GIT binary patch delta 183 zcmbQqIg@jOo*HvXYT1<9DH=U2d5O8HQ+l{vQp-|v@(XfP^Gc@lu;u2bl;)&PnViQg zFVn*V;l&qc~kTj24XUlZ{x?0bpT5IsgCw delta 221 zcmbQqIg@jOUOi8`UU6zkd__)TQfkhW+9?`6EP08!sZ)CR9V=3ck>z^Wa`RJ4bAVE! zxrrqOIr$|ynMrzyAXV`oa!L;mL_vIUMt)HV&;&GPQ%ZX{LE7R|5=#<6R%NjDOn$*= zIq{CqGb8099bF4TDCMz=OOIeooFsG!JK{ax_ hq?V=T4V;p@N+J62thE#F9k4XL@x&S5I zihyjmY!6#*eoARh>XgX`I7Ik+cp$v^;*9*F5|Ee}3v+IA&g6y6juWqhPF~L9JvoKh zk_~K+(d0&sP-fo5ipc@&!IR%GMo#|35;l1*v+Kl$kjbmr!X__da+v&%-Hg$D@_i;N zM*qp`jE0QClapAS7{ez&U~Xhgm^_csZ?ZZ^$mGTB-jnCChRX3J=jgelmZj$87v!eq um4Muy!PdhK6`I_`BEeWO`5)Vg$@0vulRvYrV9J;<`3grAYsQq)Qau26LtwT5 delta 242 zcmZ23v|os&fn}=6L>8@3zT_M|m(;SXNBBa8R2<>sf9 z=75BgGZHiNauQQgQj4ba@IZv)i!<_zN~UCRmiF)@R!n>yIPrPtNB~JDRS}w#^}j>ERK^qSS%P#CtqRrn%vH4Iaz_(b+QF(J)`gB&n!-ifs?I(tW!*3 zlckx$*!T-_@=J^+?`QU(EXT28@-8NU$vc_DCY!J?ogB;NG1;HRjJLhChch`RKDRV4 dGdW{&1C!T8J@Luc8C@oy;)noh;Abk;0|2|XSJwal diff --git a/docs/auto_examples/plot_stochastic_bornferg.ipynb b/docs/auto_examples/plot_stochastic_bornferg.ipynb index 2052a704..38cade3c 100644 --- a/docs/auto_examples/plot_stochastic_bornferg.ipynb +++ b/docs/auto_examples/plot_stochastic_bornferg.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# Stochastic Bornhuetter Ferguson\n\n\nThere are several aspects of the chainladder module that are demonstrated with\nthis example.\n\n1. We see how to use the `BootstrapODPSample` and `BornhuetterFerguson` to come\n up with a stochastic view of the Bornhuetter-Ferguson method.\n2. We see how we can use the `Triangle.values` property `numpy` to modify the\n the data underlying the Triangle\n3. We use the `broadcast_axis` method of the triangle class (new in 0.4.7)\n\n\n" + "\n# Stochastic Bornhuetter Ferguson\n\n\nThere are several aspects of the chainladder module that are demonstrated with\nthis example.\n\n1. We see how to use the `BootstrapODPSample` and `BornhuetterFerguson` to come\n up with a stochastic view of the Bornhuetter-Ferguson method.\n2. We use the `broadcast_axis` method of the triangle class (new in 0.4.7)\n\n\n" ] }, { @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "import chainladder as cl\nimport numpy as np\n\n# Simulation parameters\nrandom_state = 42\nn_sims = 1000\n\n# Get data\nloss = cl.load_dataset('genins')\npremium = loss.latest_diagonal*0+8e6\n\n# Simulate loss triangles\nsim = cl.BootstrapODPSample(random_state=random_state, n_sims=n_sims)\nsim.fit(loss, sample_weight=premium)\n\n# Repeat the premium triangle to align with simulated losses\nsim_p = premium.broadcast_axis('index', sim.resampled_triangles_.index)\n\n# Simulate aprioris using numpy\napriori_mu = 0.65\napriori_sigma = .10\naprioris = np.random.normal(apriori_mu, apriori_sigma, n_sims)\nsim_p.values = (sim_p.values * aprioris.reshape(n_sims,-1)[..., np.newaxis, np.newaxis])\n\n# Fit Bornhuetter-Ferguson to stochastically generated data\nmodel = cl.BornhuetterFerguson().fit(sim.resampled_triangles_, sample_weight=sim_p)\n\n# Grab completed triangle replacing simulated known data with actual known data\nfull_triangle = model.full_triangle_ - model.X_ + \\\n loss.broadcast_axis('index', sim.resampled_triangles_.index)\n\n# Limiting to the current year for plotting\ncurrent_year = full_triangle[full_triangle.origin==full_triangle.origin.max()].to_frame().T\n\n# Plot the data\ncurrent_year.reset_index(drop=True).plot(\n color='orange', legend=False, alpha=0.1,\n title='Current Accident Year BornFerg Distribution', grid=True);" + "import chainladder as cl\n\n# Simulation parameters\nrandom_state = 42\nn_sims = 1000\n\n# Get data\nloss = cl.load_dataset('genins')\npremium = loss.latest_diagonal*0+8e6\n\n# Simulate loss triangles\nsim = cl.BootstrapODPSample(random_state=random_state, n_sims=n_sims)\nsim.fit(loss, sample_weight=premium)\n\n\n# Fit Bornhuetter-Ferguson to stochastically generated data\nmodel = cl.BornhuetterFerguson(0.65, apriori_sigma=0.10).fit(sim.resampled_triangles_, sample_weight=premium)\n\n# Grab completed triangle replacing simulated known data with actual known data\nfull_triangle = model.full_triangle_ - model.X_ + loss.broadcast_axis('index', sim.resampled_triangles_.index)\n\n# Limiting to the current year for plotting\ncurrent_year = full_triangle[full_triangle.origin==full_triangle.origin.max()].to_frame().T\n\n# Plot the data\ncurrent_year.reset_index(drop=True).plot(\n color='orange', legend=False, alpha=0.1,\n title='Current Accident Year BornFerg Distribution', grid=True);" ] } ], diff --git a/docs/auto_examples/plot_stochastic_bornferg.py b/docs/auto_examples/plot_stochastic_bornferg.py index 7136c752..e67c85b1 100644 --- a/docs/auto_examples/plot_stochastic_bornferg.py +++ b/docs/auto_examples/plot_stochastic_bornferg.py @@ -8,13 +8,10 @@ 1. We see how to use the `BootstrapODPSample` and `BornhuetterFerguson` to come up with a stochastic view of the Bornhuetter-Ferguson method. -2. We see how we can use the `Triangle.values` property `numpy` to modify the - the data underlying the Triangle -3. We use the `broadcast_axis` method of the triangle class (new in 0.4.7) +2. We use the `broadcast_axis` method of the triangle class (new in 0.4.7) """ import chainladder as cl -import numpy as np # Simulation parameters random_state = 42 @@ -28,21 +25,12 @@ sim = cl.BootstrapODPSample(random_state=random_state, n_sims=n_sims) sim.fit(loss, sample_weight=premium) -# Repeat the premium triangle to align with simulated losses -sim_p = premium.broadcast_axis('index', sim.resampled_triangles_.index) - -# Simulate aprioris using numpy -apriori_mu = 0.65 -apriori_sigma = .10 -aprioris = np.random.normal(apriori_mu, apriori_sigma, n_sims) -sim_p.values = (sim_p.values * aprioris.reshape(n_sims,-1)[..., np.newaxis, np.newaxis]) # Fit Bornhuetter-Ferguson to stochastically generated data -model = cl.BornhuetterFerguson().fit(sim.resampled_triangles_, sample_weight=sim_p) +model = cl.BornhuetterFerguson(0.65, apriori_sigma=0.10).fit(sim.resampled_triangles_, sample_weight=premium) # Grab completed triangle replacing simulated known data with actual known data -full_triangle = model.full_triangle_ - model.X_ + \ - loss.broadcast_axis('index', sim.resampled_triangles_.index) +full_triangle = model.full_triangle_ - model.X_ + loss.broadcast_axis('index', sim.resampled_triangles_.index) # Limiting to the current year for plotting current_year = full_triangle[full_triangle.origin==full_triangle.origin.max()].to_frame().T diff --git a/docs/auto_examples/plot_stochastic_bornferg.py.md5 b/docs/auto_examples/plot_stochastic_bornferg.py.md5 index 157069e9..61f88f7f 100644 --- a/docs/auto_examples/plot_stochastic_bornferg.py.md5 +++ b/docs/auto_examples/plot_stochastic_bornferg.py.md5 @@ -1 +1 @@ -fda7e7a947a9b21b4741c8cf611c4820 \ No newline at end of file +5713e85903ed68fb0fc405467d1452c1 \ No newline at end of file diff --git a/docs/auto_examples/plot_stochastic_bornferg.rst b/docs/auto_examples/plot_stochastic_bornferg.rst index 2c723826..ed5cb3c4 100644 --- a/docs/auto_examples/plot_stochastic_bornferg.rst +++ b/docs/auto_examples/plot_stochastic_bornferg.rst @@ -16,9 +16,7 @@ this example. 1. We see how to use the `BootstrapODPSample` and `BornhuetterFerguson` to come up with a stochastic view of the Bornhuetter-Ferguson method. -2. We see how we can use the `Triangle.values` property `numpy` to modify the - the data underlying the Triangle -3. We use the `broadcast_axis` method of the triangle class (new in 0.4.7) +2. We use the `broadcast_axis` method of the triangle class (new in 0.4.7) @@ -33,10 +31,8 @@ this example. .. code-block:: none - c:\users\jboga\onedrive\documents\github\chainladder-python\chainladder\development\bootstrap.py:64: UserWarning: Process risk not yet implemented... - warn('Process risk not yet implemented...') - + @@ -48,7 +44,6 @@ this example. .. code-block:: default import chainladder as cl - import numpy as np # Simulation parameters random_state = 42 @@ -62,21 +57,12 @@ this example. sim = cl.BootstrapODPSample(random_state=random_state, n_sims=n_sims) sim.fit(loss, sample_weight=premium) - # Repeat the premium triangle to align with simulated losses - sim_p = premium.broadcast_axis('index', sim.resampled_triangles_.index) - - # Simulate aprioris using numpy - apriori_mu = 0.65 - apriori_sigma = .10 - aprioris = np.random.normal(apriori_mu, apriori_sigma, n_sims) - sim_p.values = (sim_p.values * aprioris.reshape(n_sims,-1)[..., np.newaxis, np.newaxis]) # Fit Bornhuetter-Ferguson to stochastically generated data - model = cl.BornhuetterFerguson().fit(sim.resampled_triangles_, sample_weight=sim_p) + model = cl.BornhuetterFerguson(0.65, apriori_sigma=0.10).fit(sim.resampled_triangles_, sample_weight=premium) # Grab completed triangle replacing simulated known data with actual known data - full_triangle = model.full_triangle_ - model.X_ + \ - loss.broadcast_axis('index', sim.resampled_triangles_.index) + full_triangle = model.full_triangle_ - model.X_ + loss.broadcast_axis('index', sim.resampled_triangles_.index) # Limiting to the current year for plotting current_year = full_triangle[full_triangle.origin==full_triangle.origin.max()].to_frame().T @@ -89,7 +75,7 @@ this example. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 17.268 seconds) + **Total running time of the script:** ( 0 minutes 11.573 seconds) .. _sphx_glr_download_auto_examples_plot_stochastic_bornferg.py: diff --git 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z-s^DJxLc1w$e6VqN=yh|0b-3wWZN%3COw4XdU2_lLs@>+OXkZb3qQD4+-R zb7WZLhiO@&oEC~4kH<}sXs6?FQV8>@eF8f?*)cS9eyl}#TJkeKcn}!s%}VN>UK&bb z<+nb8p&5hCc{qrGO5^5!Ru@{koJ8uKefmsTh=HMu?d}Do*uSYAyY$2k%Hh>_j4BIA zuMdz8do6K^*P90w^&s?PdxtjQktOc00~PQ?GN!nlN#)~a5}tiqgdW1S1H)ptmWJNh z8K6LYj@2QmoTz*Sc7v7{6cvs_nuSX?bkVmkE5uFHSgt;;| PIkM>O+fRS|XzTw1k6n}E diff --git a/docs/auto_examples/searchindex.dir b/docs/auto_examples/searchindex.dir index 62af0824..c20b3f65 100644 --- a/docs/auto_examples/searchindex.dir +++ b/docs/auto_examples/searchindex.dir @@ -1,3 +1,3 @@ -'C:\\Users\\jboga\\OneDrive\\Documents\\GitHub\\chainladder-python\\docs\\_build\\html\\index.html', (0, 13693) +'C:\\Users\\jboga\\OneDrive\\Documents\\GitHub\\chainladder-python\\docs\\_build\\html\\index.html', (0, 13764) 'C:\\Users\\jboga\\OneDrive\\Documents\\GitHub\\chainladder-python\\docs\\_build\\html\\_static\\documentation_options.js', (13824, 327) -'C:\\Users\\jboga\\OneDrive\\Documents\\GitHub\\chainladder-python\\docs\\_build\\html\\searchindex.js', (14336, 40893) +'C:\\Users\\jboga\\OneDrive\\Documents\\GitHub\\chainladder-python\\docs\\_build\\html\\searchindex.js', (14336, 52198) diff --git a/docs/auto_examples/sg_execution_times.rst b/docs/auto_examples/sg_execution_times.rst index 9d8b36a9..a61b988a 100644 --- a/docs/auto_examples/sg_execution_times.rst +++ b/docs/auto_examples/sg_execution_times.rst @@ -5,40 +5,40 @@ Computation times ================= -**00:30.622** total execution time for **auto_examples** files: +**00:22.923** total execution time for **auto_examples** files: +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py` (``plot_stochastic_bornferg.py``) | 00:17.268 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py` (``plot_stochastic_bornferg.py``) | 00:11.573 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_bootstrap.py` (``plot_bootstrap.py``) | 00:13.354 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_exhibits.py` (``plot_exhibits.py``) | 00:03.234 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_ave_analysis.py` (``plot_ave_analysis.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_triangle_from_pandas.py` (``plot_triangle_from_pandas.py``) | 00:02.289 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_benktander.py` (``plot_benktander.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_triangle_slicing.py` (``plot_triangle_slicing.py``) | 00:01.767 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py` (``plot_bf_apriori_from_cl.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_industry_to_company.py` (``plot_industry_to_company.py``) | 00:01.306 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_bootstrap_comparison.py` (``plot_bootstrap_comparison.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_value_at_risk.py` (``plot_value_at_risk.py``) | 00:00.715 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_capecod.py` (``plot_capecod.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_munich.py` (``plot_munich.py``) | 00:00.596 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_development_periods.py` (``plot_development_periods.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_mack.py` (``plot_mack.py``) | 00:00.558 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_exhibits.py` (``plot_exhibits.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_exposure_triangle.py` (``plot_exposure_triangle.py``) | 00:00.502 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_exposure_triangle.py` (``plot_exposure_triangle.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py` (``plot_ibnr_runoff.py``) | 00:00.382 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py` (``plot_ibnr_runoff.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_ave_analysis.py` (``plot_ave_analysis.py``) | 00:00.000 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_industry_to_company.py` (``plot_industry_to_company.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_benktander.py` (``plot_benktander.py``) | 00:00.000 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_mack.py` (``plot_mack.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py` (``plot_bf_apriori_from_cl.py``) | 00:00.000 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_munich.py` (``plot_munich.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_bootstrap.py` (``plot_bootstrap.py``) | 00:00.000 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_triangle_from_pandas.py` (``plot_triangle_from_pandas.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_bootstrap_comparison.py` (``plot_bootstrap_comparison.py``) | 00:00.000 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_triangle_slicing.py` (``plot_triangle_slicing.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_capecod.py` (``plot_capecod.py``) | 00:00.000 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_value_at_risk.py` (``plot_value_at_risk.py``) | 00:00.000 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_development_periods.py` (``plot_development_periods.py``) | 00:00.000 | 0.0 MB | +-----------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/docs/modules/generated/chainladder.BootstrapODPSample.examples b/docs/modules/generated/chainladder.BootstrapODPSample.examples index f26de802..0ca08349 100644 --- a/docs/modules/generated/chainladder.BootstrapODPSample.examples +++ b/docs/modules/generated/chainladder.BootstrapODPSample.examples @@ -5,13 +5,13 @@ Examples using ``chainladder.BootstrapODPSample`` .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_value_at_risk_thumb.png - :ref:`sphx_glr_auto_examples_plot_bootstrap.py` + :ref:`sphx_glr_auto_examples_plot_value_at_risk.py` .. raw:: html @@ -19,7 +19,7 @@ Examples using ``chainladder.BootstrapODPSample`` .. only:: not html - * :ref:`sphx_glr_auto_examples_plot_bootstrap.py` + * :ref:`sphx_glr_auto_examples_plot_value_at_risk.py` .. raw:: html diff --git a/docs/modules/generated/chainladder.Chainladder.examples b/docs/modules/generated/chainladder.Chainladder.examples index a62ed10f..f61ab762 100644 --- a/docs/modules/generated/chainladder.Chainladder.examples +++ b/docs/modules/generated/chainladder.Chainladder.examples @@ -75,78 +75,6 @@ Examples using ``chainladder.Chainladder`` * :ref:`sphx_glr_auto_examples_plot_exposure_triangle.py` -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_development_periods_thumb.png - - :ref:`sphx_glr_auto_examples_plot_development_periods.py` - -.. raw:: html - -
- -.. only:: not html - - * :ref:`sphx_glr_auto_examples_plot_development_periods.py` - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bf_apriori_from_cl_thumb.png - - :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py` - -.. raw:: html - -
- -.. only:: not html - - * :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py` - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_comparison_thumb.png - - :ref:`sphx_glr_auto_examples_plot_bootstrap_comparison.py` - -.. raw:: html - -
- -.. only:: not html - - * :ref:`sphx_glr_auto_examples_plot_bootstrap_comparison.py` - -.. raw:: html - -
- -.. 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 - -
- -.. only:: not html - - * :ref:`sphx_glr_auto_examples_plot_ave_analysis.py` - .. raw:: html
diff --git a/docs/modules/generated/chainladder.Development.examples b/docs/modules/generated/chainladder.Development.examples index 04dbd091..908f0dfa 100644 --- a/docs/modules/generated/chainladder.Development.examples +++ b/docs/modules/generated/chainladder.Development.examples @@ -5,13 +5,13 @@ Examples using ``chainladder.Development`` .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_mack_thumb.png - :ref:`sphx_glr_auto_examples_plot_bootstrap.py` + :ref:`sphx_glr_auto_examples_plot_mack.py` .. raw:: html @@ -19,4 +19,40 @@ Examples using ``chainladder.Development`` .. only:: not html - * :ref:`sphx_glr_auto_examples_plot_bootstrap.py` + * :ref:`sphx_glr_auto_examples_plot_mack.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_thumb.png + + :ref:`sphx_glr_auto_examples_plot_munich.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_munich.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_exhibits_thumb.png + + :ref:`sphx_glr_auto_examples_plot_exhibits.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_exhibits.py` diff --git a/docs/modules/generated/chainladder.Pipeline.score_samples.examples b/docs/modules/generated/chainladder.Pipeline.score_samples.examples new file mode 100644 index 00000000..e69de29b diff --git a/docs/modules/generated/chainladder.Triangle.examples b/docs/modules/generated/chainladder.Triangle.examples index 6e4fe457..35c71a56 100644 --- a/docs/modules/generated/chainladder.Triangle.examples +++ b/docs/modules/generated/chainladder.Triangle.examples @@ -5,13 +5,13 @@ Examples using ``chainladder.Triangle`` .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_industry_to_company_thumb.png - :ref:`sphx_glr_auto_examples_plot_bootstrap.py` + :ref:`sphx_glr_auto_examples_plot_industry_to_company.py` .. raw:: html @@ -19,7 +19,115 @@ Examples using ``chainladder.Triangle`` .. only:: not html - * :ref:`sphx_glr_auto_examples_plot_bootstrap.py` + * :ref:`sphx_glr_auto_examples_plot_industry_to_company.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_triangle_slicing_thumb.png + + :ref:`sphx_glr_auto_examples_plot_triangle_slicing.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_triangle_slicing.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_value_at_risk_thumb.png + + :ref:`sphx_glr_auto_examples_plot_value_at_risk.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_value_at_risk.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png + + :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_exposure_triangle_thumb.png + + :ref:`sphx_glr_auto_examples_plot_exposure_triangle.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_exposure_triangle.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_mack_thumb.png + + :ref:`sphx_glr_auto_examples_plot_mack.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_mack.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_triangle_from_pandas_thumb.png + + :ref:`sphx_glr_auto_examples_plot_triangle_from_pandas.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_triangle_from_pandas.py` .. raw:: html @@ -38,3 +146,39 @@ Examples using ``chainladder.Triangle`` .. only:: not html * :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_thumb.png + + :ref:`sphx_glr_auto_examples_plot_munich.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_munich.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_exhibits_thumb.png + + :ref:`sphx_glr_auto_examples_plot_exhibits.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_exhibits.py` diff --git a/docs/modules/generated/chainladder.Triangle.groupby.examples b/docs/modules/generated/chainladder.Triangle.groupby.examples index 1c8804b4..e2aa9988 100644 --- a/docs/modules/generated/chainladder.Triangle.groupby.examples +++ b/docs/modules/generated/chainladder.Triangle.groupby.examples @@ -20,39 +20,3 @@ Examples using ``chainladder.Triangle.groupby`` .. only:: not html * :ref:`sphx_glr_auto_examples_plot_triangle_slicing.py` - -.. raw:: html - -
- -.. 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 - -
- -.. only:: not html - - * :ref:`sphx_glr_auto_examples_plot_ave_analysis.py` - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_benktander_thumb.png - - :ref:`sphx_glr_auto_examples_plot_benktander.py` - -.. raw:: html - -
- -.. only:: not html - - * :ref:`sphx_glr_auto_examples_plot_benktander.py` diff --git a/docs/modules/generated/chainladder.Triangle.origin.examples b/docs/modules/generated/chainladder.Triangle.origin.examples index 801ee3cf..7b823dfe 100644 --- a/docs/modules/generated/chainladder.Triangle.origin.examples +++ b/docs/modules/generated/chainladder.Triangle.origin.examples @@ -3,24 +3,6 @@ Examples using ``chainladder.Triangle.origin`` ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png - - :ref:`sphx_glr_auto_examples_plot_bootstrap.py` - -.. raw:: html - -
- -.. only:: not html - - * :ref:`sphx_glr_auto_examples_plot_bootstrap.py` - .. raw:: html
diff --git a/docs/modules/generated/chainladder.Triangle.rename.examples b/docs/modules/generated/chainladder.Triangle.rename.examples index 4566f7d2..4fcc9558 100644 --- a/docs/modules/generated/chainladder.Triangle.rename.examples +++ b/docs/modules/generated/chainladder.Triangle.rename.examples @@ -20,21 +20,3 @@ Examples using ``chainladder.Triangle.rename`` .. only:: not html * :ref:`sphx_glr_auto_examples_plot_industry_to_company.py` - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_benktander_thumb.png - - :ref:`sphx_glr_auto_examples_plot_benktander.py` - -.. raw:: html - -
- -.. only:: not html - - * :ref:`sphx_glr_auto_examples_plot_benktander.py` diff --git a/docs/modules/generated/chainladder.Triangle.sum.examples b/docs/modules/generated/chainladder.Triangle.sum.examples index 397a3dba..a90ff54a 100644 --- a/docs/modules/generated/chainladder.Triangle.sum.examples +++ b/docs/modules/generated/chainladder.Triangle.sum.examples @@ -20,21 +20,3 @@ Examples using ``chainladder.Triangle.sum`` .. only:: not html * :ref:`sphx_glr_auto_examples_plot_industry_to_company.py` - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bf_apriori_from_cl_thumb.png - - :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py` - -.. raw:: html - -
- -.. only:: not html - - * :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py` diff --git a/docs/modules/generated/chainladder.core.triangle.Triangle.T.plot.examples b/docs/modules/generated/chainladder.core.triangle.Triangle.T.plot.examples index 30717387..98b84a9d 100644 --- a/docs/modules/generated/chainladder.core.triangle.Triangle.T.plot.examples +++ b/docs/modules/generated/chainladder.core.triangle.Triangle.T.plot.examples @@ -5,13 +5,13 @@ Examples using ``chainladder.core.triangle.Triangle.T.plot`` .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png - :ref:`sphx_glr_auto_examples_plot_bootstrap.py` + :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py` .. raw:: html @@ -19,4 +19,4 @@ Examples using ``chainladder.core.triangle.Triangle.T.plot`` .. only:: not html - * :ref:`sphx_glr_auto_examples_plot_bootstrap.py` + * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py` diff --git a/docs/modules/generated/chainladder.core.triangle.Triangle.origin.max.examples b/docs/modules/generated/chainladder.core.triangle.Triangle.origin.max.examples index ad39aaa2..aad8e7c3 100644 --- a/docs/modules/generated/chainladder.core.triangle.Triangle.origin.max.examples +++ b/docs/modules/generated/chainladder.core.triangle.Triangle.origin.max.examples @@ -3,24 +3,6 @@ Examples using ``chainladder.core.triangle.Triangle.origin.max`` ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png - - :ref:`sphx_glr_auto_examples_plot_bootstrap.py` - -.. raw:: html - -
- -.. only:: not html - - * :ref:`sphx_glr_auto_examples_plot_bootstrap.py` - .. raw:: html
diff --git a/docs/modules/generated/chainladder.load_dataset.examples b/docs/modules/generated/chainladder.load_dataset.examples index 4762125a..51698455 100644 --- a/docs/modules/generated/chainladder.load_dataset.examples +++ b/docs/modules/generated/chainladder.load_dataset.examples @@ -5,13 +5,13 @@ Examples using ``chainladder.load_dataset`` .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_industry_to_company_thumb.png - :ref:`sphx_glr_auto_examples_plot_bootstrap.py` + :ref:`sphx_glr_auto_examples_plot_industry_to_company.py` .. raw:: html @@ -19,7 +19,97 @@ Examples using ``chainladder.load_dataset`` .. only:: not html - * :ref:`sphx_glr_auto_examples_plot_bootstrap.py` + * :ref:`sphx_glr_auto_examples_plot_industry_to_company.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_triangle_slicing_thumb.png + + :ref:`sphx_glr_auto_examples_plot_triangle_slicing.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_triangle_slicing.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_value_at_risk_thumb.png + + :ref:`sphx_glr_auto_examples_plot_value_at_risk.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_value_at_risk.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png + + :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_exposure_triangle_thumb.png + + :ref:`sphx_glr_auto_examples_plot_exposure_triangle.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_exposure_triangle.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_mack_thumb.png + + :ref:`sphx_glr_auto_examples_plot_mack.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_mack.py` .. raw:: html @@ -38,3 +128,39 @@ Examples using ``chainladder.load_dataset`` .. only:: not html * :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_thumb.png + + :ref:`sphx_glr_auto_examples_plot_munich.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_munich.py` + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_exhibits_thumb.png + + :ref:`sphx_glr_auto_examples_plot_exhibits.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_exhibits.py` diff --git a/docs/modules/generated/chainladder.methods.chainladder.Chainladder.ultimate_.examples b/docs/modules/generated/chainladder.methods.chainladder.Chainladder.ultimate_.examples new file mode 100644 index 00000000..2d7d4ec1 --- /dev/null +++ b/docs/modules/generated/chainladder.methods.chainladder.Chainladder.ultimate_.examples @@ -0,0 +1,22 @@ + + +Examples using ``chainladder.methods.chainladder.Chainladder.ultimate_`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_thumb.png + + :ref:`sphx_glr_auto_examples_plot_munich.py` + +.. raw:: html + +
+ +.. only:: not html + + * :ref:`sphx_glr_auto_examples_plot_munich.py` diff --git a/docs/tutorials/stochastic-tutorial.ipynb b/docs/tutorials/stochastic-tutorial.ipynb index ce3a52ba..826febb7 100644 --- a/docs/tutorials/stochastic-tutorial.ipynb +++ b/docs/tutorials/stochastic-tutorial.ipynb @@ -76,7 +76,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "mack = cl.MackChainladder().fit(tri['CumPaidLoss'])" @@ -94,7 +96,7 @@ "4. `mack_std_err_`: The total prediction error by origin period\n", "5. `total_mack_std_err_`: The total prediction error across all origin periods\n", "\n", - "Notice these are all measures of uncertainty, but where do they come from? Let's start by examining the `link_ratios` underlying the triangle" + "Notice these are all measures of uncertainty, but where do they come from? Let's start by examining the `link_ratios` underlying the triangle." ] }, { @@ -247,6 +249,7 @@ "\n", "Mack noticed that this estimate for an LDF is really just a linear regression fit. For the case of the `simple` average, it is a weighted regression where the weight is set to $\\left (\\frac{1}{X} \\right )^{2}$.\n", "\n", + "Take a look at the fitted coefficient in the next cell and verify that it ties to the direct calculations above.\n", "With the regression framework in hand, we get much more information about our LDF estimate than just the coefficient." ] }, @@ -259,7 +262,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\jbogaard\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1450: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n", + "C:\\Users\\jboga\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n", " \"anyway, n=%i\" % int(n))\n" ] }, @@ -278,10 +281,10 @@ " Method: Least Squares F-statistic: 2887.\n", "\n", "\n", - " Date: Sat, 29 Feb 2020 Prob (F-statistic): 1.60e-11\n", + " Date: Sun, 01 Mar 2020 Prob (F-statistic): 1.60e-11\n", "\n", "\n", - " Time: 19:18:37 Log-Likelihood: -107.89\n", + " Time: 13:21:05 Log-Likelihood: -107.89\n", "\n", "\n", " No. Observations: 9 AIC: 217.8\n", @@ -327,8 +330,8 @@ "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: Sat, 29 Feb 2020 Prob (F-statistic): 1.60e-11\n", - "Time: 19:18:37 Log-Likelihood: -107.89\n", + "Date: Sun, 01 Mar 2020 Prob (F-statistic): 1.60e-11\n", + "Time: 13:21:05 Log-Likelihood: -107.89\n", "No. Observations: 9 AIC: 217.8\n", "Df Residuals: 8 BIC: 218.0\n", "Df Model: 1 \n", @@ -393,7 +396,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\users\\jbogaard\\documents\\bitbucket\\chainladder-python\\chainladder\\utils\\weighted_regression.py:49: RuntimeWarning: invalid value encountered in true_divide\n", + "c:\\users\\jboga\\onedrive\\documents\\github\\chainladder-python\\chainladder\\utils\\weighted_regression.py:49: RuntimeWarning: invalid value encountered in true_divide\n", " (xp.nansum(w*x*x, axis)-xp.nanmean(x, axis)*xp.nansum(w*x, axis)))\n" ] } @@ -414,13 +417,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This regression framework is what the `Development` estimator uses to set development patterns. Although we discard the information in deterministic approaches, `Development` has two useful statistics for estmating reserve variability, both of which come from the regression framework. The stastics are `std_err_` and `sigma_` and they are used by the `MackChainladder` estimator to determine the prediction error of our reserves." + "This regression framework is what the `Development` estimator uses to set development patterns. Although we discard the information in deterministic approaches, `Development` has two useful statistics for estimating reserve variability, both of which come from the regression framework. The stastics are `std_err_` and `sigma_` and they are used by the `MackChainladder` estimator to determine the prediction error of our reserves." ] }, { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "dev = cl.Development(average='simple').fit(tri['CumPaidLoss'])" @@ -768,7 +773,7 @@ "### Assumption of Independence\n", "The Mack model makes a lot of assumptions about independence (i.e. covariance between random processes is 0). This means many of the Variance estimates in the `MackChainladder` model follow the form of $Var(A+B) = Var(A)+Var(B)$.\n", "\n", - "Notice the sqaure of `mack_std_err_` is simply the sum of the sqaures of `parameter_risk_` and `process_risk_`." + "Notice the square of `mack_std_err_` is simply the sum of the sqaures of `parameter_risk_` and `process_risk_`." ] }, { @@ -851,7 +856,7 @@ "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", "\n", "### Mack Reserve Variability\n", - "The `mack_std_err_` at ultimate is the reserve variability at for each `origin`" + "The `mack_std_err_` at ultimate is the reserve variability for each `origin` period." ] }, { @@ -1065,14 +1070,12 @@ "outputs": [ { "data": { - "image/png": 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\n"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\n", 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" + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1129,7 +1130,9 @@ { "cell_type": "code", "execution_count": 21, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "samples = cl.BootstrapODPSample(n_sims=10000).fit(tri['CumPaidLoss']).resampled_triangles_" @@ -1145,7 +1148,9 @@ { "cell_type": "code", "execution_count": 22, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "samples = cl.BootstrapODPSample(n_sims=10000).fit_transform(tri['CumPaidLoss'])" @@ -1356,14 +1361,12 @@ }, { "data": { - "image/png": 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\n", 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\n", 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"text/plain": [ - "
" + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1425,7 +1426,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "99%-ile of reserve estimate is 3,151,387.0\n" + "99%-ile of reserve estimate is 3,143,466.0\n" ] } ], @@ -1449,20 +1450,18 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "ax = ibnr.plot(kind='hist', bins=50, alpha=0.9, color='yellow').plot()\n", - "dist.plot(kind='hist', bins=50, alpha=0.3, color='blue', title='Mack vs Bootstrap Variability');" + "ax = ibnr.plot(kind='hist', bins=50, alpha=0.7, color='green').plot()\n", + "dist.plot(kind='hist', bins=50, alpha=0.4, color='blue', title='Mack vs Bootstrap Variability');" ] }, { @@ -1562,7 +1561,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We don't necessarily need to simulate new premiums, because they aren't stochastic, but we will need to align it with our simulated triangles. We do this by repeating our premium vector for each simluated triangle using the `broadcast_axis` method of the `Triangle` class. This method projects the axis of our samples onto our premium vector, repeating premium values until our shapes are aligned." + "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." ] }, { @@ -1572,44 +1571,8 @@ "outputs": [ { "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \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, 1)
Index:[LOB]
Columns:[EarnedPremNet]
" - ], "text/plain": [ - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (10000, 1, 10, 1)\n", - "Index: ['LOB']\n", - "Columns: ['EarnedPremNet']" + "BornhuetterFerguson(apriori=0.65, apriori_sigma=0.1, random_state=None)" ] }, "execution_count": 32, @@ -1618,163 +1581,23 @@ } ], "source": [ - "prem_samples = tri['EarnedPremNet'].latest_diagonal.broadcast_axis('index', samples.index)\n", - "prem_samples" + "bf = cl.BornhuetterFerguson(apriori=0.65, apriori_sigma=0.10)\n", + "bf.fit(samples, sample_weight=tri['EarnedPremNet'].latest_diagonal)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `BornhuetterFerguson` method takes an apriori assumption which itself can be considered to be drawn from a Random process." + "We can use our knowledge of `Triangle` manipulation to grab most things we would want out of our model. In this example, we are using the `broadcast_axis` function of the Triangle which repeats a Triangle object along an axis according to another triangle axis." ] }, { "cell_type": "code", "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Simulate aprioris using numpy\n", - "apriori_mu = 0.65\n", - "apriori_sigma = .10\n", - "aprioris = np.random.normal(apriori_mu, apriori_sigma, 10000)\n", - "pd.Series(aprioris).plot(kind='hist', bins=50, title='Distribution of Aprioris');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recall that the `BornhuetterFerguson` apriori needs to be a constant, so we cannot use it directly. But we can exploit the fact the the premium vector and the apriori are multiplicative and embed our resampled apriori directly into our premium vector. We will modify the premium triangle using `numpy`." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \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, 1)
Index:[LOB]
Columns:[EarnedPremNet]
" - ], - "text/plain": [ - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (10000, 1, 10, 1)\n", - "Index: ['LOB']\n", - "Columns: ['EarnedPremNet']" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prem_samples.values = (prem_samples.values * aprioris.reshape(10000,-1)[..., np.newaxis, np.newaxis])\n", - "prem_samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now our premium vector is stochastic." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "prem_samples.sum('origin').plot(kind='hist', bins=50, title=\"Premium x Apriori\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With these components, fitting the `BornhuetterFerguson` or any other expected loss method is straight forward and looks just like its deterministic counterpart." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "bf = cl.BornhuetterFerguson().fit(samples, sample_weight=prem_samples)" - ] - }, - { - "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": 37, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Grab completed triangle replacing simulated known data with actual known data\n", @@ -1794,19 +1617,17 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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1xKmIgRwn46XUV5qnXBhfIBpFuLj4nm9LVKWyR3LmVo1UROnKksAL7LVLw5cY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\n3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sXhMZH3jKtGlTgVaWn5LvDvt/+eoTfk9ZXXqegV+l6/4OV/sP8B7++9jxu5RQ/b1sXg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\nGA0xZIPf2PgNXl5+mbvjuzi6w3P155iGU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" + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1846,7 +1667,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.3" } }, "nbformat": 4, diff --git a/examples/plot_stochastic_bornferg.py b/examples/plot_stochastic_bornferg.py index 7136c752..e67c85b1 100644 --- a/examples/plot_stochastic_bornferg.py +++ b/examples/plot_stochastic_bornferg.py @@ -8,13 +8,10 @@ 1. We see how to use the `BootstrapODPSample` and `BornhuetterFerguson` to come up with a stochastic view of the Bornhuetter-Ferguson method. -2. We see how we can use the `Triangle.values` property `numpy` to modify the - the data underlying the Triangle -3. We use the `broadcast_axis` method of the triangle class (new in 0.4.7) +2. We use the `broadcast_axis` method of the triangle class (new in 0.4.7) """ import chainladder as cl -import numpy as np # Simulation parameters random_state = 42 @@ -28,21 +25,12 @@ sim = cl.BootstrapODPSample(random_state=random_state, n_sims=n_sims) sim.fit(loss, sample_weight=premium) -# Repeat the premium triangle to align with simulated losses -sim_p = premium.broadcast_axis('index', sim.resampled_triangles_.index) - -# Simulate aprioris using numpy -apriori_mu = 0.65 -apriori_sigma = .10 -aprioris = np.random.normal(apriori_mu, apriori_sigma, n_sims) -sim_p.values = (sim_p.values * aprioris.reshape(n_sims,-1)[..., np.newaxis, np.newaxis]) # Fit Bornhuetter-Ferguson to stochastically generated data -model = cl.BornhuetterFerguson().fit(sim.resampled_triangles_, sample_weight=sim_p) +model = cl.BornhuetterFerguson(0.65, apriori_sigma=0.10).fit(sim.resampled_triangles_, sample_weight=premium) # Grab completed triangle replacing simulated known data with actual known data -full_triangle = model.full_triangle_ - model.X_ + \ - loss.broadcast_axis('index', sim.resampled_triangles_.index) +full_triangle = model.full_triangle_ - model.X_ + loss.broadcast_axis('index', sim.resampled_triangles_.index) # Limiting to the current year for plotting current_year = full_triangle[full_triangle.origin==full_triangle.origin.max()].to_frame().T