M/PhHdZddlZddlmZddlmZmZGddZdS)zL Created on Sun May 10 08:23:48 2015 Author: Josef Perktold License: BSD-3 N) NonePenalty)approx_fprime_cs approx_fprimeceZdZdZfdZddZdfd Zdfd ZddZdfd Z dfd Z dd Z dfd Z dfd Z xZS)PenalizedMixinaMixin class for Maximum Penalized Likelihood Parameters ---------- args and kwds for the model super class penal : None or instance of Penalized function class If penal is None, then NonePenalty is used. pen_weight : float or None factor for weighting the penalization term. If None, then pen_weight is set to nobs. TODO: missing **kwds or explicit keywords TODO: do we adjust the inherited docstrings? We would need templating to add the penalization parameters c|dd|_|dd|_tj|i||jt |j|_|jt|_d|_|j ddgt|dg|_ |j ddgdS)Npenal pen_weightr_null_drop_keys) popr r super__init__lenendogr _init_keysextendgetattrr )selfargskwds __class__s [/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/base/_penalized.pyrzPenalizedMixin.__init__ sXXgt,, 88L$77$'$''' ? "!$*ooDO : $DJDO 6777&t->CC ##Wl$;<<<<<Nc |=t|dr+||}||}nd}|S)N scaletyper)hasattrpredictestimate_scale)rparamsscalermus r _handle_scalezPenalizedMixin._handle_scale7sI =t[)) \\&))++B// rc ||j}tj|fi|}|dkr4|j|fi|}|d|z |z|j|zz}|S)z3 Log-likelihood of model at params Nrr)r rlogliker#r func)rr r rllfr!rs rr%zPenalizedMixin.loglikeCs}  Jeggof---- ??&D&v6666E 1U7Z'$*//&*A*AA AC rc ||j}tj|fi|}t|jd}|dkr7|j|fi|}|d|z |z|z |j|zz}|S)z@ Log-likelihood of model observations at params Nrr)r r loglikeobsfloatshaper#r r&)rr r rr'nobs_llfr!rs rr)zPenalizedMixin.loglikeobsQs  J egg 004001&& ??&D&v6666E 1U7Z'(2TZ__V5L5LL LC rfdc jfd}|dkrt||S|dkrt||dStd)z4score based on finite difference derivative Nc$j|fdiSNr r%prr rs rz.PenalizedMixin.score_numdiff..g LDLJJzJTJJrcsr-T)centeredz-method not recognized, should be "fd" or "cs")r rr ValueError)rr r methodrr%s` ` ` r score_numdiffzPenalizedMixin.score_numdiffasn  JJJJJJJ T>>#FG44 4 t^^ 4@@@ @LMM Mrc ||j}tj|fi|}|dkr4|j|fi|}|d|z |z|j|zz}|S)z- Gradient of model at params Nrr)r rscorer#r deriv)rr r rscr!rs rr<zPenalizedMixin.scoreps  J UWW]6 * *T * * ??&D&v6666E !E'J&)9)9&)A)AA AB rc ||j}tj|fi|}t|jd}|dkr7|j|fi|}|d|z |z|z |j|zz}|S)z: Gradient of model observations at params Nrr)r r score_obsr*r+r#r r=)rr r rr>nobs_scr!rs rr@zPenalizedMixin.score_obs~s  J UWW v . . . . $$ ??&D&v6666E !E'J&0DJ4D4DV4L4LL LB rc Ljfd}ddlm}|||S)z6hessian based on finite difference derivative Nc$j|fdiSr0r1r2s rr4z0PenalizedMixin.hessian_numdiff..r5rr) approx_hess)r statsmodels.tools.numdiffrD)rr r rr%rDs` `` rhessian_numdiffzPenalizedMixin.hessian_numdiffsL  JJJJJJJ999999{67+++rc ||j}tj|fi|}|dkrb|j|fi|}|j|}|jdkr!|d|z tj||zzz}n|d|z |z|zz}|S)z, Hessian of model at params Nrr) r rhessianr#r deriv2ndimnpdiag)rr r rhessr!hrs rrHzPenalizedMixin.hessians  Juwwv.... ??&D&v6666E !!&))Av{{%"'*q."9"999%*,q00 rc ddlm}ddlm}t |||fr|ddi|d}|d}t jd d|i|}|dur|S|d urd }tj tj |j |kd}tj tj |j |kd}| r|j |fi|} | S|S) aminimize negative penalized log-likelihood Parameters ---------- method : None or str Method specifies the scipy optimizer as in nonlinear MLE models. trim : {bool, float} Default is False or None, which uses no trimming. If trim is True or a float, then small parameters are set to zero. If True, then a default threshold is used. If trim is a float, then it will be used as threshold. The default threshold is currently 1e-4, but it will change in future and become penalty function dependent. kwds : extra keyword arguments This keyword arguments are treated in the same way as in the fit method of the underlying model class. Specifically, additional optimizer keywords and cov_type related keywords can be added. r)GLMGam)GLMmax_start_irlsNbfgsFr9Tg-C6?)*statsmodels.gam.generalized_additive_modelrP+statsmodels.genmod.generalized_linear_modelrQ isinstanceupdaterfitrKnonzeroabsr any _fit_zeros) rr9trimrrPrQres drop_index keep_indexres_auxrs rrYzPenalizedMixin.fits+2 FEEEEECCCCCC dS&M * * / KK)1- . . . >F <Deggk000400 5==Jt||BF3:$6$6$=>>qAJBF3:$6$6$=>>qAJ~~ )$/*==== r)N)Nr-)NN)__name__ __module__ __qualname____doc__rr#r%r)r:r<r@rFrHrY __classcell__)rs@rrr s6$=====.           N N N N            ,,,,$9999999999rr) rfnumpyrK _penaltiesrrErrrrTrrrjs######EEEEEEEEUUUUUUUUUUr