ó áp7]c@sRdZddlZddlmZddlmZmZdefd„ƒYZ dS(sL Created on Sun May 10 08:23:48 2015 Author: Josef Perktold License: BSD-3 iÿÿÿÿNi(t NonePenalty(tapprox_fprime_cst approx_fprimetPenalizedMixincBs‰eZdZd„Zd d„Zd d„Zd d„Zd dd„Zd d„Z d d„Z d d „Z d d „Z d d d „Z RS( sMixin 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 cOsÐ|jddƒ|_|jddƒ|_tt|ƒj||Ž|jdkrgt|jƒ|_n|jdkrŽt ƒ|_d|_n|j j ddgƒt |dgƒ|_ |j j dgƒdS(Ntpenalt pen_weightit_null_drop_keys(tpoptNoneRRtsuperRt__init__tlentendogRt _init_keystextendtgetattrR(tselftargstkwds((s:lib/python2.7/site-packages/statsmodels/base/_penalized.pyR "s  cKsI|dkrEt|dƒr<|j|ƒ}|j|ƒ}qEd}n|S(Nt scaletypei(Rthasattrtpredicttestimate_scale(RtparamstscaleRtmu((s:lib/python2.7/site-packages/statsmodels/base/_penalized.pyt _handle_scale9s   cKsz|dkr|j}ntt|ƒj||}|dkrv|j||}|d|||jj|ƒ8}n|S(s3 Log-likelihodo of model at params iiN(RRR RtloglikeRRtfunc(RRRRtllfR((s:lib/python2.7/site-packages/statsmodels/base/_penalized.pyREs   %cKs‘|dkr|j}ntt|ƒj||}t|jdƒ}|dkr|j||}|d||||jj |ƒ8}n|S(s@ Log-likelihood of model observations at params iiN( RRR Rt loglikeobstfloattshapeRRR(RRRRRtnobs_llfR((s:lib/python2.7/site-packages/statsmodels/base/_penalized.pyRSs   )tfdc suˆdkrˆj‰n‡‡‡fd†}|dkrFt||ƒS|dkret||dtƒStdƒ‚dS(s5score based on finite difference derivative csˆj|dˆˆS(NR(R(tp(RRR(s:lib/python2.7/site-packages/statsmodels/base/_penalized.pytjttcsR"tcentereds-method not recognized, should be "fd" or "cs"N(RRRRtTruet ValueError(RRRtmethodRR((RRRs:lib/python2.7/site-packages/statsmodels/base/_penalized.pyt score_numdiffcs     cKsz|dkr|j}ntt|ƒj||}|dkrv|j||}|d|||jj|ƒ8}n|S(s- Gradient of model at params iiN(RRR RtscoreRRtderiv(RRRRtscR((s:lib/python2.7/site-packages/statsmodels/base/_penalized.pyR,ss   %cKs‘|dkr|j}ntt|ƒj||}t|jdƒ}|dkr|j||}|d||||jj |ƒ8}n|S(s: Gradient of model observations at params iiN( RRR Rt score_obsRR RRR-(RRRRR.tnobs_scR((s:lib/python2.7/site-packages/statsmodels/base/_penalized.pyR/s   )c sJˆdkrˆj‰n‡‡‡fd†}ddlm}|||ƒS(s7hessian based on finite difference derivative csˆj|dˆˆS(NR(R(R#(RRR(s:lib/python2.7/site-packages/statsmodels/base/_penalized.pyR$–R%iÿÿÿÿ(t approx_hessN(RRtstatsmodels.tools.numdiffR1(RRRRRR1((RRRs:lib/python2.7/site-packages/statsmodels/base/_penalized.pythessian_numdiffs   cKs±|dkr|j}ntt|ƒj||}|dkr­|j||}|jj|ƒ}|jdkr”|d|t j ||ƒ8}q­|d|||8}n|S(s, Hessian of model at params iiN( RRR RthessianRRtderiv2tndimtnptdiag(RRRRthessRth((s:lib/python2.7/site-packages/statsmodels/base/_penalized.pyR4›s   "cKst|dƒr&|jidd6ƒn|dkr;d}n|dkrPt}ntt|ƒjd||}|tkr~|S|tkr“d}ntj tj |j ƒ|kƒd}tj tj |j ƒ|kƒd}|j ƒrÿ|j ||}|S|SdS(s‡minimize negative penalized log-likelihood Parameters ---------- method : None or str Method specifies the scipy optimizer as in nonlinear MLE models. trim : Boolean or 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. tfamilyitmax_start_irlstbfgsR*g-Cëâ6?N(RtupdateRtFalseR RtfitR(R7tnonzerotabsRtanyt _fit_zeros(RR*ttrimRtrest drop_indext keep_indextres_aux((s:lib/python2.7/site-packages/statsmodels/base/_penalized.pyR@­s"       %% N(t__name__t __module__t__doc__R RRRRR+R,R/R3R4R@(((s:lib/python2.7/site-packages/statsmodels/base/_penalized.pyRs      ( RLtnumpyR7t _penaltiesRR2RRtobjectR(((s:lib/python2.7/site-packages/statsmodels/base/_penalized.pyts