p7]c@ sddlmZdddgZddlZddlZddljjZddl jj Z ddl j jZddlmZmZmZmZmZmZmZmZmZmZmZddlmZmZmZddl m!Z!m"Z"dd l#m$Z$dd l%m&Z&d Z'd efd YZ(de(fdYZ)de(fdYZ*de(fdYZ+defdYZ,dee,fdYZ-dej.fdYZ/e j0e/e,dej.fdYZ1e j0e1e-defdYZ2dee2fdYZ3dej.fdYZ4e j0e4e2dej.fd YZ5e j0e5e3d!efd"YZ6d#ee6fd$YZ7d%ej.fd&YZ8e j0e8e6d'ej.fd(YZ9e j0e9e7dS()i(tdivisiontZeroInflatedPoissontZeroInflatedGeneralizedPoissontZeroInflatedNegativeBinomialPN( t DiscreteModelt CountModeltPoissontLogitt CountResultstL1CountResultstProbitt_discrete_results_docst_validate_l1_methodtGeneralizedPoissontNegativeBinomialP(t zipoissont zigenpoissontzinegbin(t approx_fprimet approx_hess(tcache_readonly(tConvergenceWarnings exog_infl : array_like or None Explanatory variables for the binary inflation model, i.e. for mixing probability model. If None, then a constant is used. offset : array_like Offset is added to the linear prediction with coefficient equal to 1. exposure : array_like Log(exposure) is added to the linear prediction with coefficient equal to 1. inflation : string, 'logit' or 'probit' The model for the zero inflation, either Logit (default) or Probit tGenericZeroInflatedc B seZdiejd6eejd6ZddddddZdZ dZ ddd d d dd ddd Z e j je _dd dd d ddddddd Z e j je _dZdZdZdZdZdZddddddZRS(sP Generiz Zero Inflated model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. exog_infl: array A reference to the zero-inflated exogenous design. tparamst extra_paramstlogittnonec K stt|j||d|d|d|||dkrmd|_tj|j|jfdtj|_ n||_ |j d|_t |j dkrd|_ n|j d|_ ||_ |dkrttj|j j d|j |_|j|_nS|dkrFttj|j j d|j |_|j|_ntd |||_|j|_t |jt |j krtd ng|jjjD]} d | ^q} | t|j|j(tj|j dtj|_ |jjd d gd g|_ dS(NtoffsettexposuretmissingitdtypeRitprobits%inflation == %s, which is not handleds[exog and exog_infl have different number ofobservation. `missing` handling is not supporteds inflate_%st exog_inflt inflation(!tsuperRt__init__tNonet k_inflatetnptonestsizetfloat64R tshapetlentk_exogtinflRtzerost model_inflt_hessian_logitt_hessian_inflateR t_hessian_probitt ValueErrorR!tk_extratexogtdatat param_namestlistt exog_namestasarrayt _init_keystextendt_null_drop_keys( tselftendogR5R RR!RRtkwargstit infl_names((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR#8s@           &cC stj|j|S(s9 Loglikelihood of Generic Zero Inflated model Parameters ---------- params : array-like The parameters of the model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes -------- .. math:: \ln L=\sum_{y_{i}=0}\ln(w_{i}+(1-w_{i})*P_{main\_model})+ \sum_{y_{i}>0}(\ln(1-w_{i})+L_{main\_model}) where P - pdf of main model, L - loglike function of main model. (R&tsumt loglikeobs(R>R((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pytloglikegsc C s||j }||j}|j}|jj|}tj|tjtjdtjtj}|j j |}tj |dkd}tj |d}tj |dtj } tj||d||tj||| |Rt params_inflt params_maintytwtllf_maintzero_idxt nonzero_idxtllf((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRDs   1 '#tbfgsi#it nonrobustc K s|dkrgt|ddt|dd} tj| dkrX| dkrXd} n|j}n|dkrd}ntt|jd|d|d|d |d |d || } |j|| j } |j | }|dkri}n|j d |d t d| ||S(NRiRicW s|S(N((tx((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyttt start_paramstmaxitertdisptmethodt full_outputtcallbacktcov_typetuse_selftuse_t( R$tgetattrR&R(t_get_start_paramsR"Rtfitt result_classt_resultstresult_class_wrappert_get_robustcov_resultstTrue(R>R]R`R^RaR_RbRctcov_kwdsReR@Rtmlefittzipfittresult((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRhs$ "!       tl1tdefined_by_methoditautog{Gz?g-C6?gQ?c K st|tj|dkrQ|dkrQ|j|j} |tj| }n|j|j}|jrtj|dkr||j| n|}|dkrgt|ddt|dd}tj|dkr|dkrd}n|j j d|d|d|d|d dd |d |d |d | d| d| | j }tj tj|j|}nt t|j d|d|d|d|d |d |d |d |d | d| d| | }|j||}|j|S(NiiRRR]R`R^RaR_Rbtalphat trim_modet auto_trim_tolt size_trim_toltqc_tol(R R&R(R,R%R'R4R$RfRKtfit_regularizedRtappendR"Rtresult_class_regtresult_class_reg_wrapper(R>R]R`R^RaR_RbRuRvRwRxRyR@tk_paramstextratalpha_pRtcntfitt discretefit((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRzs0 ! - "!  !cC sA||j }||j}|j}|jj|}tj|tjtjdtjtj}|j j |}|j j |}|j |}tj |dkd} tj |d} |j j|} tj |jjd|jfdtj} tj|jdtj} || jd|| tj|| j| | ddf<|| | | ddf<|jdkr|j| j|| d|| dtj|| tj|| j| | ddf<|j| j|| j | | ddfRRPRQRRRSt score_mainRTRWRURVtmutdldptdldw((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRs0   1+ 36' "cC s|j|jdS(Ni(RRC(R>R((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pytscorescC sdS(N((R>R((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyt _hessian_mainsc C s||j }||j}|j}|jj|}tj|tjtjdtjtj}|j j |}|j j |}|j |}tj |dkd} tj |d} tj |j|j|jf} tj|} x't|jD]} x t| ddD]}|j| | f|j| |f|| d|| dtj|| dd|| tj|| || || ddtj|| d| | dj|j| | f|j| |f|| d|| j| | |fRRPRQRRRSRRTRWRURVthess_arrtpmfRAtj((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR0 s,   1"!vQ]cC sdS(N((R>R((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR2GscC s|j|}|j|}|dks6|dkrFt||jS|j|j}tj||f}||d|jddf<|||jd|jdfRt hess_arr_maint hess_arr_infltdimRttri_idx((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pythessianJs"tmeancC s|dkr|j}n|dkr0|j}n|dkrQt|dd}ntj|}|dkrud}n||j }||j}d|jj||} tj |||jj d ||} |j j} |j j } t|j ddg} t|j ddg}||j _tj |j d|j _ ||j _||j _|j j|}| |j _| |j _ t| dkr| ddkr|j `n | |j _t|dkr|ddkr|j `n ||j _d| | tj|}|dkr.| tj| S|dkrGtj| S|dkrW| S|d kr|| tj| d|S|d kr|S|d kr| S|d kr|j|||||Std |dS(sH Predict response variable of a count model given exogenous variables. Parameters ---------- params : array-like The parameters of the model exog : array, optional A reference to the exogenous design. If not assigned, will be used exog from fitting. exog_infl : array, optional A reference to the zero-inflated exogenous design. If not assigned, will be used exog from fitting. offset : array, optional Offset is added to the linear prediction with coefficient equal to 1. exposure : array, optional Log(exposure) is added to the linear prediction with coefficient equal to 1. If exposure is specified, then it will be logged by the method. The user does not need to log it first. which : string, optional Define values that will be predicted. 'mean', 'mean-main', 'linear', 'mean-nonzero', 'prob-zero, 'prob', 'prob-main' Default is 'mean'. Notes ----- RiiRtnoRs mean-maintlinears mean-nonzeros prob-zeros prob-maintprobswhich = %s is not availableN(R$R5R RfR&RNR%R/RFtdotR*RKR?R.RRRDR+ROt _predict_probR3(R>RR5R RRtwhichRPRQt prob_maintlin_predttmp_exogt tmp_endogt tmp_offsett tmp_exposureRWt prob_zero((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRFns\         (       "  "          N(t__name__t __module__tbaset_model_params_doct_doc_zi_paramst_missing_param_doct__doc__R$R#RERDRhRRzRRRR0R2RRF(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR&s..  *      0   '  $ cB s]eZdiejd6eejd6Zd d d dddZdZ dZ dZ RS( sP Poisson Zero Inflated model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. exog_infl: array A reference to the zero-inflated exogenous design. RRRRc K stt|j||d|d|d|d|d||t|j|jd|d||_t|_t |_ t |_ t |_t|_dS(NRR!R RR(R"RR#RR?R5RKRt distributiontZeroInflatedPoissonResultsRit!ZeroInflatedPoissonResultsWrapperRktL1ZeroInflatedPoissonResultsR|t#L1ZeroInflatedPoissonResultsWrapperR}( R>R?R5R RRR!RR@((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR#s      cC s||j }||j}|j}|jj|}tj|tjtjdtjtj}|j |}tj |dkd}tj |d}|j j|} tj |j |j f} d||tj| |d} xt|j D]} xt| ddD]} |j|| f|j|| f| |||dd| ||| |tj| || dj| ||j|| f|j|| fj| | | fRRPRQRRRSRRURVRRtcoeffRAR((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRs$   1#< (-c C sB||j }||j}tjtjdtj|jd}t|jdkrt} tj|j j ||dddf} n+t } |j j ||dddf} tj | tjtjdtjtj} |jj ||d|dddf} |jj|| | } | r>| dS| S(NiiiR(R%R&t atleast_2dtarangetmaxR?R+R*RmR/RFR$tFalseRGRHRIRJRKRR( R>RR5R RRRPRQtcountst transformRSRRq((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR s  ++%1cC sD|jjddddj}tjtj|jd|}|S(NR_iR`tnmg?(RKRhRR&R{R'R%(R>R]((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRgs"N( RRRRRRRR$R#RRRg(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRs   cB sdeZdiejd6edejd6Zd d d ddddZdZ d Z d Z RS( s Zero Inflated Generalized Poisson model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. exog_infl: array A reference to the zero-inflated exogenous design. p: scalar P denotes parametrizations for ZIGP regression. Rsp : float dispersion power parameter for the GeneralizedPoisson model. p=1 for ZIGP-1 and p=2 for ZIGP-2. Default is p=2 RRiRc K stt|j||d|d|d|d|d|| t|j|jd|d|d||_t|_|j d7_ |j d7_ |j j dt |_t|_t|_t|_dS( NRR!R RRtpiRu(R"RR#R R?R5RKRRR,R4R9R{t%ZeroInflatedGeneralizedPoissonResultsRit,ZeroInflatedGeneralizedPoissonResultsWrapperRkt'L1ZeroInflatedGeneralizedPoissonResultsR|t.L1ZeroInflatedGeneralizedPoissonResultsWrapperR}( R>R?R5R RRR!RRR@((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR#=s      cC s-tt|j}|jjd|d<|S(NiR(R"Rt_get_init_kwdsRKtparameterization(R>tkwds((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRPscC sI||j }||j}|jj}tjtjdtj|jd} t|j dkrt } tj|j j ||dddf} n+t} |j j ||dddf} tjdd| | dk<|jj ||d|d|dddf} |jj| | |d|| } | rE| dS| S(Niiig?RRi(R%RKRR&RRRR?R+R*RmR/RFR$Rt nextafterRR(R>RR5R RRRPRQRRRRSRRq((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRUs   ++%"c C sitjEtjddtt|j|jd|jjddj }WdQXt j |d}|S(NtignoretcategoryR R_ig?( twarningstcatch_warningst simplefilterRRR?R5R RhRR&R{(R>R]((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRgjs  !N( RRRRRRRR$R#RRRg(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR%s   cB sdeZdiejd6edejd6Zd d d ddddZdZ d Z d Z RS( s Zero Inflated Generalized Negative Binomial model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. exog_infl: array A reference to the zero-inflated exogenous design. p: scalar P denotes parametrizations for ZINB regression. p=1 for ZINB-1 and p=2 for ZINB-2. Default is p=2 Rsp : float dispersion power parameter for the NegativeBinomialP model. p=1 for ZINB-1 and p=2 for ZINM-2. Default is p=2 RRiRc K stt|j||d|d|d|d|d|| t|j|jd|d|d||_t|_|j d7_ |j d7_ |j j dt |_t|_t|_t|_dS( NRR!R RRRiRu(R"RR#RR?R5RKRRR,R4R9R{t#ZeroInflatedNegativeBinomialResultsRit*ZeroInflatedNegativeBinomialResultsWrapperRkt%L1ZeroInflatedNegativeBinomialResultsR|t,L1ZeroInflatedNegativeBinomialResultsWrapperR}( R>R?R5R RRR!RRR@((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR#s      cC s)tt|j}|jj|d<|S(NR(R"RRRKR(R>R((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRscC sU||j }||j}|jj}tjdtj|jd} t|jdkrt } tj |j j ||dddf} n+t} |j j ||dddf} tj| tjtjdtjtj} |jj ||d|d|dddf} |jj| | |d|| } | rQ| dS| S(NiiiRRi(R%RKRR&RRR?R+R*RmRR/RFR$RRGRHRIRJRR(R>RR5R RRRPRQRRRRSRRq((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRs   "+%1"c C sftj6tjddt|jjddddj}WdQXtjtj |j |}|S(NRRR_iR`R( RRRRRKRhRR&R{R.R%(R>R]((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRgs  $N( RRRRRRRR$R#RRRg(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRss   RcB sGeZeidd6dd6ZedZdddeedZRS( s)A results class for Zero Inflated Poissontone_line_descriptionR\t extra_attrcC sP|jdd}d|jtj|jdd}d|tj|S(NRRi(RFR&RO(R>RRS((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyt_dispersion_factors)toveralltdydxcC stddS(shGet marginal effects of the fitted model. Not yet implemented for Zero Inflated Models s&not yet implemented for zero inflationN(tNotImplementedError(R>tatR`tatexogtdummytcount((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyt get_margeffsN( RRR RRRR$RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRs   RcB seZRS((RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRsRcB seZRS((RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRsRcB seZRS((RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRsRcB sGeZeidd6dd6ZedZdddeedZRS( s5A results class for Zero Inflated Generalized PoissonRR\RcC sq|jjj}|j|jjd}tj|jdd}d|j|}d|||d||S(NiRRii(tmodelRKRRR%R&RORF(R>RRuRRS((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRs RRcC stddS(shGet marginal effects of the fitted model. Not yet implemented for Zero Inflated Models s&not yet implemented for zero inflationN(R(R>RR`RRR((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRsN( RRR RRRR$RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRs   RcB seZRS((RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRsRcB seZRS((RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRsRcB seZRS((RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRsRcB sGeZeidd6dd6ZedZdddeedZRS( s?A results class for Zero Inflated Genaralized Negative BinomialRR\RcC sq|jjj}|j|jjd}tj|jdd}d|j|}d|||d||S(NiRRi(RRKRRR%R&RORF(R>RRuRRS((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRs RRcC stddS(shGet marginal effects of the fitted model. Not yet implemented for Zero Inflated Models s&not yet implemented for zero inflationN(R(R>RR`RRR((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRsN( RRR RRRR$RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyRs   RcB seZRS((RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR$sRcB seZRS((RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR)sRcB seZRS((RR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyR0s(:t __future__Rt__all__RtnumpyR&tstatsmodels.base.modelRRtstatsmodels.base.wrappertwrappertwrapt#statsmodels.regression.linear_modelt regressiont linear_modeltlmt#statsmodels.discrete.discrete_modelRRRRRR R R R R Rtstatsmodels.distributionsRRRtstatsmodels.tools.numdiffRRtstatsmodels.tools.decoratorsRtstatsmodels.tools.sm_exceptionsRRRRRRRRtRegressionResultsWrapperRtpopulate_wrapperRRRRRRRRR(((s?lib/python2.7/site-packages/statsmodels/discrete/count_model.pyts`   LXNN