M/Ph2ddlZddlmZddlmcmZddlm Z  dZ dd Z ddZ GddeZ GddejZejee dS)N)Results)cache_readonlycHfd}fd}fd}|||fS)ao Negative penalized log-likelihood functions. Returns the negative penalized log-likelihood, its derivative, and its Hessian. The penalty only includes the smooth (L2) term. All three functions have argument signature (x, model), where ``x`` is a point in the parameter space and ``model`` is an arbitrary statsmodels regression model. c|j}dz ztj|dzzdz }|jtj|fi}| |z |zS)N)nobsnpsumlogliker_) paramsmodelr pen_llfllfL1_wtalphak loglike_kwdss \/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/base/elastic_net.py nploglikez_gen_npfuncs..nploglike)sdz(a%i(26&!)+<+<.npscore/sWz8q5y)F2ek"%-66:66q9 9D @H}rc|j}dz z}|jtj|fid |z |z}|S)Nr)rr)r hessianr r ) rrr pen_hesshrr hess_kwdsrs rnphessz_gen_npfuncs..nphess5sRz8q5y) U]25= 6 6I 6 6t < :0yE>FTc   jjd} in in in tjrtj| z|tj| }n|}d}tjt|t} }d|d<| dd}d|vr\|dT|(tj | d}n*|tj | dz } fd t| D}d}t|D]'}|}t| D]}||r |}d ||<tj j|}|||z }jjjdd|ffd|i|}||\}}}t!|||||||z|| ||<|d kr(tj|||kr d ||<d ||<tjtj||z }||krd }n)d |tj||k<|s&t'|}||_t+|Stj|} tj| | f}!fdjD}t| d kr_jjjdd| ffi|}"|"}#|#j|| <|#j|!tj| | <n9jjjddd ffi|}"|"d }#t9|#jt:jr |#jj}$n|#j}$tA|#dr|#j!}%nd}%j"j#}'}&t| _"j$j"z _#|$||!|%}d |_%||_||_&d|dzi|_'|&|'c_"_#|S)a Return an elastic net regularized fit to a regression model. Parameters ---------- model : model object A statsmodels object implementing ``loglike``, ``score``, and ``hessian``. method : {'coord_descent'} Only the coordinate descent algorithm is implemented. maxiter : int The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha : scalar or array_like The penalty weight. If a scalar, the same penalty weight applies to all variables in the model. If a vector, it must have the same length as `params`, and contains a penalty weight for each coefficient. L1_wt : scalar The fraction of the penalty given to the L1 penalty term. Must be between 0 and 1 (inclusive). If 0, the fit is a ridge fit, if 1 it is a lasso fit. start_params : array_like Starting values for `params`. cnvrg_tol : scalar If `params` changes by less than this amount (in sup-norm) in one iteration cycle, the algorithm terminates with convergence. zero_tol : scalar Any estimated coefficient smaller than this value is replaced with zero. refit : bool If True, the model is refit using only the variables that have non-zero coefficients in the regularized fit. The refitted model is not regularized. check_step : bool If True, confirm that the first step is an improvement and search further if it is not. loglike_kwds : dict-like or None Keyword arguments for the log-likelihood function. score_kwds : dict-like or None Keyword arguments for the score function. hess_kwds : dict-like or None Keyword arguments for the Hessian function. Returns ------- Results A results object. Notes ----- The ``elastic net`` penalty is a combination of L1 and L2 penalties. The function that is minimized is: -loglike/n + alpha*((1-L1_wt)*|params|_2^2/2 + L1_wt*|params|_1) where |*|_1 and |*|_2 are the L1 and L2 norms. The computational approach used here is to obtain a quadratic approximation to the smooth part of the target function: -loglike/n + alpha*(1-L1_wt)*|params|_2^2/2 then repeatedly optimize the L1 penalized version of this function along coordinate axes. rNg-C6?)dtypeFhasconstoffsetexposurec 8g|]}t|Sr%)r&).0rrrr#rrs r z"fit_elasticnet..s;    QulJ JJ   rr)tol check_stepTr)c4i|]}|t|dSN)getattr)r3rrs r z"fit_elasticnet..s'FFFGE1d++FFFr)maxiterscaler*)r< iteration)(exogshaper isscalaroneszeroscopylenbool_get_init_kwdspoplograngedot __class__endog_opt_1dabsmaxRegularizedResults convergedRegularizedResultsWrapper flatnonzero _init_keysfitrnormalized_cov_paramsix_ issubclasswrapResultsWrapper_resultshasattrr<df_modeldf_residr regularizedmethod fit_history)(rr`r;rr start_params cnvrg_tolzero_tolrefitr6rrr#k_exogrbtol params_zero init_args model_offsetfgh_listrQitr params_saverparams0r0 model_1varfuncgradhesspchangeresultsiicovmodel1rsltklassr<pqs(` `` ``` rfit_elasticnetr|>sTZ a F%-22>H $% 2$UF33%(111   B (FF# $ $CFFFFU5EFFFI 2ww{{  KAAArE*99.799zz||[r "8BF2rNN !ejA.>LL)LLzz!z$$$.$"566 'tW  >5>qAWWENZ%.0EN E%E 2 2 2EEEOEL$cAg.E&'"ENEN Lrc|}|||} |||} |||} | | |zz } |tj| krdS| dkr || z | z } n| dkr || z | z } n tjS|s|| zS||| z||tj|| zzz}|| |tj|zzdzkr|| zSddlm}|||f|dz |dzf|}|S)az One-dimensional helper for elastic net. Parameters ---------- func : function A smooth function of a single variable to be optimized with L1 penaty. grad : function The gradient of `func`. hess : function The Hessian of `func`. model : statsmodels model The model being fit. start : real A starting value for the function argument L1_wt : non-negative real The weight for the L1 penalty function. tol : non-negative real A convergence threshold. check_step : bool If True, check that the first step is an improvement and use bisection if it is not. If False, return after the first step regardless. Notes ----- ``func``, ``grad``, and ``hess`` have argument signature (x, model), where ``x`` is a point in the parameter space and ``model`` is the model being fit. If the log-likelihood for the model is exactly quadratic, the global minimum is returned in one step. Otherwise numerical bisection is used. Returns ------- The argmin of the objective function. r)rg|=)brentr)argsbrackr5)r rNnanscipy.optimizer~)rprqrrrstartrr5r6xfbcdr"f1r~x_opts rrMrMsAj A QA QA QA AaCA rvayyr Avv QY!O QaiL1 v 1u a!eU  eBF1q5MM1 1B Qrvayy 5 (((1u %$$$$$ E$eXac1Q3ZS A A AE Lrc8eZdZdZfdZedZxZS)rPz Results for models estimated using regularization Parameters ---------- model : Model The model instance used to estimate the parameters. params : ndarray The estimated (regularized) parameters. cLt||dSr8)super__init__)selfrrrKs rrzRegularizedResults.__init__qs# '''''rc@|j|jS)zR The predicted values from the model at the estimated parameters. )rpredictr)rs r fittedvalueszRegularizedResults.fittedvaluests z!!$+...r)__name__ __module__ __qualname____doc__rrr __classcell__)rKs@rrPrPfs]  (((((//^/////rrPceZdZddddZeZdS)rRcolumnsrows)rresidrN)rrr_attrs _wrap_attrsr%rrrRrR|s*F KKKrrR) r'r(r)r*Nr+r,FTNNN)T)numpyr statsmodels.base.modelrstatsmodels.base.wrapperbasewrapperrYstatsmodels.tools.decoratorsrr&r|rMrPrZrRpopulate_wrapperr%rrrs*******'''''''''777777.&&&B;>DH:>AEOOOOfSSSSl////////, 3/(*****r