Dj3ddlZddlmZddlmcmZddlm Z  dZ d dZ d dZ GddeZ Gd d ejZej ee y) 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 `C:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\statsmodels/base/elastic_net.py nploglikez_gen_npfuncs..nploglike)sczz(a%i(266&!)+<.npscore/sWzz8q5y)F2ekk"%%-6:6q9 9D @H}rc|j}dz z}|jtj|fid |z |z}|S)Nr)rr)r hessianr r ) rrr pen_hesshrr hess_kwdsrs rnphessz_gen_npfuncs..nphess5sRzz8q5y) U]]255= 6I 6t < j@r|#jBj}$n |#j}$tE|#dr |#jF}%nd}%|jH|jJ}'}&t| |_$|jL|jHz |_%|$|||!|%}d |_'||_||_(ddzi|_)|&|'c|_$|_%|Scc}wcc}w)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?)dtypeFhasconstoffsetexposurer)tol check_stepT)maxiterscale?)r0 iteration)*exogshaper isscalaroneszeroscopylenbool_get_init_kwdspoplogranger&dot __class__endog_opt_1dabsmaxRegularizedResults convergedRegularizedResultsWrapper flatnonzero _init_keysgetattrfitrnormalized_cov_paramsix_ issubclasswrapResultsWrapper_resultshasattrr0df_modeldf_residr regularizedmethod fit_history)(rrVr/rr start_params cnvrg_tolzero_tolrefitr-rrr#k_exogrbtol params_zero init_args model_offsetrfgh_listrFitr params_saveparams0r* model_1varfuncgradhesspchangeresultsiicovmodel1rsltklassr0pqs( rfit_elasticnetrr>sTZZ  a F%-2H $% $UF3%(11  B ((FF# $C5:5E5EFGE1d++FIF 2w{  KKArE*9.79zz|[[r "88BFF2rN !ejjA.>L)Lzz!z$$..$"5"56 ''tW  >>5>>qAWENZZ%..0EN %E 2EEEOEL$cAg.E&'"ENEN LS xGs 2R-/R2c|}|||} |||} |||} | | |zz } |tj| kDry| dk\r || z | z } n| dkr || z | z } ntjS|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)argsbrackr,)r rCnanscipy.optimizert)rfrgrhrstartrr,r-xfbcdr"f1rtx_opts rrBrBsj A QA QA QA AaCA rvvay Av QY!O QaiL1 vv 1u a!eU eBFF1q5M1 1B Qrvvay 5 ((1u % $eXac1Q3ZS AE Lrc2eZdZdZfdZedZxZS)rEz Results for models estimated using regularization Parameters ---------- model : Model The model instance used to estimate the parameters. params : ndarray The estimated (regularized) parameters. c&t|||y)N)super__init__)selfrrr@s rrzRegularizedResults.__init__qs 'rcL|jj|jS)zR The predicted values from the model at the estimated parameters. )rpredictr)rs r fittedvalueszRegularizedResults.fittedvaluests zz!!$++..r)__name__ __module__ __qualname____doc__rrr __classcell__)r@s@rrErEfs! (//rrEceZdZddddZeZy)rGcolumnsrows)rresidrN)rrr_attrs _wrap_attrsr%rrrGrG|sF KrrG) coord_descentdr.r1NgHz>g:0yE>FTNNN)T)numpyr statsmodels.base.modelrstatsmodels.base.wrapperbasewrapperrOstatsmodels.tools.decoratorsrr&rrrBrErPrGpopulate_wrapperr%rrrsv*''7.&B;>DH:>AEOfSl//, 3 3/(*r