M/PhVddlmZddlmZmZmZddlmZddlm Z ddl Z ddZ ddZ ddZd Zd Z dd Zdd Zifd ZGddZGddeZdS))RegularizedResults)_calc_nodewise_row_calc_nodewise_weight_calc_approx_inv_cov)LikelihoodModelResults)OLSNcH|td|jdi|jS)aestimates the regularized fitted parameters. Parameters ---------- mod : statsmodels model class instance The model for the current partition. pnum : scalar Index of current partition partitions : scalar Total number of partitions fit_kwds : dict-like or None Keyword arguments to be given to fit_regularized Returns ------- An array of the parameters for the regularized fit NzD_est_regularized_naive currently requires that fit_kwds not be None.) ValueErrorfit_regularizedparamsmodpnum partitionsfit_kwdss g/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/base/distributed_estimation.py_est_regularized_naiverKsB&?@@ @ 3  * * * * 11cH|td|jdi|jS)aestimates the unregularized fitted parameters. Parameters ---------- mod : statsmodels model class instance The model for the current partition. pnum : scalar Index of current partition partitions : scalar Total number of partitions fit_kwds : dict-like or None Keyword arguments to be given to fit Returns ------- An array of the parameters for the fit NzF_est_unregularized_naive currently requires that fit_kwds not be None.r )r fitr rs r_est_unregularized_naiveresA&?@@ @ 37  X   %%rct|d}t|}tj|}|D]}||z }||z}d|tj||k<|S)a joins the results from each run of _est__naive and returns the mean estimate of the coefficients Parameters ---------- params_l : list A list of arrays of coefficients. threshold : scalar The threshold at which the coefficients will be cut. r)lennpzerosabs)params_l thresholdpr params_mnr s r _join_naiver"sq HQKAXJ IV  I/0IbfY)+, rc^|jtj|fi| }||d|z zz }|S)acalculates the log-likelihood gradient for the debiasing Parameters ---------- mod : statsmodels model class instance The model for the current partition. params : array_like The estimated coefficients for the current partition. 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. score_kwds : dict-like or None Keyword arguments for the score function. Returns ------- An array-like object of the same dimension as params Notes ----- In general: gradient l_k(params) where k corresponds to the index of the partition For OLS: X^T(y - X^T params) )scorerasarray)rr alphaL1_wt score_kwdsgrads r _calc_gradr+sCL CIbj(( 7 7J 7 7 7DEQY D Krctj|jtj|fi|}|dddf|jzS)acalculates the weighted design matrix necessary to generate the approximate inverse covariance matrix Parameters ---------- mod : statsmodels model class instance The model for the current partition. params : array_like The estimated coefficients for the current partition. hess_kwds : dict-like or None Keyword arguments for the hessian function. Returns ------- An array-like object, updated design matrix, same dimension as mod.exog N)rsqrthessian_factorr&exog)rr hess_kwdsrhesss r_calc_wdesign_matr2sL& G&C&rz&'9'9GGYGG H HE D>CH $$rcJ|in|}|in|}|td|d}d|vr |d}nd}|jj\}} tt jd| z|z } |jdi|j} t|| ||||z } t|| |} g}g}t|| zt|dz| z| D]O}t| ||}| |t| |||}| |P| | ||fS)aestimates the regularized fitted parameters, is the default estimation_method for class DistributedModel. Parameters ---------- mod : statsmodels model class instance The model for the current partition. mnum : scalar Index of current partition. partitions : scalar Total number of partitions. fit_kwds : dict-like or None Keyword arguments to be given to fit_regularized 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 ------- A tuple of parameters for regularized fit An array-like object of the fitted parameters, params An array-like object for the gradient A list of array like objects for nodewise_row A list of array like objects for nodewise_weight NzG_est_regularized_debiased currently requires that fit_kwds not be None.r'r(r$g?r )r r/shapeintrceilr r r+r2rangeminrappendr)rmnumrrr)r0r'r(nobsr p_partr r*wexognodewise_row_lnodewise_weight_lidx nodewise_rownodewise_weights r_est_regularized_debiasedrCsz:")zJ'YI?@@ @!(!hnGD! "q&J.// 0 0F S , ,8 , , 3F c65% < r?rapprox_inv_covdebiased_paramss r_join_debiasedrOs& IaLOAYJ IhqkkGN ''QqT 1Q4ad###  1&&&&Xn--N!233 I sZG).:KLLN."4"4W"="==O;D " "%6D "  "#%D #2D  -D  *D   DNN&DN  !3D  !.D    "D    ,D   r sequentialc"|i}|dkr||||}n1|dkr|||||}ntd|z|j|fi|j}|jdgdgfi|j}|j||fi|jS)aePerforms the distributed estimation using the corresponding DistributedModel Parameters ---------- data_generator : generator A generator that produces a sequence of tuples where the first element in the tuple corresponds to an endog array and the element corresponds to an exog array. fit_kwds : dict-like or None Keywords needed for the model fitting. parallel_method : str type of distributed estimation to be used, currently "sequential", "joblib" and "dask" are supported. parallel_backend : None or joblib parallel_backend object used to allow support for more complicated backends, ex: dask.distributed init_kwds_generator : generator or None Additional keyword generator that produces model init_kwds that may vary based on data partition. The current usecase is for WLS and GLS Returns ------- join_method result. For the default, _join_debiased, it returns a p length array. Nrgjoblibz.parallel_method: %s is currently not supportedr) fit_sequential fit_joblibr rbrcrTrQrdre) rWdata_generatorrparallel_methodparallel_backendinit_kwds_generatorrJr res_mods rrzDistributedModel.fits<  H l * *++NH,?AAII ( ((8(;==II M./00 0"!)>>t~>>#$"A3>>t~>>!t!'6GGT5FGGGrc 8g}|At|D]0\}\}}t|||||}||1nTtt||} | D]4\}\\}}} t|||||| }||5|S)a*Sequentially performs the distributed estimation using the corresponding DistributedModel Parameters ---------- data_generator : generator A generator that produces a sequence of tuples where the first element in the tuple corresponds to an endog array and the element corresponds to an exog array. fit_kwds : dict-like Keywords needed for the model fitting. init_kwds_generator : generator or None Additional keyword generator that produces model init_kwds that may vary based on data partition. The current usecase is for WLS and GLS Returns ------- join_method result. For the default, _join_debiased, it returns a p length array. ) enumerater]r9zip) rWrlrrorJrrXr/r\tup_genrYs rrjzDistributedModel.fit_sequentials0  &'0'@'@ * *#mud/dE408::  ))))  * N$7!9!9::G7> * *22}tk/dE408+GG  ))))rcJ ddlm}|tj\} }|)|'| fdt |D}n|C|A|5| fdt |D}dddn #1swxYwYn|9|7t t ||} | fd| D}nT|R|Pt t ||} |5| fd| D}dddn #1swxYwY|S)aPerforms the distributed estimation in parallel using joblib Parameters ---------- data_generator : generator A generator that produces a sequence of tuples where the first element in the tuple corresponds to an endog array and the element corresponds to an exog array. fit_kwds : dict-like Keywords needed for the model fitting. parallel_backend : None or joblib parallel_backend object used to allow support for more complicated backends, ex: dask.distributed init_kwds_generator : generator or None Additional keyword generator that produces model init_kwds that may vary based on data partition. The current usecase is for WLS and GLS Returns ------- join_method result. For the default, _join_debiased, it returns a p length array. r) parallel_funcNc3BK|]\}\}}|||VdSrar .0rrXr/frrWs r z.DistributedModel.fit_joblib..csQ:: 3mudAdD%x@@::::::rc3BK|]\}\}}|||VdSrar rxs rr{z.DistributedModel.fit_joblib..isQ > >$7D-5$!"$eT8 D D > > > > > >rc 3JK|]\}\\}}}||||VdSrar ryrrXr/rQrzrrWs rr{z.DistributedModel.fit_joblib..osX(( @&@}tiAdD%xKK((((((rc 3JK|]\}\\}}}||||VdSrar r~s rr{z.DistributedModel.fit_joblib..vsX , ,$DD*D=E4)!"$eT8Y O O , , , , , ,r)statsmodels.tools.parallelrvr]rrrrs) rWrlrrnrorvparn_jobsrJrtrzs ` ` @rrkzDistributedModel.fit_joblibDsp4 =<<<<<&' >C > > > > > >#,^#<#< > > >>>  > > > > > > > > > > > > > > >  %*=*IN4G H HIIG((((((&(((((II ).A.MN4G H HIIG! , ,C , , , , , ,#* , , ,,,  , , , , , , , , , , , , , , , s$'BB B 2DDD)NNNNNNNN)NrgNNra)__name__ __module__ __qualname____doc__rfrrjrkr rrr_r_ms=G~0437CG26--------^BN7;7H7H7H7Ht,0----`(,666666rr_c(eZdZdZfdZdZxZS)DistributedResultsaT Class to contain model results Parameters ---------- model : class instance Class instance for model used for distributed data, this particular instance uses fake data and is really only to allow use of methods like predict. params : ndarray Parameter estimates from the fit model. cLt||dSra)superrf)rWr[r __class__s rrfzDistributedResults.__init__s# '''''rc:|jj|j|g|Ri|S)aCalls self.model.predict for the provided exog. See Results.predict. Parameters ---------- exog : array_like NOT optional The values for which we want to predict, unlike standard predict this is NOT optional since the data in self.model is fake. *args : Some models can take additional arguments. See the predict method of the model for the details. **kwargs : Some models can take additional keywords arguments. See the predict method of the model for the details. Returns ------- prediction : ndarray, pandas.Series or pandas.DataFrame See self.model.predict )r[predictr )rWr/argskwargss rrzDistributedResults.predicts-."tz!$+tEdEEEfEEEr)rrrrrfr __classcell__)rs@rrr}sX  (((((FFFFFFFrrra)r)NNN)statsmodels.base.elastic_netr(statsmodels.stats.regularized_covariancerrrstatsmodels.base.modelr#statsmodels.regression.linear_modelrnumpyrrrr"r+r2rCrOr]r_rr rrrs;;;;;;0000000000999999333333@F22224&&&&42(((V%%%.?C9=>;>;>;>;B****\')""""JMMMMMMMM`(F(F(F(F(F/(F(F(F(F(Fr