Ë
    íì�i%  ã                   ó´  — d dl Z d dlZd dlZd dlmZmZ d dlmZm	Z	m
Z
 d dlmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z- d dl.m/Z/ d dl0m1Z1m2Z2 d dl3m4Z4m5Z5 d dl6m7Z7m8Z8 d d	l9m:Z: ejv                  jy                  d
 e«        e«        e«        e«        e«        e«        e«        e«        e«        e«        e«        ejz                   eddd¬«      ejv                  j}                  d¬«      ¬«       eddd¬«       e«        e«        e«        e«        e «        e!«        e%«        e&«        e)«        ejz                   e+d¬«      ejv                  j}                  d¬«      ¬«       e+dd¬«       e-d ¬«      gd„ ¬«      ejv                  jy                  dddg«      d „ «       «       Z?ejv                  j�                  d!«      ejv                  j�                  d"«      ejv                  j�                  d#«      ejv                  jy                  d$eeeeeeeeeeeeee5ee e!e#e%e&e)e+e,e-g«      ejv                  jy                  d%d&d'g«      d(„ «       «       «       «       «       ZAejv                  jy                  d)d*ge4i fei fed+ddd,œfe"i fe$i fe'i fe(i fe*i fg«      ejv                  jy                  d-d'd.g«      d/„ «       «       ZBejv                  jy                  d0edd1d2œfed3difed3dife'd4d5d6œfedd&d1d7œfedd8d1d7œfe&d4d9d5d:œfei fe)i fe(i fg
«      ejv                  jy                  d;e:«      d<„ «       «       ZCy)=é    N)ÚcloneÚis_classifier)Úmake_classificationÚmake_low_rank_matrixÚmake_regression)"ÚARDRegressionÚBayesianRidgeÚ
ElasticNetÚElasticNetCVÚGammaRegressorÚHuberRegressorÚLarsÚLarsCVÚLassoÚLassoCVÚ	LassoLarsÚLassoLarsCVÚLassoLarsICÚLinearRegressionÚLogisticRegressionÚLogisticRegressionCVÚMultiTaskElasticNetÚMultiTaskElasticNetCVÚMultiTaskLassoÚMultiTaskLassoCVÚOrthogonalMatchingPursuitÚOrthogonalMatchingPursuitCVÚPassiveAggressiveClassifierÚPassiveAggressiveRegressorÚ
PerceptronÚPoissonRegressorÚRidgeÚRidgeClassifierÚRidgeClassifierCVÚRidgeCVÚSGDClassifierÚSGDRegressorÚTheilSenRegressorÚTweedieRegressor)Úmake_pipeline)ÚMinMaxScalerÚStandardScaler)Ú	LinearSVCÚ	LinearSVR)Úassert_allcloseÚset_random_state)ÚCSR_CONTAINERSÚmodelç      à?ÚsagaçVçž¯Ò<)Úl1_ratioÚsolverÚtolz"Missing importance sampling scheme)Úreason)Úmarksg�íµ ÷Æ°>F)r   )r8   Úuse_legacy_attributesÚ	l1_ratios)r8   zInsufficient precision.Ú
elasticneti'  )ÚpenaltyÚmax_iter)Úpowerc                 ó.   — | j                   j                  S )N)Ú	__class__Ú__name__)Úxs    ú‹/var/www/ITT-ServiceAPI-Services/ITT-Document-Scan/mrzscanner/mrzenv/lib/python3.12/site-packages/sklearn/linear_model/tests/test_common.pyú<lambda>rF   \   s   € �!—+‘+×&Ñ&€ ó    )ÚidsÚwith_sample_weightTc                 ó�  — t        | «      } |rNdt        j                  | j                  «      j                  j                  «       vrt        j                  d«       d}t        | t        «      rd}nt        | d«      r| j                  dk(  rd}t        j                  j                  |«      }d\  }}}t        | t        t         t"        t$        f«      rd	}t'        |||¬
«      }|r6|j)                  dd||f¬«      t        j*                  |d¬«      d d …d f   z  }	n,|j)                  dd|¬«      t        j*                  |d¬«      z  }	t        j,                  ||	z  dz   «      }
|j/                  |
¬«      dz   }t1        | «      r%||
dz   kD  j3                  t        j4                  «      }|r"|j)                  dd|j6                  d   ¬«      }nd }| j9                  d¬«       |r| j                  |||¬«       n| j                  ||«       t1        | «      r]t        j:                  | j=                  |«      d d …df   |¬«      t        j>                  t        j:                  ||¬«      |¬«      k(  sJ ‚y t        j:                  | jA                  |«      |d¬«      t        j>                  t        j:                  ||d¬«      |¬«      k(  sJ ‚y )NÚsample_weightz)Estimator does not support sample_weight.g-Cëâ6*?çš™™™™™¹?r7   r4   ç{®Gáz„?)éd   é
   Né   )Ú	n_samplesÚ
n_featuresÚrandom_stateéþÿÿÿé   )ÚlowÚhighÚsizer   )Úaxisr3   )Úlamé   rO   T)Úfit_intercept)rK   )Úweights)Úrel)r]   rY   )!r   ÚinspectÚ	signatureÚfitÚ
parametersÚkeysÚpytestÚskipÚ
isinstancer'   Úhasattrr7   ÚnpÚrandomÚRandomStater   r   r   r   r   ÚuniformÚmaxÚexpÚpoissonr   ÚastypeÚfloat64ÚshapeÚ
set_paramsÚaverageÚpredict_probaÚapproxÚpredict)r2   rI   Úglobal_random_seedr^   ÚrngÚn_trainrR   Ú	n_targetsÚXÚcoefÚexpectationÚyÚsws                rE   Útest_balance_propertyr€   6   sg  € ôj �%‹L€EáØ¤7×#4Ñ#4°U·Y±YÓ#?×#JÑ#J×#OÑ#OÓ#QÑQä�‰Ð?Ô@à
€CÜ�%œÔ&Ø‰Ü	�˜Ô	! e§l¡l°fÒ&<Øˆä
�)‰)×
Ñ
Ð 2Ó
3€CØ%2Ñ"€GˆZ˜ÜØÜ	Ô3´^ÔEUÐVôð ˆ	Ü w¸:ÐTWÔX€AÙà�K‰K˜B Q¨j¸)Ð-DˆKÓEÜ�f‰f�Q˜QÔ¢ 4 Ñ(ñ)ñ 	ð
 �{‰{˜r¨°
ˆ{Ó;¼b¿f¹fÀQÈQÔ>OÑOˆä—&‘&˜˜T™ C™Ó(€KØ�‰˜ˆÓ$ qÑ(€AÜ�UÔØ�˜q‘Ñ ×(Ñ(¬¯©Ó4ˆáØ�[‰[˜Q R¨a¯g©g°a©jˆ[Ó9‰àˆà	×Ñ 4ÐÔ(ÙØ�	‰	�!�Q bˆ	Õ)à�	‰	�!�QŒä�UÔÜ�z‰z˜%×-Ñ-¨aÓ0²°A°Ñ6ÀÔCÄvÇ}Á}Ü�J‰J�q "Ô%¨3ôH
ò 
ð 	
ñ 
ô �z‰z˜%Ÿ-™-¨Ó*°B¸QÔ?Ä6Ç=Á=Ü�J‰J�q "¨1Ô-°3ôD
ò 
ð 	
ñ 
rG   z!ignore:The default of 'normalize'zignore:lbfgs failed to convergez5ignore:A column-vector y was passed when a 1d array.*Ú	RegressorÚndimr[   rU   c                 óœ  — | t         u rt        j                  d«       t        ddd¬«      \  }}t	        «       j                  |j                  dd«      «      dd…df   dz   }|d	k(  r|dd…t        j                  f   n|} | «       }t        |«       |j                  ||«       |j                  j                  |j                  d   fk(  sJ ‚y)
z4Check the consistency of linear models `coef` shape.z8LinearRegression does not follow `coef_` shape contract!r   éÈ   é   )rS   rQ   rR   éÿÿÿÿr[   NrU   )r   rd   Úxfailr   r+   Úfit_transformÚreshaperh   Únewaxisr0   ra   Úcoef_rq   )r�   r‚   r{   r~   Ú	regressors        rE   Ú&test_linear_model_regressor_coef_shaper�   ¢   s±   € ðF Ô$Ñ$Ü�‰ÐOÔPä¨°SÀRÔH�D€A€qÜ‹×$Ñ$ Q§Y¡Y¨r°1Ó%5Ó6²q¸!°tÑ<¸qÑ@€AØ  AšIˆŠ!ŒR�Z‰Zˆ-Ò¨1€Aá“€IÜ�YÔØ‡M�M�!�QÔØ�?‰?× Ñ  Q§W¡W¨Q¡Z MÒ1Ð1Ñ1rG   Ú
ClassifierÚparamsznewton-cholesky)r7   r;   r<   Ú	n_classesrP   c                 ó*  — | t         t        fv rt        j                  | › d�«       t	        d|d¬«      \  }}|j
                  d   } | di |¤Ž}t        |«       |j                  ||«       |dk(  rd|fn||f}|j                  j
                  |k(  sJ ‚y )Nz( does not follow `coef_` shape contract!rO   r   )Ún_informativer�   rS   r[   rU   © )	r#   r$   rd   r‡   r   rq   r0   ra   r‹   )rŽ   r�   r�   r{   r~   rR   Ú
classifierÚexpected_shapes           rE   Ú'test_linear_model_classifier_coef_shaper–   Ò   s™   € ð, ”oÔ'8Ð9Ñ9Ü�‰˜
�|Ð#KÐLÔMä¨R¸9ÐSTÔU�D€A€qØ—‘˜‘€JáÑ%˜fÑ%€JÜ�ZÔ Ø‡N�N�1�aÔØ(1°Qª�a˜‘_¸YÈ
Ð<S€NØ×Ñ×!Ñ! ^Ò3Ð3Ñ3rG   zLinearModel, paramsrM   )r8   Úalphar8   Ú	sparse_cgrL   )r7   r—   )r8   r6   r—   gñhãˆµøä>gê-�™—q=)r7   r8   r—   Úcsr_containerc                 ó   — t        t        d¬«       | di |¤Ž«      }t        t        d¬«       | di |¤Ž«      }t        j                  j	                  d«      }d}d}|j                  ||«      }d||dk  <    ||«      }	|j                  |«      }
t        |«      rt        j                  |
«      }
|j                  ||
«       |j                  |	|
«       t        |d   j                  |d   j                  d	¬
«       |j                  |«      }|j                  |	«      }t        ||«       t        |d   j                  |d   j                  «       y)z€Test that sparse and dense linear models give same results.

    Models use a preprocessing pipeline with a StandardScaler.
    F)Ú	with_meanr   rN   rU   g        rL   r[   r5   )ÚatolNr“   )r*   r,   rh   ri   rj   ÚrandnÚrandr   Úsignra   r/   r‹   rv   Ú
intercept_)ÚLinearModelr�   r™   Úmodel_denseÚmodel_sparserx   rQ   rR   r{   ÚX_sparser~   Úy_pred_denseÚy_pred_sparses                rE   Ú)test_model_pipeline_same_dense_and_sparser§   õ   s'  € ô*  ¤¸Ô ?ÁÑAVÈvÑAVÓW€Kä ¤¸%Ô!@Á+ÑBWÐPVÑBWÓX€Lô �)‰)×
Ñ
 Ó
"€CØ€IØ€JØ�	‰	�)˜ZÓ(€AØ€A€aˆ#�g�Já˜QÓ€HØ�‰�Ó€Aä�[Ô!Ü�G‰G�A‹Jˆà‡O�O�A�qÔØ×Ñ�X˜qÔ!ä�L ‘O×)Ñ)¨;°q©>×+?Ñ+?ÀeÕLØ×&Ñ& qÓ)€LØ ×(Ñ(¨Ó2€MÜ�L -Ô0ä�K ‘N×-Ñ-¨|¸A©×/IÑ/IÕJrG   )Dr_   Únumpyrh   rd   Úsklearn.baser   r   Úsklearn.datasetsr   r   r   Úsklearn.linear_modelr   r	   r
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ðF ‡�×ÑÐ?Ó@Ø‡�×ÑÐ=Ó>Ø‡�×ÑÐSÓTØ‡�×ÑØàØØØØØØØØØØØØØØØ!Ø#Ø"ØØØØØØð1óð: ‡�×Ñ˜ ! Q Ó(ñ2ó )ó;ó Uó ?ó AðB2ð ‡�×ÑØ�8Ðà	�BˆØ	˜RÐ à à+Ø).Ø!ñð	
ð 
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