§
    §éUgËj  ã                   óÎ  — d dl Z d dlmZ d dlZd dlmZ d dlmZ d dl	m
Z
 d dlmZmZ d dlmZmZ d dlmZ  G d	„ d
e¦  «        Z G d„ dee¦  «        Z G d„ de¦  «        Z G d„ de¦  «        Z G d„ de¦  «        Z G d„ de¦  «        Z G d„ de¦  «        Z G d„ de¦  «        Z G d„ de¦  «        Z G d„ de¦  «        Zd„ Zd„ Zd„ Zd „ Z d!„ Z!d"„ Z"d#„ Z#d$„ Z$d%„ Z%d&„ Z&d'„ Z'dS )(é    N)ÚPrettyPrinter)Ú_EstimatorPrettyPrinter)ÚLogisticRegressionCV)Úmake_pipeline)ÚBaseEstimatorÚTransformerMixin)ÚSelectKBestÚchi2)Úconfig_contextc                   ó:   — e Zd Z	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 dd„Zd„ ZdS )ÚLogisticRegressionÚl2Fç-Cëâ6?ç      ð?Té   NÚwarnéd   r   c                 óØ   — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        || _        d S ©N)ÚpenaltyÚdualÚtolÚCÚfit_interceptÚintercept_scalingÚclass_weightÚrandom_stateÚsolverÚmax_iterÚmulti_classÚverboseÚ
warm_startÚn_jobsÚl1_ratio)Úselfr   r   r   r   r   r   r   r   r   r   r    r!   r"   r#   r$   s                   ú^/var/www/surfInsights/venv3-11/lib/python3.11/site-packages/sklearn/utils/tests/test_pprint.pyÚ__init__zLogisticRegression.__init__   sv   € ð$ ˆŒØˆŒ	ØˆŒØˆŒØ*ˆÔØ!2ˆÔØ(ˆÔØ(ˆÔØˆŒØ ˆŒØ&ˆÔØˆŒØ$ˆŒØˆŒØ ˆŒˆˆó    c                 ó   — | S r   © )r%   ÚXÚys      r&   ÚfitzLogisticRegression.fit6   ó   € Øˆr(   )r   Fr   r   Tr   NNr   r   r   r   FNN)Ú__name__Ú
__module__Ú__qualname__r'   r-   r*   r(   r&   r   r      sg   € € € € € ð ØØØ
ØØØØØØØØØØØð! !ð  !ð  !ð  !ðDð ð ð ð r(   r   c                   ó   — e Zd Zdd„Zdd„ZdS )ÚStandardScalerTc                 ó0   — || _         || _        || _        d S r   )Ú	with_meanÚwith_stdÚcopy)r%   r7   r5   r6   s       r&   r'   zStandardScaler.__init__;   s   € Ø"ˆŒØ ˆŒØˆŒ	ˆ	ˆ	r(   Nc                 ó   — | S r   r*   ©r%   r+   r7   s      r&   Ú	transformzStandardScaler.transform@   r.   r(   )TTTr   )r/   r0   r1   r'   r:   r*   r(   r&   r3   r3   :   s<   € € € € € ðð ð ð ð
ð ð ð ð ð r(   r3   c                   ó   — e Zd Zdd„ZdS )ÚRFENr   r   c                 ó>   — || _         || _        || _        || _        d S r   )Ú	estimatorÚn_features_to_selectÚstepr!   )r%   r>   r?   r@   r!   s        r&   r'   zRFE.__init__E   s#   € Ø"ˆŒØ$8ˆÔ!ØˆŒ	ØˆŒˆˆr(   )Nr   r   ©r/   r0   r1   r'   r*   r(   r&   r<   r<   D   s(   € € € € € ðð ð ð ð ð r(   r<   c                   ó(   — e Zd Z	 	 	 	 	 	 	 	 	 d	d„ZdS )
ÚGridSearchCVNr   Tr   ú2*n_jobsúraise-deprecatingFc                 ó    — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        d S r   )r>   Ú
param_gridÚscoringr#   ÚiidÚrefitÚcvr!   Úpre_dispatchÚerror_scoreÚreturn_train_score)r%   r>   rG   rH   r#   rI   rJ   rK   r!   rL   rM   rN   s               r&   r'   zGridSearchCV.__init__M   sZ   € ð #ˆŒØ$ˆŒØˆŒØˆŒØˆŒØˆŒ
ØˆŒØˆŒØ(ˆÔØ&ˆÔØ"4ˆÔÐÐr(   )	NNr   Tr   r   rD   rE   FrA   r*   r(   r&   rC   rC   L   sE   € € € € € ð
 ØØØØØØØ'Ø ð5ð 5ð 5ð 5ð 5ð 5r(   rC   c                   óB   — e Zd Zdddddddddddd	d
dddej        fd„ZdS )ÚCountVectorizerÚcontentzutf-8ÚstrictNTz(?u)\b\w\w+\b)r   r   Úwordr   r   Fc                 óô   — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        || _	        || _
        || _        || _        |
| _        || _        || _        || _        d S r   )ÚinputÚencodingÚdecode_errorÚstrip_accentsÚpreprocessorÚ	tokenizerÚanalyzerÚ	lowercaseÚtoken_patternÚ
stop_wordsÚmax_dfÚmin_dfÚmax_featuresÚngram_rangeÚ
vocabularyÚbinaryÚdtype)r%   rU   rV   rW   rX   r\   rY   rZ   r^   r]   rb   r[   r_   r`   ra   rc   rd   re   s                     r&   r'   zCountVectorizer.__init__i   s…   € ð( ˆŒ
Ø ˆŒØ(ˆÔØ*ˆÔØ(ˆÔØ"ˆŒØ ˆŒØ"ˆŒØ*ˆÔØ$ˆŒØˆŒØˆŒØ(ˆÔØ&ˆÔØ$ˆŒØˆŒØˆŒ
ˆ
ˆ
r(   )r/   r0   r1   ÚnpÚint64r'   r*   r(   r&   rP   rP   h   s_   € € € € € ð ØØØØØØØØ&ØØØØØØØØŒhð%$ð $ð $ð $ð $ð $r(   rP   c                   ó   — e Zd Zdd„ZdS )ÚPipelineNc                 ó"   — || _         || _        d S r   )ÚstepsÚmemory)r%   rk   rl   s      r&   r'   zPipeline.__init__‘   s   € ØˆŒ
ØˆŒˆˆr(   r   rA   r*   r(   r&   ri   ri   �   s(   € € € € € ðð ð ð ð ð r(   ri   c                   ó2   — e Zd Z	 	 	 	 	 	 	 	 	 	 	 	 	 	 dd„Zd
S )ÚSVCr   Úrbfé   Úauto_deprecatedç        TFçü©ñÒMbP?éÈ   NéÿÿÿÿÚovrc                 óÊ   — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        d S r   )ÚkernelÚdegreeÚgammaÚcoef0r   r   Ú	shrinkingÚprobabilityÚ
cache_sizer   r!   r   Údecision_function_shaper   )r%   r   rx   ry   rz   r{   r|   r}   r   r~   r   r!   r   r   r   s                  r&   r'   zSVC.__init__—   sp   € ð" ˆŒØˆŒØˆŒ
ØˆŒ
ØˆŒØˆŒØ"ˆŒØ&ˆÔØ$ˆŒØ(ˆÔØˆŒØ ˆŒØ'>ˆÔ$Ø(ˆÔÐÐr(   )r   ro   rp   rq   rr   TFrs   rt   NFru   rv   NrA   r*   r(   r&   rn   rn   –   sT   € € € € € ð ØØØØØØØØØØØØ %Øð)ð )ð )ð )ð )ð )r(   rn   c                   ó$   — e Zd Z	 	 	 	 	 	 	 dd„ZdS )ÚPCANTFÚautorr   c                 óh   — || _         || _        || _        || _        || _        || _        || _        d S r   )Ún_componentsr7   ÚwhitenÚ
svd_solverr   Úiterated_powerr   )r%   r„   r7   r…   r†   r   r‡   r   s           r&   r'   zPCA.__init__¹   s>   € ð )ˆÔØˆŒ	ØˆŒØ$ˆŒØˆŒØ,ˆÔØ(ˆÔÐÐr(   )NTFr‚   rr   r‚   NrA   r*   r(   r&   r�   r�   ¸   s?   € € € € € ð ØØØØØØð)ð )ð )ð )ð )ð )r(   r�   c                   ó,   — e Zd Z	 	 	 	 	 	 	 	 	 	 	 d
d	„ZdS )ÚNMFNÚcdÚ	frobeniusr   rt   rr   r   Fc                 ó    — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        d S r   )r„   Úinitr   Ú	beta_lossr   r   r   Úalphar$   r!   Úshuffle)r%   r„   r�   r   rŽ   r   r   r   r�   r$   r!   r�   s               r&   r'   zNMF.__init__Í   sW   € ð )ˆÔØˆŒ	ØˆŒØ"ˆŒØˆŒØ ˆŒØ(ˆÔØˆŒ
Ø ˆŒØˆŒØˆŒˆˆr(   )NNrŠ   r‹   r   rt   Nrr   rr   r   FrA   r*   r(   r&   r‰   r‰   Ì   sK   € € € € € ð ØØØØØØØØØØðð ð ð ð ð r(   r‰   c                   ó*   — e Zd Zej        ddddfd„ZdS )ÚSimpleImputerÚmeanNr   Tc                 óL   — || _         || _        || _        || _        || _        d S r   )Úmissing_valuesÚstrategyÚ
fill_valuer!   r7   )r%   r•   r–   r—   r!   r7   s         r&   r'   zSimpleImputer.__init__é   s,   € ð -ˆÔØ ˆŒØ$ˆŒØˆŒØˆŒ	ˆ	ˆ	r(   )r/   r0   r1   rf   Únanr'   r*   r(   r&   r’   r’   è   s;   € € € € € ð ”vØØØØðð ð ð ð ð r(   r’   c                 ón   — t          ¦   «         }d}|dd …         }|                     ¦   «         |k    sJ ‚d S )NáE  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='warn', n_jobs=None, penalty='l2',
                   random_state=None, solver='warn', tol=0.0001, verbose=0,
                   warm_start=False)r   )r   Ú__repr__)Úprint_changed_only_falseÚlrÚexpecteds      r&   Ú
test_basicrŸ   ø   sB   € å	Ñ	Ô	€Bð(€Hð ˜˜˜Œ|€HØ�;Š;‰=Œ=˜HÒ$Ð$Ð$Ð$Ð$Ð$r(   c                  óþ  — t          d¬¦  «        } d}|                      ¦   «         |k    sJ ‚t          ddddd¬¦  «        } d	}|d
d …         }|                      ¦   «         |k    sJ ‚t          d¬¦  «        }d}|                     ¦   «         |k    sJ ‚t          t          d¦  «        ¬¦  «        }d}|                     ¦   «         |k    sJ ‚t	          t          t          j        dd
g¦  «        ¬¦  «        ¦  «         d S )Néc   ©r   zLogisticRegression(C=99)gš™™™™™Ù?FiÒ  T)r   r   r   r   r!   zk
LogisticRegression(C=99, class_weight=0.4, fit_intercept=False, tol=1234,
                   verbose=True)r   r   )r•   zSimpleImputer(missing_values=0)ÚNaNzSimpleImputer()gš™™™™™¹?)ÚCs)r   r›   r’   ÚfloatÚreprr   rf   Úarray)r�   rž   Úimputers      r&   Útest_changed_onlyr©     s  € å	˜bÐ	!Ñ	!Ô	!€BØ-€HØ�;Š;‰=Œ=˜HÒ$Ð$Ð$Ð$õ 
Ø
˜3¨e¸Àtð
ñ 
ô 
€Bð$€Hð ˜˜˜Œ|€HØ�;Š;‰=Œ=˜HÒ$Ð$Ð$Ð$å¨1Ð-Ñ-Ô-€GØ4€HØ×ÒÑÔ Ò)Ð)Ð)Ð)õ ­5°©<¬<Ð8Ñ8Ô8€GØ$€HØ×ÒÑÔ Ò)Ð)Ð)Ð)õ 	Õ	¥¤¨3°¨(Ñ!3Ô!3Ð	4Ñ	4Ô	4Ñ5Ô5Ð5Ð5Ð5r(   c                 ó¦   — t          t          ¦   «         t          d¬¦  «        ¦  «        }d}|dd …         }|                     ¦   «         |k    sJ ‚d S )Niç  r¢   a©  
Pipeline(memory=None,
         steps=[('standardscaler',
                 StandardScaler(copy=True, with_mean=True, with_std=True)),
                ('logisticregression',
                 LogisticRegression(C=999, class_weight=None, dual=False,
                                    fit_intercept=True, intercept_scaling=1,
                                    l1_ratio=None, max_iter=100,
                                    multi_class='warn', n_jobs=None,
                                    penalty='l2', random_state=None,
                                    solver='warn', tol=0.0001, verbose=0,
                                    warm_start=False))],
         verbose=False)r   )r   r3   r   r›   )rœ   Úpipelinerž   s      r&   Útest_pipeliner¬   #  s\   € å�^Ñ-Ô-Õ/AÀCÐ/HÑ/HÔ/HÑIÔI€Hð€Hð ˜˜˜Œ|€HØ×ÒÑÔ (Ò*Ð*Ð*Ð*Ð*Ð*r(   c                 ó$  — t          t          t          t          t          t          t          t          ¦   «         ¦  «        ¦  «        ¦  «        ¦  «        ¦  «        ¦  «        ¦  «        }d}|dd …         }|                     ¦   «         |k    sJ ‚d S )Naú  
RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=LogisticRegression(C=1.0,
                                                                                                                     class_weight=None,
                                                                                                                     dual=False,
                                                                                                                     fit_intercept=True,
                                                                                                                     intercept_scaling=1,
                                                                                                                     l1_ratio=None,
                                                                                                                     max_iter=100,
                                                                                                                     multi_class='warn',
                                                                                                                     n_jobs=None,
                                                                                                                     penalty='l2',
                                                                                                                     random_state=None,
                                                                                                                     solver='warn',
                                                                                                                     tol=0.0001,
                                                                                                                     verbose=0,
                                                                                                                     warm_start=False),
                                                                                        n_features_to_select=None,
                                                                                        step=1,
                                                                                        verbose=0),
                                                                          n_features_to_select=None,
                                                                          step=1,
                                                                          verbose=0),
                                                            n_features_to_select=None,
                                                            step=1, verbose=0),
                                              n_features_to_select=None, step=1,
                                              verbose=0),
                                n_features_to_select=None, step=1, verbose=0),
                  n_features_to_select=None, step=1, verbose=0),
    n_features_to_select=None, step=1, verbose=0)r   )r<   r   r›   )rœ   Úrferž   s      r&   Útest_deeply_nestedr¯   8  s{   € å
�c•#•c�#�c¥#Õ&8Ñ&:Ô&:Ñ";Ô";Ñ<Ô<Ñ=Ô=Ñ>Ô>Ñ?Ô?Ñ@Ô@Ñ
AÔ
A€Cð5€Hð< ˜˜˜Œ|€HØ�<Š<‰>Œ>˜XÒ%Ð%Ð%Ð%Ð%Ð%r(   c                 ó´   — dgddgg d¢dœdgg d¢dœg}t          t          ¦   «         |d¬	¦  «        }d
}|dd …         }|                     ¦   «         |k    sJ ‚d S )Nro   rs   r   ©r   é
   r   iè  )rx   rz   r   Úlinear)rx   r   é   )rK   aü  
GridSearchCV(cv=5, error_score='raise-deprecating',
             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
                           decision_function_shape='ovr', degree=3,
                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
                           probability=False, random_state=None, shrinking=True,
                           tol=0.001, verbose=False),
             iid='warn', n_jobs=None,
             param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],
                          'kernel': ['rbf']},
                         {'C': [1, 10, 100, 1000], 'kernel': ['linear']}],
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)r   )rC   rn   r›   )rœ   rG   Úgsrž   s       r&   Útest_gridsearchr¶   ]  sˆ   € ð �7 d¨D \Ð8JÐ8JÐ8JÐKÐKØ�:Ð$6Ð$6Ð$6Ð7Ð7ð€Jõ 
•c‘e”e˜Z¨AÐ	.Ñ	.Ô	.€Bð)€Hð ˜˜˜Œ|€HØ�;Š;‰=Œ=˜HÒ$Ð$Ð$Ð$Ð$Ð$r(   c                 ó®  — t          ddd¬¦  «        }t          dt          ¦   «         fdt          ¦   «         fg¦  «        }g d¢}g d¢}t          d¬	¦  «        t	          ¦   «         g||d
œt          t          ¦  «        g||dœg}t          |dd|¬¦  «        }d}|dd …         }|                     |¦  «        }t          j
        dd|¦  «        }||k    sJ ‚d S )NTr   )ÚcompactÚindentÚindent_at_nameÚ
reduce_dimÚclassify)é   é   é   r±   é   )r‡   )r»   Úreduce_dim__n_componentsÚclassify__C)r»   Úreduce_dim__krÂ   rp   )rK   r#   rG   a‰	  
GridSearchCV(cv=3, error_score='raise-deprecating',
             estimator=Pipeline(memory=None,
                                steps=[('reduce_dim',
                                        PCA(copy=True, iterated_power='auto',
                                            n_components=None,
                                            random_state=None,
                                            svd_solver='auto', tol=0.0,
                                            whiten=False)),
                                       ('classify',
                                        SVC(C=1.0, cache_size=200,
                                            class_weight=None, coef0=0.0,
                                            decision_function_shape='ovr',
                                            degree=3, gamma='auto_deprecated',
                                            kernel='rbf', max_iter=-1,
                                            probability=False,
                                            random_state=None, shrinking=True,
                                            tol=0.001, verbose=False))]),
             iid='warn', n_jobs=1,
             param_grid=[{'classify__C': [1, 10, 100, 1000],
                          'reduce_dim': [PCA(copy=True, iterated_power=7,
                                             n_components=None,
                                             random_state=None,
                                             svd_solver='auto', tol=0.0,
                                             whiten=False),
                                         NMF(alpha=0.0, beta_loss='frobenius',
                                             init=None, l1_ratio=0.0,
                                             max_iter=200, n_components=None,
                                             random_state=None, shuffle=False,
                                             solver='cd', tol=0.0001,
                                             verbose=0)],
                          'reduce_dim__n_components': [2, 4, 8]},
                         {'classify__C': [1, 10, 100, 1000],
                          'reduce_dim': [SelectKBest(k=10,
                                                     score_func=<function chi2 at some_address>)],
                          'reduce_dim__k': [2, 4, 8]}],
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)zfunction chi2 at 0x.*>zfunction chi2 at some_address>)r   ri   r�   rn   r‰   r	   r
   rC   ÚpformatÚreÚsub)	rœ   Úppr«   ÚN_FEATURES_OPTIONSÚ	C_OPTIONSrG   Ú	gspiplinerž   Úrepr_s	            r&   Útest_gridsearch_pipelinerÌ   w  s  € å	 ¨°aÈÐ	MÑ	MÔ	M€Bå˜,­©¬Ð.°½S¹U¼UÐ0CÐDÑEÔE€HØ"˜˜ÐØ"Ð"Ð"€Iõ ¨aÐ0Ñ0Ô0µ#±%´%Ð8Ø(:Ø$ð	
ð 	
õ '¥tÑ,Ô,Ð-Ø/Ø$ð	
ð 	
ð€Jõ ˜X¨!°AÀ*ÐMÑMÔM€Ið%)€HðN ˜˜˜Œ|€HØ�JŠJ�yÑ!Ô!€EåŒFÐ+Ð-MÈuÑUÔU€EØ�HÒÐÐÐÐÐr(   c                 ó   — d}t          ddd|¬¦  «        }d„ t          |¦  «        D ¦   «         }t          |¬¦  «        }d}|dd …         }|                     |¦  «        |k    sJ ‚d„ t          |dz   ¦  «        D ¦   «         }t          |¬¦  «        }d	}|dd …         }|                     |¦  «        |k    sJ ‚d
t	          t          |¦  «        ¦  «        i}t          t          ¦   «         |¦  «        }d}|dd …         }|                     |¦  «        |k    sJ ‚d
t	          t          |dz   ¦  «        ¦  «        i}t          t          ¦   «         |¦  «        }d}|dd …         }|                     |¦  «        |k    sJ ‚d S )Né   Tr   )r¸   r¹   rº   Ún_max_elements_to_showc                 ó   — i | ]}||“ŒS r*   r*   ©Ú.0Úis     r&   ú
<dictcomp>z/test_n_max_elements_to_show.<locals>.<dictcomp>Ã  s   € Ð>Ð>Ð>˜1�!�QÐ>Ð>Ð>r(   )rc   a÷  
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
                lowercase=True, max_df=1.0, max_features=None, min_df=1,
                ngram_range=(1, 1), preprocessor=None, stop_words=None,
                strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None,
                vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
                            8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
                            15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
                            21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
                            27: 27, 28: 28, 29: 29})c                 ó   — i | ]}||“ŒS r*   r*   rÑ   s     r&   rÔ   z/test_n_max_elements_to_show.<locals>.<dictcomp>×  s   € ÐBÐBÐB˜1�!�QÐBÐBÐBr(   aü  
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
                lowercase=True, max_df=1.0, max_features=None, min_df=1,
                ngram_range=(1, 1), preprocessor=None, stop_words=None,
                strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None,
                vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
                            8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
                            15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
                            21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
                            27: 27, 28: 28, 29: 29, ...})r   a  
GridSearchCV(cv='warn', error_score='raise-deprecating',
             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
                           decision_function_shape='ovr', degree=3,
                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
                           probability=False, random_state=None, shrinking=True,
                           tol=0.001, verbose=False),
             iid='warn', n_jobs=None,
             param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
                               15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
                               27, 28, 29]},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)a  
GridSearchCV(cv='warn', error_score='raise-deprecating',
             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
                           decision_function_shape='ovr', degree=3,
                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
                           probability=False, random_state=None, shrinking=True,
                           tol=0.001, verbose=False),
             iid='warn', n_jobs=None,
             param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
                               15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
                               27, 28, 29, ...]},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0))r   ÚrangerP   rÄ   ÚlistrC   rn   )rœ   rÏ   rÇ   rc   Ú
vectorizerrž   rG   rµ   s           r&   Útest_n_max_elements_to_showrÙ   ¹  s½  € ØÐÝ	 ØØØØ5ð	
ñ 
ô 
€Bð ?Ð>¥Ð&<Ñ =Ô =Ð>Ñ>Ô>€JÝ ¨JÐ7Ñ7Ô7€Jð8€Hð ˜˜˜Œ|€HØ�:Š:�jÑ!Ô! XÒ-Ð-Ð-Ð-ð CÐB¥Ð&<¸qÑ&@Ñ AÔ AÐBÑBÔB€JÝ ¨JÐ7Ñ7Ô7€Jð=€Hð ˜˜˜Œ|€HØ�:Š:�jÑ!Ô! XÒ-Ð-Ð-Ð-ð •t�EÐ"8Ñ9Ô9Ñ:Ô:Ð;€JÝ	•c‘e”e˜ZÑ	(Ô	(€Bð)€Hð ˜˜˜Œ|€HØ�:Š:�b‰>Œ>˜XÒ%Ð%Ð%Ð%ð •t�EÐ"8¸1Ñ"<Ñ=Ô=Ñ>Ô>Ð?€JÝ	•c‘e”e˜ZÑ	(Ô	(€Bð)€Hð ˜˜˜Œ|€HØ�:Š:�b‰>Œ>˜XÒ%Ð%Ð%Ð%Ð%Ð%r(   c                 ó¶  — t          ¦   «         }d}|dd …         }||                     d¬¦  «        k    sJ ‚d}|dd …         }||                     d¬¦  «        k    sJ ‚|                     t          d¦  «        ¬¦  «        }t          d                     |                     ¦   «         ¦  «        ¦  «        }|                     |¬¦  «        |k    sJ ‚d	|vsJ ‚d
}|dd …         }||                     |dz
  ¬¦  «        k    sJ ‚d}|dd …         }||                     |dz
  ¬¦  «        k    sJ ‚d}|dd …         }||                     |dz
  ¬¦  «        k    sJ ‚d S )Na  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   in...
                   multi_class='warn', n_jobs=None, penalty='l2',
                   random_state=None, solver='warn', tol=0.0001, verbose=0,
                   warm_start=False)r   é–   )Ú
N_CHAR_MAXz+
Lo...
                   warm_start=False)r¾   ÚinfÚ z...a@  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_i...
                   multi_class='warn', n_jobs=None, penalty='l2',
                   random_state=None, solver='warn', tol=0.0001, verbose=0,
                   warm_start=False)r²   aD  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter...,
                   multi_class='warn', n_jobs=None, penalty='l2',
                   random_state=None, solver='warn', tol=0.0001, verbose=0,
                   warm_start=False)rš   r½   )r   r›   r¥   ÚlenÚjoinÚsplit)rœ   r�   rž   Ú	full_reprÚ
n_nonblanks        r&   Útest_bruteforce_ellipsisrä     s˜  € õ 
Ñ	Ô	€Bð(€Hð ˜˜˜Œ|€HØ�r—{’{¨c�{Ñ2Ô2Ò2Ð2Ð2Ð2ð(€Hð ˜˜˜Œ|€HØ�r—{’{¨a�{Ñ0Ô0Ò0Ð0Ð0Ð0ð —’¥u¨U¡|¤|�Ñ4Ô4€IÝ�R—W’W˜YŸ_š_Ñ.Ô.Ñ/Ô/Ñ0Ô0€JØ�;Š; *ˆ;Ñ-Ô-°Ò:Ð:Ð:Ð:Ø˜	Ð!Ð!Ð!Ð!ð
(€Hð ˜˜˜Œ|€HØ�r—{’{¨j¸2©o�{Ñ>Ô>Ò>Ð>Ð>Ð>ð(€Hð ˜˜˜Œ|€HØ�r—{’{¨j¸1©n�{Ñ=Ô=Ò=Ð=Ð=Ð=ð
(€Hð ˜˜˜Œ|€HØ�r—{’{¨j¸1©n�{Ñ=Ô=Ò=Ð=Ð=Ð=Ð=Ð=r(   c                  ó`   — t          ¦   «                              t          ¦   «         ¦  «         d S r   )r   Úpprintr   r*   r(   r&   Útest_builtin_prettyprinterrç   ]  s)   € õ
 �O„O×ÒÕ-Ñ/Ô/Ñ0Ô0Ð0Ð0Ð0r(   c                  ó  —  G d„ dt           ¦  «        }  | ddd ¬¦  «        }d}||                     ¦   «         k    sJ ‚t          d¬¦  «        5  d	}||                     ¦   «         k    sJ ‚	 d d d ¦  «         d S # 1 swxY w Y   d S )
Nc                   ó.   ‡ — e Zd Zdd„Zdˆ fd„	Zd„ Zˆ xZS )	ú'test_kwargs_in_init.<locals>.WithKWargsÚ
willchangeÚ	unchangedc                 óJ   — || _         || _        i | _         | j        di |¤Ž d S )Nr*   )ÚaÚbÚ_other_paramsÚ
set_params)r%   rî   rï   Úkwargss       r&   r'   z0test_kwargs_in_init.<locals>.WithKWargs.__init__n  s6   € ØˆDŒFØˆDŒFØ!#ˆDÔØˆDŒOÐ%Ð%˜fÐ%Ð%Ð%Ð%Ð%r(   Tc                 ó€   •— t          ¦   «                              |¬¦  «        }|                     | j        ¦  «         |S )N)Údeep)ÚsuperÚ
get_paramsÚupdaterð   )r%   rô   ÚparamsÚ	__class__s      €r&   rö   z2test_kwargs_in_init.<locals>.WithKWargs.get_paramst  s7   ø€ Ý‘W”W×'Ò'¨TÐ'Ñ2Ô2ˆFØ�MŠM˜$Ô,Ñ-Ô-Ð-ØˆMr(   c                 óp   — |                      ¦   «         D ] \  }}t          | ||¦  «         || j        |<   Œ!| S r   )ÚitemsÚsetattrrð   )r%   rø   ÚkeyÚvalues       r&   rñ   z2test_kwargs_in_init.<locals>.WithKWargs.set_paramsy  sD   € Ø$Ÿlšl™nœnð 0ð 0‘
��UÝ˜˜c 5Ñ)Ô)Ð)Ø*/�Ô" 3Ñ'Ð'ØˆKr(   )rë   rì   )T)r/   r0   r1   r'   rö   rñ   Ú__classcell__)rù   s   @r&   Ú
WithKWargsrê   k  s`   ø€ € € € € ð	&ð 	&ð 	&ð 	&ð	ð 	ð 	ð 	ð 	ð 	ð
	ð 	ð 	ð 	ð 	ð 	ð 	r(   r   Ú	somethingÚabcd)rî   ÚcÚdz+WithKWargs(a='something', c='abcd', d=None)F©Úprint_changed_onlyz:WithKWargs(a='something', b='unchanged', c='abcd', d=None))r   r›   r   )r   Úestrž   s      r&   Útest_kwargs_in_initr  e  sø   € ðð ð ð ð •]ñ ô ð ð( ˆ*�{ f°Ð
5Ñ
5Ô
5€Cà<€HØ�s—|’|‘~”~Ò%Ð%Ð%Ð%å	¨5Ð	1Ñ	1Ô	1ð *ð *ØOˆØ˜3Ÿ<š<™>œ>Ò)Ð)Ð)Ð)Ð)ð*ð *ð *ñ *ô *ð *ð *ð *ð *ð *ð *ð *øøøð *ð *ð *ð *ð *ð *s   ÁA9Á9A=Â A=c                  óº  ‡—  G ˆfd„dt           t          ¦  «        Š ‰t           ‰ ‰¦   «         ¦  «         ‰¦   «         d¦  «        ¦  «        } t          d¬¦  «        5  t	          | ¦  «         ‰j        }d d d ¦  «         n# 1 swxY w Y   d‰_        t          d¬¦  «        5  t	          | ¦  «         ‰j        }d d d ¦  «         n# 1 swxY w Y   ||k    sJ ‚d S )Nc                   ó6   •‡ — e Zd ZdZdd„Zˆˆ fd„Zdd„Zˆ xZS )ú:test_complexity_print_changed_only.<locals>.DummyEstimatorr   Nc                 ó   — || _         d S r   )r>   )r%   r>   s     r&   r'   zCtest_complexity_print_changed_only.<locals>.DummyEstimator.__init__’  s   € Ø&ˆDŒNˆNˆNr(   c                 ód   •— ‰xj         dz  c_         t          ¦   «                              ¦   «         S )Nr   )Únb_times_repr_calledrõ   r›   )r%   ÚDummyEstimatorrù   s    €€r&   r›   zCtest_complexity_print_changed_only.<locals>.DummyEstimator.__repr__•  s-   ø€ ØÐ/Ô/°1Ñ4Ð/Ô/Ý‘7”7×#Ò#Ñ%Ô%Ð%r(   c                 ó   — |S r   r*   r9   s      r&   r:   zDtest_complexity_print_changed_only.<locals>.DummyEstimator.transform™  s   € ØˆHr(   r   )r/   r0   r1   r  r'   r›   r:   rÿ   )rù   r  s   @€r&   r  r  �  sl   øø€ € € € € Ø Ðð	'ð 	'ð 	'ð 	'ð	&ð 	&ð 	&ð 	&ð 	&ð 	&ð	ð 	ð 	ð 	ð 	ð 	ð 	ð 	r(   r  ÚpassthroughFr  r   T)r   r   r   r   r¦   r  )r>   Ú nb_repr_print_changed_only_falseÚnb_repr_print_changed_only_truer  s      @r&   Ú"test_complexity_print_changed_onlyr  ‰  s²  ø€ ðð ð ð ð ð ð Õ)­=ñ ô ð ð �Ý�n�n ^ ^Ñ%5Ô%5Ñ6Ô6¸¸Ñ8HÔ8HÈ-ÑXÔXñô €Iõ 
¨5Ð	1Ñ	1Ô	1ð Oð OÝˆY‰ŒˆØ+9Ô+NÐ(ðOð Oð Oñ Oô Oð Oð Oð Oð Oð Oð Oøøøð Oð Oð Oð Oð +,€NÔ'Ý	¨4Ð	0Ñ	0Ô	0ð Nð NÝˆY‰ŒˆØ*8Ô*MÐ'ðNð Nð Nñ Nô Nð Nð Nð Nð Nð Nð Nøøøð Nð Nð Nð Nð ,Ð/NÒNÐNÐNÐNÐNÐNs$   Á BÂBÂ
BÂ%CÃCÃC)(rÅ   ræ   r   Únumpyrf   Úsklearn.utils._pprintr   Úsklearn.linear_modelr   Úsklearn.pipeliner   Úsklearn.baser   r   Úsklearn.feature_selectionr	   r
   Úsklearnr   r   r3   r<   rC   rP   ri   rn   r�   r‰   r’   rŸ   r©   r¬   r¯   r¶   rÌ   rÙ   rä   rç   r  r  r*   r(   r&   ú<module>r     s  ðØ 	€	€	€	Ø  Ð  Ð  Ð  Ð  Ð  à Ð Ð Ð à 9Ð 9Ð 9Ð 9Ð 9Ð 9Ø 5Ð 5Ð 5Ð 5Ð 5Ð 5Ø *Ð *Ð *Ð *Ð *Ð *Ø 8Ð 8Ð 8Ð 8Ð 8Ð 8Ð 8Ð 8Ø 7Ð 7Ð 7Ð 7Ð 7Ð 7Ð 7Ð 7Ø "Ð "Ð "Ð "Ð "Ð "ð$ð $ð $ð $ð $˜ñ $ô $ð $ðNð ð ð ð Ð% }ñ ô ð ðð ð ð ð ˆ-ñ ô ð ð5ð 5ð 5ð 5ð 5�=ñ 5ô 5ð 5ð8%ð %ð %ð %ð %�mñ %ô %ð %ðPð ð ð ð ˆ}ñ ô ð ð)ð )ð )ð )ð )ˆ-ñ )ô )ð )ðD)ð )ð )ð )ð )ˆ-ñ )ô )ð )ð(ð ð ð ð ˆ-ñ ô ð ð8ð ð ð ð �Mñ ô ð ð %ð %ð %ð6ð 6ð 6ð:+ð +ð +ð*"&ð "&ð "&ðJ%ð %ð %ð4?ð ?ð ?ðDW&ð W&ð W&ðtG>ð G>ð G>ðT1ð 1ð 1ð!*ð !*ð !*ðHOð Oð Oð Oð Or(   