Ë
    êÿæiîk  ã                   óˆ  — d dl Z d dlmZ d dlZd dl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 d dl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 ed¬«      d„ «       Zd „ Z ed¬«      d!„ «       Z  ed¬«      d"„ «       Z!ejD                  jG                  d#d$d%g«      d&„ «       Z$ ed¬«      d'„ «       Z% ed¬«      d(„ «       Z& ed¬«      d)„ «       Z' ed¬«      d*„ «       Z(d+„ Z)d,„ Z*d-„ Z+y).é    N)ÚPrettyPrinter)Úconfig_context)ÚBaseEstimatorÚTransformerMixin)ÚSelectKBestÚchi2)ÚLogisticRegressionCV)Úmake_pipeline)Ú_EstimatorPrettyPrinterc                   ó6   — e Zd Z	 	 	 	 	 	 	 	 	 	 	 	 	 	 dd„Zd„ Zy)ÚLogisticRegressionNc                 óÈ   — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        y ©N)ÚCÚl1_ratioÚdualÚtolÚfit_interceptÚintercept_scalingÚclass_weightÚrandom_stateÚsolverÚmax_iterÚmulti_classÚverboseÚ
warm_startÚn_jobs)Úselfr   r   r   r   r   r   r   r   r   r   r   r   r   r   s                  út/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/sklearn/utils/tests/test_pprint.pyÚ__init__zLogisticRegression.__init__   sk   € 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$ˆŒØˆ�ó    c                 ó   — | S r   © )r   ÚXÚys      r   ÚfitzLogisticRegression.fit1   ó   € Øˆr!   )ç      ð?r   Fç-Cëâ6?Té   NNÚwarnéd   r+   r   FN)Ú__name__Ú
__module__Ú__qualname__r    r&   r#   r!   r   r   r      s9   „ ð ØØØØØØØØØØØØØóó@r!   r   c                   ó   — e Zd Zdd„Zdd„Zy)ÚStandardScalerc                 ó.   — || _         || _        || _        y r   )Ú	with_meanÚwith_stdÚcopy)r   r5   r3   r4   s       r   r    zStandardScaler.__init__6   s   € Ø"ˆŒØ ˆŒØˆ�	r!   Nc                 ó   — | S r   r#   ©r   r$   r5   s      r   Ú	transformzStandardScaler.transform;   r'   r!   )TTTr   )r-   r.   r/   r    r8   r#   r!   r   r1   r1   5   s   „ óô
r!   r1   c                   ó   — e Zd Zdd„Zy)ÚRFENc                 ó<   — || _         || _        || _        || _        y r   )Ú	estimatorÚn_features_to_selectÚstepr   )r   r<   r=   r>   r   s        r   r    zRFE.__init__@   s   € Ø"ˆŒØ$8ˆÔ!ØˆŒ	Øˆ�r!   )Nr*   r   ©r-   r.   r/   r    r#   r!   r   r:   r:   ?   s   „ ôr!   r:   c                   ó&   — e Zd Z	 	 	 	 	 	 	 	 	 dd„Zy)ÚGridSearchCVNc                 óž   — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        y r   )r<   Ú
param_gridÚscoringr   ÚiidÚrefitÚcvr   Úpre_dispatchÚerror_scoreÚreturn_train_score)r   r<   rC   rD   r   rE   rF   rG   r   rH   rI   rJ   s               r   r    zGridSearchCV.__init__H   sT   € ð #ˆŒØ$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Õr!   )	NNr+   Tr+   r   z2*n_jobszraise-deprecatingFr?   r#   r!   r   rA   rA   G   s$   „ ð
 ØØØØØØØ'Ø ô5r!   rA   c                   óJ   — e Zd Zdddddddddddd	d
dddej                  fd„Zy)ÚCountVectorizerÚcontentzutf-8ÚstrictNTz(?u)\b\w\w+\b)r*   r*   Úwordr(   r*   Fc                 óò   — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        || _	        || _
        || _        || _        |
| _        || _        || _        || _        y r   )ÚinputÚencodingÚdecode_errorÚstrip_accentsÚpreprocessorÚ	tokenizerÚanalyzerÚ	lowercaseÚtoken_patternÚ
stop_wordsÚmax_dfÚmin_dfÚmax_featuresÚngram_rangeÚ
vocabularyÚbinaryÚdtype)r   rQ   rR   rS   rT   rX   rU   rV   rZ   rY   r^   rW   r[   r\   r]   r_   r`   ra   s                     r   r    zCountVectorizer.__init__d   s�   € ð( ˆŒ
Ø ˆŒØ(ˆÔØ*ˆÔØ(ˆÔØ"ˆŒØ ˆŒØ"ˆŒØ*ˆÔØ$ˆŒØˆŒØˆŒØ(ˆÔØ&ˆÔØ$ˆŒØˆŒØˆ�
r!   )r-   r.   r/   ÚnpÚint64r    r#   r!   r   rL   rL   c   s@   „ ð ØØØØØØØØ&ØØØØØØØØ�h‰hô%$r!   rL   c                   ó   — e Zd Zdd„Zy)ÚPipelineNc                 ó    — || _         || _        y r   )ÚstepsÚmemory)r   rg   rh   s      r   r    zPipeline.__init__Œ   s   € ØˆŒ
Øˆ�r!   r   r?   r#   r!   r   re   re   ‹   s   „ ôr!   re   c                   ó0   — e Zd Z	 	 	 	 	 	 	 	 	 	 	 	 	 	 dd„Zy)ÚSVCNc                 óÈ   — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        y r   )ÚkernelÚdegreeÚgammaÚcoef0r   r   Ú	shrinkingÚprobabilityÚ
cache_sizer   r   r   Údecision_function_shaper   )r   r   rl   rm   rn   ro   rp   rq   r   rr   r   r   r   rs   r   s                  r   r    zSVC.__init__’   sj   € 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Õr!   )r(   Úrbfé   Úauto_deprecatedç        TFçü©ñÒMbP?éÈ   NFéÿÿÿÿÚovrNr?   r#   r!   r   rj   rj   ‘   s3   „ ð ØØØØØØØØØØØØ %Øô)r!   rj   c                   ó"   — e Zd Z	 	 	 	 	 	 	 dd„Zy)ÚPCANc                 óf   — || _         || _        || _        || _        || _        || _        || _        y r   )Ún_componentsr5   ÚwhitenÚ
svd_solverr   Úiterated_powerr   )r   r   r5   r€   r�   r   r‚   r   s           r   r    zPCA.__init__´   s8   € ð )ˆÔØˆŒ	ØˆŒØ$ˆŒØˆŒØ,ˆÔØ(ˆÕr!   )NTFÚautorw   rƒ   Nr?   r#   r!   r   r}   r}   ³   s   „ ð ØØØØØØô)r!   r}   c                   ó*   — e Zd Z	 	 	 	 	 	 	 	 	 	 	 dd„Zy)ÚNMFNc                 óž   — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        y r   )r   Úinitr   Ú	beta_lossr   r   r   Úalphar   r   Úshuffle)r   r   r‡   r   rˆ   r   r   r   r‰   r   r   rŠ   s               r   r    zNMF.__init__È   sS   € ð )ˆÔØˆŒ	ØˆŒØ"ˆŒØˆŒØ ˆŒØ(ˆÔØˆŒ
Ø ˆŒØˆŒØˆ�r!   )NNÚcdÚ	frobeniusr)   ry   Nrw   rw   r   Fr?   r#   r!   r   r…   r…   Ç   s*   „ ð ØØØØØØØØØØôr!   r…   c                   ó2   — e Zd Zej                  ddddfd„Zy)ÚSimpleImputerÚmeanNr   Tc                 óJ   — || _         || _        || _        || _        || _        y r   )Úmissing_valuesÚstrategyÚ
fill_valuer   r5   )r   r‘   r’   r“   r   r5   s         r   r    zSimpleImputer.__init__ä   s(   € ð -ˆÔØ ˆŒØ$ˆŒØˆŒØˆ�	r!   )r-   r.   r/   rb   Únanr    r#   r!   r   rŽ   rŽ   ã   s   „ ð —v‘vØØØØôr!   rŽ   F©Úprint_changed_onlyc                  óP   — t        «       } d}|dd  }| j                  «       |k(  sJ ‚y )Ná!  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=0, max_iter=100,
                   multi_class='warn', n_jobs=None, random_state=None,
                   solver='warn', tol=0.0001, verbose=0, warm_start=False)r*   )r   Ú__repr__)ÚlrÚexpecteds     r   Ú
test_basicrœ   ó   s5   € ô 
Ó	€BðN€Hð ˜˜ˆ|€HØ�;‰;‹=˜HÒ$Ð$Ñ$r!   c                  ó–  — t        d¬«      } d}| j                  «       |k(  sJ ‚t        ddddd¬«      } d	}|d
d  }| j                  «       |k(  sJ ‚t        d¬«      }d}|j                  «       |k(  sJ ‚t        t        d«      ¬«      }d}|j                  «       |k(  sJ ‚t	        t        t        j                  dd
g«      d¬«      «       y )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CsÚuse_legacy_attributes)r   r™   rŽ   ÚfloatÚreprr	   rb   Úarray)rš   r›   Úimputers      r   Útest_changed_onlyr§     sÕ   € ä	˜bÔ	!€BØ-€HØ�;‰;‹=˜HÒ$Ð$Ð$ô 
Ø
˜3¨e¸Àtô
€Bð$€Hð ˜˜ˆ|€HØ�;‰;‹=˜HÒ$Ð$Ð$ä¨1Ô-€GØ4€HØ×ÑÓ Ò)Ð)Ð)ô ¬5°«<Ô8€GØ$€HØ×ÑÓ Ò)Ð)Ð)ô 	Ô	¤§¡¨3°¨(Ó!3È5Ô	QÕRr!   c                  óx   — t        t        «       t        d¬«      «      } d}|dd  }| j                  «       |k(  sJ ‚y )Niç  rŸ   aŠ  
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=0, max_iter=100,
                                    multi_class='warn', n_jobs=None,
                                    random_state=None, solver='warn',
                                    tol=0.0001, verbose=0, warm_start=False))],
         transform_input=None, verbose=False)r*   )r
   r1   r   r™   )Úpipeliner›   s     r   Útest_pipelinerª     sD   € ô œ^Ó-Ô/AÀCÔ/HÓI€Hð1€Hð ˜˜ˆ|€HØ×ÑÓ (Ò*Ð*Ñ*r!   c                  óÎ   — t        t        t        t        t        t        t        t        «       «      «      «      «      «      «      «      } d}|dd  }| j                  «       |k(  sJ ‚y )Nat  
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=0,
                                                                                                                     max_iter=100,
                                                                                                                     multi_class='warn',
                                                                                                                     n_jobs=None,
                                                                                                                     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™   )Úrfer›   s     r   Útest_deeply_nestedr­   3  sX   € ô Œc”#”cœ#œc¤#Ô&8Ó&:Ó";Ó<Ó=Ó>Ó?Ó@Ó
A€Cð5€Hð: ˜˜ˆ|€HØ�<‰<‹>˜XÒ%Ð%Ñ%r!   )r–   r›   )TzRFE(estimator=RFE(...)))FzERFE(estimator=RFE(...), n_features_to_select=None, step=1, verbose=0)c                 óú   — t        | ¬«      5  t        d¬«      }t        t        t        t        t        t        «       «      «      «      «      «      }|j	                  |«      |k(  sJ ‚	 d d d «       y # 1 sw Y   y xY w)Nr•   r*   )Údepth)r   r   r:   r   Úpformat)r–   r›   Úppr¬   s       r   Útest_print_estimator_max_depthr²   X  s[   € ô 
Ð+=Ö	>Ü$¨1Ô-ˆä”#”cœ#œcÔ"4Ó"6Ó7Ó8Ó9Ó:Ó;ˆØ�z‰z˜#‹ (Ò*Ð*Ñ*÷	 
?×	>Ñ	>ús   �AA1Á1A:c                  óŽ   — dgddgg d¢dœdgg d¢dœg} t        t        «       | d¬	«      }d
}|dd  }|j                  «       |k(  sJ ‚y )Nrt   rx   r)   ©r*   é
   r,   iè  )rl   rn   r   Úlinear)rl   r   é   )rG   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*   )rA   rj   r™   )rC   Úgsr›   s      r   Útest_gridsearchr¹   j  sa   € ð �7 d¨D \Ò8JÑKØ�:Ò$6Ñ7ð€Jô 
”c“e˜Z¨AÔ	.€Bð)€Hð ˜˜ˆ|€HØ�;‰;‹=˜HÒ$Ð$Ñ$r!   c                  óX  — 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  }| j                  |«      }t        j                  dd|«      }||k(  sJ ‚y )NTr*   )ÚcompactÚindentÚindent_at_nameÚ
reduce_dimÚclassify)é   é   é   r´   é   )r‚   )r¾   Úreduce_dim__n_componentsÚclassify__C)r¾   Úreduce_dim__krÅ   ru   )rG   r   rC   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   re   r}   rj   r…   r   r   rA   r°   ÚreÚsub)r±   r©   ÚN_FEATURES_OPTIONSÚ	C_OPTIONSrC   Ú
gspipeliner›   Úrepr_s           r   Útest_gridsearch_pipelinerÍ   …  sÌ   € ô 
!¨°aÈÔ	M€Bä˜,¬«Ð.°¼S»UÐ0CÐDÓE€HÚ"ÐÚ"€Iô ¨aÔ0´#³%Ð8Ø(:Ø$ñ	
ô '¤tÓ,Ð-Ø/Ø$ñ	
ð€Jô ˜h¨1°QÀ:ÔN€Jð%)€HðN ˜˜ˆ|€HØ�J‰J�zÓ"€Eä�F‰FÐ+Ð-MÈuÓU€EØ�HÒÐÑr!   c                  ój  — d} t        ddd| ¬«      }t        | «      D �ci c]  }||“Œ }}t        |¬«      }d}|dd  }|j                  |«      |k(  sJ ‚t        | dz   «      D �ci c]  }||“Œ }}t        |¬«      }d}|dd  }|j                  |«      |k(  sJ ‚dt	        t        | «      «      i}t        t        «       |«      }d	}|dd  }|j                  |«      |k(  sJ ‚dt	        t        | dz   «      «      i}t        t        «       |«      }d
}|dd  }|j                  |«      |k(  sJ ‚y c c}w c c}w )Né   Tr*   )r»   r¼   r½   Ún_max_elements_to_show)r_   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})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rangerL   r°   ÚlistrA   rj   )rÐ   r±   Úir_   Ú
vectorizerr›   rC   r¸   s           r   Útest_n_max_elements_to_showrÕ   È  s€  € àÐÜ	 ØØØØ5ô	
€Bô !&Ð&<Ô =Ó>Ñ =˜1�!�Q‘$Ð =€JÐ>Ü ¨JÔ7€Jð8€Hð ˜˜ˆ|€HØ�:‰:�jÓ! XÒ-Ð-Ð-ô !&Ð&<¸qÑ&@Ô AÓBÑ A˜1�!�Q‘$Ð A€JÐBÜ ¨JÔ7€Jð=€Hð ˜˜ˆ|€HØ�:‰:�jÓ! XÒ-Ð-Ð-ð ”tœEÐ"8Ó9Ó:Ð;€JÜ	”c“e˜ZÓ	(€Bð)€Hð ˜˜ˆ|€HØ�:‰:�b‹>˜XÒ%Ð%Ð%ð ”tœEÐ"8¸1Ñ"<Ó=Ó>Ð?€JÜ	”c“e˜ZÓ	(€Bð)€Hð ˜˜ˆ|€HØ�:‰:�b‹>˜XÒ%Ð%Ñ%ùò[ ?ùò( Cs   Ÿ
D+Á$
D0c                  ó  — t        «       } d}|dd  }| j                  d¬«      |k(  sJ ‚d}|dd  }| j                  d¬«      |k(  sJ ‚| j                  t        d«      ¬«      }t        dj	                  |j                  «       «      «      }| j                  |¬«      |k(  sJ ‚d	|vsJ ‚d
}|dd  }| j                  |dz
  ¬«      |k(  sJ ‚d}|dd  }| j                  |dz
  ¬«      |k(  sJ ‚d}|dd  }| j                  |dz
  ¬«      |k(  sJ ‚y )Nzø
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   in...
                   multi_class='warn', n_jobs=None, random_state=None,
                   solver='warn', tol=0.0001, verbose=0, warm_start=False)r*   é–   )Ú
N_CHAR_MAXzQ
Lo...
                   solver='warn', tol=0.0001, verbose=0, warm_start=False)rÁ   ÚinfÚ z...a  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=0,...00,
                   multi_class='warn', n_jobs=None, random_state=None,
                   solver='warn', tol=0.0001, verbose=0, warm_start=False)rµ   a   
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=0, max...r=100,
                   multi_class='warn', n_jobs=None, random_state=None,
                   solver='warn', tol=0.0001, verbose=0, warm_start=False)r˜   rÀ   )r   r™   r£   ÚlenÚjoinÚsplit)rš   r›   Ú	full_reprÚ
n_nonblanks       r   Útest_bruteforce_ellipsisrà   #  sU  € ô
 
Ó	€BðN€Hð ˜˜ˆ|€HØ�;‰; #ˆ;Ó&¨(Ò2Ð2Ð2ðN€Hð ˜˜ˆ|€HØ�;‰; !ˆ;Ó$¨Ò0Ð0Ð0ð —‘¤u¨U£|�Ó4€IÜ�R—W‘W˜YŸ_™_Ó.Ó/Ó0€JØ�;‰; *ˆ;Ó-°Ò:Ð:Ð:Ø˜	Ñ!Ð!Ð!ð
N€Hð
 ˜˜ˆ|€HØ�;‰; *¨r¡/ˆ;Ó2°hÒ>Ð>Ð>ðN€Hð
 ˜˜ˆ|€HØ�;‰; *¨q¡.ˆ;Ó1°XÒ=Ð=Ð=ð
N€Hð
 ˜˜ˆ|€HØ�;‰; *¨q¡.ˆ;Ó1°XÒ=Ð=Ñ=r!   c                  óF   — t        «       j                  t        «       «       y r   )r   Úpprintr   r#   r!   r   Útest_builtin_prettyprinterrã   j  s   € ô
 ƒO×ÑÔ-Ó/Õ0r!   c                  óØ   —  G d„ dt         «      }  | ddd ¬«      }d}|j                  «       |k(  sJ ‚t        d¬«      5  d	}|j                  «       |k(  sJ ‚	 d d d «       y # 1 sw Y   y xY w)
Nc                   ó.   ‡ — e Zd Zdd„Zdˆ fd„	Zd„ Zˆ xZS )ú'test_kwargs_in_init.<locals>.WithKWargsc                 óR   — || _         || _        i | _         | j                  di |¤Ž y )Nr#   )ÚaÚbÚ_other_paramsÚ
set_params)r   rè   ré   Úkwargss       r   r    z0test_kwargs_in_init.<locals>.WithKWargs.__init__{  s)   € ØˆDŒFØˆDŒFØ!#ˆDÔØˆD�O‰OÑ%˜fÓ%r!   c                 ó^   •— t         ‰| �  |¬«      }|j                  | j                  «       |S )N)Údeep)ÚsuperÚ
get_paramsÚupdaterê   )r   rî   ÚparamsÚ	__class__s      €r   rð   z2test_kwargs_in_init.<locals>.WithKWargs.get_params�  s,   ø€ Ü‘WÑ'¨TÐ'Ó2ˆFØ�M‰M˜$×,Ñ,Ô-ØˆMr!   c                 ón   — |j                  «       D ]!  \  }}t        | ||«       || j                  |<   Œ# | S r   )ÚitemsÚsetattrrê   )r   rò   ÚkeyÚvalues       r   rë   z2test_kwargs_in_init.<locals>.WithKWargs.set_params†  s9   € Ø$Ÿl™lžn‘
��UÜ˜˜c 5Ô)Ø*/�×"Ñ" 3Ò'ð -ð ˆKr!   )Ú
willchangeÚ	unchanged)T)r-   r.   r/   r    rð   rë   Ú__classcell__)ró   s   @r   Ú
WithKWargsræ   x  s   ø„ ó	&õ	ö
	r!   rü   Ú	somethingÚabcd)rè   ÚcÚdz+WithKWargs(a='something', c='abcd', d=None)Fr•   z:WithKWargs(a='something', b='unchanged', c='abcd', d=None))r   r™   r   )rü   Úestr›   s      r   Útest_kwargs_in_initr  r  sd   € ô”]ô ñ( �{ f°Ô
5€Cà<€HØ�<‰<‹>˜XÒ%Ð%Ð%ä	¨5Ö	1ØOˆØ�|‰|‹~ Ò)Ð)Ñ)÷ 
2×	1Ñ	1ús   ¾A Á A)c                  ót  ‡—  G ˆfd„dt         t        «      Š ‰t         ‰ ‰«       «       ‰«       d«      «      } t        d¬«      5  t	        | «       ‰j
                  }d d d «       d‰_        t        d¬«      5  t	        | «       ‰j
                  }d d d «       k(  sJ ‚y # 1 sw Y   ŒDxY w# 1 sw Y   ŒxY w)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   c                 ó   — || _         y r   )r<   )r   r<   s     r   r    zCtest_complexity_print_changed_only.<locals>.DummyEstimator.__init__Ÿ  s	   € Ø&ˆD�Nr!   c                 óJ   •— ‰xj                   dz  c_         t        ‰| �	  «       S )Nr*   )Únb_times_repr_calledrï   r™   )r   ÚDummyEstimatorró   s    €€r   r™   zCtest_complexity_print_changed_only.<locals>.DummyEstimator.__repr__¢  s"   ø€ Ø×/Ò/°1Ñ4Õ/Ü‘7Ñ#Ó%Ð%r!   c                 ó   — |S r   r#   r7   s      r   r8   zDtest_complexity_print_changed_only.<locals>.DummyEstimator.transform¦  s   € ØˆHr!   r   )r-   r.   r/   r  r    r™   r8   rû   )ró   r	  s   @€r   r	  r  œ  s   ù„ Ø Ðó	'õ	&÷	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  –  s¤   ø€ öÔ)¬=ô ñ Ü‘n¡^Ó%5Ó6¹Ó8HÈ-ÓXó€Iô 
¨5Ö	1ÜˆYŒØ+9×+NÑ+NÐ(÷ 
2ð +,€NÔ'Ü	¨4Ö	0ÜˆYŒØ*8×*MÑ*MÐ'÷ 
1ð ,Ð/NÒNÐNÑN÷ 
2Ð	1ú÷
 
1Ð	0ús   ÁB"Á:B.Â"B+Â.B7),rÇ   râ   r   Únumpyrb   ÚpytestÚsklearnr   Úsklearn.baser   r   Úsklearn.feature_selectionr   r   Úsklearn.linear_modelr	   Úsklearn.pipeliner
   Úsklearn.utils._pprintr   r   r1   r:   rA   rL   re   rj   r}   r…   rŽ   rœ   r§   rª   r­   ÚmarkÚparametrizer²   r¹   rÍ   rÕ   rà   rã   r  r  r#   r!   r   Ú<module>r     s¢  ðÛ 	Ý  ã Û å "ß 8ß 7Ý 5Ý *Ý 9ô"˜ô "ôJÐ% }ô ôˆ-ô ô5�=ô 5ô8%�mô %ôPˆ}ô ô)ˆ-ô )ôD)ˆ-ô )ô(ˆ-ô ô8�Mô ñ   5Ô)ñ
%ó *ð
%òSñ:  5Ô)ñ+ó *ð+ñ(  5Ô)ñ!&ó *ð!&ðH ‡�×ÑØ&à)ð	
ðó	ñ+ó	ð+ñ  5Ô)ñ%ó *ð%ñ4  5Ô)ñ?ó *ð?ñD  5Ô)ñW&ó *ðW&ñt  5Ô)ñC>ó *ðC>òL1ò!*óHOr!   