§
    rŠtjsl  ã                   ó  — 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j"         #                    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+dS ).é    N)ÚPrettyPrinter)Úconfig_context)ÚBaseEstimatorÚTransformerMixin)ÚSelectKBestÚchi2)ÚLogisticRegressionCV)Úmake_pipeline)Ú_EstimatorPrettyPrinterc                   ó8   — e Zd Z	 	 	 	 	 	 	 	 	 	 	 	 	 	 dd
„Zd„ ZdS )ÚLogisticRegressionç      ð?r   Fç-Cëâ6?Té   NÚwarnéd   c                 óÊ   — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        d S ©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                  ú]/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/utils/tests/test_pprint.pyÚ__init__zLogisticRegression.__init__   so   € 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$ˆŒØˆŒˆˆó    c                 ó   — | S r   © )r#   ÚXÚys      r$   ÚfitzLogisticRegression.fit1   ó   € Øˆr&   )r   r   Fr   Tr   NNr   r   r   r   FN)Ú__name__Ú
__module__Ú__qualname__r%   r+   r(   r&   r$   r   r      sd   € € € € € ð ØØØØØØØØØØØØØðð ð ð ð@ð ð ð ð r&   r   c                   ó   — e Zd Zdd„Zdd„ZdS )ÚStandardScalerTc                 ó0   — || _         || _        || _        d S r   )Ú	with_meanÚwith_stdÚcopy)r#   r5   r3   r4   s       r$   r%   zStandardScaler.__init__6   s   € Ø"ˆŒØ ˆŒØˆŒ	ˆ	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<   € € € € € ðð ð ð ð
ð ð ð ð ð r&   r1   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__@   s#   € Ø"ˆŒØ$8ˆÔ!ØˆŒ	ØˆŒˆˆr&   )Nr   r   ©r-   r.   r/   r%   r(   r&   r$   r:   r:   ?   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<   rE   rF   r"   rG   rH   rI   r    rJ   rK   rL   s               r$   r%   zGridSearchCV.__init__H   sZ   € ð #ˆŒØ$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   rB   rC   Fr?   r(   r&   r$   rA   rA   G   sE   € € € € € ð
 ØØØØØØØ'Ø ð5ð 5ð 5ð 5ð 5ð 5r&   rA   c                   óB   — e Zd Zdddddddddddd	d
dddej        fd„ZdS )ÚCountVectorizerÚcontentzutf-8ÚstrictNTz(?u)\b\w\w+\b)r   r   Úwordr   r   Fc                 óô   — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        || _	        || _
        || _        || _        |
| _        || _        || _        || _        d S r   )ÚinputÚencodingÚdecode_errorÚstrip_accentsÚpreprocessorÚ	tokenizerÚanalyzerÚ	lowercaseÚtoken_patternÚ
stop_wordsÚmax_dfÚmin_dfÚmax_featuresÚngram_rangeÚ
vocabularyÚbinaryÚdtype)r#   rS   rT   rU   rV   rZ   rW   rX   r\   r[   r`   rY   r]   r^   r_   ra   rb   rc   s                     r$   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Œ
ˆ
ˆ
r&   )r-   r.   r/   ÚnpÚint64r%   r(   r&   r$   rN   rN   c   s_   € € € € € ð ØØØØØØØØ&ØØØØØØØØŒhð%$ð $ð $ð $ð $ð $r&   rN   c                   ó   — e Zd Zdd„ZdS )ÚPipelineNc                 ó"   — || _         || _        d S r   )ÚstepsÚmemory)r#   ri   rj   s      r$   r%   zPipeline.__init__Œ   s   € ØˆŒ
ØˆŒˆˆr&   r   r?   r(   r&   r$   rg   rg   ‹   s(   € € € € € ðð ð ð ð ð r&   rg   c                   ó2   — e Zd Z	 	 	 	 	 	 	 	 	 	 	 	 	 	 dd„Zd
S )ÚSVCr   Úrbfé   Úauto_deprecatedç        TFçü©ñÒMbP?éÈ   NéÿÿÿÿÚovrc                 óÊ   — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        d S r   )ÚkernelÚdegreeÚgammaÚcoef0r   r   Ú	shrinkingÚprobabilityÚ
cache_sizer   r    r   Údecision_function_shaper   )r#   r   rv   rw   rx   ry   rz   r{   r   r|   r   r    r   r}   r   s                  r$   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   rm   rn   ro   rp   TFrq   rr   NFrs   rt   Nr?   r(   r&   r$   rl   rl   ‘   sT   € € € € € ð ØØØØØØØØØØØØ %Øð)ð )ð )ð )ð )ð )r&   rl   c                   ó$   — e Zd Z	 	 	 	 	 	 	 dd„ZdS )ÚPCANTFÚautorp   c                 óh   — || _         || _        || _        || _        || _        || _        || _        d S r   )Ún_componentsr5   ÚwhitenÚ
svd_solverr   Úiterated_powerr   )r#   r‚   r5   rƒ   r„   r   r…   r   s           r$   r%   zPCA.__init__´   s>   € ð )ˆÔØˆŒ	ØˆŒØ$ˆŒØˆŒØ,ˆÔØ(ˆÔÐÐr&   )NTFr€   rp   r€   Nr?   r(   r&   r$   r   r   ³   s?   € € € € € ð ØØØØØØð)ð )ð )ð )ð )ð )r&   r   c                   ó,   — e Zd Z	 	 	 	 	 	 	 	 	 	 	 d
d	„ZdS )ÚNMFNÚcdÚ	frobeniusr   rr   rp   r   Fc                 ó    — || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        d S r   )r‚   Úinitr   Ú	beta_lossr   r   r   Úalphar   r    Úshuffle)r#   r‚   r‹   r   rŒ   r   r   r   r�   r   r    rŽ   s               r$   r%   zNMF.__init__È   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&   )NNrˆ   r‰   r   rr   Nrp   rp   r   Fr?   r(   r&   r$   r‡   r‡   Ç   sK   € € € € € ð ØØØØØØØØØØðð ð ð ð ð r&   r‡   c                   ó*   — e Zd Zej        ddddfd„ZdS )ÚSimpleImputerÚmeanNr   Tc                 óL   — || _         || _        || _        || _        || _        d S r   )Úmissing_valuesÚstrategyÚ
fill_valuer    r5   )r#   r“   r”   r•   r    r5   s         r$   r%   zSimpleImputer.__init__ä   s,   € ð -ˆÔØ ˆŒØ$ˆŒØˆŒØˆŒ	ˆ	ˆ	r&   )r-   r.   r/   rd   Únanr%   r(   r&   r$   r�   r�   ã   s;   € € € € € ð ”vØØØØðð ð ð ð ð r&   r�   F©Úprint_changed_onlyc                  ón   — t          ¦   «         } d}|dd …         }|                      ¦   «         |k    sJ ‚d S )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_basicrž   ó   sE   € õ 
Ñ	Ô	€BðN€Hð ˜˜˜Œ|€HØ�;Š;‰=Œ=˜HÒ$Ð$Ð$Ð$Ð$Ð$r&   c                  ó  — t          d¬¦  «        } d}|                      ¦   «         |k    sJ ‚t          ddddd¬¦  «        } d	}|d
d …         }|                      ¦   «         |k    sJ ‚t          d¬¦  «        }d}|                     ¦   «         |k    sJ ‚t          t          d¦  «        ¬¦  «        }d}|                     ¦   «         |k    sJ ‚t	          t          t          j        dd
g¦  «        dd¬¦  «        ¦  «         d S )Néc   ©r   zLogisticRegression(C=99)gš™™™™™Ù?FiÒ  T)r   r   r   r   r    zk
LogisticRegression(C=99, class_weight=0.4, fit_intercept=False, tol=1234,
                   verbose=True)r   r   )r“   zSimpleImputer(missing_values=0)ÚNaNzSimpleImputer()gš™™™™™¹?Úneg_log_loss)ÚCsÚuse_legacy_attributesrF   )r   r›   r�   ÚfloatÚreprr	   rd   Úarray)rœ   r�   Úimputers      r$   Útest_changed_onlyrª     s3  € 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 	ÝÝŒx˜˜a˜Ñ!Ô!Ø"'Ø"ð	
ñ 	
ô 	
ñô ð ð ð r&   c                  ó¦   — t          t          ¦   «         t          d¬¦  «        ¦  «        } d}|dd …         }|                      ¦   «         |k    sJ ‚d S )Niç  r¡   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$   Útest_pipeliner­   $  s^   € õ �^Ñ-Ô-Õ/AÀCÐ/HÑ/HÔ/HÑIÔI€Hð1€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 )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°   9  s}   € õ �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&   )r˜   r�   )TzRFE(estimator=RFE(...)))FzERFE(estimator=RFE(...), n_features_to_select=None, step=1, verbose=0)c                 óN  — t          | ¬¦  «        5  t          d¬¦  «        }t          t          t          t          t          t          ¦   «         ¦  «        ¦  «        ¦  «        ¦  «        ¦  «        }|                     |¦  «        |k    sJ ‚	 d d d ¦  «         d S # 1 swxY w Y   d S )Nr—   r   )Údepth)r   r   r:   r   Úpformat)r˜   r�   Úppr¯   s       r$   Útest_print_estimator_max_depthrµ   ^  sÜ   € õ 
Ð+=Ð	>Ñ	>Ô	>ð +ð +Ý$¨1Ð-Ñ-Ô-ˆå•#•c�#�cÕ"4Ñ"6Ô"6Ñ7Ô7Ñ8Ô8Ñ9Ô9Ñ:Ô:Ñ;Ô;ˆØ�zŠz˜#‰Œ (Ò*Ð*Ð*Ð*Ð*ð	+ð +ð +ñ +ô +ð +ð +ð +ð +ð +ð +ð +øøøð +ð +ð +ð +ð +ð +s   ‘A;BÂBÂ!Bc                  ó´   — dgddgg d¢dœdgg d¢dœg} t          t          ¦   «         | d¬	¦  «        }d
}|dd …         }|                     ¦   «         |k    sJ ‚d S )Nrm   rq   r   ©r   é
   r   iè  )rv   rx   r   Úlinear)rv   r   é   )rI   aü  
GridSearchCV(cv=5, error_score='raise-deprecating',
             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
                           decision_function_shape='ovr', degree=3,
                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
                           probability=False, random_state=None, shrinking=True,
                           tol=0.001, verbose=False),
             iid='warn', n_jobs=None,
             param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],
                          'kernel': ['rbf']},
                         {'C': [1, 10, 100, 1000], 'kernel': ['linear']}],
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)r   )rA   rl   r›   )rE   Úgsr�   s      r$   Útest_gridsearchr¼   p  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…   )rÁ   Úreduce_dim__n_componentsÚclassify__C)rÁ   Úreduce_dim__krÈ   rn   )rI   r"   rE   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   rg   r   rl   r‡   r   r   rA   r³   ÚreÚsub)r´   r¬   ÚN_FEATURES_OPTIONSÚ	C_OPTIONSrE   Ú
gspipeliner�   Úrepr_s           r$   Útest_gridsearch_pipelinerÐ   ‹  s  € õ 
!¨°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 ˜h¨1°QÀ:ÐNÑNÔN€Jð%)€HðN ˜˜˜Œ|€HØ�JŠJ�zÑ"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   )r¾   r¿   rÀ   Ún_max_elements_to_showc                 ó   — i | ]}||“ŒS r(   r(   ©Ú.0Úis     r$   ú
<dictcomp>z/test_n_max_elements_to_show.<locals>.<dictcomp>Ù  s   € Ð>Ð>Ð>˜1�!�QÐ>Ð>Ð>r&   )ra   a÷  
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
                lowercase=True, max_df=1.0, max_features=None, min_df=1,
                ngram_range=(1, 1), preprocessor=None, stop_words=None,
                strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None,
                vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
                            8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
                            15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
                            21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
                            27: 27, 28: 28, 29: 29})c                 ó   — i | ]}||“ŒS r(   r(   rÕ   s     r$   rØ   z/test_n_max_elements_to_show.<locals>.<dictcomp>í  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rangerN   r³   ÚlistrA   rl   )rÓ   r´   ra   Ú
vectorizerr�   rE   r»   s          r$   Útest_n_max_elements_to_showrÝ   Î  s½  € à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 )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)rÄ   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)r¸   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)rš   rÃ   )r   r›   r¦   ÚlenÚjoinÚsplit)rœ   r�   Ú	full_reprÚ
n_nonblanks       r$   Útest_bruteforce_ellipsisrè   )  s�  € õ
 
Ñ	Ô	€BðN€Hð ˜˜˜Œ|€HØ�;Š; #ˆ;Ñ&Ô&¨(Ò2Ð2Ð2Ð2ðN€Hð ˜˜˜Œ|€HØ�;Š; !ˆ;Ñ$Ô$¨Ò0Ð0Ð0Ð0ð —’¥u¨U¡|¤|�Ñ4Ô4€IÝ�R—W’W˜YŸ_š_Ñ.Ô.Ñ/Ô/Ñ0Ô0€JØ�;Š; *ˆ;Ñ-Ô-°Ò:Ð:Ð:Ð:Ø˜	Ð!Ð!Ð!Ð!ð
N€Hð
 ˜˜˜Œ|€HØ�;Š; *¨r¡/ˆ;Ñ2Ô2°hÒ>Ð>Ð>Ð>ðN€Hð
 ˜˜˜Œ|€HØ�;Š; *¨q¡.ˆ;Ñ1Ô1°XÒ=Ð=Ð=Ð=ð
N€Hð
 ˜˜˜Œ|€HØ�;Š; *¨q¡.ˆ;Ñ1Ô1°XÒ=Ð=Ð=Ð=Ð=Ð=r&   c                  ó`   — t          ¦   «                              t          ¦   «         ¦  «         d S r   )r   Úpprintr   r(   r&   r$   Útest_builtin_prettyprinterrë   p  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#   rò   ró   Úkwargss       r$   r%   z0test_kwargs_in_init.<locals>.WithKWargs.__init__�  s6   € ØˆDŒFØˆDŒFØ!#ˆDÔØˆDŒOÐ%Ð%˜fÐ%Ð%Ð%Ð%Ð%r&   Tc                 ó€   •— t          ¦   «                              |¬¦  «        }|                     | j        ¦  «         |S )N)Údeep)ÚsuperÚ
get_paramsÚupdaterô   )r#   rø   ÚparamsÚ	__class__s      €r$   rú   z2test_kwargs_in_init.<locals>.WithKWargs.get_params‡  s7   ø€ Ý‘W”W×'Ò'¨TÐ'Ñ2Ô2ˆFØ�MŠM˜$Ô,Ñ-Ô-Ð-ØˆMr&   c                 óp   — |                      ¦   «         D ] \  }}t          | ||¦  «         || j        |<   Œ!| S r   )ÚitemsÚsetattrrô   )r#   rü   ÚkeyÚvalues       r$   rõ   z2test_kwargs_in_init.<locals>.WithKWargs.set_paramsŒ  sD   € Ø$Ÿlšl™nœnð 0ð 0‘
��UÝ˜˜c 5Ñ)Ô)Ð)Ø*/�Ô" 3Ñ'Ð'ØˆKr&   )rï   rð   )T)r-   r.   r/   r%   rú   rõ   Ú__classcell__)rý   s   @r$   Ú
WithKWargsrî   ~  s`   ø€ € € € € ð	&ð 	&ð 	&ð 	&ð	ð 	ð 	ð 	ð 	ð 	ð
	ð 	ð 	ð 	ð 	ð 	ð 	r&   r  Ú	somethingÚabcd)rò   ÚcÚdz+WithKWargs(a='something', c='abcd', d=None)Fr—   z:WithKWargs(a='something', b='unchanged', c='abcd', d=None))r   r›   r   )r  Úestr�   s      r$   Útest_kwargs_in_initr
  x  sø   € ðð ð ð ð •]ñ ô ð ð( ˆ*�{ f°Ð
5Ñ
5Ô
5€Cà<€HØ�<Š<‰>Œ>˜XÒ%Ð%Ð%Ð%å	¨5Ð	1Ñ	1Ô	1ð *ð *ØOˆØ�|Š|‰~Œ~ Ò)Ð)Ð)Ð)Ð)ð*ð *ð *ñ *ô *ð *ð *ð *ð *ð *ð *ð *øøøð *ð *ð *ð *ð *ð *s   ÁA9Á9A=Â A=c                  óº  ‡—  G ˆfd„dt           t          ¦  «        Š ‰t           ‰ ‰¦   «         ¦  «         ‰¦   «         d¦  «        ¦  «        } t          d¬¦  «        5  t	          | ¦  «         ‰j        }d d d ¦  «         n# 1 swxY w Y   d‰_        t          d¬¦  «        5  t	          | ¦  «         ‰j        }d d d ¦  «         n# 1 swxY w Y   ||k    sJ ‚d S )Nc                   ó6   •‡ — e Zd ZdZdd„Zˆˆ fd„Zdd„Zˆ xZS )ú:test_complexity_print_changed_only.<locals>.DummyEstimatorr   Nc                 ó   — || _         d S r   )r<   )r#   r<   s     r$   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_calledrù   r›   )r#   ÚDummyEstimatorrý   s    €€r$   r›   zCtest_complexity_print_changed_only.<locals>.DummyEstimator.__repr__¨  s-   ø€ ØÐ/Ô/°1Ñ4Ð/Ô/Ý‘7”7×#Ò#Ñ%Ô%Ð%r&   c                 ó   — |S r   r(   r7   s      r$   r8   zDtest_complexity_print_changed_only.<locals>.DummyEstimator.transform¬  s   € ØˆHr&   r   )r-   r.   r/   r  r%   r›   r8   r  )rý   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  œ  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$   Á BÂBÂ
BÂ%CÃCÃC),rÊ   rê   r   Únumpyrd   ÚpytestÚsklearnr   Úsklearn.baser   r   Úsklearn.feature_selectionr   r   Úsklearn.linear_modelr	   Úsklearn.pipeliner
   Úsklearn.utils._pprintr   r   r1   r:   rA   rN   rg   rl   r   r‡   r�   rž   rª   r­   r°   ÚmarkÚparametrizerµ   r¼   rÐ   rÝ   rè   rë   r
  r  r(   r&   r$   ú<module>r!     s  ðØ 	€	€	€	Ø  Ð  Ð  Ð  Ð  Ð  à Ð Ð Ð Ø €€€à "Ð "Ð "Ð "Ð "Ð "Ø 8Ð 8Ð 8Ð 8Ð 8Ð 8Ð 8Ð 8Ø 7Ð 7Ð 7Ð 7Ð 7Ð 7Ð 7Ð 7Ø 5Ð 5Ð 5Ð 5Ð 5Ð 5Ø *Ð *Ð *Ð *Ð *Ð *Ø 9Ð 9Ð 9Ð 9Ð 9Ð 9ð"ð "ð "ð "ð "˜ñ "ô "ð "ðJ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  € 5Ð)Ñ)Ô)ð
%ð 
%ñ *Ô)ð
%ð ð  ð  ðF € 5Ð)Ñ)Ô)ð+ð +ñ *Ô)ð+ð( € 5Ð)Ñ)Ô)ð!&ð !&ñ *Ô)ð!&ðH „×ÒØ&à)ð	
ðñ	ô 	ð+ð +ñ	ô 	ð+ð € 5Ð)Ñ)Ô)ð%ð %ñ *Ô)ð%ð4 € 5Ð)Ñ)Ô)ð?ð ?ñ *Ô)ð?ðD € 5Ð)Ñ)Ô)ðW&ð W&ñ *Ô)ðW&ðt € 5Ð)Ñ)Ô)ðC>ð C>ñ *Ô)ðC>ðL1ð 1ð 1ð!*ð !*ð !*ðHOð Oð Oð Oð Or&   