
    0Phm                     "   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	 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!ej"        #                    d!d"d#g          d$             Z$d% Z%d& Z&d' Z'd( Z(d) Z)d* Z*d+ Z+dS ),    N)PrettyPrinter)_EstimatorPrettyPrinter)LogisticRegressionCV)make_pipeline)BaseEstimatorTransformerMixin)SelectKBestchi2)config_contextc                   :    e Zd Z	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 ddZd ZdS )LogisticRegressionl2F-C6?      ?T   Nwarnd   r   c                     || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        || _        d S N)penaltydualtolCfit_interceptintercept_scalingclass_weightrandom_statesolvermax_itermulti_classverbose
warm_startn_jobsl1_ratio)selfr   r   r   r   r   r   r   r   r   r   r    r!   r"   r#   r$   s                   _/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/utils/tests/test_pprint.py__init__zLogisticRegression.__init__   sv    $ 	*!2(( &$     c                     | S r    )r%   Xys      r&   fitzLogisticRegression.fit7       r(   )r   Fr   r   Tr   NNr   r   r   r   FNN)__name__
__module____qualname__r'   r-   r*   r(   r&   r   r      sg         
! !  !  !  !D    r(   r   c                       e Zd ZddZddZdS )StandardScalerTc                 0    || _         || _        || _        d S r   )	with_meanwith_stdcopy)r%   r7   r5   r6   s       r&   r'   zStandardScaler.__init__<   s    " 			r(   Nc                     | S r   r*   r%   r+   r7   s      r&   	transformzStandardScaler.transformA   r.   r(   )TTTr   )r/   r0   r1   r'   r:   r*   r(   r&   r3   r3   ;   s<           
     r(   r3   c                       e Zd ZddZdS )RFENr   r   c                 >    || _         || _        || _        || _        d S r   )	estimatorn_features_to_selectstepr!   )r%   r>   r?   r@   r!   s        r&   r'   zRFE.__init__F   s#    "$8!	r(   )Nr   r   r/   r0   r1   r'   r*   r(   r&   r<   r<   E   s(             r(   r<   c                   (    e Zd Z	 	 	 	 	 	 	 	 	 d	dZdS )
GridSearchCVNr   Tr   2*n_jobsraise-deprecatingFc                     || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        d S r   )r>   
param_gridscoringr#   iidrefitcvr!   pre_dispatcherror_scorereturn_train_score)r%   r>   rG   rH   r#   rI   rJ   rK   r!   rL   rM   rN   s               r&   r'   zGridSearchCV.__init__N   sZ     #$
(&"4r(   )	NNr   Tr   r   rD   rE   FrA   r*   r(   r&   rC   rC   M   sE        
 ' 5 5 5 5 5 5r(   rC   c                   B    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 )CountVectorizercontentzutf-8strictNTz(?u)\b\w\w+\b)r   r   wordr   r   Fc                     || _         || _        || _        || _        || _        || _        || _        || _        |	| _        || _	        || _
        || _        || _        |
| _        || _        || _        || _        d S r   )inputencodingdecode_errorstrip_accentspreprocessor	tokenizeranalyzer	lowercasetoken_pattern
stop_wordsmax_dfmin_dfmax_featuresngram_range
vocabularybinary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__j   s    ( 
 (*(" "*$(&$


r(   )r/   r0   r1   npint64r'   r*   r(   r&   rP   rP   i   s_         &h%$ $ $ $ $ $r(   rP   c                       e Zd ZddZdS )PipelineNc                 "    || _         || _        d S r   )stepsmemory)r%   rk   rl   s      r&   r'   zPipeline.__init__   s    
r(   r   rA   r*   r(   r&   ri   ri      s(             r(   ri   c                   2    e Zd Z	 	 	 	 	 	 	 	 	 	 	 	 	 	 ddZd
S )SVCr   rbf   auto_deprecated        TFMbP?   Novrc                     || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        d S r   )kerneldegreegammacoef0r   r   	shrinkingprobability
cache_sizer   r!   r   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    " 

"&$( '>$(r(   )r   ro   rp   rq   rr   TFrs   rt   NFru   rv   NrA   r*   r(   r&   rn   rn      sT          %) ) ) ) ) )r(   rn   c                   $    e Zd Z	 	 	 	 	 	 	 ddZdS )PCANTFautorr   c                 h    || _         || _        || _        || _        || _        || _        || _        d S r   )n_componentsr7   whiten
svd_solverr   iterated_powerr   )r%   r   r7   r   r   r   r   r   s           r&   r'   zPCA.__init__   s>     )	$,(r(   )NTFr   rr   r   NrA   r*   r(   r&   r   r      s?         ) ) ) ) ) )r(   r   c                   ,    e Zd Z	 	 	 	 	 	 	 	 	 	 	 d
d	ZdS )NMFNcd	frobeniusr   rt   rr   r   Fc                     || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        d S r   )r   initr   	beta_lossr   r   r   alphar$   r!   shuffle)r%   r   r   r   r   r   r   r   r   r$   r!   r   s               r&   r'   zNMF.__init__   sW     )	" (
 r(   )NNr   r   r   rt   Nrr   rr   r   FrA   r*   r(   r&   r   r      sK              r(   r   c                   *    e Zd Zej        ddddfdZdS )SimpleImputermeanNr   Tc                 L    || _         || _        || _        || _        || _        d S r   )missing_valuesstrategy
fill_valuer!   r7   )r%   r   r   r   r!   r7   s         r&   r'   zSimpleImputer.__init__   s,     - $			r(   )r/   r0   r1   rf   nanr'   r*   r(   r&   r   r      s;         v     r(   r   c                 n    t                      }d}|dd          }|                                |k    sJ d S )NE  
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   __repr__)print_changed_only_falselrexpecteds      r&   
test_basicr      sB    			B(H |H;;==H$$$$$$r(   c                     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 )Nc   r   zLogisticRegression(C=99)g?Fi  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)NaNzSimpleImputer()g?)Cs)r   r   r   floatreprr   rf   array)r   r   imputers      r&   test_changed_onlyr     s   	b	!	!	!B-H;;==H$$$$ 

3et
 
 
B$H |H;;==H$$$$1---G4H)))) 5<<888G$H)))) 		3(!3!3	4	4	455555r(   c                     t          t                      t          d                    }d}|dd          }|                                |k    sJ d S )Ni  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))],
         transform_input=None, verbose=False)r   )r   r3   r   r   )r   pipeliner   s      r&   test_pipeliner   $  s\    ^--/AC/H/H/HIIH1H |H(******r(   c                 $   t          t          t          t          t          t          t          t                                                                                            }d}|dd          }|                                |k    sJ d S )Na  
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   )r   rfer   s      r&   test_deeply_nestedr   9  s{    
c#c#c#&8&:&:";";<<==>>??@@
A
AC5H< |H<<>>X%%%%%%r(   )print_changed_onlyr   )TzRFE(estimator=RFE(...)))FzERFE(estimator=RFE(...), n_features_to_select=None, step=1, verbose=0)c                 N   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 )Nr   r   )depth)r   r   r<   r   pformat)r   r   ppr   s       r&   test_print_estimator_max_depthr   ^  s     
+=	>	>	> + +$1---#c#c"4"6"6778899::;;zz#(*****	+ + + + + + + + + + + + + + + + + +s   A;BB!Bc                     dgddgg dddgg ddg}t          t                      |d	          }d
}|dd          }|                                |k    sJ d S )Nro   rs   r   r   
   r   i  )rx   rz   r   linear)rx   r      )rK   a  
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   )r   rG   gsr   s       r&   test_gridsearchr   p  s     7dD\8J8J8JKK:$6$6$677J 
ceeZA	.	.	.B)H |H;;==H$$$$$$r(   c                    t          ddd          }t          dt                      fdt                      fg          }g d}g d}t          d	          t	                      g||d
t          t                    g||dg}t          |dd|          }d}|dd          }|                    |          }t          j
        dd|          }||k    sJ d S )NTr   )compactindentindent_at_name
reduce_dimclassify)         r      )r   )r   reduce_dim__n_componentsclassify__C)r   reduce_dim__kr   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   r   resub)	r   r   r   N_FEATURES_OPTIONS	C_OPTIONSrG   	gspipliner   repr_s	            r&   test_gridsearch_pipeliner     s   	 a	M	M	MB,.SUU0CDEEH""""I a000#%%8(:$	
 	
 't,,-/$	
 	
J X!A*MMMI%)HN |HJJy!!EF+-MuUUEHr(   c                     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 )N   Tr   )r   r   r   n_max_elements_to_showc                     i | ]}||S r*   r*   .0is     r&   
<dictcomp>z/test_n_max_elements_to_show.<locals>.<dictcomp>  s    >>>1!Q>>>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                     i | ]}||S r*   r*   r   s     r&   r   z/test_n_max_elements_to_show.<locals>.<dictcomp>  s    BBB1!QBBBr(   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, ...})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   rangerP   r   listrC   rn   )r   r   r   rc   
vectorizerr   rG   r   s           r&   test_n_max_elements_to_showr     s   	 5	
 
 
B ?>&< = =>>>J J777J8H |H::j!!X---- CB&<q&@ A ABBBJ J777J=H |H::j!!X---- tE"899::;J	ceeZ	(	(B)H |H::b>>X%%%% tE"81"<==>>?J	ceeZ	(	(B)H |H::b>>X%%%%%%r(   c                    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      )
N_CHAR_MAXz+
Lo...
                   warm_start=False)r   inf 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)r   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)r   r   )r   r   r   lenjoinsplit)r   r   r   	full_repr
n_nonblanks        r&   test_bruteforce_ellipsisr   &  s    
		B(H |Hr{{c{222222(H |Hr{{a{000000 uU||44IRWWY__..//00J;;*;--::::	!!!!
(H |Hr{{j2o{>>>>>>(H |Hr{{j1n{======
(H |Hr{{j1n{========r(   c                  `    t                                          t                                 d S r   )r   pprintr   r*   r(   r&   test_builtin_prettyprinterr   p  s)    
 OO-//00000r(   c                      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                   .     e Zd ZddZd fd	Zd Z xZS )	'test_kwargs_in_init.<locals>.WithKWargs
willchange	unchangedc                 J    || _         || _        i | _         | j        di | d S )Nr*   )ab_other_params
set_params)r%   r   r   kwargss       r&   r'   z0test_kwargs_in_init.<locals>.WithKWargs.__init__  s6    DFDF!#DDO%%f%%%%%r(   Tc                     t                                          |          }|                    | j                   |S )N)deep)super
get_paramsupdater   )r%   r   params	__class__s      r&   r   z2test_kwargs_in_init.<locals>.WithKWargs.get_params  s7    WW''T'22FMM$,---Mr(   c                 p    |                                 D ] \  }}t          | ||           || j        |<   !| S r   )itemssetattrr   )r%   r   keyvalues       r&   r   z2test_kwargs_in_init.<locals>.WithKWargs.set_params  sD    $llnn 0 0
Uc5)))*/"3''Kr(   )r   r   )T)r/   r0   r1   r'   r   r   __classcell__)r   s   @r&   
WithKWargsr   ~  s`        	& 	& 	& 	&	 	 	 	 	 	
	 	 	 	 	 	 	r(   r  	somethingabcd)r   cdz+WithKWargs(a='something', c='abcd', d=None)Fr   z:WithKWargs(a='something', b='unchanged', c='abcd', d=None))r   r   r   )r  estr   s      r&   test_kwargs_in_initr  x  s        ]   ( *{f
5
5
5C<Hs||~~%%%%	5	1	1	1 * *O3<<>>)))))* * * * * * * * * * * * * * * * * *s   A99A= A=c                      G fddt           t                     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                   6     e Zd ZdZddZ fdZddZ xZS ):test_complexity_print_changed_only.<locals>.DummyEstimatorr   Nc                     || _         d S r   )r>   )r%   r>   s     r&   r'   zCtest_complexity_print_changed_only.<locals>.DummyEstimator.__init__  s    &DNNNr(   c                 d    xj         dz  c_         t                                                      S )Nr   )nb_times_repr_calledr   r   )r%   DummyEstimatorr   s    r&   r   zCtest_complexity_print_changed_only.<locals>.DummyEstimator.__repr__  s-    //14//77##%%%r(   c                     |S r   r*   r9   s      r&   r:   zDtest_complexity_print_changed_only.<locals>.DummyEstimator.transform  s    Hr(   r   )r/   r0   r1   r  r'   r   r:   r  )r   r  s   @r&   r  r    sl         	' 	' 	' 	'	& 	& 	& 	& 	& 	&	 	 	 	 	 	 	 	r(   r  passthroughFr   r   T)r   r   r   r   r   r  )r>    nb_repr_print_changed_only_falsenb_repr_print_changed_only_truer  s      @r&   "test_complexity_print_changed_onlyr    s         )=    nn^^%5%5668H8H-XX I 
5	1	1	1 O OY+9+N(O O O O O O O O O O O O O O O +,N'	4	0	0	0 N NY*8*M'N N N N N N N N N N N N N N N ,/NNNNNNNs$    BB
B%CCC),r   r   r   numpyrf   pytestsklearn.utils._pprintr   sklearn.linear_modelr   sklearn.pipeliner   sklearn.baser   r   sklearn.feature_selectionr	   r
   sklearnr   r   r3   r<   rC   rP   ri   rn   r   r   r   r   r   r   r   markparametrizer   r   r   r   r   r   r  r  r*   r(   r&   <module>r"     sV   				                  9 9 9 9 9 9 5 5 5 5 5 5 * * * * * * 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 " " " " " "$ $ $ $ $ $ $ $N    %}       -   5 5 5 5 5= 5 5 58% % % % %m % % %P    }   ) ) ) ) )- ) ) )D) ) ) ) )- ) ) )(    -   8    M    % % %6 6 6:+ + +*"& "& "&J &)	
	 	+ +	 	+% % %4? ? ?DW& W& W&tG> G> G>T1 1 1!* !* !*HO O O O Or(   