
    _ndY                        d Z ddl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	 ddlm
Z
 ddlmZm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 ddlmZ ddlm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& ddl'm(Z( ddl)m*Z* ej+        ,                    d          Z-ddgddgddgddgddgddggZ.g dZ/g dZ0ddgddgddggZ1g dZ2g d Z3 e*j4                    Z5e-6                    e5j7        j8                  Z9 e&e5j:        e5j7        e-!          \  e5_:        e5_7         e*j;                    Z< e&e<j:        e<j7        e-!          \  e<_:        e<_7        d" Z=d# Z>ej?        @                    d$d%d&g          d'             ZAd( ZBd) ZCej?        @                    d*g d+          d,             ZDej?        @                    d$d%d&g          d-             ZEd. ZFd/ ZGd0 ZHd1 ZId2 ZJd3 ZKd4 ZLd5 ZMd6 ZNd7 ZOej?        @                    d$d%d&g          d8             ZPd9 ZQej?        @                    d$d%d&g          d:             ZRej?        @                    d; e            e5j:        e5j7        f e            e<j:        e<j7        fg          d<             ZSd= ZTej?        @                    d>ee#fee$fg          d?             ZUej?        @                    d@eeg          dA             ZVdB ZWdS )Cz6Testing for the boost module (sklearn.ensemble.boost).    N)
csc_matrix)
csr_matrix)
coo_matrix)
dok_matrix)
lil_matrix)assert_array_equalassert_array_less)assert_array_almost_equal)BaseEstimator)clone)DummyClassifierDummyRegressor)LinearRegression)train_test_split)GridSearchCV)AdaBoostClassifier)AdaBoostRegressor)_samme_proba)SVCSVR)DecisionTreeClassifierDecisionTreeRegressor)shuffle)NoSampleWeightWrapper)datasets      )foor    r    r   r   r   )r   r   r   r   r   r      )r    r   r   )r   r   r   random_statec                  Z   t          j        g dg dg dg dg          t          j                            d                    d d t           j        f         z   G fdd          }  |             }t          |d	t          j                            }t          |j        j                   t          j	        |          
                                sJ t          t          j        |d          g d
           t          t          j        |d          g d           d S )N)r   ư>r   )gRQ?g333333?皙?)igRQ?g      ?)r%   r   g&.>r   axisc                       e Zd Z fdZdS )'test_samme_proba.<locals>.MockEstimatorc                 <    t          |j        j                   S N)r   shape)selfXprobss     Klib/python3.11/site-packages/sklearn/ensemble/tests/test_weight_boosting.pypredict_probaz5test_samme_proba.<locals>.MockEstimator.predict_probaC   s    qw444L    N)__name__
__module____qualname__r2   )r0   s   r1   MockEstimatorr*   B   s.        	 	 	 	 	 	 	r3   r7   r!   )r   r   r   r   )r   r   r   r   )nparrayabssumnewaxisr   	ones_liker   r-   isfiniteallargminargmax)r7   mocksamme_probar0   s      @r1   test_samme_probarD   7   sJ    H	'''):):):OOOL E 
RVEII1I%%&&qqq"*}55E         
 =??DtQU(;(;<<K{(%+666;{##''))))) ry1555|||DDDry1555|||DDDDDr3   c                  <   t          j        t          t                              } t	                                          t          |           }t          |                    t                    t          j        t          t                    df                     d S )Nr   )r8   oneslenr/   r   fitr
   r2   )y_tclfs     r1   test_oneclass_adaboost_probarK   T   sj     '#a&&//C



"
"1c
*
*Cc//22BGSVVQK4H4HIIIIIr3   	algorithmSAMMESAMME.Rc                     t          | d          }|                    t          t                     t	          |                    t                    t                     t	          t          j	        t          j
        t                              |j                   |                    t                    j        t          t                    dfk    sJ |                    t                    j        t          t                    fk    sJ d S )Nr   rL   r#   r   )r   rH   r/   y_classr   predictT	y_t_classr8   uniqueasarrayclasses_r2   r-   rG   decision_function)rL   rJ   s     r1   test_classification_toyrY   ]   s     yq
A
A
ACGGAws{{1~~y111ryI!6!677FFFQ%#a&&!4444  ##)c!ffY666666r3   c                      t          d          } |                     t          t                     t	          |                     t                    t                     d S )Nr   r"   )r   rH   r/   y_regrr   rR   rS   y_t_regrrJ   s    r1   test_regression_toyr^   h   sF    

+
+
+CGGAvs{{1~~x00000r3   c                     t          j        t          j                  } d x}}dD ]t}t	          |          }|                    t          j        t          j                   t          | |j                   |	                    t          j                  }|dk    r|}|}|j
        d         t          |           k    sJ |                    t          j                  j
        d         t          |           k    sJ |                    t          j        t          j                  }|dk    sJ d||fz              t          |j                  dk    sJ t          t          d |j        D                                 t          |j                  k    sJ vd|_        t#          d	t          j        |	                    t          j                  |z
                       d S )
NrM   rN   rL   rM   r   g?z'Failed with algorithm %s and score = %fc              3   $   K   | ]}|j         V  d S r,   r"   .0ests     r1   	<genexpr>ztest_iris.<locals>.<genexpr>   s%      CCCs'CCCCCCr3   rN   r   )r8   rU   iristargetr   rH   datar   rW   r2   r-   rG   rX   scoreestimators_setrL   r	   r:   )classes	clf_samme
prob_sammealgrJ   probarj   s          r1   	test_irisrr   o   s   i$$G!!I
# 
 
 3///	4;'''7CL111!!$),,'>>IJ{1~W----$$TY//5a8CLLHHHH		$)T[11s{{{EeT{{{ 3?##a''''3CC3?CCCCCDDOI
 I
 
 
 
 
 
 $Ia	 7 7	 B BZ OPPQQQQQr3   loss)linearsquareexponentialc                    t          | d          }|                    t          j        t          j                   |                    t          j        t          j                  }|dk    sJ t          |j                  dk    sJ t          t          d |j        D                                 t          |j                  k    sJ d S )Nr   )rs   r#   g?r   c              3   $   K   | ]}|j         V  d S r,   r"   rc   s     r1   rf   z test_diabetes.<locals>.<genexpr>   s%      ??3#??????r3   )	r   rH   diabetesri   rh   rj   rG   rk   rl   )rs   regrj   s      r1   test_diabetesr{      s     A
6
6
6CGGHM8?+++IIhmX_55E4<<<< s!####s??s?????@@CDXDXXXXXXXr3   c                    t           j                            d          }|                    dt          j        j                  }|                    dt          j        j                  }t          | d          }|	                    t          j
        t          j        |           |                    t          j
                  }d |                    t          j
                  D             }|                    t          j
                  }d |                    t          j
                  D             }|                    t          j
        t          j        |          }	d |                    t          j
        t          j        |          D             }
t#          |          dk    sJ t%          ||d	                    t#          |          dk    sJ t%          ||d	                    t#          |
          dk    sJ t%          |	|
d	                    t'          dd
          }|	                    t          j
        t          j        |           |                    t          j
                  }d |                    t          j
                  D             }|                    t          j
        t          j        |          }	d |                    t          j
        t          j        |          D             }
t#          |          dk    sJ t%          ||d	                    t#          |
          dk    sJ t%          |	|
d	                    d S )Nr   
   size)rL   n_estimatorssample_weightc                     g | ]}|S  r   rd   ps     r1   
<listcomp>z'test_staged_predict.<locals>.<listcomp>   s    CCC!CCCr3   c                     g | ]}|S r   r   r   s     r1   r   z'test_staged_predict.<locals>.<listcomp>   s    DDD1QDDDr3   c                     g | ]}|S r   r   rd   ss     r1   r   z'test_staged_predict.<locals>.<listcomp>   s%         r3   r   )r   r#   c                     g | ]}|S r   r   r   s     r1   r   z'test_staged_predict.<locals>.<listcomp>   s    GGG!GGGr3   c                     g | ]}|S r   r   r   s     r1   r   z'test_staged_predict.<locals>.<listcomp>   s(        	
  r3   )r8   randomRandomStaterandintrg   rh   r-   ry   r   rH   ri   rR   staged_predictr2   staged_predict_probarj   staged_scorerG   r
   r   )rL   rngiris_weightsdiabetes_weightsrJ   predictionsstaged_predictionsrq   staged_probasrj   staged_scoress              r1   test_staged_predictr      s    )


"
"C;;r(9;::L{{2HO,A{BB
yr
B
B
BCGGDIt{,G???++di((KCCS%7%7	%B%BCCCdi((EDD 8 8 C CDDDMIIdiLIIIE ##DIt{,#WW  M !""b((((k+=b+ABBB}####e]2%6777}####e]2%6777 !
<
<
<CGGHM8?:JGKKK++hm,,KGGS%7%7%F%FGGGIIhmX_DTIUUE !!M8?:J " 
 
  M !""b((((k+=b+ABBB}####e]2%677777r3   c                  v   t          t                                } dddd}t          | |          }|                    t          j        t          j                   t          t                      d          } ddd}t          | |          }|                    t          j        t          j                   d S )N)	estimator)r   r   r`   )r   estimator__max_depthrL   r   r   r#   )r   r   )
r   r   r   rH   rg   ri   rh   r   r   ry   )boost
parametersrJ   s      r1   test_gridsearchr      s     )?)A)ABBBE &) J
 uj
)
)CGGDIt{### (=(?(?aPPPE"(&IIJ
uj
)
)CGGHM8?+++++r3   c                     dd l } dD ]}t          |          }|                    t          j        t          j                   |                    t          j        t          j                  }|                     |          }|                     |          }t          |          |j
        k    sJ |                    t          j        t          j                  }||k    sJ t          d          }|                    t          j        t          j                   |                    t          j        t          j                  }|                     |          }|                     |          }t          |          |j
        k    sJ |                    t          j        t          j                  }||k    sJ d S )Nr   r`   ra   r"   )pickler   rH   rg   ri   rh   rj   dumpsloadstype	__class__r   ry   )r   rp   objrj   r   obj2score2s          r1   test_pickler      s\   MMM $ 	 	 3///	4;'''		$)T[11LL||ADzzS]****DIt{33 
+
+
+CGGHM8?+++IIhmX_55ESA<<??D::&&&&ZZx77FF??????r3   c            	      8   t          j        ddddddd          \  } }dD ]x}t          |	          }|                    | |           |j        }|j        d         dk    sJ |d dt          j        f         |dd          k                                    sJ yd S )
Ni  r}   r!   r   Fr   )	n_samples
n_featuresn_informativen_redundant
n_repeatedr   r#   r`   ra   )	r   make_classificationr   rH   feature_importances_r-   r8   r<   r?   )r/   yrp   rJ   importancess        r1   test_importancesr      s    '  DAq $ F F 3///1. #r))))BQB
N+{122>CCEEEEEEF Fr3   c                     t                      } t          j        d          }t          j        t
          |          5  |                     t          t          t          j
        dg                     d d d            d S # 1 swxY w Y   d S )Nz*sample_weight.shape == (1,), expected (6,)matchr   r   )r   reescapepytestraises
ValueErrorrH   r/   rQ   r8   rV   )rJ   msgs     r1   ,test_adaboost_classifier_sample_weight_errorr     s    


C
)@
A
AC	z	-	-	- < <7"*bT*:*:;;;< < < < < < < < < < < < < < < < < <s   6BBBc                     ddl m}  t           |                       }|                    t          t
                     t          t                      d          }|                    t          t                     ddl m} t           |            d          }|                    t          t
                     t          t                      d          }|                    t          t
                     ddgddgddgddgg}g d}t          t                      d          }t          j        t          d	
          5  |                    ||           d d d            d S # 1 swxY w Y   d S )Nr   )RandomForestClassifierrM   ra   )RandomForestRegressorr"   r   )r    barr   r   zworse than randomr   )sklearn.ensembler   r   rH   r/   r[   r   rQ   r   r   r   r   r   r   )r   rJ   r   X_faily_fails        r1   test_estimatorr     s   777777 3355
6
6CGGAv
SUUg
6
6
6CGGAw666666
1133!
D
D
DCGGAv
CEE
2
2
2CGGAv !fq!fq!fq!f-F!!!F
SUUg
6
6
6C	z)<	=	=	=                                       s   E%%E),E)c                      d} t          ddd          }t          j        t          |           5  |                    t
          j        t
          j                   d d d            d S # 1 swxY w Y   d S )Nz+Sample weights have reached infinite values   g      7@rM   )r   learning_raterL   r   )r   r   warnsUserWarningrH   rg   ri   rh   )r   rJ   s     r1   test_sample_weights_infiniter   6  s    
7C
"DG
T
T
TC	k	-	-	- ( (	4;'''( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (s   +A((A,/A,c                      G d dt                     } t          j        dddd          \  }}t          j        |          }t          ||d	          \  }}}}t          t          t          t          t          fD ]} ||          } ||          }	t           | d
          dd                              ||          }
t           | d
          dd                              ||          }|
                    |	          }|                    |          }t          ||           |
                    |	          }|                    |          }t!          ||           |
                    |	          }|                    |          }t!          ||           |
                    |	          }|                    |          }t!          ||           |
                    |	|          }|                    ||          }t!          ||           |
                    |	          }|                    |          }t+          ||          D ]\  }}t!          ||           |
                    |	          }|                    |          }t+          ||          D ]\  }}t          ||           |
                    |	          }|                    |          }t+          ||          D ]\  }}t!          ||           |
                    |	|          }|                    ||          }t+          ||          D ]\  }}t          ||           d |
j        D             }t5          d |D                       sJ d S )Nc                   $     e Zd ZdZd fd	Z xZS )-test_sparse_classification.<locals>.CustomSVCz8SVC variant that records the nature of the training set.Nc                 x    t                                          |||           t          |          | _        | S z<Modification on fit caries data type for later verification.r   superrH   r   
data_type_r.   r/   r   r   r   s       r1   rH   z1test_sparse_classification.<locals>.CustomSVC.fitC  1    GGKK1MK:::"1ggDOKr3   r,   r4   r5   r6   __doc__rH   __classcell__r   s   @r1   	CustomSVCr   @  C        FF	 	 	 	 	 	 	 	 	 	r3   r   r         *   )	n_classesr   r   r#   r   r"   T)probabilityrM   )r   r#   rL   c                     g | ]	}|j         
S r   r   rd   is     r1   r   z.test_sparse_classification.<locals>.<listcomp>      EEE!EEEr3   c                 :    g | ]}|t           k    p
|t          k    S r   r   r   rd   ts     r1   r   z.test_sparse_classification.<locals>.<listcomp>  %    HHHQQ*_7ZHHHr3   )r   r   make_multilabel_classificationr8   ravelr   r   r   r   r   r   r   rH   rR   r   rX   r
   predict_log_probar2   rj   staged_decision_functionzipr   r   r   rk   r?   )r   r/   r   X_trainX_testy_trainy_testsparse_formatX_train_sparseX_test_sparsesparse_classifierdense_classifiersparse_resultsdense_results
sprase_res	dense_restypess                    r1   test_sparse_classificationr  =  s       C    2rab  DAq 	A'711'M'M'M$GVWf$j*j*U FJ FJ&w//%f-- /iD111
 
 
 #ng
&
&	 	 .iD111
 
 
 #gw

	 	 +22=AA(0088>=999 +<<]KK(::6BB!.-@@@ +<<]KK(::6BB!.-@@@ +88GG(66v>>!.-@@@ +00GG(..vv>>!.-@@@ +CCMRR(AA&II%(%G%G 	= 	=!J	%j)<<<< +99-HH(77??%(%G%G 	6 	6!J	z95555 +??NN(==fEE%(%G%G 	= 	=!J	%j)<<<< +77vNN(55ffEE%(%G%G 	6 	6!J	z95555 FE'8'DEEEHH%HHHIIIIIIMFJ FJr3   c                  *    G d dt                     } t          j        dddd          \  }}t          ||d	          \  }}}}t          t
          t          t          t          fD ]+} ||          } ||          }	t           |             d
          
                    ||          }
t           |             d
          
                    ||          x}}|
                    |	          }|                    |          }t          ||           |
                    |	          }|                    |          }t          ||          D ]\  }}t          ||           d |
j        D             }t!          d |D                       sJ -d S )Nc                   $     e Zd ZdZd fd	Z xZS ))test_sparse_regression.<locals>.CustomSVRz8SVR variant that records the nature of the training set.Nc                 x    t                                          |||           t          |          | _        | S r   r   r   s       r1   rH   z-test_sparse_regression.<locals>.CustomSVR.fit  r   r3   r,   r   r   s   @r1   	CustomSVRr    r   r3   r  r   2   r   r   )r   r   	n_targetsr#   r   r"   r   c                     g | ]	}|j         
S r   r   r   s     r1   r   z*test_sparse_regression.<locals>.<listcomp>  r   r3   c                 :    g | ]}|t           k    p
|t          k    S r   r   r   s     r1   r   z*test_sparse_regression.<locals>.<listcomp>  r   r3   )r   r   make_regressionr   r   r   r   r   r   r   rH   rR   r
   r   r   rk   r?   )r  r/   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r  s                    r1   test_sparse_regressionr    s       C    #qr  DAq (811'M'M'M$GVWf$j*j*U J J&w//%f-- .ikk
 
 

#ng
&
& 	
 ,=ikk,
 ,
 ,

#gw

	 =
 +22=AA(0088!.-@@@ +99-HH(77??%(%G%G 	= 	=!J	%j)<<<<EE'8'DEEEHH%HHHIIIIII7J Jr3   c                       G d dt                     } t           |             d          }|                    t          t                     t          |j                  t          |j                  k    sJ dS )z
    AdaBoostRegressor should work without sample_weights in the base estimator
    The random weighted sampling is done internally in the _boost method in
    AdaBoostRegressor.
    c                       e Zd Zd Zd ZdS )=test_sample_weight_adaboost_regressor.<locals>.DummyEstimatorc                     d S r,   r   )r.   r/   r   s      r1   rH   zAtest_sample_weight_adaboost_regressor.<locals>.DummyEstimator.fit  s    Dr3   c                 @    t          j        |j        d                   S )Nr   )r8   zerosr-   )r.   r/   s     r1   rR   zEtest_sample_weight_adaboost_regressor.<locals>.DummyEstimator.predict  s    8AGAJ'''r3   N)r4   r5   r6   rH   rR   r   r3   r1   DummyEstimatorr    s2        	 	 		( 	( 	( 	( 	(r3   r  r!   )r   N)r   r   rH   r/   r[   rG   estimator_weights_estimator_errors_)r  r   s     r1   %test_sample_weight_adaboost_regressorr    s    ( ( ( ( ( ( ( ( nn..Q???E	IIau'((C0G,H,HHHHHHHr3   c                     t           j                            d          } |                     ddd          }|                     ddgd          }|                     d          }t          t          d                    }|                    ||           |                    |           |	                    |           t          t                                }|                    ||           |                    |           dS )zX
    Check that the AdaBoost estimators can work with n-dimensional
    data matrix
    r   r  r!   r   most_frequent)strategyN)r8   r   r   randnchoicer   r   rH   rR   r2   r   r   )r   r/   ycyrr   s        r1   test_multidimensional_Xr    s    
 )


"
"C		"aA	QFB		B	2BHHHIIE	IIa	MM!	n..//E	IIa	MM!r3   c                 `   t           j        t           j        }}t          t	                                }t          ||           }d                    |j        j                  }t          j
        t          |          5  |                    ||           d d d            d S # 1 swxY w Y   d S )N)r   rL   z {} doesn't support sample_weightr   )rg   ri   rh   r   r   r   formatr   r4   r   r   r   rH   )rL   r/   r   r   rJ   err_msgs         r1   -test_adaboostclassifier_without_sample_weightr#    s    9dkqA%o&7&788I
yI
F
F
FC077	8K8TUUG	z	1	1	1  1                 s   ?B##B'*B'c                     t           j                            d          } t          j        ddd          }d|z  dz   |                     |j        d                   dz  z   }|                    d	d
          }|d	xx         dz  cc<   d|d	<   t          t                      d
d          }t          |          }t          |          }|
                    ||           |
                    |d d	         |d d	                    t          j        |          }d|d	<   |
                    |||           |                    |d d	         |d d	                   }|                    |d d	         |d d	                   }|                    |d d	         |d d	                   }	||k     sJ ||	k     sJ |t          j        |	          k    sJ d S )Nr   r   d     )numg?r&   g-C6?r   r   r}   i'  r   r   r#   r   )r8   r   r   linspacerandr-   reshaper   r   r   rH   r=   rj   r   approx)
r   r/   r   regr_no_outlierregr_with_weightregr_with_outlierr   score_with_outlierscore_no_outlierscore_with_weights
             r1   $test_adaboostregressor_sample_weightr3    s    )


#
#C
As%%%A	q3388AGAJ//&89A			"aA bEEERKEEEAbE ("$$11  O _--o.. !Q#2##2#'''LOOMM"A];;;*003B33B3@@&,,QssVQssV<<(..q"vq"v>> 00000 11111v}->????????r3   c                 0   t          t          j        d          ddi\  }}}}t          | d          }|                    ||           t          t          j        |                    |          d          |	                    |                     d S )NT)
return_X_yr#   r   rP   r   r'   )
r   r   load_digitsr   rH   r   r8   rA   r2   rR   )rL   r   r   r   r   models         r1    test_adaboost_consistent_predictr8  "  s    
 (8			.	.	.(=?( ($GVWf DDDE	IIgw
	%%%f--A666f8M8M    r3   zmodel, X, yc                     t          j        |          }d|d<   d}t          j        t          |          5  |                     |||           d d d            d S # 1 swxY w Y   d S )Nir   z1Negative values in data passed to `sample_weight`r   r   )r8   r=   r   r   r   rH   )r7  r/   r   r   r"  s        r1   #test_adaboost_negative_weight_errorr:  2  s     LOOMM"AG	z	1	1	1 5 5		!Qm	4445 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5s   AA!$A!c                     t           j                            d          } |                     d          }|                     ddgd          }t          j        |          dz  }t          dd	
          }t          |dd	          }|                    |||           t          j	        |j
                                                  dk    sJ dS )zCheck that we don't create NaN feature importance with numerically
    instable inputs.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/20320
    r   )r&  r}   r~   r   r   r&  gtDS 'T	r}      )	max_depthr#      r(  r   N)r8   r   r   normalr  r=   r   r   rH   isnanr   r;   )r   r/   r   r   tree	ada_models         r1   Ftest_adaboost_numerically_stable_feature_importance_with_small_weightsrC  B  s     )


#
#C



##A

Aq6
%%ALOOf,M!BR@@@D"TQSTTTIMM!QmM4448I2337799Q>>>>>>r3   zAdaBoost, Estimatorc                 &   t          j        ddgddgg          }t          j        ddg          } |  |                      }d}t          j        t          |          5  |                    ||           d d d            d S # 1 swxY w Y   d S )	Nr   r   r!      r   )base_estimatorzV`base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.r   )r8   r9   r   r   FutureWarningrH   )AdaBoost	Estimatorr/   r   r7  warn_msgs         r1   'test_base_estimator_argument_deprecatedrK  T  s     	1a&1a&!""A
!QAHIIKK000E	"  
m8	4	4	4  		!Q                 s   "BB
B
rH  c                     t          j        ddgddgg          }t          j        ddg          } |             }|                    ||           d}t          j        t
          |          5  |j         d d d            d S # 1 swxY w Y   d S )Nr   r   r!   rE  r   zoAttribute `base_estimator_` was deprecated in version 1.2 and will be removed in 1.4. Use `estimator_` instead.r   )r8   r9   rH   r   r   rG  base_estimator_)rH  r/   r   r7  rJ  s        r1   'test_base_estimator_property_deprecatedrN  i  s    
 	1a&1a&!""A
!QAHJJE	IIaOOO	<  
m8	4	4	4                   s   .BB
Bc                      t          t                                } t          j        t          d          5  |                     d           ddd           dS # 1 swxY w Y   dS )zCheck that setting base_estimator parameters works.

    During the deprecation cycle setting "base_estimator__*" params should
    work.

    Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/25470
    zParameter 'base_estimator' ofr   r   )base_estimator__max_depthN)r   r   r   r   rG  
set_paramsr]   s    r1   4test_deprecated_base_estimator_parameters_can_be_setrR  |  s     355
6
6C	m+J	K	K	K 4 43334 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4s   AA"A)Xr   numpyr8   r   r   scipy.sparser   r   r   r   r   sklearn.utils._testingr   r	   r
   sklearn.baser   r   sklearn.dummyr   r   sklearn.linear_modelr   sklearn.model_selectionr   r   r   r   r   !sklearn.ensemble._weight_boostingr   sklearn.svmr   r   sklearn.treer   r   sklearn.utilsr   sklearn.utils._mockingr   sklearnr   r   r   r   r/   rQ   r[   rS   rT   r\   	load_irisrg   permutationrh   r   permri   load_diabetesry   rD   rK   markparametrizerY   r^   rr   r{   r   r   r   r   r   r   r   r  r  r  r  r#  r3  r8  r:  rC  rK  rN  rR  r   r3   r1   <module>rf     s   < <      				 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # H H H H H H H H < < < < < < & & & & & &       9 9 9 9 9 9 9 9 1 1 1 1 1 1 4 4 4 4 4 4 0 0 0 0 0 0 / / / / / / . . . . . . : : : : : :                 F F F F F F F F ! ! ! ! ! ! 8 8 8 8 8 8       	iA 	"XBx"bAq6Aq6Aq6:
(
(
(			"X1v1vMM	:: x
t{'(( DKcJJJ 	4; "8!##!(M8?" " " x
E E E:J J J w	&:;;7 7 <;71 1 1R R RD !D!D!DEE
Y 
Y FE
Y w	&:;;*8 *8 <;*8Z, , ,&  8F F F,< < <     8( ( (ZJ ZJ ZJz-J -J -J`I I I&  * w	&:;;  <;#@ #@ #@L w	&:;;  <; 				ty$+6				hmX_= 5 5 5? ? ?$ 	34	12 
 
 
 *+  	 4 4 4 4 4r3   