
    Ug;c              	       L   d Z ddl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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mZ ddlmZ ddlm Z  ddl!m"Z"m#Z#m$Z$m%Z% ddl&m'Z'm(Z(m)Z)m*Z*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Z0g dZ1ddgddgddggZ2g dZ3g dZ4 ej5                    Z6e.7                    e6j8        j9                  Z: ee6j;        e6j8        e.          \  e6_;        e6_8         ej<                    Z= ee=j;        e=j8        e.          \  e=_;        e=_8        d Z>d Z?ej@        A                    d          ej@        B                    dddg          d                          ZCd! ZDej@        A                    d          d"             ZEej@        B                    d#g d$          d%             ZFej@        A                    d          ej@        B                    dddg          d&                         ZGd' ZHej@        A                    d          d(             ZIej@        A                    d          d)             ZJd* ZKd+ ZLd, ZMej@        B                    d- eNg e(e)e+e'e*e(d.e)z  z                       d/             ZOej@        B                    d- eNg e(e)e+e'e*e(d.e)z  z                       d0             ZPd1 ZQd2 ZRej@        A                    d          ej@        B                    dddg          d3                         ZSd4 ZTej@        A                    d          ej@        B                    dddg          d5                         ZUej@        B                    d6 e            e6j;        e6j8        f e            e=j;        e=j8        fg          d7             ZVd8 ZWej@        A                    d          ej@        B                    dddg          d9                         ZXd: ZYdS );z6Testing for the boost module (sklearn.ensemble.boost).    N)datasets)BaseEstimatorclone)DummyClassifierDummyRegressor)AdaBoostClassifierAdaBoostRegressor)_samme_proba)LinearRegression)GridSearchCVtrain_test_split)SVCSVR)DecisionTreeClassifierDecisionTreeRegressor)shuffle)NoSampleWeightWrapper)assert_allcloseassert_array_almost_equalassert_array_equalassert_array_less)COO_CONTAINERSCSC_CONTAINERSCSR_CONTAINERSDOK_CONTAINERSLIL_CONTAINERS      )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     j/var/www/surfInsights/venv3-11/lib/python3.11/site-packages/sklearn/ensemble/tests/test_weight_boosting.pypredict_probaz5test_samme_proba.<locals>.MockEstimator.predict_probaD   s    qw444L    N)__name__
__module____qualname__r3   )r1   s   r2   MockEstimatorr+   C   s.        	 	 	 	 	 	 	r4   r8   r"   )r    r   r   r    )r   r   r   r   )nparrayabssumnewaxisr
   	ones_liker   r.   isfiniteallargminargmax)r8   mocksamme_probar1   s      @r2   test_samme_probarE   8   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Dr4   c                  @   t          j        t          t                              } t	          d                              t          |           }t          |                    t                    t          j        t          t                    df                     d S )NSAMME	algorithmr   )r9   oneslenr0   r   fitr   r3   )y_tclfs     r2   test_oneclass_adaboost_probarO   U   so     '#a&&//C
w
/
/
/
3
3As
;
;Cc//22BGSVVQK4H4HIIIIIr4   zignore:The SAMME.R algorithmrI   rG   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   rI   r$   r    )r   rL   r0   y_classr   predictT	y_t_classr9   uniqueasarrayclasses_r3   r.   rK   decision_function)rI   rN   s     r2   test_classification_toyr[   a   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6r4   c                      t          d          } |                     t          t                     t	          |                     t                    t                     d S )Nr   r#   )r	   rL   r0   y_regrr   rT   rU   y_t_regr)rN   s    r2   test_regression_toyr_   m   sF    

+
+
+CGGAvs{{1~~x00000r4   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rG   rP   rH   rG   r   g?z'Failed with algorithm %s and score = %fc              3   $   K   | ]}|j         V  d S r-   r#   .0ests     r2   	<genexpr>ztest_iris.<locals>.<genexpr>   s%      CCCs'CCCCCCr4   rP   r   )r9   rW   iristargetr   rL   datar   rY   r3   r.   rK   rZ   scoreestimators_setrI   r   r;   )classes	clf_samme
prob_sammealgrN   probarj   s          r2   	test_irisrr   w   s    i$$G!!I
# 
 
 3///	4;'''7CL111!!$),,'>>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Qr4   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     r2   rf   z test_diabetes.<locals>.<genexpr>   s%      ??3#??????r4   )	r	   rL   diabetesri   rh   rj   rK   rk   rl   )rs   regrj   s      r2   test_diabetesr{      s     A
6
6
6CGGHM8?+++IIhmX_55E4<<<< s!####s??s?????@@CDXDXXXXXXXr4   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)rI   n_estimatorssample_weightc                     g | ]}|S  r   rd   ps     r2   
<listcomp>z'test_staged_predict.<locals>.<listcomp>   s    CCC!CCCr4   c                     g | ]}|S r   r   r   s     r2   r   z'test_staged_predict.<locals>.<listcomp>   s    DDD1QDDDr4   c                     g | ]}|S r   r   rd   ss     r2   r   z'test_staged_predict.<locals>.<listcomp>   s%         r4   r   )r   r$   c                     g | ]}|S r   r   r   s     r2   r   z'test_staged_predict.<locals>.<listcomp>   s    GGG!GGGr4   c                     g | ]}|S r   r   r   s     r2   r   z'test_staged_predict.<locals>.<listcomp>   s(        	
  r4   )r9   randomRandomStaterandintrg   rh   r.   ry   r   rL   ri   rT   staged_predictr3   staged_predict_probarj   staged_scorerK   r   r	   )rI   rngiris_weightsdiabetes_weightsrN   predictionsstaged_predictionsrq   staged_probasrj   staged_scoress              r2   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7r4   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    ra   )r   estimator__max_depthrI   r   r   r$   )r   r   )
r   r   r   rL   rg   ri   rh   r	   r   ry   )boost
parametersrN   s      r2   test_gridsearchr      s     )?)A)ABBBE &) J
 uj
)
)CGGDIt{### (=(?(?aPPPE"(&IIJ
uj
)
)CGGHM8?+++++r4   c                     dd l } dD ]}t          |          }|                    t          j        t          j                   |                    t          j        t          j                  }|                     |          }|                     |          }t          |          |j
        k    sJ |                    t          j        t          j                  }||k    sJ t          d          }|                    t          j        t          j                   |                    t          j        t          j                  }|                     |          }|                     |          }t          |          |j
        k    sJ |                    t          j        t          j                  }||k    sJ d S )Nr   ra   rH   r#   )pickler   rL   rg   ri   rh   rj   dumpsloadstype	__class__r	   ry   )r   rp   objrj   r   obj2score2s          r2   test_pickler      s^    MMM $ 	 	 3///	4;'''		$)T[11LL||ADzzS]****DIt{33 
+
+
+CGGHM8?+++IIhmX_55ESA<<??D::&&&&ZZx77FF??????r4   c            	      8   t          j        ddddddd          \  } }dD ]x}t          |	          }|                    | |           |j        }|j        d         dk    sJ |d dt          j        f         |dd          k                                    sJ yd S )
Ni  r}   r"   r   Fr   )	n_samples
n_featuresn_informativen_redundant
n_repeatedr   r$   ra   rH   )	r   make_classificationr   rL   feature_importances_r.   r9   r=   r@   )r0   yrp   rN   importancess        r2   test_importancesr     s     '  DAq $ F F 3///1. #r))))BQB
N+{122>CCEEEEEEF Fr4   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
ValueErrorrL   r0   rS   r9   rX   )rN   msgs     r2   ,test_adaboost_classifier_sample_weight_errorr   '  s    


C
)@
A
AC	z	-	-	- < <7"*bT*:*:;;;< < < < < < < < < < < < < < < < < <s   6BBBc                     ddl m}  t           |             d          }|                    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   )RandomForestClassifierrG   rH   )RandomForestRegressorr#   r   )r!   barr   r    zworse than randomr   )sklearn.ensembler   r   rL   r0   r]   r   rS   r   r	   r   r   r   r   )r   rN   r   X_faily_fails        r2   test_estimatorr   /  s   777777 3355
I
I
IC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@rG   )r   learning_raterI   r   )r   r   warnsUserWarningrL   rg   ri   rh   )r   rN   s     r2   test_sample_weights_infiniter   K  s    
7C
"DG
T
T
TC	k	-	-	- ( (	4;'''( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (s   +A((A,/A,z(sparse_container, expected_internal_type   c                     G d dt                     }t          j        dddd          \  }}t          j        |          }t          ||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             }t+          fd|D                       sJ d S )Nc                   $     e Zd ZdZd fd	Z xZS )-test_sparse_classification.<locals>.CustomSVCz8SVC variant that records the nature of the training set.Nc                 x    t                                          |||           t          |          | _        | S z<Modification on fit caries data type for later verification.r   superrL   r   
data_type_r/   r0   r   r   r   s       r2   rL   z1test_sparse_classification.<locals>.CustomSVC.fite  1    GGKK1MK:::"1ggDOKr4   r-   r5   r6   r7   __doc__rL   __classcell__r   s   @r2   	CustomSVCr   b  C        FF	 	 	 	 	 	 	 	 	 	r4   r   r         *   )	n_classesr   r   r$   r   r#   T)probabilityrG   )r   r$   rI   c                     g | ]	}|j         
S r   r   rd   is     r2   r   z.test_sparse_classification.<locals>.<listcomp>  s    AAAaQ\AAAr4   c                     g | ]}|k    	S r   r   rd   texpected_internal_types     r2   r   z.test_sparse_classification.<locals>.<listcomp>      ;;;++;;;r4   )r   r   make_multilabel_classificationr9   ravelr   r   rL   rT   r   rZ   r   predict_log_probar3   rj   staged_decision_functionzipr   r   r   rk   r@   )sparse_containerr   r   r0   r   X_trainX_testy_trainy_testX_train_sparseX_test_sparsesparse_classifierdense_classifiersparse_clf_resultsdense_clf_resultssparse_clf_resdense_clf_restypess    `                r2   test_sparse_classificationr  R  s        C    2rab  DAq 	A'711'M'M'M$GVWf%%g..N$$V,,M +)---   
c.'""	  *)---   
c'7	  +22=AA(0088)+<=== +<<]KK(::6BB02CDDD +<<]KK(::6BB02CDDD +88GG(66v>>02CDDD +00GG(..vv>>02CDDD +CCMRR(AA&II),-?AR)S)S A A%!.-@@@@ +99-HH(77??),-?AR)S)S : :%>=9999 +??NN(==fEE),-?AR)S)S A A%!.-@@@@ +77vNN(55ffEE),-?AR)S)S : :%>=9999 BA#4#@AAAE;;;;U;;;<<<<<<<r4   c                     G d dt                     }t          j        dddd          \  }}t          ||d	          \  }}}} | |          }	 | |          }
t	           |            d
                              |	|          }t	           |            d
                              ||          }|                    |
          }|                    |          }t          ||           |                    |
          }|                    |          }t          ||          D ]\  }}t          ||           d |j
        D             }t          fd|D                       sJ d S )Nc                   $     e Zd ZdZd fd	Z xZS ))test_sparse_regression.<locals>.CustomSVRz8SVR variant that records the nature of the training set.Nc                 x    t                                          |||           t          |          | _        | S r   r   r   s       r2   rL   z-test_sparse_regression.<locals>.CustomSVR.fit  r   r4   r-   r   r   s   @r2   	CustomSVRr    r   r4   r  r   2   r   r   )r   r   	n_targetsr$   r   r#   r   c                     g | ]	}|j         
S r   r   r   s     r2   r   z*test_sparse_regression.<locals>.<listcomp>  s    @@@aQ\@@@r4   c                     g | ]}|k    	S r   r   r   s     r2   r   z*test_sparse_regression.<locals>.<listcomp>  r   r4   )r   r   make_regressionr   r	   rL   rT   r   r   r   rk   r@   )r   r   r  r0   r   r   r   r   r   r   r   sparse_regressordense_regressorsparse_regr_resultsdense_regr_resultssparse_regr_resdense_regr_resr  s    `                r2   test_sparse_regressionr    s        C    #qr  DAq (811'M'M'M$GVWf%%g..N$$V,,M )99;;QOOOSS 
 ())++ANNNRR O
 +22=AA(008813EFFF +99-HH(77??+./BDV+W+W C C'!/>BBBB@@#3#?@@@E;;;;U;;;<<<<<<<r4   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/   r0   r   s      r2   rL   zAtest_sample_weight_adaboost_regressor.<locals>.DummyEstimator.fit  s    Dr4   c                 @    t          j        |j        d                   S )Nr   )r9   zerosr.   )r/   r0   s     r2   rT   zEtest_sample_weight_adaboost_regressor.<locals>.DummyEstimator.predict  s    8AGAJ'''r4   N)r5   r6   r7   rL   rT   r   r4   r2   DummyEstimatorr    s2        	 	 		( 	( 	( 	( 	(r4   r  r"   r   N)r   r	   rL   r0   r]   rK   estimator_weights_estimator_errors_)r  r   s     r2   %test_sample_weight_adaboost_regressorr    s    ( ( ( ( ( ( ( ( nn..Q???E	IIau'((C0G,H,HHHHHHHr4   c                     t           j                            d          } |                     ddd          }|                     ddgd          }|                     d          }t          t          d          d          }|                    ||           |                    |           |	                    |           t          t                                }|                    ||           |                    |           d	S )
zX
    Check that the AdaBoost estimators can work with n-dimensional
    data matrix
    r   3   r"   r   most_frequent)strategyrG   rH   N)r9   r   r   randnchoicer   r   rL   rT   r3   r	   r   )r   r0   ycyrr   s        r2   test_multidimensional_Xr'  
  s    
 )


"
"C		"aA	QFB		B	2B111W  E 
IIa	MM!	n..//E	IIa	MM!r4   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   rI   z {} doesn't support sample_weightr   )rg   ri   rh   r   r   r   formatr   r5   r   r   r   rL   )rI   r0   r   r   rN   err_msgs         r2   -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   )r9   r   r   linspacerandr.   reshaper	   r   r   rL   r>   rj   r   approx)
r   r0   r   regr_no_outlierregr_with_weightregr_with_outlierr   score_with_outlierscore_no_outlierscore_with_weights
             r2   $test_adaboostregressor_sample_weightr:  /  s    )


#
#C
As%%%A	q3388AGAJ//&89A			"aA bEEERKEEEAbE ("$$11  O _--o.. !Q#2##2#'''LOOMM"A];;;*003B33B3@@&,,QssVQssV<<(..q"vq"v>> 00000 11111v}->????????r4   c                 0   t          t          j        d          ddi\  }}}}t          | d          }|                    ||           t          t          j        |                    |          d          |	                    |                     d S )NT)
return_X_yr$   r   rR   r   r(   )
r   r   load_digitsr   rL   r   r9   rB   r3   rT   )rI   r   r   r   r   models         r2    test_adaboost_consistent_predictr?  X  s     (8			.	.	.(=?( ($GVWf DDDE	IIgw
	%%%f--A666f8M8M    r4   zmodel, X, yc                     t          j        |          }d|d<   d}t          j        t          |          5  |                     |||           d d d            d S # 1 swxY w Y   d S )Nir   z1Negative values in data passed to `sample_weight`r   r   )r9   r>   r   r   r   rL   )r>  r0   r   r   r*  s        r2   #test_adaboost_negative_weight_errorrA  i  s     LOOMM"AG	z	1	1	1 5 5		!Qm	4445 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5s   AA!$A!c                     t           j                            d          } |                     d          }|                     ddgd          }t          j        |          dz  }t          dd	
          }t          |dd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$      rG   )r   r   rI   r$   r   N)r9   r   r   normalr$  r>   r   r   rL   isnanr   r<   )r   r0   r   r   tree	ada_models         r2   Ftest_adaboost_numerically_stable_feature_importance_with_small_weightsrJ  y  s     )


#
#C



##A

Aq6
%%ALOOf,M!BR@@@D"R7  I MM!QmM4448I2337799Q>>>>>>r4   c                    d}t          j        |d|          \  }}t          d||                               ||          }|                    |          }t          |                    d          dd           | d	k    r/t          t          j	        |                    dd
|dz
  z  hk    sJ |
                    |          D ]]}t          |                    d          dd           | d	k    r/t          t          j	        |                    dd
|dz
  z  hk    sJ ^|                    d                              ||           |                    |          }t          |                    d          dd           |
                    |          D ](}t          |                    d          dd           )dS )zCheck that the decision function respects the symmetric constraint for weak
    learners.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/26520
    r"   r   )r   n_clusters_per_classr$   )r   r$   rI   r(   r   g:0yE>)atolrG   r   r   r  N)r   r   r   rL   rZ   r   r<   rl   r9   rW   r   
set_params)rI   global_random_seedr   r0   r   rN   y_scores          r2   test_adaboost_decision_functionrQ    s    I'!BT  DAq %79  	c!Qii  ##A&&GGKKQK''6666G 29W%%&&1bIM.B*CCCCC //22 H H++QT:::: ry))**q"	A2F.GGGGGNNN""&&q!,,,##A&&GGKKQK''6666//22 ; ;++QT:::::; ;r4   c                      t          d          } t          j        t          t	          j        d                    5  |                     t          t                     d d d            d S # 1 swxY w Y   d S )Nr   r  z1The SAMME.R algorithm (the default) is deprecatedr   )	r   r   r   FutureWarningr   r   rL   r0   rS   )adaboost_clfs    r2   !test_deprecated_samme_r_algorithmrU    s    %1555L	iKLL
 
 
 % % 	G$$$	% % % % % % % % % % % % % % % % % %s   !A,,A03A0)Zr   r   numpyr9   r   sklearnr   sklearn.baser   r   sklearn.dummyr   r   r   r   r	   !sklearn.ensemble._weight_boostingr
   sklearn.linear_modelr   sklearn.model_selectionr   r   sklearn.svmr   r   sklearn.treer   r   sklearn.utilsr   sklearn.utils._mockingr   sklearn.utils._testingr   r   r   r   sklearn.utils.fixesr   r   r   r   r   r   r   r   r0   rS   r]   rU   rV   r^   	load_irisrg   permutationrh   r   permri   load_diabetesry   rE   rO   markfilterwarningsparametrizer[   r_   rr   r{   r   r   r   r   r   r   r   r   r  r  r  r'  r+  r:  r?  rA  rJ  rQ  rU  r   r4   r2   <module>rj     s1   < < 				            - - - - - - - - 9 9 9 9 9 9 9 9 B B B B B B B B : : : : : : 1 1 1 1 1 1 B B B B B B B B                 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, , ,, :;;  <;> :;;F F <;F,< < <     8( ( ( .C	
	
	
 	
 		

 	
 	^++	 	 Y= Y= Y=x .C	
	
	
 	
 		

 	
 	^++	 	 ,= ,= ,=^I I I&  4 :;;w	&:;;  <; <;#@ #@ #@R :;;w	&:;;  <; <; 				ty$+6				hmX_= 5 5 5? ? ?, :;;w	&:;;&; &; <; <;&;T% % % % %r4   