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Non-regression test for gh-25635. pandas) r;rrr;rr;r;rr;r) rrr;r;rr;r;r;r;rN)r importorskipr@rSeriesrr-)rLpd y_ndarrayrX y_predictedoutputexpected_outputs rY%test_confusion_matrix_pandas_nullablersj   X &B45I YYyY .F))7w)GK fk 2F&y+>Ov/r[c tj}t|d\}}}d}t||t j t |j|j}||k(sJy)NFr]a| precision recall f1-score support setosa 0.83 0.79 0.81 24 versicolor 0.33 0.10 0.15 31 virginica 0.42 0.90 0.57 20 accuracy 0.53 75 macro avg 0.53 0.60 0.51 75 weighted avg 0.51 0.53 0.47 75 rprqr r<rZrr@rArsrqrwrXrWrxryrzs rY%test_classification_report_multiclassr sl    D'UCFFA O#yyT../0&& F _ $$ $r[c>gdgd}}d}t||}||k(sJy)N) rrrr;r;r;r4r4r4ra| precision recall f1-score support 0 0.33 0.33 0.33 3 1 0.33 0.33 0.33 3 2 0.33 0.33 0.33 3 accuracy 0.33 9 macro avg 0.33 0.33 0.33 9 weighted avg 0.33 0.33 0.33 9 r)rXrWryrzs rY.test_classification_report_multiclass_balancedr s/02MFF O#66 2F _ $$ $r[cxtj}t|d\}}}d}t||}||k(sJy)NFr]a| precision recall f1-score support 0 0.83 0.79 0.81 24 1 0.33 0.10 0.15 31 2 0.42 0.90 0.57 20 accuracy 0.53 75 macro avg 0.53 0.60 0.51 75 weighted avg 0.51 0.53 0.47 75 )r r<rZrrs rY:test_classification_report_multiclass_with_label_detectionr sF    D'UCFFA O#66 2F _ $$ $r[c tj}t|d\}}}d}t||t j t |j|jd}||k(sJy)NFr]a| precision recall f1-score support setosa 0.82609 0.79167 0.80851 24 versicolor 0.33333 0.09677 0.15000 31 virginica 0.41860 0.90000 0.57143 20 accuracy 0.53333 75 macro avg 0.52601 0.59615 0.50998 75 weighted avg 0.51375 0.53333 0.47310 75 r)rprqdigitsrrs rY1test_classification_report_multiclass_with_digitsrso    D'UCFFA O#yyT../0&& F _ $$ $r[ctd\}}}tjgd|}tjgd|}d}t||}||k(sJd}t||gd}||k(sJy)NFr)bluegreenreda| precision recall f1-score support blue 0.83 0.79 0.81 24 green 0.33 0.10 0.15 31 red 0.42 0.90 0.57 20 accuracy 0.53 75 macro avg 0.53 0.60 0.51 75 weighted avg 0.51 0.53 0.47 75 a| precision recall f1-score support a 0.83 0.79 0.81 24 b 0.33 0.10 0.15 31 c 0.42 0.90 0.57 20 accuracy 0.53 75 macro avg 0.53 0.60 0.51 75 weighted avg 0.51 0.53 0.47 75 rrqrZr@rr)rXrWrxryrzs rY7test_classification_report_multiclass_with_string_labelrs'u5FFA XX. / 7F XX. / 7F O#66 2F _ $$ $ O#66 PF _ $$ $r[ctd\}}}tjgd}||}||}d}t||}||k(sJy)NFr)ublue¢ugreen¢ured¢u precision recall f1-score support blue¢ 0.83 0.79 0.81 24 green¢ 0.33 0.10 0.15 31 red¢ 0.42 0.90 0.57 20 accuracy 0.53 75 macro avg 0.53 0.60 0.51 75 weighted avg 0.51 0.53 0.47 75 rrXrWrxrpryrzs rY8test_classification_report_multiclass_with_unicode_labelr9sW'u5FFA XX: ;F F^F F^F O#66 2F _ $$ $r[ctd\}}}tjgd}||}||}d}t||}||k(sJy)NFr)rgreengreengreengreengreenra precision recall f1-score support blue 0.83 0.79 0.81 24 greengreengreengreengreen 0.33 0.10 0.15 31 red 0.42 0.90 0.57 20 accuracy 0.53 75 macro avg 0.53 0.60 0.51 75 weighted avg 0.51 0.53 0.47 75 rrs rYpos_label=2 is not a valid label. It should be one of \[0, 1\]rrMr4rrrrrrTarget is multilabel-indicator but average='binary'. Please choose another average setting, one of \[None, 'micro', 'macro', 'weighted', 'samples'\].r)rr;r;rr4rTarget is multiclass but average='binary'. Please choose another average setting, one of \[None, 'micro', 'macro', 'weighted'\].rzJSamplewise metrics are not available outside of multilabel classification.rzNote that pos_label \(set to 3\) is ignored when average != 'binary' \(got 'micro'\). You may use labels=\[pos_label\] to specify a single positive class.rr)r@rrrrrrr)rXrWrmsg1msg2msg3rs rYtest_jaccard_score_validationr6s XXo &F XXo &FOG z 1Effh!DEXXy), -F XXy), -F 6 z .Fffh"EFXXo &F XXo &F  z .8ffh78 WD z .9ffi89  k -DffgCDDAEEFF8899DDs< F7F(F4GG F%(F14F=G  Gc tjgdgdg}tjgdgdg}t||ddk(sJt||ddk(sJt||ddk(sJt|tj|ddk(sJt|tj|ddk(sJt|tj|j ddk(sJt|tj|j ddk(sJtjgdgd g}tjgd gdg}t t||d d t t||d dt t||ddt t||dddgdt t||dddgdtt||dtjgdtjgdgdg}tjgd gdg}t t||d dt t||ddd}tjt|5t||dgd dddd}tjt|5t||dgd dddd}tjt|5ttjddggtjddggd dk(sJ dddd}tjt|5ttjddgddggtjddgddggddk(sJ dddt|rJy#1swY xYw#1swYxYw#1swYxYw#1swY 2rrrz Got -1 < 0rzXJaccard is ill-defined and being set to 0.0 in labels with no true or predicted samples.zXJaccard is ill-defined and being set to 0.0 in samples with no true or predicted labels.)r@rrrrr?r+r-rrrrr rK)recwarnrrrXrWr4r5rs rYtest_multilabel_jaccard_scorer9sw 9i( )B 9i( )B R 3t ;; ; R 3q 88 8 R 3q 88 8 R^^B/ Cq HH H R^^B/ Cq HH H RXXbhh/ Cq HH H RXXbhh/ Cq HH H XXy), -F XXy), -F ffgFP ffgFP ffiH(SffiAGffiAGffd3RXX>U5VXXy), -F XXy), -F ffgFP ffjI7S D z .CffaS'BC D z .DffbT7CD - ,C 8 "((QF8,bhhAx.@' R     , ,C 8 1a&1a&)*1a&1a&)*!       G} }ACCDD    s2N/6N<,AOAO/N9<OOOc gd}gd}gd}t}|j||j|}|j|}tt||}tt||}ddgddgddgdgdgdgdg} ddgdd gd dgdgdgd gdg} d D]2} t | | D]!\} } t || | || | #4tjddgddgddgg}tjddgddgddgg}t5t ||d dk(sJ dddt|rJy#1swYxYw)N)antr;catr<r;r<birdr=)r<r;r<r<r;r=r=r<)r;r=r<r;r=r<rr;r4)rrrNrrr) r&rI transformrrzipr+r@rr.rK)r8rXrWrplbrrmulti_jaccard_scorebin_jaccard_scoremulti_labels_listbin_labels_listrm_labelb_labels rYtest_multiclass_jaccard_scorerG4s GF GF #F  BFF6Nf%Jf%J!-@ z:F       1v1v1vsQC!dCO8 #$5 G  GW #GGD!''B  XX1v1v1v. /F XX1v1v1v. /F  FVVZ@AEEEFG} }FFs D88Ectdgdgddk(sJd}tjt|5tddgddgddk(sJ dddtdgdgddd k(sJt j gd }t j gd }t t||dd t t||dd dt|rJy#1swYxYw)Nr;rrMrrzOJaccard is ill-defined and being set to 0.0 due to no true or predicted samplesrrr)r;rr;r;r)r;rr;r;r;rr0r)rrrr r@rr+rK)r8rrXrWs rY!test_average_binary_jaccard_scorerIYs !qc8 4 ;; ; ' ,C 8FaVaVX>#EEEF !qcQ AS HH H XXo &F XXo &F ffhGQffh!DgG} }FFs CC c(tjgdgdg}tjgdgdg}d}tjt|5t ||dd}|tj dk(sJ dddy#1swYyxYw) NrrrrzJaccard is ill-defined and being set to 0.0 in samples with no true or predicted labels. Use `zero_division` parameter to control this behavior.rrrrrr)r@rrrr rr)rXrWrscores rY(test_jaccard_score_zero_division_warningrNpsXXy), -F XXy), -F C ,C 8+ffivV c****+++s *BBzzero_division, expected_scorer)r;rcHtjgdgdg}tjgdgdg}tj5tjdt t ||d|}dddtj|k(sJy#1swY$xYw)NrrKrrrL) r@rrrrr rrr)rexpected_scorerXrWrMs rY*test_jaccard_score_zero_division_set_valuerQsXXy), -F XXy), -F  " g'=> FI]  FMM.1 11 1   s *BB!chtjgdgdgdg}tjgdgdgdg}t||d\}}}}t|gddt|gddt|gd dt|gd dt ||dd }|}t|gd dt||d \}}}}t |dt |dt |d|Jt t ||dd tj |t||d\}}}}t |dt |dt |d|Jt t ||dd d|z|zd|z|zz t||d\}}}}t |dt |dt |d|Jt t ||dd tj||t||d\}}}}t |dt |dt |d|Jt t ||dd dy)Nr;rrrrr;rrr)r;rr;rr)rrrrr4)rrrr)rrr;r)r;r;r;r;rr)rrr;rrg?rg?rrrrrwrr@rrr,rr+rrrXrWrRrrrf2rbs rY+test_precision_recall_f1_score_multilabel_1rYs$ XX|\<@ AF XX|\<@ AF0NJAq!Qa!5q9a!5q9a!7;aq1 VV!T :BGb/151QJAq!Q7#3+, 99FFGr[z%zero_division, zero_division_expected)rrrc tjgdgdgdg}tjgdgdgdg}t||d|\}}}}t||dddgd t|dd d|gd d }t||d d |gd t|gdd t ||d d|} |} t| |dd |gd t||d|\}}}}tj |rd n|} dtj | z} t |d | z| z t |d| z| z d}t |||Jt t ||d d|t| dt||d|\}}}}t |d t |d t |d|Jt t ||d d|d|z|zd|z|zz t||d|\}}}}t ||d k(rdndt |d d} t |d| z |Jt t ||d d|t| | t||d\}}}}t |dt |dt |d|Jd } t t ||d d|| d y)!NrTrSrrZr[rLrrr4rrrr;r\rrrr]rr?g?rwrrfrrrrg@rrrcgZd;O?)r@rrr,rrr+r/)rzero_division_expectedrXrWrRrrr expected_frXrb value_to_sumvalues_to_averageexpected_results rY7test_precision_recall_f1_score_with_an_empty_predictionrhsXX|\<@ AF XX|\<@ AF 1MJAq!Qa"8#sC!H!La#sC1G!H!LJa*gq*!EqIaq1 VV!T WBGb:tQ "CQG0}JAq!Q!781>TL*@!AABA ,0AABC,.2CCD J:& 99  '   B% 1}JAq!Q5!301 99 FG=  ! a1q519% 1 -JAq!Q$:a$?5SI3M->>? 99 FJm  B( 1SJAq!Q5!5!5! 99O FI]   r[r)rrrrctjd}tj|}tj5tj dt |||||\}}}}t|||||} dddJtj|r# fD]} tj| rJyt|}t|t|t|t t|y#1swYxYw)Nrgrrrrrra) r@rr+rrrrrrrvr+) rrrrXrWrRrrrfbetar|s rY"test_precision_recall_f1_no_labelsrmss XXg F ]]6 "F  " g&4  '  1a  '   " 99 xx !Q& $F88F# ## $-(M=)=)=)u]34=  s ;DD ctjd}tj|}t}t j t 5||||d\}}}}dddtdtdtdJt j t 5t|||d}dddtdy#1swYoxYw#1swY"xYw)Nrjrrr) r@rr+rrrr r+r) rrXrWfuncrRrrrrls rY1test_precision_recall_f1_no_labels_check_warningsrps XXg F ]]6 "F *D , -E&&'D 1aE111 99 , -GFFG#FGq!EEGGs C %C CCctjd}tj|}tj5tj dt ||dd|\}}}}t||dd|}dddtj|}t|||gdt|||gdt|||gdtgddt|||gdy#1swYnxYw)Nrjrrrkrar4rK) r@rr+rrrrrrr,)rrXrWrRrrrrls rY/test_precision_recall_f1_no_labels_average_nonerrs XXg F ]]6 "F  "  g&4  '  1a Fd-   JJ}-Ma- !NPQRa- !NPQRa- !NPQRaA.em]M%RTUV)   s ;C''C0ctjd}tj|}tjt 5t ||dd\}}}}dddtgddtgddtgddtgddtjt 5t||dd}dddtgddy#1swYxYw#1swY%xYw)Nrjr;rrKr4rU) r@rr+rrr rr,r)rXrWrRrrrrls rY4test_precision_recall_f1_no_labels_average_none_warnrts XXg F ]]6 "F , - 4 FDq 1a aA.aA.aA.aA. , -BFFDABeY2  BBsC6C*C'*C3c tt}}dD]d}d}tj||5|gdgd|dddd}tj||5|gdgd|dddfd}tj||5|t j d d gd d ggt j d d gd d ggd dddd }tj||5|t j d d gd d ggt j d d gd d ggd dddd }tj||5|t j d d gd d ggt j d d gd d ggddddd}tj||5|t j d d gd d ggt j d d gd d ggddddd }tj||5|d d gddgddddd}tj||5|ddgd d gddddt jd5}t jdtd d gd d gdd}t|jj|k(sJd}t|jj|k(sJd }t|jj|k(sJ dddy#1swYxYw#1swY"xYw#1swYixYw#1swYxYw#1swYxYw#1swYsxYw#1swYOxYw#1swY+xYw#1swYyxYw)NNrrzPrecision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.rrr;r;r4rzRecall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.zPrecision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.r;rrzRecall is ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.rzRecall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.rrMTralwaysF-score is ill-defined and being set to 0.0 due to no true nor predicted samples. 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Please provide the true labels explicitly through the labels argument.rzDFound input variables with inconsistent numbers of samples: \[3, 2\]rr)rrrrrrrA) r@rrrr logpmfr*rrrlog) rXrWloss loss_truer error_str true_log_losscalculated_log_lossy_score2s rY test_log_lossr< s 4F XX sc3Z$SzD$<%QVXF FF #D))"((6*:e*CVAqD\RSSID)$F @F FFd 3DD)$ aKF aKF FFe 4DD-(FCj3*sCj 1F z "! !,FCj3*sCj3* =F FF #DD)$VFCj3* %Fhhc S#J/0G C z 3! !Cj3*sCj 1FWI z 3! ! WWRVVGAqDM233M"67Aq6B'7F/BH FHY 7DD266#;,'I!!$!! !!s$ I! I. I:!I+.I7:Jctjddg|}tjddg|}t||}tj|sJy)zCheck the behaviour internal eps that changes depending on the input dtype. 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No error should be raised for those types. *rhrT)rreplacerrN) r@rDrEchoiceruniformranyr)r|rrSrPrrXrWrs rY*test_classification_metric_pos_label_typesr s* ))   #Cwr{yI ZZiZ >F !!), FFi 8Fvvbhhv&'' ''r[zy_true, y_pred, expected_scorerrcPt||dtj|k(sJy)zCheck the behaviour of `zero_division` for f1-score. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/26965 rr~N)rrr)rXrWrPs rY2test_f1_for_small_binary_inputs_with_zero_divisionr s$ FF# 6&--:W WW Wr[scoringr~r4)rrctjd\}}tddj||}t ||||ddy) aZCheck that we validate `np.nan` properly for classification metrics. With `n_jobs=2` in cross-validation, the `np.nan` used for the singleton will be different in the sub-process and we should not use the `is` operator but `math.isnan`. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/27563 r)r:r) max_depthr:r4r)rn_jobs error_scoreN)r make_classificationr(rIr%)rrNrO classifiers rY9test_classification_metric_division_by_zero_nan_validatonr& sC&  ' 'Q 7DAq'!!DHHANJJ1gaWUr[c^tjgd}tjgd}d}tjt|5t ||dddd}tj t|5t |||dddy#1swY>xYw#1swYyxYw) z)Check the message for future deprecation.rrz$y_prob was deprecated in version 1.5r)y_probNz/`y_prob` and `y_proba` cannot be both specified)ry_proba)r@rrr FutureWarningrrr)rXrWra error_msgs rY)test_brier_score_loss_deprecation_warningr? sXX( )F XX5 6F5H m8 4    BI z 3         s B?B#B #B,c gd}gd}tjddgddgddgddgdd gd d gg}tjddgddgddgddgddgddgg}t|| }t||d }t||d }d||z z }|t j |k(sJtjgd}|ddj |j z |dddf<|ddj |j z |dddf<t|||}t|||d }t|||d }d||z z }|t j |k(sJtjddgddgddgddgddgddgg}t||}d|cxkrdksJJt||} | t j |k(sJtjddgddgddgddgddgddgg}t||}|dksJt||} | t j |k(sJgd}tjddgddgddgddgddgddgg}t||}|dk(sJt||} | dk(sJgd}gd}tjddgddgddgddgg}t||}|dk(sJt||} | dk(sJgd}t|||} | dk(sJgd}gd}tjgd gd gd!gd"g}t||}d|cxkrdksJJt|||}d|cxkrdksJJtjgd#gd$gd"gd%g}t||}|dksJt|||}|dksJy)&Nr]rrrerrrgffffff?g?rrr\F)rXrWrJr;)r4r;rrrr;rr)rXrWr3)rXrWr3rJrrrrr^)rr;r;r;)rrrr)r4r4r4r4r@)highr lowneutral)gffffff?rrr)rrrr)rrr)rrrrr)r@rr$rrrrT) rX y_true_stringrW y_pred_nulld2_scorelog_likelihoodlog_likelihood_null d2_score_truer3d2_score_stringd2_score_with_sample_weights rYtest_d2_log_loss_scorer+U s F;M XX #J #J #J #J 4L 4L   F(( #J #J #J #J #J #J   K!v>HVFeLN"&PUV)<<O fmmH5 55 5XX #J #J #J #J 4L #J   F!0H a<<' v>O fmmH5 55 5 F XX #J #J #J #J #J #J   F!0H q==' v>O a  F/M XXd|dD\D$<$N OF 0H q==' v>O a   M"3m# '! ++ +0F(M XX      F!0H  C    }MH  C    XX      F!0H a<< }MH a<rFsm  22 8!!;5.N3@/0.>7 )(`>DBW<61a*@A B T{aVUOi=OP))( 7DPQR$PQ*JR*JZPQMRM,>6  BHH####    . = (9)(9  BHH#####      *;+*;  =* = OF.9.9%S::%SPT<+%%;&) #"((#56""((#56  A  #"((#56""((#56  I  #"((#56""((#56  :  #"((#56""((#56  I  #"((#56""((#56  O  E),Z*[,Z*#"((?3""((?3  <  **"2>1a.9)aS1#J<8  !$  '9: ')aS1#J<8  !$ 9$2N RF5pc5\2U3UB7(t$ST ,U ,0"(".& ;< QAB '( 88  taVY'-M%%6"AB0C0"%4%$%(%6 %F%,%.X IPQ%R%< 6B(%DPJZ"J. +868:LM 2N 2PQ?UR?UDPQ=R=@PQ+&&266266"23[ R [|!%$MN1a.9"5:O&"5J$MN"O"&1a.9 W: WF38e0P1a.9: :: z61a*@A$B$N61a*@A$ B$ N61a*@A#(B#(L'>_D=8A6*"N8P4@A8>(B2::rzz2::"FG H 2::rzz2::"FG!H! 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