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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 importorskiprArSeriesrr-)rLpd y_ndarrayrY y_predictedoutputexpected_outputs rZ%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 rqrrr r=r[rrArBrtrrrxrYrXryrzr{s rZ%test_classification_report_multiclassr sl    D'UCFFA O#yyT../0&& F _ $$ $r\c>gdgd}}d}t||}||k(sJy)N) rrrr<r<r<r5r5r5ra| precision recall f1-score support 0 0.33 0.33 0.33 3 1 0.33 0.33 0.33 3 2 0.33 0.33 0.33 3 accuracy 0.33 9 macro avg 0.33 0.33 0.33 9 weighted avg 0.33 0.33 0.33 9 r)rYrXrzr{s rZ.test_classification_report_multiclass_balancedrs/02MFF O#66 2F _ $$ $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=r[rr s rZ:test_classification_report_multiclass_with_label_detectionrsF    D'UCFFA O#66 2F _ $$ $r\c tj}t|d\}}}d}t||t j t |j|jd}||k(sJy)NFr^a| precision recall f1-score support setosa 0.82609 0.79167 0.80851 24 versicolor 0.33333 0.09677 0.15000 31 virginica 0.41860 0.90000 0.57143 20 accuracy 0.53333 75 macro avg 0.52601 0.59615 0.50998 75 weighted avg 0.51375 0.53333 0.47310 75 r)rqrrdigitsr r s rZ1test_classification_report_multiclass_with_digitsrso    D'UCFFA O#yyT../0&& F _ $$ $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 rrrr[rArr)rYrXryrzr{s rZ7test_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 rrYrXryrqrzr{s rZ8test_classification_report_multiclass_with_unicode_labelr?sW'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 rZpos_label=2 is not a valid label. It should be one of \[0, 1\]rrNr5rrrrrrTarget is multilabel-indicator but average='binary'. Please choose another average setting, one of \[None, 'micro', 'macro', 'weighted', 'samples'\].r)rr<r<rr5rTarget 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)rArrrrrrr)rYrXrmsg1msg2msg3rs rZtest_jaccard_score_validationr:sd XXo &F XXo &FOG z 1ffh!D 2XXy), -F XXy), -F 6 z .ffh"E /XXo &F XXo &F  z .ffh7 / WD z .ffi8 /  k -ffgC . -A 2 1 / . / . / . . -s< 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.)rArrrrr@r+r-rrrrr rK)recwarnrrrYrXr8r9rs rZtest_multilabel_jaccard_scorer=sS 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 .ffaS'B / D z .ffbT7C / - ,C 8 "((QF8,bhhAx.@' R    9 , ,C 8 1a&1a&)*1a&1a&)*!       9G} }A / . / . 9 8 9 8s2N/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@birdrA)r@r?r@r@r?rArAr@)r?rAr@r?rAr@rr<r5)rrrNrrr) r&rJ transformrrzipr+rArr/rK)r<rYrXrqlbrrmulti_jaccard_scorebin_jaccard_scoremulti_labels_listbin_labels_listrm_labelb_labels rZtest_multiclass_jaccard_scorerK:su GF GF #F  BFF6Nf%Jf%J!-@ z:F       1v1v1vsQC!dCO8 #$5 G GW #GGD!''B !H8XX1v1v1v. /F XX1v1v1v. /F  VVZ@AEEE G} }  s D88Ectdgdgddk(sJd}tjt|5tddgddgddk(sJ dddtdgdgddd k(sJt j gd }t j gd }t t||dd t t||dd dt|rJy#1swYxYw)Nr<rrNrrzOJaccard is ill-defined and being set to 0.0 due to no true or predicted samplesrrr)r<rr<r<r)r<rr<r<r<rr4r)rrrr rArr+rK)r<rrYrXs rZ!test_average_binary_jaccard_scorerM_s !qc8 4 ;; ; ' ,C 8aVaVX>#EEE 9 !qcQ AS HH H XXo &F XXo &F ffhGQffh!DgG} } 9 8s CC c(tjgdgdg}tjgdgdg}d}tjt|5t ||dd}|tj dk(sJ dddy#1swYyxYw) NrrrrzJaccard is ill-defined and being set to 0.0 in samples with no true or predicted labels. Use `zero_division` parameter to control this behavior.rrrrrr)rArrrr rr)rYrXrscores rZ(test_jaccard_score_zero_division_warningrRvsyXXy), -F XXy), -F C ,C 8ffivV c**** 9 8 8s *BBzzero_division, expected_scorer)r<rcHtjgdgdg}tjgdgdg}tj5tjdt t ||d|}dddtj|k(sJy#1swY$xYw)NrrOrTrrP) rArrrrVr rrr)rexpected_scorerYrXrQs rZ*test_jaccard_score_zero_division_set_valuerUsXXy), -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)rrrrr5)rrrr)rrr<r)r<r<r<r<rr)rrr<rrg?rg?rrrrryrrArrr,rr+rrrYrXrSrrrf2rcs rZ+test_precision_recall_f1_score_multilabel_1r]s$ 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)!NrXrWrr\r_rPrrr5rrrr<r`rrrrarr?g?ryrrgrrrrg@rrrdgZd;O?)rArrr,rrr+r0)rzero_division_expectedrYrXrSrrr expected_fr\rc value_to_sumvalues_to_averageexpected_results rZ7test_precision_recall_f1_score_with_an_empty_predictionrlsXX|\<@ 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|}tt|||||\}}}}tt |||||} |Jtj |r#|||| fD]} tj | rJyt|}t||t||t||t| t|y)Nrhrrrrre) rArr/r.rrrrwr+) rrrrYrXrSrrrfbetar}s rZ"test_precision_recall_f1_no_labelsrqysXXg F ]]6 "F#' # JAq!Q  #  E 99 xx !Q&F88F# ##'-(M=)=)=)u]34r\ctjd}tj|}t}t j t 5||||d\}}}}dddtdtdtdJt j t 5t|||d}dddtdy#1swYoxYw#1swY"xYw)Nrnrrr) rArr/rrrr r+r) rrYrXfuncrSrrrrps rZ1test_precision_recall_f1_no_labels_check_warningsrts XXg F ]]6 "F *D , -&&'D 1a .111 99 , -FFG#F .q! . - . -s C %C CCc~tjd}tj|}tt||dd|\}}}}tt ||dd|}tj |}t||||gdt||||gdt||||gdt|gddt||||gdy)Nrnrrorer5rO)rArr/r.rrrr,)rrYrXrSrrrrps rZ/test_precision_recall_f1_no_labels_average_nonervs XXg F ]]6 "F$' # JAq!Q VV#t= EJJ}-Ma- !NPQRa- !NPQRa- !NPQRaA.em]M%RTUVr\ctjd}tj|}tjt 5t ||dd\}}}}dddtgddtgddtgddtgddtjt 5t||dd}dddtgddy#1swYxYw#1swY%xYw)Nrnr<rrOr5rY) rArr/rrr rr,r)rYrXrSrrrrps rZ4test_precision_recall_f1_no_labels_average_none_warnrxs XXg F ]]6 "F , -4 FDq 1a . aA.aA.aA.aA. , -FFDA .eY2 . - . -sC6C*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<r5rzRecall 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.rrNTralwaysF-score is ill-defined and being set to 0.0 due to no true nor predicted samples. 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