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D c/SQn/SQn[R"[SS9 [X5 SSS5 g!,(df  g=f)z8Test `confusion_matrix` warns when only one label found.rrrrzA single label was found inrN)rrrr)r[rbs rd"test_confusion_matrix_single_labelrfs0 F F k)F G( H G Gs 7 Azparams, warn_msg)rFrFrFrrrrcrbz?`positive_likelihood_ratio` is ill-defined and set to `np.nan`.)rrrrrrzdNo samples were predicted for the positive class and `positive_likelihood_ratio` is set to `np.nan`.rrrrFrFrFz?`negative_likelihood_ratio` is ill-defined and set to `np.nan`.z9No samples of the positive class are present in `y_true`.c[R"[US9 [S0UD6 SSS5 g!,(df  g=fNrr)rrrr)paramswarn_msgs rdtest_likelihood_ratios_warningsrms*X k 2)&) 3 2 2 / =zparams, err_msg)rrFrrFrrFrFrrr=zeclass_likelihood_ratios only supports binary classification problems, got targets of type: multiclassc[R"[US9 [S0UD6 SSS5 g!,(df  g=frj)rrrr)rkrs rdtest_likelihood_ratios_errorsrqs)$ z 1)&) 2 1 1rnc[R"S/S-S/S--5n[R"S/S-S/S--S/S--5n[X5up#[US5 [US 5 [X5up#[ U[R S-5 [U[R "S5S S 9 [R"S /S -S/S--5n[XUS9up#[US5 [US5 g)NrFrCrr r= r g?g_B{ %?g-q=)rtolrrrrKgUUUUUU@gqq?)rKrrr3r6nanr)rcrbposnegr>s rdtest_likelihood_ratiosrysXXqcAgb( )F XXqcAgb(A372 3F&v6HCC!C!'v6HCsBFFQJ'C!51 HHcURZ3%!)34M&v]SHCC C!rfcd[R"/SQ5n[R"/SQ5n[XSS9up#U[R"S5:Xde[R"/SQ5n[R"/SQ5n[XSS9up4U[R"S5:Xdeg)z{Test that class_likelihood_ratios returns the worst scores `1.0` for both LR+ and LR- when `replace_undefined_by=1` is set.r;rrFreplace_undefined_byrrN)rKrrrr)rcrbpositive_likelihood_ratiornegative_likelihood_ratios rd1test_likelihood_ratios_replace_undefined_by_worstrs XXi F XXi F#:Q$  % c(: :: :XXi F XXi F#:Q$ A % c(: :: :rfr|LR+rLR-g)rrrrvr^c[R"SS/5n[R"SS/5nSn[R"[US9 [ XUS9 SSS5 g!,(df  g=f)zTest that class_likelihood_ratios raises a `ValueError` if the input dict for `replace_undefined_by` is in the wrong format or contains impossible values.rFrzGThe dictionary passed as `replace_undefined_by` needs to be in the formrr{N)rKrrrrr)r|rcrbrs rd6test_likelihood_ratios_wrong_dict_replace_undefined_byrsUXXq!f F XXq!f F SC z - 1E  . - -s A  A.zreplace_undefined_by, expectedc&[R"/SQ5n[R"/SQ5n[X#US9upE[R"U5(a[R"U5(degU[R "U5:Xdeg)z|Test that the `replace_undefined_by` param returns the right value for the positive_likelihood_ratio as defined by the user.r;rr{NrKrrisnanrr)r|expectedrcrbr}rs rd0test_likelihood_ratios_replace_undefined_by_0_fpr srXXi F XXi F#:-A$  xxxx12222(FMM(,CCCCrfrc&[R"/SQ5n[R"/SQ5n[X#US9upE[R"U5(a[R"U5(degU[R "U5:Xdeg)z|Test that the `replace_undefined_by` param returns the right value for the negative_likelihood_ratio as defined by the user.rrr{Nr)r|rrcrbrr~s rd0test_likelihood_ratios_replace_undefined_by_0_tnr<sqXXi F XXi F#:-A$ A xxxx12222(FMM(,CCCCrfc[R"S/S-S/S--5n[R"S/S-S/S--S/S--S/S--5n[X5n[USS S 9 U[X5:Xde[R"US /S -5n[R"US /S -5n[XSS/S 9U:Xde[[X5S5 [R"S/S-S/S--S /S--5n[R"S/S-S/S--S /S--5n[[X5SS S 9 [R"S/S-S/S--S /S--5n[R"S/S-S/S--S /S--5n[[X5SS S 9 [[XSS9SS S 9 [[XSS9SS S 9 g)Nr(rF<rrrs2gʡE?rCrr=rrr.,4 g??g+?r@weightsg_vO? quadraticg#?)rKrrr4append)rrkappas rdtest_cohen_kapparXs 1#(aS2X% &B 1#(aS2X%b0A38; cSS/n[R"S/S-S/S--5n[R"S/S-5n[R"[SS9 [ XUS 9 S S S 5 g !,(df  g =f) zLTest that correct error is raised when users pass labels that are not in y1.rFr=rrrrsz6At least one label in `labels` must be present in `y1`rrN)rKrrrrrrs rd(test_cohen_kappa_score_error_wrong_labelrslVF 3%!)seai' (B 3%"* B R  "0   s  A-- A;zy_true, y_predrrc[R"5 [R"S5 U"XUS9nSSS5 [R"U5(a[R"W5(degWU:Xdeg!,(df  NP=f)zeCheck the behaviour of `zero_division` when setting to 0, 1 or np.nan. 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' &sE#!E(E-- E;c[RRU5n[S5nSnUR SUSS9Vs/sHn[ X$-5PM nn[ [XU5S5 /SQn/SQn[ [XV5S 5 /SQn/S Qn[ [XW5S [R"S 5- 5 /S Qn/SQn[ [XX5S5 /SQn/S Qn[ [XX5S5 /SQn /SQn [ [X5S5 /SQn/SQn/SQn [ [XXU S9S5 /SQn/SQn/SQn [ [XXU S9S5 gs snf)Nrrrrrrr)rrrFrFr=r=)r=r=rrrFrFg)rFrFrrrriir)rCrCrCr rrFr=rrFr=rrFr=) rFrFrFr=r=r=rrr)rrrFrFr=rorFrFrFrFrrKrrrFrFrr) rKrOrPordrchrr4r r) rr^ord_a n_classesrrc y_pred_bad y_pred_minrbrrr>s rd!test_matthews_corrcoef_multiclassr@sM ))   2 3C HEI&)kk!YRk&H I&Hc%)n&HF I)&93? F#J)&=tD F#J)&=sRWWWEU?UVF F)&93?F F)&93? &C %C)#3S9 F F#M& F F F M& F_JsEn_pointsdi'c^[RRU5mSnU4Sjn[R"SS/U5n[ [ XD5S5 [R"/SQU5n[ [ XD5S5 U"U5upV[ [ XU5S5 [ [ XV5U"XV55 g)Nc[X5nUSnUSnUSn[U5nX5-U- nX4-U- nX6- Xx-- n X-SU- -SU- -n U [R"U 5- $)NrFrF)rFrrrF)rr|rKr) rcrb conf_matrixtrue_pos false_pos false_negrpos_rateactivity mcc_numeratormcc_denominators rdmcc_safe1test_matthews_corrcoef_overflow..mcc_safe}s&v6 t$% % v;(H4(H4 +h.AA "-X>!h,Orww777rfct>TRU5nUSTRU5S- --nUS:nUS:nX44$)Nrr) random_sample)rx_truex_predrcrbr^s rd random_ys2test_matthews_corrcoef_overflow..random_yssL""8,#!2!28!>rfrW)Int64Float64booleanc[R"S5n[R"/SQ5nUR X S9nUR /SQSS9n[ X45n[ X$5n[ XV5 g)zcChecks that confusion_matrix works with pandas nullable dtypes. Non-regression test for gh-25635. pandas) rFrrrFrrFrFrrFr ) rrrFrFrrFrFrFrFrN)r importorskiprKrSeriesrr6)rWpd y_ndarrayrc y_predictedoutputexpected_outputs rd%test_confusion_matrix_pandas_nullabler(Asb   X &B45I YYyY .F))7w)GK f 2F&y>Ov/rfc [R"5n[USS9upnSn[UU[R "[ UR55URS9nXT:Xdeg)NFrha| 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 ryrzr rGrerrKrLr|rzrrcrbrrrs rd%test_classification_report_multiclassr-Ssh    D'UCFA O#yyT../0&& F  $$ $rfc:/SQ/SQpSn[X5nX2:Xdeg)N) rrrrFrFrFr=r=r=ra| precision recall f1-score support 0 0.33 0.33 0.33 3 1 0.33 0.33 0.33 3 2 0.33 0.33 0.33 3 accuracy 0.33 9 macro avg 0.33 0.33 0.33 9 weighted avg 0.33 0.33 0.33 9 r)rcrbrrs rd.test_classification_report_multiclass_balancedr/ms)02MF O#6 2F  $$ $rfcp[R"5n[USS9upnSn[X5nXT:Xdeg)NFrha| 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 rGrerr,s rd:test_classification_report_multiclass_with_label_detectionr1s@    D'UCFA O#6 2F  $$ $rfc [R"5n[USS9upnSn[UU[R "[ UR55URSS9nXT:Xdeg)NFrha| precision recall f1-score support setosa 0.82609 0.79167 0.80851 24 versicolor 0.33333 0.09677 0.15000 31 virginica 0.41860 0.90000 0.57143 20 accuracy 0.53333 75 macro avg 0.52601 0.59615 0.50998 75 weighted avg 0.51375 0.53333 0.47310 75 r)ryrzdigitsr+r,s rd1test_classification_report_multiclass_with_digitsr4sk    D'UCFA O#yyT../0&& F  $$ $rfc[SS9upn[R"/SQ5Un[R"/SQ5UnSn[X5nXC:XdeSn[X/SQS9nXC:Xdeg)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 rrzrerKrr)rcrbrrrs rd7test_classification_report_multiclass_with_string_labelr;sy'u5FA XX. / 7F XX. / 7F O#6 2F  $$ $ O#6 PF  $$ $rfc[SS9upn[R"/SQ5nX0nX1nSn[X5nXT:Xdeg)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 r:rcrbrryrrs rd8test_classification_report_multiclass_with_unicode_labelr>sM'u5FA XX: ;F ^F ^F O#6 2F  $$ $rfc[SS9upn[R"/SQ5nX0nX1nSn[X5nXT:Xdeg)NFr)r6greengreengreengreengreenr8a 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 r:r=s rd 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.)rKrrrrrJr4r6rrrrrrV)recwarnrrrcrbrZr[rs rdtest_multilabel_jaccard_scorer_s; 9i( )B 9i( )B  3t ;; ;  3q 88 8  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 fgFP fgFP fiH(SfiAGfiAGfd3RXX>U5VXXy), -F XXy), -F fgFP fjI7S D z .faS'B / D z .fbT7C / - ,C 8 "((QF8,bhhAx.@' R    9 , ,C 8 1a&1a&)*1a&1a&)*!       9G}} }A / . / . 9 8 9 8s2 M> N8AN"AN3> N  N" N03 Oc /SQn/SQn/SQn[5nURU5 URU5nURU5n[[X5n[[XV5nSS/SS/SS/S/S/S/S/n SS/SS /S S/S/S/S /S/n S H+n [ X5Hup[ U"XS 9U"XS 95 M M- [R"SS/SS/SS//5n[R"SS/SS/SS//5n[5 [ XS S 9S:XdeSSS5 [U5(aeg!,(df  N!=f)N)antracatrbrarbbirdrc)rbrarbrbrarcrcrb)rarcrbrarcrbrrFr=)rrrNrrr) r*rT transformrrzipr4rKrr7rV)r^rcrbrylbrrmulti_jaccard_scorebin_jaccard_scoremulti_labels_listbin_labels_listrm_labelb_labels rdtest_multiclass_jaccard_scorermsi GF GF #F  BFF6Nf%Jf%J!-@ zF       1v1v1vsQC!dCO8 #$5 G G #GD!'B !H8XX1v1v1v. /F XX1v1v1v. /F  VZ@AEEE G}} }  s D44 Ec [S/S/SS9S:XdeSn[R"[US9 [SS/SS/SS9S:XdeSSS5 [S/S/SSS9S :Xde[R "/S Q5n[R "/S Q5n[ [X#SS9S 5 [ [X#SSS 9S5 [U5(aeg!,(df  N=f)NrFrrXrrzOJaccard is ill-defined and being set to 0.0 due to no true or predicted samplesrrr)rFrrFrFr)rFrrFrFrFrrTr)rrrrrKrr4rV)r^rrcrbs rd!test_average_binary_jaccard_scoreros !qc8 4 ;; ; ' ,C 8aVaVX>#EEE 9 !qcQ AS HH H XXo &F XXo &F fhGQfh!DgG}} } 9 8s C Cc.[R"/SQ/SQ/5n[R"/SQ/SQ/5nSn[R"[US9 [ XSSS9nU[R "S5:XdeSSS5 g!,(df  g=f) 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)rKrrrrrr)rcrbrrs rd(test_jaccard_score_zero_division_warningrsswXXy), -F XXy), -F C ,C 8fivV c**** 9 8 8s (B Bzzero_division, expected_scorer )rFrcV[R"/SQ/SQ/5n[R"/SQ/SQ/5n[R"5 [R"S[ 5 [ X#SUS9nSSS5 W[R"U5:Xdeg!,(df  N+=f)Nrrqrrrr) rKrrrrrrrr)rexpected_scorercrbrs rd*test_jaccard_score_zero_division_set_valuervsXXy), -F XXy), -F  "g'=> I]  # FMM.1 11 1 # "s 'B B(c  [R"/SQ/SQ/SQ/5n[R"/SQ/SQ/SQ/5n[XSS9up#pE[U/SQS5 [U/SQS5 [U/S QS5 [U/S QS5 [ XSSS 9nUn[U/S QS5 [XS S9up#pE[ US5 [ US5 [ US5 Ube[ [ XSS S 9[R "U55 [XSS9up#pE[ US5 [ US5 [ US5 Ube[ [ XSSS 9SU-U-SU-U-- 5 [XSS9up#pE[ US5 [ US5 [ US5 Ube[ [ XSSS 9[R"XgS95 [XSS9up#pE[ US5 [ US5 [ US5 Ube[ [ XSSS 9S5 g)NrFrrrrrFrrrrFrrFrr)rrrrr=)rrrr)rrrFr)rFrFrFrFrr)rrrFrrg?rg?rrrrrrrKrr"r5rr4rrrcrbr]rrrf2rms rd+test_precision_recall_f1_score_multilabel_1r$s XX|\<@ AF XX|\<@ AF0NJA!a!5q9a!5q9a!7;aq1 V!T :BGb/151QJA!7#3+, 99FGrfz%zero_division, zero_division_expected)rrrc [R"/SQ/SQ/SQ/5n[R"/SQ/SQ/SQ/5n[X#SUS9upEpg[XASSS/S 5 [USS SU/S 5 S n[XhS S U/S 5 [U/SQS 5 [ X#S SUS9n Un [XSS U/S 5 [X#SUS9upEpg[R "U5(aS OUn S[R "U5(+-n [ US U -U - 5 [ USU -U - 5 Sn[ Xh5 Ube[ [ UUS SUS9[U SS95 [X#SUS9upEpg[ US 5 [ US 5 [ US5 Ube[ [ X#S SUS9SU-U-SU-U-- 5 [X#SUS9upEpg[ XAS :XaSOS5 [ US 5 Sn [ USU - 5 Ube[ [ X#S SUS9[XS95 [X#SS9upEpg[ US5 [ US5 [ US5 UbeS n [ [ X#S SUS9U S 5 g)!Nryrxrrerrrrrr=rrrrFrrrrrrrC?g?rrrqrrrrg@rrrngZd;O?)rKrr"r5rrr4r8)rzero_division_expectedrcrbr]rrr expected_fr~rm value_to_sumvalues_to_averageexpected_results rd7test_precision_recall_f1_score_with_an_empty_predictionrsXX|\<@ AF XX|\<@ AF 1MJA!a#sC!H!La#sC1G!H!LJagq*!EqIaq1 V!T WBGbtQ "CQG0}JA!!7881>TL*@!AABA ,0AABC,.2CCD J& 99  '   B% 1}JA!5!301 99 G=  ! a1q519% 1 -JA!a$?5SI3M->>? 99 Jm  B( 1SJA!5!5!5! 99O I]   rfr)rrrrc >[R"S5n[R"U5n[R"5 [R "S5 [ UUUUUS9upVpx[UUUUUS9n SSS5 Wbe[R"U5(a+WWWW 4H n [R"U 5(aM e g[U5n[WU5 [WU5 [WU5 [W [U55 g!,(df  N=f)NrrrCrrrrr) rKrrOrrrr"rrrr4) rrrrcrbr]rrrfbetars rd"test_precision_recall_f1_no_labelsr sXXg F ]]6 "F  "g&4  '  a  '   #" 99 xx !Q&F88F## ##'-(M=)=)=)u]34= # "s 4D Dc[R"S5n[R"U5n[n[R "[ 5 U"XUSS9upEpgSSS5 [WS5 [WS5 [WS5 Wbe[R "[ 5 [XUSS9nSSS5 [WS5 g!,(df  Nr=f!,(df  N,=f)Nrrrr) rKrrOr"rrrr4r) rrcrbfuncr]rrrrs rd1test_precision_recall_f1_no_labels_check_warningsr3s XXg F ]]6 "F *D , -&'D a .111 99 , -FG#F .q! . - . -s C$ C C C$c [R"S5n[R"U5n[R"5 [R "S5 [ UUSSUS9up4pV[XSSUS9nSSS5 [R"U5n[WXU/S5 [WXU/S5 [WXU/S5 [W/SQS5 [WXU/S5 g!,(df  Np=f)Nrrrrrr=rq) rKrrOrrrr"rrr5)rrcrbr]rrrrs rd/test_precision_recall_f1_no_labels_average_nonerGs XXg F ]]6 "F  "g&4  '  a d-  #JJ}-Ma- !NPQRa- !NPQRa- !NPQRaA.emM%RTUV) # "s 3C C-c[R"S5n[R"U5n[R"[ 5 [ XSSS9up#pESSS5 [W/SQS5 [W/SQS5 [W/SQS5 [W/SQS5 [R"[ 5 [XSSS9nSSS5 [W/SQS5 g!,(df  N=f!,(df  N/=f)NrrFrrqr=r{) rKrrOrrrr"r5r)rcrbr]rrrrs rd4test_precision_recall_f1_no_labels_average_none_warnrks XXg F ]]6 "F , -4 Dq a . aA.aA.aA.aA. , -FDA .eY2 . - . -sC4 C) C&) C7c ([[pSHYnSn[R"XS9 U"/SQ/SQUS9 SSS5 Sn[R"XS9 U"/SQ/SQUS9 SSS5 M[ Sn[R"XS9 U"[R "S S /S S //5[R "S S /S S //5S S9 SSS5 S n[R"XS9 U"[R "S S /S S //5[R "S S /S S //5S S9 SSS5 S n[R"XS9 U"[R "S S /S S //5[R "S S /S S //5SS9 SSS5 Sn[R"XS9 U"[R "S S /S S //5[R "S S /S S //5SS9 SSS5 S n[R"XS9 U"S S /SS/SS9 SSS5 Sn[R"XS9 U"SS/S S /SS9 SSS5 [ R"SS9n[ R"S5 [S S /S S /SS9 Sn[UR5R5U:XdeSn[UR5R5U:XdeS n[UR5R5U:XdeSSS5 g!,(df  GN=f!,(df  GM=f!,(df  GN`=f!,(df  GN=f!,(df  GN=f!,(df  GN=f!,(df  GNl=f!,(df  GNS=f!,(df  g=f)NNrrzPrecision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.rrrFrFr=rzRecall 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.rFrrzRecall 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.rrXTrrF-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.) r"rrrrKrrrrrpopr)rrQrrrs rdtest_prf_warningsrs. *,Bq.  \\! ' iG 4(  \\! ' iG 4( '!/*  a # "((QFQF# $bhhAA/?&@)T $  a # "((QFQF# $bhhAA/?&@)T $   a # "((QFQF# $bhhAA/?&@'R $  a # "((QFQF# $bhhAA/?&@'R $   a # 1a&2r(H- $  a # 2r(QFH- $   -h''AAI  6::<''(C///  6::<''(C///  6::<''(C///- . -K( '( ' $ # $ # $ # $ # $ # $ # . -sl K2 L=L=L)7=L;=M - M M1B'N2 L  L  L&) L8; M  M M.1 N Nc [R"5 [R"S5 SH!n[/SQ/SQXS9 [/SQ/SQXS9 M# [[R "SS/SS//5[R "SS/SS//5SUS9 [[R "SS/SS//5[R "SS/SS//5SUS9 [[R "SS/SS//5[R "SS/SS//5S US9 [[R "SS/SS//5[R "SS/SS//5S US9 [SS/S S /S US9 [S S /SS/S US9 SSS5 [R"S S 9n[R"S5 [SS/SS/S US9 [ U5S:XdeSSS5 g!,(df  Nd=f!,(df  g=f)NrrrrrrrFrrrrrXTrr)rrrr"rKrr|)rrrs rd)test_prf_no_warnings_if_zero_division_setrs  "g&3G +9g  ,9g  3 ( HHq!fq!f% & HHq!fq!f% &'   ( HHq!fq!f% & HHq!fq!f% &'   ( HHq!fq!f% & HHq!fq!f% &'   ( HHq!fq!f% & HHq!fq!f% &'   ( FRHhm  ( Hq!fhm a #h   -h'' FQFHM 6{a . -i # "h . -sEG8G# G # G1c [R"5 [R"S5 [[R "SS/SS//5[R "SS/SS//5SUS9 SSS5 [R"SS9n[R"S5 [[R "SS/SS//5[R "SS/SS//5SUS9 US :Xa*[ UR5R5S :XdeO[U5S:Xde[SS/SS/5 US :Xa)[ UR5R5S :XdeSSS5 g!,(df  GN=f!,(df  g=f) NrrFrrrrTrrrr) rrrr$rKrrrrr|rrs rdtest_recall_warningsr- sc  "g& HHq!fq!f% & HHq!fq!f% &'   #   -h' HHq!fq!f% & HHq!fq!f% &'  F "FJJL(()."" " v;!# ##aVaV$ F "FJJL(()."" "+ . - # " . -sAE) CE;) E8; F c [R"SS9n[R"S5 [[R "SS/SS//5[R "SS/SS//5SUS9 US:Xa*[ UR5R5S :XdeO[U5S:Xde[SS/SS/5 US:Xa)[ UR5R5S :XdeSSS5 [R"5 [R"S 5 [[R "SS/SS//5[R "SS/SS//5SUS9 SSS5 g!,(df  N=f!,(df  g=f) NTrrrFrrrrrrr) rrrr#rKrrrrr|rs rdtest_precision_warningsrU sc   -h' HHq!fq!f% & HHq!fq!f% &'  F "FJJL(()."" " v;!# ##AA' F "FJJL(()."" "+ .6  "g& HHq!fq!f% & HHq!fq!f% &'   # "7 . -6 # "sCE) AE:) E7: Fc [R"SS9n[R"S5 [[ [ SS94GH nU"[ R"SS/SS//5[ R"SS/SS//5SUS 9 [U5S:XdeU"[ R"SS/SS//5[ R"SS/SS//5SUS 9 [U5S:XdeU"[ R"SS/SS//5[ R"SS/SS//5SUS 9 US :Xa,[UR5R5S :XdeGM[U5S:XaGM!e SSS5 g!,(df  g=f) NTrrr=rrFrrrrrr) rrrrrrrKrr|rrr)rrrs rdtest_fscore_warningsr} sp   -h' ! <=E 1a&1a&)*1a&1a&)*+   v;!# ## 1a&1a&)*1a&1a&)*+   v;!# ## 1a&1a&)*1a&1a&)*+   & ,,-2--- 6{a'''?> . - -sEE.!E.. E<c ~/SQn/SQnSn[R"/SQ/SQ/SQ/5n[R"/SQ/SQ/SQ/5nSnXU4X4U44HUupgn[[[[ [ S S 94H-n [R"[US 9 U "Xg5 SSS5 M/ MW g!,(df  ME=f) N)rFr=rCrC)rFr=rCrFrVrrrr:rUr=rr) rKrr#r$rrrrrr) y_true_mc y_pred_mcmsg_mc y_true_ind y_pred_indmsg_indrcrbrrs rd'test_prf_average_binary_data_non_binaryr sII 1 9i;=b.4;;EBGzA&r.BA/ /=R.D +KE5!* **%%l33||u,,,||u,,,"5"**R.9"5"**R.9z*r#2w++*A%@H ) B B 3 z -r . -S .u -H .+*>= BA+* . -sN?L" L% L., M0M0 M'LL L+ . L>  M  M$ ' M5cSn[R"[[R"U5S9 [ [ R"/5[ R"/55 SSS5 g!,(df  g=f)NzIFound empty input array (e.g., `y_true` or `y_pred`) while a minimum of 1r)rrrrWrXr&rKrrs rd*test__check_targets_raises_on_empty_inputsr$ sC UC z3 8rxx|RXXb\2 9 8 8s 5A.. A<c<SS/nSS/n[X5SS:Xdeg)NrrFrr)r&rgs rdAtest__check_targets_multiclass_with_both_y_true_and_y_pred_binaryr* s,VFWF & )! , << a/C,:1////  E)))  E)))  s A11 A?c[R"/SQ5n[R"/SQ5n[X5S:Xde[R"/SQ5n[R"/SQ5n[X5S:Xdeg)N)rrFrFr)g!rrg333333ӿr)rr=r=r)rKrrrc pred_decisions rdtest_hinge_loss_binaryrI sb XXn %FHH34M f , 77 7 XXl #FHH34M f , 77 7rfc [R"/SQ/SQ/SQ/SQ/SQ/SQ/5n[R"/SQ5n[R"SUSS- USS-SUSS- USS -SUS S - US S -SUS S- US S -SUS S - US S -SUS S - US S -/5n[R"USSUS 9 [R"U5n[ X5U:Xdeg)N ףp= ?(\ſ(\gGz)HzGgGz׿Q޿r333333rRQؿr)rrrrgzGgHzGgHzGѿgQ?)rrFr=rFrCr=rFrr=rCrroutrKrcliprr)rrc dummy_lossesdummy_hinge_losss rdtest_hinge_loss_multiclassrS sHHH ( ( ( ( ( (   MXX( )F88 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9   LGGL!T|4ww|, f ,0@ @@ @rfc[R"/SQ5n[R"/SQ/SQ/SQ/SQ/5nSn[R"[US9 [ X5 SSS5 g!,(df  g=f)N)rrFr=r=)gRQ?g rh?g(\gffffffrrzDPlease include all labels in y_true or pass labels as third argumentr)rKrrrrr)rcr error_messages rd:test_hinge_loss_multiclass_missing_labels_with_labels_nonern s^ XXl #FHH ( ( ( (  M O z 76) 8 7 7s  A++ A9c  [R"/SQ5n[R"/SQ5nSn[R"[[ R "U5S9 [XS9 SSS5 [R"SS/SS/SS/SS/SS/SS/SS//5n/S QnS n[R"[[ R "U5S9 [XUS 9 SSS5 g!,(df  N=f!,(df  g=f) N)r=rFrrFrrFrF)rrFr=rFrr=rFzThe shape of pred_decision cannot be 1d arraywith a multiclass target. pred_decision shape must be (n_samples, n_classes), that is (7, 3). Got: (7,)rrrrFr=rzThe shape of pred_decision is not consistent with the number of classes. With a multiclass target, pred_decision shape must be (n_samples, n_classes), that is (7, 3). Got: (7, 2))rcrry)rKrrrrrWrXr)rcrrrys rd CHHq!fq!fq!fq!fq!fq!fqRSfUVM F  z=)A B&fM C B C B C Bs C$ C5$ C25 Dc [R"/SQ/SQ/SQ/SQ/SQ/5n[R"/SQ5n[R"/SQ5n[R"SUSS- USS-SUSS- USS-SUSS- USS -SUS S- US S-SUS S- US S -/5n[R"USSUS 9 [R"U5n[ XUS 9U:Xdeg) Nr皙rrrr)rrFr=rFr=)rrFr=rCrFrr=rCrrrrrrcryrrs rd.test_hinge_loss_multiclass_with_missing_labelsr s3HH ( ( ( ( (  MXXo &F XXl #F88 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9  LGGL!T|4ww|, fF ;?O OO Orfc [R"/SQ/SQ/SQ/SQ/SQ/5n[R"/SQ5n[R"/SQ5n[R"SUSS- USS-SUSS - USS-SUS S - US S-SUS S- US S -SUS S - US S-/5n[R"USSUS 9 [R"U5n[ [ XUS 9U5 g)N)rrr)g333333ÿrr)rrr)rg(\gzGڿ)rr=r=rr=rrFrr=rCrrr)rKrrrr4rrs rd@test_hinge_loss_multiclass_missing_labels_only_two_unq_in_y_truer s4 HH ! ! ! ! !  MXXo &F XXi F88 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9  LGGL!T|4ww|,68:Jrfc /SQn/SQ/SQ/SQ/SQ/SQ/SQ/n[R"SUSS- USS-SUSS- USS-SUSS- USS -SUS S- US S-SUS S - US S-SUS S- US S -/5n[R"USSUS 9 [R"U5n[ X5U:Xdeg) N)r6r7r8r7whiter8rrrrrFrr=rCrrrr)rcrrrs rd+test_hinge_loss_multiclass_invariance_listsr s4?F$$$$$$ M88 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9 a # #mA&6q&9 9   LGGL!T|4ww|, f ,0@ @@ @rfc &/SQn[R"SS/SS/SS/SS/SS/S S //5n[X5n[R"[R "[R"U5S :HUSS2S 455*n[ X#5 /S Qn/SQ/SQ/SQ/n[XSS9n[ US5 US-nUS-n[XSS9n[ US5 /S QnSS/SS/SS//n[R"[5 [X5 SSS5 /SQn/SQ/SQ/SQ/n/SQnS n[R"[[R"U5S!9 [XUS"9 SSS5 /S#QnSS/SS/SS/SS//n[X5n[ US$5 SS/nS%S&/SS//n[R"SS/SS//5nS'n[R"[[R"U5S!9 [X5 SSS5 S%S&/SS/SS//nS(n[R"[[R"U5S!9 [X5 SSS5 [R"[R"USS2S 455*n[XS S/S"9n[ X5 /S)Qn/S*Q/SQ/S+Q/n [X /S,QS"9n[ U[R"S5*5 g!,(df  GN=f!,(df  GN=f!,(df  GN=f!,(df  N=f)-Nnorryesrrrrrp{Gz?Gz?rrgMbP?g+?rrFrMrrrrrrrrrTrTg躕ʀ?r=Fg.L`@rrrrr)rprr)rrprrrr)rrrzPy_true contains values {'b'} not belonging to the passed labels ['a', 'c', 'd'].rrhamspamrrCT?rrzy_true contains only one label (2). Please provide the list of all expected class labels explicitly through the labels argument.zBFound input variables with inconsistent numbers of samples: [3, 2]r)rrrrrrrL) rKrrrr logpmfr3rrrrWrXlog) rcy_probaloss loss_truery error_strr true_log_losscalculated_log_lossy_score2s rd test_log_lossr s 4Fhh sc3Z$SzD$<%QVXG F $D))"((6*:e*CWQPQT]STTID$FAG Ft 4DD)$ aKF qLG Fu 5DD-(FSzC:Sz2G z "! #FAG F " z9)= >0 ?,FSzC:SzC:>G F $DD)$VFSzC:&Ghhc S#J/0G H z9)= >! ?SzC:Sz2GTI z9)= >! ? WWRVVGAqDM233M"6Aq6B'7F/BH FY 7DD266#;,'_ # " ? >$ ? > ? >s0; K  K$ K02 L K K-0 K? Lc[R"SS/US9n[R"SS/US9n[X5n[R"U5(deg)zCheck the behaviour internal eps that changes depending on the input dtype. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/24315 rrFr N)rKrrisfinite)rWrcr rs rdtest_log_loss_epsr= sKXXq!fE *Fhh1vU+G F $D ;;t   rfc[R"/SQ5n[R"SS/SS/SS/SS//US9n[R"[S S 9 [ X5 S S S 5 g !,(df  g =f) zHCheck that log_loss raises a warning when y_proba values don't sum to 1.rrrrrrrr z$The y_prob values do not sum to one.rN)rKrrrrr)rWrcr s rd'test_log_loss_not_probabilities_warningrK s_XXl #Fhhc S#Jc S#JGuUG k)O P! Q P Ps  A(( A6zy_true, y_probarrcN[X5[R"S5:Xdeg)z6Check that log_loss returns 0 for perfect predictions.rN)rrrrcr s rd!test_log_loss_perfect_predictionsrU s  F $ a(8 88 8rfcJ[R"/SQ5n[R"SS/SS/SS/SS//5n[[4/nSSKJnJn UR XC45 UH+upVU"U5U"U5p[Xx5n [U S5 M- g![a N>f=f) Nrrrrrr) DataFramer"r ) rKrr1r rr"r ImportErrorrr3) y_try_prtypesrr" TrueInputType PredInputTypercr rs rdtest_log_loss_pandas_inputr%c s 882 3D 88c3Z#sc3Z#sD ED] + ,E , f()).$ '-}T/B(i( ).    s B B"!B"c[R"S5n[R"[US9 [ /SQ/SQ/SQ/SQ//SQS9 SSS5 g!,(df  g=f NzLabels passed were ['spam', 'eggs', 'ham']. But this function assumes labels are ordered lexicographically. Pass the ordered labels=['eggs', 'ham', 'spam'] and ensure that the columns of y_prob correspond to this ordering.reggsrrrr:r)rr)rr)rWrXrrrrexpected_messages rdtest_log_loss_warningsr,u sKyy = k)9 : #  9 -*  ; : : A A c[R"/SQ5n[R"SS/SS/SS/SS//5n[X5 Sn[R"[ [ R"U5S 9 [XS 9 S S S 5 S n[R"[[ R"U5S 9 [XUS 9 S S S 5 g !,(df  NQ=f!,(df  g =f)z?Test `y_pred` deprecation in favor of `y_proba` for `log_loss`.rrrprrffffff??E`y_pred` was renamed to `y_proba` in version 1.9 and will be removed rrbN@Cannot use both `y_pred` and `y_proba`. `y_pred` is deprecated, rbr ) rKrrrr FutureWarningrWrXrrrcr rs rd test_log_loss_y_pred_deprecationr7 s XXl #Fhhc S#Jc T4LIJG V QC m299S> :( ; MC z3 89 9 8 ; : 9 85 C 6 C C C)c[R"/SQ5n[R"/SQ5n[R"X- 5S-[ U5- n[ [ X5S5 [ [ X5U5 [ [ SU-U5U5 [ [ SU-S- U5U5 [R"SU- U45n[R"SU- U45n[ [ X5U5 [ [ XC5U5 [ [ XSS9U5 [ [ XS S9U5 [ [ XS S9SU-5 [ [ S /S /5S 5 [ [ S/S /5S 5 [ [ S/S /5S5 [ [ S/S /SS9S 5 [ [ S/S /SS9S5 g)NrrFrFrrFrFrrrprrgffffff?r=rrrFauto) scale_by_halfTFrrg|Gz?rrfoobarr)rKrrnormr|r4r column_stack)rcy_prob true_scorey_prob_reshapedy_true_reshapeds rdtest_brier_score_loss_binaryrF s XX( )F XX5 6FV_-2S[@J(8#>(8*E(vv> K(Va@*Mooq6z6&:;Oooq6z6&:;O(A:N(JJWv> t(!se4f=(!se4nE(%3%5I6R%3%59rfc [[/SQ/SQ/SQ/SQ//SQS9S5 [[/SQ/SQ/S Q/S Q/5S 5 [[/S Q/S Q/SQ/SQ/5S5 [[/S Q/SQ/S Q/S Q/5S5 g)Nr(rxry)r)rryamsrrrMrrrg t?r)rrr)rrr)rrrrr=)r4rrrfrd test_brier_score_loss_multiclassrI s # < 62   /J    /J   /J  rfcz[R"/SQ5n[R"/SQ5n[R"[5 [ XSS5 SSS5 [R"[5 [ XS-5 SSS5 [R"[5 [ XS- 5 SSS5 [R"/SQ5n[R"/SQ/SQ/SQ/5n[R"[5 [ XSS5 SSS5 [R"[5 [ XS-5 SSS5 [R"[5 [ XS- 5 SSS5 [R"/S Q5n[R"/S Q5n[ R"S 5n[R"[US 9 [ X5 SSS5 /S QnSS/SS/SS//nSn[R"[[ R"U5S 9 [ X5 SSS5 /SQn/SQ/SQ/SQ/n/SQnSn[R"[[ R"U5S 9 [ XUS9 SSS5 S/nSS//nSn[R"[[ R"U5S 9 [ X5 SSS5 [[ XSS/S9S5 g!,(df  GN=f!,(df  GN=f!,(df  GNy=f!,(df  GN#=f!,(df  GN=f!,(df  GN=f!,(df  GN=f!,(df  GND=f!,(df  GN=f!,(df  N=f)Nr:r;rFrrMrrr)rrFr=rrrrrzpThe type of the target inferred from y_true is multiclass but should be binary according to the shape of y_prob.rrrzy_true and y_prob contain different number of classes: 3 vs 2. Please provide the true labels explicitly through the labels argument. Classes found in y_true: [0 1 2]r(rr:)r)rrrHzwThe number of classes in labels is different from that in y_prob. Classes found in labels: ['eggs' 'ham' 'spam' 'yams']rr)rprzy_true contains only one label (eggs). Please provide the list of all expected class labels explicitly through the labels argument.rr) rKrrrrrrWrXr4)rcrBrrys rd$test_brier_score_loss_invalid_inputsrL s XX( )F XX5 6F z ", # z "#. # z "#. # XXi F XXI JF z ", # z "#. # z "#. # XXl #F XX* +FII AM z 7( 8F!fq!fq!f %F  z=)A B( C%FI .F ,F / z=)A B7 CXFCj\F  z=)A B( C(PRVWK # " # " # " # " # " # " 8 7 C B C B C Bsx L <L-L.MM7M$- M6? N N N, L L+. L= M M!$ M36 N N N), N:c[R"S5n[R"[US9 [ /SQ/SQ/SQ/SQ//SQS9 SSS5 g!,(df  g=fr')rWrXrrrrr*s rdtest_brier_score_loss_warningsrN, sQyy = k)9 : #  +  ; : :r-cSn[R"[US9 [/SQ/SQ5 SSS5 g!,(df  g=f)Nz%y_pred contains classes not in y_truerrqr)rrrrrs rd#test_balanced_accuracy_score_unseenrP? s+ 1C k - 95 . - -rz y_true,y_pred)rrrr)rrrr)rrrrcR[XS[R"U5S9n[5 [ X5nSSS5 W[ R "U5:Xde[ XSS9n[ U[R"XS55nXCU- SU- - :Xdeg!,(df  Nh=f)NrrT)adjustedrrF)r$rKuniquer7rrr full_like)rcrb macro_recallbalancedrRchances rdtest_balanced_accuracy_scorerXE s  &0AL  *6:  v}}\2 22 2&vEH $VR\\&)-L MF 6)a&j9 99 9  s B B&r))FTr)rr)zeroonec0[RRS5nSUSpCURXSS9nU[LaUR US9nOUR 5nU"XVUS9n[R"[R"U55(aeg) zCheck that the metric works with different types of `pos_label`. We can expect `pos_label` to be a bool, an integer, a float, a string. No error should be raised for those types. *rsrT)rreplacerrN) rKrOrPchoiceruniformr anyr)rrr^r[rrcrbrs rd*test_classification_metric_pos_label_typesraZ s* ))   #Cwr{y ZZZ >F !!), Fi 8Fvvbhhv&''' ''rfzy_true, y_pred, expected_scorecL[XSS9[R"U5:Xdeg)zCheck the behaviour of `zero_division` for f1-score. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/26965 rrN)rrr)rcrbrus rd2test_f1_for_small_binary_inputs_with_zero_divisionrc{ s" F# 6&--:W WW Wrfscoringr)rrc z[R"SS9up[SSS9RX5n[ X1X SSS9 g) aBCheck that we validate `np.nan` properly for classification metrics. With `n_jobs=2` in cross-validation, the `np.nan` used for the singleton will be different in the sub-process and we should not use the `is` operator but `math.isnan`. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/27563 r)rBrC) max_depthrBr=r)rdn_jobs error_scoreN)r make_classificationr,rTr))rdrYrZ classifiers rd:test_classification_metric_division_by_zero_nan_validationrk s;(  ' 'Q 7DA'!!DHHNJJ1aWUrfc /SQn/SQn[R"SS/SS/SS/SS/SS /S S //5n[R"SS/SS/SS/SS/SS/SS//5n[XS 9n[XS S9n[XS S9nSXV- - nU[R "U5:Xde[R"/SQ5nUSSR 5UR 5- USS2S4'USSR 5UR 5- USS2S4'[XUS9n[UUUS S9n[UUUS S9nSXV- - nU[R "U5:Xde[R"SS/SS/SS/SS/SS/SS//5n[X5nSUs=:aS:de e[X5n U [R "U5:Xde[R"SS/SS/SS/SS/SS/SS//5n[X5nUS:de[X5n U [R "U5:Xde/SQn[R"SS/SS/SS/SS/SS/SS//5n[X5nUS:Xde[X5n U S:Xde/SQn/SQn[R"SS/SS/SS/SS//5n[X5nUS:Xde[X5n U S:Xde/SQn[XUS9n U S:Xde/SQn/SQn[R"/S Q/S Q/S!Q/S"Q/5n[X5nSUs=:aS:de e[XUS9nSUs=:aS:de e[R"/S#Q/S$Q/S"Q/S%Q/5n[X5nUS:de[XUS9nUS:deg)&Nrhrrrprrrr/r0rrrF)rcr rUrF)r=rFrCrrCrFrCrrcr r>)rcr r>rUrrrrr)rrFrFrF)rrrr)r=r=r=r=rK)highrnlowneutral)ffffff?rrr)rrrrr)rrrr r)rKrr(rrrr_) rc y_true_stringr  y_proba_nulld2_scorelog_likelihoodlog_likelihood_null d2_score_truer>d2_score_stringd2_score_with_sample_weights rdtest_d2_log_loss_scorerz s\ F;Mhh #J #J #J #J 4L 4L   G88 #J #J #J #J #J #J   L!@HVNN"&RWX<ry)rrrrFN)rKtilerr(r3) rcryr>r  log_loss_obsrs log_loss_nullexpected_d2_scorerts rd%test_d2_log_loss_score_missing_labelsr> sy F F(Mggi(GF=XL77=&1LMML88 }HH0rfc/SQn[R"/SQS5n[X/SQS9n[X/SQS9n[X#5 g)zGCheck that d2_log_loss_score doesn't depend on the order of the labels.r|rr}rrr2N)rKr~r(r3)rcr rtd2_score_others rd"test_d2_log_loss_score_label_orderr[ s7 Fggi(G CH&vyINH-rfc/SQnSS/SS/SS//nSn[R"[US9 [X5 S S S 5 /SQnSS/SS/SS//n/SQnS n[R"[US9 [XUS 9 S S S 5 /SQn/S Q/S Q/nSn[R"[US9 [X5 S S S 5 S/nSS//nSn[R"[ US9 [X5 S S S 5 /SQnSS/SS/SS//nSn[R"[US9 [X5 S S S 5 /SQnS/nSS/SS/SS//nSn[R"[US9 [XUS 9 S S S 5 g !,(df  GNA=f!,(df  GN=f!,(df  N=f!,(df  N=f!,(df  N=f!,(df  g =f)zLTest that d2_log_loss_score raises the appropriate errors on invalid inputs.rrrrrrz#contain different number of classesrNz(number of classes in labels is differentr)rrr)rrrrJrFzscore is not well-definedry_true contains only one labelz.The labels array needs to contain at least two)rrrr(rr)rcr errrys rdtest_d2_log_loss_score_raisesrf sFSzC:Sz2G /C z -&* . FSzC:Sz2G F 4C z -&&9 .F0G +C z -&* .SFSzlG %C ,C 8&* 9FSzC:Sz2G *C z -&* . FSFSzC:Sz2G :C z -&&9 . -O . - . - . - 9 8 . - . -sG E1- F' F F& F7 G1 F F F#& F47 G Gc[R"/SQ5n[R"SS/SS/SS/SS//5n[X5 Sn[R"[ [ R"U5S 9 [XS 9 S S S 5 S n[R"[[ R"U5S 9 [XUS 9 S S S 5 g !,(df  NQ=f!,(df  g =f)zHTest `y_pred` deprecation in favor of `y_proba` for `d2_log_loss_score`.rrrprrr/r0r1rr2Nr3r4) rKrr(rrr5rWrXrrr6s rd)test_d2_log_loss_score_y_pred_deprecationr s XXl #Fhhc S#Jc T4LIJGf& QC m299S> :&1 ; MC z3 8&'B 9 8 ; : 9 8r8c v/SQn/SQn/SQn/SQn/SQn[XS9n[XS9n[XS9nSXg- - n[R"U5U:Xde/SQn[XS9nUS:Xde[X#S S 9nUS:Xde/S Qn[XUS 9nUS:Xde[UUUS S 9nUS:Xde/SQn/SQn/SQn/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/ n[XUS 9nUS:Xde[UUUS 9nUS:Xde/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/ n[XUS 9nUS:de[UUUS 9nUS:deg)z]Test that d2_brier_score gives expected outcomes in both the binary and multiclass settings. )r=r=rCrFrFrF)rrFrFrrrF)rrrrrr)rrrrrpr)rrrrrrrrFrr)rcr r)rrrrrrrm)rcr r>r) r=rFrCrFrFr=rFrrFr) rCrCr=r=r=rFrFrFrFr) ddrccrrbbrrraa)rrrr)rrrr)rrrr)rrrr)rrrrrN)r'rrr) r>rcrrr  y_proba_refrtbrier_score_modelbrier_score_refd2_score_expecteds rdtest_d2_brier_scorer s 'M F;M-G0KV=H(H&fJO-?? == "&7 77 7-GV=H q==]uUH q== -GmH q==# H q==3M +FPM  GmH q==#H q==  GmH c>>#H c>>rfc/SQn/SQn/SQ/SQ/SQ/SQ/n[XUS9nUS:Xde/SQn[XUS9nU[R"U5:Xde/SQ/SQ/SQ/SQ/n[XUS9n[R"U5S :Xdeg ) zGTest that d2_brier_score gives expected outcomes when labels are passed)rr=rr=r)rrr)rcr ryrrrrN)r'rr)rcryr rt new_d2_score neg_d2_scores rdtest_d2_brier_score_with_labelsr s F F G VVLH q==F!PL 6==2 22 2  G "PL == &" ,, ,rfz!y_true, y_pred, labels, error_msg)rFr=rFrCrKz7inferred from y_true is multiclass but should be binary)rrrrzpos_label is not specified)rrFrrrFrFrz.variables with inconsistent numbers of samples)rrFrrF)g?rrrz%y_prob contains values greater than 1)grrrz"y_prob contains values less than 0rrrz)r=rCrCr=)rrrr)rrrrz(Multioutput target data is not supportedr)rrrz"not belonging to the passed labelsrqz*labels array needs to contain at least twoc[R"U5n[R"U5n[R"[US9 [ XUS9 SSS5 g!,(df  g=f)zITest that d2_brier_score raises the appropriate errors on invalid inputs.rrN)rKasarrayrrrr')rcrbry error_msgs rdtest_d2_brier_score_raisesr,sE|ZZ F ZZ F z 3vf5 4 3 3s  A A(c[R"S/5n[R"S/5nSn[R"[US9 [ X5 SSS5 g!,(df  g=f)zMTest that d2_brier_score emits a warning when there are less than two samplesrFrz+not well-defined with less than two samplesrN)rKrrrrr')rcrbwarning_messages rd4test_d2_brier_score_warning_on_less_than_two_samplesrpsKXXqc]F XXse_FCO ,O Dv& E D Ds A A-z(array_namespace, device_name, dtype_namec\[XU5up4UR/SQUS9nUR/SQUS9nUR/SQUS9n[SS9 [XVUS9n[ U5S[ U5S:Xde[ U5[ U5:XdeSSS5 g!,(df  g=f) zTest that `confusion_matrix` works for all array types when `labels` are passed such that the inner boolean `need_index_conversion` evaluates to `True`.rLr-)rrrT)array_api_dispatchrrN)r2rr rr/array_api_device) array_namespace device_name dtype_namexpr.rcrbryrs rdtest_confusion_matrix_array_apirzs&oJOJB ZZ &Z 1F ZZ &Z 1F ZZ &Z 1F 4 0!&@V$Q'=+@+CCCC'+;F+CCCC 1 0 0s AB B+)NF)rWr functoolsr itertoolsrrrnumpyrKrscipyrrscipy.spatial.distancer rP scipy.statsr sklearnr r sklearn.baser sklearn.calibrationrsklearn.datasetsrsklearn.exceptionsrsklearn.metricsrrrrrrrrrrrrrrrr r!r"r#r$r%sklearn.metrics._classificationr&r'r(sklearn.model_selectionr)sklearn.preprocessingr*r+ sklearn.treer,sklearn.utils._array_apir.rr/r0sklearn.utils._mockingr1sklearn.utils._testingr2r3r4r5r6r7sklearn.utils.extmathr8sklearn.utils.fixesr9r:sklearn.utils.validationr;rermark parametrizervrrrrrrrrrrrrrr r&r6rHrNr\rcrfrmrqryrrrrrrrrrrrrrrrrrrrrrr rr(r-r/r1r4r;r>rArFrHrJrLrRr\r_rmrorsrvrrrrrrrrrrrrrrrr csr_matrixrrrrrrrrrrrfloat16rrrr%r,r7rFrIrLrNrPrXrarc thread_unsaferkrzrrrrrrrrrrrfrdrs~ 22 8!!'6;5. 4@/1.>7 +(d>DB61a*@AB&T{aVUOi=OP))( 7DPQR$PQ*JR*JZPQMRM,>6  HH####    . = (9)(9  HH#####      *;+*;  =* = OF.9.9%S::%SPT<+%%;&) ((#56((#56  N  ((#56((#56  B ((#56((#56  N  ((#56((#56  H  ;$'P*Q'P*((?3((?3  <  **"2;0   S!E"C C       $S !3' $bff-S !3' '0   D D$$S !3' $c* $c* '0   D D$>01 qA37 QC"Hq!fd3 rA38T4( qA37 QC!GqcAg-1vt< qA37 QC!GqcAg-1vx@ "/#rvv?A@#2&A(1.11a.9)aS1#J<8  !$  '9: ')aS1#J<8  !$ 9$2N RF5pc5\2U3UB7(t$ST ,U ,0"(".& :; QAB '( 88 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'>^B3 =   QC!qcA3/ 0(;   QC!qcA3/ 0,?   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