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permutationsN)linalg)datasets)svm)make_multilabel_classification)label_binarizeLabelBinarizer)check_random_state)assert_almost_equal)assert_array_equal)assert_array_almost_equal)assert_allclose)assert_no_warnings)ignore_warnings) MockDataFrame)accuracy_scoreaverage_precision_score)balanced_accuracy_scoreclassification_report)cohen_kappa_score)confusion_matrix)f1_score) fbeta_score) hamming_loss) hinge_loss) jaccard_score)log_lossmatthews_corrcoef)precision_recall_fscore_support)precision_score) recall_score) zero_one_loss)brier_score_loss)multilabel_confusion_matrix_check_targets)UndefinedMetricWarning)hammingFc Cs|durt}|j}|j}|r||dk||dk}}|j\}}t|}td}||||||}}t |d}tj d}tj || |d|f}tjdddd} | |d||d|||d} |ry| ddd f} | ||d} ||d} | | | fS) zMake some classification predictions on a toy dataset using a SVC If binary is True restrict to a binary classification problem instead of a multiclass classification problem N%rlinearT)kernel probability random_state)r load_irisdatatargetshapenparanger shuffleintrandom RandomStatec_randnrSVCfit predict_probapredict) datasetbinaryXy n_samples n_featuresprnghalfclf probas_predy_predy_truerS/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_classification.pymake_prediction7s*     *  rUc Cstt}t|dd\}}}ddddddd d d dd d ddddddddddddddd}t||tt|j|jdd}||ksIJ|D]<}|dkret ||t sZJ||||ksdJqK||||kssJ||D]}t ||||||qwqKt |ddt ksJt |ddt ksJt |ddt ksJt |ddt ksJdS) NFrFrGg7Mo?gUUUUUU?ghQ?) precisionrecallf1-scoresupportUUUUUU?gc1Ƹ?g433333?g)k??$I$I?gCFQ?gc?gƢ?KrZrXrYr[g?gD~WG?g]3p?)setosa versicolor virginica macro avgaccuracy weighted avgT)labels target_names output_dictrgrcrXrfr[)rr6rUrr:r;lenrjkeys isinstancefloatr typer=)irisrRrQ_expected_reportreportkeymetricrSrSrT,test_classification_report_dictionary_outputgsb"  rwcCstggdd}dtjtjtjdddddddd}t|ts!J||ks+J|D]<}|dkrGt||ts.testcSg|]}t|qSrSr.0rIrSrSrT z0test_confusion_matrix_binary..rUrRrQrrrrSrSrTtest_confusion_matrix_binarys "rcCr)NTrcSs4t||}t|ddgddggddgddgggdS)Nrrrrr)r)rRrQrrSrSrTrs *z5test_multilabel_confusion_matrix_binary..testcSrrSrrrSrSrTrrz;test_multilabel_confusion_matrix_binary..rrrSrSrT'test_multilabel_confusion_matrix_binarys "rcCsRtdd\}}}d dd}||||tdd|Dtdd|DdddS) NFrcSst||}t|ddgddggddgddggd d gd d ggg|r%gd ngd}t|||d}t|ddgddggd d gd d ggddgddggg|rPgdngd}t|||d}t|ddgddggd d gd d ggddgddggddgddgggdS)N/rr&rrrr.)021rr.r5r)r r r3)rr.r5rrarr)rRrQ string_typerrirSrSrTrs& ..z9test_multilabel_confusion_matrix_multiclass..testcss|]}t|VqdS)NrrrSrSrT sz>test_multilabel_confusion_matrix_multiclass..T)r)F)rUlistrrSrSrT+test_multilabel_confusion_matrix_multiclasss  .rcCsddlm}m}tgdgdgdg}tgdgdgdg}||}||}||}||}tgd }d dgd d ggd dgd d ggdd gd dggg} |||g} |||g} | D]} | D] } t| | }t|| qbq^t||d d }t|d dgd d ggd d gdd ggdd gd dgggt||d dgd}t|dd gd dggd dgd d gggt||d dgd d}t|ddgd d ggd d gddggdd gd dgggt|||d d}t|d dgd d ggd d gdd ggddgddgggdS)Nr) csc_matrix csr_matrixrrr5rr5r5rrrr)r.r5rr5r.T samplewiser)rir) sample_weightrrr) scipy.sparserrr:rr)r)rrrRrQ y_true_csr y_pred_csr y_true_csc y_pred_cscrreal_cmtruespreds y_true_tmp y_pred_tmprrSrSrT+test_multilabel_confusion_matrix_multilabels4.    4&48r&cCstgdgdgdg}tgdgdgdg}tjtddt||d d gd Wdn1s7wYtjtd dt||gd gdgdgd Wdn1s^wYd}tjt|dt||dgdWdn1swYd}tjt|dt||dgdWdn1swYtjtddtgdgdddWdn1swYd}tjt|dtgdgdggdgdgWddS1swYdS)Nrrrrrrzinconsistent numbers of samplesrr5r.rzshould be a 1d arrayr5r.r)r.rr)rrrz%All labels must be in \[0, n labels\)rrrzSamplewise metricsrrTrz'multiclass-multioutput is not supported)r.r5rr5rr.)r:rrrrr))rRrQrrSrSrT'test_multilabel_confusion_matrix_errorss2$"r*z%normalize, cm_dtype, expected_results))truerTUU?)predrr,)allrgeq?)Nrr.cCsLgdd}tttgd}t|||d}t|||jj|ks$JdS)Nrr normalize)rrrrrdtypekind)r0cm_dtypeexpected_resultsy_testrQrrSrSrTtest_confusion_matrix_normalize0s  r6cCsRgd}gd}tjtddt||ddWddS1s"wYdS)Nrrrrr5r5r5r5rrrrrrrrznormalize must be one ofrTr/)rrrr)r5rQrSrSrT,test_confusion_matrix_normalize_wrong_optionAs "r9cCsgd}gd}t||dd}|tdksJttdtt||dd}Wdn1s6wY|tdksFJttdtt||ddWddS1scwYdS) Nr7r8r+r/@errorr-r)rsumrrrr simplefilterRuntimeWarning)r5rQcm_truecm_predrSrSrT,test_confusion_matrix_normalize_single_classHs    "rAcCstdgddgd}tdgddgddgddgd}t||}t|dd d |t||ks9Jt|d gd }t|d gd }t||ddgd |ksXJtt||dtdgddgdd gd}tdgddgdd gd}tt||dd d tdgddgdd gd}tdgddgdd gd}tt||dd d tt||dddd d tt||dddd d dS)Nr(r5<r`r2gʡE?rrr.rrr.,4 g??g+?r1weightsg_vO? quadraticg#?)r:rrr append)rrkapparSrSrTtest_cohen_kappa[s&. $$$$ rOcCs4tdgdgdks JtddgddgdksJdS)Nrr5rxr"rSrSrSrTtest_matthews_corrcoef_nanzsrPcCsNtjd}|jdddd}|jdddd}tt||t||dddS)Nrr.r`rrr5r)r:r>r?rr r#corrcoef)rMrRrQrSrSrT-test_matthews_corrcoef_against_numpy_corrcoefs rSc stjd}|jdddd}|jdddd}|d}t|||dttfddtD}tfddtD}tfd dtD}|t ||}t |||d}t ||d dS) Nrr.r`rr'c sXg|](}tD]!}tD]}||f||f||f||fqqqSrSrange)rkmlCNrSrTrs. z9test_matthews_corrcoef_against_jurman..cs@g|]ddftfddtDqS)Ncs.g|]}tD] }|kr||fqqSrSrTrrgrZr[rVrSrTr.Dtest_matthews_corrcoef_against_jurman...r<r:rUrrYrVrTr cs@g|]ddftfddtDqS)Ncs.g|]}tD] }|kr||fqqSrSrTr\r^rSrTrr_r`rarbrYrcrTrrdr) r:r>r?rrrrlr<rUrr#r ) rMrRrQrcov_ytypcov_ytytcov_ypyp mcc_jurmanmcc_oursrSrYrT%test_matthews_corrcoef_against_jurmans0     rjcCs,tjd}dd|jddddD}tt||ddd|D}tt||d t|d d gd }t|d d }tt||d ttgd gd dtt|d gt|dgd}gd}tt||ddgddgd}t t tt|||ddWddS1swYdS)NrcSsg|] }|dkr dndqS)rrrrSrrrSrSrTrz*test_matthews_corrcoef..r.r`rrcSsg|] }|dkr dndqS)rrrSrkrSrSrTrrlrrrrrrrrrx)r5rr5r5rr5r5r5rr5r5r5r5r5r5r5rr5r5r5)r5r5r5rrr5r5r5r5rr5r5r5rr5r5r5rr5r5r5rr') r:r>r?rr r#r whererlrrAssertionError)rMrR y_true_inv y_true_inv2y_1y_2maskrSrSrTtest_matthews_corrcoefs"  "ruc sLtjd}tdd}fdd|jd|ddD}tt||dgd }gd }tt||d gd }gd }tt||d tdgd}gd}tt||dgd}gd}tt||dgd}gd}tt||dgd}gd}gd}tt|||ddgd}gd}gd}tt|||dddS)Nrrrcsg|]}t|qSrS)chrrkord_arSrTrsz5test_matthews_corrcoef_multiclass..r`rr)rrr5r5r.r.)r.r.rrr5r5g)r5r5rrrriir)rrrrx rr5r.rr5r.rr5r.) r5r5r5r.r.r.rrr)rrr5r5r.)r5r5rrr.r5r5r5r5rr'r)rrr5r.r5r5rr)r:r>r?ordrr r#r) rM n_classesrR y_pred_bad y_pred_minrQrrrsrrSrwrT!test_matthews_corrcoef_multiclasss@ rn_pointsdi'cstjddd}fdd}tddg|}tt||dtgd|}tt||d||\}}tt||dtt|||||dS) Ni3c Ssxt||}|d}|d}|d}t|}|||}|||}||||} ||d|d|} | t| S)Nr5r5)r5rrQr5)rrlr:r) rRrQ conf_matrixtrue_pos false_pos false_negrpos_rateactivity mcc_numeratormcc_denominatorrSrSrTmcc_safes   z1test_matthews_corrcoef_overflow..mcc_safecs8|}|d|d}|dk}|dk}||fS)N皙?r) random_sample)rx_truex_predrRrQrMrSrT random_yss z2test_matthews_corrcoef_overflow..random_ysrxr)rxrr:)r:r>r?repeatr r#)rrrarrrRrQrSrrTtest_matthews_corrcoef_overflow s   rc Cstdd\}}}t||dd\}}}}t|gddt|gddt|gddt|gdt||d d d }t|d dt||d d}t|d dt||d d} t| d dt||d d}t|d dt||d d}t|ddt||d d} t| ddt||dd}t|ddt||dd}t|d dt||dd} t| ddtt t||ddWdn1swYtt t||ddWdn1swYtt t||ddWdn1swYtt t ||dddWdn 1swYt||gddd\}}}}t|gddt|gddt|gddt|gddS)NFrr)(\?Q?gzG?r.)HzG?g ףp= ?r^)Q?333333?r)rWr]r`r5rrg(\?rrgRQ?rgGz?rrrrrr)rg= ףp=?r)rr^r)rrr)rWr`r]) rUr$rrr%r&rrrrr) rRrQrrrLrrrrrrrSrSrT)test_precision_recall_f1_score_multiclass-sV              rr)rrrrNcCsztgdg}tgdg}t||gdg|d\}}}}t|dt|dt|d|dur;t|gddSdS)Nr{rrr5r5)rrr5r.)riwarn_forrrrr5r5r)r:rr$r)rrRrQrLrrrrSrSrT;test_precision_refcall_f1_score_multilabel_unordered_labelsgs   rc Cstgd}tgd}t||dd\}}}}t||dd\}}}}|t|ks-J|t|ks6J|t|ks?Jt||dd\}}}}t|} |tj|| dksZJ|tj|| dkseJ|tj|| dkspJdS)N)rr5rrr5r5rr5rrr5rr5rr5)r5r5rr5rr5r5r5r5rr5rr5rr5rrrrJ)r:rr$rbincountr) rRrQrrrrrrLrrr[rSrSrT.test_precision_recall_f1_score_binary_averagedvs rc Cstjdd}z:tgd}tgd}tt||ddddtt||ddddtt||ddddWtjd i|dStjd i|w) Nraise)r.)rr5r.rr5r.)r.rr5r5r.rrrrxr.rS)r:seterrrr r%r&r)old_error_settingsrRrQrSrSrTtest_zero_precision_recalls &rcCstdd\}}}t||ddgd}t|ddgddggt||d dgd}t|d d gd dggt|d}t||d |gd}t|d dgddggdS) NFrrr5rrrrr.r rW)rUrrr:max)rRrQrrr extra_labelrSrSrT.test_confusion_matrix_multiclass_subset_labelssrzlabels, err_msgz,'labels' should contains at least one label.rrz.At least one label specified must be in y_truez empty listzunknown labels)idscCsRtdd\}}}tjt|dt|||dWddS1s"wYdS)NFrrr)rUrrrr)rirrRrQrrrSrSrTtest_confusion_matrix_errors "rrir5r)NonerG multiclasscCs>|rt|nd}tj||ftd}tgg|d}t||dS)Nrr1r)rlr:rr=rr)riexpected_n_classesexpectedrrSrSrT*test_confusion_matrix_on_zero_length_inputsrcCs8gd}tt|}t||}|jtjksJtjtjtjfD]}t|||j |ddd}|jtjks6Jq tj tj dt fD]}t|||j |ddd}|jtj ksUJq?tj t|dtjd}t|||d}|ddkspJ|dd ksxJtj t|d tjd}t|||d}|dd ksJ|dd ksJdS) NrF)copyr'lrrrrll)r:onesrlrr1int64bool_int32uint64astypefloat32float64objectfulluint32)rIweightrr1rSrSrTtest_confusion_matrix_dtypes$ rcCsLt}t|dd\}}}d}t||tt|j|jd}||ks$JdS)NFrVa| precision recall f1-score support setosa 0.83 0.79 0.81 24 versicolor 0.33 0.10 0.15 31 virginica 0.42 0.90 0.57 20 accuracy 0.53 75 macro avg 0.53 0.60 0.51 75 weighted avg 0.51 0.53 0.47 75 rirjrr6rUrr:r;rlrjrqrRrQrrrsrtrSrSrT%test_classification_report_multiclasss rcCs0gdgd}}d}t||}||ksJdS)N) rrrr5r5r5r.r.r.rya| precision recall f1-score support 0 0.33 0.33 0.33 3 1 0.33 0.33 0.33 3 2 0.33 0.33 0.33 3 accuracy 0.33 9 macro avg 0.33 0.33 0.33 9 weighted avg 0.33 0.33 0.33 9 r)rRrQrsrtrSrSrT.test_classification_report_multiclass_balanceds rcCs8t}t|dd\}}}d}t||}||ksJdS)NFrVa| precision recall f1-score support 0 0.83 0.79 0.81 24 1 0.33 0.10 0.15 31 2 0.42 0.90 0.57 20 accuracy 0.53 75 macro avg 0.53 0.60 0.51 75 weighted avg 0.51 0.53 0.47 75 )rr6rUrrrSrSrT:test_classification_report_multiclass_with_label_detection s  rcCsNt}t|dd\}}}d}t||tt|j|jdd}||ks%JdS)NFrVa| precision recall f1-score support setosa 0.82609 0.79167 0.80851 24 versicolor 0.33333 0.09677 0.15000 31 virginica 0.41860 0.90000 0.57143 20 accuracy 0.53333 75 macro avg 0.52601 0.59615 0.50998 75 weighted avg 0.51375 0.53333 0.47310 75 r)rirjdigitsrrrSrSrT1test_classification_report_multiclass_with_digitss rcCsttdd\}}}tgd|}tgd|}d}t||}||ks'Jd}t||gdd}||ks8JdS)NFr)bluegreenreda| precision recall f1-score support blue 0.83 0.79 0.81 24 green 0.33 0.10 0.15 31 red 0.42 0.90 0.57 20 accuracy 0.53 75 macro avg 0.53 0.60 0.51 75 weighted avg 0.51 0.53 0.47 75 a| precision recall f1-score support a 0.83 0.79 0.81 24 b 0.33 0.10 0.15 31 c 0.42 0.90 0.57 20 accuracy 0.53 75 macro avg 0.53 0.60 0.51 75 weighted avg 0.51 0.53 0.47 75 r~rjrUr:rr)rRrQrrrsrtrSrSrT7test_classification_report_multiclass_with_string_label9s  rcCLtdd\}}}tgd}||}||}d}t||}||ks$JdS)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 rrRrQrrrirsrtrSrSrT8test_classification_report_multiclass_with_unicode_label\s rcCr)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 rrrSrSrTpos_label=2 is not a valid label. It should be one of \[0, 1\]rrGr.rrrrrrTarget is multilabel-indicator but average='binary'. Please choose another average setting, one of \[None, 'micro', 'macro', 'weighted', 'samples'\].r)rr5r5rr.rzTarget 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:rrrrr rr)rRrQrmsg1msg2msg3rrSrSrTtest_jaccard_score_validations:"rcCsbtgdgdg}tgdgdg}t||dddks!Jt||dddks,Jt||dddks7Jt|t|dddksEJt|t|dddksSJt|t|jdddksbJt|t|jdddksqJtgdgd g}tgd gdg}tt||d dd tt||d ddtt||dddtt||dddgddtt||dddgddtt||ddtgdtgdgdg}tgd gdg}tt||d ddtt||dddd}tj t |dt||dgd dWdn 1swYd}tj t |dt||dgd dWdn 1s9wYd}tj t |d ttddggtddggd ddks`JWdn 1skwYd}tj t |d&ttddgddggtddgddggdddksJWdn 1swYt |rJdS)Nrrrrrrr5rrrrrrrg?r.rr)rrrrrg?z Got 4 > 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:rr rrr9r rrrrrr,r)recwarnrrrRrQrrrrSrSrTtest_multilabel_jaccard_score sl$   rcCsXgd}gd}gd}t}||||}||}tt||}tt||}ddgddgddgdgdgdgdg} ddgdd gd dgdgdgd gdg} d D]} t| | D]\} } t|| | d || | d qWqPtddgddgddgg}tddgddgddgg}t t||d d dksJWdn1swYt |rJdS)N)antrcatrrrbirdr)rrrrrrrr)rrrrrrrr5r.)rrrNrrr) r rC transformrr zipr r:rrr)rrRrQrilbrrmulti_jaccard_scorebin_jaccard_scoremulti_labels_listbin_labels_listrm_labelb_labelrSrSrTtest_multiclass_jaccard_scoreWs>     $   rcCstdgdgdddks Jd}tjt|dtddgddgdddks&JWdn1s0wYtdgdgdddd ksCJtgd }tgd }tt||ddd tt||ddd dt|rlJdS)Nr5rrGrrxzOJaccard is ill-defined and being set to 0.0 due to no true or predicted samplesrrr)r5rr5r5r)r5rr5r5r5rrr)r rrr,r:rr r)rrrRrQrSrSrT!test_average_binary_jaccard_score|s rcCstgdgdg}tgdgdg}d}tjt|dt||ddd}|tdks1JWddS1sJtddgddg|dkrVt|jd ksVJWdn1s`wYt ttddgddggtddgddggd|ddS) NTrrr5rrrr}zPrecision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.) rrr=r%r:rrrrrlrrrSrSrTtest_precision_warningss6     rc Cs>tjdd}tdtttddfD]x}|tddgddggtddgddggd|d t|dks8J|tddgddggtddgddggd|d t|dksZJ|tddgddggtddgddggd|d |d krt | j d ksJqt|dksJqWddS1swYdS) NTrrr.rr5rrrr}zF-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.) rrr=rrrr:rrlrrr)r|rrrSrSrTtest_fscore_warningss<   "r c Csgd}gd}d}tgdgdgdg}tgdgdgdg}d}|||f|||ffD].\}}}tttttd d fD]} tjt |d  | ||Wdn1sZwYqAq2dS) N)r5r.rr)r5r.rr5rrrrrrr.rr) r:rr%r&rrrrrr) y_true_mc y_pred_mcmsg_mc y_true_ind y_pred_indmsg_indrRrQrrvrSrSrT'test_prf_average_binary_data_non_binarys,  r'cCsd}d}d}d}d}d}|tgdgdgd gf|td d gd d gd d ggf|gd f|gdf|gd f|tdgdgd ggf|td gd gd ggf|tdgdgdggf|td dgd dgddggf|tddgddgddggfg }i||f|||f|||f|||fd||fd||f|||fd||fd||fd||fd||fd||fd||fd||fd||fd||fd||fd||fd||fd||fd||fdi}t|ddD]\\}} \} } z||| f} Wnty || |f} Ynw| durtt t| | Wdn 1s(wY|| krVd|| } tjt| d t| | Wdn 1sPwYq||||fvrd|} tjt| d t| | Wdn 1s{wYqt| | \}}}|| ksJ| dr|jdksJ|jdksJnt |t | t |t | ttt| dd| Wdn 1swYqddg} d d!g} d"}tjt|dt| | WddS1swYdS)#Nzmultilabel-indicatorrrG continuouszmulticlass-multioutputzcontinuous-multioutputrrrrr5)r.rr5)rxr rr.rrxr rrr:g?g@)rz@Classification metrics can't handle a mix of {0} and {1} targetsrz{0} is not supported multilabelcsrr)r5r.)rr.r)r.)rr.zYou appear to be using a legacy multi-label data representation. Sequence of sequences are no longer supported; use a binary array or sparse matrix instead - the MultiLabelBinarizer transformer can convert to this format.) r:rrKeyErrorrrrr+format startswithrsqueeze)INDMCBINCNTMMCMCNEXAMPLESEXPECTEDtype1rtype2rrr merged_typey1outy2outrrSrSrTtest__check_targets.s                  $r<cCs*ddg}ddg}t||ddksJdS)Nrr5rrr*)rRrQrSrSrTAtest__check_targets_multiclass_with_both_y_true_and_y_pred_binarysr=cCs`tgd}tgd}t||dksJtgd}tgd}t||dks.JdS)N)rr5r5r)g!rr g333333ӿ333333?)rr.r.r)r:rrrR pred_decisionrSrSrTtest_hinge_loss_binarys rAc Cstgdgdgdgdgdgdg}tgd}td|dd|ddd|dd|dd d|d d |d d d|d d|d d d|d d |d d d|d d |d d g}tj|dd|d t|}t|||ksJdS)N ףp= ?(\ſ(\gGz)HzGgGz׿Q޿rE333333rERQؿrD)rFrJrGrEgzGgHzGgHzGѿgQ?)rr5r.r5rr.r5rr.rrroutr:rcliprr)r@rR dummy_lossesdummy_hinge_lossrSrSrTtest_hinge_loss_multiclasss,  rRcCsrtgd}tgdgdgdgdg}d}tjt|dt||WddS1s2wYdS)N)rr5r.r.)gRQ?g rh?g(\gffffffrHrKzDPlease include all labels in y_true or pass labels as third argumentr)r:rrrrr)rRr@ error_messagerSrSrT:test_hinge_loss_multiclass_missing_labels_with_labels_nones  "rTc Cstgd}tgd}d}tjtt|dt||dWdn1s+wYtddgddgddgddgddgddgddgg}gd }d }tjtt|dt|||d WddS1smwYdS) N)r.r5rr5rr5r5)rr5r.r5rr.r5zThe 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,)rr?rr5r.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))rRr@ri)r:rrrrreescaper)rRr@rSrirSrSrTTr/g躕ʀ?r.Fg.L`@rr)r0epsrrarrhamspamrerdL7?zly_true contains only one label \(2\). Please provide the true labels explicitly through the labels argument.rr>zBFound input variables with inconsistent numbers of samples: [3, 2]r5rr)rrar>)rrr>)r>r^rr(gPv0?) r:rr!r asarrayrOrrrrlog)rRrQlossr error_str true_log_losscalculated_log_lossy_score2rSrSrT test_log_loss? sX&        rnc Cstgd}tddgddgddgddgg}ttfg}zdd lm}m}|||fWn ty7Ynw|D]\}}||||}}t||} t | d d d q:dS) Nrcrrarrrrr)Series DataFramerfrr) r:rrpandasrorprM ImportErrorr!r ) y_try_prtypesrorp TrueInputType PredInputTyperRrQrirSrSrTtest_log_loss_pandas_input s"    rxcCstgd}tgd}t||dt|}tt||dtt|||ttd|||ttd|d||tt t||ddWdn1sZwYtt t||dWdn1svwYtt t||dWdn1swYtgd}tgd}d }tjt |d  t||Wdn1swYttd gd gd ttdgd gd ttdgd gdttdgd gddd ttdgd gddddS)N)rr5r5rr5r5)rrr^r>rgffffff?r.rxrr5)rr5r.r)rrrrzMOnly binary classification is supported. The type of the target is multiclassrrrg{Gz?rrCfoobar)r) r:rrnormrlr r(rrr)rRrQ true_scorerSrSrSrTtest_brier_score_loss s8    r}cCsJd}tjt|dtgdgdWddS1swYdS)Nz%y_pred contains classes not in y_truerrr)rrrrrrSrSrT#test_balanced_accuracy_score_unseen s"r~z y_true,y_pred)rrrr)rrrr)rrrrcCst||dt|d}t t||}Wdn1swY|t|ks,Jt||dd}t|t||d}|||d|ksJJdS)NrrT)adjustedrr5)r&r:uniquerrrr full_like)rRrQ macro_recallbalancedrchancerSrSrTtest_balanced_accuracy_score s  r)NF) functoolsr itertoolsrrrrrUnumpyr:scipyrrsklearnrrsklearn.datasetsr sklearn.preprocessingr r sklearn.utils.validationr sklearn.utils._testingr rrrrrsklearn.utils._mockingrsklearn.metricsrrrrrrrrrrr r!r#r$r%r&r'r(r)sklearn.metrics._classificationr+sklearn.exceptionsr,scipy.spatial.distancer-rrUrwr{mark parametrizerrrrrrrrrrrrrrr&r*r6r9rArOrPrSrjrurrrrrrrrrrrrrrrrrrrrrrrrrrrrrr rrrrrrrrrr r'r<r=rArRrTrWr[r\r^rnrxr}r~rrSrSrSrTsP                                      0B "  -     #)  '#8 !:    #  (M%  B @ L  !c @ & ' 'b  D#