o tBhx@s ddlmZddlmZddlmZddlmZddlmZddlZ ddl m Z ddl Z ddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlm Z ddlm!Z!ddlm"Z"ddlm#Z#ddlm$Z$ddlm%Z%ddlm&Z&ddlm'Z'ddlm(Z(ddlm)Z)ddlm*Z*ddlm+Z+ddlm,Z,ddlm-Z-dd lm.Z.dd!lm/Z/dd"lm0Z0dd#lm1Z1dd$lm2Z2dd%lm3Z3dd&lm4Z4dd'lm5Z5dd(lm6Z6dd)lm7Z7dd*lm8Z8dd+lm9Z9dd,lm:Z:dd-lm;Z;dd.lmZ>dd1lm?Z?dd2lm@Z@dd3lmAZAdd4lmBZBdd5lmCZCdd6lmDZDdd7lmEZEdd8lmFZFdd9lmGZGdd:lmHZHdd;lImJZJe4e6e8e>ee:e;ee9d?d>ee'd?d>e(e)d@ZKidAe dBe"dCee"dDdEdFee dGdHdIe%dJdKdLdMe=dNee=dDdOdPe.dQeEdReeEdGdHdSe0dTe@dUeBdVe,dWee-dXdYdZee-d[dYid\e5d]ee-d^d[d_d`ee,d^dadbee-d^dXd_dcee@d^daddeeBd^dadeee0d^dadfee-dgd[d_dhee,dgdadiee-dgdXd_djee@dgdadkeeBdgdadlee0dgdadmee-dnd[d_doee,dndadpee-dndXd_dqee@dndaeeBdndaee0dndaee-drd[d_ee,drdaee-drdXd_ee@drdaeeBdrdaee0drdae$ds ZLdtduZMeDeMe*dvZNidwe&dxe2dye3dzee3dGdHd{e/d|e#d}eCd~eeCd^dadeeCdrdadeeCdgdadeeCdndddeeCd^dddeeCdndddeeCd^dddeeCd[dde!dee!d^daee!drdaee!dgdae1eFeGeHdZOePZQeQReOeQReLeQReKeQReNhdZShdZTeSUeTZVhdZWhdZXhdZYhdZZhdZ[hdZ\hdZ]hdZ^hdZ_hdZ`hdZahdZbddZcddZde jefdege_ddZhe jefdege`ddZie jefdegejeQeVddZkeddZle jefdegejeQeVddZme jefdegejeLeVddZne jefdeOddZoddge jpe jpgfddge jqe jqgfddge jqe jpgfddge jpdgfddge jqdgfgZre jefdeeOseKse jefderddZte jefdeLse jefdere jqddXggdfe jpddXggdfgddZue jefdeLsddZveddZweddZxe jefdegejeQeVejeOddZye jefdege^e]BddZze jefdege^ddZ{e jefdege^ddÄZ|eddńZ}e jefdege]ddDŽZ~e jefdege[ddɄZe jefdege[dd˄Ze jefdege[e]dd̈́ZeddτZddфZe jefdegeWddӄZe jefdegeWeXBddՄZe jefdegeWddׄZddلZe jefdegeWddۄZedd݄Ze jefdegejeQejeKeadd߄Ze jefdegejeQejeKeaeSddZe jefdegejeQejeKeaeVddZe jefdege]e\Be^BeaddZeddZe jefdege]dMhddZe jefdege\e^BddZe jefdegejeOeVddZe jefdeLddZe jefde!dDfe#dDfe,dGfee-ddYdGfe0dGfe?dDfe@dGfeBdGfeDdDfg e jefdeegddZdS))partial) signature)product)chain) permutationsN)make_multilabel_classification)LabelBinarizer)type_of_target) _num_samples)check_random_state)shuffle)assert_allclose)assert_almost_equal)assert_array_equal)assert_array_less)ignore_warnings)accuracy_score)average_precision_score)balanced_accuracy_score)brier_score_loss)cohen_kappa_score)confusion_matrix)coverage_error)d2_tweedie_score)d2_pinball_score)d2_absolute_error_score) det_curve)explained_variance_score)f1_score) fbeta_score) hamming_loss) hinge_loss) jaccard_score)%label_ranking_average_precision_score)label_ranking_loss)log_loss) max_error)matthews_corrcoef)mean_absolute_error)mean_absolute_percentage_error)mean_squared_error)mean_tweedie_deviance)mean_poisson_deviance)mean_gamma_deviance)median_absolute_error)multilabel_confusion_matrix)mean_pinball_loss)precision_recall_curve)precision_score)r2_score) recall_score) roc_auc_score) roc_curve) zero_one_loss) ndcg_score) dcg_score)top_k_accuracy_score)_average_binary_scorevariance_weighted) multioutput)powergffffff?)r&r(r*r0r.r)rr3mean_normal_deviancer,r-mean_compound_poisson_deviancerrrrr adjusted_balanced_accuracy_scoreT)adjustedunnormalized_accuracy_scoreF normalizeunnormalized_confusion_matrixnormalized_confusion_matrixcOs8t|i|dt|i|jddddtjfS)Nfloataxis)rastypesumnpnewaxis)argskwargsrRx/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_common.pys"rT(unnormalized_multilabel_confusion_matrix/unnormalized_multilabel_confusion_matrix_sample) samplewiser r7unnormalized_zero_one_lossr"r2r4rf2_score)beta f0.5_scoreg?matthews_corrcoef_scoreweighted_f0.5_scoreweighted)averager[weighted_f1_scorer`weighted_f2_scoreweighted_precision_scoreweighted_recall_scoreweighted_jaccard_scoremicro_f0.5_scoremicromicro_f1_scoremicro_f2_scoremicro_precision_scoremicro_recall_scoremicro_jaccard_scoremacro_f0.5_scoremacromacro_f1_scoremacro_f2_scoremacro_precision_scoresamples) macro_recall_scoremacro_jaccard_scoresamples_f0.5_scoresamples_f1_scoresamples_f2_scoresamples_precision_scoresamples_recall_scoresamples_jaccard_scorerc OsRt|i|\}}}t|t|}t||tj|tjd|fdtjgdgS)a' The dimensions of precision-recall pairs and the threshold array as returned by the precision_recall_curve do not match. See func:`sklearn.metrics.precision_recall_curve` This prevents implicit conversion of return value triple to an higher dimensional np.array of dtype('float64') (it will be of dtype('object) instead). This again is needed for assert_array_equal to work correctly. 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Cstd}|jdddd}|jdddd}|j|jd}t|||dd\}}}tD]}t|}t||||||d|dq(tD]}t|}t||||||d|dq@t D]$}t|}t||||||d|dt||||||d|dqXdS)NrrZrrrrr) r rnormalshaper rrr THRESHOLDED_MULTILABEL_METRICSMULTIOUTPUT_METRICS) rrry_scorerry_score_shufflerrrRrRrS7test_sample_order_invariance_multilabel_and_multioutput|sD rc Csntd}|jdddd}|jdddd}|tvrt||\}}t|}t|}t|t|}}t|jdt|jdt |d}t |d} t |d} t |d} t Rt |} | ||} t | ||| d|d t | ||| d |d t | || | d |d t | ||| d |d t | ||| d |d t | || | d |d t | ||| d |d t | || | d|d t | ||| d|d t t | || Wdn1swYt t | | |Wdn1swYt t | || Wdn 1swYt t | | |Wdn 1s5wYt t | || Wdn 1sPwYt t | | | Wdn 1skwY|ttBtBvrt t | | | Wdn1swYWddSWddSWddS1swYdS)NrrZrrrI)rI)rIrz,%s is not representation invariant with listrz3%s is not representation invariant with np-array-1dz7%s is not representation invariant with np-array-columnz@%s is not representation invariant with mix np-array-1d and listzK%s is not representation invariant with mix np-array-1d and np-array-columnzD%s is not representation invariant with mix 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rrrrrrrrrrrrRrRrS4test_thresholded_invariance_string_vs_numbers_labelsXsJ     "rrIrzy_true, y_scorecCs>tjtdd|||WddS1swYdS)Nzcontains (NaN|infinity)matchrrr)rrrrRrRrS)test_regression_thresholded_inf_nan_inputs "r)rIrZcCst|sd}t|rd}nd}nd}t|r"d}nd}d|d|}tjt|d|||WddS1sDwYdS) z{check that classification metrics raise a message mentioning the occurrence of non-finite values in the target vectors.rNaNzinfinity or a value too largerzInput z contains rN)rNisfiniteallisnananyrrr)rrr input_nameunexpected_valuerrRrRrS!test_classification_inf_nan_inputs "rcCsTgdgd}}d}tjt|d|||WddS1s#wYdS)zocheck that classification metrics raise a message of mixed type data with continuous/binary target vectors.)abr)g?g?333333?zJClassification metrics can't handle a mix of binary and continuous targetsrNr)rrrrrRrRrS+test_classification_binary_continuous_inputs  "rcCsJt|}|tvr ddg}nddg}t|ddD] \}}||g|gqdS)NrIrZrrepeat)rrr)rrvaluesijrRrRrScheck_single_samples rcCsLt|}tddgddD]\}}}}|t||ggt||ggq 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failed representation invariance between dense and list of array indicator formats.)rrNvstacksp coo_matrixrrr isinstancer __module____name__r r)rrrrry1_sparse_indicatory2_sparse_indicatory1_list_array_indicatory2_list_array_indicatory1_list_list_indicatory2_list_list_indicatorrrrrRrRrS)test_multilabel_representation_invariance sh         r3c Csdgdgddgggdggdgtjgddggddg}t|}|D]}tt |||Wdn1s8wYq!dS)NrIrZr)rRrZ)rrIrRobjectdtyper)rmultilabel_sequencesrseqrRrRrS+test_raise_value_error_multilabel_sequencesWs  r9c Csd}d}td}|jd||fd}|jd||fd}|j|jd}t|}|tvr+|n|}|||dd} |||dd} td| dd d t| | |d |d dS) NrZrrrTrDF0We failed to test correctly the normalize optionr Failed with )r rrrrrrr rrrrrrrmetricspredmeasure_normalizedmeasure_not_normalizedrRrRrS+test_normalize_option_binary_classificationhs( rBc Csd}d}td}|jd||fd}|jd||fd}|j||fd}t|}|tvr,|n|}|||dd} |||dd} td| dd d t| | |d |d dS) NrrrrTrDFr:r;rr<)r rrrrrr r=rRrRrS/test_normalize_option_multiclass_classifications( rCc Csd}d}td}td|dd|d\}}td|dd|d\}}|j|jd}|dg|7}|dg|7}t|}|tvr=|n|} ||| dd} ||| d d} td | dd d t| | |d |d dS)NrdrrIT)rrrrrrrDFr:r;rr<)r rrrrrrr ) rrrrrrrrr>r?r@rArRrRrS/test_normalize_option_multilabel_classificationsD   rEcsj\}}||dd}t|fddt|D||dd} t| ||dd} t| t|tjdtd} t| dkr_||dd} t| tj|| d n ||dd} t| d|r||d d} t| tfd dt|Dt t ||d dWdn1swYt t ||d dWddS1swYdS)Nrbcs.g|]}dd|fdd|fqSrrRr#rry_pred_binarizey_true_binarizerRrSr$s z$_check_averaging..rhror)rKr6r_)weightsrscsg|] }||qSrRrRrFrGrRrSr$sunknowngarbage) rr rravelrNmeanrMintr`rrr)rrrrIrH is_multilabelrr label_measure micro_measure macro_measurerJweighted_measuresample_measurerRrGrS_check_averagingsH     "rVcCsZt|d}t|}|tvrt||||||dS|tvr)t||||||dStd)N multilabelz2Metric is not recorded as having an average option)r startswithrMETRICS_WITH_AVERAGINGrV"THRESHOLDED_METRICS_WITH_AVERAGINGr)rrrIrrHrrPrrRrRrScheck_averaging s  r[c Cszd\}}td}|jd||fd}|jd||fd}|j||fd}t|}||}||} t||||| |dS)N)rrrr)r rrrfit transformr[) rrrrrrrlbrIrHrRrRrStest_averaging_multiclasss   r_c Cshd\}}td|d|dd\}}|dd}|dd}tdjd|fd}|}|} t||||| |dS) N)(rrIrFrrrr)rr rr[) rrrryrrrrIrHrRrRrStest_averaging_multilabel+s  rbcC<td}td}td}|}|}t||||||dSNrr)rNzerosr[rrrrrIrHrRrRrS$test_averaging_multilabel_all_zeroes@   rhcCs>td}td}|}|}ddd}t|||||dddS)NrerocSstt|||Sr)r;r2)rrr`rRrRrSrTQsz=test_averaging_binary_multilabel_all_zeroes..T)rP)ro)rNrfrV)rrrIrH binary_metricrRrRrS+test_averaging_binary_multilabel_all_zeroesKs    rkcCrcrd)rNonesr[rgrRrRrS"test_averaging_multilabel_all_ones^rirmcCs,tjd}|jddt|d}|dkrt|ddn|}|||dd}t||||tjt|ddd |d ||||d}t t t||t d |||f1sUwY|||| d}t||d |||fd |tj ||dd tj ||dd dd} t|| d|d |ddd} t|} d| ddd<|ddd} |ddd} || | | d}|||| d}t||d|||fd |dsdD]}t||||||dd|d qdt|t|t|d}tj t |d|||t||gdWddS1swYdS)NrrI rr:)r ) sample_weight)rzAFor %s sample_weight=None is not equivalent to sample_weight=onesrzQUnweighted and weighted scores are unexpectedly almost equal (%s) and (%s) for %szVWeighted scores for array and list sample_weight input are not equal (%s != %s) for %srJz.Weighting %s is not equal to repeating samplesrZzeZeroing weights does not give the same result as removing the corresponding samples (%s != %s) for %s unnormalized)rZrz/%s sample_weight is not invariant under scalingzJFound input variables with inconsistent numbers of samples: \[{}, {}, {}\]r)rNrandom RandomStaterrrr rlrrrrtolistrcopyrXrr hstack)rrrrrngrounweighted_scoreweighted_scoreweighted_score_listrepeat_weighted_scoresample_weight_subsetsample_weight_zeroed y1_subset y2_subsetweighted_score_subsetweighted_score_zeroedscaling error_messagerRrRrScheck_sample_weight_invarianceis        $rcCsBd}td}|j|fd}|j|fd}t|}t||||dS)Nrrr)r random_samplerr)rrrrrrrRrRrS(test_regression_sample_weight_invariances rcCsrd}td}|jdd|fd}|jdd|fd}|j|fd}t|}|tvr0t||||dSt||||dS)NrrrZr)r rrrrr)rrrrrrrrRrRrS$test_binary_sample_weight_invariances rc Csd}td}|jdd|fd}|jdd|fd}|j|dfd}t|}|tvrCt| }||jdddd}t ||||dSt ||||dS)NrrrrrrJrI) r rrrrrNexprMrr) rrrrrrrtemp 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}||jdddd}|jd||d}t|}|||}tt ||D]*} tj |t d} t || t | <|dd| f} t| |} || | } t|| q8dS)N)rDrrrrJrIrr5)r randrNrrMrrrrrrfrOarangertaker)rrrrrrrrrr inverse_permrrrrRrRrS.test_thresholded_metric_permutation_invariancexs       r metric_namecCstjd}tjdgddgdtd}|jdd|jd}d }tjt |d t |||WddS1s:wYdS) N*rrrrZr5rrz7Labels in y_true and y_pred should be of the same type.r) rNrqrrrr4rrrr TypeErrorr)rrvrrrrRrRrS"test_metrics_consistent_type_errors "rzmetric, y_pred_threshold dtype_y_strc Cstjd}tjdgddgd|d}|jdd|jd}|s+tjddg|d|}d }d }t|jd j}|d kr=|n|} t j t | d |||WddS1sWwYdS)NrrrrrZr5rrzy_true takes value in {'eggs', 'spam'} and pos_label is not specified: either make y_true take value in {0, 1} or {-1, 1} or pass pos_label explicitzHpos_label=1 is not a valid label. It should be one of \['eggs', 'spam'\]rrIr) rNrqrrrrrr parametersdefaultrrr) ry_pred_thresholdrrvrrerr_msg_pos_label_Noneerr_msg_pos_label_1pos_label_defaultrrRrRrS test_metrics_pos_label_error_strs  "r) functoolsrinspectr itertoolsrrrnumpyrN scipy.sparsesparser(rsklearn.datasetsrsklearn.preprocessingrsklearn.utils.multiclassr sklearn.utils.validationr r sklearn.utilsr sklearn.utils._testingr rrrrsklearn.metricsrrrrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:sklearn.metrics._baser;REGRESSION_METRICSrr CURVE_METRICSrdictrupdaterMETRIC_UNDEFINED_MULTICLASSunionrrYrZrrMETRICS_WITH_NORMALIZE_OPTIONrrrrrMETRICS_WITHOUT_SAMPLE_WEIGHTrrrmark parametrizesortedrrrrrrrrinfrinvalids_nan_infrrrrrr rrrrr3r9rBrC intersectionrErVr[r_rbrhrkrmrrrrrrrrrrstrr4rrRrRrRrSs(                                                          %              ! "# $% & ' () *+ , -       9         "      $"!%    * v  5 +            K    / 6       a