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Estimators are only allowed to change attributes started or ended with _, but %s changed)rur^rrrrrrrnrrrNrKrJrrlrrrkr) r~rr}rrrZpublic_keys_after_fitZattrs_added_by_fitZattrs_changed_by_fitrrrr8sF           rcCstjd}d|jdd}t||}|dddft}t|}t||}t |dr^d|_ t |drnd|_ t |d| ||dD]&}t ||rttd t|||dqdS) NrrPrrrNr8rKrzReshape your data)rrrrrrnrrrrurNrKrJrrFrr)r~rrrrr}rrrrrss(        rcs~|}|jdfdd|D}t|tkrL|d}ttdd|}t|rj|j}dd|D}t |t |fS)Nr8csg|]}|dqS)r8)r)rbatchrr6rrrsz%_apply_on_subsets..rcSs|dS)Nrr)xrrrz#_apply_on_subsets..cSsg|] }|qSr)Ztoarray)rrrrrrs) rr%tuplelistmaprissparseArravel)rr result_fullresult_by_batchrrr_apply_on_subsetss   rc Cstjd}d|jdd}t||}|dddft}t|}t||}t |dr^d|_ t |drnd|_ t |d| ||dD]@}d j||d }t ||rtt|||\}} t|| d |d qdS) NrrPrrrNr8rKrrr{ score_samplesr}z={method} of {name} is not invariant when applied to a subset.rr~Hz>rzr)rrrrrrnrrrrurNrKrJrrrrrA) r~rrrrr}rrrrrrrrs.        rc Cstjd}d|jdd}t||}|dddftj}t|ddrVd||dk<t|}t ||}t |d rxd|_ t |d rd|_ t |d|||tj|jd}d D]H}d j||d }t ||rtt||||t||||d|dqdS)NrrPrrrrr8r rNrKrzY{method} of {name} is not invariant when applied to a datasetwith different sample order.r& .>r)rrrrrrnrjr>rrrurNrKrJrZ permutationrrrBr) r~rrrrr}idxrrrrrrs8          rc Cstjd}d|jdd}t||}|dddft}t|}t||}t |dr^d|_ t |drnd|_ t |d|dkr|j d d |d kr|j d d ddddddg}tt|dd|||W5QRXdS)NrrP)r8rrrNr8rKZOPTICSrIrUr<rMr=z1 samplez n_samples = 1z n_samples=1z one samplez1 classz one classTr)rrrrrrnrrrrurNrKrJrVrIrrr~rrrrr}Zmsgsrrrrs0         rc Cstjd}d|jdd}t||}|dddft}t|}t||}t |dr^d|_ t |drnd|_ |dkr|d|_ |d krd |_ t||}t|dd d d g}tt|dd|||W5QRXdS)NrrP)rr8rrNr8rKZRandomizedLogisticRegressionrrMz1 feature\(s\)zn_features = 1z n_features=1Tr)rrrrrrnrrrrurNrKZsample_fractionZresidual_thresholdrJrIrrrrrrrs&        rc Cstjd}d|jdd}|t}t|}t||}t|drHd|_ t|drXd|_ t |dt t |||W5QRXdS)NrrPr3rrNr8rK)rrrrrnrrrrurNrKrJrIrr)r~rrrrr}rrrr7s       rcCsbtddddgdddggdddd\}}t|}t||}|rPt||g\}}t||||dSNrrr8r rr5centersr r6 cluster_std)rr+r9rrG_check_transformer)r~rrTrrrrrrJs   rcCsztddddgdddggdddd\}}t|}t||}t|}tt|}t||||t||||dSr) rr+r9rrZrr[rtolist)r~rrrZthis_XZthis_yrrrr\s   rc CsDt\}}t|}tttfd|dd||W5QRXdS)NzThe unfitted transformer z\ does not raise an error when transform is called. 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Got arguments: %r.)rrrrrrrrrJrr parametersvaluesrkr%rb) r~rrr5rrr}rrrrrrrre s*       rec Cstjd}d|jddtj}t||}|tj}|tj}|tj }|dddf}t ||}ddddg}||||fD]D} t |} t | d | | ||D]} t| | rt| | | qqdS) NrrPrrrrr{r}r8)rrrrrnr^rr]rjint32rrrJrrur) r~rrZ X_train_32Z X_train_64ZX_train_int_64ZX_train_int_32rrX_trainr}rrrrrd(s           rdc Cstddddgdddggddd\}}t|}t||}t|ddD]}||}t|}t||||}||| |}t ||gdd gD]H\} } t | t r| d} | j |kst|d | d |jd | j d qqFdS)Nrrr8r)r5rr rrrr9rz (method=z3) does not preserve dtype. Original/Expected dtype=z , got dtype=.)rr+r9rr>rnrrJrrrrrr`rkrb) r~rrrr`ZX_castrZX_trans1ZX_trans2ZXtrrrrr?s(       rcCst|}t|dtddd}d|d}tt|d||gW5QRXtddd}t|t ddddddddddddg }d}tt|d |||W5QRXdS) Nr8rrPThe estimator zc does not raise a ValueError when an empty data is used to train. Perhaps use check_array in train.rrzG0 feature\(s\) \(shape=\(\d*, 0\)\) while a minimum of \d* is required.r) rrJremptyrrIrrrr)r~reZX_zero_samplesrZX_zero_featuresrrrrrro_s  (roc Csftjd}t||jdd}|jdd}tj|d<|jdd}tj|d<td}d|dd<t||}d|d}d|d }d|d } ||fD]} t t d t |} t | d t td dg|d| | |W5QRX| ||t| dr"t td dg|d| | W5QRXt| drVt td dg| d| | W5QRXW5QRXqdS)NrrrPrrrrrz& doesn't check for NaN and inf in fit.z* doesn't check for NaN and inf in predict.z, doesn't check for NaN and inf in transform.rr8rNaNrrr)rrrrrrrrPrrHrrrJrIrrrurr) r~rrZX_train_finiteZ X_train_nanZ X_train_infrerror_string_fiterror_string_predictZerror_string_transformrr}rrrrrwsJ                  rrc CsHtddd\}}t|}ttd|dd|||W5QRXdS)z8Test that error is thrown when non-square data provided.r3r)r5r6zThe pairwise estimator z+ does not raise an error on non-square datarN)rrrIrrr~rrrr}rrrrss rscCsLddddg}tddddgdddggddd d \}}t||td }t|}|d r~tjd }|j|jddd}tj | d|<t |} t | |}t | | |||rt| } nBt| } | j} | drd| ks| dsd| kstt| } t} |D] }t| |rt| ||| |<q| D]"}t| ||}t| ||q$dS)z'Test that we can pickle all estimators.rrr{r}rrr8r rrrrVr4rF)replacerrrrsversionN)rrr&r>rrrchoicerrrrrrJrrGpickledumpsrrrrkloadsdictrurrB)r~rrT check_methodsrrrrmaskr}Zunpickled_estimatorZpickled_estimatorrrrZunpickled_resultrrrrxsH           rxc Cst|dsdSt|}tddd\}}t||}t||}z4t|r`t|}|j|||dn |||Wnt k rYdSXt t d|dd"||dddd f|W5QRXdS) Nr2r8r5r classesrzZ does not raise an error when the number of features changes between calls to partial_fit.rr) rurrrrrruniquerNotImplementedErrorrIr)r~rr}rrr rrrrs$     rcCs&d\}}}t|}t|}td|||d\}}|||||}|j||fkshtd||f|j|jj dksxtt |dr| |} t | t jst| j||fkstd||f| j| dkt} |j| } t| |t |d r||} t | trx|d sxt|D]^} | | j|d fksHtd |d f| | jtt j| | d dt|dd| fqnD|d s| j||fkstd ||f| jt| t|t |dr"t |d r"t|D]D} ||dd| f}| |}tt|t|dd| fqdS)N)r4rrPr4)r r5n_labels n_classesSThe shape of the prediction for multioutput data is incorrect. Expected {}, got {}.ir{zaThe shape of the decision function output for multioutput data is incorrect. Expected {}, got {}.rr} poor_scorer zTThe shape of the probability for multioutput data is incorrect. Expected {}, got {}.r8Zaxis)r>rrrrrrkrr`rQrur{rrrtrnrclasses_rDr}rrangeargmaxroundr )r~r}r5rrrrry_preddecisiondec_preddec_expy_probrZy_probaZ y_decisionrrrr sr               rcCst|}d}}t|s |d}tdd||d\}}t||}|||||}|jtdksttd |j|j |j kstddS) Nrr8r4r)r  n_targetsr5r6r]zbMultioutput predictions by a regressor are expected to be floating-point precision. Got {} insteadr) rrfrrrrr`rrkrr)r~r}r5r6rrrrrrrHs*    rc Cst|}tddd\}}t||dd\}}t|}tjd}t||j dddd g}|rtt |||g\}}}|j \}} t |d r|j dd t||d kr|j d d|j dd|||||j} | j |fkstt| |dkstt|ddrdSt|tjdd||} W5QRXt| | | jtdtdfksbt| jtdtdfkst||} t| } t| t| d| dd| ddkstt |d rt|d }|d| dkstdS)Nrr8r r rP)rr rrK)rKZAffinityPropagationi)Z preferencerCrAg?rrTrecordrrjrr)rr)rrr;r+r9rrrZ concatenaterrGrrurVrJrrZlabels_rkr"r>rcatch_warningsrrDr`r rr)r~clusterer_origrTrrrrZX_noiser5r6rpred2labelsZ labels_sortedrKrrrrbsL               rcCsdtddd\}}t|}t|t|dr`|||}|jdd|||}t||dS)z2Check that predict is invariant of compute_labels.r3rr compute_labelsF)r&N)rrrJrurrrVrD)r~r#rrrrrrrrrs  rc Csd}d}tjd}|jdd}|jdd}td}ttd^t|}tt dd |d } | ||W5QRX| j rW5QRdSt | |||d W5QRXdS) Nz6Classifier can't train when only one class is present.z8Classifier can't predict when only one class is present.rrrrrclassTrr)rrrrrPrHrrrIrrraised_and_matchedrDr) r~classifier_origrrrrX_testrrcmrrrrs&     rc Cs|d}|d}tjd}|jdd}|jdd}tdd}|}t|} t| drxd |g} tt f|} } nd } t t fd } } t | | d | d D} | j |||d| j rW5QRd St| |td|dW5QRXd S)zCheck that classifiers accepting sample_weight fit or throws a ValueError with an explicit message if the problem is reduced to one class. zu failed when fitted on one label after sample_weight trimming. Error message is not explicit, it should have 'class'.z; prediction results should only output the remaining class.r)rrrrr rNz\bclass(es)?\bz\bsample_weight\bNTrrr)rrrrrrlrrLrkrr,rIrr(rDrrP)r~r)Z error_fitZ error_predictrrr*rrNrrZerr_typerr+rrrrs.     rc Cs`tddd\}}||}t||dd\}}t|}||dk}||dk}|dkrp||8}||8}|rt||||g\}}}}||fg}t|} | ds|||f|D]\} } t | } t | } | j \}}t |}t|| } t|| } t|| d s@ttd |d d || | dd W5QRX|| | || | t|dspt|| }|j |fkst| dst| |dkstd}d}| d s&| drtt||dd || d dW5QRXn,tt||dd || jW5QRXt|drPz|| }| dkr| dsh|j |fks|tn|j |dfks|t|dkt}t||n(|j || fksttt j |dd|| d s6| dr tt||dd || d dW5QRXn,tt||dd || jW5QRXWnt!k rNYnXt|dr|"| }|j || fksxttt j |dd|t#t j$|ddt %|| d s| drtt||dd |"| d dW5QRXn,tt||dd |"| jW5QRXt|dr|&| }t'|t (|dddtt )|t )|qdS)N,rr rr r ) BernoulliNBZ MultinomialNB ComplementNBZ CategoricalNBrrUThe classifier does not raise an error when incorrect/malformed input data for fit is passed. The number of training examples is not the same as the number of labels. Perhaps use check_X_y in fit.rrrrg(\?zuThe classifier {} does not raise an error when shape of X in {} is not equal to (n_test_samples, n_training_samples)z|The classifier {} does not raise an error when the number of features in {} is different from the number of features in fit.rOrr8r{rrr}r|r)rz)*rrnr;r+r9rWrGr>appendrr rrrrrrJrIrrrrurkrr!rrTr{rrrDrr r}rCsumrPr|rAlogargsort)r~r)rTrZX_mZy_mZy_bZX_bproblemsrrrr rr5r6rrZ msg_pairwiserrrrZ y_log_probrrrrs                                     rcCsV||kr|}|d}n |}|d}t|}d}tt|||dksRt|dS)Nr8zThe number of predicted outliers is not equal to the expected number of outliers and this difference is not explained by the number of ties in the decision_function values)rsortrr rk) num_outliersexpected_outliersrstartendZsorted_decisionrrrrcheck_outlier_corruptiont s  r=Tc Csd}t|dd\}}t|dd}|r,t|}|j\}}t|}t|||||||}|j|fksxt |j j dkst t t |t ddg||} ||} | | fD](} | j t d kst | j|fkst qtt||jW5QRX| dkt} d| | dk<t | |tt||jW5QRX| |j} t| | tt||jW5QRXt|d rt|d sd }||}|j|d ||||}t |dk}||kr||} t||| dS)Nr,rr rr rrr8floatrZnoveltyrr)rr;rGrrrJrrrrkr`rQrDrr rr{rrIrr3rnrZoffset_rArurVr4r=)r~rrTr5rrr6r}rrZscoresoutputrZy_decr:rr9rrrr sP                   rcCst|dsdSd|jkrdS|jd}tdd|Ds@td|D]H}t|trD|jtkr|jdkr|j dkr|jdksD|j d ksDtd qDdS) N_parameter_constraintsrcSsg|]}t|tqSr)rr<)rcrrrr sz/check_outlier_contamination..zDcontamination constraints should contain a Real Interval constraint.rMr>rightneitherz:contamination constraint should be an interval in (0, 0.5]) rurAr+rkrr<r%rleftrDclosed)r~rZcontamination_constraints constraintrrrr s.    rc Cstdddddddd\}}t|}|dd |dd }}|d d}|}t|}t|} t| | |||} | |||} | |||} t| | t| | | j | j kst | j | j kst t | t | kst t | t | kst dS) NrCr rrPrTrr5r6rrlengthZallow_unlabeledr P) rr,rrrrJrrrDr`rkr%) r~r)rrry_trainr*Zy_train_list_of_listsZy_train_list_of_arraysrrZy_pred_list_of_listsZy_pred_list_of_arraysrrrr s:      rc Cs8t|}t|d\}}}t|d|ddddd\}}t|}|d| || d}} |d| || d} } ||| d } t|| d} | dkrt|d | d | | }t|tj st |d t |d |j | j kst |d|j d| j d|j | j ks4t |d|j d| j ddS)zeCheck the output of the `predict` method for classifiers supporting multilabel-indicator targets.rCrr rPrTrrINr does not have a  method.z2.predict is expected to output a NumPy array. Got instead.z(.predict outputs a NumPy array of shape instead of rz>.predict does not output the same dtype than the targets. Got )rrJrr,rrr?rrrtrkr%rr`)r~r)rr5 test_size n_outputsrrrr*rLy_testresponse_method_nameZpredict_methodrrrrr s<    rc Cst|}t|d\}}}t|d|ddddd\}}t|}|d| || d}} |d| } ||| d } t|| d} | dkrt|d | d | | } t| trft | |kst d |d t | d|d| D]~}|j |dfkst d |d|j d|dfd|j j dks@t d |d|j dd |d}t|jddd|dqnt| tjr| j ||fkst d |d| j d||fd| j j dkst d |d| j dd |d}td| |dt| d|dntdt| d|ddS)zkCheck the output of the `predict_proba` method for classifiers supporting multilabel-indicator targets.rMr rPrTrrINr}rOrPzWhen zn.predict_proba returns a list, the list should be of length n_outputs and contain NumPy arrays. Got length of rRrzx.predict_proba returns a list, this list should contain NumPy arrays of shape (n_samples, 2). Got NumPy arrays of shape fzW.predict_proba returns a list, it should contain NumPy arrays with floating dtype. Got rQz.predict_proba returns a list, each NumPy array should contain probabilities for each class and thus each row should sum to 1 (or close to 1 due to numerical errors).r8rrzX.predict_proba returns a NumPy array, the expected shape is (n_samples, n_outputs). Got zN.predict_proba returns a NumPy array, the expected data type is floating. Got z.predict_proba returns a NumPy array, this array is expected to provide probabilities of the positive class and should therefore contain values between 0 and 1.zUnknown returned type z by z4.predict_proba. A list or a Numpy array is expected.)rrJrr,rrr?rrrrkrr`rQrAr4rrtrErr%)r~r)rr5rSrTrrrr*rLrVZpredict_proba_methodrrrrrrrC sb       rc Cs"t|}t|d\}}}t|d|ddddd\}}t|}|d| || d}} |d| } ||| d } t|| d} | dkrt|d | d | | } t| tj st |d t | d | j ||fkst |d| j d||fd| j jdkst |d| j d dS)zoCheck the output of the `decision_function` method for classifiers supporting multilabel-indicator targets.rMr rPrTrrINr{rOrPz<.decision_function is expected to output a NumPy array. Got rQz].decision_function is expected to provide a NumPy array of shape (n_samples, n_outputs). Got rRrrWz?.decision_function is expected to output a floating dtype. Got )rrJrr,rrr?rrrtrkr%rr`rQ)r~r)rr5rSrTrrrr*rLrVZdecision_function_methodrrrrr s<    rc Cs8t|}d|d}tt|d|W5QRXdS)zCheck the error raised by get_feature_names_out when called before fit. Unfitted estimators with get_feature_names_out should raise a NotFittedError. rzU should have raised a NotFitted error when fit is called before get_feature_names_outrN)rrIrget_feature_names_out)r~rr}rrrr!check_get_feature_names_out_error s  rYcCs`tddd\}}t||}t|}t||}|r@t||g\}}t|||||ks\tdS)z+Check if self is returned when calling fit.rr r5N)rrrrrGrJrrk)r~rrTrrr}rrrrl s  rlc CsLt\}}t|}dD]0}t||rttt|||W5QRXqdS)z}Check that predict raises an exception in an unfitted estimator. Unfitted estimators should raise a NotFittedError. )r{rr}r|N)r;rrurIrr)r~rrrr}rrrrr s    rc Cs"t|}tjd}d}t||j|dfd}t|d}t||}t|}t || ||| |}t |t j dd8} t dtt dt| ||ddtjfW5QRX| |} d d d d | D} |d s t| dkst| d| ks tt|| dS)NrrrPrTr alwaysrSz)expected 1 DataConversionWarning, got: %srcSsg|] }t|qSrr)rZw_xrrrr sz)check_supervised_y_2d..rzPDataConversionWarning('A column-vector y was passed when a 1d array was expected)r>rrrrrrrrrJrrrr" simplefilterrRuntimeWarningZnewaxisrrrkrAr) r~rrrr5rrr}rwZ y_pred_2drrrrr s4      $   rc Cs|t|}t|}|dkr&||k}t||||||}t|dr$||}t |tj sjt t |dkr| dkt}|j|} t| |d|dtt| dtt|fdn`t|dd d kr$tj|d d t} |j| } t| |d|dtt| dtt|fd|d krDtt|t|t||jd |dtt|dtt|jfddS)Nr-r{r rzKdecision_function does not match classifier for %r: expected '%s', got '%s'rrZdecision_function_shapeZovrr8rr.z=Unexpected classes_ attribute for %r: expected '%s', got '%s')rr rmeanrJrrrur{rrtrkrrrnrrrDrrrrr) rrr~r)r rrrrrZ decision_yZy_exprrrcheck_classifiers_predictions s`           rbcCs|dkr |S|S)N)ZLabelPropagationZLabelSpreadingSelfTrainingClassifierr)r~ry_namesrrr _choose_check_classifiers_labels_ srecCstdddd\}}t||dd\}}t|}||dk}||dk}t||}t||}dd d g}dd g}t||}t||} ||| fg} t|d d s| |||f| D]8\} } } | | d fD]}t || |}t | |||qqddg}t||} t ||| }t ||||dS)Nrrr)r5r rrr r onetwothreerrOrr8) rr;r+r9rrZtaker>r2rnrerb)r~r)Z X_multiclassZ y_multiclassZX_binaryZy_binaryZlabels_multiclassZ labels_binaryZy_names_multiclassZy_names_binaryr7rrrdZ y_names_irrrrri s4              rc Cst\}}t||dd}tjd}|jd|jdd}t||}tjd}t|}t|}t |t ||t krt |d||jdt |dg}|j }n|}|||||} |||t||} t| | d|ddS)NrrrPrr rr)r;rrrrrrrrrJrprrr3rrrnr>rA) r~regressor_origrrrrZ regressor_1Z regressor_2rpred1r$rrrr s&    $   rc Cs\t\}}||}t|}t|}t||}t||}|tkrxtj d}t |d||j dt |dg}|j }n|}|rt|||g\}}}t|dst|drd|_|dkrd|_ttd|d d |||dd W5QRXt|||||||||} | j|jks4tt|d d sX|||dksXtdS)Nrr rZalphasrrZPassiveAggressiveRegressorr/r0rrrrrM)r;rnr,rrrrprrrrrrr3rGrurr rIrrrJrrrrkr>r) r~rjrTrrrrrrrrrrr s:     $    rcCsrtjd}t|}|jdd}t||}t||dddf}|||dddg}|D]}t||rZt qZdS)Nr)rrrr{r}r|) rrrrnormalrrrrurk)r~rjrrrrrrrrrr s     rc CsNt|ddrdg}nddg}|D]&}t|ddd\}}t||ddd \}}}} t|d drpt||}t||}tt|}|dkrd d d } n d d d d} t|j| d} t | dr| jddt | dr| jd dt | dr| jddt | dr| jddt | | ||| |} t|dds t | dkdks tq dS)Nrrr rPrr3)rr rrMrSr rOg-C6?)rr8)rr8r rr>rCr?r@rAmin_weight_fraction_leafr)rpn_iter_no_change)rqrgףp= ?)r>rr(r&rrr rrVrurJrrrark) r~r)r7Z n_centersrrrr*rLrUrrrrrrr s@                  rc Cst|}t|dr|jddt|dr4|jddt||||||}|jdd|||||} t|| d d t||d d kstdS) Nr>rCr?r@rnrAbalancedroZweighted)Zaverage)rrurVrJrrr#rk) r~r)rrLr*rUrrrZy_pred_balancedrrr'check_class_weight_balanced_classifiers s          rsc Cs&tddgddgddgddgddgg}tdddddg}|}t|drX|jd d t|d rn|jd d t|d r|jddt||jdd|||j}t|}t tt |}|t |dk||t |dk|d}|j|d|||j} t || d|ddS)z4Test class weights with non-contiguous class labels.rgrIrCr8rr>rnr?r@rArOrPrQrrro)r8rz>Classifier %s is not computing class_weight=balanced properly.rN) rrrurVrJrcoef_rlrr>r r4rA) r~ ClassifierrrrZ coef_balancedr5rrZ coef_manualrrr-check_class_weight_balanced_linear_classifier. s0(        rwc Cstddd\}}t||td}t|}t||}t||}t|}||||}| D]8\}} ||} t | t | ksdt d||| | fqddS)NrrZr[rzTEstimator %s should not change or mutate the parameter %s from %s to %s during fit.) rrr&rrrJrrrrjoblibhashrk) r~rrrr}rXZoriginal_params new_params param_nameoriginal_value new_valuerrrrtW s    rtcCsz t|}Wn$tk r0td|dYnXtt|jdrFdStt|j}trtdD]}||kr\||q\dddd t|jDD}t t |t |t |}t d d|D}|rt d |t |fdS) zCheck setting during init.rz9 should store all parameters as an attribute during init.rN)rcSsg|]}|D]}|q qSrr)rZ params_parentrrrrr sz3check_no_attributes_set_in_init..css|]}t|VqdSr$)r@)rparentrrrr' sz2check_no_attributes_set_in_init..cSsg|]}|ds|qS)r)r)rrrrrr s zaEstimator %s should not set any attribute apart from parameters during init. 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It is recommended to delay parameter validation until fit.z9Estimator's parameters changed after set_params raised {})rrrVrrrkrrrrr,rrarbrrr)r~rr}Z orig_paramsrZ curr_paramsrrZ test_valuesZ test_paramsr{ default_valuerrZe_typeZchange_warning_msgZparams_before_exceptionrrrr sX        " "rc CsTt\}}t||}t|}d}t|ddsPtt|d|||W5QRXdS)NzUnknown label type: rUrr)r;rrr>rIrr)r~rrrrrrrrr4s   rc sddg}tddd|ddd\}}t||d dd \}}}}t|} t| d r t| d r | ||| |ddd fjdd} | |jddt| t} zt | WnTt k rt fddt D} t | } t| t t| YnXdS)N)r r )rrrCrrrIT)r5r r6rrr;皙?rmr{r}r8r)Zdecimalscsg|]}|kqSr)ra)rgroupbZ rank_probarrrisz4check_decision_proba_consistency..)rr(rrurr}rr{r rCrkrrr r6rDrr)r~rrrrrr*rLrUr}aZ rank_scoreZgrouped_y_scoreZ sorted_idxrrrrBs:   rc Csd}t|dd\}}t|dd}|j\}}t|}t|||}|j|fksTt|jjdksdtt t |t ddgt |d r|||}t ||t |d rd } t| |} |j| d ||}t |dk} | | krt |d r||} t| | | dS)Nr,rr rr rrr8rrrr?r{)rr;rrrJrrkr`rQrDrr rrurrr>rVr4r{r=) r~rr5rrr6r}rZy_pred_2r:rr9rrrrros2           rc CsPtddgddgg}tddg}t|}tt|||W5QRXdS)Nrtr8r )rrrrIrrrrrrrs  rc sddddg}tjd}t|tdkrDjddd }|jd |d fd }t |}t |rz|j|d }n|j dd |d }t |}t td|d|\}}t|||\} } t||||\} | | fdd|D} t| | |D]} t| r t| }t|jtjrPd t|jj}nd ttjj}t| | |t|dt|dd| dq dS)Nrrr{r}r warm_startFrrCr locrrrrrmcs&i|]}t|r|t|qSr)rur)rrr*r}rrrvs z(check_fit_idempotent..rrz&Idempotency check failed for method {})rzZrtolr)rrrrrJrrrVrlrrrrnextr'splitr)rrurZ issubdtyper`Zfloatingrrr]rBmaxr)r~rrrr5rrtraintestrrLrUrrZ new_resultZtolrrrrsF         rc Cstjd}t|}t|d|kr4|jddd}|jd|dfd}t||}t |rj|j|d}n|j d d|d }t ||}t | d dszt|t|jjd Wntk rYnX|||z t|Wn.tk r}ztd |W5d}~XYnXdS)Nr4rFrrCr rrrrrz) passes check_is_fitted before being fit!zFEstimator fails to pass `check_is_fitted` even though it has been fit.)rrrrrJrrVrlrrrrr>getr7rkrarbrr)r~rrr}r5rrrrrrrs:        rcCstjd}t|}t|d|kr4|jddd}|jd|dfd}t||}t |rj|j|d}n|j dd|d }t ||}t |d rt |||t |d st |j|jd kst dS) NrrFrrCr rrrn_features_in_r8)rrrrrJrrVrlrrrrrurkrrr)r~rrr}r5rrrrrrs       rc stjd}t|}t|d}|jd|dfd}t||}d}z||dWn<tk rzt fdd|Ds|W5dXYnXdS)NrrCr r)z1requires y to be passed, but the target y is Nonez.) rrrrrJrlrrrr+)r~rrr}r5rZexpected_err_msgsrrrrs  rc Cst|}d|dkpd|dk}|r,|dr0dStjd}t|}t|d|krd|jddd }|j|d fd }t ||}t |r|j|d }n|j dd |d }t ||}| |||j|jdkstdddddg} |dddgf} d|jdd} | D]V} t|| sqt|| } | dkrrrrrrJrrVrlrrrrrrrrkrurrrIrrrr )r~rris_supported_X_typesrr}r5rrrX_badrrcallable_methodrrr!check_n_features_in_after_fitting9sX              rcCs`t|}t|dsdSt|}tt}|||ks\t|d|||dS)N _get_tagsz@._get_tags() is missing entries for the following default tags: )rrurrrr= intersectionrk)r~rr}Z tags_keysZdefault_tags_keysrrrry~s  ryc Csz ddl}Wntk r(tdYnXt|}d|dkpHd|dk}|rV|drZdStjd}t|}t||j dd}t ||}|j \}} t d d t | D} |j|| d d } t|r|j |d} n|jdd |d} t|| } t$tjddtdd|| | W5QRXt|ds4tdt|jtjsHt|jjtksZtt|j| |j } | !drd| ks| "dsd|j#krtd|dg}dD]D}t||sƐqt$||}|dkrt%|| d}|&||fq|D]8\}}t tjddtdd|| W5QRXq| ddddfdd t | Dd f| dd!d"t'| d!dd#fg}d$d%|()D}t*d&d'|+D}|D]\}}|j| |d d }t,-d(|}|D]2\}}t.t||d)d*||W5QRXqt|d+r|r&qt|}t/|rTt0| }|j1| | |d,n |1| | t.t|d-|1|| W5QRXqdS).NrHpandas is not installed: not checking column name consistency for pandasrrrrU)rr1rcSsg|]}d|qS)Zcol_rrrrrrrsz.Fcolumnsrlr rerrorz#X does not have valid feature namesZsklearn)messagermodulefeature_names_in_zTEstimator does not have a feature_names_in_ attribute after fitting with a dataframerrrrz2 does not document its feature_names_in_ attribute)rrr{r}rrr|rrrz.css|]}|dkVqdS)TNr)rrrrrr'sz;check_dataframe_column_names_consistency..zBThe feature names should match those that were passed during fit. z did not raiserrr r)2rrr?r>rrrrrJrlrrrrrrrrrr"filterwarnings UserWarningrrurrrrtrkr`rrDrrrrcrrr2rWrrr+rrescaperIrr r)r~rrrrrr}ZX_origr5r6namesrrrrrrr invalid_namesrXZearly_stopping_enabledZ invalid_nameZadditional_messagerZ expected_msgr rrr(check_dataframe_column_names_consistencys                         rc Cs|}d|dks|dr dStddddgdddggdddd \}}t|}t|}t||}|jd}t||}|tkrt j t |t |f}|ddddfd9<|j||d }d d t |D} t td d|| dddW5QRX|| } | dk stt| t js,t| jtks.z'input_features should have length equalrcss|]}t|tVqdSr$)rr)rr~rrrr'@sz:check_transformer_get_feature_names_out.. Expected  feature names, got )rrr+r9rrrrJrprrr[rrIrrXrkrrtr`rrrr) r~rrrrrr6r X_transformZinput_featuresZfeature_names_outn_features_outrrr'check_transformer_get_feature_names_outsF        rc Csz ddl}Wntk r(tdYnX|}d|dksF|drJdStddddgdddggddd d \}}t|}t|}t||}|j d}t ||}|t krt j t |t |f}|ddddfd9<d d t|D} |j|| d d} |j| |d} dd t|D} ttdd|| W5QRX|} || }t| |t| tr|| dj d}n | j d}t| |kstd|dt| dS)NrrrrrUrr8r rrcSsg|]}d|qSrorrrrrrlszBcheck_transformer_get_feature_names_out_pandas..FrrcSsg|]}d|qS)badrrrrrrqsz0input_features is not equal to feature_names_in_rrr)rrr?rrr+r9rrrrJrprrr[rrrIrrXrDrrrrk)r~rrrrrrr6rfeature_names_indfrZinvalid_feature_namesZfeature_names_out_defaultZfeature_names_in_explicit_namesrrrr.check_transformer_get_feature_names_out_pandasLsV          rc Cstjd}|jdd}|jdddd}t||}|jdd}|r|j}t |t |}t |t |}d|d |d |} ||kst | t d d i} d dddg} |D]} |j| } | dkrqt dd| Dr t dd| Dr t d| d|dd| d|d}|d| d} t|}|jf| | i| D]l}t||s^qJtt|| dBt ddt|dd Drt|||nt||||W5QRXqJd!d"| D} | D]}z t|}Wntk rYqYnX|jf| |i| D]l}t||s q tt|| dBt d#dt|dd Dr\t|||nt||||W5QRXq qqdS)$Nrrrr r3Frz>Mismatch between _parameter_constraints and the parameters of z%. Consider the unexpected parameters z% and expected but missing parameters ZBadTyperrrr9rrUcss"|]}t|to|jtkVqdSr$)rr<r%rrrHrrrr'sz)check_param_validation..css"|]}t|to|jtkVqdSr$)rr<r%rrrrrr'szThe constraint for parameter z of zc can't have a mix of intervals of Integral and Real types. 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