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When `obj` is an estimator, this returns the pprint version of the estimator (with `print_changed_only=True`). When `obj` is a function, the name of the function is returned with its keyword arguments. `_get_check_estimator_ids` is designed to be used as the `id` in `pytest.mark.parametrize` where `check_estimator(..., generate_only=True)` is yielding estimators and checks. Parameters ---------- obj : estimator or function Items generated by `check_estimator`. Returns ------- id : str or None See Also -------- check_estimator ,cSsg|] \}}d||qS)z{}={})r).0kvrnrnro vz,_get_check_estimator_ids..z{}({})rT)print_changed_onlyz\sN)callablerrrSkeywordsfuncjoinitemsrrfr resubstr)objkwstringrnrnro_get_check_estimator_idsWs   $rcCst|dg}t|r|dgdgfvrAt|tr|t}|St|tr*|t}|St|tr8|tdd}|S|t dd}|S|dgfvrrt|tr_|d td d fd tdd fgd }|S|d t d dfd t ddfgd }|Sd|j d|}t |t t||}|S)z)Construct Estimator instance if possible._required_parametersrkbase_estimatorr random_stater)C estimatorsest1皙?alphaest2)rzCan't instantiate estimator z parameters )getattrlen issubclassrrr&rr,r rrSrrr0r) Estimatorrequired_parametersrkmsgrnrnro_construct_instance}sD          rcCs4t||\}}|s ||fS|j|||jj|ddS)N)reason)marks)_should_be_skipped_or_markedparammarkxfail)rkrpytestshould_be_markedrrnrnro_maybe_mark_xfailsrcsJt|\}|s |St|tr|jjn|jt|fdd}|S)Ncstddjjd)Nz Skipping z for z: )rrRrS)argskwargs check_namerkrrnrowrappedsz_maybe_skip..wrapped)rrrrrSr)rkrshould_be_skippedr"rnr ro _maybe_skipsr$cCs@t|tr |jjn|j}t|ddpi}||vrd||fSdS)N _xfail_checksrT)Fz*placeholder reason that will never be used)rrrrSr;)rkrr! xfail_checksrnrnrors  rcsHddltddDrd}t|fdd}jjd|td S) aPytest specific decorator for parametrizing estimator checks. The `id` of each check is set to be a pprint version of the estimator and the name of the check with its keyword arguments. This allows to use `pytest -k` to specify which tests to run:: pytest test_check_estimators.py -k check_estimators_fit_returns_self Parameters ---------- estimators : list of estimators instances Estimators to generated checks for. .. versionchanged:: 0.24 Passing a class was deprecated in version 0.23, and support for classes was removed in 0.24. Pass an instance instead. .. versionadded:: 0.24 Returns ------- decorator : `pytest.mark.parametrize` See Also -------- check_estimator : Check if estimator adheres to scikit-learn conventions. Examples -------- >>> from sklearn.utils.estimator_checks import parametrize_with_checks >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.tree import DecisionTreeRegressor >>> @parametrize_with_checks([LogisticRegression(), ... DecisionTreeRegressor()]) ... def test_sklearn_compatible_estimator(estimator, check): ... check(estimator) rNcs|]}t|tVqdSN)rtype)restrnrnro z*parametrize_with_checks..uPassing a class was deprecated in version 0.23 and isn't supported anymore from 0.24.Please pass an instance instead.c3s@D]}t|j}t|D]}t||}t||VqqdSr()r)rSrrr)rkrlrrrrnrochecks_generator s   z1parametrize_with_checks..checks_generatorzestimator, check)ids)rany TypeErrorr parametrizer)rrr/rnr.roparametrize_with_checkss(r4F deprecatedc sdur|dkrd}t||dkrd}t|t|ttr'd}t|tjfdd}|r8|S|D]$\}z|Wq;ty_}ztt |t WYd}~q;d}~wwdS)aCheck if estimator adheres to scikit-learn conventions. This function will run an extensive test-suite for input validation, shapes, etc, making sure that the estimator complies with `scikit-learn` conventions as detailed in :ref:`rolling_your_own_estimator`. Additional tests for classifiers, regressors, clustering or transformers will be run if the Estimator class inherits from the corresponding mixin from sklearn.base. Setting `generate_only=True` returns a generator that yields (estimator, check) tuples where the check can be called independently from each other, i.e. `check(estimator)`. This allows all checks to be run independently and report the checks that are failing. scikit-learn provides a pytest specific decorator, :func:`~sklearn.utils.parametrize_with_checks`, making it easier to test multiple estimators. Parameters ---------- estimator : estimator object Estimator instance to check. .. versionadded:: 1.1 Passing a class was deprecated in version 0.23, and support for classes was removed in 0.24. generate_only : bool, default=False When `False`, checks are evaluated when `check_estimator` is called. When `True`, `check_estimator` returns a generator that yields (estimator, check) tuples. The check is run by calling `check(estimator)`. .. versionadded:: 0.22 Estimator : estimator object Estimator instance to check. .. deprecated:: 1.1 ``Estimator`` was deprecated in favor of ``estimator`` in version 1.1 and will be removed in version 1.3. Returns ------- checks_generator : generator Generator that yields (estimator, check) tuples. Returned when `generate_only=True`. See Also -------- parametrize_with_checks : Pytest specific decorator for parametrizing estimator checks. Nr5zBEither estimator or Estimator should be passed to check_estimator.ze'Estimator' was deprecated in favor of 'estimator' in version 1.1 and will be removed in version 1.3.r-c3s.tD]}t|}t|fVqdSr()rr$r)rrkrlrnror/es   z)check_estimator..checks_generator) rrr FutureWarningrr)r2rSrrr0)rk generate_onlyrrr/r exceptionrnr6rocheck_estimators07    r:cCs8tdurtddddddd\}}t|}||fatS)Nrr@*) n_samples n_features n_informativebiasnoiser)REGRESSION_DATASETrCr> fit_transform)rrrnrnro_regression_datasetvs rFcCsr|}|jj}d|vr|dkr|jddd|vr^|jdur)|jtd|jd|dvr3|jdd|d kr=|jd d|jjd krJ|jdd d |dvrT|jdd|dkr^|jddd|vrh|jddd|vrv|jtd|jdd|vr|jddd|vr|jddd|vr|jdd|dkr|jdd|d krd!|_|d"kr|jdd#t|d$rt|j d|_ t|d%rd!|_ |d&kr|jd'd(|d)krd|_ t |t r|jdd*t |tr|jd!d+|d,vr|jdd-|d.kr|jd/d0d1d2g}||vrt|d3r|jd4d5t|d6r|jd4d7|d8kr*|jd9d:|tvr7|jd!d*dSdS);Nn_iterTSNErrGmax_iterrJ) LinearSVR LinearSVCr=NMFi MiniBatchNMFT)rJfresh_restarts) MLPClassifier MLPRegressordMiniBatchDictionaryLearning n_resampling)rU n_estimators)rV max_trialsr)rWn_initr )rX batch_size)rY MeanShift?) bandwidth TruncatedSVDr LassoLarsIC)noise_variance n_clustersn_best SelectFdr?r TheilSenRegressor) n_components)r)HistGradientBoostingClassifierHistGradientBoostingRegressor)min_samples_leafDummyClassifier stratified)strategyRidgeCVRidgeClassifierCVcvrnn_splits)rq OneHotEncoderignore)handle_unknown)rrRrS set_paramsrJminrVrerfr`ramax_subpopulationrr*r+ra)rkparamsrlloo_cvrnrnro_set_checking_parameterssv                            rzc@s*eZdZdZddZd ddZddZdS) _NotAnArrayzvAn object that is convertible to an array. 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Estimators are only allowed to add private attributes either started with _ or ended with _ but %s added, cs g|] }||ur|qSrnrnrdict_after_fitrrnrors zEstimator changes public attribute(s) during the fit method. Estimators are only allowed to change attributes started or ended with _, but %s changed)rfrr!rrrrrrrrrer`rrrrrr) rlrrkrrrpublic_keys_after_fitattrs_added_by_fitattrs_changed_by_fitrnrrorsF           rcCstjd}d|jdd}t||}|dddft}t|}t||}t |dr/d|_ t |dr7d|_ t |d| ||dD]}t ||rWttd t|||dqDdS) Nrrorrrerr`rzReshape your data)rrrrrrrr!rrfrer`rrrrr)rlrrrrrkrrnrnrors&        rcs~|}|jdfdd|D}t|tkr&|d}ttdd|}t|r5|j}dd|D}t |t |fS)Nrcsg|] }|dqS)r)r)rbatchrr@rnrorrz%_apply_on_subsets..rcSs|dS)Nrrn)xrnrnrosz#_apply_on_subsets..cSg|]}|jqSrn)A)rrrnrnror) rr)tuplelistmaprissparserrravel)rr result_fullresult_by_batchrnrro_apply_on_subsetss   rc Cstjd}d|jdd}t||}|dddft}t|}t||}t |dr/d|_ t |dr7d|_ t |d| ||dD] }d j||d }t ||rdtt|||\}} t|| d |d qDdS) Nrrorrrerr`rrr score_samplesrz={method} of {name} is not invariant when applied to a subset.rrlHz>atolr)rrrrrrrr!rrfrer`rrrrrr) rlrrrrrkrrrrrnrnror$s.         rc Cstjd}d|jdd}t||}|dddftj}t|ddr+d||dk<t|}t ||}t |d rr)rrrrrrrr;r!rrfrer`rr permutationrrrr) rlrrrrrkidxrrrnrnrorJs8          rcCstjd}d|jdd}t||}|dddft}t|}t||}t |dr/d|_ t |dr7d|_ t |d|dkrF|j dd gd }tt|d d |||WddS1scwYdS) Nrro)rrrrerr`OPTICS) min_samples)z1 samplez n_samples = 1z n_samples=1z one samplez1 classz one classTr)rrrrrrrr!rrfrer`rrurrrrlrrrrrkmsgsrnrnrorvs"        "rcCstjd}d|jdd}t||}|dddft}t|}t||}t |dr/d|_ t |dr7d|_ |dkr>d|_ |d krEd |_ t||}t|dgd }tt|d d |||WddS1slwYdS)Nrro)rrrrerr`RandomizedLogisticRegressionrrc)z1 feature\(s\)zn_features = 1z n_features=1Tr)rrrrrrrr!rrfrer`sample_fractionresidual_thresholdrrrrrrnrnrors(       "rcCstjd}d|jdd}|t}t|}t||}t|dr$d|_ t|dr,d|_ t |dt t |||WddS1sGwYdS)Nrror=rrerr`)rrrrrrr!rrfrer`rrrr)rlrrrrrkrnrnrors       "rcCsjtdgdgdgdddd\}}t|}||8}t||}|r,t||g\}}t||||dSNrrrrrrrrr r r?centersrr@ cluster_std)rAr>rErvrr_check_transformer)rlrrNrrrnrnrors   rcCstdgdgdgdddd\}}t|}||d8}t||}t|}tt|}t||||t||| | dSr) rAr>rErvrr{rr|rtolist)rlrrrthis_Xthis_yrnrnrors  rcCsZt\}}t|}tttfd|dd||WddS1s&wYdS)NzThe unfitted transformer z\ does not raise an error when transform is called. Perhaps use check_is_fitted in transform.r)rFr!rAttributeErrorrr)rlrrrrnrnrors  "rcCsrt|j\}}t|}t||tvr8tjt|t|f}|ddddfd9<t|tr7t|}n|}| ||t|}|j ||d} t| t r_| D] } | jd|ks]JqRn | jd|kshJt |dr/|tvr| ||} |j ||d} n | |} |j ||d} t|ddr|d} t| t| t rt| t rt| | | D]\} }}t| |d d |d t| |d d |d qn$t| | d |d d t| | d d |d t| |ksJt| |ksJt |dr1t|dds3|jdkr5|jddkr7ttd|dd| |ddddfWddS1s(wYdSdSdSdSdSdS)Nr rrrrrr is non deterministic{Gz?z9fit_transform and transform outcomes not consistent in %srz7consecutive fit_transform outcomes not consistent in %s)rr rrzThe transformer zr does not raise an error when the number of features in transform is different from the number of features in fit.rr)rr|rr!rrac_rr{rrErrfrr;rziprr=ndimrr)rltransformer_origrrr?r@ry_transformer_cloneX_predx_predrX_pred3rx_pred2x_pred3rnrnrors          $.rc Cst|ddr|d}t|tdgdgdgddd d \}}||8}t||td }t|}t||}t|t |}| ||| ||d d g}|D] }t ||d} | durot ||} | ||} | ||} t | | qOdS)Nrrr rrrrr r rrscorerE) r;rrArvrr4r!rrr-rrr) rlrrrrrkpipelinefuncs func_namer func_pipelineresult result_pipernrnrorbbs8          rbc Cstjd}d}|j|dfd}t||}t|d}t|}t||}t|gd}|D];}t ||d} | durj| ||ddt | j D} | ddkrX| d d} | d d vsjJd |t |j| fq/dS) Nrrror)rr. partial_fitrrEcSrrn)rlrprnrnrorrz+check_fit_score_takes_y..r~r)rYzJExpected y or Y as second argument for method %s of %s. Got arguments: %r.)rrrrrrr!rrrr parametersvaluesr)rS) rlrrr?rrrkr0r1rrrnrnrorVs.       rVc Cstjd}d|jddtj}t||}|tj}|tj}|tj }|dddf}t ||}gd}||||fD]"} t |} t | d| | ||D]} t| | rat| | | qSq@dS)Nrro)r=rrrr)rrrrrrrrrrint32rr!rrrfr) rlrr X_train_32 X_train_64X_train_int_64X_train_int_32rmethodsX_trainrkrrnrnrorUs&         rUcCstdgdgdgddd\}}t|}||8}t||}t|ddD]/}||}t|}t||||}t |t rE|d}|j |ksVJd |j d |j q'dS) Nrrrrr )r?rrrrrzEstimator transform dtype: z - original/expected dtype: ) rAr>rErvrr;rr!rrrrrS)rlr%rrrX_castrX_transrnrnrors,        rcCst|}t|dtddd}d|d}tt|d||gWdn1s/wYtddd}t|t gd}d }tt|d |||WddS1sawYdS) NrrroThe estimator zc does not raise a ValueError when an empty data is used to train. Perhaps use check_array in train.rr) rrrrrrrrrrrrzG0 feature\(s\) \(shape=\(\d*, 0\)\) while a minimum of \d* is required.r) r!rremptyrrrrrr)rlreX_zero_samplesrX_zero_featuresrrrnrnror`s  "r`c Cstjd}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| | |Wdn1svwY| ||t| drt td dg|d | | Wdn1swYt| drt td dg| d | | Wdn1swYWdn1swYqKdS)Nrrrorrrrrz& 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.rrrNaNrrr)rrrrrrrrJrrr7r!rrrrrfrr) rlrrX_train_finite X_train_nan X_train_infrerror_string_fiterror_string_predicterror_string_transformrArkrnrnrorcsV                    rccCs^tddd\}}t|}ttd|dd|||WddS1s(wYdS)z8Test that error is thrown when non-square data provided.r=r)r?r@zThe pairwise estimator z+ does not raise an error on non-square datarN)rAr!rrrrlrrrrkrnrnrord!s "rdcCs@gd}tdgdgdgdddd\}}||8}t||td }t|}|d rAtjd }|j|j d d d}tj | d|<t |}t ||}t||||t|} |j} | drpd| vsp| dspd| vspJt| } t} |D]} t|| rt|| || | <qz| D]} t| | |}t| | |qdS)z'Test that we can pickle all estimators.rrrrrr r rr-rPr>rF)replacerrrrsversionN)rArvrr4r;rrrchoicerrrr!rrrpickledumpsrrrloadsdictrfrr)rlr check_methodsrrrmrmaskrkpickled_estimatorrunpickled_estimatorr3runpickled_resultrnrnrori1sD         ricCst|dsdSt|}tddd\}}||8}t||}zt|r1t|}|j|||dn|||Wn t yBYdSwt t d|dd||dddd f|WddS1sgwYdS) Nr52rr?rclassesrDzZ does not raise an error when the number of features changes between calls to partial_fit.rr) rfr!rArvrr#runiquer5NotImplementedErrorrr)rlrrkrrr`rnrnror|fs*       "r|cCsd\}}}t|}t|}td|||d\}}|||||}|j||fks4Jd||f|j|jjdksrror>)rr?n_labels n_classesSThe shape of the prediction for multioutput data is incorrect. Expected {}, got {}.irzaThe 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 {}.raxis)r;r!rBrrrrrrKrfrrrndarrayrrclasses_rrrrangeargmaxroundr)rlrkr?rcrdrmrry_preddecisiondec_preddec_expy_probrfy_proba y_decisionrnrnror}sl           $     r}cCst|}d}}t|s|d}tdd||d\}}t||}|||||}|jtdks:Jd|j|j |j ksDJddS) Nrrr>r)r n_targetsr?r@rzbMultioutput predictions by a regressor are expected to be floating-point precision. Got {} insteadre) r!rrCrrrrrrr)rlrkr?r@rrrornrnrors"   rcCst|}tddd\}}t||dd\}}t|}tjd}t||j dddd g}|r:t |||g\}}}|j \}} t |d rJ|j dd t||d kr^|j d d|j dd|||||j} | j |fksuJt| |dks~Jt|ddrdSt|tjdd ||} Wdn1swYt| | | jtdtdfvsJ| jtdtdfvsJ||} t| } t| t| d| dd| ddvsJt |d rt|d }|d| dksJdSdS)Nr]rr^rro)rr rr`)r`AffinityPropagationi) preferencerSrKg?rrTrecordr;rrr)rr)r!rAr9r>rErrr concatenaterrrrfrurrrlabels_r(r;rcatch_warningsrrrrarr)rlclusterer_origrNrrrrX_noiser?r@rpred2labels labels_sortedr`rnrnrorsP               rcCshtddd\}}t|}t|t|dr2|||}|jdd|||}t||dSdS)z2Check that predict is invariant of compute_labels.r=rr^compute_labelsF)rN)rAr!rrfrrrur)rlrrrrrrrnrnrors  rc Csd}d}tjd}|jdd}|jdd}td}ttdAt|}tt dd |d } | ||Wdn1s>wY| j rO WddSt | |||d WddS1sdwYdS) Nz6Classifier can't train when only one class is present.z8Classifier can't predict when only one class is present.rrIrrrclassTrr)rrrrrJrr7r!rrrraised_and_matchedrr) rlclassifier_origrNrOrrAX_testrrcmrnrnrorz%s&      "rzcs&tddtDfdd|DS)Ncss4|]}|ddkr|dddkrdVqdS) internal_apiopenblas architectureprescottTN)getlower)rinfornrnror+?s z-_create_memmap_backed_data..csg|]}t|dqS))alignedr)rrhas_prescott_openblasrnrorFs z._create_memmap_backed_data..)r1r7) numpy_arraysrnrro_create_memmap_backed_data<s  rrc Cstddd\}}||}t||dd\}}t|}||dk}||dk}|dvr8||8}||8}|rFt||||g\}}}}||fg}t|} | dsZ|||f|D]\} } t | } t | } | j \}}t |}t| |} t|| } t|| d sttd |d d || | dd Wdn1swY|| | || | t|dsJ|| }|j |fksJ| dst| |dksJd}d}| d s.| dr tt||dd || d dWdn 1swYn!tt||dd || jWdn 1s)wYt|drz|| }| dkrg| dsN|j |fksMJn |j |dfksXJ|dkt}t||n|j || fksqJtt j|dd|| d s| drtt||dd || d dWdn 1swYn!tt||dd || jWdn 1swYWn t yYnwt|drt|!| }|j || fksJtt j|dd|t"t j#|ddt $|| d sS| dr2tt||dd |!| d dWdn 1s,wYn!tt||dd |!| jWdn 1sNwYt|drt|%| }t&|t '|dddtt (|t (|q\dS)N,rr^rwrr ) BernoulliNB MultinomialNB ComplementNB CategoricalNBrrOThe 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.rrrkrgg(\?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.rIrrrrvrhrpredict_log_probar )r ))rArr9r>rErvrr;appendrrarrr!rrrrrrrrfrr'rrTrrrrrmrbrrsumrJrrlogargsort)rlrrNrtX_my_my_bX_bproblemsrmrrr`rdr?r@rro msg_pairwiserrprqrs y_log_probrnrnror~Ls                                   r~cCsV||kr |}|d}n|}|d}t|}d}tt|||dks)J|dS)NrzThe 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)rsortrra) num_outliersexpected_outliersrpstartendsorted_decisionrrnrnrocheck_outlier_corruptions  &rTc Csd}t|dd\}}t|dd}|rt|}|j\}}t|}t|||||||}|j|fks.predict does not output the same dtype than the targets. Got ) r!rrBr?rrrrrrjr)rr)rlrrr? test_size n_outputsrrrArry_testresponse_method_namepredict_methodrornrnrore sL     rc Cst|}t|d\}}}t|d|ddddd\}}t|}|d| || d}} |d| } ||| d } t|| d} | durPt|d | d | | } t| trt | |ksoJd |d t | d|d| D]=}|j |dfksJd |d|j d|dfd|j j dksJd |d|j dd |d}t |jddd|dqqdSt| tjr| j ||fksJd |d| j d||fd| j j dksJd |d| j dd |d}td| |dt| d|ddStdt| d|d)zkCheck the output of the `predict_proba` method for classifiers supporting multilabel-indicator targets.rr ror]TrrNrrrzWhen zn.predict_proba returns a list, the list should be of length n_outputs and contain NumPy arrays. Got length of rrzx.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 rz.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).rrhrzX.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.)r!rrBr?rrrrrrrrrKrrrrjrrr))rlrrr?rrrrrArrrpredict_proba_methodrorrrnrnror s         rc Cs t|}t|d\}}}t|d|ddddd\}}t|}|d| || d}} |d| } ||| d } t|| d} | durPt|d | d | | } t| tj sfJ|d t | d | j ||fks}J|d| j d||fd| j j dksJ|d| j d dS)zoCheck the output of the `decision_function` method for classifiers supporting multilabel-indicator targets.rr ror]TrrNrrrz<.decision_function is expected to output a NumPy array. Got rz].decision_function is expected to provide a NumPy array of shape (n_samples, n_outputs). Got rrrz?.decision_function is expected to output a floating dtype. Got )r!rrBr?rrrrrrjr)rrrK)rlrrr?rrrrrArrrdecision_function_methodrornrnror sL     rcCsltddd\}}||8}t||}t|}t||}|r&t||g\}}t|||||us4JdS)z+Check if self is returned when calling fit.rrr?N)rArvrr!rrrr)rlrrNrrrkrnrnror] s   r]c Cs`t\}}t|}dD]"}t||r-ttt|||Wdn1s(wYq dS)z}Check that predict raises an exception in an unfitted estimator. Unfitted estimators should raise a NotFittedError. )rrrrN)rFr!rfrr/r)rlrrrrkrrnrnror& s   rc Cs2t|}tjd}d}t|j|dfd|}t|d}t||}t|}t || ||| |}t |t j dd!} t dtt dt| ||ddtjfWdn1sdwY| |} d d d d | D} |d st| dksJ| d| vsJt|| dS)NrrrorTr{alwaysrsz)expected 1 DataConversionWarning, got: %srcSsg|]}t|qSrnr)rw_xrnrnrorR sz)check_supervised_y_2d..rqzPDataConversionWarning('A column-vector y was passed when a 1d array was expected)r;rrrrrrrr!rrrrr simplefilterr.RuntimeWarningnewaxisrrrr) rlrrmrr?rrrkrow y_pred_2drrnrnror; s4        rc Csvt|}t|}|dkr||k}t||||||}t|dr||}t |tj s4Jt |dkra| dk t}|j|} t| |d|dtt| dtt|fdn/t|dd d krtj|d d  t} |j| } t| |d|dtt| dtt|fd|d krtt|t|t||jd |dtt|dtt|jfddS)Nrrr rzKdecision_function does not match classifier for %r: expected '%s', got '%s'rrdecision_function_shapeovrrrhrz=Unexpected classes_ attribute for %r: expected '%s', got '%s')rrar!meanrrrrfrrrjrrrrrkrrrrrrm) rrrlrr`rrorprqrr decision_yy_exprnrnrocheck_classifiers_predictions_ sZ             rcCs|dvr|S|S)N)LabelPropagationLabelSpreadingSelfTrainingClassifierrn)rlry_namesrnrnro _choose_check_classifiers_labels s rcCs,tdddd\}}t||dd\}}t|}||d8}||dk}||dk}t||}t||}gd}d d g}t||}t||} ||| fg} t|d d s^| |||f| D]\} } } | | d fD]}t || |}t | |||qlq`ddg}t||} t ||| }t ||||dS)Nrrr )r?rrrwrr )onetwothreerrrrOrr) rAr9r>rErvrrtaker;rrrr)rlr X_multiclass y_multiclassX_binaryy_binarylabels_multiclass labels_binaryy_names_multiclassy_names_binaryrrrr y_names_ir&rnrnror{ s4             r{c Cst\}}t|dd|}tjd}|jd|jdd}t||}tjd}t|}t|}t |t ||t vrSt |d||jdt |dg}|j }n|}|||||} |||t||} t| | d|ddS)Nr]rrorr r!r)rFrrrrrrrr!rrarrrrrrrr) rlregressor_origrrrr regressor_1 regressor_2r&pred1rrnrnror s&    $   rc Csnt\}}||}t||}t|}t|}t||}|tvr                 rc Cst|}t|dr|jddt|dr|jddt||||||}|jdd|||||} t|| d d t||d d ksJJdS) NrGrSrIrJrrKbalancedrweighted)average)r!rfrurrrr)) rlrrArrrrrroy_pred_balancedrnrnro'check_class_weight_balanced_classifiersY s         r c Cs tddgddgddgddgddgg}tgd}|}t|dr)|jdd t|d r4|jdd t|d r?|jd dt||jdd|||j}t|}t tt |}|t |dk||t |dk|d}|j|d|||j} t || d|ddS)z4Test class weights with non-contiguous class labels.rgr[)rrrrrrGrrIrJrKrnrorprrrr)rrz>Classifier %s is not computing class_weight=balanced properly.rN) rrrfrurrcoef_rrrrarr) rl Classifierrrr coef_balancedr?rdrx coef_manualrnrnro-check_class_weight_balanced_linear_classifiero s0(         rc Cstddd\}}||8}t||td}t|}t||}t||}t|}| |||}| D]\}} ||} t | t | ksTJd||| | fq8dS)Nrrrr-zTEstimator %s should not change or mutate the parameter %s from %s to %s during fit.) rArvrr4r!rrrrrrjoblibhash) rlrrrrkrxoriginal_params new_params param_nameoriginal_value new_valuernrnrore s$    recCszt|}Wntytd|dwtt|jdr dStt|j}tr7dD] }||vr6||q+dddd t|jDD}t t |t |t |}|r`Jd |t |fdS) zCheck setting during init.rz9 should store all parameters as an attribute during init.rN)rcSsg|] }|D]}|qqSrnrn)r params_parentrrnrnror sz3check_no_attributes_set_in_init..css|]}t|VqdSr(r )rparentrnrnror+ z2check_no_attributes_set_in_init..zaEstimator %s should not set any attribute apart from parameters during init. Found attributes %s.) r!rrfr)rr r remove__mro__setvarssorted)rlrrk init_paramsrparents_init_params invalid_attrrnrnrorT s0     rTc Cstddgddgddgddgddgddgddgddgddgg }tgd}t||}t|}|||||}|t|j sGJ||}t ||t t |}t|j saJ||}t ||dS)Nrrr ) rrrr r r rororo)rrrr!rrrQrrr rrTrVrU)rlrrrr* pred_origrrnrnrorg s2      rgcCstddgddgddgddgddgddgddgddgddgddgddgddgg }t||}tgd}t||}dD] }t|||||q.param_filtercsg|]}|r|qSrnrnr6r1rnroru rz:check_parameters_default_constructible..rz(parameter %s for %s has no default valuez Parameter 'z' of estimator 'z ' is of type z which is not allowed. All init parameters have to be immutable to make cloning possible. Therefore we restrict the set of legal types to css|]}|jVqdSr()rS)rr)rnrnror+ sz9check_parameters_default_constructible..rzEstimator parameter 'zT' is not returned by get_params. If it is deprecated, set its default value to None.z Parameter z> was mutated on init. All parameters must be stored unchanged.))rRrr7rr!reprrurrrr9r:r2rrrdefaultrErlr)rSrrrrrr FunctionTyperMemoryupdatercore numerictypesallTypesrrrrjrr) rlrrkinitr!rx init_param allowed_types param_value failure_textrnr2rorT s|            $rcCstt|ddr|dt|7}t|ddr,|jdkr,t||jdk||jdd}t|ddr8t|dS|S)Nrequires_positive_yrrrrrv)rr)r;absrvrrwhereflatr)rkrrnrnror s "  rcCsxt|ddr ||j}dt|ddvr|dddf}t|ddr(||8}dt|ddvr:||tj}|S)NrIr1darrayrrr categorical)r;dotrrvrrr;)rkrrnrnro_enforce_estimator_tags_x s    rGcCsgd}|t7}||vrdS|dkrt|jdd}nt|}t|drMt}|j|j}}t||}t|dt ||}| ||t |j dksOJdSdS)N)rRidgeClassifierrrrMrr LassoLarsr r rJrr)rar!rurfr@r}targetrrrrralln_iter_)rlrnot_run_check_n_iterrkirisrr&rnrnror s"        rcCst|}t|drg|tvr*gdgdgdgdg}ddgdd gdd gd dgg}ntd gd gdgdddd\}}||d8}t|d||||tvr`|jD]}|dks]JqUdS|jdksiJdSdS)NrJ)r r r[)r[r r )@rOrO)rOr<g@r gɿg?g?rg333333?rrrrr rr)r!rfrarArvrrrL)rlrrkrr&iter_rnrnror s,     rcsBt|}|jdd}|jddtfdd|DsJdS)NFdeepTc3s|] }|vVqdSr()r)ritem deep_paramsrnror++ z.check_get_params_invariance..)r!rrKr)rlrrFshallow_paramsrnrTror# s  "rc Cst|}|jdd}d}|jdi||jdd}t|t|ks*J||D]\}}|||usget_params result does not match what was passed to set_paramszn{0} occurred during set_params of param {1} on {2}. It is recommended to delay parameter validation until fit.z9Estimator's parameters changed after set_params raised {}rn)r!rrurrrrrrr2rrRrSrrrAssertionError)rlrrk orig_paramsr curr_paramsrr test_values test_paramsr default_valuerrFe_typechange_warning_msgparams_before_exceptionrnrnror. s^            rcCs|t\}}|dt|jdd}t|}d}t|dds s z(check_fit_idempotent..r rz&Idempotency check failed for method {})r rtolr)rrrr!rrrrurrr$rrnextr2splitr3rrfr issubdtyperfloatingfinfoepsrrmaxr)rlrrXrr?rrtraintestrArrr3r new_resulttolrnrjror sJ        rc Cstjd}t|}t|d|vr|jddd}|jd|dfd}t||}t |r5|j|d}n|j d d|d }t ||}t | d dsaz t|t|jjd ty`Ynw|||zt|WdSty}ztd |d}~ww)Nr>rfFrgrSr rhrrrrz) passes check_is_fitted before being fit!zFEstimator fails to pass `check_is_fitted` even though it has been fit.)rrrr!rrrurrr$rrr;rr8rXrRrSr/r)rlrrrkr?rrrFrnrnror s@        rcCstjd}t|}t|d|vr|jddd}|jd|dfd}t||}t |r5|j|d}n|j dd|d }t ||}t |d rIJ| ||t |d sVJ|j|jd ks`JdS) NrrfFrgrSr rhrrn_features_in_r)rrrr!rrrurrr$rrrfrrxr)rlrrrkr?rrrnrnror2s      rc stjd}t|}t|d}|jd|dfd}t||}d}z ||dWdStyIzt fdd|Ds>WYddSdww)NrrSr rh)z1requires y to be passed, but the target y is Nonez.) rrrr!rrrrrr1)rlrrrkr?rexpected_err_msgsrnryrorLs  rc Cst|}d|dvpd|dv}|r|drdStjd}t|}t|d|vr2|jddd }|j|d fd }t ||}t ||}t |rQ|j|d }n|j dd |d }t ||}||||j|jdksnJgd} |dddgf} d|jdd} | D]1} t|| sqt|| } | dkrt| |d} tt| d | | Wdn1swYqt|dsdSt|}t|r|j||t|dn||||j|jdksJtt| d|| |WddS1swYdS)NrrrErOrrfFrgrrr rr)rrrrr.z'X has 1 features, but \w+ is expecting z features as inputr.rrr5r_)r;rrrr!rrrurrGrr$rrrrxrrfrrrrr#r5ra)rlrrmis_supported_X_typesrrkr?rrrXX_badrrcallable_methodrnrnro!check_n_features_in_after_fittingfsV              "rcCs`t|}t|ds dSt|}tt}|||ks.J|d|||dS)N _get_tagsz@._get_tags() is missing entries for the following default tags: )r!rfrrrr: intersection)rlrrk tags_keysdefault_tags_keysrnrnrorjs   rjc CsPzddl}Wn tytdwt|}d|dvp!d|dv}|r(|dr*dStjd}t|}t||j dd}|| d 8}t ||}t ||}|j \}} td d t| D} |j|| d } t|rs|j |d} n|jdd |d} t|| } ttjddtdd|| | Wdn1swYt|dstdt|jtjsJ|jjtksJt |j| |j!} | "drd| vs| #dsd|j$vrtd|dg}dD]}t||sqt%||}|dkrt&|| d}|'||fq|D]'\}}ttjddtdd|| Wdn 1s'wYq| ddddfdd t| Dd f| dd!d"t | d!dd#fg}d$d%|()D}t*d&d'|+D}|D]\}}|j| |d }t,-d(|}|D]A\}}t/tjdt.dd)t/t.||d*d+ ||Wdn 1swYWdn 1swYqt|d,r|r͐qkt|}t0|rt1| }|j2| | |d-n|2| | t-tjdt.dd)t/t.|d.|2|| Wdn 1swYWdn 1swYqkdS)/NrHpandas is not installed: not checking column name consistency for pandasrrrErO)r|rrrccSg|]}d|qS)col_rnrrfrnrnrorz.columnsr rerrorz#X does not have valid feature namessklearn)messagermodulefeature_names_in_zTEstimator does not have a feature_names_in_ attribute after fitting with a dataframerrrrz2 does not document its feature_names_in_ attribute)rrrrr.rrr.rrz.css|]}|duVqdS)TNrn)rrrnrnror+,rz;check_dataframe_column_names_consistency..zqThe feature names should match those that were passed during fit. Starting version 1.2, an error will be raised. )rrz did not raiserr5r_r)3rrrr;rrrr!rrrvrGrrrrlrr$rrrrfilterwarnings UserWarningrrfrrrrjrrrrrrrrrrrrr1r:rescaper7rr#rar5)rlrrrmr}rrkX_origr?r@namesrrrrXrrr invalid_namesrxearly_stopping_enabled invalid_nameadditional_messager~ expected_msgr`rnrnro(check_dataframe_column_names_consistencys                                 rc Cs|}d|dvs|drdStdgdgdgddd d \}}t|}||8}t|}t||}t||}|jd }t ||}|t vrdt j t |t |f}|dddd fd9<|j||d }d dt|D} ttdd|| dddWdn1swY|| } | dusJt| t jsJ| jtksJtdd| DsJt|tr|djd } n|jd } t| | ksJd| dt| dS)NrrrOrrrrr r rrrcSr)featurernrrnrnrorqrz;check_transformer_get_feature_names_out..z'input_features should have length equalrcsr'r()rr)rrlrnrnror+{r,z:check_transformer_get_feature_names_out.. 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