o iN@sddlZddlmZddlmZddlZddlmZddl m Z m Z m Z m Z mZmZddlmZmZddlmZddlmZdd lmZdd lmZmZmZdd lmZd7d dZe fddZ!eeddZ"d8ddZ#ddZ$Gddde%Z&Gddde e Z'Gddde e Z(Gddde e Z)Gdd d e e Z*Gd!d"d"e*Z+Gd#d$d$e*Z,Gd%d&d&e*Z-Gd'd(d(ee Z.Gd)d*d*e Z/Gd+d,d,eZ0Gd-d.d.eeZ1Gd/d0d0e e e Z2Gd1d2d2e e e Z3Gd3d4d4e e e Z4Gd5d6d6e ee Z5dS)9N) defaultdict)partial)assert_array_equal) BaseEstimatorClassifierMixinMetaEstimatorMixinRegressorMixinTransformerMixinclone)_Scorermean_squared_error)BaseCrossValidator)GroupsConsumerMixin)SIMPLE_METHODS)MetadataRouter MethodMappingprocess_routing)_check_partial_fit_first_callTcKsbt}|dj}|dj}t|dstdd|_|s%dd|D}|j|||dS) zUtility function to store passed metadata to a method of obj. If record_default is False, kwargs whose values are "default" are skipped. This is so that checks on keyword arguments whose default was not changed are skipped. _recordscSsttSN)rlistrrt/var/www/html/eduruby.in/lip-sync/lip-sync-env/lib/python3.10/site-packages/sklearn/tests/metadata_routing_common.py*sz!record_metadata..cSs(i|]\}}t|tr|dkr||qS)default isinstancestr).0keyvalrrr ,sz#record_metadata..N)inspectstackfunctionhasattrrritemsappend)objrecord_defaultkwargsr%calleecallerrrrrecord_metadatas   r/c Kst|dt|t|t}|D]Y}t|t|ks/Jd|d||D]8\}}||} ||vrN| durNt| | sMJq3t | tj rZt | |q3| |uskJd| d|d|q3qdS)aCheck whether the expected metadata is passed to the object's method. Parameters ---------- obj : estimator object sub-estimator to check routed params for method : str sub-estimator's method where metadata is routed to, or otherwise in the context of metadata routing referred to as 'callee' parent : str the parent method which should have called `method`, or otherwise in the context of metadata routing referred to as 'caller' split_params : tuple, default=empty specifies any parameters which are to be checked as being a subset of the original values **kwargs : dict passed metadata rz Expected z vs Nz . Method: ) getattrdictgetrsetkeysr(npisinallrZndarrayr) r*methodparentZ split_paramsr,Z all_recordsrecordr!valueZrecorded_valuerrrcheck_recorded_metadata4s"    r<F)r+cCst|tr$|D]\}}|dur||vr||}nd}t|j|dqdS|dur*gn|}tD]}||vr5q.t||}dd|jD}|rHJq.dS)aCheck if a metadata request dict is empty. One can exclude a method or a list of methods from the check using the ``exclude`` parameter. If metadata_request is a MetadataRouter, then ``exclude`` can be of the form ``{"object" : [method, ...]}``. N)excludecSs&g|]\}}t|ts|dur|qSrr)r propaliasrrr wsz+assert_request_is_empty..)rrassert_request_is_emptyrouterrr0requestsr()Zmetadata_requestr=nameZ route_mappingZ_excluder8mmrpropsrrrrAbs"     rAcs^D]\}}t||}|j|ksJqfddtD}|D] }tt||jr,Jq dS)Ncsg|]}|vr|qSrr)r r8 dictionaryrrr@sz(assert_request_equal..)r(r0rCrlen)requestrHr8rCrEZ empty_methodsrrGrassert_request_equals rKc@seZdZddZddZdS) _RegistrycC|Srr)selfmemorrr __deepcopy__z_Registry.__deepcopy__cCrMrrrNrrr__copy__rQz_Registry.__copy__N)__name__ __module__ __qualname__rPrSrrrrrLs rLc@sBeZdZdZdddZdddZddd Zdd d Zdd d ZdS)ConsumingRegressoracA regressor consuming metadata. Parameters ---------- registry : list, default=None If a list, the estimator will append itself to the list in order to have a reference to the estimator later on. Since that reference is not required in all tests, registration can be skipped by leaving this value as None. NcC ||_dSrregistryrNrZrrr__init__ zConsumingRegressor.__init__rcC(|jdur |j|t|||d|SN sample_weightmetadatarZr)record_metadata_not_defaultrNXyrarbrrr partial_fit  zConsumingRegressor.partial_fitcCr^r_rcrerrrfitrizConsumingRegressor.fitcCs t|||dtjt|fdS)Nr`shape)rdr5ZzerosrIrerrrpredictszConsumingRegressor.predictcCt|||ddSNr`rrdrerrrscorezConsumingRegressor.scorerrrNrr) rTrUrV__doc__r\rhrjrmrqrrrrrWs  rWc@sLeZdZdZdddZddZddd Zd d Zd d ZddZ ddZ dS)NonConsumingClassifier5A classifier which accepts no metadata on any method.cCrXr)alpha)rNryrrrr\r]zNonConsumingClassifier.__init__cCst||_t||_|Sr)r5uniqueclasses_ ones_likecoef_rNrfrgrrrrjs  zNonConsumingClassifier.fitNcCrMrr)rNrfrgclassesrrrrhrQz"NonConsumingClassifier.partial_fitcC ||Sr)rmrNrfrrrdecision_functionr]z(NonConsumingClassifier.decision_functioncCs>tjt|fd}d|dt|d<d|t|dd<|S)Nrkrrr)r5emptyrI)rNrfZy_predrrrrmszNonConsumingClassifier.predictcCsJtjt|t|jftjd}tjjtt|jt|d|dd<|S)NrlZdtyperysize)r5rrIr{float32random dirichletones)rNrfy_probarrr predict_probas(z$NonConsumingClassifier.predict_probacCrr)rrrrrpredict_log_probas z(NonConsumingClassifier.predict_log_proba)rxr) rTrUrVrur\rjrhrrmrrrrrrrvs   rvc@(eZdZdZddZddZddZdS) NonConsumingRegressorrwcCrMrrr~rrrrjrQzNonConsumingRegressor.fitcCrMrrr~rrrrhrQz!NonConsumingRegressor.partial_fitcCstt|Sr)r5rrIrrrrrmszNonConsumingRegressor.predictN)rTrUrVrurjrhrmrrrrrs  rc@sbeZdZdZdddZ dddZdd d Zdd d Zdd dZdddZ dddZ dddZ dS)ConsumingClassifieraA classifier consuming metadata. Parameters ---------- registry : list, default=None If a list, the estimator will append itself to the list in order to have a reference to the estimator later on. Since that reference is not required in all tests, registration can be skipped by leaving this value as None. alpha : float, default=0 This parameter is only used to test the ``*SearchCV`` objects, and doesn't do anything. NrxcCs||_||_dSr)ryrZ)rNrZryrrrr\ zConsumingClassifier.__init__rcCs2|jdur |j|t|||dt|||Sr_)rZr)rdr)rNrfrgrrarbrrrrhs   zConsumingClassifier.partial_fitcCs@|jdur |j|t|||dt||_t||_|Sr_)rZr)rdr5rzr{r|r}rerrrrjs    zConsumingClassifier.fitcCsNt|||dtjt|fdd}d|t|dd<d|dt|d<|S)Nr`Zint8rrrrrdr5rrIrNrfrarbZy_scorerrrrm szConsumingClassifier.predictcCsXt|||dtjt|t|jftjd}tjjtt|jt|d|dd<|S)Nr`rr) rdr5rrIr{rrrr)rNrfrarbrrrrr)s (z!ConsumingClassifier.predict_probacCst|||d||Sr_)rdrrNrfrarbrrrr2s z%ConsumingClassifier.predict_log_probacCsLt|||dtjt|fd}d|t|dd<d|dt|d<|S)Nr`rkrrrrrrrrr8sz%ConsumingClassifier.decision_functioncCrnrorprerrrrqArrzConsumingClassifier.score)Nrxrtrs) rTrUrVrur\rhrjrmrrrrqrrrrrs   rc@eZdZdZeddZdS)&ConsumingClassifierWithoutPredictProbazConsumingClassifier without a predict_proba method, but with predict_log_proba. 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Used to mimic dynamic method selection such as in `BaggingClassifier.predict_log_proba()`. cCrrrrRrrrrgrz0ConsumingClassifierWithOnlyPredict.predict_probacCrrrrRrrrrkrz4ConsumingClassifierWithOnlyPredict.predict_log_probaN)rTrUrVrurrrrrrrr`s  rc@sBeZdZdZdddZdddZddd Zdd d Zdd d ZdS)ConsumingTransformera~A transformer which accepts metadata on fit and transform. Parameters ---------- registry : list, default=None If a list, the estimator will append itself to the list in order to have a reference to the estimator later on. Since that reference is not required in all tests, registration can be skipped by leaving this value as None. NcCrXrrYr[rrrr\|r]zConsumingTransformer.__init__rcCs.|jdur |j|t|||dd|_|S)Nr`T)rZr)rdZfitted_rerrrrjs  zConsumingTransformer.fitcCst|||d|dSrorprrrr transformzConsumingTransformer.transformcCs,t|||d|j||||dj|||dSr_)rdrjrrerrr fit_transforms z"ConsumingTransformer.fit_transformcCst|||d|dSrorprrrrinverse_transformrz&ConsumingTransformer.inverse_transformrrtrsNN) rTrUrVrur\rjrrrrrrrrps   rc@s.eZdZdZd ddZd ddZd ddZdS) "ConsumingNoFitTransformTransformerzA metadata consuming transformer that doesn't inherit from TransformerMixin, and thus doesn't implement `fit_transform`. Note that TransformerMixin's `fit_transform` doesn't route metadata to `transform`.NcCrXrrYr[rrrr\r]z+ConsumingNoFitTransformTransformer.__init__cCr^r_)rZr)r/rerrrrjs  z&ConsumingNoFitTransformTransformer.fitcCst|||d|Sr_)r/rrrrrsz,ConsumingNoFitTransformTransformer.transformrNNNr)rTrUrVrur\rjrrrrrrs   rcs*eZdZdfdd ZfddZZS)ConsumingScorerNcstjtdidd||_dS)Nrrm)Z score_funcsignr,Zresponse_method)superr\r rZr[ __class__rrr\s zConsumingScorer.__init__c sH|jdur |j|t|fi||dd}tj|||||dS)Nrara)rZr)rdr2r_score)rNZ method_callerZclfrfrgr,rarrrrs   zConsumingScorer._scorer)rTrUrVr\r __classcell__rrrrrsrc@s4eZdZd ddZd ddZd ddZdd d ZdS)ConsumingSplitterNcCrXrrYr[rrrr\r]zConsumingSplitter.__init__rccsj|jdur |j|t|||dt|d}ttd|}tt|t|}||fV||fVdS)N)groupsrbrr)rZr)rdrIrrange)rNrfrgrrb split_index train_indices test_indicesrrrsplits    zConsumingSplitter.splitcCsdS)Nrr)rNrfrgrrbrrr get_n_splitsrQzConsumingSplitter.get_n_splitsccs>t|d}ttd|}tt|t|}|V|VdS)Nrr)rIrr)rNrfrgrrrrrrr_iter_test_indicess   z$ConsumingSplitter._iter_test_indicesrrt)NNNNr)rTrUrVr\rrrrrrrrs    rc@r) MetaRegressorz(A meta-regressor which is only a router.cCrXr) estimator)rNrrrrr\r]zMetaRegressor.__init__cKs6t|dfi|}t|jj||fi|jj|_dSNrj)rr rrj estimator_rNrfrg fit_paramsparamsrrrrjs$zMetaRegressor.fitcCs*t|jjdj|jtjdddd}|SNownerrjr.r-rmethod_mapping)rrrTaddrrrNrBrrrget_metadata_routings z"MetaRegressor.get_metadata_routingNrTrUrVrur\rjrrrrrrs  rc@s4eZdZdZd ddZd ddZddZd d ZdS) WeightedMetaRegressorz*A meta-regressor which is also a consumer.NcC||_||_dSrrrZrNrrZrrrr\rzWeightedMetaRegressor.__init__cK\|jdur |j|t||dt|dfd|i|}t|jj||fi|jj|_|SNrrjrarZr)r/rr rrjr)rNrfrgrarrrrrrj    zWeightedMetaRegressor.fitcKs*t|dfi|}|jj|fi|jjS)Nrm)rrrmr)rNrfZpredict_paramsrrrrrmzWeightedMetaRegressor.predictcCs:t|jjd|j|jtjdddjdddd}|S)NrrjrrmrrrrTZadd_self_requestrrrrrrrr s  z*WeightedMetaRegressor.get_metadata_routingr)rTrUrVrur\rjrmrrrrrrs    rc@s,eZdZdZd ddZd ddZddZdS) WeightedMetaClassifierzEA meta-estimator which also consumes sample_weight itself in ``fit``.NcCrrrrrrrr\rzWeightedMetaClassifier.__init__cKrrr)rNrfrgrar,rrrrrjrzWeightedMetaClassifier.fitcCs0t|jjd|j|jtjdddd}|Srrrrrrr's z+WeightedMetaClassifier.get_metadata_routingrrrrrrrs    rc@s4eZdZdZddZd ddZd ddZd d ZdS) MetaTransformerzA simple meta-transformer.cCrXr) transformer)rNrrrrr\6r]zMetaTransformer.__init__NcKs6t|dfi|}t|jj||fi|jj|_|Sr)rr rrj transformer_rrrrrj9s zMetaTransformer.fitcKs*t|dfi|}|jj|fi|jjS)Nr)rrrr)rNrfrgZtransform_paramsrrrrr>rzMetaTransformer.transformcCs0t|jjdj|jtjdddjddddS)Nrrjrr)rr)rrrTrrrrRrrrrBsz$MetaTransformer.get_metadata_routingr)rTrUrVrur\rjrrrrrrr3s    r)Tr)6r$ collectionsr functoolsrnumpyr5Z numpy.testingrZ sklearn.baserrrrr r Zsklearn.metrics._scorerr r Zsklearn.model_selectionr Zsklearn.model_selection._splitrZ sklearn.utils._metadata_requestsrZsklearn.utils.metadata_routingrrrZsklearn.utils.multiclassrr/tupler<rdrArKrrLrWrvrrrrrrrrrrrrrrrrrsB          +   .# T  2"