o ?HhI@sddlZddlmZddlmZddlZddlmZddl m Z m Z m Z m Z mZmZddlmZmZddlmZddlmZdd lmZdd lmZmZmZdd lmZd1d dZe fddZ!eeddZ"d2ddZ#ddZ$Gddde%Z&Gddde e Z'Gddde e Z(Gddde e Z)Gdd d e e Z*Gd!d"d"ee Z+Gd#d$d$e Z,Gd%d&d&eZ-Gd'd(d(eeZ.Gd)d*d*e e e Z/Gd+d,d,e e e Z0Gd-d.d.e e e Z1Gd/d0d0e ee Z2dS)3N) defaultdict)partial)assert_array_equal) BaseEstimatorClassifierMixinMetaEstimatorMixinRegressorMixinTransformerMixinclone)_Scorermean_squared_error)BaseCrossValidator)GroupsConsumerMixin)SIMPLE_METHODS)MetadataRouter MethodMappingprocess_routing)_check_partial_fit_first_callTcKsbt}|dj}|dj}t|dstdd|_|s%dd|D}|j|||dS) zUtility function to store passed metadata to a method of obj. If record_default is False, kwargs whose values are "default" are skipped. This is so that checks on keyword arguments whose default was not changed are skipped. _recordscSsttSN)rlistrre/home/air/sanwanet/gpt-api/venv/lib/python3.10/site-packages/sklearn/tests/metadata_routing_common.py*sz!record_metadata..cSs(i|]\}}t|tr|dkr||qS)default isinstancestr).0keyvalrrr ,sz#record_metadata..N)inspectstackfunctionhasattrrritemsappend)objrecord_defaultkwargsr%calleecallerrrrrecord_metadatas   r/c Kst|dt|t|t}|D]Y}t|t|ks/Jd|d||D]8\}}||} ||vrN| durNt| | sMJq3t | tj rZt | |q3| |uskJd| d|d|q3qdS)aCheck whether the expected metadata is passed to the object's method. Parameters ---------- obj : estimator object sub-estimator to check routed params for method : str sub-estimator's method where metadata is routed to, or otherwise in the context of metadata routing referred to as 'callee' parent : str the parent method which should have called `method`, or otherwise in the context of metadata routing referred to as 'caller' split_params : tuple, default=empty specifies any parameters which are to be checked as being a subset of the original values **kwargs : dict passed metadata rz Expected z vs Nz . Method: ) getattrdictgetrsetkeysr(npisinallrndarrayr) r*methodparent split_paramsr, all_recordsrecordr!valuerecorded_valuerrrcheck_recorded_metadata4s&     r@F)r+cCst|tr$|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()metadata_requestrAname route_mapping_excluder9mmrpropsrrrrEbs"     rEcs^D]\}}t||}|j|ksJqfddtD}|D] }tt||jr,Jq dS)Ncsg|]}|vr|qSrr)r r9 dictionaryrrrDsz(assert_request_equal..)r(r0rGrlen)requestrOr9rGrL empty_methodsrrNrassert_request_equals rSc@seZdZddZddZdS) _RegistrycC|Srr)selfmemorrr __deepcopy__z_Registry.__deepcopy__cCrUrrrVrrr__copy__rYz_Registry.__copy__N)__name__ __module__ __qualname__rXr[rrrrrTs rTc@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 ||_dSrregistryrVrbrrr__init__ zConsumingRegressor.__init__rcC(|jdur |j|t|||d|SN sample_weightmetadatarbr)record_metadata_not_defaultrVXyrirjrrr partial_fit  zConsumingRegressor.partial_fitcCrfrgrkrmrrrfitrqzConsumingRegressor.fitcCs t|||dtjt|fdS)Nrhshaperlr5zerosrPrmrrrpredictszConsumingRegressor.predictcCt|||ddSNrhrrlrmrrrscorezConsumingRegressor.scorerrrNrr) r\r]r^__doc__rdrprrrwr{rrrrr_s  r_c@sLeZdZdZdddZddZddd Zd d Zd d ZddZ ddZ dS)NonConsumingClassifier5A classifier which accepts no metadata on any method.cCr`r)alpha)rVrrrrrdrezNonConsumingClassifier.__init__cCst||_t||_|Sr)r5uniqueclasses_ ones_likecoef_rVrnrorrrrrs  zNonConsumingClassifier.fitNcCrUrr)rVrnroclassesrrrrprYz"NonConsumingClassifier.partial_fitcC ||Sr)rwrVrnrrrdecision_functionrez(NonConsumingClassifier.decision_functioncCs>tjt|fd}d|dt|d<d|t|dd<|S)Nrsrrr)r5emptyrP)rVrny_predrrrrwszNonConsumingClassifier.predictcCsdtjt|dfd}tddg|dt|dddf<tddg|t|ddddf<|S)Nrrs?r)r5rrPasarray)rVrny_probarrr predict_probas&&z$NonConsumingClassifier.predict_probacCrr)rrrrrpredict_log_probas z(NonConsumingClassifier.predict_log_proba)rr) r\r]r^rrdrrrprrwrrrrrrrs   rc@(eZdZdZddZddZddZdS) NonConsumingRegressorrcCrUrrrrrrrrrYzNonConsumingRegressor.fitcCrUrrrrrrrprYz!NonConsumingRegressor.partial_fitcCstt|Sr)r5onesrPrrrrrwszNonConsumingRegressor.predictN)r\r]r^rrrrprwrrrrrs  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. NrcCs||_||_dSr)rrb)rVrbrrrrrd zConsumingClassifier.__init__rcCs2|jdur |j|t|||dt|||Srg)rbr)rlr)rVrnrorrirjrrrrps   zConsumingClassifier.partial_fitcCs@|jdur |j|t|||dt||_t||_|Srg)rbr)rlr5rrrrrmrrrrrs    zConsumingClassifier.fitcCsNt|||dtjt|fdd}d|t|dd<d|dt|d<|S)Nrhint8)rtdtyperrrrlr5rrPrVrnrirjy_scorerrrrw szConsumingClassifier.predictcCsrt|||dtjt|dfd}tddg|dt|dddf<tddg|t|ddddf<|S)Nrhrrsrr)rlr5rrPr)rVrnrirjrrrrr)s&&z!ConsumingClassifier.predict_probacCs"t|||dtjt|dfdS)NrhrrsrurVrnrirjrrrr2sz%ConsumingClassifier.predict_log_probacCsLt|||dtjt|fd}d|t|dd<d|dt|d<|S)Nrhrsrrrrrrrrr8sz%ConsumingClassifier.decision_functioncCrxryrzrmrrrr{Ar|zConsumingClassifier.score)Nrr~r}) r\r]r^rrdrprrrwrrrr{rrrrrs   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. NcCr`rrarcrrrrdTrezConsumingTransformer.__init__rcCs.|jdur |j|t|||dd|_|S)NrhT)rbr)rlfitted_rmrrrrrWs  zConsumingTransformer.fitcCst|||d|dSryrzrrrr transformazConsumingTransformer.transformcCs,t|||d|j||||dj|||dSrg)rlrrrrmrrr fit_transformgs z"ConsumingTransformer.fit_transformcCst|||d|dSryrzrrrrinverse_transformsrz&ConsumingTransformer.inverse_transformrr~r}NN) r\r]r^rrdrrrrrrrrrrHs   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`.NcCr`rrarcrrrrdrez+ConsumingNoFitTransformTransformer.__init__cCrfrg)rbr)r/rmrrrrrs  z&ConsumingNoFitTransformTransformer.fitcCst|||d|Srg)r/rrrrrsz,ConsumingNoFitTransformTransformer.transformrNNNr)r\r]r^rrdrrrrrrrrzs   rcs*eZdZdfdd ZfddZZS)ConsumingScorerNcstjtdidd||_dS)Nrrw) score_funcsignr,response_method)superrdr rbrc __class__rrrds zConsumingScorer.__init__c sH|jdur |j|t|fi||dd}tj|||||dS)Nriri)rbr)rlr2r_score)rV method_callerclfrnror,rirrrrs   zConsumingScorer._scorer)r\r]r^rdr __classcell__rrrrrsrc@s4eZdZd ddZd ddZd ddZdd d ZdS)ConsumingSplitterNcCr`rrarcrrrrdrezConsumingSplitter.__init__rccsj|jdur |j|t|||dt|d}ttd|}tt|t|}||fV||fVdS)N)groupsrjrr)rbr)rlrPrrange)rVrnrorrj split_index train_indices test_indicesrrrsplits    zConsumingSplitter.splitcCsdS)Nrr)rVrnrorrjrrr get_n_splitsrYzConsumingSplitter.get_n_splitsccs>t|d}ttd|}tt|t|}|V|VdS)Nrr)rPrr)rVrnrorrrrrrr_iter_test_indicess   z$ConsumingSplitter._iter_test_indicesrr~)NNNNr)r\r]r^rdrrrrrrrrs    rc@r) MetaRegressorz(A meta-regressor which is only a router.cCr`r) estimator)rVrrrrrdrezMetaRegressor.__init__cKs6t|dfi|}t|jj||fi|jj|_dSNrr)rr rrr estimator_rVrnro fit_paramsparamsrrrrrs$zMetaRegressor.fitcCs*t|jjdj|jtjdddd}|SNownerrrr.r-rmethod_mapping)rrr\addrrrVrFrrrget_metadata_routings z"MetaRegressor.get_metadata_routingNr\r]r^rrdrrrrrrrrs  rc@s4eZdZdZd ddZd ddZddZd d ZdS) WeightedMetaRegressorz*A meta-regressor which is also a consumer.NcC||_||_dSrrrbrVrrbrrrrdrzWeightedMetaRegressor.__init__cK\|jdur |j|t||dt|dfd|i|}t|jj||fi|jj|_|SNrrrrirbr)r/rr rrrr)rVrnrorirrrrrrr    zWeightedMetaRegressor.fitcKs*t|dfi|}|jj|fi|jjS)Nrw)rrrwr)rVrnpredict_paramsrrrrrwzWeightedMetaRegressor.predictcCs:t|jjd|j|jtjdddjdddd}|S)Nrrrrrwrrrr\add_self_requestrrrrrrrrs  z*WeightedMetaRegressor.get_metadata_routingr)r\r]r^rrdrrrwrrrrrrs    rc@s,eZdZdZd ddZd ddZddZdS) WeightedMetaClassifierzEA meta-estimator which also consumes sample_weight itself in ``fit``.NcCrrrrrrrrdrzWeightedMetaClassifier.__init__cKrrr)rVrnrorir,rrrrrrrzWeightedMetaClassifier.fitcCs0t|jjd|j|jtjdddd}|Srrrrrrrs z+WeightedMetaClassifier.get_metadata_routingrrrrrrrs    rc@s4eZdZdZddZd ddZd ddZd d ZdS) MetaTransformerzA simple meta-transformer.cCr`r) transformer)rVrrrrrdrezMetaTransformer.__init__NcKs6t|dfi|}t|jj||fi|jj|_|Sr)rr rrr transformer_rrrrrrs zMetaTransformer.fitcKs*t|dfi|}|jj|fi|jjS)Nr)rrrr)rVrnrotransform_paramsrrrrrrzMetaTransformer.transformcCs0t|jjdj|jtjdddjddddS)Nrrrrr)rr)rrr\rrrrZrrrrsz$MetaTransformer.get_metadata_routingr)r\r]r^rrdrrrrrrrrr s    r)Tr)3r$ collectionsr functoolsrnumpyr5 numpy.testingr sklearn.baserrrrr r sklearn.metrics._scorerr r sklearn.model_selectionr sklearn.model_selection._splitr sklearn.utils._metadata_requestsrsklearn.utils.metadata_routingrrrsklearn.utils.multiclassrr/tupler@rlrErSrrTr_rrrrrrrrrrrrrrrs<          +   .# T2"