a jK@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$e Z,Gd%d&d&eZ-Gd'd(d(eeZ.Gd)d*d*e e e Z/Gd+d,d,e e e Z0Gd-d.d.e e e Z1Gd/d0d0e ee Z2dS)3N) defaultdict)partial)assert_array_equal) BaseEstimatorClassifierMixinMetaEstimatorMixinRegressorMixinTransformerMixinclone)_Scorermean_squared_error)BaseCrossValidator)GroupsConsumerMixin)SIMPLE_METHODS)MetadataRouter MethodMappingprocess_routing)_check_partial_fit_first_callTcKsbt}|dj}|dj}t|ds4tdd|_|sJdd|D}|j|||dS) zUtility function to store passed metadata to a method of obj. If record_default is False, kwargs whose values are "default" are skipped. This is so that checks on keyword arguments whose default was not changed are skipped. _recordscSsttSN)rlistrrKE:\soccer\ml_env\Lib\site-packages\sklearn/tests/metadata_routing_common.py*z!record_metadata..cSs(i|] \}}t|tr|dkr||qS)default isinstancestr).0keyvalrrr ,sz#record_metadata..N)inspectstackfunctionhasattrrritemsappend)objrecord_defaultkwargsr&calleecallerrrrrecord_metadatas   r0c Kst|dt|t|t}|D]}t|t|ks^Jd|d||D]p\}}||} ||vr| durt| | sJqft | tj rt | |qf| |usfJd| d|d|qfq&dS)aCheck whether the expected metadata is passed to the object's method. Parameters ---------- obj : estimator object sub-estimator to check routed params for method : str sub-estimator's method where metadata is routed to, or otherwise in the context of metadata routing referred to as 'callee' parent : str the parent method which should have called `method`, or otherwise in the context of metadata routing referred to as 'caller' split_params : tuple, default=empty specifies any parameters which are to be checked as being a subset of the original values **kwargs : dict passed metadata rz Expected z vs Nz . Method: ) getattrdictgetrsetkeysr)npisinallrZndarrayr) r+methodparentZ split_paramsr-Z all_recordsrecordr"valueZrecorded_valuerrrcheck_recorded_metadata4s$    r=F)r,cCst|trH|D]4\}}|dur0||vr0||}nd}t|j|dqdS|durTgn|}tD]4}||vrjq\t||}dd|jD}|r\Jq\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_emptyrouterrr1requestsr))Zmetadata_requestr>nameZ route_mappingZ_excluder9mmrpropsrrrrBbs     rBcs^D] \}}t||}|j|ksJqfddtD}|D]}tt||jr@Jq@dS)Ncsg|]}|vr|qSrr)r!r9 dictionaryrrrArz(assert_request_equal..)r)r1rDrlen)requestrIr9rDrFZ empty_methodsrrHrassert_request_equals  rLc@seZdZddZddZdS) _RegistrycCs|Srr)selfmemorrr __deepcopy__sz_Registry.__deepcopy__cCs|SrrrNrrr__copy__sz_Registry.__copy__N)__name__ __module__ __qualname__rPrRrrrrrMsrMc@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. NcCs ||_dSrregistryrNrXrrr__init__szConsumingRegressor.__init__rcCs(|jdur|j|t|||d|SN sample_weightmetadatarXr*record_metadata_not_defaultrNXyr]r^rrr partial_fits   zConsumingRegressor.partial_fitcCs(|jdur|j|t|||d|Sr[r_rarrrfits   zConsumingRegressor.fitcCs t|||dtjt|fdS)Nr\shaper`r6ZzerosrJrarrrpredictszConsumingRegressor.predictcCst|||ddSNr\rr`rarrrscoreszConsumingRegressor.score)N)rr)rr)Nrr)rr) rSrTrU__doc__rZrdrerirlrrrrrVs   rVc@sLeZdZdZdddZddZddd Zd d Zd d ZddZ ddZ dS)NonConsumingClassifier5A classifier which accepts no metadata on any method.cCs ||_dSr)alpha)rNrqrrrrZszNonConsumingClassifier.__init__cCst||_t||_|Sr)r6uniqueclasses_ ones_likecoef_rNrbrcrrrres  zNonConsumingClassifier.fitNcCs|Srr)rNrbrcclassesrrrrdsz"NonConsumingClassifier.partial_fitcCs ||Sr)rirNrbrrrdecision_functionsz(NonConsumingClassifier.decision_functioncCs>tjt|fd}d|dt|d<d|t|dd<|S)Nrfrrr)r6emptyrJ)rNrbZy_predrrrriszNonConsumingClassifier.predictcCsdtjt|dfd}tddg|dt|dddf<tddg|t|ddddf<|S)Nrrf?rp)r6rzrJasarray)rNrby_probarrr predict_probas&&z$NonConsumingClassifier.predict_probacCs ||Sr)r~rxrrrpredict_log_probasz(NonConsumingClassifier.predict_log_proba)rp)N) rSrTrUrmrZrerdryrir~rrrrrrns  rnc@s(eZdZdZddZddZddZdS) NonConsumingRegressorrocCs|SrrrvrrrreszNonConsumingRegressor.fitcCs|Srrrvrrrrdsz!NonConsumingRegressor.partial_fitcCstt|Sr)r6ZonesrJrxrrrriszNonConsumingRegressor.predictN)rSrTrUrmrerdrirrrrrsrc@s`eZdZdZdddZdddZdd 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. 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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. NcCs ||_dSrrWrYrrrrZTszConsumingTransformer.__init__rcCs.|jdur|j|t|||dd|_|S)Nr\T)rXr*r`Zfitted_rarrrreWs  zConsumingTransformer.fitcCst|||d|dSrjrkrrrr transformaszConsumingTransformer.transformcCs,t|||d|j||||dj|||dSr[)r`rerrarrr fit_transformgs z"ConsumingTransformer.fit_transformcCst|||d|dSrjrkrrrrinverse_transformssz&ConsumingTransformer.inverse_transform)N)Nrr)rr)rr)NN) rSrTrUrmrZrerrrrrrrrHs    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`.NcCs ||_dSrrWrYrrrrZsz+ConsumingNoFitTransformTransformer.__init__cCs(|jdur|j|t|||d|Sr[)rXr*r0rarrrres  z&ConsumingNoFitTransformTransformer.fitcCst|||d|Sr[)r0rrrrrsz,ConsumingNoFitTransformTransformer.transform)N)NNN)NN)rSrTrUrmrZrerrrrrrzs  rcs*eZdZdfdd ZfddZZS)ConsumingScorerNcstjtdidd||_dS)Nrri)Z score_funcsignr-Zresponse_method)superrZr rXrY __class__rrrZszConsumingScorer.__init__c sH|jdur|j|t|fi||dd}tj|||||dS)Nr]r])rXr*r`r3r_score)rNZ method_callerZclfrbrcr-r]rrrrs    zConsumingScorer._score)N)rSrTrUrZr __classcell__rrrrrsrc@s4eZdZd ddZd ddZd ddZdd d ZdS)ConsumingSplitterNcCs ||_dSrrWrYrrrrZszConsumingSplitter.__init__rccsh|jdur|j|t|||dt|d}ttd|}tt|t|}||fV||fVdS)N)groupsr^rr)rXr*r`rJrrange)rNrbrcrr^ split_index train_indices test_indicesrrrsplits    zConsumingSplitter.splitcCsdS)Nrr)rNrbrcrr^rrr get_n_splitsszConsumingSplitter.get_n_splitsccs<t|d}ttd|}tt|t|}|V|VdS)Nrr)rJrr)rNrbrcrrrrrrr_iter_test_indicess  z$ConsumingSplitter._iter_test_indices)N)Nrr)NNNN)NNN)rSrTrUrZrrrrrrrrs  rc@s(eZdZdZddZddZddZdS) MetaRegressorz(A meta-regressor which is only a router.cCs ||_dSr) estimator)rNrrrrrZszMetaRegressor.__init__cKs6t|dfi|}t|jj||fi|jj|_dSNre)rr rre estimator_rNrbrc fit_paramsparamsrrrreszMetaRegressor.fitcCs*t|jjdj|jtjdddd}|SNownerrer/r.rmethod_mapping)rrrSaddrrrNrCrrrget_metadata_routings z"MetaRegressor.get_metadata_routingNrSrTrUrmrZrerrrrrrsrc@s4eZdZdZd ddZd ddZddZd d ZdS) WeightedMetaRegressorz*A meta-regressor which is also a consumer.NcCs||_||_dSrrrXrNrrXrrrrZszWeightedMetaRegressor.__init__cKs\|jdur|j|t||dt|dfd|i|}t|jj||fi|jj|_|SNrrer]rXr*r0rr rrer)rNrbrcr]rrrrrres     zWeightedMetaRegressor.fitcKs*t|dfi|}|jj|fi|jjS)Nri)rrrir)rNrbZpredict_paramsrrrrriszWeightedMetaRegressor.predictcCs:t|jjd|j|jtjdddjdddd}|S)NrrerrirrrrSZadd_self_requestrrrrrrrrs z*WeightedMetaRegressor.get_metadata_routing)N)N)rSrTrUrmrZrerirrrrrrs   rc@s,eZdZdZd ddZd ddZddZdS) WeightedMetaClassifierzEA meta-estimator which also consumes sample_weight itself in ``fit``.NcCs||_||_dSrrrrrrrZszWeightedMetaClassifier.__init__cKs\|jdur|j|t||dt|dfd|i|}t|jj||fi|jj|_|Srr)rNrbrcr]r-rrrrres     zWeightedMetaClassifier.fitcCs0t|jjd|j|jtjdddd}|Srrrrrrrsz+WeightedMetaClassifier.get_metadata_routing)N)Nrrrrrrs  rc@s4eZdZdZddZd ddZd ddZd d ZdS) MetaTransformerzA simple meta-transformer.cCs ||_dSr) transformer)rNrrrrrZszMetaTransformer.__init__NcKs6t|dfi|}t|jj||fi|jj|_|Sr)rr rre transformer_rrrrres zMetaTransformer.fitcKs*t|dfi|}|jj|fi|jjS)Nr)rrrr)rNrbrcZtransform_paramsrrrrrszMetaTransformer.transformcCs0t|jjdj|jtjdddjddddS)Nrrerr)rr)rrrSrrrrQrrrrsz$MetaTransformer.get_metadata_routing)N)N)rSrTrUrmrZrerrrrrrr s   r)T)N)3r% collectionsr functoolsrnumpyr6Z numpy.testingrZ sklearn.baserrrrr r Zsklearn.metrics._scorerr r Zsklearn.model_selectionr Zsklearn.model_selection._splitrZ sklearn.utils._metadata_requestsrZsklearn.utils.metadata_routingrrrZsklearn.utils.multiclassrr0tupler=r`rBrLrrMrVrnrrrrrrrrrrrrrrs:         +    .# T2"