B \@sjddlZddlmZmZddlmZddlmZm Z Gddde Z Gd d d e Z Gd d d eeZ dS) N) BaseEstimatorClassifierMixin) assert_true) _num_samples check_arrayc@s eZdZdZddZddZdS)ArraySlicingWrapperz- Parameters ---------- array cCs ||_dS)N)array)selfr r 4lib/python3.7/site-packages/sklearn/utils/mocking.py__init__szArraySlicingWrapper.__init__cCst|j|S)N) MockDataFramer )r Zaslicer r r __getitem__szArraySlicingWrapper.__getitem__N)__name__ __module__ __qualname____doc__rrr r r r r sr c@s:eZdZdZddZddZd ddZd d Zd d ZdS)rz- Parameters ---------- array cCs*||_||_|j|_|j|_t||_dS)N)r valuesshapendimr Ziloc)r r r r r rs zMockDataFrame.__init__cCs t|jS)N)lenr )r r r r __len__$szMockDataFrame.__len__NcCs|jS)N)r )r dtyper r r __array__'szMockDataFrame.__array__cCst|j|jkS)N)rr )r otherr r r __eq__-szMockDataFrame.__eq__cCs ||k S)Nr )r rr r r __ne__0szMockDataFrame.__ne__)N) rrrrrrrrrr r r r rs  rc@s4eZdZdZd ddZddZdd Zd d d ZdS)CheckingClassifiera7Dummy classifier to test pipelining and meta-estimators. Checks some property of X and y in fit / predict. This allows testing whether pipelines / cross-validation or metaestimators changed the input. Parameters ---------- check_y check_X foo_param expected_fit_params NrcCs||_||_||_||_dS)N)check_ycheck_X foo_paramexpected_fit_params)r r r!r"r#r r r rBszCheckingClassifier.__init__cKst|t|kst|jdk r,||s,t|jdk rD||sDttt|ddd|_|jrt |jt |}t t|dkdt |x<| D]0\}}t t|t|kd|t|t|fqW|S)a0 Fit classifier Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vector, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of the estimator NFT)Z ensure_2dZallow_ndrz&Expected fit parameter(s) %s not seen.z,Fit parameter %s has length %d; expected %d.) rAssertionErrorr!r npuniquerclasses_r#setrlistitems)r XyZ fit_paramsZmissingkeyvaluer r r fitIs      zCheckingClassifier.fitcCs2|jdk r||st|jtjt|tjdS)zX Parameters ----------- T : indexable, length n_samples N)r)r!r$r'r%Zzerosrint)r Tr r r predictls zCheckingClassifier.predictcCs|jdkrd}nd}|S)a Parameters ---------- X : array-like, shape = [n_samples, n_features] Input data, where n_samples is the number of samples and n_features is the number of features. Y : array-like, shape = [n_samples] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning. rg?g)r")r r+Yscorer r r r4vs zCheckingClassifier.score)NNrN)NN)rrrrrr/r2r4r r r r r4s   # r)Znumpyr%baserrZtestingrZ validationrrobjectr rrr r r r s