ó ‡ˆ\c@s‘ddlZddlmZmZddlmZddlmZm Z de fd„ƒYZ d e fd „ƒYZ d eefd „ƒYZ dS( iÿÿÿÿNi(t BaseEstimatortClassifierMixini(t assert_true(t _num_samplest check_arraytArraySlicingWrappercBs eZdZd„Zd„ZRS(s- Parameters ---------- array cCs ||_dS(N(tarray(tselfR((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyt__init__scCst|j|ƒS(N(t MockDataFrameR(Rtaslice((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyt __getitem__s(t__name__t __module__t__doc__RR (((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyRs R cBs>eZdZd„Zd„Zdd„Zd„Zd„ZRS(s- Parameters ---------- array cCs=||_||_|j|_|j|_t|ƒ|_dS(N(RtvaluestshapetndimRtiloc(RR((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyRs     cCs t|jƒS(N(tlenR(R((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyt__len__$scCs|jS(N(R(Rtdtype((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyt __array__'scCst|j|jkƒS(N(R R(Rtother((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyt__eq__-scCs ||k S(N((RR((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyt__ne__0sN( R R RRRtNoneRRR(((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyR s     tCheckingClassifiercBsDeZdZddddd„Zd„Zd„Zddd„ZRS(s7Dummy 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 icCs(||_||_||_||_dS(N(tcheck_ytcheck_Xt foo_paramtexpected_fit_params(RRRRR((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyRBs   cKs1t|ƒt|ƒkst‚|jdk rE|j|ƒsEt‚n|jdk rl|j|ƒslt‚ntjt|dtdt ƒƒ|_ |j r-t |j ƒt |ƒ}t t|ƒdkdt|ƒƒxU|jƒD]D\}}t t|ƒt|ƒkd|t|ƒt|ƒfƒqâWn|S(s0 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 t ensure_2dtallow_ndis&Expected fit parameter(s) %s not seen.s,Fit parameter %s has length %d; expected %d.N(RtAssertionErrorRRRtnptuniqueRtFalsetTruetclasses_RtsetRtlisttitems(RtXtyt fit_paramstmissingtkeytvalue((s4lib/python2.7/site-packages/sklearn/utils/mocking.pytfitIs  $cCsJ|jdk r'|j|ƒs't‚n|jtjt|ƒdtjƒS(sX Parameters ----------- T : indexable, length n_samples RN(RRR"R'R#tzerosRtint(RtT((s4lib/python2.7/site-packages/sklearn/utils/mocking.pytpredictlscCs"|jdkrd}nd}|S(s 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. igð?g(R(RR+tYtscore((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyR7vs  N(R R RRRR1R5R7(((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyR4s     # (tnumpyR#tbaseRRttestingRt validationRRtobjectRR R(((s4lib/python2.7/site-packages/sklearn/utils/mocking.pyts