a j6@sddlZddlmZmZddlmZddlmZddl m Z m Z m Z m Z mZGdd d ZGd d d ZGd d d eeZedgdde_GdddeZddZGdddeZdS)N) BaseEstimatorClassifierMixin) RequestMethod) available_if)_check_sample_weight _num_samples check_arraycheck_is_fittedcheck_random_statec@s eZdZdZddZddZdS)ArraySlicingWrapper- Parameters ---------- array cCs ||_dSNarrayselfrr 1`, the output of :meth:`score` will be 1 otherwise it is 0. expected_sample_weight : bool, default=False Whether to check if a valid `sample_weight` was passed to `fit`. expected_fit_params : list of str, default=None A list of the expected parameters given when calling `fit`. Attributes ---------- classes_ : int The classes seen during `fit`. n_features_in_ : int The number of features seen during `fit`. Examples -------- >>> from sklearn.utils._mocking import CheckingClassifier This helper allow to assert to specificities regarding `X` or `y`. In this case we expect `check_X` or `check_y` to return a boolean. >>> from sklearn.datasets import load_iris >>> X, y = load_iris(return_X_y=True) >>> clf = CheckingClassifier(check_X=lambda x: x.shape == (150, 4)) >>> clf.fit(X, y) CheckingClassifier(...) We can also provide a check which might raise an error. In this case, we expect `check_X` to return `X` and `check_y` to return `y`. >>> from sklearn.utils import check_array >>> clf = CheckingClassifier(check_X=check_array) >>> clf.fit(X, y) CheckingClassifier(...) Nallr check_ycheck_y_paramscheck_Xcheck_X_paramsmethods_to_check foo_paramexpected_sample_weightexpected_fit_params random_statec Cs:||_||_||_||_||_||_||_||_| |_dSrr/) rr0r1r2r3r4r5r6r7r8rrrrs zCheckingClassifier.__init__TcCs|r t||jdurZ|jdur$in|j}|j|fi|}t|ttjfrV|sZJn|}|dur|jdur|jdurzin|j}|j|fi|}t|ttjfr|sJn|}||fS)atValidate X and y and make extra check. Parameters ---------- X : array-like of shape (n_samples, n_features) The data set. `X` is checked only if `check_X` is not `None` (default is None). y : array-like of shape (n_samples), default=None The corresponding target, by default `None`. `y` is checked only if `check_y` is not `None` (default is None). should_be_fitted : bool, default=True Whether or not the classifier should be already fitted. By default True. Returns ------- X, y N) r r2r3 isinstanceboolnpZbool_r0r1)rXyshould_be_fittedparamsZ checked_XZ checked_yrrr _check_X_ys   zCheckingClassifier._check_X_yc Kst|t|ksJ|jdks(d|jvr<|j||dd\}}t|d|_tt|ddd|_|j rt |j t |}|rt dt |d | D]<\}}t|t|krt d |d t|d t|d q|jr|durt dt|||S)aFit classifier. Parameters ---------- X : array-like of 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 of shape (n_samples, n_outputs) or (n_samples,), default=None Target relative to X for classification or regression; None for unsupervised learning. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of the estimator Returns ------- self r.fitF)r>rT)Z ensure_2dZallow_ndzExpected fit parameter(s) z not seen.zFit parameter z has length z ; expected .Nz#Expected sample_weight to be passed)r r4r@r;rZn_features_in_uniquer classes_r7setAssertionErrorlistitemsr6r)rr<r=Z sample_weightZ fit_paramsmissingkeyvaluerrrrAs. zCheckingClassifier.fitcCs@|jdksd|jvr"||\}}t|j}|j|jt|dS)a>Predict the first class seen in `classes_`. Parameters ---------- X : array-like of shape (n_samples, n_features) The input data. Returns ------- preds : ndarray of shape (n_samples,) Predictions of the first class seens in `classes_`. r.predict)size)r4r@r r8choicerDr rr<r=rngrrrrLs  zCheckingClassifier.predictcCst|jdksd|jvr"||\}}t|j}|t|t|j}tj ||d}|tj |ddddtj f}|S)aPredict probabilities for each class. Here, the dummy classifier will provide a probability of 1 for the first class of `classes_` and 0 otherwise. Parameters ---------- X : array-like of shape (n_samples, n_features) The input data. Returns ------- proba : ndarray of shape (n_samples, n_classes) The probabilities for each sample and class. r. predict_proba)outrr)N) r4r@r r8randnr r!rDr;abssumZnewaxis)rr<r=rPZprobarrrrQs  z CheckingClassifier.predict_probacCsb|jdksd|jvr"||\}}t|j}t|jdkrH|t|S|t|t|jSdS)aBConfidence score. Parameters ---------- X : array-like of shape (n_samples, n_features) The input data. Returns ------- decision : ndarray of shape (n_samples,) if n_classes == 2 else (n_samples, n_classes) Confidence score. r.decision_functionrN)r4r@r r8r!rDrSr rOrrrrVs z$CheckingClassifier.decision_functioncCs8|jdksd|jvr ||||jdkr0d}nd}|S)aQFake score. Parameters ---------- X : array-like of 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 of shape (n_samples, n_output) or (n_samples,) Target relative to X for classification or regression; None for unsupervised learning. Returns ------- score : float Either 0 or 1 depending of `foo_param` (i.e. `foo_param > 1 => score=1` otherwise `score=0`). r.scorerg?g)r4r@r5)rr<YrWrrrrW7s   zCheckingClassifier.scorecs$t}d|_d|j_d|j_|S)NTF)super__sklearn_tags__ _skip_testZ input_tagsZ two_d_arrayZ target_tagsZ one_d_labelsrtags __class__rrrZRs  z#CheckingClassifier.__sklearn_tags__)NT)N)NN) rrrrrr@rArLrQrVrWrZ __classcell__rrr^rr-Ds$D  % 0 r-rAF)namekeysZ validate_keyscsBeZdZdZd ddZddZddZd d Zfd d ZZ S)NoSampleWeightWrapperzWrap estimator which will not expose `sample_weight`. Parameters ---------- est : estimator, default=None The estimator to wrap. NcCs ||_dSr)est)rrdrrrrkszNoSampleWeightWrapper.__init__cCs|j||Sr)rdrArr<r=rrrrAnszNoSampleWeightWrapper.fitcCs |j|Sr)rdrLrr<rrrrLqszNoSampleWeightWrapper.predictcCs |j|Sr)rdrQrfrrrrQtsz#NoSampleWeightWrapper.predict_probacst}d|_|S)NT)rYrZr[r\r^rrrZws z&NoSampleWeightWrapper.__sklearn_tags__)N) rrrrrrArLrQrZr`rrr^rrcbs  rccsfdd}|S)Ncs|jduo|jvSrresponse_methodsr"methodrrcheck~sz_check_response..checkr)rjrkrrir_check_response}s rlc@s^eZdZdZdddZddZeeddd Zeed d d Z eed ddZ dS)_MockEstimatorOnOffPredictionaEstimator for which we can turn on/off the prediction methods. Parameters ---------- response_methods: list of {"predict", "predict_proba", "decision_function"}, default=None List containing the response implemented by the estimator. When, the response is in the list, it will return the name of the response method when called. Otherwise, an `AttributeError` is raised. It allows to use `getattr` as any conventional estimator. By default, no response methods are mocked. NcCs ||_dSrrg)rrhrrrrsz&_MockEstimatorOnOffPrediction.__init__cCst||_|Sr)r;rCrDrerrrrAs z!_MockEstimatorOnOffPrediction.fitrLcCsdS)NrLrrfrrrrLsz%_MockEstimatorOnOffPrediction.predictrQcCsdS)NrQrrfrrrrQsz+_MockEstimatorOnOffPrediction.predict_probarVcCsdS)NrVrrfrrrrVsz/_MockEstimatorOnOffPrediction.decision_function)N) rrrrrrArrlrLrQrVrrrrrms      rm)numpyr;baserrZutils._metadata_requestsrZmetaestimatorsrZ validationrr r r r r rr-Zset_fit_requestrcrlrmrrrrs   $