o i]5@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 cC ||_dSNarrayselfrre/var/www/html/eduruby.in/lip-sync/lip-sync-env/lib/python3.10/site-packages/sklearn/utils/_mocking.py__init__ zArraySlicingWrapper.__init__cCst|j|Sr MockDataFramer)rZaslicerrr __getitem__zArraySlicingWrapper.__getitem__N)__name__ __module__ __qualname____doc__rrrrrrr s r c@sDeZdZdZddZddZdddZd d Zd d ZdddZ dS)rrcCs*||_||_|j|_|j|_t||_dSr)rvaluesshapendimr Zilocrrrrr)s zMockDataFrame.__init__cCs t|jSr)lenrrrrr__len__1rzMockDataFrame.__len__NcCs|jSrr)rZdtyperrr __array__4szMockDataFrame.__array__cCst|j|jkSrrrotherrrr__eq__:szMockDataFrame.__eq__cCs ||k Srrr(rrr__ne__=rzMockDataFrame.__ne__rcCst|jj||dS)Naxis)rrtake)rindicesr-rrrr.@zMockDataFrame.taker)r) rrrr rr&r'r*r+r.rrrrr s rc steZdZdZdddddddddd ddZdd d Zdd d Zd dZddZddZ dddZ fddZ Z S)CheckingClassifiera$ Dummy 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. Can also be used to check if `fit_params` are passed correctly, and to force a certain score to be returned. Parameters ---------- check_y, check_X : callable, default=None The callable used to validate `X` and `y`. These callable should return a bool where `False` will trigger an `AssertionError`. If `None`, the data is not validated. Default is `None`. check_y_params, check_X_params : dict, default=None The optional parameters to pass to `check_X` and `check_y`. If `None`, then no parameters are passed in. methods_to_check : "all" or list of str, default="all" The methods in which the checks should be applied. By default, all checks will be done on all methods (`fit`, `predict`, `predict_proba`, `decision_function` and `score`). foo_param : int, default=0 A `foo` param. When `foo > 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:||_||_||_||_||_||_||_||_| |_dSrr3) rr4r5r6r7r8r9r:r;r<rrrrs  zCheckingClassifier.__init__TcCs|rt||jdur-|jdurin|j}|j|fi|}t|ttjfr+|s*Jn|}|dur[|jdur[|jdur=in|j}|j|fi|}t|ttjfrY|sUJ||fS|}||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 r6r7 isinstanceboolnpZbool_r4r5)rXyshould_be_fittedparamsZ checked_XZ checked_yrrr _check_X_ys   zCheckingClassifier._check_X_yc Kst|t|ks J|jdksd|jvr|j||dd\}}t|d|_tt|ddd|_|j rlt |j t |}|rIt dt |d | D]\}}t|t|krkt d |d t|d t|d qM|jr||durwt 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 r2fitF)rBrT)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 r8rDr?r"Zn_features_in_uniquer classes_r;setAssertionErrorlistitemsr:r)rr@rAZ sample_weightZ fit_paramsmissingkeyvaluerrrrEs0 zCheckingClassifier.fitcCs@|jdks d|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 seen in `classes_`. r2predict)size)r8rDr r<choicerHr rr@rArngrrrrPs  zCheckingClassifier.predictcCst|jdks d|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. r2 predict_proba)outrr,N) r8rDr r<randnr r$rHr?abssumZnewaxis)rr@rArTZprobarrrrUs  z CheckingClassifier.predict_probacCs^|jdks d|jvr||\}}t|j}t|jdkr$|t|S|t|t|jS)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. r2decision_functionr)r8rDr r<r$rHrWr rSrrrrZs   z$CheckingClassifier.decision_functioncCs:|jdks d|jvr||||jdkrd}|Sd}|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`). r2scorerg?g)r8rDr9)rr@Yr[rrrr[7s  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__rrr^Rs z#CheckingClassifier.__sklearn_tags__NTr)NN) rrrr rrDrErPrUrZr[r^ __classcell__rrrbrr1Ds&D  %0 r1rEF)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. NcCrr)est)rrirrrrkrzNoSampleWeightWrapper.__init__cCs|j||Sr)rirErr@rArrrrEnrzNoSampleWeightWrapper.fitcC |j|Sr)rirPrr@rrrrPq zNoSampleWeightWrapper.predictcCrkr)rirUrlrrrrUtrmz#NoSampleWeightWrapper.predict_probacst}d|_|Srd)r]r^r_r`rbrrr^ws z&NoSampleWeightWrapper.__sklearn_tags__r) rrrr rrErPrUr^rerrrbrrhbs rhcsfdd}|S)Ncs|jduo |jvSrresponse_methodsr%methodrrcheck~r0z_check_response..checkr)rqrrrrpr_check_response}s rsc@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. NcCrrrn)rrorrrrrz&_MockEstimatorOnOffPrediction.__init__cCst||_|Sr)r?rGrHrjrrrrEs z!_MockEstimatorOnOffPrediction.fitrPcCdS)NrPrrlrrrrPz%_MockEstimatorOnOffPrediction.predictrUcCru)NrUrrlrrrrUrvz+_MockEstimatorOnOffPrediction.predict_probarZcCru)NrZrrlrrrrZrvz/_MockEstimatorOnOffPrediction.decision_functionr) rrrr rrErrsrPrUrZrrrrrts      rt)numpyr?baserrZutils._metadata_requestsrZmetaestimatorsrZ validationrr r r r r rr1Zset_fit_requestrhrsrtrrrrs    $