o tBhj+@szddlZddlmZmZddlmZmZmZddlm Z GdddZ Gd d d Z Gd d d eeZ Gd ddeZ dS)N) BaseEstimatorClassifierMixin)_check_sample_weight _num_samples check_array)check_is_fittedc@s eZdZdZddZddZdS)ArraySlicingWrapper- Parameters ---------- array cC ||_dSNarrayselfrrm/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/utils/_mocking.py__init__ zArraySlicingWrapper.__init__cCst|j|Sr  MockDataFramer)raslicerrr __getitem__zArraySlicingWrapper.__getitem__N)__name__ __module__ __qualname____doc__rrrrrrr s r c@sDeZdZdZddZddZdddZd d Zd d ZdddZ dS)rr cCs*||_||_|j|_|j|_t||_dSr )rvaluesshapendimr ilocrrrrrs zMockDataFrame.__init__cCs t|jSr )lenrrrrr__len__'rzMockDataFrame.__len__NcCs|jSr r)rdtyperrr __array__*szMockDataFrame.__array__cCst|j|jkSr rrotherrrr__eq__0szMockDataFrame.__eq__cCs ||k Sr rr(rrr__ne__3rzMockDataFrame.__ne__rcCst|jj||dS)N)axis)rrtake)rindicesr,rrrr-6szMockDataFrame.taker )r) rrrrrr%r'r*r+r-rrrrrs rc @sjeZdZdZdddddddddddZdd d Zdd d Zd dZddZddZ dddZ ddZ dS)CheckingClassifieraDummy 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`. check_y_params, check_X_params : dict, default=None The optional parameters to pass to `check_X` and `check_y`. 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(...) Nallrcheck_ycheck_y_paramscheck_Xcheck_X_paramsmethods_to_check foo_paramexpected_sample_weightexpected_fit_paramsc Cs4||_||_||_||_||_||_||_||_dSr r1) rr2r3r4r5r6r7r8r9rrrrzs  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)aValidate X and y and make extra check. Parameters ---------- X : array-like of shape (n_samples, n_features) The data set. y : array-like of shape (n_samples), default=None The corresponding target, by default None. should_be_fitted : bool, default=True Whether or not the classifier should be already fitted. By default True. Returns ------- X, y N) r r4r5 isinstanceboolnpbool_r2r3)rXyshould_be_fittedparams checked_X 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 r0fitF)r@rT) ensure_2dallow_ndzExpected fit parameter(s) z not seen.zFit parameter z has length z ; expected .Nz#Expected sample_weight to be passed)rr6rDr<r n_features_in_uniquerclasses_r9setAssertionErrorlistitemsr8r)rr>r? sample_weight fit_paramsmissingkeyvaluerrrrEs0 zCheckingClassifier.fitcCs:|jdks d|jvr||\}}|jtjt|tdS)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_`. r0predict)r&)r6rDrKr<zerosrintrr>r?rrrrUs zCheckingClassifier.predictcCsN|jdks d|jvr||\}}tt|t|jf}d|dddf<|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. r0 predict_probarNr)r6rDr<rVrr#rK)rr>r?probarrrrYs z CheckingClassifier.predict_probacCsj|jdks d|jvr||\}}t|jdkrtt|Stt|t|jf}d|dddf<|S)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. r0decision_functionrrNr)r6rDr#rKr<rVr)rr>r?decisionrrrr[ s  z$CheckingClassifier.decision_functioncCs:|jdks d|jvr||||jdkrd}|Sd}|S)aQFake score. Parameters ---------- X : array-like of shape (n_samples, n_features) Input data, where `n_samples` is the number of samples and `n_features` is the number of features. Y : array-like of shape (n_samples, n_output) or (n_samples,) Target relative to X for classification or regression; None for unsupervised learning. Returns ------- score : float Either 0 or 1 depending of `foo_param` (i.e. `foo_param > 1 => score=1` otherwise `score=0`). r0scorerg?g)r6rDr7)rr>Yr]rrrr]%s  zCheckingClassifier.scorecCs ddgdS)NT1dlabel) _skip_testX_typesrr$rrr _more_tags@ zCheckingClassifier._more_tags)NTr )NN) rrrrrrDrErUrYr[r]rbrrrrr/:s$B  #0  r/c@s:eZdZdZd ddZddZddZd d Zd d ZdS)NoSampleWeightWrapperzWrap estimator which will not expose `sample_weight`. Parameters ---------- est : estimator, default=None The estimator to wrap. NcCr r )est)rrerrrrMrzNoSampleWeightWrapper.__init__cCs|j||Sr )rerErXrrrrEPrzNoSampleWeightWrapper.fitcC |j|Sr )rerUrr>rrrrUSrczNoSampleWeightWrapper.predictcCrfr )rerYrgrrrrYVrcz#NoSampleWeightWrapper.predict_probacCsddiS)Nr`Trr$rrrrbYsz NoSampleWeightWrapper._more_tagsr ) rrrrrrErUrYrbrrrrrdDs  rd)numpyr<baserr validationrrrr r rr/rdrrrrs $