:i54SSKrSSKJrJr SSKJr SSKJr SSK J r J r J r J r Jr "SS 5r"S S 5r"S S \\5r\"S/SS9\l"SS\5rSr"SS\5rg)N) BaseEstimatorClassifierMixin) RequestMethod) available_if)_check_sample_weight _num_samples check_arraycheck_is_fittedcheck_random_statec$\rSrSrSrSrSrSrg)ArraySlicingWrapper Parameters ---------- array cXlgNarrayselfrs Y/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/sklearn/utils/_mocking.py__init__ArraySlicingWrapper.__init__s c2[URU5$r MockDataFramer)raslices r __getitem__ArraySlicingWrapper.__getitem__sTZZ/00rrN)__name__ __module__ __qualname____firstlineno____doc__rr __static_attributes__rrrrs 1rrcD\rSrSrSrSrSrS SjrSrSr S S jr S r g) rrcXlXlURUlURUl[ U5Ulgr)rvaluesshapendimrilocrs rrMockDataFrame.__init__&s.  [[ JJ '. rc,[UR5$r)lenrrs r__len__MockDataFrame.__len__.s4::rNcUR$rr)rdtypes r __array__MockDataFrame.__array__1szzrcF[URUR:H5$rrrothers r__eq__MockDataFrame.__eq__7sTZZ5;;677rc X:X+$rr(r;s r__ne__MockDataFrame.__ne__:s   rcF[URRXS95$)Naxis)rrtake)rindicesrDs rrEMockDataFrame.take=sTZZ__W_@AAr)rr/r.r-r,r)r) r"r#r$r%r&rr4r8r=r@rEr'r(rrrrs&/ 8!Brrc l\rSrSrSrSSSSSSSSSS. SjrSSjrSS jrS rS r S r SS jr Sr Sr g)CheckingClassifierAadDummy 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 pXlX lX0lX@lXPlX`lXplXlXlgrrL) rrMrNrOrPrQrRrSrTrUs rrCheckingClassifier.__init__s7 , , 0"&<##6 (rcU(a [U5 URb_URc0O URnUR"U40UD6n[U[[ R 45(a U(deOUnUboURbbURc0O URnUR"U40UD6n[U[[ R 45(a U(deX4$UnX4$)aValidate 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 ) r rOrP isinstanceboolnpbool_rMrN)rXyshould_be_fittedparams checked_X checked_ys r _check_X_yCheckingClassifier._check_X_ys&  D ! << #..6RD object Parameters passed to the ``fit`` method of the estimator Returns ------- self rKfitF)r_rT) ensure_2dallow_ndzExpected fit parameter(s) z not seen.zFit parameter z has length z ; expected .z#Expected sample_weight to be passed)r rQrcr[r-n_features_in_uniquer classes_rTsetAssertionErrorlistitemsrSr )rr]r^ sample_weight fit_paramsmissingkeyvalues rrfCheckingClassifier.fits=0A,q/111  E )Ud6K6K-K??1%?@DA hhqk!n +a54"PQ  # #$223c*oEG$0gzJ)..0 &,q/9((\,u:M9N%l1o%6a91  & &$$%JKK  2 rcURS:XdSUR;aURU5up[UR5nUR UR [ U5S9$)zPredict 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_`. rKpredict)size)rQrcr rUchoicerlr rr]r^rngs rrxCheckingClassifier.predictsZ  E )Y$:O:O-O??1%DA !2!23zz$--l1oz>>rctURS:XdSUR;aURU5up[UR5nUR [ U5[ UR55n[R"XDS9nU[R"USS9SS2[R4-nU$)abPredict 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. rK predict_proba)outrrCN) rQrcr rUrandnr r2rlr[abssumnewaxis)rr]r^r|probas rr CheckingClassifier.predict_probas  E )_@U@U-U??1%DA !2!23 ,q/3t}}+=>u( A&q"**}55 rcTURS:XdSUR;aURU5up[UR5n[ UR 5S:XaUR [U55$UR [U5[ UR 55$)zConfidence 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. rKdecision_functionr)rQrcr rUr2rlrr r{s rr$CheckingClassifier.decision_functions  ! !U *"d&;&;;??1%DA !2!23 t}}  "99\!_- -99\!_c$--.@A ArcURS:XdSUR;aURX5 URS:aSnU$SnU$)aFake 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`). rKscorerg?g)rQrcrR)rr]Yrs rrCheckingClassifier.score4sO&  E )W8M8M-M OOA ! >>A E E rcSS/S.$)NT1dlabel) _skip_testX_typesr(r3s r _more_tagsCheckingClassifier._more_tagsOs" {;;r) rOrPrMrNrlrTrSrRrQrjrU)NTr)NN)r"r#r$r%r&rrcrfrxrrrrr'r(rrrIrIAsP?H# ).#J.`?$0B66URSL=(a TUR;$rresponse_methods)rmethods rcheck_check_response..checkus$$$D0TVt?T?T5TTrr()rrs` r_check_responsertsU Lrc\rSrSrSrS SjrSr\"\"S55S5r \"\"S55S 5r \"\"S 55S 5r S r g)_MockEstimatorOnOffPredictioni{aEstimator 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. NcXlgrr)rrs rr&_MockEstimatorOnOffPrediction.__init__s 0rc<[R"U5UlU$r)r[rkrlrs rrf!_MockEstimatorOnOffPrediction.fits !   rrxcg)Nrxr(rs rrx%_MockEstimatorOnOffPrediction.predictsrrcg)Nrr(rs rr+_MockEstimatorOnOffPrediction.predict_probasrrcg)Nrr(rs rr/_MockEstimatorOnOffPrediction.decision_functions"r)rlrr) r"r#r$r%r&rrfrrrxrrr'r(rrrr{sl 1/),-.//234/"567#8#rr)numpyr[baserrutils._metadata_requestsrmetaestimatorsr validationr r r r r rrrIset_fit_requestrrrr(rrrsw14( 1 1!B!BHO<-O