o `^hD5@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 ||_dSNarrayselfrrT/home/air/shanriGPT/back/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)rrcCs*||_||_|j|_|j|_t||_dSr)rvaluesshapendimr ilocrrrrr)s zMockDataFrame.__init__cCs t|jSr)lenrrrrr__len__1rzMockDataFrame.__len__NcCs|jSrr)rdtyperrr __array__4szMockDataFrame.__array__cCst|j|jkSrrrotherrrr__eq__:szMockDataFrame.__eq__cCs ||k Srrr+rrr__ne__=rzMockDataFrame.__ne__rcCst|jj||dS)Naxis)rrtake)rindicesr0rrrr1@zMockDataFrame.taker)r) rrr r!rr(r*r-r.r1rrrrr 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:||_||_||_||_||_||_||_||_| |_dSrr6) rr7r8r9r:r;r<r=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 r9r: isinstanceboolnpbool_r7r8)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 r5fitF)rFrT) ensure_2dallow_ndzExpected fit parameter(s) z not seen.zFit parameter z has length z ; expected .Nz#Expected sample_weight to be passed)r r;rJrBr#n_features_in_uniquer classes_r>setAssertionErrorlistitemsr=r)rrDrE sample_weight fit_paramsmissingkeyvaluerrrrKs0 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 seens in `classes_`. r5predict)size)r;rJr r?choicerQr rrDrErngrrrr[s  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. r5 predict_proba)outrr/N) r;rJr r?randnr r&rQrBabssumnewaxis)rrDrEr_probarrrr`s  z CheckingClassifier.predict_probacCs^|jdks d|jvr||\}}t|j}t|jdkr$|t|S|t|t|jS)aBConfidence score. Parameters ---------- X : array-like of shape (n_samples, n_features) The input data. Returns ------- decision : ndarray of shape (n_samples,) if n_classes == 2 else (n_samples, n_classes) Confidence score. r5decision_functionr)r;rJr r?r&rQrbr r^rrrrgs   z$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`). r5scorerg?g)r;rJr<)rrDYrhrrrrh7s  zCheckingClassifier.scorecs$t}d|_d|j_d|j_|S)NTF)super__sklearn_tags__ _skip_test input_tags two_d_array target_tags one_d_labelsrtags __class__rrrkRs z#CheckingClassifier.__sklearn_tags__NTr)NN) rrr r!rrJrKr[r`rgrhrk __classcell__rrrsrr4Ds&D  %0 r4rKF)namekeys 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)rr{rrrrkrzNoSampleWeightWrapper.__init__cCs|j||Sr)r{rKrrDrErrrrKnrzNoSampleWeightWrapper.fitcC |j|Sr)r{r[rrDrrrr[q zNoSampleWeightWrapper.predictcCr}r)r{r`r~rrrr`trz#NoSampleWeightWrapper.predict_probacst}d|_|Sru)rjrkrlrqrsrrrkws z&NoSampleWeightWrapper.__sklearn_tags__r) rrr r!rrKr[r`rkrvrrrsrrzbs rzcsfdd}|S)Ncs|jduo |jvSrresponse_methodsr'methodrrcheck~r3z_check_response..checkr)rrrrr_check_response}s rc@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. NcCrrr)rrrrrrrz&_MockEstimatorOnOffPrediction.__init__cCst||_|Sr)rBrPrQr|rrrrKs z!_MockEstimatorOnOffPrediction.fitr[cCdS)Nr[rr~rrrr[z%_MockEstimatorOnOffPrediction.predictr`cCr)Nr`rr~rrrr`rz+_MockEstimatorOnOffPrediction.predict_probargcCr)Nrgrr~rrrrgrz/_MockEstimatorOnOffPrediction.decision_functionr) rrr r!rrKrrr[r`rgrrrrrs      r)numpyrBbaserrutils._metadata_requestsrmetaestimatorsr validationrr r r r r rr4set_fit_requestrzrrrrrrs    $