"""Custom warnings and errors used across scikit-learn.""" # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause __all__ = [ "NotFittedError", "ConvergenceWarning", "DataConversionWarning", "DataDimensionalityWarning", "EfficiencyWarning", "FitFailedWarning", "SkipTestWarning", "UndefinedMetricWarning", "PositiveSpectrumWarning", "UnsetMetadataPassedError", "EstimatorCheckFailedWarning", ] class UnsetMetadataPassedError(ValueError): """Exception class to raise if a metadata is passed which is not explicitly \ requested (metadata=True) or not requested (metadata=False). .. versionadded:: 1.3 Parameters ---------- message : str The message unrequested_params : dict A dictionary of parameters and their values which are provided but not requested. routed_params : dict A dictionary of routed parameters. """ def __init__(self, *, message, unrequested_params, routed_params): super().__init__(message) self.unrequested_params = unrequested_params self.routed_params = routed_params class NotFittedError(ValueError, AttributeError): """Exception class to raise if estimator is used before fitting. This class inherits from both ValueError and AttributeError to help with exception handling and backward compatibility. Examples -------- >>> from sklearn.svm import LinearSVC >>> from sklearn.exceptions import NotFittedError >>> try: ... LinearSVC().predict([[1, 2], [2, 3], [3, 4]]) ... except NotFittedError as e: ... print(repr(e)) NotFittedError("This LinearSVC instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."...) .. versionchanged:: 0.18 Moved from sklearn.utils.validation. """ class ConvergenceWarning(UserWarning): """Custom warning to capture convergence problems .. versionchanged:: 0.18 Moved from sklearn.utils. """ class DataConversionWarning(UserWarning): """Warning used to notify implicit data conversions happening in the code. This warning occurs when some input data needs to be converted or interpreted in a way that may not match the user's expectations. For example, this warning may occur when the user - passes an integer array to a function which expects float input and will convert the input - requests a non-copying operation, but a copy is required to meet the implementation's data-type expectations; - passes an input whose shape can be interpreted ambiguously. .. versionchanged:: 0.18 Moved from sklearn.utils.validation. """ class DataDimensionalityWarning(UserWarning): """Custom warning to notify potential issues with data dimensionality. For example, in random projection, this warning is raised when the number of components, which quantifies the dimensionality of the target projection space, is higher than the number of features, which quantifies the dimensionality of the original source space, to imply that the dimensionality of the problem will not be reduced. .. versionchanged:: 0.18 Moved from sklearn.utils. """ class EfficiencyWarning(UserWarning): """Warning used to notify the user of inefficient computation. This warning notifies the user that the efficiency may not be optimal due to some reason which may be included as a part of the warning message. This may be subclassed into a more specific Warning class. .. versionadded:: 0.18 """ class FitFailedWarning(RuntimeWarning): """Warning class used if there is an error while fitting the estimator. This Warning is used in meta estimators GridSearchCV and RandomizedSearchCV and the cross-validation helper function cross_val_score to warn when there is an error while fitting the estimator. .. versionchanged:: 0.18 Moved from sklearn.cross_validation. """ class SkipTestWarning(UserWarning): """Warning class used to notify the user of a test that was skipped. For example, one of the estimator checks requires a pandas import. If the pandas package cannot be imported, the test will be skipped rather than register as a failure. """ class UndefinedMetricWarning(UserWarning): """Warning used when the metric is invalid .. versionchanged:: 0.18 Moved from sklearn.base. """ class PositiveSpectrumWarning(UserWarning): """Warning raised when the eigenvalues of a PSD matrix have issues This warning is typically raised by ``_check_psd_eigenvalues`` when the eigenvalues of a positive semidefinite (PSD) matrix such as a gram matrix (kernel) present significant negative eigenvalues, or bad conditioning i.e. very small non-zero eigenvalues compared to the largest eigenvalue. .. versionadded:: 0.22 """ class InconsistentVersionWarning(UserWarning): """Warning raised when an estimator is unpickled with a inconsistent version. Parameters ---------- estimator_name : str Estimator name. current_sklearn_version : str Current scikit-learn version. original_sklearn_version : str Original scikit-learn version. """ def __init__( self, *, estimator_name, current_sklearn_version, original_sklearn_version ): self.estimator_name = estimator_name self.current_sklearn_version = current_sklearn_version self.original_sklearn_version = original_sklearn_version def __str__(self): return ( f"Trying to unpickle estimator {self.estimator_name} from version" f" {self.original_sklearn_version} when " f"using version {self.current_sklearn_version}. This might lead to breaking" " code or " "invalid results. Use at your own risk. " "For more info please refer to:\n" "https://scikit-learn.org/stable/model_persistence.html" "#security-maintainability-limitations" ) class EstimatorCheckFailedWarning(UserWarning): """Warning raised when an estimator check from the common tests fails. Parameters ---------- estimator : estimator object Estimator instance for which the test failed. check_name : str Name of the check that failed. exception : Exception Exception raised by the failed check. status : str Status of the check. expected_to_fail : bool Whether the check was expected to fail. expected_to_fail_reason : str Reason for the expected failure. """ def __init__( self, *, estimator, check_name: str, exception: Exception, status: str, expected_to_fail: bool, expected_to_fail_reason: str, ): self.estimator = estimator self.check_name = check_name self.exception = exception self.status = status self.expected_to_fail = expected_to_fail self.expected_to_fail_reason = expected_to_fail_reason def __repr__(self): expected_to_fail_str = ( f"Expected to fail: {self.expected_to_fail_reason}" if self.expected_to_fail else "Not expected to fail" ) return ( f"Test {self.check_name} failed for estimator {self.estimator!r}.\n" f"Expected to fail reason: {expected_to_fail_str}\n" f"Exception: {self.exception}" ) def __str__(self): return self.__repr__()