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