187 lines
6.2 KiB
Python
187 lines
6.2 KiB
Python
"""
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The :mod:`sklearn.exceptions` module includes all custom warnings and error
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classes used across scikit-learn.
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"""
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__all__ = ['NotFittedError',
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'ChangedBehaviorWarning',
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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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'NonBLASDotWarning',
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'SkipTestWarning',
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'UndefinedMetricWarning',
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'PositiveSpectrumWarning']
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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 ChangedBehaviorWarning(UserWarning):
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"""Warning class used to notify the user of any change in the behavior.
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.. versionchanged:: 0.18
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Moved from sklearn.base.
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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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Examples
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--------
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>>> import numpy as np
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>>> import warnings
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>>> from sklearn.cluster import KMeans
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>>> from sklearn.exceptions import ConvergenceWarning
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>>> warnings.simplefilter("always", ConvergenceWarning)
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>>> X = np.asarray([[0, 0],
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... [0, 1],
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... [1, 0],
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... [1, 0]]) # last point is duplicated
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>>> with warnings.catch_warnings(record=True) as w:
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... km = KMeans(n_clusters=4).fit(X)
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... print(w[-1].message)
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Number of distinct clusters (3) found smaller than n_clusters (4).
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Possibly due to duplicate points in X.
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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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Examples
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--------
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>>> from sklearn.model_selection import GridSearchCV
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>>> from sklearn.svm import LinearSVC
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>>> from sklearn.exceptions import FitFailedWarning
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>>> import warnings
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>>> warnings.simplefilter('always', FitFailedWarning)
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>>> gs = GridSearchCV(LinearSVC(), {'C': [-1, -2]}, error_score=0, cv=2)
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>>> X, y = [[1, 2], [3, 4], [5, 6], [7, 8]], [0, 0, 1, 1]
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>>> with warnings.catch_warnings(record=True) as w:
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... try:
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... gs.fit(X, y) # This will raise a ValueError since C is < 0
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... except ValueError:
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... pass
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... print(repr(w[-1].message))
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FitFailedWarning('Estimator fit failed. The score on this train-test
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partition for these parameters will be set to 0.000000.
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Details:...ValueError: Penalty term must be positive; got (C=-2)...
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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 NonBLASDotWarning(EfficiencyWarning):
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"""Warning used when the dot operation does not use BLAS.
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This warning is used to notify the user that BLAS was not used for dot
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operation and hence the efficiency may be affected.
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.. versionchanged:: 0.18
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Moved from sklearn.utils.validation, extends EfficiencyWarning.
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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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