74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
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# Source: https://github.com/scikit-learn/scikit-learn
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"""Utilities for input validation"""
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# Authors: Olivier Grisel
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# Gael Varoquaux
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# Andreas Mueller
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# Lars Buitinck
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# Alexandre Gramfort
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# Nicolas Tresegnie
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# License: BSD 3 clause
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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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... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
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NotFittedError('This LinearSVC instance is not fitted yet',)
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.. versionchanged:: 0.18
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Moved from sklearn.utils.validation.
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"""
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def check_is_fitted(estimator, attributes, msg=None, all_or_any=all):
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"""Perform is_fitted validation for estimator.
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Checks if the estimator is fitted by verifying the presence of
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"all_or_any" of the passed attributes and raises a NotFittedError with the
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given message.
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Parameters
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----------
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estimator : estimator instance.
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estimator instance for which the check is performed.
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attributes : attribute name(s) given as string or a list/tuple of strings
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Eg.:
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``["coef_", "estimator_", ...], "coef_"``
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msg : string
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The default error message is, "This %(name)s instance is not fitted
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yet. Call 'fit' with appropriate arguments before using this method."
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For custom messages if "%(name)s" is present in the message string,
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it is substituted for the estimator name.
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Eg. : "Estimator, %(name)s, must be fitted before sparsifying".
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all_or_any : callable, {all, any}, default all
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Specify whether all or any of the given attributes must exist.
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Returns
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-------
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None
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Raises
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------
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NotFittedError
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If the attributes are not found.
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"""
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if msg is None:
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msg = ("This %(name)s instance is not fitted yet. Call 'fit' with "
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"appropriate arguments before using this method.")
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if not hasattr(estimator, 'fit'):
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raise TypeError("%s is not an estimator instance." % (estimator))
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if not isinstance(attributes, (list, tuple)):
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attributes = [attributes]
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if not all_or_any([hasattr(estimator, attr) for attr in attributes]):
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raise NotFittedError(msg % {'name': type(estimator).__name__})
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