68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
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import numpy as np
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_DEFAULT_TAGS = {
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'non_deterministic': False,
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'requires_positive_X': False,
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'requires_positive_y': False,
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'X_types': ['2darray'],
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'poor_score': False,
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'no_validation': False,
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'multioutput': False,
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"allow_nan": False,
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'stateless': False,
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'multilabel': False,
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'_skip_test': False,
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'_xfail_checks': False,
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'multioutput_only': False,
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'binary_only': False,
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'requires_fit': True,
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'preserves_dtype': [np.float64],
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'requires_y': False,
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'pairwise': False,
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}
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def _safe_tags(estimator, key=None):
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"""Safely get estimator tags.
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:class:`~sklearn.BaseEstimator` provides the estimator tags machinery.
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However, if an estimator does not inherit from this base class, we should
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fall-back to the default tags.
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For scikit-learn built-in estimators, we should still rely on
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`self._get_tags()`. `_safe_tags(est)` should be used when we are not sure
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where `est` comes from: typically `_safe_tags(self.base_estimator)` where
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`self` is a meta-estimator, or in the common checks.
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Parameters
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----------
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estimator : estimator object
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The estimator from which to get the tag.
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key : str, default=None
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Tag name to get. By default (`None`), all tags are returned.
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Returns
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-------
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tags : dict or tag value
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The estimator tags. A single value is returned if `key` is not None.
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"""
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if hasattr(estimator, "_get_tags"):
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tags_provider = "_get_tags()"
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tags = estimator._get_tags()
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elif hasattr(estimator, "_more_tags"):
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tags_provider = "_more_tags()"
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tags = {**_DEFAULT_TAGS, **estimator._more_tags()}
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else:
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tags_provider = "_DEFAULT_TAGS"
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tags = _DEFAULT_TAGS
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if key is not None:
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if key not in tags:
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raise ValueError(
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f"The key {key} is not defined in {tags_provider} for the "
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f"class {estimator.__class__.__name__}."
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)
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return tags[key]
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return tags
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