291 lines
11 KiB
Python
291 lines
11 KiB
Python
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# Authors: Gilles Louppe, Mathieu Blondel, Maheshakya Wijewardena
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# License: BSD 3 clause
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import numpy as np
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import numbers
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from ._base import SelectorMixin
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from ._base import _get_feature_importances
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from ..base import BaseEstimator, clone, MetaEstimatorMixin
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from ..utils._tags import _safe_tags
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from ..utils.validation import check_is_fitted
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from ..exceptions import NotFittedError
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from ..utils.metaestimators import if_delegate_has_method
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from ..utils.validation import _deprecate_positional_args
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def _calculate_threshold(estimator, importances, threshold):
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"""Interpret the threshold value"""
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if threshold is None:
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# determine default from estimator
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est_name = estimator.__class__.__name__
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if ((hasattr(estimator, "penalty") and estimator.penalty == "l1") or
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"Lasso" in est_name):
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# the natural default threshold is 0 when l1 penalty was used
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threshold = 1e-5
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else:
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threshold = "mean"
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if isinstance(threshold, str):
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if "*" in threshold:
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scale, reference = threshold.split("*")
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scale = float(scale.strip())
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reference = reference.strip()
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if reference == "median":
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reference = np.median(importances)
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elif reference == "mean":
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reference = np.mean(importances)
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else:
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raise ValueError("Unknown reference: " + reference)
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threshold = scale * reference
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elif threshold == "median":
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threshold = np.median(importances)
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elif threshold == "mean":
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threshold = np.mean(importances)
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else:
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raise ValueError("Expected threshold='mean' or threshold='median' "
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"got %s" % threshold)
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else:
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threshold = float(threshold)
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return threshold
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class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):
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"""Meta-transformer for selecting features based on importance weights.
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.. versionadded:: 0.17
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Read more in the :ref:`User Guide <select_from_model>`.
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Parameters
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----------
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estimator : object
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The base estimator from which the transformer is built.
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This can be both a fitted (if ``prefit`` is set to True)
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or a non-fitted estimator. The estimator should have a
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``feature_importances_`` or ``coef_`` attribute after fitting.
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Otherwise, the ``importance_getter`` parameter should be used.
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threshold : string or float, default=None
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The threshold value to use for feature selection. Features whose
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importance is greater or equal are kept while the others are
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discarded. If "median" (resp. "mean"), then the ``threshold`` value is
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the median (resp. the mean) of the feature importances. A scaling
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factor (e.g., "1.25*mean") may also be used. If None and if the
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estimator has a parameter penalty set to l1, either explicitly
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or implicitly (e.g, Lasso), the threshold used is 1e-5.
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Otherwise, "mean" is used by default.
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prefit : bool, default=False
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Whether a prefit model is expected to be passed into the constructor
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directly or not. If True, ``transform`` must be called directly
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and SelectFromModel cannot be used with ``cross_val_score``,
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``GridSearchCV`` and similar utilities that clone the estimator.
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Otherwise train the model using ``fit`` and then ``transform`` to do
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feature selection.
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norm_order : non-zero int, inf, -inf, default=1
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Order of the norm used to filter the vectors of coefficients below
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``threshold`` in the case where the ``coef_`` attribute of the
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estimator is of dimension 2.
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max_features : int, default=None
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The maximum number of features to select.
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To only select based on ``max_features``, set ``threshold=-np.inf``.
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.. versionadded:: 0.20
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importance_getter : str or callable, default='auto'
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If 'auto', uses the feature importance either through a ``coef_``
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attribute or ``feature_importances_`` attribute of estimator.
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Also accepts a string that specifies an attribute name/path
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for extracting feature importance (implemented with `attrgetter`).
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For example, give `regressor_.coef_` in case of
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:class:`~sklearn.compose.TransformedTargetRegressor` or
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`named_steps.clf.feature_importances_` in case of
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:class:`~sklearn.pipeline.Pipeline` with its last step named `clf`.
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If `callable`, overrides the default feature importance getter.
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The callable is passed with the fitted estimator and it should
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return importance for each feature.
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.. versionadded:: 0.24
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Attributes
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----------
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estimator_ : an estimator
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The base estimator from which the transformer is built.
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This is stored only when a non-fitted estimator is passed to the
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``SelectFromModel``, i.e when prefit is False.
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threshold_ : float
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The threshold value used for feature selection.
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Notes
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-----
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Allows NaN/Inf in the input if the underlying estimator does as well.
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Examples
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--------
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>>> from sklearn.feature_selection import SelectFromModel
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>>> from sklearn.linear_model import LogisticRegression
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>>> X = [[ 0.87, -1.34, 0.31 ],
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... [-2.79, -0.02, -0.85 ],
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... [-1.34, -0.48, -2.55 ],
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... [ 1.92, 1.48, 0.65 ]]
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>>> y = [0, 1, 0, 1]
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>>> selector = SelectFromModel(estimator=LogisticRegression()).fit(X, y)
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>>> selector.estimator_.coef_
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array([[-0.3252302 , 0.83462377, 0.49750423]])
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>>> selector.threshold_
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0.55245...
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>>> selector.get_support()
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array([False, True, False])
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>>> selector.transform(X)
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array([[-1.34],
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[-0.02],
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[-0.48],
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[ 1.48]])
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See Also
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--------
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RFE : Recursive feature elimination based on importance weights.
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RFECV : Recursive feature elimination with built-in cross-validated
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selection of the best number of features.
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SequentialFeatureSelector : Sequential cross-validation based feature
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selection. Does not rely on importance weights.
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"""
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@_deprecate_positional_args
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def __init__(self, estimator, *, threshold=None, prefit=False,
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norm_order=1, max_features=None,
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importance_getter='auto'):
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self.estimator = estimator
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self.threshold = threshold
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self.prefit = prefit
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self.importance_getter = importance_getter
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self.norm_order = norm_order
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self.max_features = max_features
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def _get_support_mask(self):
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# SelectFromModel can directly call on transform.
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if self.prefit:
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estimator = self.estimator
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elif hasattr(self, 'estimator_'):
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estimator = self.estimator_
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else:
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raise ValueError('Either fit the model before transform or set'
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' "prefit=True" while passing the fitted'
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' estimator to the constructor.')
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scores = _get_feature_importances(
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estimator=estimator, getter=self.importance_getter,
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transform_func='norm', norm_order=self.norm_order)
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threshold = _calculate_threshold(estimator, scores, self.threshold)
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if self.max_features is not None:
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mask = np.zeros_like(scores, dtype=bool)
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candidate_indices = \
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np.argsort(-scores, kind='mergesort')[:self.max_features]
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mask[candidate_indices] = True
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else:
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mask = np.ones_like(scores, dtype=bool)
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mask[scores < threshold] = False
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return mask
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def fit(self, X, y=None, **fit_params):
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"""Fit the SelectFromModel meta-transformer.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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The training input samples.
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y : array-like of shape (n_samples,), default=None
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The target values (integers that correspond to classes in
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classification, real numbers in regression).
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**fit_params : Other estimator specific parameters
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Returns
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-------
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self : object
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"""
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if self.max_features is not None:
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if not isinstance(self.max_features, numbers.Integral):
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raise TypeError("'max_features' should be an integer between"
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" 0 and {} features. Got {!r} instead."
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.format(X.shape[1], self.max_features))
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elif self.max_features < 0 or self.max_features > X.shape[1]:
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raise ValueError("'max_features' should be 0 and {} features."
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"Got {} instead."
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.format(X.shape[1], self.max_features))
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if self.prefit:
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raise NotFittedError(
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"Since 'prefit=True', call transform directly")
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self.estimator_ = clone(self.estimator)
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self.estimator_.fit(X, y, **fit_params)
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return self
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@property
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def threshold_(self):
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scores = _get_feature_importances(estimator=self.estimator_,
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getter=self.importance_getter,
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transform_func='norm',
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norm_order=self.norm_order)
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return _calculate_threshold(self.estimator, scores, self.threshold)
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@if_delegate_has_method('estimator')
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def partial_fit(self, X, y=None, **fit_params):
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"""Fit the SelectFromModel meta-transformer only once.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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The training input samples.
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y : array-like of shape (n_samples,), default=None
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The target values (integers that correspond to classes in
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classification, real numbers in regression).
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**fit_params : Other estimator specific parameters
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Returns
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-------
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self : object
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"""
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if self.prefit:
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raise NotFittedError(
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"Since 'prefit=True', call transform directly")
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if not hasattr(self, "estimator_"):
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self.estimator_ = clone(self.estimator)
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self.estimator_.partial_fit(X, y, **fit_params)
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return self
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@property
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def n_features_in_(self):
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# For consistency with other estimators we raise a AttributeError so
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# that hasattr() fails if the estimator isn't fitted.
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try:
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check_is_fitted(self)
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except NotFittedError as nfe:
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raise AttributeError(
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"{} object has no n_features_in_ attribute."
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.format(self.__class__.__name__)
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) from nfe
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return self.estimator_.n_features_in_
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def _more_tags(self):
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return {
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'allow_nan': _safe_tags(self.estimator, key="allow_nan")
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}
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