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