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import numpy as np
from ..base import TransformerMixin
from ..utils import safe_mask
class SelectorMixin(TransformerMixin):
""""Transformer mixin selecting features based on importance weights.
This implementation can be mixin on any estimator that exposes a
``feature_importances_`` or ``coef_`` attribute to evaluate the relative
importance of individual features for feature selection.
"""
def transform(self, X, threshold=None):
"""Reduce X to its most important features.
Parameters
----------
X : array or scipy sparse matrix of shape [n_samples, n_features]
The input samples.
threshold : string, float or None, optional (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
available, the object attribute ``threshold`` is used. Otherwise,
"mean" is used by default.
Returns
-------
X_r : array of shape [n_samples, n_selected_features]
The input samples with only the selected features.
"""
# Retrieve importance vector
if hasattr(self, "feature_importances_"):
importances = self.feature_importances_
if importances is None:
raise ValueError("Importance weights not computed. Please "
"Set the compute_importances parameter before fit.")
elif hasattr(self, "coef_"):
if self.coef_.ndim == 1:
importances = np.abs(self.coef_)
else:
importances = np.sum(np.abs(self.coef_), axis=0)
else:
raise ValueError("Missing `feature_importances_` or `coef_`"
" attribute, did you forget to set the "
"estimator's parameter to compute it?")
# Retrieve threshold
if threshold is None:
if hasattr(self, "penalty") and self.penalty == "l1":
# the natural default threshold is 0 when l1 penalty was used
threshold = getattr(self, "threshold", 1e-5)
else:
threshold = getattr(self, "threshold", "mean")
if isinstance(threshold, basestring):
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:
threshold = float(threshold)
# Selection
mask = importances >= threshold
if np.any(mask):
mask = safe_mask(X, mask)
return X[:, mask]
else:
raise ValueError("Invalid threshold: all features are discarded.")
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