102 lines
3.2 KiB
Python
102 lines
3.2 KiB
Python
# Author: Lars Buitinck
|
|
# License: 3-clause BSD
|
|
|
|
import numpy as np
|
|
from ..base import BaseEstimator
|
|
from ._base import SelectorMixin
|
|
from ..utils.sparsefuncs import mean_variance_axis, min_max_axis
|
|
from ..utils.validation import check_is_fitted
|
|
|
|
|
|
class VarianceThreshold(SelectorMixin, BaseEstimator):
|
|
"""Feature selector that removes all low-variance features.
|
|
|
|
This feature selection algorithm looks only at the features (X), not the
|
|
desired outputs (y), and can thus be used for unsupervised learning.
|
|
|
|
Read more in the :ref:`User Guide <variance_threshold>`.
|
|
|
|
Parameters
|
|
----------
|
|
threshold : float, optional
|
|
Features with a training-set variance lower than this threshold will
|
|
be removed. The default is to keep all features with non-zero variance,
|
|
i.e. remove the features that have the same value in all samples.
|
|
|
|
Attributes
|
|
----------
|
|
variances_ : array, shape (n_features,)
|
|
Variances of individual features.
|
|
|
|
Notes
|
|
-----
|
|
Allows NaN in the input.
|
|
|
|
Examples
|
|
--------
|
|
The following dataset has integer features, two of which are the same
|
|
in every sample. These are removed with the default setting for threshold::
|
|
|
|
>>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
|
|
>>> selector = VarianceThreshold()
|
|
>>> selector.fit_transform(X)
|
|
array([[2, 0],
|
|
[1, 4],
|
|
[1, 1]])
|
|
"""
|
|
|
|
def __init__(self, threshold=0.):
|
|
self.threshold = threshold
|
|
|
|
def fit(self, X, y=None):
|
|
"""Learn empirical variances from X.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix}, shape (n_samples, n_features)
|
|
Sample vectors from which to compute variances.
|
|
|
|
y : any
|
|
Ignored. This parameter exists only for compatibility with
|
|
sklearn.pipeline.Pipeline.
|
|
|
|
Returns
|
|
-------
|
|
self
|
|
"""
|
|
X = self._validate_data(X, accept_sparse=('csr', 'csc'),
|
|
dtype=np.float64,
|
|
force_all_finite='allow-nan')
|
|
|
|
if hasattr(X, "toarray"): # sparse matrix
|
|
_, self.variances_ = mean_variance_axis(X, axis=0)
|
|
if self.threshold == 0:
|
|
mins, maxes = min_max_axis(X, axis=0)
|
|
peak_to_peaks = maxes - mins
|
|
else:
|
|
self.variances_ = np.nanvar(X, axis=0)
|
|
if self.threshold == 0:
|
|
peak_to_peaks = np.ptp(X, axis=0)
|
|
|
|
if self.threshold == 0:
|
|
# Use peak-to-peak to avoid numeric precision issues
|
|
# for constant features
|
|
compare_arr = np.array([self.variances_, peak_to_peaks])
|
|
self.variances_ = np.nanmin(compare_arr, axis=0)
|
|
|
|
if np.all(~np.isfinite(self.variances_) |
|
|
(self.variances_ <= self.threshold)):
|
|
msg = "No feature in X meets the variance threshold {0:.5f}"
|
|
if X.shape[0] == 1:
|
|
msg += " (X contains only one sample)"
|
|
raise ValueError(msg.format(self.threshold))
|
|
|
|
return self
|
|
|
|
def _get_support_mask(self):
|
|
check_is_fitted(self)
|
|
|
|
return self.variances_ > self.threshold
|
|
|
|
def _more_tags(self):
|
|
return {'allow_nan': True}
|