Source code for sklearn.feature_selection.variance_threshold

# Author: Lars Buitinck
# License: 3-clause BSD

import numpy as np
from ..base import BaseEstimator
from .base import SelectorMixin
from ..utils import check_array
from ..utils.sparsefuncs import mean_variance_axis
from ..utils.validation import check_is_fitted

class VarianceThreshold(BaseEstimator, SelectorMixin):
    """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>`.

    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.

    variances_ : array, shape (n_features,)
        Variances of individual features.

    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

[docs] 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 = check_array(X, ('csr', 'csc'), dtype=np.float64) if hasattr(X, "toarray"): # sparse matrix _, self.variances_ = mean_variance_axis(X, axis=0) else: self.variances_ = np.var(X, axis=0) if np.all(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, 'variances_') return self.variances_ > self.threshold