137 lines
4.4 KiB
Python
137 lines
4.4 KiB
Python
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# Author: Lars Buitinck
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# License: 3-clause BSD
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from numbers import Real
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import numpy as np
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from ..base import BaseEstimator, _fit_context
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from ..utils._param_validation import Interval
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from ..utils.sparsefuncs import mean_variance_axis, min_max_axis
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from ..utils.validation import check_is_fitted
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from ._base import SelectorMixin
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class VarianceThreshold(SelectorMixin, BaseEstimator):
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"""Feature selector that removes all low-variance features.
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This feature selection algorithm looks only at the features (X), not the
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desired outputs (y), and can thus be used for unsupervised learning.
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Read more in the :ref:`User Guide <variance_threshold>`.
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Parameters
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----------
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threshold : float, default=0
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Features with a training-set variance lower than this threshold will
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be removed. The default is to keep all features with non-zero variance,
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i.e. remove the features that have the same value in all samples.
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Attributes
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----------
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variances_ : array, shape (n_features,)
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Variances of individual features.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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See Also
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--------
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SelectFromModel: Meta-transformer for selecting features based on
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importance weights.
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SelectPercentile : Select features according to a percentile of the highest
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scores.
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SequentialFeatureSelector : Transformer that performs Sequential Feature
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Selection.
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Notes
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-----
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Allows NaN in the input.
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Raises ValueError if no feature in X meets the variance threshold.
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Examples
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--------
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The following dataset has integer features, two of which are the same
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in every sample. These are removed with the default setting for threshold::
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>>> from sklearn.feature_selection import VarianceThreshold
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>>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
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>>> selector = VarianceThreshold()
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>>> selector.fit_transform(X)
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array([[2, 0],
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[1, 4],
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[1, 1]])
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"""
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_parameter_constraints: dict = {
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"threshold": [Interval(Real, 0, None, closed="left")]
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}
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def __init__(self, threshold=0.0):
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self.threshold = threshold
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@_fit_context(prefer_skip_nested_validation=True)
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def fit(self, X, y=None):
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"""Learn empirical variances from X.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape (n_samples, n_features)
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Data from which to compute variances, where `n_samples` is
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the number of samples and `n_features` is the number of features.
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y : any, default=None
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Ignored. This parameter exists only for compatibility with
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sklearn.pipeline.Pipeline.
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Returns
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-------
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self : object
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Returns the instance itself.
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"""
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X = self._validate_data(
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X,
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accept_sparse=("csr", "csc"),
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dtype=np.float64,
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force_all_finite="allow-nan",
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)
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if hasattr(X, "toarray"): # sparse matrix
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_, self.variances_ = mean_variance_axis(X, axis=0)
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if self.threshold == 0:
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mins, maxes = min_max_axis(X, axis=0)
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peak_to_peaks = maxes - mins
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else:
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self.variances_ = np.nanvar(X, axis=0)
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if self.threshold == 0:
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peak_to_peaks = np.ptp(X, axis=0)
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if self.threshold == 0:
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# Use peak-to-peak to avoid numeric precision issues
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# for constant features
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compare_arr = np.array([self.variances_, peak_to_peaks])
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self.variances_ = np.nanmin(compare_arr, axis=0)
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if np.all(~np.isfinite(self.variances_) | (self.variances_ <= self.threshold)):
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msg = "No feature in X meets the variance threshold {0:.5f}"
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if X.shape[0] == 1:
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msg += " (X contains only one sample)"
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raise ValueError(msg.format(self.threshold))
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return self
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def _get_support_mask(self):
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check_is_fitted(self)
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return self.variances_ > self.threshold
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def _more_tags(self):
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return {"allow_nan": True}
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