450 lines
16 KiB
Python
450 lines
16 KiB
Python
# Author: Nikolay Mayorov <n59_ru@hotmail.com>
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# License: 3-clause BSD
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import numpy as np
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from scipy.sparse import issparse
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from scipy.special import digamma
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from ..metrics.cluster import mutual_info_score
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from ..neighbors import NearestNeighbors, KDTree
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from ..preprocessing import scale
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from ..utils import check_random_state
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from ..utils.fixes import _astype_copy_false
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from ..utils.validation import check_array, check_X_y
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from ..utils.validation import _deprecate_positional_args
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from ..utils.multiclass import check_classification_targets
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def _compute_mi_cc(x, y, n_neighbors):
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"""Compute mutual information between two continuous variables.
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Parameters
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----------
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x, y : ndarray, shape (n_samples,)
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Samples of two continuous random variables, must have an identical
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shape.
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n_neighbors : int
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Number of nearest neighbors to search for each point, see [1]_.
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Returns
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-------
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mi : float
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Estimated mutual information. If it turned out to be negative it is
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replace by 0.
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Notes
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-----
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True mutual information can't be negative. If its estimate by a numerical
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method is negative, it means (providing the method is adequate) that the
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mutual information is close to 0 and replacing it by 0 is a reasonable
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strategy.
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References
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----------
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.. [1] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
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information". Phys. Rev. E 69, 2004.
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"""
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n_samples = x.size
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x = x.reshape((-1, 1))
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y = y.reshape((-1, 1))
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xy = np.hstack((x, y))
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# Here we rely on NearestNeighbors to select the fastest algorithm.
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nn = NearestNeighbors(metric='chebyshev', n_neighbors=n_neighbors)
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nn.fit(xy)
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radius = nn.kneighbors()[0]
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radius = np.nextafter(radius[:, -1], 0)
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# KDTree is explicitly fit to allow for the querying of number of
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# neighbors within a specified radius
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kd = KDTree(x, metric='chebyshev')
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nx = kd.query_radius(x, radius, count_only=True, return_distance=False)
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nx = np.array(nx) - 1.0
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kd = KDTree(y, metric='chebyshev')
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ny = kd.query_radius(y, radius, count_only=True, return_distance=False)
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ny = np.array(ny) - 1.0
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mi = (digamma(n_samples) + digamma(n_neighbors) -
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np.mean(digamma(nx + 1)) - np.mean(digamma(ny + 1)))
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return max(0, mi)
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def _compute_mi_cd(c, d, n_neighbors):
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"""Compute mutual information between continuous and discrete variables.
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Parameters
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----------
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c : ndarray, shape (n_samples,)
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Samples of a continuous random variable.
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d : ndarray, shape (n_samples,)
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Samples of a discrete random variable.
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n_neighbors : int
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Number of nearest neighbors to search for each point, see [1]_.
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Returns
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-------
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mi : float
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Estimated mutual information. If it turned out to be negative it is
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replace by 0.
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Notes
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-----
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True mutual information can't be negative. If its estimate by a numerical
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method is negative, it means (providing the method is adequate) that the
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mutual information is close to 0 and replacing it by 0 is a reasonable
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strategy.
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References
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----------
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.. [1] B. C. Ross "Mutual Information between Discrete and Continuous
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Data Sets". PLoS ONE 9(2), 2014.
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"""
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n_samples = c.shape[0]
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c = c.reshape((-1, 1))
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radius = np.empty(n_samples)
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label_counts = np.empty(n_samples)
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k_all = np.empty(n_samples)
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nn = NearestNeighbors()
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for label in np.unique(d):
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mask = d == label
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count = np.sum(mask)
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if count > 1:
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k = min(n_neighbors, count - 1)
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nn.set_params(n_neighbors=k)
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nn.fit(c[mask])
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r = nn.kneighbors()[0]
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radius[mask] = np.nextafter(r[:, -1], 0)
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k_all[mask] = k
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label_counts[mask] = count
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# Ignore points with unique labels.
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mask = label_counts > 1
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n_samples = np.sum(mask)
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label_counts = label_counts[mask]
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k_all = k_all[mask]
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c = c[mask]
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radius = radius[mask]
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kd = KDTree(c)
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m_all = kd.query_radius(c, radius, count_only=True, return_distance=False)
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m_all = np.array(m_all) - 1.0
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mi = (digamma(n_samples) + np.mean(digamma(k_all)) -
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np.mean(digamma(label_counts)) -
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np.mean(digamma(m_all + 1)))
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return max(0, mi)
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def _compute_mi(x, y, x_discrete, y_discrete, n_neighbors=3):
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"""Compute mutual information between two variables.
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This is a simple wrapper which selects a proper function to call based on
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whether `x` and `y` are discrete or not.
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"""
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if x_discrete and y_discrete:
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return mutual_info_score(x, y)
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elif x_discrete and not y_discrete:
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return _compute_mi_cd(y, x, n_neighbors)
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elif not x_discrete and y_discrete:
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return _compute_mi_cd(x, y, n_neighbors)
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else:
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return _compute_mi_cc(x, y, n_neighbors)
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def _iterate_columns(X, columns=None):
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"""Iterate over columns of a matrix.
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Parameters
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----------
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X : ndarray or csc_matrix, shape (n_samples, n_features)
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Matrix over which to iterate.
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columns : iterable or None, default=None
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Indices of columns to iterate over. If None, iterate over all columns.
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Yields
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------
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x : ndarray, shape (n_samples,)
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Columns of `X` in dense format.
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"""
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if columns is None:
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columns = range(X.shape[1])
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if issparse(X):
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for i in columns:
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x = np.zeros(X.shape[0])
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start_ptr, end_ptr = X.indptr[i], X.indptr[i + 1]
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x[X.indices[start_ptr:end_ptr]] = X.data[start_ptr:end_ptr]
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yield x
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else:
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for i in columns:
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yield X[:, i]
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def _estimate_mi(X, y, discrete_features='auto', discrete_target=False,
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n_neighbors=3, copy=True, random_state=None):
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"""Estimate mutual information between the features and the target.
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Parameters
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----------
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X : array-like or sparse matrix, shape (n_samples, n_features)
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Feature matrix.
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y : array-like of shape (n_samples,)
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Target vector.
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discrete_features : {'auto', bool, array-like}, default='auto'
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If bool, then determines whether to consider all features discrete
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or continuous. If array, then it should be either a boolean mask
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with shape (n_features,) or array with indices of discrete features.
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If 'auto', it is assigned to False for dense `X` and to True for
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sparse `X`.
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discrete_target : bool, default=False
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Whether to consider `y` as a discrete variable.
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n_neighbors : int, default=3
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Number of neighbors to use for MI estimation for continuous variables,
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see [1]_ and [2]_. Higher values reduce variance of the estimation, but
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could introduce a bias.
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copy : bool, default=True
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Whether to make a copy of the given data. If set to False, the initial
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data will be overwritten.
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random_state : int, RandomState instance or None, default=None
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Determines random number generation for adding small noise to
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continuous variables in order to remove repeated values.
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Pass an int for reproducible results across multiple function calls.
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See :term:`Glossary <random_state>`.
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Returns
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-------
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mi : ndarray, shape (n_features,)
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Estimated mutual information between each feature and the target.
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A negative value will be replaced by 0.
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References
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----------
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.. [1] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
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information". Phys. Rev. E 69, 2004.
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.. [2] B. C. Ross "Mutual Information between Discrete and Continuous
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Data Sets". PLoS ONE 9(2), 2014.
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"""
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X, y = check_X_y(X, y, accept_sparse='csc', y_numeric=not discrete_target)
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n_samples, n_features = X.shape
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if isinstance(discrete_features, (str, bool)):
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if isinstance(discrete_features, str):
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if discrete_features == 'auto':
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discrete_features = issparse(X)
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else:
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raise ValueError("Invalid string value for discrete_features.")
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discrete_mask = np.empty(n_features, dtype=bool)
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discrete_mask.fill(discrete_features)
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else:
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discrete_features = check_array(discrete_features, ensure_2d=False)
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if discrete_features.dtype != 'bool':
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discrete_mask = np.zeros(n_features, dtype=bool)
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discrete_mask[discrete_features] = True
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else:
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discrete_mask = discrete_features
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continuous_mask = ~discrete_mask
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if np.any(continuous_mask) and issparse(X):
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raise ValueError("Sparse matrix `X` can't have continuous features.")
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rng = check_random_state(random_state)
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if np.any(continuous_mask):
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if copy:
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X = X.copy()
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if not discrete_target:
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X[:, continuous_mask] = scale(X[:, continuous_mask],
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with_mean=False, copy=False)
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# Add small noise to continuous features as advised in Kraskov et. al.
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X = X.astype(float, **_astype_copy_false(X))
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means = np.maximum(1, np.mean(np.abs(X[:, continuous_mask]), axis=0))
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X[:, continuous_mask] += 1e-10 * means * rng.randn(
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n_samples, np.sum(continuous_mask))
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if not discrete_target:
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y = scale(y, with_mean=False)
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y += 1e-10 * np.maximum(1, np.mean(np.abs(y))) * rng.randn(n_samples)
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mi = [_compute_mi(x, y, discrete_feature, discrete_target, n_neighbors) for
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x, discrete_feature in zip(_iterate_columns(X), discrete_mask)]
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return np.array(mi)
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@_deprecate_positional_args
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def mutual_info_regression(X, y, *, discrete_features='auto', n_neighbors=3,
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copy=True, random_state=None):
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"""Estimate mutual information for a continuous target variable.
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Mutual information (MI) [1]_ between two random variables is a non-negative
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value, which measures the dependency between the variables. It is equal
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to zero if and only if two random variables are independent, and higher
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values mean higher dependency.
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The function relies on nonparametric methods based on entropy estimation
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from k-nearest neighbors distances as described in [2]_ and [3]_. Both
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methods are based on the idea originally proposed in [4]_.
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It can be used for univariate features selection, read more in the
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:ref:`User Guide <univariate_feature_selection>`.
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Parameters
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----------
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X : array-like or sparse matrix, shape (n_samples, n_features)
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Feature matrix.
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y : array-like of shape (n_samples,)
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Target vector.
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discrete_features : {'auto', bool, array-like}, default='auto'
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If bool, then determines whether to consider all features discrete
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or continuous. If array, then it should be either a boolean mask
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with shape (n_features,) or array with indices of discrete features.
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If 'auto', it is assigned to False for dense `X` and to True for
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sparse `X`.
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n_neighbors : int, default=3
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Number of neighbors to use for MI estimation for continuous variables,
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see [2]_ and [3]_. Higher values reduce variance of the estimation, but
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could introduce a bias.
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copy : bool, default=True
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Whether to make a copy of the given data. If set to False, the initial
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data will be overwritten.
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random_state : int, RandomState instance or None, default=None
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Determines random number generation for adding small noise to
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continuous variables in order to remove repeated values.
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Pass an int for reproducible results across multiple function calls.
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See :term:`Glossary <random_state>`.
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Returns
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-------
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mi : ndarray, shape (n_features,)
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Estimated mutual information between each feature and the target.
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Notes
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-----
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1. The term "discrete features" is used instead of naming them
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"categorical", because it describes the essence more accurately.
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For example, pixel intensities of an image are discrete features
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(but hardly categorical) and you will get better results if mark them
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as such. Also note, that treating a continuous variable as discrete and
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vice versa will usually give incorrect results, so be attentive about
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that.
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2. True mutual information can't be negative. If its estimate turns out
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to be negative, it is replaced by zero.
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References
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----------
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.. [1] `Mutual Information
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<https://en.wikipedia.org/wiki/Mutual_information>`_
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on Wikipedia.
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.. [2] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
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information". Phys. Rev. E 69, 2004.
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.. [3] B. C. Ross "Mutual Information between Discrete and Continuous
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Data Sets". PLoS ONE 9(2), 2014.
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.. [4] L. F. Kozachenko, N. N. Leonenko, "Sample Estimate of the Entropy
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of a Random Vector", Probl. Peredachi Inf., 23:2 (1987), 9-16
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"""
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return _estimate_mi(X, y, discrete_features, False, n_neighbors,
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copy, random_state)
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@_deprecate_positional_args
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def mutual_info_classif(X, y, *, discrete_features='auto', n_neighbors=3,
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copy=True, random_state=None):
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"""Estimate mutual information for a discrete target variable.
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Mutual information (MI) [1]_ between two random variables is a non-negative
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value, which measures the dependency between the variables. It is equal
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|
to zero if and only if two random variables are independent, and higher
|
|
values mean higher dependency.
|
|
|
|
The function relies on nonparametric methods based on entropy estimation
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|
from k-nearest neighbors distances as described in [2]_ and [3]_. Both
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|
methods are based on the idea originally proposed in [4]_.
|
|
|
|
It can be used for univariate features selection, read more in the
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:ref:`User Guide <univariate_feature_selection>`.
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Parameters
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----------
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X : array-like or sparse matrix, shape (n_samples, n_features)
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Feature matrix.
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y : array-like of shape (n_samples,)
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Target vector.
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discrete_features : {'auto', bool, array-like}, default='auto'
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If bool, then determines whether to consider all features discrete
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or continuous. If array, then it should be either a boolean mask
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with shape (n_features,) or array with indices of discrete features.
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If 'auto', it is assigned to False for dense `X` and to True for
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sparse `X`.
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n_neighbors : int, default=3
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Number of neighbors to use for MI estimation for continuous variables,
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see [2]_ and [3]_. Higher values reduce variance of the estimation, but
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could introduce a bias.
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copy : bool, default=True
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Whether to make a copy of the given data. If set to False, the initial
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data will be overwritten.
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random_state : int, RandomState instance or None, default=None
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Determines random number generation for adding small noise to
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continuous variables in order to remove repeated values.
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Pass an int for reproducible results across multiple function calls.
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See :term:`Glossary <random_state>`.
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Returns
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-------
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mi : ndarray, shape (n_features,)
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|
Estimated mutual information between each feature and the target.
|
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|
|
Notes
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-----
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|
1. The term "discrete features" is used instead of naming them
|
|
"categorical", because it describes the essence more accurately.
|
|
For example, pixel intensities of an image are discrete features
|
|
(but hardly categorical) and you will get better results if mark them
|
|
as such. Also note, that treating a continuous variable as discrete and
|
|
vice versa will usually give incorrect results, so be attentive about
|
|
that.
|
|
2. True mutual information can't be negative. If its estimate turns out
|
|
to be negative, it is replaced by zero.
|
|
|
|
References
|
|
----------
|
|
.. [1] `Mutual Information
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|
<https://en.wikipedia.org/wiki/Mutual_information>`_
|
|
on Wikipedia.
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|
.. [2] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
|
|
information". Phys. Rev. E 69, 2004.
|
|
.. [3] B. C. Ross "Mutual Information between Discrete and Continuous
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|
Data Sets". PLoS ONE 9(2), 2014.
|
|
.. [4] L. F. Kozachenko, N. N. Leonenko, "Sample Estimate of the Entropy
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of a Random Vector:, Probl. Peredachi Inf., 23:2 (1987), 9-16
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"""
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check_classification_targets(y)
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return _estimate_mi(X, y, discrete_features, True, n_neighbors,
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copy, random_state)
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