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MAINT: mutual information using upstream KDTree #31347

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MAINT: mutual information using upstream KDTree #31347

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13 changes: 7 additions & 6 deletions 13 sklearn/feature_selection/_mutual_info.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,10 +5,11 @@

import numpy as np
from scipy.sparse import issparse
from scipy.spatial import KDTree
from scipy.special import digamma

from ..metrics.cluster import mutual_info_score
from ..neighbors import KDTree, NearestNeighbors
from ..neighbors import NearestNeighbors
from ..preprocessing import scale
from ..utils import check_random_state
from ..utils._param_validation import Interval, StrOptions, validate_params
Expand Down Expand Up @@ -62,12 +63,12 @@ def _compute_mi_cc(x, y, n_neighbors):

# KDTree is explicitly fit to allow for the querying of number of
# neighbors within a specified radius
kd = KDTree(x, metric="chebyshev")
nx = kd.query_radius(x, radius, count_only=True, return_distance=False)
kd = KDTree(x)
nx = kd.query_ball_point(x, radius, p=np.inf, return_length=True)
nx = np.array(nx) - 1.0

kd = KDTree(y, metric="chebyshev")
ny = kd.query_radius(y, radius, count_only=True, return_distance=False)
kd = KDTree(y)
ny = kd.query_ball_point(y, radius, p=np.inf, return_length=True)
ny = np.array(ny) - 1.0

mi = (
Expand Down Expand Up @@ -140,7 +141,7 @@ def _compute_mi_cd(c, d, n_neighbors):
radius = radius[mask]

kd = KDTree(c)
m_all = kd.query_radius(c, radius, count_only=True, return_distance=False)
m_all = kd.query_ball_point(c, radius, return_length=True)
m_all = np.array(m_all)

mi = (
Expand Down
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