MAINT fix the way to call stats.mode (scikit-learn#23633)

Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Meekail Zain <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]>
neurodata · Aug 5, 2022 · 02a4b34 · 02a4b34
1 parent f2e1d9d
commit 02a4b34
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Showing 4 changed files with 13 additions and 6 deletions.
diff --git a/sklearn/impute/_base.py b/sklearn/impute/_base.py
@@ -9,10 +9,10 @@
 import numpy as np
 import numpy.ma as ma
 from scipy import sparse as sp
-from scipy import stats
 
 from ..base import BaseEstimator, TransformerMixin
 from ..utils._param_validation import StrOptions
+from ..utils.fixes import _mode
 from ..utils.sparsefuncs import _get_median
 from ..utils.validation import check_is_fitted
 from ..utils.validation import FLOAT_DTYPES
@@ -52,7 +52,7 @@ def _most_frequent(array, extra_value, n_repeat):
  if count == most_frequent_count
  )
  else:
- mode = stats.mode(array)
+ mode = _mode(array)
  most_frequent_value = mode[0][0]
  most_frequent_count = mode[1][0]
  else:

diff --git a/sklearn/neighbors/_classification.py b/sklearn/neighbors/_classification.py
@@ -10,7 +10,7 @@
 from numbers import Integral
 
 import numpy as np
-from scipy import stats
+from ..utils.fixes import _mode
 from ..utils.extmath import weighted_mode
 from ..utils.validation import _is_arraylike, _num_samples
 
@@ -249,7 +249,7 @@ def predict(self, X):
  y_pred = np.empty((n_queries, n_outputs), dtype=classes_[0].dtype)
  for k, classes_k in enumerate(classes_):
  if weights is None:
- mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
+ mode, _ = _mode(_y[neigh_ind, k], axis=1)
  else:
  mode, _ = weighted_mode(_y[neigh_ind, k], weights, axis=1)
 

diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py
@@ -163,3 +163,10 @@ def threadpool_info():
 
 
 threadpool_info.__doc__ = threadpoolctl.threadpool_info.__doc__
+
+
+# TODO: Remove when SciPy 1.9 is the minimum supported version
+def _mode(a, axis=0):
+ if sp_version >= parse_version("1.9.0"):
+ return scipy.stats.mode(a, axis=axis, keepdims=True)
+ return scipy.stats.mode(a, axis=axis)
diff --git a/sklearn/utils/tests/test_extmath.py b/sklearn/utils/tests/test_extmath.py
@@ -6,7 +6,6 @@
 import numpy as np
 from scipy import sparse
 from scipy import linalg
-from scipy import stats
 from scipy.sparse.linalg import eigsh
 from scipy.special import expit
 
@@ -19,6 +18,7 @@
 from sklearn.utils._testing import assert_array_equal
 from sklearn.utils._testing import assert_array_almost_equal
 from sklearn.utils._testing import skip_if_32bit
+from sklearn.utils.fixes import _mode
 
 from sklearn.utils.extmath import density, _safe_accumulator_op
 from sklearn.utils.extmath import randomized_svd, _randomized_eigsh
@@ -56,7 +56,7 @@ def test_uniform_weights():
  weights = np.ones(x.shape)
 
  for axis in (None, 0, 1):
- mode, score = stats.mode(x, axis)
+ mode, score = _mode(x, axis)
  mode2, score2 = weighted_mode(x, weights, axis=axis)
 
  assert_array_equal(mode, mode2)