scikit-learn · glemaitre · Dec 19, 2022 · Dec 19, 2022 · Dec 19, 2022 · Dec 19, 2022
diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst
@@ -128,6 +128,12 @@ Changelog
   when the provided `sample_weight` reduces the problem to a single class in `fit`.
   :pr:`24140` by :user:`Jonathan Ohayon <Johayon>` and :user:`Chiara Marmo <cmarmo>`.
 
+:mod:`sklearn.neighbors`
+........................
+- |Fix| Remove support for `"KulsinskiDistance"` in :class:`neighbors.BallTree`. This
+  metric is not a proper metric and cannot be supported by the BallTree.
+  :pr:`25212` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 :mod:`sklearn.neural_network`
 .............................
 

diff --git a/sklearn/cluster/_optics.py b/sklearn/cluster/_optics.py
@@ -81,7 +81,7 @@ class OPTICS(ClusterMixin, BaseEstimator):
           'manhattan']
 
         - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
-          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
+          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'kulczynski1',
           'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
           'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
           'yule']
@@ -90,6 +90,10 @@ class OPTICS(ClusterMixin, BaseEstimator):
         See the documentation for scipy.spatial.distance for details on these
         metrics.
 
+        .. note::
+           `'kulsinski'` is deprecated from SciPy 1.8. Use `'kulczynski1'` instead.
+           Note that the two metrics are not identical.
+
     p : float, default=2
         Parameter for the Minkowski metric from
         :class:`~sklearn.metrics.pairwise_distances`. When p = 1, this is
@@ -481,14 +485,18 @@ def compute_optics_graph(
           'manhattan']
 
         - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
-          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
+          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'kulczynski1',
           'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
           'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
           'yule']
 
         See the documentation for scipy.spatial.distance for details on these
         metrics.
 
+        .. note::
+           `'kulsinski'` is deprecated from SciPy 1.8. Use `'kulczynski1'` instead.
+           Note that the two metrics are not identical.
+
     p : int, default=2
         Parameter for the Minkowski metric from
         :class:`~sklearn.metrics.pairwise_distances`. When p = 1, this is

diff --git a/sklearn/metrics/_dist_metrics.pyx.tp b/sklearn/metrics/_dist_metrics.pyx.tp
@@ -42,6 +42,7 @@ from ..utils._typedefs cimport DTYPE_t, ITYPE_t, DTYPECODE
 from ..utils._typedefs import DTYPE, ITYPE
 from ..utils._readonly_array_wrapper import ReadonlyArrayWrapper
 from ..utils import check_array
+from ..utils.fixes import parse_version, sp_version
 
 cdef inline double fmax(double a, double b) nogil:
     return max(a, b)
@@ -59,12 +60,17 @@ BOOL_METRICS = [
     "matching",
     "jaccard",
     "dice",
-    "kulsinski",
     "rogerstanimoto",
     "russellrao",
     "sokalmichener",
     "sokalsneath",
 ]
+if sp_version >= parse_version("1.8"):
+    # Introduced in SciPy 1.8
+    BOOL_METRICS += ["kulczynski1"]
+if sp_version < parse_version("1.11"):
+    # Deprecated in SciPy 1.8 and removed in SciPy 1.10
+    BOOL_METRICS += ["kulsinski"]
 
 def get_valid_metric_ids(L):
     """Given an iterable of metric class names or class identifiers,
@@ -105,6 +111,7 @@ METRIC_MAPPING{{name_suffix}} = {
     'jaccard': JaccardDistance{{name_suffix}},
     'dice': DiceDistance{{name_suffix}},
     'kulsinski': KulsinskiDistance{{name_suffix}},
+    'kulczynski1': Kulczynski1Distance{{name_suffix}},
     'rogerstanimoto': RogersTanimotoDistance{{name_suffix}},
     'russellrao': RussellRaoDistance{{name_suffix}},
     'sokalmichener': SokalMichenerDistance{{name_suffix}},
@@ -213,6 +220,7 @@ cdef class DistanceMetric{{name_suffix}}:
     "matching"         MatchingDistance         NNEQ / N
     "dice"             DiceDistance             NNEQ / (NTT + NNZ)
     "kulsinski"        KulsinskiDistance        (NNEQ + N - NTT) / (NNEQ + N)
+    "kulczynski1"      Kulczynski1Distance        NTT / NNEQ
     "rogerstanimoto"   RogersTanimotoDistance   2 * NNEQ / (N + NNEQ)
     "russellrao"       RussellRaoDistance       (N - NTT) / N
     "sokalmichener"    SokalMichenerDistance    2 * NNEQ / (N + NNEQ)
@@ -2312,6 +2320,102 @@ cdef class KulsinskiDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
 
         return (n_neq - n_tt + size) * 1.0 / (n_neq + size)
 
+
+#------------------------------------------------------------
+# Kulczynski Distance (boolean)
+#  D(x, y) = c_11 / (c_01 + c_10)
+cdef class Kulczynski1Distance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Kulsinski Distance
+
+    Kulczynski Distance is a dissimilarity measure for boolean-valued
+    vectors. All nonzero entries will be treated as True, zero entries will
+    be treated as False.
+
+        D(x, y) = c_11 / (c_01 + c_10)
+
+    """
+    cdef inline DTYPE_t dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        ITYPE_t size,
+    ) nogil except -1:
+        cdef int tf1, tf2, c_11 = 0, c_01 = 0, c_10 = 0
+        cdef cnp.intp_t j
+        for j in range(size):
+            tf1 = x1[j] != 0
+            tf2 = x2[j] != 0
+            if tf1 == 0 and tf2 == 0:
+                continue
+            elif tf1 == tf2:
+                c_11 += (tf1 and tf2)
+            elif tf1 == 0:
+                c_01 += tf2
+            else:
+                c_10 += tf1
+        return <double> c_11 / <double> (c_01 + c_10)
+
+    cdef inline DTYPE_t dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const SPARSE_INDEX_TYPE_t[:] x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const SPARSE_INDEX_TYPE_t[:] x2_indices,
+        const SPARSE_INDEX_TYPE_t x1_start,
+        const SPARSE_INDEX_TYPE_t x1_end,
+        const SPARSE_INDEX_TYPE_t x2_start,
+        const SPARSE_INDEX_TYPE_t x2_end,
+        const ITYPE_t size,
+    ) nogil except -1:
+
+        cdef:
+            cnp.npy_intp ix1, ix2
+            cnp.npy_intp i1 = x1_start
+            cnp.npy_intp i2 = x2_start
+
+            ITYPE_t tf1, tf2, c_11 = 0, c_01 = 0, c_10 = 0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            tf1 = x1_data[i1] != 0
+            tf2 = x2_data[i2] != 0
+
+            if ix1 == ix2:
+                if tf1 == 0 and tf2 == 0:
+                    pass
+                elif tf1 == tf2:
+                    c_11 += (tf1 and tf2)
+                elif tf1 == 0:
+                    c_01 += tf2
+                else:
+                    c_10 += tf1
+                i1 += 1
+                i2 += 1
+            elif ix1 < ix2:
+                # non-zero value in x1 but not in x2
+                c_10 += tf1
+                i1 += 1
+            else:
+                # non-zero value in x2 but not in x1
+                c_01 += tf2
+                i2 += 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                tf2 = x2_data[i2] != 0
+                c_01 += tf2
+                i2 += 1
+        else:
+            while i1 < x1_end:
+                tf1 = x1_data[i1] != 0
+                c_10 += tf1
+                i1 += 1
+
+        return <double> c_11 / <double> (c_10 + c_01)
+
+
 #------------------------------------------------------------
 # Rogers-Tanimoto Distance (boolean)
 #  D(x, y) = 2 * n_neq / (n + n_neq)

diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
@@ -635,14 +635,18 @@ def pairwise_distances_argmin_min(
           'manhattan']
 
         - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
-          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
+          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'kulczynski1',
           'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
           'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
           'yule']
 
         See the documentation for scipy.spatial.distance for details on these
         metrics.
 
+        .. note::
+           `'kulsinski'` is deprecated from SciPy 1.8. Use `'kulczynski1'` instead.
+           Note that the two metrics are not identical.
+
     metric_kwargs : dict, default=None
         Keyword arguments to pass to specified metric function.
 
@@ -752,14 +756,18 @@ def pairwise_distances_argmin(X, Y, *, axis=1, metric="euclidean", metric_kwargs
           'manhattan']
 
         - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
-          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
+          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'kulczynski1',
           'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
           'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
           'yule']
 
         See the documentation for scipy.spatial.distance for details on these
         metrics.
 
+        .. note::
+           `'kulsinski'` is deprecated from SciPy 1.8. Use `'kulczynski1'` instead.
+           Note that the two metrics are not identical.
+
     metric_kwargs : dict, default=None
         Keyword arguments to pass to specified metric function.
 
@@ -1639,7 +1647,6 @@ def _pairwise_callable(X, Y, metric, force_all_finite=True, **kwds):
     "dice",
     "hamming",
     "jaccard",
-    "kulsinski",
     "mahalanobis",
     "matching",
     "minkowski",
@@ -1654,6 +1661,12 @@ def _pairwise_callable(X, Y, metric, force_all_finite=True, **kwds):
     "nan_euclidean",
     "haversine",
 ]
+if sp_version >= parse_version("1.8"):
+    # Introduced in SciPy 1.8
+    _VALID_METRICS += ["kulczynski1"]
+if sp_version < parse_version("1.11"):
+    # Deprecated in SciPy 1.8 and removed in SciPy 1.10
+    _VALID_METRICS += ["kulsinski"]
 
 _NAN_METRICS = ["nan_euclidean"]
 
@@ -1902,12 +1915,16 @@ def pairwise_distances(
       ['nan_euclidean'] but it does not yet support sparse matrices.
 
     - From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
-      'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis',
-      'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
+      'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'kulczynski1',
+      'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
       'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule']
       See the documentation for scipy.spatial.distance for details on these
       metrics. These metrics do not support sparse matrix inputs.
 
+    .. note::
+        `'kulsinski'` is deprecated from SciPy 1.8. Use `'kulczynski1'` instead.
+        Note that the two metrics are not identical.
+
     Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are
     valid scipy.spatial.distance metrics), the scikit-learn implementation
     will be used, which is faster and has support for sparse matrices (except
@@ -2043,14 +2060,19 @@ def pairwise_distances(
 PAIRWISE_BOOLEAN_FUNCTIONS = [
     "dice",
     "jaccard",
-    "kulsinski",
     "matching",
     "rogerstanimoto",
     "russellrao",
     "sokalmichener",
     "sokalsneath",
     "yule",
 ]
+if sp_version >= parse_version("1.8"):
+    # Introduced in SciPy 1.8
+    PAIRWISE_BOOLEAN_FUNCTIONS += ["kulczynski1"]
+if sp_version < parse_version("1.11"):
+    # Deprecated in SciPy 1.8 and removed in SciPy 1.10
+    PAIRWISE_BOOLEAN_FUNCTIONS += ["kulsinski"]
 
 # Helper functions - distance
 PAIRWISE_KERNEL_FUNCTIONS = {

diff --git a/sklearn/metrics/tests/test_pairwise.py b/sklearn/metrics/tests/test_pairwise.py
@@ -225,7 +225,7 @@ def test_pairwise_boolean_distance(metric):
     with ignore_warnings(category=DataConversionWarning):
         for Z in [Y, None]:
             res = pairwise_distances(X, Z, metric=metric)
-            res[np.isnan(res)] = 0
+            np.nan_to_num(res, nan=0, posinf=0, neginf=0, copy=False)
             assert np.sum(res != 0) == 0
 
     # non-boolean arrays are converted to boolean for boolean

diff --git a/sklearn/neighbors/_ball_tree.pyx b/sklearn/neighbors/_ball_tree.pyx
@@ -11,7 +11,7 @@ VALID_METRICS = ['EuclideanDistance', 'SEuclideanDistance',
                  'MahalanobisDistance', 'HammingDistance',
                  'CanberraDistance', 'BrayCurtisDistance',
                  'JaccardDistance', 'MatchingDistance',
-                 'DiceDistance', 'KulsinskiDistance',
+                 'DiceDistance',
                  'RogersTanimotoDistance', 'RussellRaoDistance',
                  'SokalMichenerDistance', 'SokalSneathDistance',
                  'PyFuncDistance', 'HaversineDistance']

diff --git a/sklearn/neighbors/_base.py b/sklearn/neighbors/_base.py
@@ -57,7 +57,7 @@
                 "dice",
                 "hamming",
                 "jaccard",
-                "kulsinski",
+                "kulsinski" if sp_version < parse_version("1.8") else "kulczynski1",
                 "mahalanobis",
                 "matching",
                 "minkowski",

diff --git a/sklearn/neighbors/tests/test_ball_tree.py b/sklearn/neighbors/tests/test_ball_tree.py
@@ -30,7 +30,6 @@
     "matching",
     "jaccard",
     "dice",
-    "kulsinski",
     "rogerstanimoto",
     "russellrao",
     "sokalmichener",