glemaitre
diff --git a/‎sklearn/neighbors/_binary_tree.pxi
Lines changed: 8 additions & 6 deletions b/‎sklearn/neighbors/_binary_tree.pxi
Lines changed: 8 additions & 6 deletions
diff --git a/‎sklearn/neighbors/_classification.py
Lines changed: 30 additions & 14 deletions b/‎sklearn/neighbors/_classification.py
Lines changed: 30 additions & 14 deletions
diff --git a/‎sklearn/neighbors/_graph.py
Lines changed: 36 additions & 56 deletions b/‎sklearn/neighbors/_graph.py
Lines changed: 36 additions & 56 deletions
diff --git a/‎sklearn/neighbors/_kde.py
Lines changed: 11 additions & 6 deletions b/‎sklearn/neighbors/_kde.py
Lines changed: 11 additions & 6 deletions
diff --git a/‎sklearn/neighbors/_lof.py
Lines changed: 12 additions & 23 deletions b/‎sklearn/neighbors/_lof.py
Lines changed: 12 additions & 23 deletions
diff --git a/‎sklearn/neighbors/_nearest_centroid.py
Lines changed: 10 additions & 5 deletions b/‎sklearn/neighbors/_nearest_centroid.py
Lines changed: 10 additions & 5 deletions
diff --git a/‎sklearn/neighbors/_regression.py
Lines changed: 30 additions & 14 deletions b/‎sklearn/neighbors/_regression.py
Lines changed: 30 additions & 14 deletions
@@ -230,12 +230,14 @@ leaf_size : positive int, default=40
     satisfy ``leaf_size <= n_points <= 2 * leaf_size``, except in
     the case that ``n_samples < leaf_size``.
 
-metric : str or DistanceMetric object
-    The distance metric to use for the tree.  Default='minkowski'
-    with p=2 (that is, a euclidean metric). See the documentation
-    of the DistanceMetric class for a list of available metrics.
-    {binary_tree}.valid_metrics gives a list of the metrics which
-    are valid for {BinaryTree}.
+metric : str or DistanceMetric object, default='minkowski'
+    Metric to use for distance computation. Default is "minkowski", which
+    results in the standard Euclidean distance when p = 2.
+    {binary_tree}.valid_metrics gives a list of the metrics which are valid for
+    {BinaryTree}. See the documentation of `scipy.spatial.distance
+    <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and the
+    metrics listed in :class:`~sklearn.metrics.pairwise.distance_metrics` for
+    more information.
 
 Additional keywords are passed to the distance metric class.
 Note: Callable functions in the metric parameter are NOT supported for KDTree
 
@@ -65,15 +65,22 @@ class KNeighborsClassifier(KNeighborsMixin, ClassifierMixin, NeighborsBase):
         (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
 
     metric : str or callable, default='minkowski'
-        The distance metric to use for the tree.  The default metric is
-        minkowski, and with p=2 is equivalent to the standard Euclidean
-        metric. For a list of available metrics, see the documentation of
-        :class:`~sklearn.metrics.DistanceMetric` and the metrics listed in
-        `sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. Note that the
-        "cosine" metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`.
+        Metric to use for distance computation. Default is "minkowski", which
+        results in the standard Euclidean distance when p = 2. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values.
+
         If metric is "precomputed", X is assumed to be a distance matrix and
-        must be square during fit. X may be a :term:`sparse graph`,
-        in which case only "nonzero" elements may be considered neighbors.
+        must be square during fit. X may be a :term:`sparse graph`, in which
+        case only "nonzero" elements may be considered neighbors.
+
+        If metric is a callable function, it takes two arrays representing 1D
+        vectors as inputs and must return one value indicating the distance
+        between those vectors. This works for Scipy's metrics, but is less
+        efficient than passing the metric name as a string.
 
     metric_params : dict, default=None
         Additional keyword arguments for the metric function.
@@ -357,13 +364,22 @@ class RadiusNeighborsClassifier(RadiusNeighborsMixin, ClassifierMixin, Neighbors
         (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
 
     metric : str or callable, default='minkowski'
-        Distance metric to use for the tree.  The default metric is
-        minkowski, and with p=2 is equivalent to the standard Euclidean
-        metric. For a list of available metrics, see the documentation of
-        :class:`~sklearn.metrics.DistanceMetric`.
+        Metric to use for distance computation. Default is "minkowski", which
+        results in the standard Euclidean distance when p = 2. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values.
+
         If metric is "precomputed", X is assumed to be a distance matrix and
-        must be square during fit. X may be a :term:`sparse graph`,
-        in which case only "nonzero" elements may be considered neighbors.
+        must be square during fit. X may be a :term:`sparse graph`, in which
+        case only "nonzero" elements may be considered neighbors.
+
+        If metric is a callable function, it takes two arrays representing 1D
+        vectors as inputs and must return one value indicating the distance
+        between those vectors. This works for Scipy's metrics, but is less
+        efficient than passing the metric name as a string.
 
     outlier_label : {manual label, 'most_frequent'}, default=None
         Label for outlier samples (samples with no neighbors in given radius).
 
@@ -65,13 +65,13 @@ def kneighbors_graph(
         between neighbors according to the given metric.
 
     metric : str, default='minkowski'
-        The distance metric to use for the tree. The default metric is
-        minkowski, and with p=2 is equivalent to the standard Euclidean
-        metric.
-        For a list of available metrics, see the documentation of
-        :class:`~sklearn.metrics.DistanceMetric` and the metrics listed in
-        `sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. Note that the
-        "cosine" metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`.
+        Metric to use for distance computation. Default is "minkowski", which
+        results in the standard Euclidean distance when p = 2. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values.
 
     p : int, default=2
         Power parameter for the Minkowski metric. When p = 1, this is
@@ -160,13 +160,13 @@ def radius_neighbors_graph(
         between neighbors according to the given metric.
 
     metric : str, default='minkowski'
-        The distance metric to use for the tree. The default metric is
-        minkowski, and with p=2 is equivalent to the standard Euclidean
-        metric.
-        For a list of available metrics, see the documentation of
-        :class:`~sklearn.metrics.DistanceMetric` and the metrics listed in
-        `sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. Note that the
-        "cosine" metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`.
+        Metric to use for distance computation. Default is "minkowski", which
+        results in the standard Euclidean distance when p = 2. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values.
 
     p : int, default=2
         Power parameter for the Minkowski metric. When p = 1, this is
@@ -266,31 +266,21 @@ class KNeighborsTransformer(
         nature of the problem.
 
     metric : str or callable, default='minkowski'
-        Metric to use for distance computation. Any metric from scikit-learn
-        or scipy.spatial.distance can be used.
-
-        If metric is a callable function, it is called on each
-        pair of instances (rows) and the resulting value recorded. The callable
-        should take two arrays as input and return one value indicating the
-        distance between them. This works for Scipy's metrics, but is less
+        Metric to use for distance computation. Default is "minkowski", which
+        results in the standard Euclidean distance when p = 2. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values.
+
+        If metric is a callable function, it takes two arrays representing 1D
+        vectors as inputs and must return one value indicating the distance
+        between those vectors. This works for Scipy's metrics, but is less
         efficient than passing the metric name as a string.
 
         Distance matrices are not supported.
 
-        Valid values for metric are:
-
-        - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
-          'manhattan']
-
-        - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
-          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
-          'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
-          'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
-          'yule']
-
-        See the documentation for scipy.spatial.distance for details on these
-        metrics.
-
     p : int, default=2
         Parameter for the Minkowski metric from
         sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
@@ -493,31 +483,21 @@ class RadiusNeighborsTransformer(
         nature of the problem.
 
     metric : str or callable, default='minkowski'
-        Metric to use for distance computation. Any metric from scikit-learn
-        or scipy.spatial.distance can be used.
-
-        If metric is a callable function, it is called on each
-        pair of instances (rows) and the resulting value recorded. The callable
-        should take two arrays as input and return one value indicating the
-        distance between them. This works for Scipy's metrics, but is less
+        Metric to use for distance computation. Default is "minkowski", which
+        results in the standard Euclidean distance when p = 2. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values.
+
+        If metric is a callable function, it takes two arrays representing 1D
+        vectors as inputs and must return one value indicating the distance
+        between those vectors. This works for Scipy's metrics, but is less
         efficient than passing the metric name as a string.
 
         Distance matrices are not supported.
 
-        Valid values for metric are:
-
-        - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
-          'manhattan']
-
-        - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
-          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
-          'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
-          'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
-          'yule']
-
-        See the documentation for scipy.spatial.distance for details on these
-        metrics.
-
     p : int, default=2
         Parameter for the Minkowski metric from
         sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
 
@@ -47,12 +47,17 @@ class KernelDensity(BaseEstimator):
         The kernel to use.
 
     metric : str, default='euclidean'
-        The distance metric to use.  Note that not all metrics are
-        valid with all algorithms.  Refer to the documentation of
-        :class:`BallTree` and :class:`KDTree` for a description of
-        available algorithms.  Note that the normalization of the density
-        output is correct only for the Euclidean distance metric. Default
-        is 'euclidean'.
+        Metric to use for distance computation. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values.
+
+        Not all metrics are valid with all algorithms: refer to the
+        documentation of :class:`BallTree` and :class:`KDTree`. Note that the
+        normalization of the density output is correct only for the Euclidean
+        distance metric.
 
     atol : float, default=0
         The desired absolute tolerance of the result.  A larger tolerance will
 
@@ -58,34 +58,23 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):
         nature of the problem.
 
     metric : str or callable, default='minkowski'
-        The metric is used for distance computation. Any metric from scikit-learn
-        or scipy.spatial.distance can be used.
+        Metric to use for distance computation. Default is "minkowski", which
+        results in the standard Euclidean distance when p = 2. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values.
 
         If metric is "precomputed", X is assumed to be a distance matrix and
-        must be square. X may be a sparse matrix, in which case only "nonzero"
-        elements may be considered neighbors.
+        must be square during fit. X may be a :term:`sparse graph`, in which
+        case only "nonzero" elements may be considered neighbors.
 
-        If metric is a callable function, it is called on each
-        pair of instances (rows) and the resulting value recorded. The callable
-        should take two arrays as input and return one value indicating the
-        distance between them. This works for Scipy's metrics, but is less
+        If metric is a callable function, it takes two arrays representing 1D
+        vectors as inputs and must return one value indicating the distance
+        between those vectors. This works for Scipy's metrics, but is less
         efficient than passing the metric name as a string.
 
-        Valid values for metric are:
-
-        - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
-          'manhattan']
-
-        - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
-          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
-          'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
-          'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
-          'yule']
-
-        See the documentation for scipy.spatial.distance for details on these
-        metrics:
-        https://docs.scipy.org/doc/scipy/reference/spatial.distance.html.
-
     p : int, default=2
         Parameter for the Minkowski metric from
         :func:`sklearn.metrics.pairwise.pairwise_distances`. When p = 1, this
 
@@ -30,11 +30,16 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):
     Parameters
     ----------
     metric : str or callable, default="euclidean"
-        The metric to use when calculating distance between instances in a
-        feature array. If metric is a string or callable, it must be one of
-        the options allowed by
-        :func:`~sklearn.metrics.pairwise_distances` for its metric
-        parameter. The centroids for the samples corresponding to each class is
+        Metric to use for distance computation. Default is "minkowski", which
+        results in the standard Euclidean distance when p = 2. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values. Note that "wminkowski", "seuclidean" and "mahalanobis" are not
+        supported.
+
+        The centroids for the samples corresponding to each class is
         the point from which the sum of the distances (according to the metric)
         of all samples that belong to that particular class are minimized.
         If the `"manhattan"` metric is provided, this centroid is the median
 
@@ -72,15 +72,22 @@ class KNeighborsRegressor(KNeighborsMixin, RegressorMixin, NeighborsBase):
         (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
 
     metric : str or callable, default='minkowski'
-        The distance metric to use for the tree.  The default metric is
-        minkowski, and with p=2 is equivalent to the standard Euclidean
-        metric. For a list of available metrics, see the documentation of
-        :class:`~sklearn.metrics.DistanceMetric` and the metrics listed in
-        `sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. Note that the
-        "cosine" metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`.
+        Metric to use for distance computation. Default is "minkowski", which
+        results in the standard Euclidean distance when p = 2. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values.
+
         If metric is "precomputed", X is assumed to be a distance matrix and
-        must be square during fit. X may be a :term:`sparse graph`,
-        in which case only "nonzero" elements may be considered neighbors.
+        must be square during fit. X may be a :term:`sparse graph`, in which
+        case only "nonzero" elements may be considered neighbors.
+
+        If metric is a callable function, it takes two arrays representing 1D
+        vectors as inputs and must return one value indicating the distance
+        between those vectors. This works for Scipy's metrics, but is less
+        efficient than passing the metric name as a string.
 
     metric_params : dict, default=None
         Additional keyword arguments for the metric function.
@@ -300,13 +307,22 @@ class RadiusNeighborsRegressor(RadiusNeighborsMixin, RegressorMixin, NeighborsBa
         (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
 
     metric : str or callable, default='minkowski'
-        The distance metric to use for the tree.  The default metric is
-        minkowski, and with p=2 is equivalent to the standard Euclidean
-        metric. See the documentation of :class:`DistanceMetric` for a
-        list of available metrics.
+        Metric to use for distance computation. Default is "minkowski", which
+        results in the standard Euclidean distance when p = 2. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values.
+
         If metric is "precomputed", X is assumed to be a distance matrix and
-        must be square during fit. X may be a :term:`sparse graph`,
-        in which case only "nonzero" elements may be considered neighbors.
+        must be square during fit. X may be a :term:`sparse graph`, in which
+        case only "nonzero" elements may be considered neighbors.
+
+        If metric is a callable function, it takes two arrays representing 1D
+        vectors as inputs and must return one value indicating the distance
+        between those vectors. This works for Scipy's metrics, but is less
+        efficient than passing the metric name as a string.
 
     metric_params : dict, default=None
         Additional keyword arguments for the metric function.