MNT Move DistanceMetric under metrics (#21177)

doc/glossary.rst

@@ -644,9 +644,8 @@ General Concepts
 
         Note that for most distance metrics, we rely on implementations from
         :mod:`scipy.spatial.distance`, but may reimplement for efficiency in
-        our context.  The :mod:`neighbors` module also duplicates some metric
-        implementations for integration with efficient binary tree search data
-        structures.
+        our context. The :class:`metrics.DistanceMetric` interface is used to implement
+        distance metrics for integration with efficient neighbors search.
 
     pd
         A shorthand for `Pandas <https://pandas.pydata.org>`_ due to the
@@ -1023,7 +1022,7 @@ such as:
 
 Further examples:
 
-* :class:`neighbors.DistanceMetric`
+* :class:`metrics.DistanceMetric`
 * :class:`gaussian_process.kernels.Kernel`
 * ``tree.Criterion``
 

doc/modules/classes.rst

@@ -1058,6 +1058,16 @@ further details.
 
    metrics.consensus_score
 
+Distance metrics
+----------------
+
+.. currentmodule:: sklearn
+
+.. autosummary::
+   :toctree: generated/
+   :template: class.rst
+
+   metrics.DistanceMetric
 
 Pairwise metrics
 ----------------
@@ -1317,7 +1327,6 @@ Model validation
    :template: class.rst
 
    neighbors.BallTree
-   neighbors.DistanceMetric
    neighbors.KDTree
    neighbors.KernelDensity
    neighbors.KNeighborsClassifier

doc/modules/density.rst

@@ -136,9 +136,9 @@ The form of these kernels is as follows:
   :math:`K(x; h) \propto \cos(\frac{\pi x}{2h})` if :math:`x < h`
 
 The kernel density estimator can be used with any of the valid distance
-metrics (see :class:`~sklearn.neighbors.DistanceMetric` for a list of available metrics), though
-the results are properly normalized only for the Euclidean metric.  One
-particularly useful metric is the
+metrics (see :class:`~sklearn.metrics.DistanceMetric` for a list of
+available metrics), though the results are properly normalized only
+for the Euclidean metric.  One particularly useful metric is the
 `Haversine distance <https://en.wikipedia.org/wiki/Haversine_formula>`_
 which measures the angular distance between points on a sphere.  Here
 is an example of using a kernel density estimate for a visualization

doc/whats_new/v1.1.rst

@@ -60,6 +60,29 @@ Changelog
   message when the solver does not support sparse matrices with int64 indices.
   :pr:`21093` by `Tom Dupre la Tour`_.
 
+:mod:`sklearn.metrics`
+......................
+
+- |API| :class:`metrics.DistanceMetric` has been moved from
+  :mod:`sklearn.neighbors` to :mod:`sklearn.metric`.
+  Using `neighbors.DistanceMetric` for imports is still valid for
+  backward compatibility, but this alias will be removed in 1.3.
+  :pr:`21177` by :user:`Julien Jerphanion <jjerphan>`.
+
+:mod:`sklearn.model_selection`
+..............................
+
+- |Enhancement| raise an error during cross-validation when the fits for all the
+  splits failed. Similarly raise an error during grid-search when the fits for
+  all the models and all the splits failed. :pr:`21026` by :user:`Loïc Estève <lesteve>`.
+
+:mod:`sklearn.pipeline`
+.......................
+
+- |Enhancement| Added support for "passthrough" in :class:`FeatureUnion`.
+  Setting a transformer to "passthrough" will pass the features unchanged.
+  :pr:`20860` by :user:`Shubhraneel Pal <shubhraneel>`.
+
 :mod:`sklearn.utils`
 ....................
 

sklearn/cluster/_agglomerative.py

@@ -16,8 +16,8 @@
 
 from ..base import BaseEstimator, ClusterMixin
 from ..metrics.pairwise import paired_distances
-from ..neighbors import DistanceMetric
-from ..neighbors._dist_metrics import METRIC_MAPPING
+from ..metrics import DistanceMetric
+from ..metrics._dist_metrics import METRIC_MAPPING
 from ..utils import check_array
 from ..utils._fast_dict import IntFloatDict
 from ..utils.fixes import _astype_copy_false

sklearn/cluster/_hierarchical_fast.pyx

@@ -13,7 +13,7 @@ ctypedef np.int8_t INT8
 
 np.import_array()
 
-from ..neighbors._dist_metrics cimport DistanceMetric
+from ..metrics._dist_metrics cimport DistanceMetric
 from ..utils._fast_dict cimport IntFloatDict
 
 # C++
@@ -236,8 +236,8 @@ def max_merge(IntFloatDict a, IntFloatDict b,
 def average_merge(IntFloatDict a, IntFloatDict b,
               np.ndarray[ITYPE_t, ndim=1] mask,
               ITYPE_t n_a, ITYPE_t n_b):
-    """Merge two IntFloatDicts with the average strategy: when the 
-    same key is present in the two dicts, the weighted average of the two 
+    """Merge two IntFloatDicts with the average strategy: when the
+    same key is present in the two dicts, the weighted average of the two
     values is used.
 
     Parameters
@@ -290,13 +290,13 @@ def average_merge(IntFloatDict a, IntFloatDict b,
 
 
 ###############################################################################
-# An edge object for fast comparisons 
+# An edge object for fast comparisons
 
 cdef class WeightedEdge:
     cdef public ITYPE_t a
     cdef public ITYPE_t b
     cdef public DTYPE_t weight
-    
+
     def __init__(self, DTYPE_t weight, ITYPE_t a, ITYPE_t b):
         self.weight = weight
         self.a = a
@@ -326,7 +326,7 @@ cdef class WeightedEdge:
             return self.weight > other.weight
         elif op == 5:
             return self.weight >= other.weight
-        
+
     def __repr__(self):
         return "%s(weight=%f, a=%i, b=%i)" % (self.__class__.__name__,
                                               self.weight,
@@ -475,7 +475,7 @@ def mst_linkage_core(
 
     dist_metric: DistanceMetric
         A DistanceMetric object conforming to the API from
-        ``sklearn.neighbors._dist_metrics.pxd`` that will be
+        ``sklearn.metrics._dist_metrics.pxd`` that will be
         used to compute distances.
 
     Returns
@@ -534,4 +534,3 @@ def mst_linkage_core(
         current_node = new_node
 
     return np.array(result)
-

sklearn/cluster/tests/test_hierarchical.py

@@ -17,7 +17,7 @@
 from scipy.sparse.csgraph import connected_components
 
 from sklearn.metrics.cluster import adjusted_rand_score
-from sklearn.neighbors.tests.test_dist_metrics import METRICS_DEFAULT_PARAMS
+from sklearn.metrics.tests.test_dist_metrics import METRICS_DEFAULT_PARAMS
 from sklearn.utils._testing import assert_almost_equal, create_memmap_backed_data
 from sklearn.utils._testing import assert_array_almost_equal
 from sklearn.utils._testing import ignore_warnings
@@ -31,14 +31,15 @@
     _fix_connectivity,
 )
 from sklearn.feature_extraction.image import grid_to_graph
+from sklearn.metrics import DistanceMetric
 from sklearn.metrics.pairwise import (
     PAIRED_DISTANCES,
     cosine_distances,
     manhattan_distances,
     pairwise_distances,
 )
 from sklearn.metrics.cluster import normalized_mutual_info_score
-from sklearn.neighbors import kneighbors_graph, DistanceMetric
+from sklearn.neighbors import kneighbors_graph
 from sklearn.cluster._hierarchical_fast import (
     average_merge,
     max_merge,

sklearn/metrics/__init__.py

@@ -36,6 +36,8 @@
 from ._classification import brier_score_loss
 from ._classification import multilabel_confusion_matrix
 
+from ._dist_metrics import DistanceMetric
+
 from . import cluster
 from .cluster import adjusted_mutual_info_score
 from .cluster import adjusted_rand_score
@@ -115,6 +117,7 @@
     "davies_bouldin_score",
     "DetCurveDisplay",
     "det_curve",
+    "DistanceMetric",
     "euclidean_distances",
     "explained_variance_score",
     "f1_score",

sklearn/neighbors/_dist_metrics.pxd → sklearn/metrics/_dist_metrics.pxd

@@ -1,14 +1,12 @@
-#!python
-#cython: boundscheck=False
-#cython: wraparound=False
-#cython: cdivision=True
+# cython: boundscheck=False
+# cython: cdivision=True
+# cython: initializedcheck=False
+# cython: wraparound=False
 
-cimport cython
 cimport numpy as np
-from libc.math cimport fabs, sqrt, exp, cos, pow
+from libc.math cimport sqrt, exp
 
-from ._typedefs cimport DTYPE_t, ITYPE_t, DITYPE_t
-from ._typedefs import DTYPE, ITYPE
+from ..utils._typedefs cimport DTYPE_t, ITYPE_t
 
 ######################################################################
 # Inline distance functions
@@ -60,7 +58,7 @@ cdef class DistanceMetric:
     cdef DTYPE_t dist(self, const DTYPE_t* x1, const DTYPE_t* x2,
                       ITYPE_t size) nogil except -1
 
-    cdef DTYPE_t rdist(self, DTYPE_t* x1, DTYPE_t* x2,
+    cdef DTYPE_t rdist(self, const DTYPE_t* x1, const DTYPE_t* x2,
                        ITYPE_t size) nogil except -1
 
     cdef int pdist(self, const DTYPE_t[:, ::1] X, DTYPE_t[:, ::1] D) except -1

sklearn/neighbors/_dist_metrics.pyx → sklearn/metrics/_dist_metrics.pyx

@@ -1,8 +1,7 @@
-#!python
-#cython: boundscheck=False
-#cython: wraparound=False
-#cython: initializedcheck=False
-#cython: cdivision=True
+# cython: boundscheck=False
+# cython: cdivision=True
+# cython: initializedcheck=False
+# cython: wraparound=False
 
 # By Jake Vanderplas (2013) <jakevdp@cs.washington.edu>
 # written for the scikit-learn project
@@ -19,7 +18,7 @@ cdef extern from "arrayobject.h":
                                      int typenum, void* data)
 
 
-cdef inline np.ndarray _buffer_to_ndarray(DTYPE_t* x, np.npy_intp n):
+cdef inline np.ndarray _buffer_to_ndarray(const DTYPE_t* x, np.npy_intp n):
     # Wrap a memory buffer with an ndarray. Warning: this is not robust.
     # In particular, if x is deallocated before the returned array goes
     # out of scope, this could cause memory errors.  Since there is not
@@ -33,8 +32,8 @@ cdef inline np.ndarray _buffer_to_ndarray(DTYPE_t* x, np.npy_intp n):
 from libc.math cimport fabs, sqrt, exp, pow, cos, sin, asin
 cdef DTYPE_t INF = np.inf
 
-from ._typedefs cimport DTYPE_t, ITYPE_t, DITYPE_t, DTYPECODE
-from ._typedefs import DTYPE, ITYPE
+from ..utils._typedefs cimport DTYPE_t, ITYPE_t, DITYPE_t, DTYPECODE
+from ..utils._typedefs import DTYPE, ITYPE
 
 
 ######################################################################
@@ -98,7 +97,7 @@ cdef class DistanceMetric:
 
     Examples
     --------
-    >>> from sklearn.neighbors import DistanceMetric
+    >>> from sklearn.metrics import DistanceMetric
     >>> dist = DistanceMetric.get_metric('euclidean')
     >>> X = [[0, 1, 2],
              [3, 4, 5]]
@@ -291,14 +290,13 @@ cdef class DistanceMetric:
 
     cdef DTYPE_t rdist(self, const DTYPE_t* x1, const DTYPE_t* x2,
                        ITYPE_t size) nogil except -1:
-        """Compute the reduced distance between vectors x1 and x2.
+        """Compute the rank-preserving surrogate distance between vectors x1 and x2.
 
         This can optionally be overridden in a base class.
 
-        The reduced distance is any measure that yields the same rank as the
-        distance, but is more efficient to compute.  For example, for the
-        Euclidean metric, the reduced distance is the squared-euclidean
-        distance.
+        The rank-preserving surrogate distance is any measure that yields the same
+        rank as the distance, but is more efficient to compute. For example, for the
+        Euclidean metric, the surrogate distance is the squared-euclidean distance.
         """
         return self.dist(x1, x2, size)
 
@@ -323,25 +321,24 @@ cdef class DistanceMetric:
         return 0
 
     cdef DTYPE_t _rdist_to_dist(self, DTYPE_t rdist) nogil except -1:
-        """Convert the reduced distance to the distance"""
+        """Convert the rank-preserving surrogate distance to the distance"""
         return rdist
 
     cdef DTYPE_t _dist_to_rdist(self, DTYPE_t dist) nogil except -1:
-        """Convert the distance to the reduced distance"""
+        """Convert the distance to the rank-preserving surrogate distance"""
         return dist
 
     def rdist_to_dist(self, rdist):
-        """Convert the Reduced distance to the true distance.
+        """Convert the rank-preserving surrogate distance to the distance.
 
-        The reduced distance, defined for some metrics, is a computationally
-        more efficient measure which preserves the rank of the true distance.
-        For example, in the Euclidean distance metric, the reduced distance
-        is the squared-euclidean distance.
+        The surrogate distance is any measure that yields the same rank as the
+        distance, but is more efficient to compute. For example, for the
+        Euclidean metric, the surrogate distance is the squared-euclidean distance.
 
         Parameters
         ----------
         rdist : double
-            Reduced distance.
+            Surrogate distance.
 
         Returns
         -------
@@ -351,12 +348,11 @@ cdef class DistanceMetric:
         return rdist
 
     def dist_to_rdist(self, dist):
-        """Convert the true distance to the reduced distance.
+        """Convert the true distance to the rank-preserving surrogate distance.
 
-        The reduced distance, defined for some metrics, is a computationally
-        more efficient measure which preserves the rank of the true distance.
-        For example, in the Euclidean distance metric, the reduced distance
-        is the squared-euclidean distance.
+        The surrogate distance is any measure that yields the same rank as the
+        distance, but is more efficient to compute. For example, for the
+        Euclidean metric, the surrogate distance is the squared-euclidean distance.
 
         Parameters
         ----------
@@ -366,7 +362,7 @@ cdef class DistanceMetric:
         Returns
         -------
         double
-            Reduced distance.
+            Surrogate distance.
         """
         return dist
 
@@ -519,7 +515,7 @@ cdef class ChebyshevDistance(DistanceMetric):
 
     Examples
     --------
-    >>> from sklearn.neighbors.dist_metrics import DistanceMetric
+    >>> from sklearn.metrics.dist_metrics import DistanceMetric
     >>> dist = DistanceMetric.get_metric('chebyshev')
     >>> X = [[0, 1, 2],
     ...      [3, 4, 5]]

sklearn/metrics/pairwise.py

@@ -780,7 +780,7 @@ def haversine_distances(X, Y=None):
     array([[    0.        , 11099.54035582],
            [11099.54035582,     0.        ]])
     """
-    from ..neighbors import DistanceMetric
+    from ..metrics import DistanceMetric
 
     return DistanceMetric.get_metric("haversine").pairwise(X, Y)
 

sklearn/metrics/setup.py

@@ -1,4 +1,5 @@
 import os
+import numpy as np
 
 from numpy.distutils.misc_util import Configuration
 
@@ -18,6 +19,13 @@ def configuration(parent_package="", top_path=None):
         "_pairwise_fast", sources=["_pairwise_fast.pyx"], libraries=libraries
     )
 
+    config.add_extension(
+        "_dist_metrics",
+        sources=["_dist_metrics.pyx"],
+        include_dirs=[np.get_include(), os.path.join(np.get_include(), "numpy")],
+        libraries=libraries,
+    )
+
     config.add_subpackage("tests")
 
     return config