scikit-learn · jjerphan · Jun 11, 2021 · Jun 14, 2021 · Jun 14, 2021 · Jun 15, 2021
diff --git a/.circleci/config.yml b/.circleci/config.yml
@@ -116,6 +116,8 @@ jobs:
     environment:
       - OMP_NUM_THREADS: 2
       - OPENBLAS_NUM_THREADS: 2
+      - NUMPY_VERSION: 'latest'
+      - SCIPY_VERSION: 'latest'
       - CYTHON_VERSION: 'latest'
       - JOBLIB_VERSION: 'latest'
       - THREADPOOLCTL_VERSION: 'latest'

diff --git a/build_tools/circle/build_test_arm.sh b/build_tools/circle/build_test_arm.sh
@@ -21,39 +21,51 @@ source build_tools/shared.sh
 
 sudo add-apt-repository --remove ppa:ubuntu-toolchain-r/test
 sudo apt-get update
-sudo apt-get install python3-virtualenv ccache
-python3 -m virtualenv --system-site-packages --python=python3 testenv
-source testenv/bin/activate
-pip install --upgrade pip
+
+# Setup conda environment
+MINICONDA_URL="https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-aarch64.sh"
+
+# Install Mambaforge
+wget $MINICONDA_URL -O mambaforge.sh
+MINICONDA_PATH=$HOME/miniconda
+chmod +x mambaforge.sh && ./mambaforge.sh -b -p $MINICONDA_PATH
+export PATH=$MINICONDA_PATH/bin:$PATH
+mamba update --yes conda
+
+# Create environment and install dependencies
+mamba create -n testenv --yes python=3.7
+source activate testenv
+
+# Use the latest by default
+mamba install --verbose -y  ccache \
+                            pip \
+                            $(get_dep numpy $NUMPY_VERSION) \
+                            $(get_dep scipy $SCIPY_VERSION) \
+                            $(get_dep cython $CYTHON_VERSION) \
+                            $(get_dep joblib $JOBLIB_VERSION) \
+                            $(get_dep threadpoolctl $THREADPOOLCTL_VERSION) \
+                            $(get_dep pytest $PYTEST_VERSION) \
+                            $(get_dep pytest-xdist $PYTEST_XDIST_VERSION)
 setup_ccache
-python -m pip install $(get_dep cython $CYTHON_VERSION) \
-                      $(get_dep joblib $JOBLIB_VERSION)
-python -m pip install $(get_dep threadpoolctl $THREADPOOLCTL_VERSION) \
-                      $(get_dep pytest $PYTEST_VERSION) \
-                      $(get_dep pytest-xdist $PYTEST_XDIST_VERSION)
 
 if [[ "$COVERAGE" == "true" ]]; then
-    python -m pip install codecov pytest-cov
-fi
-
-if [[ "$PYTEST_XDIST_VERSION" != "none" ]]; then
-    python -m pip install pytest-xdist
+    mamba install --verbose -y codecov pytest-cov
 fi
 
 if [[ "$TEST_DOCSTRINGS" == "true" ]]; then
     # numpydoc requires sphinx
-    python -m pip install sphinx
-    python -m pip install numpydoc
+    mamba install --verbose -y sphinx
+    mamba install --verbose -y numpydoc
 fi
 
 python --version
+conda list
 
 # Set parallelism to 3 to overlap IO bound tasks with CPU bound tasks on CI
 # workers with 2 cores when building the compiled extensions of scikit-learn.
 export SKLEARN_BUILD_PARALLEL=3
 
-python -m pip list
-pip install --verbose --editable .
+pip install --verbose --editable . --no-build-isolation
 ccache -s
 python -c "import sklearn; sklearn.show_versions()"
 python -m threadpoolctl --import sklearn

diff --git a/doc/glossary.rst b/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``
 

diff --git a/doc/modules/classes.rst b/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

diff --git a/doc/modules/density.rst b/doc/modules/density.rst
@@ -136,7 +136,7 @@ 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
+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>`_

diff --git a/sklearn/cluster/_affinity_propagation.py b/sklearn/cluster/_affinity_propagation.py
@@ -523,7 +523,9 @@ def predict(self, X):
 
         if self.cluster_centers_.shape[0] > 0:
             with config_context(assume_finite=True):
-                return pairwise_distances_argmin(X, self.cluster_centers_)
+                return pairwise_distances_argmin(
+                    X, self.cluster_centers_, metric="fast_euclidean"
+                )
         else:
             warnings.warn(
                 "This model does not have any cluster centers "

diff --git a/sklearn/cluster/_agglomerative.py b/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

diff --git a/sklearn/cluster/_birch.py b/sklearn/cluster/_birch.py
@@ -12,7 +12,6 @@
 from ..metrics import pairwise_distances_argmin
 from ..metrics.pairwise import euclidean_distances
 from ..base import TransformerMixin, ClusterMixin, BaseEstimator
-from ..utils.extmath import row_norms
 from ..utils import deprecated
 from ..utils.validation import check_is_fitted
 from ..exceptions import ConvergenceWarning
@@ -654,11 +653,10 @@ def predict(self, X):
         """
         check_is_fitted(self)
         X = self._validate_data(X, accept_sparse="csr", reset=False)
-        kwargs = {"Y_norm_squared": self._subcluster_norms}
 
         with config_context(assume_finite=True):
             argmin = pairwise_distances_argmin(
-                X, self.subcluster_centers_, metric_kwargs=kwargs
+                X, self.subcluster_centers_, metric="fast_euclidean"
             )
         return self.subcluster_labels_[argmin]
 
@@ -704,9 +702,6 @@ def _global_clustering(self, X=None):
                 "n_clusters should be an instance of ClusterMixin or an int"
             )
 
-        # To use in predict to avoid recalculation.
-        self._subcluster_norms = row_norms(self.subcluster_centers_, squared=True)
-
         if clusterer is None or not_enough_centroids:
             self.subcluster_labels_ = np.arange(len(centroids))
             if not_enough_centroids:

diff --git a/sklearn/cluster/_hierarchical_fast.pyx b/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)
-
diff --git a/sklearn/cluster/_mean_shift.py b/sklearn/cluster/_mean_shift.py
@@ -512,4 +512,6 @@ def predict(self, X):
         check_is_fitted(self)
         X = self._validate_data(X, reset=False)
         with config_context(assume_finite=True):
-            return pairwise_distances_argmin(X, self.cluster_centers_)
+            return pairwise_distances_argmin(
+                X, self.cluster_centers_, metric="fast_euclidean"
+            )
diff --git a/sklearn/cluster/tests/test_hierarchical.py b/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,

diff --git a/sklearn/metrics/__init__.py b/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",

diff --git a/sklearn/neighbors/_dist_metrics.pxd → sklearn/metrics/_dist_metrics.pxd b/sklearn/neighbors/_dist_metrics.pxd → sklearn/metrics/_dist_metrics.pxd
@@ -1,14 +1,13 @@
 #!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
@@ -60,9 +59,25 @@ 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 DTYPE_t csr_dist(
+        self,
+        const DTYPE_t[:] x1_data,
+        const ITYPE_t[:] x1_indices,
+        const DTYPE_t[:] x2_data,
+        const ITYPE_t[:] x2_indices,
+    ) nogil except -1
+
+    cdef DTYPE_t csr_rdist(
+        self,
+        const DTYPE_t[:] x1_data,
+        const ITYPE_t[:] x1_indices,
+        const DTYPE_t[:] x2_data,
+        const ITYPE_t[:] x2_indices,
+    ) nogil except -1
+
     cdef int pdist(self, const DTYPE_t[:, ::1] X, DTYPE_t[:, ::1] D) except -1
 
     cdef int cdist(self, const DTYPE_t[:, ::1] X, const DTYPE_t[:, ::1] Y,
@@ -71,3 +86,24 @@ cdef class DistanceMetric:
     cdef DTYPE_t _rdist_to_dist(self, DTYPE_t rdist) nogil except -1
 
     cdef DTYPE_t _dist_to_rdist(self, DTYPE_t dist) nogil except -1
+
+
+######################################################################
+# DatasetsPair base class
+cdef class DatasetsPair:
+    cdef DistanceMetric distance_metric
+
+    cdef ITYPE_t n_X(self) nogil
+
+    cdef ITYPE_t n_Y(self) nogil
+
+    cdef DTYPE_t dist(self, ITYPE_t i, ITYPE_t j) nogil
+
+    cdef DTYPE_t ranking_preserving_dist(self, ITYPE_t i, ITYPE_t j) nogil
+
+
+cdef class DenseDenseDatasetsPair(DatasetsPair):
+    cdef:
+        const DTYPE_t[:, ::1] X
+        const DTYPE_t[:, ::1] Y
+        ITYPE_t d