scikit-learn · thomasjpfan · Apr 14, 2021 · Apr 13, 2021 · Apr 13, 2021 · Apr 13, 2021
diff --git a/doc/whats_new/v1.0.rst b/doc/whats_new/v1.0.rst
@@ -98,6 +98,9 @@ Changelog
 - |API| :class:`cluster.Birch` attributes, `fit_` and `partial_fit_`, are
   deprecated and will be removed in 1.2. :pr:`19297` by `Thomas Fan`_.
 
+- |FIX| :class:`cluster.AgglomerativeClustering` now supports readonly
+  memory-mapped datasets. :pr:`19883` by `Julien Jerphanion <jjerphan>`.
+
 :mod:`sklearn.compose`
 ......................
 
@@ -306,6 +309,9 @@ Changelog
   :pr:`19473` by :user:`jiefangxuanyan <jiefangxuanyan>` and
   :user:`Julien Jerphanion <jjerphan>`.
 
+- |FIX| :class:`neighbors.DistanceMetric` subclasses now support readonly
+  memory-mapped datasets. :pr:`19883` by `Julien Jerphanion <jjerphan>`.
+
 :mod:`sklearn.pipeline`
 .......................
 

diff --git a/sklearn/cluster/_hierarchical_fast.pyx b/sklearn/cluster/_hierarchical_fast.pyx
@@ -455,7 +455,7 @@ def single_linkage_label(L):
 @cython.boundscheck(False)
 @cython.nonecheck(False)
 def mst_linkage_core(
-        DTYPE_t [:, ::1] raw_data,
+        const DTYPE_t [:, ::1] raw_data,
         DistanceMetric dist_metric):
     """
     Compute the necessary elements of a minimum spanning

diff --git a/sklearn/cluster/tests/test_hierarchical.py b/sklearn/cluster/tests/test_hierarchical.py
@@ -5,6 +5,7 @@
 # Authors: Vincent Michel, 2010, Gael Varoquaux 2012,
 #          Matteo Visconti di Oleggio Castello 2014
 # License: BSD 3 clause
+import itertools
 from tempfile import mkdtemp
 import shutil
 import pytest
@@ -15,7 +16,11 @@
 from scipy.cluster import hierarchy
 
 from sklearn.metrics.cluster import adjusted_rand_score
-from sklearn.utils._testing import assert_almost_equal
+from sklearn.neighbors.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
 
@@ -28,8 +33,12 @@
 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
-from sklearn.cluster._hierarchical_fast import average_merge, max_merge
+from sklearn.neighbors import kneighbors_graph, DistanceMetric
+from sklearn.cluster._hierarchical_fast import (
+    average_merge,
+    max_merge,
+    mst_linkage_core
+)
 from sklearn.utils._fast_dict import IntFloatDict
 from sklearn.utils._testing import assert_array_equal
 from sklearn.datasets import make_moons, make_circles
@@ -264,6 +273,16 @@ def test_agglomerative_clustering():
     assert_array_equal(clustering.labels_, clustering2.labels_)
 
 
+def test_agglomerative_clustering_memory_mapped():
+    """AgglomerativeClustering must work on mem-mapped dataset.
+
+    Non-regression test for issue #19875.
+    """
+    rng = np.random.RandomState(0)
+    Xmm = create_memmap_backed_data(rng.randn(50, 100))
+    AgglomerativeClustering(affinity="euclidean", linkage="single").fit(Xmm)
+
+
 def test_ward_agglomeration():
     # Check that we obtain the correct solution in a simplistic case
     rng = np.random.RandomState(0)
@@ -375,6 +394,25 @@ def test_vector_scikit_single_vs_scipy_single(seed):
     assess_same_labelling(cut, cut_scipy)
 
 
+@pytest.mark.parametrize('metric', METRICS_DEFAULT_PARAMS)
+def test_mst_linkage_core_memory_mapped(metric):
+    """The MST-LINKAGE-CORE algorithm must work on mem-mapped dataset.
+
+    Non-regression test for issue #19875.
+    """
+    rng = np.random.RandomState(seed=1)
+    X = rng.normal(size=(20, 4))
+    Xmm = create_memmap_backed_data(X)
+    argdict = METRICS_DEFAULT_PARAMS[metric]
+    keys = argdict.keys()
+    for vals in itertools.product(*argdict.values()):
+        kwargs = dict(zip(keys, vals))
+        distance_metric = DistanceMetric.get_metric(metric, **kwargs)
+        mst = mst_linkage_core(X, distance_metric)
+        mst_mm = mst_linkage_core(Xmm, distance_metric)
+        np.testing.assert_equal(mst, mst_mm)
+
+
 def test_identical_points():
     # Ensure identical points are handled correctly when using mst with
     # a sparse connectivity matrix

diff --git a/sklearn/neighbors/_dist_metrics.pxd b/sklearn/neighbors/_dist_metrics.pxd
@@ -15,7 +15,7 @@ from ._typedefs import DTYPE, ITYPE
 #
 #  We use these for the default (euclidean) case so that they can be
 #  inlined.  This leads to faster computation for the most common case
-cdef inline DTYPE_t euclidean_dist(DTYPE_t* x1, DTYPE_t* x2,
+cdef inline DTYPE_t euclidean_dist(const DTYPE_t* x1, const DTYPE_t* x2,
                                    ITYPE_t size) nogil except -1:
     cdef DTYPE_t tmp, d=0
     cdef np.intp_t j
@@ -25,7 +25,7 @@ cdef inline DTYPE_t euclidean_dist(DTYPE_t* x1, DTYPE_t* x2,
     return sqrt(d)
 
 
-cdef inline DTYPE_t euclidean_rdist(DTYPE_t* x1, DTYPE_t* x2,
+cdef inline DTYPE_t euclidean_rdist(const DTYPE_t* x1, const DTYPE_t* x2,
                                     ITYPE_t size) nogil except -1:
     cdef DTYPE_t tmp, d=0
     cdef np.intp_t j
@@ -35,11 +35,11 @@ cdef inline DTYPE_t euclidean_rdist(DTYPE_t* x1, DTYPE_t* x2,
     return d
 
 
-cdef inline DTYPE_t euclidean_dist_to_rdist(DTYPE_t dist) nogil except -1:
+cdef inline DTYPE_t euclidean_dist_to_rdist(const DTYPE_t dist) nogil except -1:
     return dist * dist
 
 
-cdef inline DTYPE_t euclidean_rdist_to_dist(DTYPE_t dist) nogil except -1:
+cdef inline DTYPE_t euclidean_rdist_to_dist(const DTYPE_t dist) nogil except -1:
     return sqrt(dist)
 
 
@@ -61,7 +61,7 @@ cdef class DistanceMetric:
     cdef object func
     cdef object kwargs
 
-    cdef DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
+    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,