913 lines
32 KiB
Python
913 lines
32 KiB
Python
"""
|
|
Several basic tests for hierarchical clustering procedures
|
|
|
|
"""
|
|
# 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
|
|
from functools import partial
|
|
|
|
import numpy as np
|
|
from scipy import sparse
|
|
from scipy.cluster import hierarchy
|
|
from scipy.sparse.csgraph import connected_components
|
|
|
|
from sklearn.metrics.cluster import adjusted_rand_score
|
|
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
|
|
|
|
from sklearn.cluster import ward_tree
|
|
from sklearn.cluster import AgglomerativeClustering, FeatureAgglomeration
|
|
from sklearn.cluster._agglomerative import (
|
|
_hc_cut,
|
|
_TREE_BUILDERS,
|
|
linkage_tree,
|
|
_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
|
|
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
|
|
|
|
|
|
def test_linkage_misc():
|
|
# Misc tests on linkage
|
|
rng = np.random.RandomState(42)
|
|
X = rng.normal(size=(5, 5))
|
|
|
|
with pytest.raises(ValueError):
|
|
linkage_tree(X, linkage="foo")
|
|
|
|
with pytest.raises(ValueError):
|
|
linkage_tree(X, connectivity=np.ones((4, 4)))
|
|
|
|
# Smoke test FeatureAgglomeration
|
|
FeatureAgglomeration().fit(X)
|
|
|
|
# test hierarchical clustering on a precomputed distances matrix
|
|
dis = cosine_distances(X)
|
|
|
|
res = linkage_tree(dis, affinity="precomputed")
|
|
assert_array_equal(res[0], linkage_tree(X, affinity="cosine")[0])
|
|
|
|
# test hierarchical clustering on a precomputed distances matrix
|
|
res = linkage_tree(X, affinity=manhattan_distances)
|
|
assert_array_equal(res[0], linkage_tree(X, affinity="manhattan")[0])
|
|
|
|
|
|
def test_structured_linkage_tree():
|
|
# Check that we obtain the correct solution for structured linkage trees.
|
|
rng = np.random.RandomState(0)
|
|
mask = np.ones([10, 10], dtype=bool)
|
|
# Avoiding a mask with only 'True' entries
|
|
mask[4:7, 4:7] = 0
|
|
X = rng.randn(50, 100)
|
|
connectivity = grid_to_graph(*mask.shape)
|
|
for tree_builder in _TREE_BUILDERS.values():
|
|
children, n_components, n_leaves, parent = tree_builder(
|
|
X.T, connectivity=connectivity
|
|
)
|
|
n_nodes = 2 * X.shape[1] - 1
|
|
assert len(children) + n_leaves == n_nodes
|
|
# Check that ward_tree raises a ValueError with a connectivity matrix
|
|
# of the wrong shape
|
|
with pytest.raises(ValueError):
|
|
tree_builder(X.T, connectivity=np.ones((4, 4)))
|
|
# Check that fitting with no samples raises an error
|
|
with pytest.raises(ValueError):
|
|
tree_builder(X.T[:0], connectivity=connectivity)
|
|
|
|
|
|
def test_unstructured_linkage_tree():
|
|
# Check that we obtain the correct solution for unstructured linkage trees.
|
|
rng = np.random.RandomState(0)
|
|
X = rng.randn(50, 100)
|
|
for this_X in (X, X[0]):
|
|
# With specified a number of clusters just for the sake of
|
|
# raising a warning and testing the warning code
|
|
with ignore_warnings():
|
|
with pytest.warns(UserWarning):
|
|
children, n_nodes, n_leaves, parent = ward_tree(this_X.T, n_clusters=10)
|
|
n_nodes = 2 * X.shape[1] - 1
|
|
assert len(children) + n_leaves == n_nodes
|
|
|
|
for tree_builder in _TREE_BUILDERS.values():
|
|
for this_X in (X, X[0]):
|
|
with ignore_warnings():
|
|
with pytest.warns(UserWarning):
|
|
children, n_nodes, n_leaves, parent = tree_builder(
|
|
this_X.T, n_clusters=10
|
|
)
|
|
n_nodes = 2 * X.shape[1] - 1
|
|
assert len(children) + n_leaves == n_nodes
|
|
|
|
|
|
def test_height_linkage_tree():
|
|
# Check that the height of the results of linkage tree is sorted.
|
|
rng = np.random.RandomState(0)
|
|
mask = np.ones([10, 10], dtype=bool)
|
|
X = rng.randn(50, 100)
|
|
connectivity = grid_to_graph(*mask.shape)
|
|
for linkage_func in _TREE_BUILDERS.values():
|
|
children, n_nodes, n_leaves, parent = linkage_func(
|
|
X.T, connectivity=connectivity
|
|
)
|
|
n_nodes = 2 * X.shape[1] - 1
|
|
assert len(children) + n_leaves == n_nodes
|
|
|
|
|
|
def test_zero_cosine_linkage_tree():
|
|
# Check that zero vectors in X produce an error when
|
|
# 'cosine' affinity is used
|
|
X = np.array([[0, 1], [0, 0]])
|
|
msg = "Cosine affinity cannot be used when X contains zero vectors"
|
|
with pytest.raises(ValueError, match=msg):
|
|
linkage_tree(X, affinity="cosine")
|
|
|
|
|
|
@pytest.mark.parametrize("n_clusters, distance_threshold", [(None, 0.5), (10, None)])
|
|
@pytest.mark.parametrize("compute_distances", [True, False])
|
|
@pytest.mark.parametrize("linkage", ["ward", "complete", "average", "single"])
|
|
def test_agglomerative_clustering_distances(
|
|
n_clusters, compute_distances, distance_threshold, linkage
|
|
):
|
|
# Check that when `compute_distances` is True or `distance_threshold` is
|
|
# given, the fitted model has an attribute `distances_`.
|
|
rng = np.random.RandomState(0)
|
|
mask = np.ones([10, 10], dtype=bool)
|
|
n_samples = 100
|
|
X = rng.randn(n_samples, 50)
|
|
connectivity = grid_to_graph(*mask.shape)
|
|
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=n_clusters,
|
|
connectivity=connectivity,
|
|
linkage=linkage,
|
|
distance_threshold=distance_threshold,
|
|
compute_distances=compute_distances,
|
|
)
|
|
clustering.fit(X)
|
|
if compute_distances or (distance_threshold is not None):
|
|
assert hasattr(clustering, "distances_")
|
|
n_children = clustering.children_.shape[0]
|
|
n_nodes = n_children + 1
|
|
assert clustering.distances_.shape == (n_nodes - 1,)
|
|
else:
|
|
assert not hasattr(clustering, "distances_")
|
|
|
|
|
|
def test_agglomerative_clustering(global_random_seed):
|
|
# Check that we obtain the correct number of clusters with
|
|
# agglomerative clustering.
|
|
rng = np.random.RandomState(global_random_seed)
|
|
mask = np.ones([10, 10], dtype=bool)
|
|
n_samples = 100
|
|
X = rng.randn(n_samples, 50)
|
|
connectivity = grid_to_graph(*mask.shape)
|
|
for linkage in ("ward", "complete", "average", "single"):
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=10, connectivity=connectivity, linkage=linkage
|
|
)
|
|
clustering.fit(X)
|
|
# test caching
|
|
try:
|
|
tempdir = mkdtemp()
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=10,
|
|
connectivity=connectivity,
|
|
memory=tempdir,
|
|
linkage=linkage,
|
|
)
|
|
clustering.fit(X)
|
|
labels = clustering.labels_
|
|
assert np.size(np.unique(labels)) == 10
|
|
finally:
|
|
shutil.rmtree(tempdir)
|
|
# Turn caching off now
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=10, connectivity=connectivity, linkage=linkage
|
|
)
|
|
# Check that we obtain the same solution with early-stopping of the
|
|
# tree building
|
|
clustering.compute_full_tree = False
|
|
clustering.fit(X)
|
|
assert_almost_equal(normalized_mutual_info_score(clustering.labels_, labels), 1)
|
|
clustering.connectivity = None
|
|
clustering.fit(X)
|
|
assert np.size(np.unique(clustering.labels_)) == 10
|
|
# Check that we raise a TypeError on dense matrices
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=10,
|
|
connectivity=sparse.lil_matrix(connectivity.toarray()[:10, :10]),
|
|
linkage=linkage,
|
|
)
|
|
with pytest.raises(ValueError):
|
|
clustering.fit(X)
|
|
|
|
# Test that using ward with another metric than euclidean raises an
|
|
# exception
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=10,
|
|
connectivity=connectivity.toarray(),
|
|
metric="manhattan",
|
|
linkage="ward",
|
|
)
|
|
with pytest.raises(ValueError):
|
|
clustering.fit(X)
|
|
|
|
# Test using another metric than euclidean works with linkage complete
|
|
for metric in PAIRED_DISTANCES.keys():
|
|
# Compare our (structured) implementation to scipy
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=10,
|
|
connectivity=np.ones((n_samples, n_samples)),
|
|
metric=metric,
|
|
linkage="complete",
|
|
)
|
|
clustering.fit(X)
|
|
clustering2 = AgglomerativeClustering(
|
|
n_clusters=10, connectivity=None, metric=metric, linkage="complete"
|
|
)
|
|
clustering2.fit(X)
|
|
assert_almost_equal(
|
|
normalized_mutual_info_score(clustering2.labels_, clustering.labels_), 1
|
|
)
|
|
|
|
# Test that using a distance matrix (affinity = 'precomputed') has same
|
|
# results (with connectivity constraints)
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=10, connectivity=connectivity, linkage="complete"
|
|
)
|
|
clustering.fit(X)
|
|
X_dist = pairwise_distances(X)
|
|
clustering2 = AgglomerativeClustering(
|
|
n_clusters=10,
|
|
connectivity=connectivity,
|
|
metric="precomputed",
|
|
linkage="complete",
|
|
)
|
|
clustering2.fit(X_dist)
|
|
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(metric="euclidean", linkage="single").fit(Xmm)
|
|
|
|
|
|
def test_ward_agglomeration(global_random_seed):
|
|
# Check that we obtain the correct solution in a simplistic case
|
|
rng = np.random.RandomState(global_random_seed)
|
|
mask = np.ones([10, 10], dtype=bool)
|
|
X = rng.randn(50, 100)
|
|
connectivity = grid_to_graph(*mask.shape)
|
|
agglo = FeatureAgglomeration(n_clusters=5, connectivity=connectivity)
|
|
agglo.fit(X)
|
|
assert np.size(np.unique(agglo.labels_)) == 5
|
|
|
|
X_red = agglo.transform(X)
|
|
assert X_red.shape[1] == 5
|
|
X_full = agglo.inverse_transform(X_red)
|
|
assert np.unique(X_full[0]).size == 5
|
|
assert_array_almost_equal(agglo.transform(X_full), X_red)
|
|
|
|
# Check that fitting with no samples raises a ValueError
|
|
with pytest.raises(ValueError):
|
|
agglo.fit(X[:0])
|
|
|
|
|
|
def test_single_linkage_clustering():
|
|
# Check that we get the correct result in two emblematic cases
|
|
moons, moon_labels = make_moons(noise=0.05, random_state=42)
|
|
clustering = AgglomerativeClustering(n_clusters=2, linkage="single")
|
|
clustering.fit(moons)
|
|
assert_almost_equal(
|
|
normalized_mutual_info_score(clustering.labels_, moon_labels), 1
|
|
)
|
|
|
|
circles, circle_labels = make_circles(factor=0.5, noise=0.025, random_state=42)
|
|
clustering = AgglomerativeClustering(n_clusters=2, linkage="single")
|
|
clustering.fit(circles)
|
|
assert_almost_equal(
|
|
normalized_mutual_info_score(clustering.labels_, circle_labels), 1
|
|
)
|
|
|
|
|
|
def assess_same_labelling(cut1, cut2):
|
|
"""Util for comparison with scipy"""
|
|
co_clust = []
|
|
for cut in [cut1, cut2]:
|
|
n = len(cut)
|
|
k = cut.max() + 1
|
|
ecut = np.zeros((n, k))
|
|
ecut[np.arange(n), cut] = 1
|
|
co_clust.append(np.dot(ecut, ecut.T))
|
|
assert (co_clust[0] == co_clust[1]).all()
|
|
|
|
|
|
def test_sparse_scikit_vs_scipy(global_random_seed):
|
|
# Test scikit linkage with full connectivity (i.e. unstructured) vs scipy
|
|
n, p, k = 10, 5, 3
|
|
rng = np.random.RandomState(global_random_seed)
|
|
|
|
# Not using a lil_matrix here, just to check that non sparse
|
|
# matrices are well handled
|
|
connectivity = np.ones((n, n))
|
|
for linkage in _TREE_BUILDERS.keys():
|
|
for i in range(5):
|
|
X = 0.1 * rng.normal(size=(n, p))
|
|
X -= 4.0 * np.arange(n)[:, np.newaxis]
|
|
X -= X.mean(axis=1)[:, np.newaxis]
|
|
|
|
out = hierarchy.linkage(X, method=linkage)
|
|
|
|
children_ = out[:, :2].astype(int, copy=False)
|
|
children, _, n_leaves, _ = _TREE_BUILDERS[linkage](
|
|
X, connectivity=connectivity
|
|
)
|
|
|
|
# Sort the order of child nodes per row for consistency
|
|
children.sort(axis=1)
|
|
assert_array_equal(
|
|
children,
|
|
children_,
|
|
"linkage tree differs from scipy impl for linkage: " + linkage,
|
|
)
|
|
|
|
cut = _hc_cut(k, children, n_leaves)
|
|
cut_ = _hc_cut(k, children_, n_leaves)
|
|
assess_same_labelling(cut, cut_)
|
|
|
|
# Test error management in _hc_cut
|
|
with pytest.raises(ValueError):
|
|
_hc_cut(n_leaves + 1, children, n_leaves)
|
|
|
|
|
|
# Make sure our custom mst_linkage_core gives
|
|
# the same results as scipy's builtin
|
|
def test_vector_scikit_single_vs_scipy_single(global_random_seed):
|
|
n_samples, n_features, n_clusters = 10, 5, 3
|
|
rng = np.random.RandomState(global_random_seed)
|
|
X = 0.1 * rng.normal(size=(n_samples, n_features))
|
|
X -= 4.0 * np.arange(n_samples)[:, np.newaxis]
|
|
X -= X.mean(axis=1)[:, np.newaxis]
|
|
|
|
out = hierarchy.linkage(X, method="single")
|
|
children_scipy = out[:, :2].astype(int)
|
|
|
|
children, _, n_leaves, _ = _TREE_BUILDERS["single"](X)
|
|
|
|
# Sort the order of child nodes per row for consistency
|
|
children.sort(axis=1)
|
|
assert_array_equal(
|
|
children,
|
|
children_scipy,
|
|
"linkage tree differs from scipy impl for single linkage.",
|
|
)
|
|
|
|
cut = _hc_cut(n_clusters, children, n_leaves)
|
|
cut_scipy = _hc_cut(n_clusters, children_scipy, n_leaves)
|
|
assess_same_labelling(cut, cut_scipy)
|
|
|
|
|
|
# TODO: Remove filterwarnings in 1.3 when wminkowski is removed
|
|
@pytest.mark.filterwarnings("ignore:WMinkowskiDistance:FutureWarning:sklearn")
|
|
@pytest.mark.parametrize("metric_param_grid", METRICS_DEFAULT_PARAMS)
|
|
def test_mst_linkage_core_memory_mapped(metric_param_grid):
|
|
"""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)
|
|
metric, param_grid = metric_param_grid
|
|
keys = param_grid.keys()
|
|
for vals in itertools.product(*param_grid.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
|
|
X = np.array([[0, 0, 0], [0, 0, 0], [1, 1, 1], [1, 1, 1], [2, 2, 2], [2, 2, 2]])
|
|
true_labels = np.array([0, 0, 1, 1, 2, 2])
|
|
connectivity = kneighbors_graph(X, n_neighbors=3, include_self=False)
|
|
connectivity = 0.5 * (connectivity + connectivity.T)
|
|
connectivity, n_components = _fix_connectivity(X, connectivity, "euclidean")
|
|
|
|
for linkage in ("single", "average", "average", "ward"):
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=3, linkage=linkage, connectivity=connectivity
|
|
)
|
|
clustering.fit(X)
|
|
|
|
assert_almost_equal(
|
|
normalized_mutual_info_score(clustering.labels_, true_labels), 1
|
|
)
|
|
|
|
|
|
def test_connectivity_propagation():
|
|
# Check that connectivity in the ward tree is propagated correctly during
|
|
# merging.
|
|
X = np.array(
|
|
[
|
|
(0.014, 0.120),
|
|
(0.014, 0.099),
|
|
(0.014, 0.097),
|
|
(0.017, 0.153),
|
|
(0.017, 0.153),
|
|
(0.018, 0.153),
|
|
(0.018, 0.153),
|
|
(0.018, 0.153),
|
|
(0.018, 0.153),
|
|
(0.018, 0.153),
|
|
(0.018, 0.153),
|
|
(0.018, 0.153),
|
|
(0.018, 0.152),
|
|
(0.018, 0.149),
|
|
(0.018, 0.144),
|
|
]
|
|
)
|
|
connectivity = kneighbors_graph(X, 10, include_self=False)
|
|
ward = AgglomerativeClustering(
|
|
n_clusters=4, connectivity=connectivity, linkage="ward"
|
|
)
|
|
# If changes are not propagated correctly, fit crashes with an
|
|
# IndexError
|
|
ward.fit(X)
|
|
|
|
|
|
def test_ward_tree_children_order(global_random_seed):
|
|
# Check that children are ordered in the same way for both structured and
|
|
# unstructured versions of ward_tree.
|
|
|
|
# test on five random datasets
|
|
n, p = 10, 5
|
|
rng = np.random.RandomState(global_random_seed)
|
|
|
|
connectivity = np.ones((n, n))
|
|
for i in range(5):
|
|
X = 0.1 * rng.normal(size=(n, p))
|
|
X -= 4.0 * np.arange(n)[:, np.newaxis]
|
|
X -= X.mean(axis=1)[:, np.newaxis]
|
|
|
|
out_unstructured = ward_tree(X)
|
|
out_structured = ward_tree(X, connectivity=connectivity)
|
|
|
|
assert_array_equal(out_unstructured[0], out_structured[0])
|
|
|
|
|
|
def test_ward_linkage_tree_return_distance(global_random_seed):
|
|
# Test return_distance option on linkage and ward trees
|
|
|
|
# test that return_distance when set true, gives same
|
|
# output on both structured and unstructured clustering.
|
|
n, p = 10, 5
|
|
rng = np.random.RandomState(global_random_seed)
|
|
|
|
connectivity = np.ones((n, n))
|
|
for i in range(5):
|
|
X = 0.1 * rng.normal(size=(n, p))
|
|
X -= 4.0 * np.arange(n)[:, np.newaxis]
|
|
X -= X.mean(axis=1)[:, np.newaxis]
|
|
|
|
out_unstructured = ward_tree(X, return_distance=True)
|
|
out_structured = ward_tree(X, connectivity=connectivity, return_distance=True)
|
|
|
|
# get children
|
|
children_unstructured = out_unstructured[0]
|
|
children_structured = out_structured[0]
|
|
|
|
# check if we got the same clusters
|
|
assert_array_equal(children_unstructured, children_structured)
|
|
|
|
# check if the distances are the same
|
|
dist_unstructured = out_unstructured[-1]
|
|
dist_structured = out_structured[-1]
|
|
|
|
assert_array_almost_equal(dist_unstructured, dist_structured)
|
|
|
|
for linkage in ["average", "complete", "single"]:
|
|
structured_items = linkage_tree(
|
|
X, connectivity=connectivity, linkage=linkage, return_distance=True
|
|
)[-1]
|
|
unstructured_items = linkage_tree(X, linkage=linkage, return_distance=True)[
|
|
-1
|
|
]
|
|
structured_dist = structured_items[-1]
|
|
unstructured_dist = unstructured_items[-1]
|
|
structured_children = structured_items[0]
|
|
unstructured_children = unstructured_items[0]
|
|
assert_array_almost_equal(structured_dist, unstructured_dist)
|
|
assert_array_almost_equal(structured_children, unstructured_children)
|
|
|
|
# test on the following dataset where we know the truth
|
|
# taken from scipy/cluster/tests/hierarchy_test_data.py
|
|
X = np.array(
|
|
[
|
|
[1.43054825, -7.5693489],
|
|
[6.95887839, 6.82293382],
|
|
[2.87137846, -9.68248579],
|
|
[7.87974764, -6.05485803],
|
|
[8.24018364, -6.09495602],
|
|
[7.39020262, 8.54004355],
|
|
]
|
|
)
|
|
# truth
|
|
linkage_X_ward = np.array(
|
|
[
|
|
[3.0, 4.0, 0.36265956, 2.0],
|
|
[1.0, 5.0, 1.77045373, 2.0],
|
|
[0.0, 2.0, 2.55760419, 2.0],
|
|
[6.0, 8.0, 9.10208346, 4.0],
|
|
[7.0, 9.0, 24.7784379, 6.0],
|
|
]
|
|
)
|
|
|
|
linkage_X_complete = np.array(
|
|
[
|
|
[3.0, 4.0, 0.36265956, 2.0],
|
|
[1.0, 5.0, 1.77045373, 2.0],
|
|
[0.0, 2.0, 2.55760419, 2.0],
|
|
[6.0, 8.0, 6.96742194, 4.0],
|
|
[7.0, 9.0, 18.77445997, 6.0],
|
|
]
|
|
)
|
|
|
|
linkage_X_average = np.array(
|
|
[
|
|
[3.0, 4.0, 0.36265956, 2.0],
|
|
[1.0, 5.0, 1.77045373, 2.0],
|
|
[0.0, 2.0, 2.55760419, 2.0],
|
|
[6.0, 8.0, 6.55832839, 4.0],
|
|
[7.0, 9.0, 15.44089605, 6.0],
|
|
]
|
|
)
|
|
|
|
n_samples, n_features = np.shape(X)
|
|
connectivity_X = np.ones((n_samples, n_samples))
|
|
|
|
out_X_unstructured = ward_tree(X, return_distance=True)
|
|
out_X_structured = ward_tree(X, connectivity=connectivity_X, return_distance=True)
|
|
|
|
# check that the labels are the same
|
|
assert_array_equal(linkage_X_ward[:, :2], out_X_unstructured[0])
|
|
assert_array_equal(linkage_X_ward[:, :2], out_X_structured[0])
|
|
|
|
# check that the distances are correct
|
|
assert_array_almost_equal(linkage_X_ward[:, 2], out_X_unstructured[4])
|
|
assert_array_almost_equal(linkage_X_ward[:, 2], out_X_structured[4])
|
|
|
|
linkage_options = ["complete", "average", "single"]
|
|
X_linkage_truth = [linkage_X_complete, linkage_X_average]
|
|
for linkage, X_truth in zip(linkage_options, X_linkage_truth):
|
|
out_X_unstructured = linkage_tree(X, return_distance=True, linkage=linkage)
|
|
out_X_structured = linkage_tree(
|
|
X, connectivity=connectivity_X, linkage=linkage, return_distance=True
|
|
)
|
|
|
|
# check that the labels are the same
|
|
assert_array_equal(X_truth[:, :2], out_X_unstructured[0])
|
|
assert_array_equal(X_truth[:, :2], out_X_structured[0])
|
|
|
|
# check that the distances are correct
|
|
assert_array_almost_equal(X_truth[:, 2], out_X_unstructured[4])
|
|
assert_array_almost_equal(X_truth[:, 2], out_X_structured[4])
|
|
|
|
|
|
def test_connectivity_fixing_non_lil():
|
|
# Check non regression of a bug if a non item assignable connectivity is
|
|
# provided with more than one component.
|
|
# create dummy data
|
|
x = np.array([[0, 0], [1, 1]])
|
|
# create a mask with several components to force connectivity fixing
|
|
m = np.array([[True, False], [False, True]])
|
|
c = grid_to_graph(n_x=2, n_y=2, mask=m)
|
|
w = AgglomerativeClustering(connectivity=c, linkage="ward")
|
|
with pytest.warns(UserWarning):
|
|
w.fit(x)
|
|
|
|
|
|
def test_int_float_dict():
|
|
rng = np.random.RandomState(0)
|
|
keys = np.unique(rng.randint(100, size=10).astype(np.intp, copy=False))
|
|
values = rng.rand(len(keys))
|
|
|
|
d = IntFloatDict(keys, values)
|
|
for key, value in zip(keys, values):
|
|
assert d[key] == value
|
|
|
|
other_keys = np.arange(50, dtype=np.intp)[::2]
|
|
other_values = np.full(50, 0.5)[::2]
|
|
other = IntFloatDict(other_keys, other_values)
|
|
# Complete smoke test
|
|
max_merge(d, other, mask=np.ones(100, dtype=np.intp), n_a=1, n_b=1)
|
|
average_merge(d, other, mask=np.ones(100, dtype=np.intp), n_a=1, n_b=1)
|
|
|
|
|
|
def test_connectivity_callable():
|
|
rng = np.random.RandomState(0)
|
|
X = rng.rand(20, 5)
|
|
connectivity = kneighbors_graph(X, 3, include_self=False)
|
|
aglc1 = AgglomerativeClustering(connectivity=connectivity)
|
|
aglc2 = AgglomerativeClustering(
|
|
connectivity=partial(kneighbors_graph, n_neighbors=3, include_self=False)
|
|
)
|
|
aglc1.fit(X)
|
|
aglc2.fit(X)
|
|
assert_array_equal(aglc1.labels_, aglc2.labels_)
|
|
|
|
|
|
def test_connectivity_ignores_diagonal():
|
|
rng = np.random.RandomState(0)
|
|
X = rng.rand(20, 5)
|
|
connectivity = kneighbors_graph(X, 3, include_self=False)
|
|
connectivity_include_self = kneighbors_graph(X, 3, include_self=True)
|
|
aglc1 = AgglomerativeClustering(connectivity=connectivity)
|
|
aglc2 = AgglomerativeClustering(connectivity=connectivity_include_self)
|
|
aglc1.fit(X)
|
|
aglc2.fit(X)
|
|
assert_array_equal(aglc1.labels_, aglc2.labels_)
|
|
|
|
|
|
def test_compute_full_tree():
|
|
# Test that the full tree is computed if n_clusters is small
|
|
rng = np.random.RandomState(0)
|
|
X = rng.randn(10, 2)
|
|
connectivity = kneighbors_graph(X, 5, include_self=False)
|
|
|
|
# When n_clusters is less, the full tree should be built
|
|
# that is the number of merges should be n_samples - 1
|
|
agc = AgglomerativeClustering(n_clusters=2, connectivity=connectivity)
|
|
agc.fit(X)
|
|
n_samples = X.shape[0]
|
|
n_nodes = agc.children_.shape[0]
|
|
assert n_nodes == n_samples - 1
|
|
|
|
# When n_clusters is large, greater than max of 100 and 0.02 * n_samples.
|
|
# we should stop when there are n_clusters.
|
|
n_clusters = 101
|
|
X = rng.randn(200, 2)
|
|
connectivity = kneighbors_graph(X, 10, include_self=False)
|
|
agc = AgglomerativeClustering(n_clusters=n_clusters, connectivity=connectivity)
|
|
agc.fit(X)
|
|
n_samples = X.shape[0]
|
|
n_nodes = agc.children_.shape[0]
|
|
assert n_nodes == n_samples - n_clusters
|
|
|
|
|
|
def test_n_components():
|
|
# Test n_components returned by linkage, average and ward tree
|
|
rng = np.random.RandomState(0)
|
|
X = rng.rand(5, 5)
|
|
|
|
# Connectivity matrix having five components.
|
|
connectivity = np.eye(5)
|
|
|
|
for linkage_func in _TREE_BUILDERS.values():
|
|
assert ignore_warnings(linkage_func)(X, connectivity=connectivity)[1] == 5
|
|
|
|
|
|
def test_affinity_passed_to_fix_connectivity():
|
|
# Test that the affinity parameter is actually passed to the pairwise
|
|
# function
|
|
|
|
size = 2
|
|
rng = np.random.RandomState(0)
|
|
X = rng.randn(size, size)
|
|
mask = np.array([True, False, False, True])
|
|
|
|
connectivity = grid_to_graph(n_x=size, n_y=size, mask=mask, return_as=np.ndarray)
|
|
|
|
class FakeAffinity:
|
|
def __init__(self):
|
|
self.counter = 0
|
|
|
|
def increment(self, *args, **kwargs):
|
|
self.counter += 1
|
|
return self.counter
|
|
|
|
fa = FakeAffinity()
|
|
|
|
linkage_tree(X, connectivity=connectivity, affinity=fa.increment)
|
|
|
|
assert fa.counter == 3
|
|
|
|
|
|
@pytest.mark.parametrize("linkage", ["ward", "complete", "average"])
|
|
def test_agglomerative_clustering_with_distance_threshold(linkage, global_random_seed):
|
|
# Check that we obtain the correct number of clusters with
|
|
# agglomerative clustering with distance_threshold.
|
|
rng = np.random.RandomState(global_random_seed)
|
|
mask = np.ones([10, 10], dtype=bool)
|
|
n_samples = 100
|
|
X = rng.randn(n_samples, 50)
|
|
connectivity = grid_to_graph(*mask.shape)
|
|
# test when distance threshold is set to 10
|
|
distance_threshold = 10
|
|
for conn in [None, connectivity]:
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=None,
|
|
distance_threshold=distance_threshold,
|
|
connectivity=conn,
|
|
linkage=linkage,
|
|
)
|
|
clustering.fit(X)
|
|
clusters_produced = clustering.labels_
|
|
num_clusters_produced = len(np.unique(clustering.labels_))
|
|
# test if the clusters produced match the point in the linkage tree
|
|
# where the distance exceeds the threshold
|
|
tree_builder = _TREE_BUILDERS[linkage]
|
|
children, n_components, n_leaves, parent, distances = tree_builder(
|
|
X, connectivity=conn, n_clusters=None, return_distance=True
|
|
)
|
|
num_clusters_at_threshold = (
|
|
np.count_nonzero(distances >= distance_threshold) + 1
|
|
)
|
|
# test number of clusters produced
|
|
assert num_clusters_at_threshold == num_clusters_produced
|
|
# test clusters produced
|
|
clusters_at_threshold = _hc_cut(
|
|
n_clusters=num_clusters_produced, children=children, n_leaves=n_leaves
|
|
)
|
|
assert np.array_equiv(clusters_produced, clusters_at_threshold)
|
|
|
|
|
|
def test_small_distance_threshold(global_random_seed):
|
|
rng = np.random.RandomState(global_random_seed)
|
|
n_samples = 10
|
|
X = rng.randint(-300, 300, size=(n_samples, 3))
|
|
# this should result in all data in their own clusters, given that
|
|
# their pairwise distances are bigger than .1 (which may not be the case
|
|
# with a different random seed).
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=None, distance_threshold=1.0, linkage="single"
|
|
).fit(X)
|
|
# check that the pairwise distances are indeed all larger than .1
|
|
all_distances = pairwise_distances(X, metric="minkowski", p=2)
|
|
np.fill_diagonal(all_distances, np.inf)
|
|
assert np.all(all_distances > 0.1)
|
|
assert clustering.n_clusters_ == n_samples
|
|
|
|
|
|
def test_cluster_distances_with_distance_threshold(global_random_seed):
|
|
rng = np.random.RandomState(global_random_seed)
|
|
n_samples = 100
|
|
X = rng.randint(-10, 10, size=(n_samples, 3))
|
|
# check the distances within the clusters and with other clusters
|
|
distance_threshold = 4
|
|
clustering = AgglomerativeClustering(
|
|
n_clusters=None, distance_threshold=distance_threshold, linkage="single"
|
|
).fit(X)
|
|
labels = clustering.labels_
|
|
D = pairwise_distances(X, metric="minkowski", p=2)
|
|
# to avoid taking the 0 diagonal in min()
|
|
np.fill_diagonal(D, np.inf)
|
|
for label in np.unique(labels):
|
|
in_cluster_mask = labels == label
|
|
max_in_cluster_distance = (
|
|
D[in_cluster_mask][:, in_cluster_mask].min(axis=0).max()
|
|
)
|
|
min_out_cluster_distance = (
|
|
D[in_cluster_mask][:, ~in_cluster_mask].min(axis=0).min()
|
|
)
|
|
# single data point clusters only have that inf diagonal here
|
|
if in_cluster_mask.sum() > 1:
|
|
assert max_in_cluster_distance < distance_threshold
|
|
assert min_out_cluster_distance >= distance_threshold
|
|
|
|
|
|
@pytest.mark.parametrize("linkage", ["ward", "complete", "average"])
|
|
@pytest.mark.parametrize(
|
|
("threshold", "y_true"), [(0.5, [1, 0]), (1.0, [1, 0]), (1.5, [0, 0])]
|
|
)
|
|
def test_agglomerative_clustering_with_distance_threshold_edge_case(
|
|
linkage, threshold, y_true
|
|
):
|
|
# test boundary case of distance_threshold matching the distance
|
|
X = [[0], [1]]
|
|
clusterer = AgglomerativeClustering(
|
|
n_clusters=None, distance_threshold=threshold, linkage=linkage
|
|
)
|
|
y_pred = clusterer.fit_predict(X)
|
|
assert adjusted_rand_score(y_true, y_pred) == 1
|
|
|
|
|
|
def test_dist_threshold_invalid_parameters():
|
|
X = [[0], [1]]
|
|
with pytest.raises(ValueError, match="Exactly one of "):
|
|
AgglomerativeClustering(n_clusters=None, distance_threshold=None).fit(X)
|
|
|
|
with pytest.raises(ValueError, match="Exactly one of "):
|
|
AgglomerativeClustering(n_clusters=2, distance_threshold=1).fit(X)
|
|
|
|
X = [[0], [1]]
|
|
with pytest.raises(ValueError, match="compute_full_tree must be True if"):
|
|
AgglomerativeClustering(
|
|
n_clusters=None, distance_threshold=1, compute_full_tree=False
|
|
).fit(X)
|
|
|
|
|
|
def test_invalid_shape_precomputed_dist_matrix():
|
|
# Check that an error is raised when affinity='precomputed'
|
|
# and a non square matrix is passed (PR #16257).
|
|
rng = np.random.RandomState(0)
|
|
X = rng.rand(5, 3)
|
|
with pytest.raises(
|
|
ValueError,
|
|
match=r"Distance matrix should be square, got matrix of shape \(5, 3\)",
|
|
):
|
|
AgglomerativeClustering(metric="precomputed", linkage="complete").fit(X)
|
|
|
|
|
|
def test_precomputed_connectivity_affinity_with_2_connected_components():
|
|
"""Check that connecting components works when connectivity and
|
|
affinity are both precomputed and the number of connected components is
|
|
greater than 1. Non-regression test for #16151.
|
|
"""
|
|
|
|
connectivity_matrix = np.array(
|
|
[
|
|
[0, 1, 1, 0, 0],
|
|
[0, 0, 1, 0, 0],
|
|
[0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 1],
|
|
[0, 0, 0, 0, 0],
|
|
]
|
|
)
|
|
# ensure that connectivity_matrix has two connected components
|
|
assert connected_components(connectivity_matrix)[0] == 2
|
|
|
|
rng = np.random.RandomState(0)
|
|
X = rng.randn(5, 10)
|
|
|
|
X_dist = pairwise_distances(X)
|
|
clusterer_precomputed = AgglomerativeClustering(
|
|
affinity="precomputed", connectivity=connectivity_matrix, linkage="complete"
|
|
)
|
|
msg = "Completing it to avoid stopping the tree early"
|
|
with pytest.warns(UserWarning, match=msg):
|
|
clusterer_precomputed.fit(X_dist)
|
|
|
|
clusterer = AgglomerativeClustering(
|
|
connectivity=connectivity_matrix, linkage="complete"
|
|
)
|
|
with pytest.warns(UserWarning, match=msg):
|
|
clusterer.fit(X)
|
|
|
|
assert_array_equal(clusterer.labels_, clusterer_precomputed.labels_)
|
|
assert_array_equal(clusterer.children_, clusterer_precomputed.children_)
|
|
|
|
|
|
# TODO(1.4): Remove
|
|
def test_deprecate_affinity():
|
|
rng = np.random.RandomState(42)
|
|
X = rng.randn(50, 10)
|
|
|
|
af = AgglomerativeClustering(affinity="euclidean")
|
|
msg = (
|
|
"Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4."
|
|
" Use `metric` instead"
|
|
)
|
|
with pytest.warns(FutureWarning, match=msg):
|
|
af.fit(X)
|
|
with pytest.warns(FutureWarning, match=msg):
|
|
af.fit_predict(X)
|
|
|
|
af = AgglomerativeClustering(metric="euclidean", affinity="euclidean")
|
|
msg = "Both `affinity` and `metric` attributes were set. Attribute"
|
|
with pytest.raises(ValueError, match=msg):
|
|
af.fit(X)
|
|
with pytest.raises(ValueError, match=msg):
|
|
af.fit_predict(X)
|