251 lines
9.6 KiB
Python
251 lines
9.6 KiB
Python
|
"""Testing for Spectral Clustering methods"""
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from scipy import sparse
|
||
|
|
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
import pickle
|
||
|
|
|
||
|
|
from sklearn.utils import check_random_state
|
||
|
|
from sklearn.utils._testing import assert_array_equal
|
||
|
|
from sklearn.utils._testing import assert_warns_message
|
||
|
|
|
||
|
|
from sklearn.cluster import SpectralClustering, spectral_clustering
|
||
|
|
from sklearn.cluster._spectral import discretize
|
||
|
|
from sklearn.feature_extraction import img_to_graph
|
||
|
|
from sklearn.metrics import pairwise_distances
|
||
|
|
from sklearn.metrics import adjusted_rand_score
|
||
|
|
from sklearn.metrics.pairwise import kernel_metrics, rbf_kernel
|
||
|
|
from sklearn.neighbors import NearestNeighbors
|
||
|
|
from sklearn.datasets import make_blobs
|
||
|
|
|
||
|
|
try:
|
||
|
|
from pyamg import smoothed_aggregation_solver # noqa
|
||
|
|
amg_loaded = True
|
||
|
|
except ImportError:
|
||
|
|
amg_loaded = False
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('eigen_solver', ('arpack', 'lobpcg'))
|
||
|
|
@pytest.mark.parametrize('assign_labels', ('kmeans', 'discretize'))
|
||
|
|
def test_spectral_clustering(eigen_solver, assign_labels):
|
||
|
|
S = np.array([[1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
|
||
|
|
[1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
|
||
|
|
[1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
|
||
|
|
[0.2, 0.2, 0.2, 1.0, 1.0, 1.0, 1.0],
|
||
|
|
[0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
|
||
|
|
[0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
|
||
|
|
[0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0]])
|
||
|
|
|
||
|
|
for mat in (S, sparse.csr_matrix(S)):
|
||
|
|
model = SpectralClustering(random_state=0, n_clusters=2,
|
||
|
|
affinity='precomputed',
|
||
|
|
eigen_solver=eigen_solver,
|
||
|
|
assign_labels=assign_labels
|
||
|
|
).fit(mat)
|
||
|
|
labels = model.labels_
|
||
|
|
if labels[0] == 0:
|
||
|
|
labels = 1 - labels
|
||
|
|
|
||
|
|
assert adjusted_rand_score(labels, [1, 1, 1, 0, 0, 0, 0]) == 1
|
||
|
|
|
||
|
|
model_copy = pickle.loads(pickle.dumps(model))
|
||
|
|
assert model_copy.n_clusters == model.n_clusters
|
||
|
|
assert model_copy.eigen_solver == model.eigen_solver
|
||
|
|
assert_array_equal(model_copy.labels_, model.labels_)
|
||
|
|
|
||
|
|
|
||
|
|
def test_spectral_unknown_mode():
|
||
|
|
# Test that SpectralClustering fails with an unknown mode set.
|
||
|
|
centers = np.array([
|
||
|
|
[0., 0., 0.],
|
||
|
|
[10., 10., 10.],
|
||
|
|
[20., 20., 20.],
|
||
|
|
])
|
||
|
|
X, true_labels = make_blobs(n_samples=100, centers=centers,
|
||
|
|
cluster_std=1., random_state=42)
|
||
|
|
D = pairwise_distances(X) # Distance matrix
|
||
|
|
S = np.max(D) - D # Similarity matrix
|
||
|
|
S = sparse.coo_matrix(S)
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
spectral_clustering(S, n_clusters=2, random_state=0,
|
||
|
|
eigen_solver="<unknown>")
|
||
|
|
|
||
|
|
|
||
|
|
def test_spectral_unknown_assign_labels():
|
||
|
|
# Test that SpectralClustering fails with an unknown assign_labels set.
|
||
|
|
centers = np.array([
|
||
|
|
[0., 0., 0.],
|
||
|
|
[10., 10., 10.],
|
||
|
|
[20., 20., 20.],
|
||
|
|
])
|
||
|
|
X, true_labels = make_blobs(n_samples=100, centers=centers,
|
||
|
|
cluster_std=1., random_state=42)
|
||
|
|
D = pairwise_distances(X) # Distance matrix
|
||
|
|
S = np.max(D) - D # Similarity matrix
|
||
|
|
S = sparse.coo_matrix(S)
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
spectral_clustering(S, n_clusters=2, random_state=0,
|
||
|
|
assign_labels="<unknown>")
|
||
|
|
|
||
|
|
|
||
|
|
def test_spectral_clustering_sparse():
|
||
|
|
X, y = make_blobs(n_samples=20, random_state=0,
|
||
|
|
centers=[[1, 1], [-1, -1]], cluster_std=0.01)
|
||
|
|
|
||
|
|
S = rbf_kernel(X, gamma=1)
|
||
|
|
S = np.maximum(S - 1e-4, 0)
|
||
|
|
S = sparse.coo_matrix(S)
|
||
|
|
|
||
|
|
labels = SpectralClustering(random_state=0, n_clusters=2,
|
||
|
|
affinity='precomputed').fit(S).labels_
|
||
|
|
assert adjusted_rand_score(y, labels) == 1
|
||
|
|
|
||
|
|
|
||
|
|
def test_precomputed_nearest_neighbors_filtering():
|
||
|
|
# Test precomputed graph filtering when containing too many neighbors
|
||
|
|
X, y = make_blobs(n_samples=200, random_state=0,
|
||
|
|
centers=[[1, 1], [-1, -1]], cluster_std=0.01)
|
||
|
|
|
||
|
|
n_neighbors = 2
|
||
|
|
results = []
|
||
|
|
for additional_neighbors in [0, 10]:
|
||
|
|
nn = NearestNeighbors(
|
||
|
|
n_neighbors=n_neighbors + additional_neighbors).fit(X)
|
||
|
|
graph = nn.kneighbors_graph(X, mode='connectivity')
|
||
|
|
labels = SpectralClustering(random_state=0, n_clusters=2,
|
||
|
|
affinity='precomputed_nearest_neighbors',
|
||
|
|
n_neighbors=n_neighbors).fit(graph).labels_
|
||
|
|
results.append(labels)
|
||
|
|
|
||
|
|
assert_array_equal(results[0], results[1])
|
||
|
|
|
||
|
|
|
||
|
|
def test_affinities():
|
||
|
|
# Note: in the following, random_state has been selected to have
|
||
|
|
# a dataset that yields a stable eigen decomposition both when built
|
||
|
|
# on OSX and Linux
|
||
|
|
X, y = make_blobs(n_samples=20, random_state=0,
|
||
|
|
centers=[[1, 1], [-1, -1]], cluster_std=0.01)
|
||
|
|
# nearest neighbors affinity
|
||
|
|
sp = SpectralClustering(n_clusters=2, affinity='nearest_neighbors',
|
||
|
|
random_state=0)
|
||
|
|
assert_warns_message(UserWarning, 'not fully connected', sp.fit, X)
|
||
|
|
assert adjusted_rand_score(y, sp.labels_) == 1
|
||
|
|
|
||
|
|
sp = SpectralClustering(n_clusters=2, gamma=2, random_state=0)
|
||
|
|
labels = sp.fit(X).labels_
|
||
|
|
assert adjusted_rand_score(y, labels) == 1
|
||
|
|
|
||
|
|
X = check_random_state(10).rand(10, 5) * 10
|
||
|
|
|
||
|
|
kernels_available = kernel_metrics()
|
||
|
|
for kern in kernels_available:
|
||
|
|
# Additive chi^2 gives a negative similarity matrix which
|
||
|
|
# doesn't make sense for spectral clustering
|
||
|
|
if kern != 'additive_chi2':
|
||
|
|
sp = SpectralClustering(n_clusters=2, affinity=kern,
|
||
|
|
random_state=0)
|
||
|
|
labels = sp.fit(X).labels_
|
||
|
|
assert (X.shape[0],) == labels.shape
|
||
|
|
|
||
|
|
sp = SpectralClustering(n_clusters=2, affinity=lambda x, y: 1,
|
||
|
|
random_state=0)
|
||
|
|
labels = sp.fit(X).labels_
|
||
|
|
assert (X.shape[0],) == labels.shape
|
||
|
|
|
||
|
|
def histogram(x, y, **kwargs):
|
||
|
|
# Histogram kernel implemented as a callable.
|
||
|
|
assert kwargs == {} # no kernel_params that we didn't ask for
|
||
|
|
return np.minimum(x, y).sum()
|
||
|
|
|
||
|
|
sp = SpectralClustering(n_clusters=2, affinity=histogram, random_state=0)
|
||
|
|
labels = sp.fit(X).labels_
|
||
|
|
assert (X.shape[0],) == labels.shape
|
||
|
|
|
||
|
|
# raise error on unknown affinity
|
||
|
|
sp = SpectralClustering(n_clusters=2, affinity='<unknown>')
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
sp.fit(X)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('n_samples', [50, 100, 150, 500])
|
||
|
|
def test_discretize(n_samples):
|
||
|
|
# Test the discretize using a noise assignment matrix
|
||
|
|
random_state = np.random.RandomState(seed=8)
|
||
|
|
for n_class in range(2, 10):
|
||
|
|
# random class labels
|
||
|
|
y_true = random_state.randint(0, n_class + 1, n_samples)
|
||
|
|
y_true = np.array(y_true, np.float)
|
||
|
|
# noise class assignment matrix
|
||
|
|
y_indicator = sparse.coo_matrix((np.ones(n_samples),
|
||
|
|
(np.arange(n_samples),
|
||
|
|
y_true)),
|
||
|
|
shape=(n_samples,
|
||
|
|
n_class + 1))
|
||
|
|
y_true_noisy = (y_indicator.toarray()
|
||
|
|
+ 0.1 * random_state.randn(n_samples,
|
||
|
|
n_class + 1))
|
||
|
|
y_pred = discretize(y_true_noisy, random_state=random_state)
|
||
|
|
assert adjusted_rand_score(y_true, y_pred) > 0.8
|
||
|
|
|
||
|
|
|
||
|
|
# TODO: Remove when pyamg does replaces sp.rand call with np.random.rand
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/issues/15913
|
||
|
|
@pytest.mark.filterwarnings(
|
||
|
|
"ignore:scipy.rand is deprecated:DeprecationWarning:pyamg.*")
|
||
|
|
def test_spectral_clustering_with_arpack_amg_solvers():
|
||
|
|
# Test that spectral_clustering is the same for arpack and amg solver
|
||
|
|
# Based on toy example from plot_segmentation_toy.py
|
||
|
|
|
||
|
|
# a small two coin image
|
||
|
|
x, y = np.indices((40, 40))
|
||
|
|
|
||
|
|
center1, center2 = (14, 12), (20, 25)
|
||
|
|
radius1, radius2 = 8, 7
|
||
|
|
|
||
|
|
circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1 ** 2
|
||
|
|
circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2 ** 2
|
||
|
|
|
||
|
|
circles = circle1 | circle2
|
||
|
|
mask = circles.copy()
|
||
|
|
img = circles.astype(float)
|
||
|
|
|
||
|
|
graph = img_to_graph(img, mask=mask)
|
||
|
|
graph.data = np.exp(-graph.data / graph.data.std())
|
||
|
|
|
||
|
|
labels_arpack = spectral_clustering(
|
||
|
|
graph, n_clusters=2, eigen_solver='arpack', random_state=0)
|
||
|
|
|
||
|
|
assert len(np.unique(labels_arpack)) == 2
|
||
|
|
|
||
|
|
if amg_loaded:
|
||
|
|
labels_amg = spectral_clustering(
|
||
|
|
graph, n_clusters=2, eigen_solver='amg', random_state=0)
|
||
|
|
assert adjusted_rand_score(labels_arpack, labels_amg) == 1
|
||
|
|
else:
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
spectral_clustering(graph, n_clusters=2, eigen_solver='amg',
|
||
|
|
random_state=0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_n_components():
|
||
|
|
# Test that after adding n_components, result is different and
|
||
|
|
# n_components = n_clusters by default
|
||
|
|
X, y = make_blobs(n_samples=20, random_state=0,
|
||
|
|
centers=[[1, 1], [-1, -1]], cluster_std=0.01)
|
||
|
|
sp = SpectralClustering(n_clusters=2, random_state=0)
|
||
|
|
labels = sp.fit(X).labels_
|
||
|
|
# set n_components = n_cluster and test if result is the same
|
||
|
|
labels_same_ncomp = SpectralClustering(n_clusters=2, n_components=2,
|
||
|
|
random_state=0).fit(X).labels_
|
||
|
|
# test that n_components=n_clusters by default
|
||
|
|
assert_array_equal(labels, labels_same_ncomp)
|
||
|
|
|
||
|
|
# test that n_components affect result
|
||
|
|
# n_clusters=8 by default, and set n_components=2
|
||
|
|
labels_diff_ncomp = SpectralClustering(n_components=2,
|
||
|
|
random_state=0).fit(X).labels_
|
||
|
|
assert not np.array_equal(labels, labels_diff_ncomp)
|