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"""Testing for Spectral Clustering methods"""
from __future__ import division
import numpy as np
from scipy import sparse
import pytest
from sklearn.externals.six.moves import cPickle
from sklearn.utils import check_random_state
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
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.datasets.samples_generator 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 = cPickle.loads(cPickle.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)
assert_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)
assert_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_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_equal(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>')
assert_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)
assert adjusted_rand_score(y_true, y_pred) > 0.8
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:
assert_raises(
ValueError, spectral_clustering,
graph, n_clusters=2, eigen_solver='amg', random_state=0)
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