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"""Testing for Spectral Clustering methods"""
from cPickle import dumps, loads
import nose
import numpy as np
from numpy.testing import assert_equal
from scipy import sparse
from sklearn.utils.testing import assert_greater
from .. import SpectralClustering
def test_spectral_clustering():
S = np.array([[1, 5, 2, 1, 0, 0, 0],
[5, 1, 3, 1, 0, 0, 0],
[2, 3, 1, 1, 0, 0, 0],
[1, 1, 1, 1, 2, 1, 1],
[0, 0, 0, 2, 2, 3, 2],
[0, 0, 0, 1, 3, 1, 4],
[0, 0, 0, 1, 2, 4, 1],
])
for mat in (S, sparse.csr_matrix(S)):
model = SpectralClustering(random_state=0, k=2).fit(mat)
labels = model.labels_
if labels[0] == 0:
labels = 1 - labels
assert_equal(labels, [1, 1, 1, 0, 0, 0, 0])
model_copy = loads(dumps(model))
assert_equal(model_copy.k, model.k)
assert_equal(model_copy.mode, model.mode)
assert_equal(model_copy.random_state.get_state(),
model.random_state.get_state())
assert_equal(model_copy.labels_, model.labels_)
def test_spectral_clustering_sparse():
# We need a large matrice, or the lobpcg solver will fallback to its
# non-sparse and buggy mode
raise nose.SkipTest("XFailed Test")
S = np.array([[1, 5, 2, 2, 1, 0, 0, 0, 0, 0],
[5, 1, 3, 2, 1, 0, 0, 0, 0, 0],
[2, 3, 1, 1, 1, 0, 0, 0, 0, 0],
[2, 2, 1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 2, 1, 1, 1],
[0, 0, 0, 0, 1, 2, 2, 3, 3, 2],
[0, 0, 0, 0, 2, 2, 3, 3, 3, 4],
[0, 0, 0, 0, 1, 3, 3, 1, 2, 4],
[0, 0, 0, 0, 1, 3, 3, 2, 1, 4],
[0, 0, 0, 0, 1, 2, 4, 4, 4, 1],
])
S = sparse.coo_matrix(S)
labels = SpectralClustering(random_state=0, k=2).fit(S).labels_
if labels[0] == 0:
labels = 1 - labels
assert_greater(np.mean(labels == [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]), .9)
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