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_equal(adjusted_rand_score(y, sp.labels_), 1)
sp = SpectralClustering(n_clusters=2, gamma=2, random_state=0)
labels = sp.fit(X).labels_
assert_equal(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_equal((X.shape[0],), labels.shape)
sp = SpectralClustering(n_clusters=2, affinity=lambda x, y: 1,
random_state=0)
labels = sp.fit(X).labels_
assert_equal((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_equal((X.shape[0],), labels.shape)
# raise error on unknown affinity
sp = SpectralClustering(n_clusters=2, affinity='<unknown>')
assert_raises(ValueError, sp.fit, X)
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