def test_shc_threshold():
"""Test changing threshold in SHC."""
X, _ = generate_data(supervised=False, affinity=True)
clusterer = ScipyHierarchicalClustering(affinity="precomputed",
scoring_data="affinity",
n_clusters=4)
labels = clusterer.fit_predict(X)
clusterer.set_params(threshold=clusterer.linkage_[-4, 2])
labels = clusterer.labels_
assert_array_equal([25, 25, 25, 25], np.bincount(labels))
def test_shc_n_clusters():
"""Test changing number of clusters in SHC."""
X, _ = generate_data(supervised=False, affinity=True)
clusterer = ScipyHierarchicalClustering(affinity="precomputed",
n_clusters=4)
labels = clusterer.fit_predict(X)
assert_equal(len(np.unique(labels)), 4)
clusterer.set_params(n_clusters=10)
labels = clusterer.labels_
assert_equal(len(np.unique(labels)), 10)
def test_shc_n_clusters():
"""Test changing number of clusters in SHC."""
X, _ = generate_data(supervised=False, affinity=True)
clusterer = ScipyHierarchicalClustering(affinity="precomputed",
n_clusters=4)
labels = clusterer.fit_predict(X)
assert_equal(len(np.unique(labels)), 4)
clusterer.set_params(n_clusters=10)
labels = clusterer.labels_
assert_equal(len(np.unique(labels)), 10)
def test_shc_threshold():
"""Test changing threshold in SHC."""
X, _ = generate_data(supervised=False, affinity=True)
# n_clusters has precedence over threshold
clusterer = ScipyHierarchicalClustering(affinity="precomputed",
n_clusters=2)
labels1 = clusterer.fit_predict(X)
clusterer.set_params(threshold=clusterer.linkage_[-4, 2])
labels2 = clusterer.labels_
assert_array_equal(labels1, labels2)
assert_equal(len(np.unique(labels1)), 2)
# change threshold
clusterer.set_params(best_threshold_precedence=False)
clusterer.set_params(n_clusters=None, threshold=clusterer.linkage_[-5, 2])
labels = clusterer.labels_
assert_equal(len(np.unique(labels)), 5)
clusterer.set_params(threshold=clusterer.linkage_[-4, 2])
labels = clusterer.labels_
assert_equal(len(np.unique(labels)), 4)
def test_shc_threshold():
"""Test changing threshold in SHC."""
X, _ = generate_data(supervised=False, affinity=True)
# n_clusters has precedence over threshold
clusterer = ScipyHierarchicalClustering(affinity="precomputed",
n_clusters=2)
labels1 = clusterer.fit_predict(X)
clusterer.set_params(threshold=clusterer.linkage_[-4, 2])
labels2 = clusterer.labels_
assert_array_equal(labels1, labels2)
assert_equal(len(np.unique(labels1)), 2)
# change threshold
clusterer.set_params(best_threshold_precedence=False)
clusterer.set_params(n_clusters=None,
threshold=clusterer.linkage_[-5, 2])
labels = clusterer.labels_
assert_equal(len(np.unique(labels)), 5)
clusterer.set_params(threshold=clusterer.linkage_[-4, 2])
labels = clusterer.labels_
assert_equal(len(np.unique(labels)), 4)
