def test_affinity_passed_to_fix_connectivity(): # Test that the affinity parameter is actually passed to the pairwise # function size = 2 rng = np.random.RandomState(0) X = rng.randn(size, size) mask = np.array([True, False, False, True]) connectivity = grid_to_graph(n_x=size, n_y=size, mask=mask, return_as=np.ndarray) class FakeAffinity: def __init__(self): self.counter = 0 def increment(self, *args, **kwargs): self.counter += 1 return self.counter fa = FakeAffinity() linkage_tree(X, connectivity=connectivity, affinity=fa.increment) assert fa.counter == 3
def test_linkage_misc(): # Misc tests on linkage rng = np.random.RandomState(42) X = rng.normal(size=(5, 5)) with pytest.raises(ValueError): AgglomerativeClustering(linkage='foo').fit(X) with pytest.raises(ValueError): linkage_tree(X, linkage='foo') with pytest.raises(ValueError): linkage_tree(X, connectivity=np.ones((4, 4))) # Smoke test FeatureAgglomeration FeatureAgglomeration().fit(X) # test hierarchical clustering on a precomputed distances matrix dis = cosine_distances(X) res = linkage_tree(dis, affinity="precomputed") assert_array_equal(res[0], linkage_tree(X, affinity="cosine")[0]) # test hierarchical clustering on a precomputed distances matrix res = linkage_tree(X, affinity=manhattan_distances) assert_array_equal(res[0], linkage_tree(X, affinity="manhattan")[0])
def test_ward_linkage_tree_return_distance(): # Test return_distance option on linkage and ward trees # test that return_distance when set true, gives same # output on both structured and unstructured clustering. n, p = 10, 5 rng = np.random.RandomState(0) connectivity = np.ones((n, n)) for i in range(5): X = .1 * rng.normal(size=(n, p)) X -= 4. * np.arange(n)[:, np.newaxis] X -= X.mean(axis=1)[:, np.newaxis] out_unstructured = ward_tree(X, return_distance=True) out_structured = ward_tree(X, connectivity=connectivity, return_distance=True) # get children children_unstructured = out_unstructured[0] children_structured = out_structured[0] # check if we got the same clusters assert_array_equal(children_unstructured, children_structured) # check if the distances are the same dist_unstructured = out_unstructured[-1] dist_structured = out_structured[-1] assert_array_almost_equal(dist_unstructured, dist_structured) for linkage in ['average', 'complete', 'single']: structured_items = linkage_tree( X, connectivity=connectivity, linkage=linkage, return_distance=True)[-1] unstructured_items = linkage_tree( X, linkage=linkage, return_distance=True)[-1] structured_dist = structured_items[-1] unstructured_dist = unstructured_items[-1] structured_children = structured_items[0] unstructured_children = unstructured_items[0] assert_array_almost_equal(structured_dist, unstructured_dist) assert_array_almost_equal( structured_children, unstructured_children) # test on the following dataset where we know the truth # taken from scipy/cluster/tests/hierarchy_test_data.py X = np.array([[1.43054825, -7.5693489], [6.95887839, 6.82293382], [2.87137846, -9.68248579], [7.87974764, -6.05485803], [8.24018364, -6.09495602], [7.39020262, 8.54004355]]) # truth linkage_X_ward = np.array([[3., 4., 0.36265956, 2.], [1., 5., 1.77045373, 2.], [0., 2., 2.55760419, 2.], [6., 8., 9.10208346, 4.], [7., 9., 24.7784379, 6.]]) linkage_X_complete = np.array( [[3., 4., 0.36265956, 2.], [1., 5., 1.77045373, 2.], [0., 2., 2.55760419, 2.], [6., 8., 6.96742194, 4.], [7., 9., 18.77445997, 6.]]) linkage_X_average = np.array( [[3., 4., 0.36265956, 2.], [1., 5., 1.77045373, 2.], [0., 2., 2.55760419, 2.], [6., 8., 6.55832839, 4.], [7., 9., 15.44089605, 6.]]) n_samples, n_features = np.shape(X) connectivity_X = np.ones((n_samples, n_samples)) out_X_unstructured = ward_tree(X, return_distance=True) out_X_structured = ward_tree(X, connectivity=connectivity_X, return_distance=True) # check that the labels are the same assert_array_equal(linkage_X_ward[:, :2], out_X_unstructured[0]) assert_array_equal(linkage_X_ward[:, :2], out_X_structured[0]) # check that the distances are correct assert_array_almost_equal(linkage_X_ward[:, 2], out_X_unstructured[4]) assert_array_almost_equal(linkage_X_ward[:, 2], out_X_structured[4]) linkage_options = ['complete', 'average', 'single'] X_linkage_truth = [linkage_X_complete, linkage_X_average] for (linkage, X_truth) in zip(linkage_options, X_linkage_truth): out_X_unstructured = linkage_tree( X, return_distance=True, linkage=linkage) out_X_structured = linkage_tree( X, connectivity=connectivity_X, linkage=linkage, return_distance=True) # check that the labels are the same assert_array_equal(X_truth[:, :2], out_X_unstructured[0]) assert_array_equal(X_truth[:, :2], out_X_structured[0]) # check that the distances are correct assert_array_almost_equal(X_truth[:, 2], out_X_unstructured[4]) assert_array_almost_equal(X_truth[:, 2], out_X_structured[4])