From c6dce71d88ab863c969e2eff873465469d43284e Mon Sep 17 00:00:00 2001 From: Dax Pryce Date: Fri, 5 Nov 2021 09:27:40 -0700 Subject: [PATCH] Fixing formatting from trying to do this using the github editor --- tests/embed/test_n2v.py | 92 ++++++++++++++++++++--------------------- 1 file changed, 46 insertions(+), 46 deletions(-) diff --git a/tests/embed/test_n2v.py b/tests/embed/test_n2v.py index a876cb8d5..753ae3779 100644 --- a/tests/embed/test_n2v.py +++ b/tests/embed/test_n2v.py @@ -14,68 +14,68 @@ class Node2VecEmbedTest(unittest.TestCase): # these are not deterministic tests and can't be trusted to work consistently. # See Issue #880 -# def test_n2v_returns_same_labels_with_different_nodeid_types(self): -# probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) -# number_of_nodes_per_community = [20, 20] + # def test_n2v_returns_same_labels_with_different_nodeid_types(self): + # probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) + # number_of_nodes_per_community = [20, 20] -# sbm_sample = gc.simulations.sbm( -# number_of_nodes_per_community, probability_matrix -# ) -# sbm_graph = nx.from_numpy_array(sbm_sample) + # sbm_sample = gc.simulations.sbm( + # number_of_nodes_per_community, probability_matrix + # ) + # sbm_graph = nx.from_numpy_array(sbm_sample) -# graph = nx.Graph() -# graph_as_strings = nx.Graph() -# for s, t in sbm_graph.edges(): -# graph.add_edge(s, t, weight=1) -# graph_as_strings.add_edge(str(s), str(t), weight=1) + # graph = nx.Graph() + # graph_as_strings = nx.Graph() + # for s, t in sbm_graph.edges(): + # graph.add_edge(s, t, weight=1) + # graph_as_strings.add_edge(str(s), str(t), weight=1) -# original_embedding = gc.embed.node2vec_embed(graph, random_seed=1) -# string_embedding = gc.embed.node2vec_embed(graph_as_strings, random_seed=1) + # original_embedding = gc.embed.node2vec_embed(graph, random_seed=1) + # string_embedding = gc.embed.node2vec_embed(graph_as_strings, random_seed=1) -# k = KMeans(n_clusters=2) -# original_labels = k.fit_predict(original_embedding[0]) -# string_labels = k.fit_predict(string_embedding[0]) + # k = KMeans(n_clusters=2) + # original_labels = k.fit_predict(original_embedding[0]) + # string_labels = k.fit_predict(string_embedding[0]) -# expected_labels = np.zeros(40, dtype=int) -# expected_labels[20:] = 1 + # expected_labels = np.zeros(40, dtype=int) + # expected_labels[20:] = 1 -# original_ari = adjusted_rand_score(original_labels, expected_labels) -# string_ari = adjusted_rand_score(string_labels, expected_labels) + # original_ari = adjusted_rand_score(original_labels, expected_labels) + # string_ari = adjusted_rand_score(string_labels, expected_labels) -# self.assertEqual(original_ari, string_ari) + # self.assertEqual(original_ari, string_ari) -# def test_n2v_directed_undirected_returns_same_clustering(self): -# probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) -# number_of_nodes_per_community = [20, 20] + # def test_n2v_directed_undirected_returns_same_clustering(self): + # probability_matrix = np.array([[0.95, 0.01], [0.01, 0.95]]) + # number_of_nodes_per_community = [20, 20] -# sbm_sample = gc.simulations.sbm( -# number_of_nodes_per_community, probability_matrix -# ) -# sbm_graph = nx.from_numpy_array(sbm_sample) + # sbm_sample = gc.simulations.sbm( + # number_of_nodes_per_community, probability_matrix + # ) + # sbm_graph = nx.from_numpy_array(sbm_sample) -# graph = nx.Graph() -# graph_directed = nx.DiGraph() -# for s, t in sbm_graph.edges(): -# graph.add_edge(s, t, weight=1) + # graph = nx.Graph() + # graph_directed = nx.DiGraph() + # for s, t in sbm_graph.edges(): + # graph.add_edge(s, t, weight=1) -# graph_directed.add_edge(s, t, weight=1) -# graph_directed.add_edge(t, s, weight=1) + # graph_directed.add_edge(s, t, weight=1) + # graph_directed.add_edge(t, s, weight=1) -# undirected_embedding = gc.embed.node2vec_embed(graph, random_seed=1) -# directed_embedding = gc.embed.node2vec_embed(graph_directed, random_seed=1) + # undirected_embedding = gc.embed.node2vec_embed(graph, random_seed=1) + # directed_embedding = gc.embed.node2vec_embed(graph_directed, random_seed=1) -# k = KMeans(n_clusters=2, random_state=1234) -# undirected_labels = k.fit_predict(undirected_embedding[0]) -# k = KMeans(n_clusters=2, random_state=1234) -# directed_labels = k.fit_predict(directed_embedding[0]) + # k = KMeans(n_clusters=2, random_state=1234) + # undirected_labels = k.fit_predict(undirected_embedding[0]) + # k = KMeans(n_clusters=2, random_state=1234) + # directed_labels = k.fit_predict(directed_embedding[0]) -# expected_labels = np.zeros(40, dtype=int) -# expected_labels[20:] = 1 + # expected_labels = np.zeros(40, dtype=int) + # expected_labels[20:] = 1 -# undirected_ari = adjusted_rand_score(undirected_labels, expected_labels) -# directed_ari = adjusted_rand_score(directed_labels, expected_labels) + # undirected_ari = adjusted_rand_score(undirected_labels, expected_labels) + # directed_ari = adjusted_rand_score(directed_labels, expected_labels) -# self.assertEqual(undirected_ari, directed_ari) + # self.assertEqual(undirected_ari, directed_ari) def test_node2vec_embed(self): g = nx.florentine_families_graph()