Fixing formatting from trying to do this using the github editor

graspologic-org · Nov 5, 2021 · c6dce71 · c6dce71
1 parent 899616f
commit c6dce71
Showing 1 changed file with 46 additions and 46 deletions.
diff --git 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()