Merge pull request #988 from microsoft/main

main into dev pre 2.0.1

mypi.ini

@@ -0,0 +1,2 @@
+[mypy]
+ignore_missing_imports = True

tests/test_n2v.py

@@ -0,0 +1,185 @@
+# Copyright (c) Microsoft Corporation and contributors.
+# Licensed under the MIT License.
+
+import unittest
+
+import networkx as nx
+import numpy as np
+
+import graspologic.embed.n2v as n2v
+from graspologic.embed.n2v import _Node2VecGraph
+
+
+class Node2VecEmbedTest(unittest.TestCase):
+    def test_node2vec_embedding_correct_shape_is_returned(self):
+        import io
+
+        graph = nx.read_edgelist(
+            io.StringIO(_edge_list), nodetype=int, create_using=nx.DiGraph()
+        )
+
+        model = n2v.node2vec_embed(graph)
+        model_matrix: np.ndarray = model[0]
+        vocab_list = model[1]
+        self.assertIsNotNone(model)
+        self.assertIsNotNone(model[0])
+        self.assertIsNotNone(model[1])
+
+        # model matrix should be 34 x 128
+        self.assertEqual(model_matrix.shape[0], 34)
+        self.assertEqual(model_matrix.shape[1], 128)
+
+        # vocab list should have exactly 34 elements
+        self.assertEqual(len(vocab_list), 34)
+
+    def test_node2vec_embedding_florentine_graph_correct_shape_is_returned(self):
+        graph = nx.florentine_families_graph()
+        for s, t in graph.edges():
+            graph.add_edge(s, t, weight=1)
+
+        model = n2v.node2vec_embed(graph)
+        model_matrix: np.ndarray = model[0]
+        vocab_list = model[1]
+        self.assertIsNotNone(model)
+        self.assertIsNotNone(model[0])
+        self.assertIsNotNone(model[1])
+
+        # model matrix should be 34 x 128
+        self.assertEqual(model_matrix.shape[0], 15)
+        self.assertEqual(model_matrix.shape[1], 128)
+
+        # vocab list should have exactly 34 elements
+        self.assertEqual(len(vocab_list), 15)
+
+    def test_node2vec_embedding_barbell_graph_correct_shape_is_returned(self):
+        graph = nx.barbell_graph(25, 2)
+        for s, t in graph.edges():
+            graph.add_edge(s, t, weight=1)
+
+        model = n2v.node2vec_embed(graph)
+        model_matrix: np.ndarray = model[0]
+        vocab_list = model[1]
+        self.assertIsNotNone(model)
+        self.assertIsNotNone(model[0])
+        self.assertIsNotNone(model[1])
+
+        # model matrix should be 34 x 128
+        self.assertEqual(model_matrix.shape[0], 52)
+        self.assertEqual(model_matrix.shape[1], 128)
+
+        # vocab list should have exactly 34 elements
+        self.assertEqual(len(vocab_list), 52)
+
+    def test_get_walk_length_lower_defaults_to_1(self):
+        expected_walk_length = 1
+
+        g = _Node2VecGraph(nx.Graph(), 1, 1)
+        w = g._get_walk_length_interpolated(
+            degree=0, percentiles=[1, 2, 3, 4, 10, 100], max_walk_length=10
+        )
+
+        self.assertEqual(w, expected_walk_length)
+
+    def test_get_walk_length_higher_default_to_walk_length(self):
+        expected_walk_length = 100
+
+        g = _Node2VecGraph(nx.Graph(), 1, 1)
+        w = g._get_walk_length_interpolated(
+            degree=10,
+            percentiles=[2, 3, 4, 5, 6, 7, 8, 9],
+            max_walk_length=expected_walk_length,
+        )
+
+        self.assertEqual(w, expected_walk_length)
+
+    def test_get_walk_length_in_middle_selects_interpolated_bucket(self):
+        expected_walk_length = 5
+
+        g = _Node2VecGraph(nx.Graph(), 1, 1)
+        w = g._get_walk_length_interpolated(
+            degree=5, percentiles=[2, 3, 4, 5, 6, 7, 8, 9], max_walk_length=10
+        )
+
+        self.assertEqual(w, expected_walk_length)
+
+
+_edge_list = """
+1 32
+1 22
+1 20
+1 18
+1 14
+1 13
+1 12
+1 11
+1 9
+1 8
+1 7
+1 6
+1 5
+1 4
+1 3
+1 2
+2 31
+2 22
+2 20
+2 18
+2 14
+2 8
+2 4
+2 3
+3 14
+3 9
+3 10
+3 33
+3 29
+3 28
+3 8
+3 4
+4 14
+4 13
+4 8
+5 11
+5 7
+6 17
+6 11
+6 7
+7 17
+9 34
+9 33
+9 33
+10 34
+14 34
+15 34
+15 33
+16 34
+16 33
+19 34
+19 33
+20 34
+21 34
+21 33
+23 34
+23 33
+24 30
+24 34
+24 33
+24 28
+24 26
+25 32
+25 28
+25 26
+26 32
+27 34
+27 30
+28 34
+29 34
+29 32
+30 34
+30 33
+31 34
+31 33
+32 34
+32 33
+33 34
+"""