diff env/lib/python3.9/site-packages/networkx/linalg/tests/test_modularity.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/env/lib/python3.9/site-packages/networkx/linalg/tests/test_modularity.py	Mon Mar 22 18:12:50 2021 +0000
@@ -0,0 +1,86 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+npt = pytest.importorskip("numpy.testing")
+scipy = pytest.importorskip("scipy")
+
+import networkx as nx
+from networkx.generators.degree_seq import havel_hakimi_graph
+
+
+class TestModularity:
+    @classmethod
+    def setup_class(cls):
+        deg = [3, 2, 2, 1, 0]
+        cls.G = havel_hakimi_graph(deg)
+        # Graph used as an example in Sec. 4.1 of Langville and Meyer,
+        # "Google's PageRank and Beyond". (Used for test_directed_laplacian)
+        cls.DG = nx.DiGraph()
+        cls.DG.add_edges_from(
+            (
+                (1, 2),
+                (1, 3),
+                (3, 1),
+                (3, 2),
+                (3, 5),
+                (4, 5),
+                (4, 6),
+                (5, 4),
+                (5, 6),
+                (6, 4),
+            )
+        )
+
+    def test_modularity(self):
+        "Modularity matrix"
+        # fmt: off
+        B = np.array([[-1.125,  0.25,  0.25,  0.625,  0.],
+                      [0.25, -0.5,  0.5, -0.25,  0.],
+                      [0.25,  0.5, -0.5, -0.25,  0.],
+                      [0.625, -0.25, -0.25, -0.125,  0.],
+                      [0.,  0.,  0.,  0.,  0.]])
+        # fmt: on
+
+        permutation = [4, 0, 1, 2, 3]
+        npt.assert_equal(nx.modularity_matrix(self.G), B)
+        npt.assert_equal(
+            nx.modularity_matrix(self.G, nodelist=permutation),
+            B[np.ix_(permutation, permutation)],
+        )
+
+    def test_modularity_weight(self):
+        "Modularity matrix with weights"
+        # fmt: off
+        B = np.array([[-1.125,  0.25,  0.25,  0.625,  0.],
+                      [0.25, -0.5,  0.5, -0.25,  0.],
+                      [0.25,  0.5, -0.5, -0.25,  0.],
+                      [0.625, -0.25, -0.25, -0.125,  0.],
+                      [0.,  0.,  0.,  0.,  0.]])
+        # fmt: on
+
+        G_weighted = self.G.copy()
+        for n1, n2 in G_weighted.edges():
+            G_weighted.edges[n1, n2]["weight"] = 0.5
+        # The following test would fail in networkx 1.1
+        npt.assert_equal(nx.modularity_matrix(G_weighted), B)
+        # The following test that the modularity matrix get rescaled accordingly
+        npt.assert_equal(nx.modularity_matrix(G_weighted, weight="weight"), 0.5 * B)
+
+    def test_directed_modularity(self):
+        "Directed Modularity matrix"
+        # fmt: off
+        B = np.array([[-0.2,  0.6,  0.8, -0.4, -0.4, -0.4],
+                      [0.,  0.,  0.,  0.,  0.,  0.],
+                      [0.7,  0.4, -0.3, -0.6,  0.4, -0.6],
+                      [-0.2, -0.4, -0.2, -0.4,  0.6,  0.6],
+                      [-0.2, -0.4, -0.2,  0.6, -0.4,  0.6],
+                      [-0.1, -0.2, -0.1,  0.8, -0.2, -0.2]])
+        # fmt: on
+        node_permutation = [5, 1, 2, 3, 4, 6]
+        idx_permutation = [4, 0, 1, 2, 3, 5]
+        mm = nx.directed_modularity_matrix(self.DG, nodelist=sorted(self.DG))
+        npt.assert_equal(mm, B)
+        npt.assert_equal(
+            nx.directed_modularity_matrix(self.DG, nodelist=node_permutation),
+            B[np.ix_(idx_permutation, idx_permutation)],
+        )