speed up tests, made explicit communities

Author: mb3152

brainx/tests/test_nodal_roles.py

@@ -1,48 +1,36 @@
 #test nodal roles 
 import unittest
 import networkx as nx
-from .. import nodal_roles as nr
-from .. import weighted_modularity as wm
+import nodal_roles as nr
+import weighted_modularity as wm
 
 class TestNodalRoles(unittest.TestCase):
 	def test_participation_coefficient_edgeless(self):
 		graph = nx.Graph([(0,1)])
 		graph.add_node(2)
-		louvain = wm.LouvainCommunityDetection(graph)
-		weighted_partitions = louvain.run()
-		weighted_partition = weighted_partitions[0]
+		partition = wm.WeightedPartition(graph, communities=[ set([0,1]), set([2])])
 		with self.assertRaises(ValueError):
-			nr.participation_coefficient(weighted_partition)
-		graph = nx.Graph([(0,1),(1,2),(2,0),(3,4),(3,5),(4,5)])
-		louvain = wm.LouvainCommunityDetection(graph)
-		partition = louvain.run()[0]
+			nr.participation_coefficient(partition)
 	def test_within_community_degree_edgeless(self):
 		graph = nx.Graph([(0,1)])
 		graph.add_node(2)
-		louvain = wm.LouvainCommunityDetection(graph)
-		weighted_partitions = louvain.run()
-		weighted_partition = weighted_partitions[0]
+		partition = wm.WeightedPartition(graph, communities=[ set([0,1]), set([2])])
 		with self.assertRaises(ValueError):
-			nr.within_community_degree(weighted_partition)
+			nr.within_community_degree(partition)
 	def test_disconnected_communites(self):
 		graph = nx.Graph([(0,1),(1,2),(2,0),(3,4),(3,5),(4,5)])
-		louvain = wm.LouvainCommunityDetection(graph)
-		partition = louvain.run()[0]
+		partition = wm.WeightedPartition(graph, communities=[set([0, 1, 2]), set([3, 4, 5])])
 		wcd = nr.within_community_degree(partition)
-		self.assertEqual(wcd, {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0})
+		self.assertAlmostEqual(wcd, {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0})
 		pc = nr.participation_coefficient(partition)
 		self.assertEqual(pc, {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0})
 	def test_high_low_pc(self):
 		graph = nx.Graph([(0,1),(1,2),(2,0),(0,3),(3,4),(3,5),(4,5)])
-		louvain = wm.LouvainCommunityDetection(graph)
-		partition = louvain.run()[0]
+		partition = wm.WeightedPartition(graph, communities=[set([0, 1, 2]), set([3, 4, 5])])
 		pc = nr.participation_coefficient(partition)
-		self.assertEqual(pc,{0: 0.8888888888888888, 1: 0.0, 2: 0.0, 3: 0.8888888888888888, 4: 0.0, 5: 0.0})
+		self.assertAlmostEqual(pc,{0: 0.8888888888888888, 1: 0.0, 2: 0.0, 3: 0.8888888888888888, 4: 0.0, 5: 0.0})
 	def test_high_low_wcd(self):
 		graph = nx.Graph([(0,1),(0,2),(0,3),(0,4),(0,5),(6,7),(7,8),(8,6)])
-		louvain = wm.LouvainCommunityDetection(graph)
-		partition = louvain.run()[0]
+		partition = wm.WeightedPartition(graph, communities=[set([0, 1, 2, 3, 4, 5]), set([8, 6, 7])])
 		wcd = nr.within_community_degree(partition)
-		self.assertEqual(wcd, {0: 3.8819660112501051, 1: -0.11803398874989512, 2: -0.11803398874989512, 3: -0.11803398874989512,4: -0.11803398874989512, 5: -0.11803398874989512, 6: 0.0, 7: 0.0, 8: 0.0})
-
-
+		self.assertAlmostEqual(wcd, {0: 3.8819660112501051, 1: -0.11803398874989512, 2: -0.11803398874989512, 3: -0.11803398874989512,4: -0.11803398874989512, 5: -0.11803398874989512, 6: 0.0, 7: 0.0, 8: 0.0})