def test_central_node(self):
"""
Checks if, given a graph that has been tested for centrality,
no nodes are identified as hubs (actually the p-value is too low).
"""
results = cluster_graph(deepcopy(g), limit, max_clusters, min_clusters,
min_cluster_size, iterations, subset, ratio, edgescale, permutations, verbose)
graph = results[0]
central_edge(graph, percentile, permutations, error, verbose)
central_node(graph)
self.assertEqual(len(nx.get_node_attributes(graph, 'hub')), 0)
def test_default_manta(self):
"""
Checks whether the main function carries out both clustering and centrality estimates.
"""
clustered_graph = cluster_graph(deepcopy(g), limit, max_clusters, min_clusters,
min_cluster_size, iterations, subset, ratio, edgescale, permutations, verbose)
graph = clustered_graph[0]
central_edge(graph, percentile, rel,
error, verbose)
central_node(graph)
clusters = nx.get_node_attributes(graph, 'cluster')
hubs = nx.get_edge_attributes(graph, 'hub')
self.assertGreater(len(hubs), 0)
def test_center_manta(self):
"""
Checks if the edge between 1 and 2 is identified as a positive hub.
WARNING: at the moment the test indicates that centrality measures
are not stable.
"""
results = cluster_graph(deepcopy(g), limit, max_clusters, min_clusters,
min_cluster_size, iterations, subset, ratio, edgescale, permutations, verbose)
graph = results[0]
central_edge(graph, percentile, permutations, error, verbose)
hubs = nx.get_edge_attributes(graph, 'hub')
if ('OTU_4', 'OTU_3') in hubs:
hubs['OTU_3', 'OTU_4'] = hubs['OTU_4', 'OTU_3']
# 3.5 test sometimes swaps keys around apparently
self.assertEqual(hubs[('OTU_3', 'OTU_4')], 'negative hub')