def test_predict_with_different_ancestor_false_2(self):
g = nx.Graph()
g.add_edge(0, 1, weight=1)
g.add_edge(2, 3, weight=1)
ic = info_clustering_prediction()
ic.fit(g, weight_method=None)
self.assertFalse(ic.predict(0, 3))
def test_predict_trivial(self):
g = nx.Graph() # undirected graph
g.add_edge(0, 1, weight=1)
ic = info_clustering_prediction()
ic.fit(g)
self.assertTrue(ic.predict(0, 1))
self.assertTrue(ic.predict(1, 0))
def test_predict_with_different_ancestor_true(self):
g = nx.Graph()
g.add_edge(0, 1, weight=2)
g.add_edge(1, 2, weight=1.1)
g.add_edge(2, 3, weight=1)
g.add_edge(3, 4, weight=1)
g.add_edge(2, 4, weight=1)
ic = info_clustering_prediction()
ic.fit(g, weight_method=None)
self.assertTrue(ic.predict(0, 2))
self.assertTrue(ic.predict(2, 0))
def test_predict_with_same_ancestor(self):
g = nx.Graph()
g.add_edge(0, 1, weight=1)
g.add_edge(1, 2, weight=1)
g.add_edge(2, 3, weight=1)
ic = info_clustering_prediction()
ic.fit(g, weight_method=None)
self.assertTrue(ic.predict(0, 3))
self.assertTrue(ic.predict(3, 0))
self.assertFalse(ic.predict(0, 2))
self.assertFalse(ic.predict(2, 0))
def test_evaluation_single(self):
g = nx.Graph()
g.add_edge(0, 1, weight=1)
g.add_edge(1, 2, weight=1)
g.add_edge(2, 3, weight=1)
ic = info_clustering_prediction()
ic.fit(g, weight_method=None)
test_index_list = [(0, 3)]
res = evaluate_single(ic, test_index_list, need_fit=False)
self.assertAlmostEqual(res["tpr"], 1.0)
self.assertAlmostEqual(res["tnr"], 1.0)
self.assertAlmostEqual(res["acc"], 1.0)
pdb.set_trace()
if(args.load_graph):
G = nx.read_gml(os.path.join('build', args.load_graph))
if(type(G) is nx.DiGraph):
G = G.to_undirected()
if(G.nodes.get(0) is None):
G = nx.convert_node_labels_to_integers(G)
else:
G = nx.read_gml(os.path.join('build', 'nips-234.gml'))
if(args.save_graph):
raise NotImplementedError("")
methods = []
if(args.alg.count('all') > 0):
args.alg = method_chocies
if(args.alg.count('GBIC') > 0):
methods.append(info_clustering_prediction())
if(args.alg.count('BHCD') > 0):
methods.append(BHCD())
if args.alg.count('RAI') > 0:
methods.append(ResourceAllocationIndex())
if(len(methods)==0):
raise ValueError('unknown algorithm')
np.random.seed(args.seed)
if(args.evaluate == 2):
print('logging to', LOGGING_FILE)
tabulate_dic = {}
method_dic = {}
for i, method in enumerate(methods):