def test_closeness_centrality():
"""
The following test checks closeness centrality before any perturbation.
"""
g = ParallelGeneralGraph()
g.load("tests/TOY_graph.csv")
closeness_centrality = {
'1': 0.0,
'2': 0.05555555555555555,
'3': 0.05555555555555555,
'4': 0.07407407407407407,
'5': 0.07407407407407407,
'6': 0.1736111111111111,
'7': 0.11574074074074076,
'8': 0.11574074074074076,
'9': 0.14327485380116958,
'10': 0.12077294685990338,
'11': 0.17386831275720163,
'12': 0.1866925064599483,
'13': 0.16055555555555556,
'14': 0.1866925064599483,
'15': 0.0,
'16': 0.16071428571428573,
'17': 0.125,
'18': 0.17307692307692307,
'19': 0.22299382716049382
}
np.testing.assert_array_almost_equal(
np.asarray(sorted(closeness_centrality.values())),
np.asarray(sorted(g.closeness_centrality.values())),
err_msg="CLOSENESS CENTRALITY failure (PARALLEL)")
def test_nodal_efficiency():
"""
The following test checks nodal efficiency before any perturbation.
"""
g = ParallelGeneralGraph()
g.load("tests/TOY_graph.csv")
nodal_eff_before = {
'1': 0.3213624338624339,
'2': 0.19689554272887605,
'3': 0.15185185185185185,
'4': 0.20222663139329808,
'5': 0.14814814814814814,
'6': 0.22583774250440916,
'7': 0.17444885361552032,
'8': 0.2492063492063492,
'9': 0.16124338624338624,
'10': 0.14814814814814814,
'11': 0.14814814814814814,
'12': 0.15740740740740738,
'13': 0.16666666666666666,
'14': 0.19444444444444445,
'15': 0.16587301587301587,
'16': 0.15648148148148147,
'17': 0.20740740740740743,
'18': 0.0,
'19': 0.16666666666666666
}
np.testing.assert_array_almost_equal(
np.asarray(sorted(nodal_eff_before.values())),
np.asarray(sorted(g.nodal_efficiency.values())),
err_msg="ORIGINAL NODAL EFFICIENCY failure (PARALLEL)")
def test_local_efficiency():
"""
The following test checks local efficiency before any perturbation.
"""
g = ParallelGeneralGraph()
g.load("tests/TOY_graph.csv")
local_eff_before = {
'1': 0.17437369729036395,
'2': 0.20222663139329808,
'3': 0.14814814814814814,
'4': 0.22583774250440916,
'5': 0.14814814814814814,
'6': 0.21182760141093476,
'7': 0.22583774250440916,
'8': 0.19354056437389772,
'9': 0.15648148148148147,
'10': 0.14814814814814814,
'11': 0.16666666666666666,
'12': 0.16666666666666666,
'13': 0.17592592592592593,
'14': 0.1111111111111111,
'15': 0.16124338624338624,
'16': 0.20740740740740743,
'17': 0.1523148148148148,
'18': 0.0,
'19': 0.17592592592592593
}
np.testing.assert_array_almost_equal(
np.asarray(sorted(local_eff_before.values())),
np.asarray(sorted(g.local_efficiency.values())),
err_msg="ORIGINAL LOCAL EFFICIENCY failure (PARALLEL)")
def test_degree_centrality():
"""
The following test checks degree centrality before any perturbation.
"""
g = ParallelGeneralGraph()
g.load("tests/TOY_graph.csv")
degree_centrality = {
'1': 0.1111111111111111,
'2': 0.1111111111111111,
'3': 0.1111111111111111,
'4': 0.1111111111111111,
'5': 0.1111111111111111,
'6': 0.2777777777777778,
'7': 0.1111111111111111,
'8': 0.16666666666666666,
'9': 0.16666666666666666,
'10': 0.1111111111111111,
'11': 0.16666666666666666,
'12': 0.2222222222222222,
'13': 0.2222222222222222,
'14': 0.2777777777777778,
'15': 0.05555555555555555,
'16': 0.16666666666666666,
'17': 0.16666666666666666,
'18': 0.05555555555555555,
'19': 0.2777777777777778
}
np.testing.assert_array_almost_equal(
np.asarray(sorted(degree_centrality.values())),
np.asarray(sorted(g.degree_centrality.values())),
err_msg="DEGREE CENTRALITY failure (PARALLEL)")
def test_betweenness_centrality():
"""
The following test checks betweenness centrality before any perturbation.
"""
g = ParallelGeneralGraph()
g.load("tests/TOY_graph.csv")
betweenness_centrality = {
'1': 0.0,
'2': 0.05161290322580645,
'3': 0.04516129032258064,
'4': 0.12903225806451613,
'5': 0.07741935483870968,
'6': 0.2709677419354839,
'7': 0.0,
'8': 0.2838709677419355,
'9': 0.36774193548387096,
'10': 0.34838709677419355,
'11': 0.41935483870967744,
'12': 0.1032258064516129,
'13': 0.0,
'14': 0.10967741935483871,
'15': 0.0,
'16': 0.3741935483870968,
'17': 0.36774193548387096,
'18': 0.0,
'19': 0.38064516129032255
}
np.testing.assert_array_almost_equal(
np.asarray(sorted(betweenness_centrality.values())),
np.asarray(sorted(g.betweenness_centrality.values())),
err_msg="BETWENNESS CENTRALITY failure (PARALLEL)")
def test_floyd_warshall_parallel(self):
"""
The following test checks the parallel Floyd Warshall's APSP algorithm.
"""
g = ParallelGeneralGraph()
g.load("tests/TOY_graph.csv")
g.num = mp.cpu_count()
shpath, shpath_len = g.floyd_warshall_predecessor_and_distance()
self.check_shortest_paths(self, self.initial, shpath, shpath_len)
def test_Dijkstra_parallel(self):
"""
The following test checks the parallel SSSP algorithm based
on Dijkstra's method.
"""
g = ParallelGeneralGraph()
g.load("tests/TOY_graph.csv")
g.num = mp.cpu_count()
shpath, shpath_len = g.dijkstra_single_source_shortest_path()
self.check_shortest_paths(self, self.initial, shpath, shpath_len)
def test_global_efficiency():
"""
The following test checks global efficiency before any perturbation.
"""
g = ParallelGeneralGraph()
g.load("tests/TOY_graph.csv")
np.testing.assert_almost_equal(
g.global_efficiency,
0.1759191750419821,
err_msg="ORIGINAL GLOBAL EFFICIENCY failure")