def test_n_iterations():
Q = nx.karate_club_graph()
#Q = nx.Graph()
#Q.add_edge(0,1,weight=8)
#Q.add_edge(2,1,weight=1)
#Q.add_edge(3,1,weight=1)
gpu_comm = gpucommunity.Community(Q)
print(f"[T]Initial modularity {gpu_comm.modularity}")
gpu_comm.compute_one_level(limit=4)
res = gpu_comm.partition
print(f"[T]partition after one level {res}")
gpu_comm.merge_communities()
gpu_comm.compute_one_level(limit=4)
res = gpu_comm.partition
print(f"[T]partition after 2nd level {res}")
gpu_comm.merge_communities()
gpu_comm.compute_one_level(limit=4)
res = gpu_comm.partition
print(f"[T]partition after 3nd level {res}")
for _ in range(10):
gpu_comm.merge_communities()
gpu_comm.compute_one_level(limit=4)
res = gpu_comm.partition.copy()
print(f"[T]partition after {_} level {res}")
print(f"[T]modularity is {gpu_comm.modularity}")
def test_best_partition1():
__MIN = 0.001
status_list = []
Q = nx.karate_club_graph()
gpu_comm = gpucommunity.Community(Q)
old_mod = gpu_comm.modularity
print(f"[T]Initial modularity {old_mod}")
gpu_comm.compute_one_level(limit=2)
new_mod = gpu_comm.modularity
partition = gpu_comm.partition.copy()
status_list.append(partition)
old_mod = new_mod
gpu_comm.merge_communities()
loop = 0
while True:
loop += 1
gpu_comm.compute_one_level(limit=2)
new_mod = gpu_comm.modularity
if new_mod - old_mod < __MIN:
print('[T] BREAK!!!')
break
partition = gpu_comm.partition.copy()
status_list.append(partition)
old_mod = new_mod
gpu_comm.merge_communities()
print(f"levels {len(status_list)}")
print(f"[T]{status_list[-1]}")
print(f"[T]{set(status_list[-1].values())}")
print(f"[T]{new_mod}")
print(f'loops{loop}')
def test_best_partition2():
MIN = 0.001
#Q = nx.karate_club_graph()
coo_mtx = io.mmread(os.path.join(os.getcwd(), 'graphs/bio-diseasome.mtx'))
Q = nx.from_scipy_sparse_matrix(coo_mtx)
gpu_comm = gpucommunity.Community(Q)
gpu_comm.compute_one_level(limit=6)
res = gpu_comm.partition
print(f"[T]result {res}")
print(f"[T]incidents {gpu_comm.communities_sum_incidents}")
print(f"[T]inner weights {gpu_comm.communities_sum_inside}")
#status_list = gpu_comm.best_partition(limit=6, __MIN=MIN)
mod = gpu_comm.modularity
def test_community():
karate = nx.karate_club_graph()
Q = karate
#Q = nx.Graph()
#Q.add_edge(0,1,weight=8)
#Q.add_edge(2,1,weight=1)
#Q.add_edge(3,1,weight=1)
gpu_comm = gpucommunity.Community(Q)
print(f"[T]Initial modularity {gpu_comm.modularity}")
#do some mod
gpu_comm.compute_one_level(limit=1)
res = gpu_comm.partition
print(f"[T]result {res}")
print(f"[T]incidents {gpu_comm.communities_sum_incidents}")
print(f"[T]inner weights {gpu_comm.communities_sum_inside}")
def test_merge():
Q = nx.Graph()
Q.add_edge(0, 1, weight=8)
Q.add_edge(2, 1, weight=1)
Q.add_edge(3, 1, weight=1)
gpu_comm = gpucommunity.Community(Q)
print(f"[T]Initial modularity {gpu_comm.modularity}")
#do some mod
gpu_comm.compute_one_level(limit=6)
res = gpu_comm.partition
print(f"[T]result {res}")
print(f"[T]incidents {gpu_comm.communities_sum_incidents}")
print(f"[T]inner weights {gpu_comm.communities_sum_inside}")
gpu_comm.merge_communities()
res = gpu_comm.nxgraph.edges(data=True)
print(f"[T]result {res}")
def test_create():
karate = nx.karate_club_graph()
gpu_comm = gpucommunity.Community(karate)
def main():
coo_mtx = io.mmread(os.path.join(os.getcwd(),'graphs/bio-diseasome.mtx'))
G = nx.from_scipy_sparse_matrix(coo_mtx)
MIN = 0.001
gpu_comm = gpucommunity.Community(G)
gpu_comm.compute_one_level(limit=6)