def test_modularity_clustering_multi_column(graph_file, partitions):
gc.collect()
# Read in the graph and get a cugraph object
cu_M = utils.read_csv_file(graph_file, read_weights_in_sp=False)
cu_M.rename(columns={'0': 'src_0', '1': 'dst_0'}, inplace=True)
cu_M['src_1'] = cu_M['src_0'] + 1000
cu_M['dst_1'] = cu_M['dst_0'] + 1000
G1 = cugraph.Graph()
G1.from_cudf_edgelist(cu_M,
source=["src_0", "src_1"],
destination=["dst_0", "dst_1"],
edge_attr="2")
df1 = cugraph.spectralModularityMaximizationClustering(
G1, partitions, num_eigen_vects=(partitions - 1))
cu_score = cugraph.analyzeClustering_modularity(G1, partitions, df1,
['0_vertex', '1_vertex'],
'cluster')
G2 = cugraph.Graph()
G2.from_cudf_edgelist(cu_M,
source="src_0",
destination="dst_0",
edge_attr="2")
rand_score = random_call(G2, partitions)
# Assert that the partitioning has better modularity than the random
# assignment
assert cu_score > rand_score
def cugraph_call(G, partitions):
df = cugraph.spectralModularityMaximizationClustering(
G, partitions, num_eigen_vects=(partitions - 1)
)
score = cugraph.analyzeClustering_modularity(G, partitions, df,
'vertex',
'cluster')
return score
