def test_edge_cut_clustering_with_edgevals_nx(graph_file, partitions):
gc.collect()
# Read in the graph and create a NetworkX Graph
# FIXME: replace with utils.generate_nx_graph_from_file()
NM = utils.read_csv_for_nx(graph_file, read_weights_in_sp=True)
G = nx.from_pandas_edgelist(NM,
create_using=nx.Graph(),
source="0",
target="1",
edge_attr="weight")
# Get the edge_cut score for partitioning versus random assignment
df = cugraph.spectralBalancedCutClustering(G,
partitions,
num_eigen_vects=partitions)
pdf = pd.DataFrame.from_dict(df, orient='index').reset_index()
pdf.columns = ["vertex", "cluster"]
gdf = cudf.from_pandas(pdf)
cu_score = cugraph.analyzeClustering_edge_cut(G, partitions, gdf, 'vertex',
'cluster')
df = set(gdf["vertex"].to_array())
Gcu = cugraph.utilities.convert_from_nx(G)
rand_vid, rand_score = random_call(Gcu, partitions)
# Assert that the partitioning has better edge_cut than the random
# assignment
print(cu_score, rand_score)
assert cu_score < rand_score
def cugraph_call(G, partitions):
df = cugraph.spectralBalancedCutClustering(G,
partitions,
num_eigen_vects=partitions)
score = cugraph.analyzeClustering_edge_cut(G, partitions, df, 'vertex',
'cluster')
return set(df["vertex"].to_array()), score
def cugraph_call(G, partitions):
df = cugraph.spectralBalancedCutClustering(
G, partitions, num_eigen_vects=partitions
)
df = df.sort_values("vertex")
score = cugraph.analyzeClustering_edge_cut(G, partitions, df["cluster"])
return set(df["vertex"].to_array()), score
def annotate_nodes(graph, nodes, edges):
return nodes.assign(
# add node names
name=nodes["name"] if "name" in nodes else nodes["id"],
# add node sizes
size=(nodes["degree"].scale() * 254 + 1).astype(np.uint8),
# add node colors
color=category_to_color(
cugraph.spectralBalancedCutClustering(graph, 9)
.sort_values(by="vertex").reset_index(drop=True)["cluster"],
color_palette=[
# https://colorbrewer2.org/#type=diverging&scheme=Spectral&n=9
4292165199, 4294208835,
4294815329, 4294893707,
4294967231, 4293326232,
4289453476, 4284924581,
4281501885
])
)