def test_multigraph(graph_file):
# FIXME: Migrate to new test fixtures for Graph setup once available
cuM = utils.read_csv_file(graph_file)
G = cugraph.MultiDiGraph()
G.from_cudf_edgelist(cuM, source="0", destination="1", edge_attr="2")
nxM = utils.read_csv_for_nx(graph_file, read_weights_in_sp=True)
Gnx = nx.from_pandas_edgelist(
nxM,
source="0",
target="1",
edge_attr="weight",
create_using=nx.MultiDiGraph(),
)
assert G.number_of_edges() == Gnx.number_of_edges()
assert G.number_of_nodes() == Gnx.number_of_nodes()
cuedges = cugraph.to_pandas_edgelist(G)
cuedges.rename(columns={"src": "source", "dst": "target",
"weights": "weight"}, inplace=True)
cuedges["weight"] = cuedges["weight"].round(decimals=3)
nxedges = nx.to_pandas_edgelist(Gnx).astype(dtype={"source": "int32",
"target": "int32",
"weight": "float32"})
cuedges = cuedges.sort_values(by=["source", "target"]).\
reset_index(drop=True)
nxedges = nxedges.sort_values(by=["source", "target"]).\
reset_index(drop=True)
nxedges["weight"] = nxedges["weight"].round(decimals=3)
assert nxedges.equals(cuedges[["source", "target", "weight"]])
def test_to_from_pandas(graph_file):
# Read in the graph
M = utils.read_csv_for_nx(graph_file, read_weights_in_sp=True)
# create a NetworkX DiGraph and convert to pandas adjacency
nxG = nx.from_pandas_edgelist(M,
source="0",
target="1",
edge_attr="weight",
create_using=nx.DiGraph)
nx_pdf = nx.to_pandas_adjacency(nxG)
nx_pdf = nx_pdf[sorted(nx_pdf.columns)]
nx_pdf.sort_index(inplace=True)
# create a cugraph DiGraph and convert to pandas adjacency
cuG = cugraph.from_pandas_edgelist(M,
source="0",
destination="1",
edge_attr="weight",
create_using=cugraph.DiGraph)
cu_pdf = cugraph.to_pandas_adjacency(cuG)
cu_pdf = cu_pdf[sorted(cu_pdf.columns)]
cu_pdf.sort_index(inplace=True)
# Compare pandas adjacency list
assert nx_pdf.equals(cu_pdf)
# Convert pandas adjacency list to graph
new_nxG = nx.from_pandas_adjacency(nx_pdf, create_using=nx.DiGraph)
new_cuG = cugraph.from_pandas_adjacency(cu_pdf,
create_using=cugraph.DiGraph)
# Compare pandas edgelist
exp_pdf = nx.to_pandas_edgelist(new_nxG)
res_pdf = cugraph.to_pandas_edgelist(new_cuG)
exp_pdf = exp_pdf.rename(columns={
"source": "src",
"target": "dst",
"weight": "weights"
})
exp_pdf = exp_pdf.sort_values(by=["src", "dst"]).reset_index(drop=True)
res_pdf = res_pdf.sort_values(by=["src", "dst"]).reset_index(drop=True)
res_pdf = res_pdf[['src', 'dst', 'weights']]
assert exp_pdf.equals(res_pdf)
def test_from_to_numpy(graph_file):
# Read in the graph
M = utils.read_csv_for_nx(graph_file, read_weights_in_sp=True)
# create NetworkX and cugraph DiGraph
nxG = nx.from_pandas_edgelist(M,
source="0",
target="1",
edge_attr="weight",
create_using=nx.DiGraph)
cuG = cugraph.from_pandas_edgelist(M,
source="0",
destination="1",
edge_attr="weight",
create_using=cugraph.DiGraph)
# convert graphs to numpy array
nparray_nx = nx.to_numpy_array(nxG, nodelist=cuG.nodes().values_host)
nparray_cu = cugraph.to_numpy_array(cuG)
npmatrix_nx = nx.to_numpy_matrix(nxG, nodelist=cuG.nodes().values_host)
npmatrix_cu = cugraph.to_numpy_matrix(cuG)
# Compare arrays and matrices
assert np.array_equal(nparray_nx, nparray_cu)
assert np.array_equal(np.asarray(npmatrix_nx), np.asarray(npmatrix_cu))
# Create graphs from numpy array
new_nxG = nx.from_numpy_array(nparray_nx, create_using=nx.DiGraph)
new_cuG = cugraph.from_numpy_array(nparray_cu,
create_using=cugraph.DiGraph)
# Assert graphs are same
exp_pdf = nx.to_pandas_edgelist(new_nxG)
res_pdf = cugraph.to_pandas_edgelist(new_cuG)
exp_pdf = exp_pdf.rename(columns={
"source": "src",
"target": "dst",
"weight": "weights"
})
exp_pdf = exp_pdf.sort_values(by=["src", "dst"]).reset_index(drop=True)
res_pdf = res_pdf.sort_values(by=["src", "dst"]).reset_index(drop=True)
res_pdf = res_pdf[['src', 'dst', 'weights']]
assert exp_pdf.equals(res_pdf)
# Create graphs from numpy matrix
new_nxG = nx.from_numpy_matrix(npmatrix_nx, create_using=nx.DiGraph)
new_cuG = cugraph.from_numpy_matrix(npmatrix_cu,
create_using=cugraph.DiGraph)
# Assert graphs are same
exp_pdf = nx.to_pandas_edgelist(new_nxG)
res_pdf = cugraph.to_pandas_edgelist(new_cuG)
exp_pdf = exp_pdf.rename(columns={
"source": "src",
"target": "dst",
"weight": "weights"
})
exp_pdf = exp_pdf.sort_values(by=["src", "dst"]).reset_index(drop=True)
res_pdf = res_pdf.sort_values(by=["src", "dst"]).reset_index(drop=True)
res_pdf = res_pdf[['src', 'dst', 'weights']]
assert exp_pdf.equals(res_pdf)