def cugraph_call(cu_M, max_iter, pos_list, outbound_attraction_distribution,
lin_log_mode, prevent_overlapping, edge_weight_influence,
jitter_tolerance, barnes_hut_theta, barnes_hut_optimize,
scaling_ratio, strong_gravity_mode, gravity):
G = cugraph.Graph()
G.from_cudf_edgelist(cu_M,
source='0',
destination='1',
edge_attr='2',
renumber=False)
# cugraph Force Atlas 2 Call
t1 = time.time()
pos = cugraph.force_atlas2(
G,
max_iter=max_iter,
pos_list=pos_list,
outbound_attraction_distribution=outbound_attraction_distribution,
lin_log_mode=lin_log_mode,
prevent_overlapping=prevent_overlapping,
edge_weight_influence=edge_weight_influence,
jitter_tolerance=jitter_tolerance,
barnes_hut_optimize=barnes_hut_optimize,
barnes_hut_theta=barnes_hut_theta,
scaling_ratio=scaling_ratio,
strong_gravity_mode=strong_gravity_mode,
gravity=gravity)
t2 = time.time() - t1
print('Cugraph Time : ' + str(t2))
return pos
def test_force_atlas2_multi_column_pos_list(graph_file, score, max_iter,
barnes_hut_optimize):
cu_M = utils.read_csv_file(graph_file)
test_callback = TestCallback()
pos = cugraph_call(cu_M,
max_iter=max_iter,
pos_list=None,
outbound_attraction_distribution=True,
lin_log_mode=False,
prevent_overlapping=False,
edge_weight_influence=1.0,
jitter_tolerance=1.0,
barnes_hut_optimize=False,
barnes_hut_theta=0.5,
scaling_ratio=2.0,
strong_gravity_mode=False,
gravity=1.0,
callback=test_callback)
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
G = cugraph.Graph()
G.from_cudf_edgelist(
cu_M, source=["src_0", "src_1"],
destination=["dst_0", "dst_1"],
edge_attr="2"
)
pos_list = cudf.DataFrame()
pos_list['vertex_0'] = pos['vertex']
pos_list['vertex_1'] = pos_list['vertex_0'] + 1000
pos_list['x'] = pos['x']
pos_list['y'] = pos['y']
cu_pos = cugraph.force_atlas2(
G,
max_iter=max_iter,
pos_list=pos_list,
outbound_attraction_distribution=True,
lin_log_mode=False,
prevent_overlapping=False,
edge_weight_influence=1.0,
jitter_tolerance=1.0,
barnes_hut_optimize=False,
barnes_hut_theta=0.5,
scaling_ratio=2.0,
strong_gravity_mode=False,
gravity=1.0,
callback=test_callback)
cu_pos = cu_pos.sort_values('0_vertex')
matrix_file = graph_file.with_suffix(".mtx")
M = scipy.io.mmread(matrix_file)
M = M.todense()
cu_trust = trustworthiness(M, cu_pos[["x", "y"]].to_pandas())
print(cu_trust, score)
assert cu_trust > score
async def main(zmq_ctx):
def map_positions(pos):
return cudf.DataFrame(pos, columns=["x", "y"]).astype("float32")
callback = GraphZmqCallback(
zmq_ctx=zmq_ctx,
map_positions=map_positions,
nodes=nodes[["id", "color", "size"]],
edges=edges[["edge", "bundle", "color"]],
edge_col_names=["edge", "color", "bundle"],
node_col_names=["id", "color", "size", "x", "y"],
)
cugraph.force_atlas2(
graph,
max_iter=500,
callback=callback,
)
callback.update(msg=b"close")
callback.close()