def test_graph_roundtrip_undirected_weighted_nodevals(default_plugin_resolver):
rt = RoundTripper(default_plugin_resolver)
g = nx.Graph()
nodes = [1, 3, 5, 7, 8, 9, 10, 11, 15]
node_weights = [1.1, 0.0, -4.4, 4.4, 6.5, 1.2, 2.0, 0.01, 15.2]
edges = [(1, 3), (3, 5), (5, 7), (7, 9), (9, 3), (5, 5), (11, 10)]
edge_weights = [1.1, 0.0, -4.4, 4.4, 6.5, 1.2, 2.0]
g.add_nodes_from(nodes)
g.add_edges_from(edges)
# nodevals as floats, edges as bools
nx.set_node_attributes(
g, {node: wgt for node, wgt in zip(nodes, node_weights)}, name="weight"
)
nx.set_edge_attributes(
g, {edge: bool(wgt) for edge, wgt in zip(edges, edge_weights)}, name="weight"
)
rt.verify_round_trip(NetworkXGraph(g))
# nodevals as ints, edges as floats
nx.set_node_attributes(
g, {node: int(wgt) for node, wgt in zip(nodes, node_weights)}, name="weight"
)
nx.set_edge_attributes(
g, {edge: wgt for edge, wgt in zip(edges, edge_weights)}, name="weight"
)
rt.verify_round_trip(NetworkXGraph(g))
# nodevals as bools, edges as ints
nx.set_node_attributes(
g, {node: bool(wgt) for node, wgt in zip(nodes, node_weights)}, name="weight"
)
nx.set_edge_attributes(
g, {edge: int(wgt) for edge, wgt in zip(edges, edge_weights)}, name="weight"
)
rt.verify_round_trip(NetworkXGraph(g))
def test_graph_roundtrip_directed_symmetric(default_plugin_resolver):
rt = RoundTripper(default_plugin_resolver)
g = nx.DiGraph()
g.add_nodes_from([1, 3, 5, 7, 8, 9, 10, 11, 15])
edges = [(1, 3), (3, 1), (3, 5), (5, 3), (3, 9), (9, 3), (5, 5), (11, 10), (10, 11)]
edge_weights = [1.1, 1.1, 0.0, -4.4, 4.4, 6.5, 1.2, 2.0]
# float with neg weights
g.add_weighted_edges_from(
[(src, dst, wgt) for (src, dst), wgt in zip(edges, edge_weights)]
)
rt.verify_round_trip(NetworkXGraph(g))
# float without neg weights
g.add_weighted_edges_from(
[(src, dst, abs(wgt)) for (src, dst), wgt in zip(edges, edge_weights)]
)
rt.verify_round_trip(NetworkXGraph(g))
# int with neg weights
g.add_weighted_edges_from(
[(src, dst, int(wgt)) for (src, dst), wgt in zip(edges, edge_weights)]
)
rt.verify_round_trip(NetworkXGraph(g))
# int without neg weights
g.add_weighted_edges_from(
[(src, dst, abs(int(wgt))) for (src, dst), wgt in zip(edges, edge_weights)]
)
rt.verify_round_trip(NetworkXGraph(g))
# bool
g.add_weighted_edges_from(
[(src, dst, bool(wgt)) for (src, dst), wgt in zip(edges, edge_weights)]
)
rt.verify_round_trip(NetworkXGraph(g))
def test_graph_roundtrip_directed_unweighted(default_plugin_resolver):
dpr = default_plugin_resolver
rt = RoundTripper(dpr)
g = nx.DiGraph()
g.add_nodes_from([1, 3, 5, 7, 8, 9, 10, 11, 15])
g.add_edges_from([(1, 3), (3, 1), (3, 5), (5, 7), (7, 9), (9, 3), (5, 5), (11, 10)])
graph = NetworkXGraph(g)
rt.verify_round_trip(graph)
# networkx: edgetype=set overrides weight label
g2 = nx.DiGraph()
g2.add_nodes_from([1, 3, 5, 7, 8])
g2.add_weighted_edges_from([(1, 3, 2), (3, 5, 4), (5, 7, 6)])
graph2 = NetworkXGraph(g2, aprops={"edge_type": "set"})
rt.verify_round_trip(graph2)
def build_standard_graph(directed=True):
r"""
0 <--2-- 1 5 --10-> 6
| ^ | ^ ^ /
| / | / | /
1 7 3 9 5 11
| / | / | /
v v / v
3 --8--> 4 <--4-- 2 --6--> 7
"""
ebunch = [
(0, 3, 1),
(1, 0, 2),
(1, 4, 3),
(2, 4, 4),
(2, 5, 5),
(2, 7, 6),
(3, 1, 7),
(3, 4, 8),
(4, 5, 9),
(5, 6, 10),
(6, 2, 11),
]
nx_graph = nx.DiGraph() if directed else nx.Graph()
nx_graph.add_weighted_edges_from(ebunch)
return NetworkXGraph(nx_graph)
def test_graph_roundtrip_undirected_unweighted(default_plugin_resolver):
rt = RoundTripper(default_plugin_resolver)
g = nx.Graph()
g.add_nodes_from([1, 3, 5, 7, 8, 9, 10, 11, 15])
g.add_edges_from([(1, 3), (3, 5), (5, 7), (7, 9), (9, 3), (5, 5), (11, 10)])
graph = NetworkXGraph(g)
rt.verify_round_trip(graph)
def test_graph_edgeset_oneway_undirected(default_plugin_resolver):
rt = RoundTripper(default_plugin_resolver)
g = nx.Graph()
g.add_nodes_from([1, 3, 5, 7, 8, 9, 10, 11, 15])
g.add_edges_from([(1, 3), (3, 5), (5, 7), (7, 9), (9, 3), (5, 5), (11, 10)])
graph = NetworkXGraph(g)
df = pd.DataFrame(
{"source": [11, 1, 3, 5, 7, 9, 5], "target": [10, 3, 5, 7, 9, 3, 5]}
)
edgeset = PandasEdgeSet(df, is_directed=False)
rt.verify_one_way(graph, edgeset)
def test_graph_nodeset_oneway(default_plugin_resolver):
rt = RoundTripper(default_plugin_resolver)
g = nx.Graph()
nodes = [1, 3, 5, 7, 8, 9, 10, 11, 15]
node_weights = [1.1, 0.0, -4.4, 4.4, 6.5, 1.2, 2.0, 0.01, 15.2]
edges = [(1, 3), (3, 5), (5, 7), (7, 9), (9, 3), (5, 5), (11, 10)]
g.add_nodes_from(nodes)
g.add_edges_from(edges)
nx.set_node_attributes(
g, {node: wgt for node, wgt in zip(nodes, node_weights)}, name="weight"
)
rt.verify_one_way(NetworkXGraph(g), NumpyNodeSet(nodes))
def test_networkx_scipy_graph_from_edgeset(default_plugin_resolver):
dpr = default_plugin_resolver
g = nx.DiGraph()
g.add_edges_from([(2, 2), (2, 7), (7, 7), (7, 0), (0, 7)])
x = NetworkXGraph(g)
# Convert networkx -> scipy adjacency
# 0 2 7
# 0 [ 1]
# 2 [ 1 1]
# 7 [1 1]
m = ss.coo_matrix(([1, 1, 1, 1, 1], ([0, 1, 1, 2, 2], [2, 1, 2, 0, 2])), dtype=bool)
intermediate = ScipyGraph(m, [0, 2, 7])
y = dpr.translate(x, ScipyGraph)
dpr.assert_equal(y, intermediate)
def graph_stellargraph_to_networkx(x: StellarGraph,
**props) -> NetworkXGraph:
aprops = StellarGraph.Type.compute_abstract_properties(
x, {
"is_directed", "node_type", "node_dtype", "edge_type",
"edge_dtype"
})
nx_multi_graph = x.value.to_networkx(
node_type_attr=x.node_sg_type,
edge_type_attr=x.edge_sg_type,
edge_weight_attr="weight",
feature_attr="weight",
)
has_node_weights = aprops["node_type"] == "map"
if has_node_weights:
node_weight_label = "weight"
caster = dtype_casting[aprops["node_dtype"]]
else:
node_weight_label = None
for node, node_attributes in nx_multi_graph.nodes(data=True):
del node_attributes[x.node_sg_type]
if has_node_weights:
node_attributes["weight"] = caster(
node_attributes["weight"][x.node_weight_index])
else:
del node_attributes["weight"]
is_weighted = aprops["edge_type"] == "map"
if is_weighted:
edge_weight_label = "weight"
caster = dtype_casting[aprops["edge_dtype"]]
else:
edge_weight_label = None
for src, dst, edge_attributes in nx_multi_graph.edges(data=True):
del edge_attributes[x.edge_sg_type]
if is_weighted:
edge_attributes["weight"] = caster(edge_attributes["weight"])
else:
del edge_attributes["weight"]
nx_graph = (nx.DiGraph(nx_multi_graph)
if aprops["is_directed"] else nx.Graph(nx_multi_graph))
return NetworkXGraph(
nx_graph,
node_weight_label=node_weight_label,
edge_weight_label=edge_weight_label,
)
def test_networkx_scipy_graph_from_edgemap(default_plugin_resolver):
dpr = default_plugin_resolver
g = nx.DiGraph()
g.add_weighted_edges_from([(2, 2, 1), (2, 7, 2), (7, 7, 0), (7, 0, 3), (0, 7, 3)])
x = NetworkXGraph(g)
# Convert networkx -> scipy adjacency
# 0 2 7
# 0 [ 3]
# 2 [ 1 2]
# 7 [3 0]
m = ss.coo_matrix(
([3, 1, 2, 3, 0], ([0, 1, 1, 2, 2], [2, 1, 2, 0, 2])), dtype=np.int64
)
intermediate = ScipyGraph(m, [0, 2, 7])
y = dpr.translate(x, ScipyGraph)
dpr.assert_equal(y, intermediate)
def test_graph_edgeset_oneway_directed_symmetric(default_plugin_resolver):
rt = RoundTripper(default_plugin_resolver)
g = nx.DiGraph()
g.add_nodes_from([1, 3, 5, 7, 8, 9, 10, 11, 15])
g.add_edges_from(
[(1, 3), (3, 1), (3, 5), (5, 3), (3, 9), (9, 3), (5, 5), (11, 10), (10, 11)]
)
graph = NetworkXGraph(g)
df = pd.DataFrame(
{
"source": [1, 3, 3, 5, 3, 9, 5, 11, 10],
"target": [3, 1, 5, 3, 9, 3, 5, 10, 11],
}
)
edgeset = PandasEdgeSet(df, is_directed=True)
rt.verify_one_way(graph, edgeset)
def test_weighted_directed_edge_list_cugraph_to_nextworkx():
"""
0 <--2-- 1 5 --10-> 6
| ^ | ^ ^ /
| / | / | /
1 7 3 9 5 11
| / | / | /
v v / v
3 --8--> 4 <--4-- 2 --6--> 7
"""
dpr = mg.resolver
sources = [0, 1, 1, 2, 2, 2, 3, 3, 4, 5, 6]
destinations = [3, 0, 4, 4, 5, 7, 1, 4, 5, 6, 2]
weights = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
gdf = cudf.DataFrame({
"source": sources,
"destination": destinations,
"weight": weights
})
cugraph_graph = cugraph.DiGraph()
cugraph_graph.from_cudf_edgelist(gdf,
source="source",
destination="destination",
edge_attr="weight")
x = dpr.wrappers.Graph.CuGraph(cugraph_graph)
networkx_graph_data = [
(0, 3, 1),
(1, 0, 2),
(1, 4, 3),
(2, 4, 4),
(2, 5, 5),
(2, 7, 6),
(3, 1, 7),
(3, 4, 8),
(4, 5, 9),
(5, 6, 10),
(6, 2, 11),
]
networkx_graph_unwrapped = nx.DiGraph()
networkx_graph_unwrapped.add_weighted_edges_from(networkx_graph_data)
intermediate = NetworkXGraph(networkx_graph_unwrapped)
y = dpr.translate(x, NetworkXGraph)
dpr.assert_equal(y, intermediate)
assert len(dpr.plan.translate(x, NetworkXGraph)) == 1
def test_unweighted_directed_edge_list_cugraph_to_nextworkx():
"""
0 < - 1 5 - > 6
^ ^ ^
| / | / | /
v v / v
3 - > 4 < - 2 - > 7
"""
dpr = mg.resolver
sources = [0, 1, 1, 2, 2, 2, 3, 3, 4, 5, 6]
destinations = [3, 0, 4, 4, 5, 7, 1, 4, 5, 6, 2]
gdf = cudf.DataFrame({"source": sources, "dst": destinations})
cugraph_graph_unwrapped = cugraph.DiGraph()
cugraph_graph_unwrapped.from_cudf_edgelist(gdf,
source="source",
destination="dst")
x = dpr.wrappers.Graph.CuGraph(cugraph_graph_unwrapped)
networkx_graph_data = [
(0, 3),
(1, 0),
(1, 4),
(2, 4),
(2, 5),
(2, 7),
(3, 1),
(3, 4),
(4, 5),
(5, 6),
(6, 2),
]
networkx_graph_unwrapped = nx.DiGraph()
networkx_graph_unwrapped.add_edges_from(networkx_graph_data)
intermediate = NetworkXGraph(networkx_graph_unwrapped)
y = dpr.translate(x, NetworkXGraph)
dpr.assert_equal(y, intermediate)
assert len(dpr.plan.translate(x, NetworkXGraph)) == 1
def test_networkx():
# 0 -> 0 (weight=1)
# 0 -> 1 (weight=2)
# 1 -> 1 (weight=0)
# 1 -> 2 (weight=3)
# 2 -> 1 (weight=3)
aprops = {
"is_directed": True,
"node_type": "set",
"edge_type": "map",
"edge_dtype": "int",
}
g_int = nx.DiGraph()
g_int.add_weighted_edges_from([(0, 0, 1), (0, 1, 2), (1, 1, 0), (1, 2, 3),
(2, 1, 3)])
g_float = nx.DiGraph()
g_float.add_weighted_edges_from([(0, 0, 1.0), (0, 1, 2.0), (1, 1, 0.0),
(1, 2, 3.0), (2, 1, 3.0)])
NetworkXGraph.Type.assert_equal(NetworkXGraph(g_int),
NetworkXGraph(g_int.copy()), aprops,
aprops, {}, {})
g_close = g_float.copy()
g_close.edges[(0, 0)]["weight"] = 1.0000000000001
NetworkXGraph.Type.assert_equal(
NetworkXGraph(g_close),
NetworkXGraph(g_float),
{
**aprops, "edge_dtype": "float"
},
{
**aprops, "edge_dtype": "float"
},
{},
{},
)
g_diff1 = nx.DiGraph()
g_diff1.add_weighted_edges_from([(0, 0, 1), (0, 1, 2), (1, 1, 0),
(1, 2, 333), (2, 1, 3)])
with pytest.raises(AssertionError):
NetworkXGraph.Type.assert_equal(NetworkXGraph(g_int),
NetworkXGraph(g_diff1), aprops, aprops,
{}, {})
g_diff2 = nx.DiGraph()
g_diff2.add_weighted_edges_from([(0, 0, 1), (0, 1, 2), (1, 1, 0),
(1, 2, 3), (2, 0, 3)])
with pytest.raises(AssertionError):
NetworkXGraph.Type.assert_equal(NetworkXGraph(g_int),
NetworkXGraph(g_diff2), aprops, aprops,
{}, {})
g_extra = nx.DiGraph()
g_extra.add_weighted_edges_from([
(0, 0, 1),
(0, 1, 2),
(1, 1, 0),
(1, 2, 3),
(2, 1, 3),
(2, 0, 2),
])
with pytest.raises(AssertionError):
NetworkXGraph.Type.assert_equal(NetworkXGraph(g_int),
NetworkXGraph(g_extra), aprops, aprops,
{}, {})
# Undirected vs Directed
g_undir = nx.Graph()
g_undir.add_weighted_edges_from([(0, 0, 1), (0, 1, 2), (1, 1, 0),
(1, 2, 3)])
g_dir = nx.DiGraph()
g_dir.add_weighted_edges_from([(0, 0, 1), (0, 1, 2), (1, 1, 0), (1, 2, 3)])
with pytest.raises(AssertionError):
NetworkXGraph.Type.assert_equal(
NetworkXGraph(g_undir),
NetworkXGraph(g_dir),
{
**aprops, "is_directed": False
},
aprops,
{},
{},
)
NetworkXGraph.Type.assert_equal(
NetworkXGraph(g_undir),
NetworkXGraph(g_undir),
{
**aprops, "is_directed": False
},
{
**aprops, "is_directed": False
},
{},
{},
)
# Different weight_label
g_wgt = nx.DiGraph()
g_wgt.add_weighted_edges_from(
[(0, 0, 1), (0, 1, 2), (1, 1, 0), (1, 2, 3), (2, 1, 3)],
weight="WGT",
)
NetworkXGraph.Type.assert_equal(
NetworkXGraph(g_int, edge_weight_label="weight"),
NetworkXGraph(g_wgt, edge_weight_label="WGT"),
aprops,
aprops,
{},
{},
)
# Exercise NetworkXGraph
with pytest.raises(TypeError, match="unable to determine dtype"):
g = nx.Graph()
g.add_weighted_edges_from([(0, 2, "a"), (1, 0, "b"), (1, 2, "c")])
gr = NetworkXGraph(g)
NetworkXGraph.Type.compute_abstract_properties(gr, {"edge_dtype"})
