chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,266 @@
|
||||
from collections import defaultdict
|
||||
|
||||
import backend as F
|
||||
import dgl
|
||||
import networkx as nx
|
||||
import numpy as np
|
||||
import scipy.sparse as ssp
|
||||
|
||||
case_registry = defaultdict(list)
|
||||
|
||||
|
||||
def register_case(labels):
|
||||
def wrapper(fn):
|
||||
for lbl in labels:
|
||||
case_registry[lbl].append(fn)
|
||||
fn.__labels__ = labels
|
||||
return fn
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
def get_cases(labels=None, exclude=[]):
|
||||
"""Get all graph instances of the given labels."""
|
||||
cases = set()
|
||||
if labels is None:
|
||||
# get all the cases
|
||||
labels = case_registry.keys()
|
||||
for lbl in labels:
|
||||
for case in case_registry[lbl]:
|
||||
if not any([l in exclude for l in case.__labels__]):
|
||||
cases.add(case)
|
||||
return [fn() for fn in cases]
|
||||
|
||||
|
||||
@register_case(["bipartite", "zero-degree"])
|
||||
def bipartite1():
|
||||
return dgl.heterograph(
|
||||
{("_U", "_E", "_V"): ([0, 0, 0, 2, 2, 3], [0, 1, 4, 1, 4, 3])}
|
||||
)
|
||||
|
||||
|
||||
@register_case(["bipartite"])
|
||||
def bipartite_full():
|
||||
return dgl.heterograph(
|
||||
{
|
||||
("_U", "_E", "_V"): (
|
||||
[0, 0, 0, 0, 1, 1, 1, 1],
|
||||
[0, 1, 2, 3, 0, 1, 2, 3],
|
||||
)
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
@register_case(["homo"])
|
||||
def graph0():
|
||||
return dgl.graph(
|
||||
(
|
||||
[0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 9],
|
||||
[4, 5, 1, 2, 4, 7, 9, 8, 6, 4, 1, 0, 1, 0, 2, 3, 5],
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
@register_case(["homo", "zero-degree", "homo-zero-degree"])
|
||||
def bipartite1():
|
||||
return dgl.graph(([0, 0, 0, 2, 2, 3], [0, 1, 4, 1, 4, 3]))
|
||||
|
||||
|
||||
@register_case(["homo", "has_feature"])
|
||||
def graph1():
|
||||
g = dgl.graph(
|
||||
(
|
||||
[0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 9],
|
||||
[4, 5, 1, 2, 4, 7, 9, 8, 6, 4, 1, 0, 1, 0, 2, 3, 5],
|
||||
),
|
||||
device=F.cpu(),
|
||||
)
|
||||
g.ndata["h"] = F.copy_to(F.randn((g.num_nodes(), 2)), F.cpu())
|
||||
g.edata["w"] = F.copy_to(F.randn((g.num_edges(), 3)), F.cpu())
|
||||
return g
|
||||
|
||||
|
||||
@register_case(["homo", "has_scalar_e_feature"])
|
||||
def graph1():
|
||||
g = dgl.graph(
|
||||
(
|
||||
[0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 9],
|
||||
[4, 5, 1, 2, 4, 7, 9, 8, 6, 4, 1, 0, 1, 0, 2, 3, 5],
|
||||
),
|
||||
device=F.cpu(),
|
||||
)
|
||||
g.ndata["h"] = F.copy_to(F.randn((g.num_nodes(), 2)), F.cpu())
|
||||
g.edata["scalar_w"] = F.copy_to(F.abs(F.randn((g.num_edges(),))), F.cpu())
|
||||
return g
|
||||
|
||||
|
||||
@register_case(["homo", "row_sorted"])
|
||||
def graph2():
|
||||
return dgl.graph(
|
||||
(
|
||||
[0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 9],
|
||||
[4, 5, 1, 2, 4, 7, 9, 8, 6, 4, 1, 0, 1, 0, 2, 3, 5],
|
||||
),
|
||||
row_sorted=True,
|
||||
)
|
||||
|
||||
|
||||
@register_case(["homo", "row_sorted", "col_sorted"])
|
||||
def graph3():
|
||||
return dgl.graph(
|
||||
(
|
||||
[0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 9],
|
||||
[1, 4, 5, 2, 4, 7, 8, 9, 1, 4, 6, 0, 0, 1, 2, 3, 5],
|
||||
),
|
||||
row_sorted=True,
|
||||
col_sorted=True,
|
||||
)
|
||||
|
||||
|
||||
@register_case(["hetero", "has_feature"])
|
||||
def heterograph0():
|
||||
g = dgl.heterograph(
|
||||
{
|
||||
("user", "plays", "game"): ([0, 1, 1, 2], [0, 0, 1, 1]),
|
||||
("developer", "develops", "game"): ([0, 1], [0, 1]),
|
||||
},
|
||||
device=F.cpu(),
|
||||
)
|
||||
g.nodes["user"].data["h"] = F.copy_to(
|
||||
F.randn((g.num_nodes("user"), 3)), F.cpu()
|
||||
)
|
||||
g.nodes["game"].data["h"] = F.copy_to(
|
||||
F.randn((g.num_nodes("game"), 2)), F.cpu()
|
||||
)
|
||||
g.nodes["developer"].data["h"] = F.copy_to(
|
||||
F.randn((g.num_nodes("developer"), 3)), F.cpu()
|
||||
)
|
||||
g.edges["plays"].data["h"] = F.copy_to(
|
||||
F.randn((g.num_edges("plays"), 1)), F.cpu()
|
||||
)
|
||||
g.edges["develops"].data["h"] = F.copy_to(
|
||||
F.randn((g.num_edges("develops"), 5)), F.cpu()
|
||||
)
|
||||
return g
|
||||
|
||||
|
||||
@register_case(["batched", "homo"])
|
||||
def batched_graph0():
|
||||
g1 = dgl.add_self_loop(dgl.graph(([0, 1, 2], [1, 2, 3])))
|
||||
g2 = dgl.add_self_loop(dgl.graph(([1, 1], [2, 0])))
|
||||
g3 = dgl.add_self_loop(dgl.graph(([0], [1])))
|
||||
return dgl.batch([g1, g2, g3])
|
||||
|
||||
|
||||
@register_case(["block", "bipartite", "block-bipartite"])
|
||||
def block_graph0():
|
||||
g = dgl.graph(([2, 3, 4], [5, 6, 7]), num_nodes=100)
|
||||
g = g.to(F.cpu())
|
||||
return dgl.to_block(g)
|
||||
|
||||
|
||||
@register_case(["block"])
|
||||
def block_graph1():
|
||||
g = dgl.heterograph(
|
||||
{
|
||||
("user", "plays", "game"): ([0, 1, 2], [1, 1, 0]),
|
||||
("user", "likes", "game"): ([1, 2, 3], [0, 0, 2]),
|
||||
("store", "sells", "game"): ([0, 1, 1], [0, 1, 2]),
|
||||
},
|
||||
device=F.cpu(),
|
||||
)
|
||||
return dgl.to_block(g)
|
||||
|
||||
|
||||
@register_case(["clique"])
|
||||
def clique():
|
||||
g = dgl.graph(([0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]))
|
||||
return g
|
||||
|
||||
|
||||
def random_dglgraph(size):
|
||||
return dgl.DGLGraph(nx.erdos_renyi_graph(size, 0.3))
|
||||
|
||||
|
||||
def random_graph(size):
|
||||
return dgl.from_networkx(nx.erdos_renyi_graph(size, 0.3))
|
||||
|
||||
|
||||
def random_bipartite(size_src, size_dst):
|
||||
return dgl.bipartite_from_scipy(
|
||||
ssp.random(size_src, size_dst, 0.1),
|
||||
utype="_U",
|
||||
etype="_E",
|
||||
vtype="V",
|
||||
)
|
||||
|
||||
|
||||
def random_block(size):
|
||||
g = dgl.from_networkx(nx.erdos_renyi_graph(size, 0.1))
|
||||
return dgl.to_block(g, np.unique(F.zerocopy_to_numpy(g.edges()[1])))
|
||||
|
||||
|
||||
@register_case(["two_hetero_batch"])
|
||||
def two_hetero_batch():
|
||||
g1 = dgl.heterograph(
|
||||
{
|
||||
("user", "follows", "user"): ([0, 1], [1, 2]),
|
||||
("user", "follows", "developer"): ([0, 1], [1, 2]),
|
||||
("user", "plays", "game"): ([0, 1, 2, 3], [0, 0, 1, 1]),
|
||||
}
|
||||
)
|
||||
g2 = dgl.heterograph(
|
||||
{
|
||||
("user", "follows", "user"): ([0, 1], [1, 2]),
|
||||
("user", "follows", "developer"): ([0, 1], [1, 2]),
|
||||
("user", "plays", "game"): ([0, 1, 2], [0, 0, 1]),
|
||||
}
|
||||
)
|
||||
return [g1, g2]
|
||||
|
||||
|
||||
@register_case(["two_hetero_batch"])
|
||||
def two_hetero_batch_with_isolated_ntypes():
|
||||
g1 = dgl.heterograph(
|
||||
{
|
||||
("user", "follows", "user"): ([0, 1], [1, 2]),
|
||||
("user", "follows", "developer"): ([0, 1], [1, 2]),
|
||||
("user", "plays", "game"): ([0, 1, 2, 3], [0, 0, 1, 1]),
|
||||
},
|
||||
num_nodes_dict={"user": 4, "game": 2, "developer": 3, "platform": 2},
|
||||
)
|
||||
g2 = dgl.heterograph(
|
||||
{
|
||||
("user", "follows", "user"): ([0, 1], [1, 2]),
|
||||
("user", "follows", "developer"): ([0, 1], [1, 2]),
|
||||
("user", "plays", "game"): ([0, 1, 2], [0, 0, 1]),
|
||||
},
|
||||
num_nodes_dict={"user": 3, "game": 2, "developer": 3, "platform": 3},
|
||||
)
|
||||
return [g1, g2]
|
||||
|
||||
|
||||
@register_case(["batched", "hetero"])
|
||||
def batched_heterograph0():
|
||||
g1 = dgl.heterograph(
|
||||
{
|
||||
("user", "follows", "user"): ([0, 1], [1, 2]),
|
||||
("user", "follows", "developer"): ([0, 1], [1, 2]),
|
||||
("user", "plays", "game"): ([0, 1, 2, 3], [0, 0, 1, 1]),
|
||||
}
|
||||
)
|
||||
g2 = dgl.heterograph(
|
||||
{
|
||||
("user", "follows", "user"): ([0, 1], [1, 2]),
|
||||
("user", "follows", "developer"): ([0, 1], [1, 2]),
|
||||
("user", "plays", "game"): ([0, 1, 2], [0, 0, 1]),
|
||||
}
|
||||
)
|
||||
g3 = dgl.heterograph(
|
||||
{
|
||||
("user", "follows", "user"): ([1], [2]),
|
||||
("user", "follows", "developer"): ([0, 1, 2], [0, 2, 2]),
|
||||
("user", "plays", "game"): ([0, 1], [0, 0]),
|
||||
}
|
||||
)
|
||||
return dgl.batch([g1, g2, g3])
|
||||
Reference in New Issue
Block a user