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dgl/tests/python/common/transforms/test_transform.py

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##
# Copyright 2019-2021 Contributors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import math
import os
import unittest
import backend as F
import dgl
import dgl.function as fn
import dgl.partition
import networkx as nx
import numpy as np
import pytest
from scipy import sparse as spsp
from utils import parametrize_idtype
from utils.graph_cases import get_cases
D = 5
def create_test_heterograph3(idtype):
g = dgl.heterograph(
{
("user", "plays", "game"): (
F.tensor([0, 1, 1, 2], dtype=idtype),
F.tensor([0, 0, 1, 1], dtype=idtype),
),
("developer", "develops", "game"): (
F.tensor([0, 1], dtype=idtype),
F.tensor([0, 1], dtype=idtype),
),
},
idtype=idtype,
device=F.ctx(),
)
g.nodes["user"].data["h"] = F.copy_to(
F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx()
)
g.nodes["game"].data["h"] = F.copy_to(
F.tensor([2, 2], dtype=idtype), ctx=F.ctx()
)
g.nodes["developer"].data["h"] = F.copy_to(
F.tensor([3, 3], dtype=idtype), ctx=F.ctx()
)
g.edges["plays"].data["h"] = F.copy_to(
F.tensor([1, 1, 1, 1], dtype=idtype), ctx=F.ctx()
)
return g
def create_test_heterograph4(idtype):
g = dgl.heterograph(
{
("user", "follows", "user"): (
F.tensor([0, 1, 1, 2, 2, 2], dtype=idtype),
F.tensor([0, 0, 1, 1, 2, 2], dtype=idtype),
),
("user", "plays", "game"): (
F.tensor([0, 1], dtype=idtype),
F.tensor([0, 1], dtype=idtype),
),
},
idtype=idtype,
device=F.ctx(),
)
g.nodes["user"].data["h"] = F.copy_to(
F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx()
)
g.nodes["game"].data["h"] = F.copy_to(
F.tensor([2, 2], dtype=idtype), ctx=F.ctx()
)
g.edges["follows"].data["h"] = F.copy_to(
F.tensor([1, 2, 3, 4, 5, 6], dtype=idtype), ctx=F.ctx()
)
g.edges["plays"].data["h"] = F.copy_to(
F.tensor([1, 2], dtype=idtype), ctx=F.ctx()
)
return g
def create_test_heterograph5(idtype):
g = dgl.heterograph(
{
("user", "follows", "user"): (
F.tensor([1, 2], dtype=idtype),
F.tensor([0, 1], dtype=idtype),
),
("user", "plays", "game"): (
F.tensor([0, 1], dtype=idtype),
F.tensor([0, 1], dtype=idtype),
),
},
idtype=idtype,
device=F.ctx(),
)
g.nodes["user"].data["h"] = F.copy_to(
F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx()
)
g.nodes["game"].data["h"] = F.copy_to(
F.tensor([2, 2], dtype=idtype), ctx=F.ctx()
)
g.edges["follows"].data["h"] = F.copy_to(
F.tensor([1, 2], dtype=idtype), ctx=F.ctx()
)
g.edges["plays"].data["h"] = F.copy_to(
F.tensor([1, 2], dtype=idtype), ctx=F.ctx()
)
return g
# line graph related
def test_line_graph1():
N = 5
G = dgl.from_networkx(nx.star_graph(N)).to(F.ctx())
G.edata["h"] = F.randn((2 * N, D))
L = G.line_graph(shared=True)
assert L.num_nodes() == 2 * N
assert F.allclose(L.ndata["h"], G.edata["h"])
assert G.device == F.ctx()
@parametrize_idtype
def test_line_graph2(idtype):
g = dgl.heterograph(
{("user", "follows", "user"): ([0, 1, 1, 2, 2], [2, 0, 2, 0, 1])},
idtype=idtype,
)
lg = dgl.line_graph(g)
assert lg.num_nodes() == 5
assert lg.num_edges() == 8
row, col = lg.edges()
assert np.array_equal(F.asnumpy(row), np.array([0, 0, 1, 2, 2, 3, 4, 4]))
assert np.array_equal(F.asnumpy(col), np.array([3, 4, 0, 3, 4, 0, 1, 2]))
lg = dgl.line_graph(g, backtracking=False)
assert lg.num_nodes() == 5
assert lg.num_edges() == 4
row, col = lg.edges()
assert np.array_equal(F.asnumpy(row), np.array([0, 1, 2, 4]))
assert np.array_equal(F.asnumpy(col), np.array([4, 0, 3, 1]))
g = dgl.heterograph(
{("user", "follows", "user"): ([0, 1, 1, 2, 2], [2, 0, 2, 0, 1])},
idtype=idtype,
).formats("csr")
lg = dgl.line_graph(g)
assert lg.num_nodes() == 5
assert lg.num_edges() == 8
row, col = lg.edges()
assert np.array_equal(F.asnumpy(row), np.array([0, 0, 1, 2, 2, 3, 4, 4]))
assert np.array_equal(F.asnumpy(col), np.array([3, 4, 0, 3, 4, 0, 1, 2]))
g = dgl.heterograph(
{("user", "follows", "user"): ([0, 1, 1, 2, 2], [2, 0, 2, 0, 1])},
idtype=idtype,
).formats("csc")
lg = dgl.line_graph(g)
assert lg.num_nodes() == 5
assert lg.num_edges() == 8
row, col, eid = lg.edges("all")
row = F.asnumpy(row)
col = F.asnumpy(col)
eid = F.asnumpy(eid).astype(int)
order = np.argsort(eid)
assert np.array_equal(row[order], np.array([0, 0, 1, 2, 2, 3, 4, 4]))
assert np.array_equal(col[order], np.array([3, 4, 0, 3, 4, 0, 1, 2]))
def test_no_backtracking():
N = 5
G = dgl.from_networkx(nx.star_graph(N))
L = G.line_graph(backtracking=False)
assert L.num_nodes() == 2 * N
for i in range(1, N):
e1 = G.edge_ids(0, i)
e2 = G.edge_ids(i, 0)
assert not L.has_edges_between(e1, e2)
assert not L.has_edges_between(e2, e1)
# reverse graph related
@parametrize_idtype
def test_reverse(idtype):
g = dgl.graph([])
g = g.astype(idtype).to(F.ctx())
g.add_nodes(5)
# The graph need not to be completely connected.
g.add_edges([0, 1, 2], [1, 2, 1])
g.ndata["h"] = F.tensor([[0.0], [1.0], [2.0], [3.0], [4.0]])
g.edata["h"] = F.tensor([[5.0], [6.0], [7.0]])
rg = g.reverse()
assert g.is_multigraph == rg.is_multigraph
assert g.num_nodes() == rg.num_nodes()
assert g.num_edges() == rg.num_edges()
assert F.allclose(
F.astype(rg.has_edges_between([1, 2, 1], [0, 1, 2]), F.float32),
F.ones((3,)),
)
assert g.edge_ids(0, 1) == rg.edge_ids(1, 0)
assert g.edge_ids(1, 2) == rg.edge_ids(2, 1)
assert g.edge_ids(2, 1) == rg.edge_ids(1, 2)
# test dgl.reverse
# test homogeneous graph
g = dgl.graph((F.tensor([0, 1, 2]), F.tensor([1, 2, 0])))
g.ndata["h"] = F.tensor([[0.0], [1.0], [2.0]])
g.edata["h"] = F.tensor([[3.0], [4.0], [5.0]])
g_r = dgl.reverse(g)
assert g.num_nodes() == g_r.num_nodes()
assert g.num_edges() == g_r.num_edges()
u_g, v_g, eids_g = g.all_edges(form="all")
u_rg, v_rg, eids_rg = g_r.all_edges(form="all")
assert F.array_equal(u_g, v_rg)
assert F.array_equal(v_g, u_rg)
assert F.array_equal(eids_g, eids_rg)
assert F.array_equal(g.ndata["h"], g_r.ndata["h"])
assert len(g_r.edata) == 0
# without share ndata
g_r = dgl.reverse(g, copy_ndata=False)
assert g.num_nodes() == g_r.num_nodes()
assert g.num_edges() == g_r.num_edges()
assert len(g_r.ndata) == 0
assert len(g_r.edata) == 0
# with share ndata and edata
g_r = dgl.reverse(g, copy_ndata=True, copy_edata=True)
assert g.num_nodes() == g_r.num_nodes()
assert g.num_edges() == g_r.num_edges()
assert F.array_equal(g.ndata["h"], g_r.ndata["h"])
assert F.array_equal(g.edata["h"], g_r.edata["h"])
# add new node feature to g_r
g_r.ndata["hh"] = F.tensor([0, 1, 2])
assert ("hh" in g.ndata) is False
assert ("hh" in g_r.ndata) is True
# add new edge feature to g_r
g_r.edata["hh"] = F.tensor([0, 1, 2])
assert ("hh" in g.edata) is False
assert ("hh" in g_r.edata) is True
# test heterogeneous graph
g = dgl.heterograph(
{
("user", "follows", "user"): (
[0, 1, 2, 4, 3, 1, 3],
[1, 2, 3, 2, 0, 0, 1],
),
("user", "plays", "game"): (
[0, 0, 2, 3, 3, 4, 1],
[1, 0, 1, 0, 1, 0, 0],
),
("developer", "develops", "game"): ([0, 1, 1, 2], [0, 0, 1, 1]),
},
idtype=idtype,
device=F.ctx(),
)
g.nodes["user"].data["h"] = F.tensor([0, 1, 2, 3, 4])
g.nodes["user"].data["hh"] = F.tensor([1, 1, 1, 1, 1])
g.nodes["game"].data["h"] = F.tensor([0, 1])
g.edges["follows"].data["h"] = F.tensor([0, 1, 2, 4, 3, 1, 3])
g.edges["follows"].data["hh"] = F.tensor([1, 2, 3, 2, 0, 0, 1])
g_r = dgl.reverse(g)
for etype_g, etype_gr in zip(g.canonical_etypes, g_r.canonical_etypes):
assert etype_g[0] == etype_gr[2]
assert etype_g[1] == etype_gr[1]
assert etype_g[2] == etype_gr[0]
assert g.num_edges(etype_g) == g_r.num_edges(etype_gr)
for ntype in g.ntypes:
assert g.num_nodes(ntype) == g_r.num_nodes(ntype)
assert F.array_equal(g.nodes["user"].data["h"], g_r.nodes["user"].data["h"])
assert F.array_equal(
g.nodes["user"].data["hh"], g_r.nodes["user"].data["hh"]
)
assert F.array_equal(g.nodes["game"].data["h"], g_r.nodes["game"].data["h"])
assert len(g_r.edges["follows"].data) == 0
u_g, v_g, eids_g = g.all_edges(
form="all", etype=("user", "follows", "user")
)
u_rg, v_rg, eids_rg = g_r.all_edges(
form="all", etype=("user", "follows", "user")
)
assert F.array_equal(u_g, v_rg)
assert F.array_equal(v_g, u_rg)
assert F.array_equal(eids_g, eids_rg)
u_g, v_g, eids_g = g.all_edges(form="all", etype=("user", "plays", "game"))
u_rg, v_rg, eids_rg = g_r.all_edges(
form="all", etype=("game", "plays", "user")
)
assert F.array_equal(u_g, v_rg)
assert F.array_equal(v_g, u_rg)
assert F.array_equal(eids_g, eids_rg)
u_g, v_g, eids_g = g.all_edges(
form="all", etype=("developer", "develops", "game")
)
u_rg, v_rg, eids_rg = g_r.all_edges(
form="all", etype=("game", "develops", "developer")
)
assert F.array_equal(u_g, v_rg)
assert F.array_equal(v_g, u_rg)
assert F.array_equal(eids_g, eids_rg)
# withour share ndata
g_r = dgl.reverse(g, copy_ndata=False)
for etype_g, etype_gr in zip(g.canonical_etypes, g_r.canonical_etypes):
assert etype_g[0] == etype_gr[2]
assert etype_g[1] == etype_gr[1]
assert etype_g[2] == etype_gr[0]
assert g.num_edges(etype_g) == g_r.num_edges(etype_gr)
for ntype in g.ntypes:
assert g.num_nodes(ntype) == g_r.num_nodes(ntype)
assert len(g_r.nodes["user"].data) == 0
assert len(g_r.nodes["game"].data) == 0
g_r = dgl.reverse(g, copy_ndata=True, copy_edata=True)
print(g_r)
for etype_g, etype_gr in zip(g.canonical_etypes, g_r.canonical_etypes):
assert etype_g[0] == etype_gr[2]
assert etype_g[1] == etype_gr[1]
assert etype_g[2] == etype_gr[0]
assert g.num_edges(etype_g) == g_r.num_edges(etype_gr)
assert F.array_equal(
g.edges["follows"].data["h"], g_r.edges["follows"].data["h"]
)
assert F.array_equal(
g.edges["follows"].data["hh"], g_r.edges["follows"].data["hh"]
)
# add new node feature to g_r
g_r.nodes["user"].data["hhh"] = F.tensor([0, 1, 2, 3, 4])
assert ("hhh" in g.nodes["user"].data) is False
assert ("hhh" in g_r.nodes["user"].data) is True
# add new edge feature to g_r
g_r.edges["follows"].data["hhh"] = F.tensor([1, 2, 3, 2, 0, 0, 1])
assert ("hhh" in g.edges["follows"].data) is False
assert ("hhh" in g_r.edges["follows"].data) is True
@parametrize_idtype
def test_reverse_shared_frames(idtype):
g = dgl.graph([])
g = g.astype(idtype).to(F.ctx())
g.add_nodes(3)
g.add_edges([0, 1, 2], [1, 2, 1])
g.ndata["h"] = F.tensor([[0.0], [1.0], [2.0]])
g.edata["h"] = F.tensor([[3.0], [4.0], [5.0]])
rg = g.reverse(copy_ndata=True, copy_edata=True)
assert F.allclose(g.ndata["h"], rg.ndata["h"])
assert F.allclose(g.edata["h"], rg.edata["h"])
assert F.allclose(
g.edges[[0, 2], [1, 1]].data["h"], rg.edges[[1, 1], [0, 2]].data["h"]
)
@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
def test_to_bidirected():
# homogeneous graph
elist = [(0, 0), (0, 1), (1, 0), (1, 1), (2, 1), (2, 2)]
num_edges = 7
g = dgl.graph(tuple(zip(*elist)))
elist.append((1, 2))
elist = set(elist)
big = dgl.to_bidirected(g)
assert big.num_edges() == num_edges
src, dst = big.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == set(elist)
# heterogeneous graph
elist1 = [(0, 0), (0, 1), (1, 0), (1, 1), (2, 1), (2, 2)]
elist2 = [(0, 0), (0, 1)]
g = dgl.heterograph(
{
("user", "wins", "user"): tuple(zip(*elist1)),
("user", "follows", "user"): tuple(zip(*elist2)),
}
)
g.nodes["user"].data["h"] = F.ones((3, 1))
elist1.append((1, 2))
elist1 = set(elist1)
elist2.append((1, 0))
elist2 = set(elist2)
big = dgl.to_bidirected(g)
assert big.num_edges("wins") == 7
assert big.num_edges("follows") == 3
src, dst = big.edges(etype="wins")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == set(elist1)
src, dst = big.edges(etype="follows")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == set(elist2)
big = dgl.to_bidirected(g, copy_ndata=True)
assert F.array_equal(g.nodes["user"].data["h"], big.nodes["user"].data["h"])
def test_add_reverse_edges():
# homogeneous graph
g = dgl.graph((F.tensor([0, 1, 3, 1]), F.tensor([1, 2, 0, 2])))
g.ndata["h"] = F.tensor([[0.0], [1.0], [2.0], [1.0]])
g.edata["h"] = F.tensor([[3.0], [4.0], [5.0], [6.0]])
bg = dgl.add_reverse_edges(g, copy_ndata=True, copy_edata=True)
u, v = g.edges()
ub, vb = bg.edges()
assert F.array_equal(F.cat([u, v], dim=0), ub)
assert F.array_equal(F.cat([v, u], dim=0), vb)
assert F.array_equal(g.ndata["h"], bg.ndata["h"])
assert F.array_equal(
F.cat([g.edata["h"], g.edata["h"]], dim=0), bg.edata["h"]
)
bg.ndata["hh"] = F.tensor([[0.0], [1.0], [2.0], [1.0]])
assert ("hh" in g.ndata) is False
bg.edata["hh"] = F.tensor(
[[0.0], [1.0], [2.0], [1.0], [0.0], [1.0], [2.0], [1.0]]
)
assert ("hh" in g.edata) is False
# donot share ndata and edata
bg = dgl.add_reverse_edges(g, copy_ndata=False, copy_edata=False)
ub, vb = bg.edges()
assert F.array_equal(F.cat([u, v], dim=0), ub)
assert F.array_equal(F.cat([v, u], dim=0), vb)
assert ("h" in bg.ndata) is False
assert ("h" in bg.edata) is False
# zero edge graph
g = dgl.graph(([], []))
bg = dgl.add_reverse_edges(
g, copy_ndata=True, copy_edata=True, exclude_self=False
)
# heterogeneous graph
g = dgl.heterograph(
{
("user", "wins", "user"): (
F.tensor([0, 2, 0, 2, 2]),
F.tensor([1, 1, 2, 1, 0]),
),
("user", "plays", "game"): (
F.tensor([1, 2, 1]),
F.tensor([2, 1, 1]),
),
("user", "follows", "user"): (
F.tensor([1, 2, 1]),
F.tensor([0, 0, 0]),
),
}
)
g.nodes["game"].data["hv"] = F.ones((3, 1))
g.nodes["user"].data["hv"] = F.ones((3, 1))
g.edges["wins"].data["h"] = F.tensor([0, 1, 2, 3, 4])
bg = dgl.add_reverse_edges(
g, copy_ndata=True, copy_edata=True, ignore_bipartite=True
)
assert F.array_equal(
g.nodes["game"].data["hv"], bg.nodes["game"].data["hv"]
)
assert F.array_equal(
g.nodes["user"].data["hv"], bg.nodes["user"].data["hv"]
)
u, v = g.all_edges(order="eid", etype=("user", "wins", "user"))
ub, vb = bg.all_edges(order="eid", etype=("user", "wins", "user"))
assert F.array_equal(F.cat([u, v], dim=0), ub)
assert F.array_equal(F.cat([v, u], dim=0), vb)
assert F.array_equal(
F.cat([g.edges["wins"].data["h"], g.edges["wins"].data["h"]], dim=0),
bg.edges["wins"].data["h"],
)
u, v = g.all_edges(order="eid", etype=("user", "follows", "user"))
ub, vb = bg.all_edges(order="eid", etype=("user", "follows", "user"))
assert F.array_equal(F.cat([u, v], dim=0), ub)
assert F.array_equal(F.cat([v, u], dim=0), vb)
u, v = g.all_edges(order="eid", etype=("user", "plays", "game"))
ub, vb = bg.all_edges(order="eid", etype=("user", "plays", "game"))
assert F.array_equal(u, ub)
assert F.array_equal(v, vb)
assert set(bg.edges["plays"].data.keys()) == {dgl.EID}
assert set(bg.edges["follows"].data.keys()) == {dgl.EID}
# donot share ndata and edata
bg = dgl.add_reverse_edges(
g, copy_ndata=False, copy_edata=False, ignore_bipartite=True
)
assert len(bg.edges["wins"].data) == 0
assert len(bg.edges["plays"].data) == 0
assert len(bg.edges["follows"].data) == 0
assert len(bg.nodes["game"].data) == 0
assert len(bg.nodes["user"].data) == 0
u, v = g.all_edges(order="eid", etype=("user", "wins", "user"))
ub, vb = bg.all_edges(order="eid", etype=("user", "wins", "user"))
assert F.array_equal(F.cat([u, v], dim=0), ub)
assert F.array_equal(F.cat([v, u], dim=0), vb)
u, v = g.all_edges(order="eid", etype=("user", "follows", "user"))
ub, vb = bg.all_edges(order="eid", etype=("user", "follows", "user"))
assert F.array_equal(F.cat([u, v], dim=0), ub)
assert F.array_equal(F.cat([v, u], dim=0), vb)
u, v = g.all_edges(order="eid", etype=("user", "plays", "game"))
ub, vb = bg.all_edges(order="eid", etype=("user", "plays", "game"))
assert F.array_equal(u, ub)
assert F.array_equal(v, vb)
# test the case when some nodes have zero degree
# homogeneous graph
g = dgl.graph((F.tensor([0, 1, 3, 1]), F.tensor([1, 2, 0, 2])), num_nodes=6)
g.ndata["h"] = F.tensor([[0.0], [1.0], [2.0], [1.0], [1.0], [1.0]])
g.edata["h"] = F.tensor([[3.0], [4.0], [5.0], [6.0]])
bg = dgl.add_reverse_edges(g, copy_ndata=True, copy_edata=True)
assert g.num_nodes() == bg.num_nodes()
assert F.array_equal(g.ndata["h"], bg.ndata["h"])
assert F.array_equal(
F.cat([g.edata["h"], g.edata["h"]], dim=0), bg.edata["h"]
)
# heterogeneous graph
g = dgl.heterograph(
{
("user", "wins", "user"): (
F.tensor([0, 2, 0, 2, 2]),
F.tensor([1, 1, 2, 1, 0]),
),
("user", "plays", "game"): (
F.tensor([1, 2, 1]),
F.tensor([2, 1, 1]),
),
("user", "follows", "user"): (
F.tensor([1, 2, 1]),
F.tensor([0, 0, 0]),
),
},
num_nodes_dict={"user": 5, "game": 3},
)
g.nodes["game"].data["hv"] = F.ones((3, 1))
g.nodes["user"].data["hv"] = F.ones((5, 1))
g.edges["wins"].data["h"] = F.tensor([0, 1, 2, 3, 4])
bg = dgl.add_reverse_edges(
g, copy_ndata=True, copy_edata=True, ignore_bipartite=True
)
assert g.num_nodes("user") == bg.num_nodes("user")
assert g.num_nodes("game") == bg.num_nodes("game")
assert F.array_equal(
g.nodes["game"].data["hv"], bg.nodes["game"].data["hv"]
)
assert F.array_equal(
g.nodes["user"].data["hv"], bg.nodes["user"].data["hv"]
)
assert F.array_equal(
F.cat([g.edges["wins"].data["h"], g.edges["wins"].data["h"]], dim=0),
bg.edges["wins"].data["h"],
)
# test exclude_self
g = dgl.heterograph(
{
("A", "r1", "A"): (F.tensor([0, 0, 1, 1]), F.tensor([0, 1, 1, 2])),
("A", "r2", "A"): (F.tensor([0, 1]), F.tensor([1, 2])),
}
)
g.edges["r1"].data["h"] = F.tensor([0, 1, 2, 3])
rg = dgl.add_reverse_edges(g, copy_edata=True, exclude_self=True)
assert rg.num_edges("r1") == 6
assert rg.num_edges("r2") == 4
assert F.array_equal(rg.edges["r1"].data["h"], F.tensor([0, 1, 2, 3, 1, 3]))
@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
def test_simple_graph():
elist = [(0, 1), (0, 2), (1, 2), (0, 1)]
g = dgl.graph(elist)
assert g.is_multigraph
sg = dgl.to_simple(g)
assert not sg.is_multigraph
assert sg.num_edges() == 3
src, dst = sg.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == set(elist)
@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
def _test_bidirected_graph():
def _test(in_readonly, out_readonly):
elist = [(0, 0), (0, 1), (1, 0), (1, 1), (2, 1), (2, 2)]
num_edges = 7
g = dgl.graph(elist)
elist.append((1, 2))
elist = set(elist)
big = dgl.to_bidirected_stale(g, out_readonly)
assert big.num_edges() == num_edges
src, dst = big.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == set(elist)
_test(True, True)
_test(True, False)
_test(False, True)
_test(False, False)
@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
def test_khop_graph():
N = 20
feat = F.randn((N, 5))
def _test(g):
for k in range(4):
g_k = dgl.khop_graph(g, k)
# use original graph to do message passing for k times.
g.ndata["h"] = feat
for _ in range(k):
g.update_all(fn.copy_u("h", "m"), fn.sum("m", "h"))
h_0 = g.ndata.pop("h")
# use k-hop graph to do message passing for one time.
g_k.ndata["h"] = feat
g_k.update_all(fn.copy_u("h", "m"), fn.sum("m", "h"))
h_1 = g_k.ndata.pop("h")
assert F.allclose(h_0, h_1, rtol=1e-3, atol=1e-3)
# Test for random undirected graphs
g = dgl.from_networkx(nx.erdos_renyi_graph(N, 0.3))
_test(g)
# Test for random directed graphs
g = dgl.from_networkx(nx.erdos_renyi_graph(N, 0.3, directed=True))
_test(g)
@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
def test_khop_adj():
N = 20
feat = F.randn((N, 5))
g = dgl.from_networkx(nx.erdos_renyi_graph(N, 0.3, directed=True))
for k in range(3):
adj = F.tensor(F.swapaxes(dgl.khop_adj(g, k), 0, 1))
# use original graph to do message passing for k times.
g.ndata["h"] = feat
for _ in range(k):
g.update_all(fn.copy_u("h", "m"), fn.sum("m", "h"))
h_0 = g.ndata.pop("h")
# use k-hop adj to do message passing for one time.
h_1 = F.matmul(adj, feat)
assert F.allclose(h_0, h_1, rtol=1e-3, atol=1e-3)
@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
def test_laplacian_lambda_max():
N = 20
eps = 1e-6
# test DGLGraph
g = dgl.from_networkx(nx.erdos_renyi_graph(N, 0.3))
l_max = dgl.laplacian_lambda_max(g)
assert l_max[0] < 2 + eps
# test batched DGLGraph
"""
N_arr = [20, 30, 10, 12]
bg = dgl.batch([
dgl.from_networkx(nx.erdos_renyi_graph(N, 0.3))
for N in N_arr
])
l_max_arr = dgl.laplacian_lambda_max(bg)
assert len(l_max_arr) == len(N_arr)
for l_max in l_max_arr:
assert l_max < 2 + eps
"""
def create_large_graph(num_nodes, idtype=F.int64):
row = np.random.choice(num_nodes, num_nodes * 10)
col = np.random.choice(num_nodes, num_nodes * 10)
spm = spsp.coo_matrix((np.ones(len(row)), (row, col)))
spm.sum_duplicates()
return dgl.from_scipy(spm, idtype=idtype)
# Disabled since everything will be on heterogeneous graphs
@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
def test_partition_with_halo():
g = create_large_graph(1000)
node_part = np.random.choice(4, g.num_nodes())
subgs, _, _ = dgl.transforms.partition_graph_with_halo(
g, node_part, 2, reshuffle=True
)
for part_id, subg in subgs.items():
node_ids = np.nonzero(node_part == part_id)[0]
lnode_ids = np.nonzero(F.asnumpy(subg.ndata["inner_node"]))[0]
orig_nids = F.asnumpy(subg.ndata["orig_id"])[lnode_ids]
assert np.all(np.sort(orig_nids) == node_ids)
assert np.all(
F.asnumpy(subg.in_degrees(lnode_ids))
== F.asnumpy(g.in_degrees(orig_nids))
)
assert np.all(
F.asnumpy(subg.out_degrees(lnode_ids))
== F.asnumpy(g.out_degrees(orig_nids))
)
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
@unittest.skipIf(
F._default_context_str == "gpu", reason="METIS doesn't support GPU"
)
@parametrize_idtype
def test_metis_partition(idtype):
# TODO(zhengda) Metis fails to partition a small graph.
g = create_large_graph(1000, idtype=idtype)
if idtype == F.int64:
check_metis_partition(g, 0)
check_metis_partition(g, 1)
check_metis_partition(g, 2)
check_metis_partition_with_constraint(g)
else:
assert_fail = False
try:
check_metis_partition(g, 1)
except:
assert_fail = True
assert assert_fail
def check_metis_partition_with_constraint(g):
ntypes = np.zeros((g.num_nodes(),), dtype=np.int32)
ntypes[0 : int(g.num_nodes() / 4)] = 1
ntypes[int(g.num_nodes() * 3 / 4) :] = 2
subgs = dgl.transforms.metis_partition(
g, 4, extra_cached_hops=1, balance_ntypes=ntypes
)
if subgs is not None:
for i in subgs:
subg = subgs[i]
parent_nids = F.asnumpy(subg.ndata[dgl.NID])
sub_ntypes = ntypes[parent_nids]
print("type0:", np.sum(sub_ntypes == 0))
print("type1:", np.sum(sub_ntypes == 1))
print("type2:", np.sum(sub_ntypes == 2))
subgs = dgl.transforms.metis_partition(
g, 4, extra_cached_hops=1, balance_ntypes=ntypes, balance_edges=True
)
if subgs is not None:
for i in subgs:
subg = subgs[i]
parent_nids = F.asnumpy(subg.ndata[dgl.NID])
sub_ntypes = ntypes[parent_nids]
print("type0:", np.sum(sub_ntypes == 0))
print("type1:", np.sum(sub_ntypes == 1))
print("type2:", np.sum(sub_ntypes == 2))
def check_metis_partition(g, extra_hops):
subgs = dgl.transforms.metis_partition(g, 4, extra_cached_hops=extra_hops)
num_inner_nodes = 0
num_inner_edges = 0
if subgs is not None:
for part_id, subg in subgs.items():
lnode_ids = np.nonzero(F.asnumpy(subg.ndata["inner_node"]))[0]
ledge_ids = np.nonzero(F.asnumpy(subg.edata["inner_edge"]))[0]
num_inner_nodes += len(lnode_ids)
num_inner_edges += len(ledge_ids)
assert np.sum(F.asnumpy(subg.ndata["part_id"]) == part_id) == len(
lnode_ids
)
assert num_inner_nodes == g.num_nodes()
print(g.num_edges() - num_inner_edges)
if extra_hops == 0:
return
# partitions with node reshuffling
subgs = dgl.transforms.metis_partition(
g, 4, extra_cached_hops=extra_hops, reshuffle=True
)
num_inner_nodes = 0
num_inner_edges = 0
edge_cnts = np.zeros((g.num_edges(),))
if subgs is not None:
for part_id, subg in subgs.items():
lnode_ids = np.nonzero(F.asnumpy(subg.ndata["inner_node"]))[0]
ledge_ids = np.nonzero(F.asnumpy(subg.edata["inner_edge"]))[0]
num_inner_nodes += len(lnode_ids)
num_inner_edges += len(ledge_ids)
assert np.sum(F.asnumpy(subg.ndata["part_id"]) == part_id) == len(
lnode_ids
)
nids = F.asnumpy(subg.ndata[dgl.NID])
# ensure the local node Ids are contiguous.
parent_ids = F.asnumpy(subg.ndata[dgl.NID])
parent_ids = parent_ids[: len(lnode_ids)]
assert np.all(
parent_ids == np.arange(parent_ids[0], parent_ids[-1] + 1)
)
# count the local edges.
parent_ids = F.asnumpy(subg.edata[dgl.EID])[ledge_ids]
edge_cnts[parent_ids] += 1
orig_ids = subg.ndata["orig_id"]
inner_node = F.asnumpy(subg.ndata["inner_node"])
for nid in range(subg.num_nodes()):
neighs = subg.predecessors(nid)
old_neighs1 = F.gather_row(orig_ids, neighs)
old_nid = F.asnumpy(orig_ids[nid])
old_neighs2 = g.predecessors(old_nid)
# If this is an inner node, it should have the full neighborhood.
if inner_node[nid]:
assert np.all(
np.sort(F.asnumpy(old_neighs1))
== np.sort(F.asnumpy(old_neighs2))
)
# Normally, local edges are only counted once.
assert np.all(edge_cnts == 1)
assert num_inner_nodes == g.num_nodes()
print(g.num_edges() - num_inner_edges)
@unittest.skipIf(
F._default_context_str == "gpu", reason="It doesn't support GPU"
)
def test_reorder_nodes():
g = create_large_graph(1000)
new_nids = np.random.permutation(g.num_nodes())
# TODO(zhengda) we need to test both CSR and COO.
new_g = dgl.partition.reorder_nodes(g, new_nids)
new_in_deg = new_g.in_degrees()
new_out_deg = new_g.out_degrees()
in_deg = g.in_degrees()
out_deg = g.out_degrees()
new_in_deg1 = F.scatter_row(in_deg, F.tensor(new_nids), in_deg)
new_out_deg1 = F.scatter_row(out_deg, F.tensor(new_nids), out_deg)
assert np.all(F.asnumpy(new_in_deg == new_in_deg1))
assert np.all(F.asnumpy(new_out_deg == new_out_deg1))
orig_ids = F.asnumpy(new_g.ndata["orig_id"])
for nid in range(g.num_nodes()):
neighs = F.asnumpy(g.successors(nid))
new_neighs1 = new_nids[neighs]
new_nid = new_nids[nid]
new_neighs2 = new_g.successors(new_nid)
assert np.all(np.sort(new_neighs1) == np.sort(F.asnumpy(new_neighs2)))
for nid in range(new_g.num_nodes()):
neighs = F.asnumpy(new_g.successors(nid))
old_neighs1 = orig_ids[neighs]
old_nid = orig_ids[nid]
old_neighs2 = g.successors(old_nid)
assert np.all(np.sort(old_neighs1) == np.sort(F.asnumpy(old_neighs2)))
neighs = F.asnumpy(new_g.predecessors(nid))
old_neighs1 = orig_ids[neighs]
old_nid = orig_ids[nid]
old_neighs2 = g.predecessors(old_nid)
assert np.all(np.sort(old_neighs1) == np.sort(F.asnumpy(old_neighs2)))
@parametrize_idtype
def test_compact(idtype):
g1 = dgl.heterograph(
{
("user", "follow", "user"): ([1, 3], [3, 5]),
("user", "plays", "game"): ([2, 3, 2], [4, 4, 5]),
("game", "wished-by", "user"): ([6, 5], [7, 7]),
},
{"user": 20, "game": 10},
idtype=idtype,
device=F.ctx(),
)
g2 = dgl.heterograph(
{
("game", "clicked-by", "user"): ([3], [1]),
("user", "likes", "user"): ([1, 8], [8, 9]),
},
{"user": 20, "game": 10},
idtype=idtype,
device=F.ctx(),
)
g3 = dgl.heterograph(
{("user", "_E", "user"): ((0, 1), (1, 2))},
{"user": 10},
idtype=idtype,
device=F.ctx(),
)
g4 = dgl.heterograph(
{("user", "_E", "user"): ((1, 3), (3, 5))},
{"user": 10},
idtype=idtype,
device=F.ctx(),
)
def _check(g, new_g, induced_nodes):
assert g.ntypes == new_g.ntypes
assert g.canonical_etypes == new_g.canonical_etypes
for ntype in g.ntypes:
assert -1 not in induced_nodes[ntype]
for etype in g.canonical_etypes:
g_src, g_dst = g.all_edges(order="eid", etype=etype)
g_src = F.asnumpy(g_src)
g_dst = F.asnumpy(g_dst)
new_g_src, new_g_dst = new_g.all_edges(order="eid", etype=etype)
new_g_src_mapped = induced_nodes[etype[0]][F.asnumpy(new_g_src)]
new_g_dst_mapped = induced_nodes[etype[2]][F.asnumpy(new_g_dst)]
assert (g_src == new_g_src_mapped).all()
assert (g_dst == new_g_dst_mapped).all()
# Test default
new_g1 = dgl.compact_graphs(g1)
induced_nodes = {
ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes
}
induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
assert new_g1.idtype == idtype
assert set(induced_nodes["user"]) == set([1, 3, 5, 2, 7])
assert set(induced_nodes["game"]) == set([4, 5, 6])
_check(g1, new_g1, induced_nodes)
# Test with always_preserve given a dict
new_g1 = dgl.compact_graphs(
g1, always_preserve={"game": F.tensor([4, 7], idtype)}
)
assert new_g1.idtype == idtype
induced_nodes = {
ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes
}
induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
assert set(induced_nodes["user"]) == set([1, 3, 5, 2, 7])
assert set(induced_nodes["game"]) == set([4, 5, 6, 7])
_check(g1, new_g1, induced_nodes)
# Test with always_preserve given a tensor
new_g3 = dgl.compact_graphs(g3, always_preserve=F.tensor([1, 7], idtype))
induced_nodes = {
ntype: new_g3.nodes[ntype].data[dgl.NID] for ntype in new_g3.ntypes
}
induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
assert new_g3.idtype == idtype
assert set(induced_nodes["user"]) == set([0, 1, 2, 7])
_check(g3, new_g3, induced_nodes)
# Test multiple graphs
new_g1, new_g2 = dgl.compact_graphs([g1, g2])
induced_nodes = {
ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes
}
induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
assert new_g1.idtype == idtype
assert new_g2.idtype == idtype
assert set(induced_nodes["user"]) == set([1, 3, 5, 2, 7, 8, 9])
assert set(induced_nodes["game"]) == set([3, 4, 5, 6])
_check(g1, new_g1, induced_nodes)
_check(g2, new_g2, induced_nodes)
# Test multiple graphs with always_preserve given a dict
new_g1, new_g2 = dgl.compact_graphs(
[g1, g2], always_preserve={"game": F.tensor([4, 7], dtype=idtype)}
)
induced_nodes = {
ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes
}
induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
assert new_g1.idtype == idtype
assert new_g2.idtype == idtype
assert set(induced_nodes["user"]) == set([1, 3, 5, 2, 7, 8, 9])
assert set(induced_nodes["game"]) == set([3, 4, 5, 6, 7])
_check(g1, new_g1, induced_nodes)
_check(g2, new_g2, induced_nodes)
# Test multiple graphs with always_preserve given a tensor
new_g3, new_g4 = dgl.compact_graphs(
[g3, g4], always_preserve=F.tensor([1, 7], dtype=idtype)
)
induced_nodes = {
ntype: new_g3.nodes[ntype].data[dgl.NID] for ntype in new_g3.ntypes
}
induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
assert new_g3.idtype == idtype
assert new_g4.idtype == idtype
assert set(induced_nodes["user"]) == set([0, 1, 2, 3, 5, 7])
_check(g3, new_g3, induced_nodes)
_check(g4, new_g4, induced_nodes)
@unittest.skipIf(
F._default_context_str == "gpu", reason="GPU to simple not implemented"
)
@parametrize_idtype
def test_to_simple(idtype):
# homogeneous graph
g = dgl.graph((F.tensor([0, 1, 2, 1]), F.tensor([1, 2, 0, 2])))
g.ndata["h"] = F.tensor([[0.0], [1.0], [2.0]])
g.edata["h"] = F.tensor([[3.0], [4.0], [5.0], [6.0]])
sg, wb = dgl.to_simple(g, writeback_mapping=True)
u, v = g.all_edges(form="uv", order="eid")
u = F.asnumpy(u).tolist()
v = F.asnumpy(v).tolist()
uv = list(zip(u, v))
eid_map = F.asnumpy(wb)
su, sv = sg.all_edges(form="uv", order="eid")
su = F.asnumpy(su).tolist()
sv = F.asnumpy(sv).tolist()
suv = list(zip(su, sv))
sc = F.asnumpy(sg.edata["count"])
assert set(uv) == set(suv)
for i, e in enumerate(suv):
assert sc[i] == sum(e == _e for _e in uv)
for i, e in enumerate(uv):
assert eid_map[i] == suv.index(e)
# shared ndata
assert F.array_equal(sg.ndata["h"], g.ndata["h"])
assert "h" not in sg.edata
# new ndata to sg
sg.ndata["hh"] = F.tensor([[0.0], [1.0], [2.0]])
assert "hh" not in g.ndata
sg = dgl.to_simple(g, writeback_mapping=False, copy_ndata=False)
assert "h" not in sg.ndata
assert "h" not in sg.edata
# test coalesce edge feature
sg = dgl.to_simple(g, copy_edata=True, aggregator="arbitrary")
assert F.allclose(sg.edata["h"][1], F.tensor([4.0]))
sg = dgl.to_simple(g, copy_edata=True, aggregator="sum")
assert F.allclose(sg.edata["h"][1], F.tensor([10.0]))
sg = dgl.to_simple(g, copy_edata=True, aggregator="mean")
assert F.allclose(sg.edata["h"][1], F.tensor([5.0]))
# heterogeneous graph
g = dgl.heterograph(
{
("user", "follow", "user"): (
[0, 1, 2, 1, 1, 1],
[1, 3, 2, 3, 4, 4],
),
("user", "plays", "game"): (
[3, 2, 1, 1, 3, 2, 2],
[5, 3, 4, 4, 5, 3, 3],
),
},
idtype=idtype,
device=F.ctx(),
)
g.nodes["user"].data["h"] = F.tensor([0, 1, 2, 3, 4])
g.nodes["user"].data["hh"] = F.tensor([0, 1, 2, 3, 4])
g.edges["follow"].data["h"] = F.tensor([0, 1, 2, 3, 4, 5])
sg, wb = dgl.to_simple(
g, return_counts="weights", writeback_mapping=True, copy_edata=True
)
g.nodes["game"].data["h"] = F.tensor([0, 1, 2, 3, 4, 5])
for etype in g.canonical_etypes:
u, v = g.all_edges(form="uv", order="eid", etype=etype)
u = F.asnumpy(u).tolist()
v = F.asnumpy(v).tolist()
uv = list(zip(u, v))
eid_map = F.asnumpy(wb[etype])
su, sv = sg.all_edges(form="uv", order="eid", etype=etype)
su = F.asnumpy(su).tolist()
sv = F.asnumpy(sv).tolist()
suv = list(zip(su, sv))
sw = F.asnumpy(sg.edges[etype].data["weights"])
assert set(uv) == set(suv)
for i, e in enumerate(suv):
assert sw[i] == sum(e == _e for _e in uv)
for i, e in enumerate(uv):
assert eid_map[i] == suv.index(e)
# shared ndata
assert F.array_equal(sg.nodes["user"].data["h"], g.nodes["user"].data["h"])
assert F.array_equal(
sg.nodes["user"].data["hh"], g.nodes["user"].data["hh"]
)
assert "h" not in sg.nodes["game"].data
# new ndata to sg
sg.nodes["user"].data["hhh"] = F.tensor([0, 1, 2, 3, 4])
assert "hhh" not in g.nodes["user"].data
# share edata
feat_idx = F.asnumpy(wb[("user", "follow", "user")])
_, indices = np.unique(feat_idx, return_index=True)
assert np.array_equal(
F.asnumpy(sg.edges["follow"].data["h"]),
F.asnumpy(g.edges["follow"].data["h"])[indices],
)
sg = dgl.to_simple(g, writeback_mapping=False, copy_ndata=False)
for ntype in g.ntypes:
assert g.num_nodes(ntype) == sg.num_nodes(ntype)
assert "h" not in sg.nodes["user"].data
assert "hh" not in sg.nodes["user"].data
# verify DGLGraph.edge_ids() after dgl.to_simple()
# in case ids are not initialized in underlying coo2csr()
u = F.tensor([0, 1, 2])
v = F.tensor([1, 2, 3])
eids = F.tensor([0, 1, 2])
g = dgl.graph((u, v))
assert F.array_equal(g.edge_ids(u, v), eids)
sg = dgl.to_simple(g)
assert F.array_equal(sg.edge_ids(u, v), eids)
@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
@parametrize_idtype
def test_remove_edges(idtype):
def check(g1, etype, g, edges_removed):
src, dst, eid = g.edges(etype=etype, form="all")
src1, dst1 = g1.edges(etype=etype, order="eid")
if etype is not None:
eid1 = g1.edges[etype].data[dgl.EID]
else:
eid1 = g1.edata[dgl.EID]
src1 = F.asnumpy(src1)
dst1 = F.asnumpy(dst1)
eid1 = F.asnumpy(eid1)
src = F.asnumpy(src)
dst = F.asnumpy(dst)
eid = F.asnumpy(eid)
sde_set = set(zip(src, dst, eid))
for s, d, e in zip(src1, dst1, eid1):
assert (s, d, e) in sde_set
assert not np.isin(edges_removed, eid1).any()
assert g1.idtype == g.idtype
for fmt in ["coo", "csr", "csc"]:
for edges_to_remove in [[2], [2, 2], [3, 2], [1, 3, 1, 2]]:
g = dgl.graph(([0, 2, 1, 3], [1, 3, 2, 4]), idtype=idtype).formats(
fmt
)
g1 = dgl.remove_edges(g, F.tensor(edges_to_remove, idtype))
check(g1, None, g, edges_to_remove)
g = dgl.from_scipy(
spsp.csr_matrix(
([1, 1, 1, 1], ([0, 2, 1, 3], [1, 3, 2, 4])), shape=(5, 5)
),
idtype=idtype,
).formats(fmt)
g1 = dgl.remove_edges(g, F.tensor(edges_to_remove, idtype))
check(g1, None, g, edges_to_remove)
g = dgl.heterograph(
{
("A", "AA", "A"): ([0, 2, 1, 3], [1, 3, 2, 4]),
("A", "AB", "B"): ([0, 1, 3, 1], [1, 3, 5, 6]),
("B", "BA", "A"): ([2, 3], [3, 2]),
},
idtype=idtype,
)
g2 = dgl.remove_edges(
g,
{
"AA": F.tensor([2], idtype),
"AB": F.tensor([3], idtype),
"BA": F.tensor([1], idtype),
},
)
check(g2, "AA", g, [2])
check(g2, "AB", g, [3])
check(g2, "BA", g, [1])
g3 = dgl.remove_edges(
g,
{
"AA": F.tensor([], idtype),
"AB": F.tensor([3], idtype),
"BA": F.tensor([1], idtype),
},
)
check(g3, "AA", g, [])
check(g3, "AB", g, [3])
check(g3, "BA", g, [1])
g4 = dgl.remove_edges(g, {"AB": F.tensor([3, 1, 2, 0], idtype)})
check(g4, "AA", g, [])
check(g4, "AB", g, [3, 1, 2, 0])
check(g4, "BA", g, [])
@parametrize_idtype
def test_add_edges(idtype):
# homogeneous graph
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
u = 0
v = 1
g = dgl.add_edges(g, u, v)
assert g.device == F.ctx()
assert g.num_nodes() == 3
assert g.num_edges() == 3
u = [0]
v = [1]
g = dgl.add_edges(g, u, v)
assert g.device == F.ctx()
assert g.num_nodes() == 3
assert g.num_edges() == 4
u = F.tensor(u, dtype=idtype)
v = F.tensor(v, dtype=idtype)
g = dgl.add_edges(g, u, v)
assert g.device == F.ctx()
assert g.num_nodes() == 3
assert g.num_edges() == 5
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0, 1, 0, 0, 0], dtype=idtype))
assert F.array_equal(v, F.tensor([1, 2, 1, 1, 1], dtype=idtype))
g = dgl.add_edges(g, [], [])
g = dgl.add_edges(g, 0, [])
g = dgl.add_edges(g, [], 0)
assert g.device == F.ctx()
assert g.num_nodes() == 3
assert g.num_edges() == 5
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0, 1, 0, 0, 0], dtype=idtype))
assert F.array_equal(v, F.tensor([1, 2, 1, 1, 1], dtype=idtype))
# node id larger than current max node id
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
u = F.tensor([0, 1], dtype=idtype)
v = F.tensor([2, 3], dtype=idtype)
g = dgl.add_edges(g, u, v)
assert g.num_nodes() == 4
assert g.num_edges() == 4
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0, 1, 0, 1], dtype=idtype))
assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype))
# has data
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
g.ndata["h"] = F.copy_to(F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx())
g.edata["h"] = F.copy_to(F.tensor([1, 1], dtype=idtype), ctx=F.ctx())
u = F.tensor([0, 1], dtype=idtype)
v = F.tensor([2, 3], dtype=idtype)
e_feat = {
"h": F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()),
"hh": F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()),
}
g = dgl.add_edges(g, u, v, e_feat)
assert g.num_nodes() == 4
assert g.num_edges() == 4
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0, 1, 0, 1], dtype=idtype))
assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype))
assert F.array_equal(g.ndata["h"], F.tensor([1, 1, 1, 0], dtype=idtype))
assert F.array_equal(g.edata["h"], F.tensor([1, 1, 2, 2], dtype=idtype))
assert F.array_equal(g.edata["hh"], F.tensor([0, 0, 2, 2], dtype=idtype))
# zero data graph
g = dgl.graph(([], []), num_nodes=0, idtype=idtype, device=F.ctx())
u = F.tensor([0, 1], dtype=idtype)
v = F.tensor([2, 2], dtype=idtype)
e_feat = {
"h": F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()),
"hh": F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()),
}
g = dgl.add_edges(g, u, v, e_feat)
assert g.num_nodes() == 3
assert g.num_edges() == 2
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0, 1], dtype=idtype))
assert F.array_equal(v, F.tensor([2, 2], dtype=idtype))
assert F.array_equal(g.edata["h"], F.tensor([2, 2], dtype=idtype))
assert F.array_equal(g.edata["hh"], F.tensor([2, 2], dtype=idtype))
# bipartite graph
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1], [1, 2])},
idtype=idtype,
device=F.ctx(),
)
u = 0
v = 1
g = dgl.add_edges(g, u, v)
assert g.device == F.ctx()
assert g.num_nodes("user") == 2
assert g.num_nodes("game") == 3
assert g.num_edges() == 3
u = [0]
v = [1]
g = dgl.add_edges(g, u, v)
assert g.device == F.ctx()
assert g.num_nodes("user") == 2
assert g.num_nodes("game") == 3
assert g.num_edges() == 4
u = F.tensor(u, dtype=idtype)
v = F.tensor(v, dtype=idtype)
g = dgl.add_edges(g, u, v)
assert g.device == F.ctx()
assert g.num_nodes("user") == 2
assert g.num_nodes("game") == 3
assert g.num_edges() == 5
u, v = g.edges(form="uv")
assert F.array_equal(u, F.tensor([0, 1, 0, 0, 0], dtype=idtype))
assert F.array_equal(v, F.tensor([1, 2, 1, 1, 1], dtype=idtype))
# node id larger than current max node id
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1], [1, 2])},
idtype=idtype,
device=F.ctx(),
)
u = F.tensor([0, 2], dtype=idtype)
v = F.tensor([2, 3], dtype=idtype)
g = dgl.add_edges(g, u, v)
assert g.device == F.ctx()
assert g.num_nodes("user") == 3
assert g.num_nodes("game") == 4
assert g.num_edges() == 4
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0, 1, 0, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype))
# has data
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1], [1, 2])},
idtype=idtype,
device=F.ctx(),
)
g.nodes["user"].data["h"] = F.copy_to(
F.tensor([1, 1], dtype=idtype), ctx=F.ctx()
)
g.nodes["game"].data["h"] = F.copy_to(
F.tensor([2, 2, 2], dtype=idtype), ctx=F.ctx()
)
g.edata["h"] = F.copy_to(F.tensor([1, 1], dtype=idtype), ctx=F.ctx())
u = F.tensor([0, 2], dtype=idtype)
v = F.tensor([2, 3], dtype=idtype)
e_feat = {
"h": F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()),
"hh": F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()),
}
g = dgl.add_edges(g, u, v, e_feat)
assert g.num_nodes("user") == 3
assert g.num_nodes("game") == 4
assert g.num_edges() == 4
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0, 1, 0, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype))
assert F.array_equal(
g.nodes["user"].data["h"], F.tensor([1, 1, 0], dtype=idtype)
)
assert F.array_equal(
g.nodes["game"].data["h"], F.tensor([2, 2, 2, 0], dtype=idtype)
)
assert F.array_equal(g.edata["h"], F.tensor([1, 1, 2, 2], dtype=idtype))
assert F.array_equal(g.edata["hh"], F.tensor([0, 0, 2, 2], dtype=idtype))
# heterogeneous graph
g = create_test_heterograph3(idtype)
u = F.tensor([0, 2], dtype=idtype)
v = F.tensor([2, 3], dtype=idtype)
g = dgl.add_edges(g, u, v, etype="plays")
assert g.num_nodes("user") == 3
assert g.num_nodes("game") == 4
assert g.num_nodes("developer") == 2
assert g.num_edges("plays") == 6
assert g.num_edges("develops") == 2
u, v = g.edges(form="uv", order="eid", etype="plays")
assert F.array_equal(u, F.tensor([0, 1, 1, 2, 0, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([0, 0, 1, 1, 2, 3], dtype=idtype))
assert F.array_equal(
g.nodes["user"].data["h"], F.tensor([1, 1, 1], dtype=idtype)
)
assert F.array_equal(
g.nodes["game"].data["h"], F.tensor([2, 2, 0, 0], dtype=idtype)
)
assert F.array_equal(
g.edges["plays"].data["h"], F.tensor([1, 1, 1, 1, 0, 0], dtype=idtype)
)
# add with feature
e_feat = {"h": F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())}
u = F.tensor([0, 2], dtype=idtype)
v = F.tensor([2, 3], dtype=idtype)
g.nodes["game"].data["h"] = F.copy_to(
F.tensor([2, 2, 1, 1], dtype=idtype), ctx=F.ctx()
)
g = dgl.add_edges(g, u, v, data=e_feat, etype="develops")
assert g.num_nodes("user") == 3
assert g.num_nodes("game") == 4
assert g.num_nodes("developer") == 3
assert g.num_edges("plays") == 6
assert g.num_edges("develops") == 4
u, v = g.edges(form="uv", order="eid", etype="develops")
assert F.array_equal(u, F.tensor([0, 1, 0, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([0, 1, 2, 3], dtype=idtype))
assert F.array_equal(
g.nodes["developer"].data["h"], F.tensor([3, 3, 0], dtype=idtype)
)
assert F.array_equal(
g.nodes["game"].data["h"], F.tensor([2, 2, 1, 1], dtype=idtype)
)
assert F.array_equal(
g.edges["develops"].data["h"], F.tensor([0, 0, 2, 2], dtype=idtype)
)
@parametrize_idtype
def test_add_nodes(idtype):
# homogeneous Graphs
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
g.ndata["h"] = F.copy_to(F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx())
new_g = dgl.add_nodes(g, 1)
assert g.num_nodes() == 3
assert new_g.num_nodes() == 4
assert F.array_equal(new_g.ndata["h"], F.tensor([1, 1, 1, 0], dtype=idtype))
# zero node graph
g = dgl.graph(([], []), num_nodes=3, idtype=idtype, device=F.ctx())
g.ndata["h"] = F.copy_to(F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx())
g = dgl.add_nodes(
g, 1, data={"h": F.copy_to(F.tensor([2], dtype=idtype), ctx=F.ctx())}
)
assert g.num_nodes() == 4
assert F.array_equal(g.ndata["h"], F.tensor([1, 1, 1, 2], dtype=idtype))
# bipartite graph
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1], [1, 2])},
idtype=idtype,
device=F.ctx(),
)
g = dgl.add_nodes(
g,
2,
data={"h": F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())},
ntype="user",
)
assert g.num_nodes("user") == 4
assert g.num_nodes("game") == 3
assert F.array_equal(
g.nodes["user"].data["h"], F.tensor([0, 0, 2, 2], dtype=idtype)
)
g = dgl.add_nodes(g, 2, ntype="game")
assert g.num_nodes("user") == 4
assert g.num_nodes("game") == 5
# heterogeneous graph
g = create_test_heterograph3(idtype)
g = dgl.add_nodes(g, 1, ntype="user")
g = dgl.add_nodes(
g,
2,
data={"h": F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())},
ntype="game",
)
assert g.num_nodes("user") == 4
assert g.num_nodes("game") == 4
assert g.num_nodes("developer") == 2
assert F.array_equal(
g.nodes["user"].data["h"], F.tensor([1, 1, 1, 0], dtype=idtype)
)
assert F.array_equal(
g.nodes["game"].data["h"], F.tensor([2, 2, 2, 2], dtype=idtype)
)
@parametrize_idtype
def test_remove_edges(idtype):
# homogeneous Graphs
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
e = 0
g = dgl.remove_edges(g, e)
assert g.num_edges() == 1
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([1], dtype=idtype))
assert F.array_equal(v, F.tensor([2], dtype=idtype))
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
e = [0]
g = dgl.remove_edges(g, e)
assert g.num_edges() == 1
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([1], dtype=idtype))
assert F.array_equal(v, F.tensor([2], dtype=idtype))
e = F.tensor([0], dtype=idtype)
g = dgl.remove_edges(g, e)
assert g.num_edges() == 0
# has node data
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
g.ndata["h"] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx())
g = dgl.remove_edges(g, 1)
assert g.num_edges() == 1
assert F.array_equal(g.ndata["h"], F.tensor([1, 2, 3], dtype=idtype))
# has edge data
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
g.edata["h"] = F.copy_to(F.tensor([1, 2], dtype=idtype), ctx=F.ctx())
g = dgl.remove_edges(g, 0)
assert g.num_edges() == 1
assert F.array_equal(g.edata["h"], F.tensor([2], dtype=idtype))
# invalid eid
assert_fail = False
try:
g = dgl.remove_edges(g, 1)
except:
assert_fail = True
assert assert_fail
# bipartite graph
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1], [1, 2])},
idtype=idtype,
device=F.ctx(),
)
e = 0
g = dgl.remove_edges(g, e)
assert g.num_edges() == 1
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([1], dtype=idtype))
assert F.array_equal(v, F.tensor([2], dtype=idtype))
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1], [1, 2])},
idtype=idtype,
device=F.ctx(),
)
e = [0]
g = dgl.remove_edges(g, e)
assert g.num_edges() == 1
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([1], dtype=idtype))
assert F.array_equal(v, F.tensor([2], dtype=idtype))
e = F.tensor([0], dtype=idtype)
g = dgl.remove_edges(g, e)
assert g.num_edges() == 0
# has data
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1], [1, 2])},
idtype=idtype,
device=F.ctx(),
)
g.nodes["user"].data["h"] = F.copy_to(
F.tensor([1, 1], dtype=idtype), ctx=F.ctx()
)
g.nodes["game"].data["h"] = F.copy_to(
F.tensor([2, 2, 2], dtype=idtype), ctx=F.ctx()
)
g.edata["h"] = F.copy_to(F.tensor([1, 2], dtype=idtype), ctx=F.ctx())
g = dgl.remove_edges(g, 1)
assert g.num_edges() == 1
assert F.array_equal(
g.nodes["user"].data["h"], F.tensor([1, 1], dtype=idtype)
)
assert F.array_equal(
g.nodes["game"].data["h"], F.tensor([2, 2, 2], dtype=idtype)
)
assert F.array_equal(g.edata["h"], F.tensor([1], dtype=idtype))
# heterogeneous graph
g = create_test_heterograph3(idtype)
g.edges["plays"].data["h"] = F.copy_to(
F.tensor([1, 2, 3, 4], dtype=idtype), ctx=F.ctx()
)
g = dgl.remove_edges(g, 1, etype="plays")
assert g.num_edges("plays") == 3
u, v = g.edges(form="uv", order="eid", etype="plays")
assert F.array_equal(u, F.tensor([0, 1, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([0, 1, 1], dtype=idtype))
assert F.array_equal(
g.edges["plays"].data["h"], F.tensor([1, 3, 4], dtype=idtype)
)
# remove all edges of 'develops'
g = dgl.remove_edges(g, [0, 1], etype="develops")
assert g.num_edges("develops") == 0
assert F.array_equal(
g.nodes["user"].data["h"], F.tensor([1, 1, 1], dtype=idtype)
)
assert F.array_equal(
g.nodes["game"].data["h"], F.tensor([2, 2], dtype=idtype)
)
assert F.array_equal(
g.nodes["developer"].data["h"], F.tensor([3, 3], dtype=idtype)
)
# batched graph
ctx = F.ctx()
g1 = dgl.graph(([0, 1], [1, 2]), num_nodes=5, idtype=idtype, device=ctx)
g2 = dgl.graph(([], []), idtype=idtype, device=ctx)
g3 = dgl.graph(([2, 3, 4], [3, 2, 1]), idtype=idtype, device=ctx)
bg = dgl.batch([g1, g2, g3])
bg_r = dgl.remove_edges(bg, 2)
assert bg.batch_size == bg_r.batch_size
assert F.array_equal(bg.batch_num_nodes(), bg_r.batch_num_nodes())
assert F.array_equal(
bg_r.batch_num_edges(), F.tensor([2, 0, 2], dtype=idtype)
)
bg_r = dgl.remove_edges(bg, [0, 2])
assert bg.batch_size == bg_r.batch_size
assert F.array_equal(bg.batch_num_nodes(), bg_r.batch_num_nodes())
assert F.array_equal(
bg_r.batch_num_edges(), F.tensor([1, 0, 2], dtype=idtype)
)
bg_r = dgl.remove_edges(bg, F.tensor([0, 2], dtype=idtype))
assert bg.batch_size == bg_r.batch_size
assert F.array_equal(bg.batch_num_nodes(), bg_r.batch_num_nodes())
assert F.array_equal(
bg_r.batch_num_edges(), F.tensor([1, 0, 2], dtype=idtype)
)
# batched heterogeneous graph
g1 = dgl.heterograph(
{
("user", "follows", "user"): ([0, 1], [1, 2]),
("user", "plays", "game"): ([1, 3], [0, 1]),
},
num_nodes_dict={"user": 4, "game": 3},
idtype=idtype,
device=ctx,
)
g2 = dgl.heterograph(
{
("user", "follows", "user"): ([0, 2], [3, 4]),
("user", "plays", "game"): ([], []),
},
num_nodes_dict={"user": 6, "game": 2},
idtype=idtype,
device=ctx,
)
g3 = dgl.heterograph(
{
("user", "follows", "user"): ([], []),
("user", "plays", "game"): ([1, 2], [1, 2]),
},
idtype=idtype,
device=ctx,
)
bg = dgl.batch([g1, g2, g3])
bg_r = dgl.remove_edges(bg, 1, etype="follows")
assert bg.batch_size == bg_r.batch_size
ntypes = bg.ntypes
for nty in ntypes:
assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty))
assert F.array_equal(
bg_r.batch_num_edges("follows"), F.tensor([1, 2, 0], dtype=idtype)
)
assert F.array_equal(
bg_r.batch_num_edges("plays"), bg.batch_num_edges("plays")
)
bg_r = dgl.remove_edges(bg, 2, etype="plays")
assert bg.batch_size == bg_r.batch_size
for nty in ntypes:
assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty))
assert F.array_equal(
bg.batch_num_edges("follows"), bg_r.batch_num_edges("follows")
)
assert F.array_equal(
bg_r.batch_num_edges("plays"), F.tensor([2, 0, 1], dtype=idtype)
)
bg_r = dgl.remove_edges(bg, [0, 1, 3], etype="follows")
assert bg.batch_size == bg_r.batch_size
for nty in ntypes:
assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty))
assert F.array_equal(
bg_r.batch_num_edges("follows"), F.tensor([0, 1, 0], dtype=idtype)
)
assert F.array_equal(
bg.batch_num_edges("plays"), bg_r.batch_num_edges("plays")
)
bg_r = dgl.remove_edges(bg, [1, 2], etype="plays")
assert bg.batch_size == bg_r.batch_size
for nty in ntypes:
assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty))
assert F.array_equal(
bg.batch_num_edges("follows"), bg_r.batch_num_edges("follows")
)
assert F.array_equal(
bg_r.batch_num_edges("plays"), F.tensor([1, 0, 1], dtype=idtype)
)
bg_r = dgl.remove_edges(
bg, F.tensor([0, 1, 3], dtype=idtype), etype="follows"
)
assert bg.batch_size == bg_r.batch_size
for nty in ntypes:
assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty))
assert F.array_equal(
bg_r.batch_num_edges("follows"), F.tensor([0, 1, 0], dtype=idtype)
)
assert F.array_equal(
bg.batch_num_edges("plays"), bg_r.batch_num_edges("plays")
)
bg_r = dgl.remove_edges(bg, F.tensor([1, 2], dtype=idtype), etype="plays")
assert bg.batch_size == bg_r.batch_size
for nty in ntypes:
assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty))
assert F.array_equal(
bg.batch_num_edges("follows"), bg_r.batch_num_edges("follows")
)
assert F.array_equal(
bg_r.batch_num_edges("plays"), F.tensor([1, 0, 1], dtype=idtype)
)
@parametrize_idtype
def test_remove_nodes(idtype):
# homogeneous Graphs
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
n = 0
g = dgl.remove_nodes(g, n)
assert g.num_nodes() == 2
assert g.num_edges() == 1
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0], dtype=idtype))
assert F.array_equal(v, F.tensor([1], dtype=idtype))
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
n = [1]
g = dgl.remove_nodes(g, n)
assert g.num_nodes() == 2
assert g.num_edges() == 0
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
n = F.tensor([2], dtype=idtype)
g = dgl.remove_nodes(g, n)
assert g.num_nodes() == 2
assert g.num_edges() == 1
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0], dtype=idtype))
assert F.array_equal(v, F.tensor([1], dtype=idtype))
# invalid nid
assert_fail = False
try:
g.remove_nodes(3)
except:
assert_fail = True
assert assert_fail
# has node and edge data
g = dgl.graph(([0, 0, 2], [0, 1, 2]), idtype=idtype, device=F.ctx())
g.ndata["hv"] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx())
g.edata["he"] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx())
g = dgl.remove_nodes(g, F.tensor([0], dtype=idtype))
assert g.num_nodes() == 2
assert g.num_edges() == 1
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([1], dtype=idtype))
assert F.array_equal(v, F.tensor([1], dtype=idtype))
assert F.array_equal(g.ndata["hv"], F.tensor([2, 3], dtype=idtype))
assert F.array_equal(g.edata["he"], F.tensor([3], dtype=idtype))
# node id larger than current max node id
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1], [1, 2])},
idtype=idtype,
device=F.ctx(),
)
n = 0
g = dgl.remove_nodes(g, n, ntype="user")
assert g.num_nodes("user") == 1
assert g.num_nodes("game") == 3
assert g.num_edges() == 1
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0], dtype=idtype))
assert F.array_equal(v, F.tensor([2], dtype=idtype))
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1], [1, 2])},
idtype=idtype,
device=F.ctx(),
)
n = [1]
g = dgl.remove_nodes(g, n, ntype="user")
assert g.num_nodes("user") == 1
assert g.num_nodes("game") == 3
assert g.num_edges() == 1
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0], dtype=idtype))
assert F.array_equal(v, F.tensor([1], dtype=idtype))
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1], [1, 2])},
idtype=idtype,
device=F.ctx(),
)
n = F.tensor([0], dtype=idtype)
g = dgl.remove_nodes(g, n, ntype="game")
assert g.num_nodes("user") == 2
assert g.num_nodes("game") == 2
assert g.num_edges() == 2
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0, 1], dtype=idtype))
assert F.array_equal(v, F.tensor([0, 1], dtype=idtype))
# heterogeneous graph
g = create_test_heterograph3(idtype)
g.edges["plays"].data["h"] = F.copy_to(
F.tensor([1, 2, 3, 4], dtype=idtype), ctx=F.ctx()
)
g = dgl.remove_nodes(g, 0, ntype="game")
assert g.num_nodes("user") == 3
assert g.num_nodes("game") == 1
assert g.num_nodes("developer") == 2
assert g.num_edges("plays") == 2
assert g.num_edges("develops") == 1
assert F.array_equal(
g.nodes["user"].data["h"], F.tensor([1, 1, 1], dtype=idtype)
)
assert F.array_equal(g.nodes["game"].data["h"], F.tensor([2], dtype=idtype))
assert F.array_equal(
g.nodes["developer"].data["h"], F.tensor([3, 3], dtype=idtype)
)
u, v = g.edges(form="uv", order="eid", etype="plays")
assert F.array_equal(u, F.tensor([1, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([0, 0], dtype=idtype))
assert F.array_equal(
g.edges["plays"].data["h"], F.tensor([3, 4], dtype=idtype)
)
u, v = g.edges(form="uv", order="eid", etype="develops")
assert F.array_equal(u, F.tensor([1], dtype=idtype))
assert F.array_equal(v, F.tensor([0], dtype=idtype))
# batched graph
ctx = F.ctx()
g1 = dgl.graph(([0, 1], [1, 2]), num_nodes=5, idtype=idtype, device=ctx)
g2 = dgl.graph(([], []), idtype=idtype, device=ctx)
g3 = dgl.graph(([2, 3, 4], [3, 2, 1]), idtype=idtype, device=ctx)
bg = dgl.batch([g1, g2, g3])
bg_r = dgl.remove_nodes(bg, 1)
assert bg_r.batch_size == bg.batch_size
assert F.array_equal(
bg_r.batch_num_nodes(), F.tensor([4, 0, 5], dtype=idtype)
)
assert F.array_equal(
bg_r.batch_num_edges(), F.tensor([0, 0, 3], dtype=idtype)
)
bg_r = dgl.remove_nodes(bg, [1, 7])
assert bg_r.batch_size == bg.batch_size
assert F.array_equal(
bg_r.batch_num_nodes(), F.tensor([4, 0, 4], dtype=idtype)
)
assert F.array_equal(
bg_r.batch_num_edges(), F.tensor([0, 0, 1], dtype=idtype)
)
bg_r = dgl.remove_nodes(bg, F.tensor([1, 7], dtype=idtype))
assert bg_r.batch_size == bg.batch_size
assert F.array_equal(
bg_r.batch_num_nodes(), F.tensor([4, 0, 4], dtype=idtype)
)
assert F.array_equal(
bg_r.batch_num_edges(), F.tensor([0, 0, 1], dtype=idtype)
)
# batched heterogeneous graph
g1 = dgl.heterograph(
{
("user", "follows", "user"): ([0, 1], [1, 2]),
("user", "plays", "game"): ([1, 3], [0, 1]),
},
num_nodes_dict={"user": 4, "game": 3},
idtype=idtype,
device=ctx,
)
g2 = dgl.heterograph(
{
("user", "follows", "user"): ([0, 2], [3, 4]),
("user", "plays", "game"): ([], []),
},
num_nodes_dict={"user": 6, "game": 2},
idtype=idtype,
device=ctx,
)
g3 = dgl.heterograph(
{
("user", "follows", "user"): ([], []),
("user", "plays", "game"): ([1, 2], [1, 2]),
},
idtype=idtype,
device=ctx,
)
bg = dgl.batch([g1, g2, g3])
bg_r = dgl.remove_nodes(bg, 1, ntype="user")
assert bg_r.batch_size == bg.batch_size
assert F.array_equal(
bg_r.batch_num_nodes("user"), F.tensor([3, 6, 3], dtype=idtype)
)
assert F.array_equal(
bg.batch_num_nodes("game"), bg_r.batch_num_nodes("game")
)
assert F.array_equal(
bg_r.batch_num_edges("follows"), F.tensor([0, 2, 0], dtype=idtype)
)
assert F.array_equal(
bg_r.batch_num_edges("plays"), F.tensor([1, 0, 2], dtype=idtype)
)
bg_r = dgl.remove_nodes(bg, 6, ntype="game")
assert bg_r.batch_size == bg.batch_size
assert F.array_equal(
bg.batch_num_nodes("user"), bg_r.batch_num_nodes("user")
)
assert F.array_equal(
bg_r.batch_num_nodes("game"), F.tensor([3, 2, 2], dtype=idtype)
)
assert F.array_equal(
bg.batch_num_edges("follows"), bg_r.batch_num_edges("follows")
)
assert F.array_equal(
bg_r.batch_num_edges("plays"), F.tensor([2, 0, 1], dtype=idtype)
)
bg_r = dgl.remove_nodes(bg, [1, 5, 6, 11], ntype="user")
assert bg_r.batch_size == bg.batch_size
assert F.array_equal(
bg_r.batch_num_nodes("user"), F.tensor([3, 4, 2], dtype=idtype)
)
assert F.array_equal(
bg.batch_num_nodes("game"), bg_r.batch_num_nodes("game")
)
assert F.array_equal(
bg_r.batch_num_edges("follows"), F.tensor([0, 1, 0], dtype=idtype)
)
assert F.array_equal(
bg_r.batch_num_edges("plays"), F.tensor([1, 0, 1], dtype=idtype)
)
bg_r = dgl.remove_nodes(bg, [0, 3, 4, 7], ntype="game")
assert bg_r.batch_size == bg.batch_size
assert F.array_equal(
bg.batch_num_nodes("user"), bg_r.batch_num_nodes("user")
)
assert F.array_equal(
bg_r.batch_num_nodes("game"), F.tensor([2, 0, 2], dtype=idtype)
)
assert F.array_equal(
bg.batch_num_edges("follows"), bg_r.batch_num_edges("follows")
)
assert F.array_equal(
bg_r.batch_num_edges("plays"), F.tensor([1, 0, 1], dtype=idtype)
)
bg_r = dgl.remove_nodes(
bg, F.tensor([1, 5, 6, 11], dtype=idtype), ntype="user"
)
assert bg_r.batch_size == bg.batch_size
assert F.array_equal(
bg_r.batch_num_nodes("user"), F.tensor([3, 4, 2], dtype=idtype)
)
assert F.array_equal(
bg.batch_num_nodes("game"), bg_r.batch_num_nodes("game")
)
assert F.array_equal(
bg_r.batch_num_edges("follows"), F.tensor([0, 1, 0], dtype=idtype)
)
assert F.array_equal(
bg_r.batch_num_edges("plays"), F.tensor([1, 0, 1], dtype=idtype)
)
bg_r = dgl.remove_nodes(
bg, F.tensor([0, 3, 4, 7], dtype=idtype), ntype="game"
)
assert bg_r.batch_size == bg.batch_size
assert F.array_equal(
bg.batch_num_nodes("user"), bg_r.batch_num_nodes("user")
)
assert F.array_equal(
bg_r.batch_num_nodes("game"), F.tensor([2, 0, 2], dtype=idtype)
)
assert F.array_equal(
bg.batch_num_edges("follows"), bg_r.batch_num_edges("follows")
)
assert F.array_equal(
bg_r.batch_num_edges("plays"), F.tensor([1, 0, 1], dtype=idtype)
)
@parametrize_idtype
def test_add_selfloop(idtype):
# homogeneous graph
# test for fill_data is float
g = dgl.graph(([0, 0, 2], [2, 1, 0]), idtype=idtype, device=F.ctx())
g.edata["he"] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx())
g.edata["he1"] = F.copy_to(
F.tensor([[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]), ctx=F.ctx()
)
g.ndata["hn"] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx())
g = dgl.add_self_loop(g)
assert g.num_nodes() == 3
assert g.num_edges() == 6
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0, 0, 2, 0, 1, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([2, 1, 0, 0, 1, 2], dtype=idtype))
assert F.array_equal(
g.edata["he"], F.tensor([1, 2, 3, 1, 1, 1], dtype=idtype)
)
assert F.array_equal(
g.edata["he1"],
F.tensor(
[
[0.0, 1.0],
[2.0, 3.0],
[4.0, 5.0],
[1.0, 1.0],
[1.0, 1.0],
[1.0, 1.0],
]
),
)
# test for fill_data is int
g = dgl.graph(([0, 0, 2], [2, 1, 0]), idtype=idtype, device=F.ctx())
g.edata["he"] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx())
g.edata["he1"] = F.copy_to(
F.tensor([[0, 1], [2, 3], [4, 5]], dtype=idtype), ctx=F.ctx()
)
g.ndata["hn"] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx())
g = dgl.add_self_loop(g, fill_data=1)
assert g.num_nodes() == 3
assert g.num_edges() == 6
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0, 0, 2, 0, 1, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([2, 1, 0, 0, 1, 2], dtype=idtype))
assert F.array_equal(
g.edata["he"], F.tensor([1, 2, 3, 1, 1, 1], dtype=idtype)
)
assert F.array_equal(
g.edata["he1"],
F.tensor(
[[0, 1], [2, 3], [4, 5], [1, 1], [1, 1], [1, 1]], dtype=idtype
),
)
# test for fill_data is str
g = dgl.graph(([0, 0, 2], [2, 1, 0]), idtype=idtype, device=F.ctx())
g.edata["he"] = F.copy_to(F.tensor([1.0, 2.0, 3.0]), ctx=F.ctx())
g.edata["he1"] = F.copy_to(
F.tensor([[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]), ctx=F.ctx()
)
g.ndata["hn"] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx())
g = dgl.add_self_loop(g, fill_data="sum")
assert g.num_nodes() == 3
assert g.num_edges() == 6
u, v = g.edges(form="uv", order="eid")
assert F.array_equal(u, F.tensor([0, 0, 2, 0, 1, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([2, 1, 0, 0, 1, 2], dtype=idtype))
assert F.array_equal(
g.edata["he"], F.tensor([1.0, 2.0, 3.0, 3.0, 2.0, 1.0])
)
assert F.array_equal(
g.edata["he1"],
F.tensor(
[
[0.0, 1.0],
[2.0, 3.0],
[4.0, 5.0],
[4.0, 5.0],
[2.0, 3.0],
[0.0, 1.0],
]
),
)
# bipartite graph
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1, 2], [1, 2, 2])},
idtype=idtype,
device=F.ctx(),
)
# nothing will happend
raise_error = False
try:
g = dgl.add_self_loop(g)
except:
raise_error = True
assert raise_error
# test for fill_data is float
g = create_test_heterograph5(idtype)
g.edges["follows"].data["h1"] = F.copy_to(
F.tensor([[0.0, 1.0], [1.0, 2.0]]), ctx=F.ctx()
)
g = dgl.add_self_loop(g, etype="follows")
assert g.num_nodes("user") == 3
assert g.num_nodes("game") == 2
assert g.num_edges("follows") == 5
assert g.num_edges("plays") == 2
u, v = g.edges(form="uv", order="eid", etype="follows")
assert F.array_equal(u, F.tensor([1, 2, 0, 1, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([0, 1, 0, 1, 2], dtype=idtype))
assert F.array_equal(
g.edges["follows"].data["h"], F.tensor([1, 2, 1, 1, 1], dtype=idtype)
)
assert F.array_equal(
g.edges["follows"].data["h1"],
F.tensor([[0.0, 1.0], [1.0, 2.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]),
)
assert F.array_equal(
g.edges["plays"].data["h"], F.tensor([1, 2], dtype=idtype)
)
# test for fill_data is int
g = create_test_heterograph5(idtype)
g.edges["follows"].data["h1"] = F.copy_to(
F.tensor([[0, 1], [1, 2]], dtype=idtype), ctx=F.ctx()
)
g = dgl.add_self_loop(g, fill_data=1, etype="follows")
assert g.num_nodes("user") == 3
assert g.num_nodes("game") == 2
assert g.num_edges("follows") == 5
assert g.num_edges("plays") == 2
u, v = g.edges(form="uv", order="eid", etype="follows")
assert F.array_equal(u, F.tensor([1, 2, 0, 1, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([0, 1, 0, 1, 2], dtype=idtype))
assert F.array_equal(
g.edges["follows"].data["h"], F.tensor([1, 2, 1, 1, 1], dtype=idtype)
)
assert F.array_equal(
g.edges["follows"].data["h1"],
F.tensor([[0, 1], [1, 2], [1, 1], [1, 1], [1, 1]], dtype=idtype),
)
assert F.array_equal(
g.edges["plays"].data["h"], F.tensor([1, 2], dtype=idtype)
)
# test for fill_data is str
g = dgl.heterograph(
{
("user", "follows", "user"): (
F.tensor([1, 2], dtype=idtype),
F.tensor([0, 1], dtype=idtype),
),
("user", "plays", "game"): (
F.tensor([0, 1], dtype=idtype),
F.tensor([0, 1], dtype=idtype),
),
},
idtype=idtype,
device=F.ctx(),
)
g.nodes["user"].data["h"] = F.copy_to(
F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx()
)
g.nodes["game"].data["h"] = F.copy_to(
F.tensor([2, 2], dtype=idtype), ctx=F.ctx()
)
g.edges["follows"].data["h"] = F.copy_to(F.tensor([1.0, 2.0]), ctx=F.ctx())
g.edges["follows"].data["h1"] = F.copy_to(
F.tensor([[0.0, 1.0], [1.0, 2.0]]), ctx=F.ctx()
)
g.edges["plays"].data["h"] = F.copy_to(F.tensor([1.0, 2.0]), ctx=F.ctx())
g = dgl.add_self_loop(g, fill_data="mean", etype="follows")
assert g.num_nodes("user") == 3
assert g.num_nodes("game") == 2
assert g.num_edges("follows") == 5
assert g.num_edges("plays") == 2
u, v = g.edges(form="uv", order="eid", etype="follows")
assert F.array_equal(u, F.tensor([1, 2, 0, 1, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([0, 1, 0, 1, 2], dtype=idtype))
assert F.array_equal(
g.edges["follows"].data["h"], F.tensor([1.0, 2.0, 1.0, 2.0, 0.0])
)
assert F.array_equal(
g.edges["follows"].data["h1"],
F.tensor([[0.0, 1.0], [1.0, 2.0], [0.0, 1.0], [1.0, 2.0], [0.0, 0.0]]),
)
assert F.array_equal(g.edges["plays"].data["h"], F.tensor([1.0, 2.0]))
raise_error = False
try:
g = dgl.add_self_loop(g, etype="plays")
except:
raise_error = True
assert raise_error
@parametrize_idtype
def test_remove_selfloop(idtype):
# homogeneous graph
g = dgl.graph(([0, 0, 0, 1], [1, 0, 0, 2]), idtype=idtype, device=F.ctx())
g.edata["he"] = F.copy_to(F.tensor([1, 2, 3, 4], dtype=idtype), ctx=F.ctx())
g = dgl.remove_self_loop(g)
assert g.num_nodes() == 3
assert g.num_edges() == 2
assert F.array_equal(g.edata["he"], F.tensor([1, 4], dtype=idtype))
# bipartite graph
g = dgl.heterograph(
{("user", "plays", "game"): ([0, 1, 2], [1, 2, 2])},
idtype=idtype,
device=F.ctx(),
)
# nothing will happend
raise_error = False
try:
g = dgl.remove_self_loop(g, etype="plays")
except:
raise_error = True
assert raise_error
g = create_test_heterograph4(idtype)
g = dgl.remove_self_loop(g, etype="follows")
assert g.num_nodes("user") == 3
assert g.num_nodes("game") == 2
assert g.num_edges("follows") == 2
assert g.num_edges("plays") == 2
u, v = g.edges(form="uv", order="eid", etype="follows")
assert F.array_equal(u, F.tensor([1, 2], dtype=idtype))
assert F.array_equal(v, F.tensor([0, 1], dtype=idtype))
assert F.array_equal(
g.edges["follows"].data["h"], F.tensor([2, 4], dtype=idtype)
)
assert F.array_equal(
g.edges["plays"].data["h"], F.tensor([1, 2], dtype=idtype)
)
raise_error = False
try:
g = dgl.remove_self_loop(g, etype="plays")
except:
raise_error = True
assert raise_error
# batch information
g = dgl.graph(
([0, 0, 0, 1, 3, 3, 4], [1, 0, 0, 2, 3, 4, 4]),
idtype=idtype,
device=F.ctx(),
)
g.set_batch_num_nodes([3, 2])
g.set_batch_num_edges([4, 3])
g = dgl.remove_self_loop(g)
assert g.num_nodes() == 5
assert g.num_edges() == 3
assert F.array_equal(g.batch_num_nodes(), F.tensor([3, 2], dtype=idtype))
assert F.array_equal(g.batch_num_edges(), F.tensor([2, 1], dtype=idtype))
@parametrize_idtype
def test_reorder_graph(idtype):
g = dgl.graph(
([0, 1, 2, 3, 4], [2, 2, 3, 2, 3]), idtype=idtype, device=F.ctx()
)
g.ndata["h"] = F.copy_to(F.randn((g.num_nodes(), 3)), ctx=F.ctx())
g.edata["w"] = F.copy_to(F.randn((g.num_edges(), 2)), ctx=F.ctx())
# call with default: node_permute_algo=None, edge_permute_algo='src'
rg = dgl.reorder_graph(g)
assert dgl.EID in rg.edata.keys()
src = F.asnumpy(rg.edges()[0])
assert np.array_equal(src, np.sort(src))
# call with 'rcmk' node_permute_algo
rg = dgl.reorder_graph(g, node_permute_algo="rcmk")
assert dgl.NID in rg.ndata.keys()
assert dgl.EID in rg.edata.keys()
src = F.asnumpy(rg.edges()[0])
assert np.array_equal(src, np.sort(src))
# call with 'dst' edge_permute_algo
rg = dgl.reorder_graph(g, edge_permute_algo="dst")
dst = F.asnumpy(rg.edges()[1])
assert np.array_equal(dst, np.sort(dst))
# call with unknown edge_permute_algo
raise_error = False
try:
dgl.reorder_graph(g, edge_permute_algo="none")
except:
raise_error = True
assert raise_error
# reorder back to original according to stored ids
rg = dgl.reorder_graph(g, node_permute_algo="rcmk")
rg2 = dgl.reorder_graph(
rg,
"custom",
permute_config={"nodes_perm": np.argsort(F.asnumpy(rg.ndata[dgl.NID]))},
)
assert F.array_equal(g.ndata["h"], rg2.ndata["h"])
assert F.array_equal(g.edata["w"], rg2.edata["w"])
# do not store ids
rg = dgl.reorder_graph(g, store_ids=False)
assert not dgl.NID in rg.ndata.keys()
assert not dgl.EID in rg.edata.keys()
# metis does not work on windows.
if os.name == "nt":
pass
else:
# metis_partition may fail for small graph.
mg = create_large_graph(1000).to(F.ctx())
# call with metis strategy, but k is not specified
raise_error = False
try:
dgl.reorder_graph(mg, node_permute_algo="metis")
except:
raise_error = True
assert raise_error
# call with metis strategy, k is specified
raise_error = False
try:
dgl.reorder_graph(
mg, node_permute_algo="metis", permute_config={"k": 2}
)
except:
raise_error = True
assert not raise_error
# call with qualified nodes_perm specified
nodes_perm = np.random.permutation(g.num_nodes())
raise_error = False
try:
dgl.reorder_graph(
g,
node_permute_algo="custom",
permute_config={"nodes_perm": nodes_perm},
)
except:
raise_error = True
assert not raise_error
# call with unqualified nodes_perm specified
raise_error = False
try:
dgl.reorder_graph(
g,
node_permute_algo="custom",
permute_config={"nodes_perm": nodes_perm[: g.num_nodes() - 1]},
)
except:
raise_error = True
assert raise_error
# call with unsupported strategy
raise_error = False
try:
dgl.reorder_graph(g, node_permute_algo="cmk")
except:
raise_error = True
assert raise_error
# heterograph: not supported
raise_error = False
try:
hg = dgl.heterogrpah(
{("user", "follow", "user"): ([0, 1], [1, 2])},
idtype=idtype,
device=F.ctx(),
)
dgl.reorder_graph(hg)
except:
raise_error = True
assert raise_error
# TODO: shall we fix them?
# add 'csc' format if needed
# fg = g.formats('csr')
# assert 'csc' not in sum(fg.formats().values(), [])
# rfg = dgl.reorder_graph(fg)
# assert 'csc' in sum(rfg.formats().values(), [])
@unittest.skipIf(
dgl.backend.backend_name == "tensorflow",
reason="TF doesn't support a slicing operation",
)
@parametrize_idtype
def test_norm_by_dst(idtype):
# Case1: A homogeneous graph
g = dgl.graph(([0, 1, 1], [1, 1, 2]), idtype=idtype, device=F.ctx())
eweight = dgl.norm_by_dst(g)
assert F.allclose(eweight, F.tensor([0.5, 0.5, 1.0]))
# Case2: A heterogeneous graph
g = dgl.heterograph(
{
("user", "follows", "user"): ([0, 1], [1, 2]),
("user", "plays", "game"): ([0, 1, 1], [1, 1, 2]),
},
idtype=idtype,
device=F.ctx(),
)
eweight = dgl.norm_by_dst(g, etype=("user", "plays", "game"))
assert F.allclose(eweight, F.tensor([0.5, 0.5, 1.0]))
@parametrize_idtype
def test_module_add_self_loop(idtype):
g = dgl.graph(([1, 1], [1, 2]), idtype=idtype, device=F.ctx())
g.ndata["h"] = F.randn((g.num_nodes(), 2))
g.edata["w"] = F.randn((g.num_edges(), 3))
# Case1: add self-loops with the default setting
transform = dgl.AddSelfLoop()
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.num_nodes() == g.num_nodes()
assert new_g.num_edges() == 4
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 0), (1, 1), (1, 2), (2, 2)}
assert "h" in new_g.ndata
assert "w" in new_g.edata
# Case2: remove self-loops first to avoid duplicate ones
transform = dgl.AddSelfLoop(allow_duplicate=True)
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.num_nodes() == g.num_nodes()
assert new_g.num_edges() == 5
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 0), (1, 1), (1, 2), (2, 2)}
assert "h" in new_g.ndata
assert "w" in new_g.edata
# Case3: add self-loops for a homogeneous graph (the example in doc)
transform = dgl.AddSelfLoop(fill_data="sum")
g = dgl.graph(([0, 0, 2], [2, 1, 0]), idtype=idtype, device=F.ctx())
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.num_nodes() == g.num_nodes()
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 2), (0, 1), (2, 0), (0, 0), (1, 1), (2, 2)}
# Create a heterogeneous graph
g = dgl.heterograph(
{
("user", "plays", "game"): ([0], [1]),
("user", "follows", "user"): ([1], [3]),
},
idtype=idtype,
device=F.ctx(),
)
g.nodes["user"].data["h1"] = F.randn((4, 2))
g.edges["plays"].data["w1"] = F.randn((1, 3))
g.nodes["game"].data["h2"] = F.randn((2, 4))
g.edges["follows"].data["w2"] = F.randn((1, 5))
# Case4: add self-loops for a heterogeneous graph
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.ntypes == g.ntypes
assert new_g.canonical_etypes == g.canonical_etypes
for nty in new_g.ntypes:
assert new_g.num_nodes(nty) == g.num_nodes(nty)
assert new_g.num_edges("plays") == 1
assert new_g.num_edges("follows") == 5
assert "h1" in new_g.nodes["user"].data
assert "h2" in new_g.nodes["game"].data
assert "w1" in new_g.edges["plays"].data
assert "w2" in new_g.edges["follows"].data
# Case5: add self-etypes for a heterogeneous graph
transform = dgl.AddSelfLoop(new_etypes=True)
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.ntypes == g.ntypes
assert set(new_g.canonical_etypes) == {
("user", "plays", "game"),
("user", "follows", "user"),
("user", "self", "user"),
("game", "self", "game"),
}
for nty in new_g.ntypes:
assert new_g.num_nodes(nty) == g.num_nodes(nty)
assert new_g.num_edges("plays") == 1
assert new_g.num_edges("follows") == 5
assert new_g.num_edges(("user", "self", "user")) == 4
assert new_g.num_edges(("game", "self", "game")) == 2
assert "h1" in new_g.nodes["user"].data
assert "h2" in new_g.nodes["game"].data
assert "w1" in new_g.edges["plays"].data
assert "w2" in new_g.edges["follows"].data
@parametrize_idtype
def test_module_remove_self_loop(idtype):
transform = dgl.RemoveSelfLoop()
# Case1: homogeneous graph
g = dgl.graph(([1, 1], [1, 2]), idtype=idtype, device=F.ctx())
g.ndata["h"] = F.randn((g.num_nodes(), 2))
g.edata["w"] = F.randn((g.num_edges(), 3))
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.num_nodes() == g.num_nodes()
assert new_g.num_edges() == 1
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(1, 2)}
assert "h" in new_g.ndata
assert "w" in new_g.edata
# Case2: heterogeneous graph
g = dgl.heterograph(
{
("user", "plays", "game"): ([0, 1], [1, 1]),
("user", "follows", "user"): ([1, 2], [2, 2]),
},
idtype=idtype,
device=F.ctx(),
)
g.nodes["user"].data["h1"] = F.randn((3, 2))
g.edges["plays"].data["w1"] = F.randn((2, 3))
g.nodes["game"].data["h2"] = F.randn((2, 4))
g.edges["follows"].data["w2"] = F.randn((2, 5))
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.ntypes == g.ntypes
assert new_g.canonical_etypes == g.canonical_etypes
for nty in new_g.ntypes:
assert new_g.num_nodes(nty) == g.num_nodes(nty)
assert new_g.num_edges("plays") == 2
assert new_g.num_edges("follows") == 1
assert "h1" in new_g.nodes["user"].data
assert "h2" in new_g.nodes["game"].data
assert "w1" in new_g.edges["plays"].data
assert "w2" in new_g.edges["follows"].data
@parametrize_idtype
def test_module_add_reverse(idtype):
transform = dgl.AddReverse()
# Case1: Add reverse edges for a homogeneous graph
g = dgl.graph(([0], [1]), idtype=idtype, device=F.ctx())
g.ndata["h"] = F.randn((g.num_nodes(), 3))
g.edata["w"] = F.randn((g.num_edges(), 2))
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert g.num_nodes() == new_g.num_nodes()
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (1, 0)}
assert F.allclose(g.ndata["h"], new_g.ndata["h"])
assert F.allclose(g.edata["w"], F.narrow_row(new_g.edata["w"], 0, 1))
assert F.allclose(
F.narrow_row(new_g.edata["w"], 1, 2),
F.zeros((1, 2), F.float32, F.ctx()),
)
# Case2: Add reverse edges for a homogeneous graph and copy edata
transform = dgl.AddReverse(copy_edata=True)
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert g.num_nodes() == new_g.num_nodes()
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (1, 0)}
assert F.allclose(g.ndata["h"], new_g.ndata["h"])
assert F.allclose(g.edata["w"], F.narrow_row(new_g.edata["w"], 0, 1))
assert F.allclose(g.edata["w"], F.narrow_row(new_g.edata["w"], 1, 2))
# Case3: Add reverse edges for a heterogeneous graph
g = dgl.heterograph(
{
("user", "plays", "game"): ([0, 1], [1, 1]),
("user", "follows", "user"): ([1, 2], [2, 2]),
},
device=F.ctx(),
)
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert g.ntypes == new_g.ntypes
assert set(new_g.canonical_etypes) == {
("user", "plays", "game"),
("user", "follows", "user"),
("game", "rev_plays", "user"),
}
for nty in g.ntypes:
assert g.num_nodes(nty) == new_g.num_nodes(nty)
src, dst = new_g.edges(etype="plays")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (1, 1)}
src, dst = new_g.edges(etype="follows")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(1, 2), (2, 2), (2, 1)}
src, dst = new_g.edges(etype="rev_plays")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(1, 1), (1, 0)}
# Case4: Enforce reverse edge types for symmetric canonical edge types
transform = dgl.AddReverse(sym_new_etype=True)
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert g.ntypes == new_g.ntypes
assert set(new_g.canonical_etypes) == {
("user", "plays", "game"),
("user", "follows", "user"),
("game", "rev_plays", "user"),
("user", "rev_follows", "user"),
}
for nty in g.ntypes:
assert g.num_nodes(nty) == new_g.num_nodes(nty)
src, dst = new_g.edges(etype="plays")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (1, 1)}
src, dst = new_g.edges(etype="follows")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(1, 2), (2, 2)}
src, dst = new_g.edges(etype="rev_plays")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(1, 1), (1, 0)}
src, dst = new_g.edges(etype="rev_follows")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(2, 1), (2, 2)}
@unittest.skipIf(
F._default_context_str == "gpu", reason="GPU not supported for to_simple"
)
@parametrize_idtype
def test_module_to_simple(idtype):
transform = dgl.ToSimple()
g = dgl.graph(([0, 1, 1], [1, 2, 2]), idtype=idtype, device=F.ctx())
g.ndata["h"] = F.randn((g.num_nodes(), 2))
g.edata["w"] = F.tensor([[0.1], [0.2], [0.3]])
sg = transform(g)
assert sg.device == g.device
assert sg.idtype == g.idtype
assert sg.num_nodes() == g.num_nodes()
assert sg.num_edges() == 2
src, dst = sg.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (1, 2)}
assert F.allclose(sg.edata["count"], F.tensor([1, 2]))
assert F.allclose(sg.ndata["h"], g.ndata["h"])
g = dgl.heterograph(
{
("user", "follows", "user"): ([0, 1, 1], [1, 2, 2]),
("user", "plays", "game"): ([0, 1, 0], [1, 1, 1]),
}
)
sg = transform(g)
assert sg.device == g.device
assert sg.idtype == g.idtype
assert sg.ntypes == g.ntypes
assert sg.canonical_etypes == g.canonical_etypes
for nty in sg.ntypes:
assert sg.num_nodes(nty) == g.num_nodes(nty)
for ety in sg.canonical_etypes:
assert sg.num_edges(ety) == 2
src, dst = sg.edges(etype="follows")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (1, 2)}
src, dst = sg.edges(etype="plays")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (1, 1)}
@parametrize_idtype
def test_module_line_graph(idtype):
transform = dgl.LineGraph()
g = dgl.graph(([0, 1, 1], [1, 0, 2]), idtype=idtype, device=F.ctx())
g.ndata["h"] = F.tensor([[0.0], [1.0], [2.0]])
g.edata["w"] = F.tensor([[0.0], [0.1], [0.2]])
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.num_nodes() == g.num_edges()
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (0, 2), (1, 0)}
transform = dgl.LineGraph(backtracking=False)
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.num_nodes() == g.num_edges()
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 2)}
@parametrize_idtype
def test_module_khop_graph(idtype):
transform = dgl.KHopGraph(2)
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
g.ndata["h"] = F.randn((g.num_nodes(), 2))
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.num_nodes() == g.num_nodes()
assert F.allclose(g.ndata["h"], new_g.ndata["h"])
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 2)}
@parametrize_idtype
def test_module_add_metapaths(idtype):
g = dgl.heterograph(
{
("person", "author", "paper"): ([0, 0, 1], [1, 2, 2]),
("paper", "accepted", "venue"): ([1], [0]),
("paper", "rejected", "venue"): ([2], [1]),
},
idtype=idtype,
device=F.ctx(),
)
g.nodes["venue"].data["h"] = F.randn((g.num_nodes("venue"), 2))
g.edges["author"].data["h"] = F.randn((g.num_edges("author"), 3))
# Case1: keep_orig_edges is True
metapaths = {
"accepted": [
("person", "author", "paper"),
("paper", "accepted", "venue"),
],
"rejected": [
("person", "author", "paper"),
("paper", "rejected", "venue"),
],
}
transform = dgl.AddMetaPaths(metapaths)
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.ntypes == g.ntypes
assert set(new_g.canonical_etypes) == {
("person", "author", "paper"),
("paper", "accepted", "venue"),
("paper", "rejected", "venue"),
("person", "accepted", "venue"),
("person", "rejected", "venue"),
}
for nty in new_g.ntypes:
assert new_g.num_nodes(nty) == g.num_nodes(nty)
for ety in g.canonical_etypes:
assert new_g.num_edges(ety) == g.num_edges(ety)
assert F.allclose(
g.nodes["venue"].data["h"], new_g.nodes["venue"].data["h"]
)
assert F.allclose(
g.edges["author"].data["h"], new_g.edges["author"].data["h"]
)
src, dst = new_g.edges(etype=("person", "accepted", "venue"))
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 0)}
src, dst = new_g.edges(etype=("person", "rejected", "venue"))
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (1, 1)}
# Case2: keep_orig_edges is False
transform = dgl.AddMetaPaths(metapaths, keep_orig_edges=False)
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.ntypes == g.ntypes
assert len(new_g.canonical_etypes) == 2
for nty in new_g.ntypes:
assert new_g.num_nodes(nty) == g.num_nodes(nty)
assert F.allclose(
g.nodes["venue"].data["h"], new_g.nodes["venue"].data["h"]
)
src, dst = new_g.edges(etype=("person", "accepted", "venue"))
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 0)}
src, dst = new_g.edges(etype=("person", "rejected", "venue"))
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (1, 1)}
@parametrize_idtype
def test_module_compose(idtype):
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
transform = dgl.Compose([dgl.AddReverse(), dgl.AddSelfLoop()])
new_g = transform(g)
assert new_g.device == g.device
assert new_g.idtype == g.idtype
assert new_g.num_edges() == 7
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (1, 2), (1, 0), (2, 1), (0, 0), (1, 1), (2, 2)}
@parametrize_idtype
def test_module_gcnnorm(idtype):
g = dgl.heterograph(
{
("A", "r1", "A"): ([0, 1, 2], [0, 0, 1]),
("A", "r2", "B"): ([0, 0], [1, 1]),
("B", "r3", "B"): ([0, 1, 2], [0, 0, 1]),
},
idtype=idtype,
device=F.ctx(),
)
g.edges["r3"].data["w"] = F.tensor([0.1, 0.2, 0.3])
transform = dgl.GCNNorm()
new_g = transform(g)
assert "w" not in new_g.edges[("A", "r2", "B")].data
assert F.allclose(
new_g.edges[("A", "r1", "A")].data["w"],
F.tensor([1.0 / 2, 1.0 / math.sqrt(2), 0.0]),
)
assert F.allclose(
new_g.edges[("B", "r3", "B")].data["w"],
F.tensor([1.0 / 3, 2.0 / 3, 0.0]),
)
@unittest.skipIf(
dgl.backend.backend_name != "pytorch", reason="Only support PyTorch for now"
)
@parametrize_idtype
def test_module_ppr(idtype):
g = dgl.graph(
([0, 1, 2, 3, 4], [2, 3, 4, 5, 3]), idtype=idtype, device=F.ctx()
)
g.ndata["h"] = F.randn((6, 2))
transform = dgl.PPR(avg_degree=2)
new_g = transform(g)
assert new_g.idtype == g.idtype
assert new_g.device == g.device
assert new_g.num_nodes() == g.num_nodes()
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {
(0, 0),
(0, 2),
(0, 4),
(1, 1),
(1, 3),
(1, 5),
(2, 2),
(2, 3),
(2, 4),
(3, 3),
(3, 5),
(4, 3),
(4, 4),
(4, 5),
(5, 5),
}
assert F.allclose(g.ndata["h"], new_g.ndata["h"])
assert "w" in new_g.edata
# Prior edge weights
g.edata["w"] = F.tensor([0.1, 0.2, 0.3, 0.4, 0.5])
new_g = transform(g)
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {
(0, 0),
(1, 1),
(1, 3),
(2, 2),
(2, 3),
(2, 4),
(3, 3),
(3, 5),
(4, 3),
(4, 4),
(4, 5),
(5, 5),
}
@unittest.skipIf(
dgl.backend.backend_name != "pytorch", reason="Only support PyTorch for now"
)
@parametrize_idtype
def test_module_heat_kernel(idtype):
# Case1: directed graph
g = dgl.graph(
([0, 1, 2, 3, 4], [2, 3, 4, 5, 3]), idtype=idtype, device=F.ctx()
)
g.ndata["h"] = F.randn((6, 2))
transform = dgl.HeatKernel(avg_degree=1)
new_g = transform(g)
assert new_g.idtype == g.idtype
assert new_g.device == g.device
assert new_g.num_nodes() == g.num_nodes()
assert F.allclose(g.ndata["h"], new_g.ndata["h"])
assert "w" in new_g.edata
# Case2: weighted undirected graph
g = dgl.graph(([0, 1, 2, 3], [1, 0, 3, 2]), idtype=idtype, device=F.ctx())
g.edata["w"] = F.tensor([0.1, 0.2, 0.3, 0.4])
new_g = transform(g)
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 0), (1, 1), (2, 2), (3, 3)}
@unittest.skipIf(
dgl.backend.backend_name != "pytorch", reason="Only support PyTorch for now"
)
@parametrize_idtype
def test_module_gdc(idtype):
transform = dgl.GDC([0.1, 0.2, 0.1], avg_degree=1)
g = dgl.graph(
([0, 1, 2, 3, 4], [2, 3, 4, 5, 3]), idtype=idtype, device=F.ctx()
)
g.ndata["h"] = F.randn((6, 2))
new_g = transform(g)
assert new_g.idtype == g.idtype
assert new_g.device == g.device
assert new_g.num_nodes() == g.num_nodes()
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {
(0, 0),
(0, 2),
(0, 4),
(1, 1),
(1, 3),
(1, 5),
(2, 2),
(2, 3),
(2, 4),
(3, 3),
(3, 5),
(4, 3),
(4, 4),
(4, 5),
(5, 5),
}
assert F.allclose(g.ndata["h"], new_g.ndata["h"])
assert "w" in new_g.edata
# Prior edge weights
g.edata["w"] = F.tensor([0.1, 0.2, 0.3, 0.4, 0.5])
new_g = transform(g)
src, dst = new_g.edges()
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 0), (1, 1), (2, 2), (3, 3), (4, 3), (4, 4), (5, 5)}
@unittest.skipIf(
dgl.backend.backend_name == "tensorflow",
reason="TF doesn't support a slicing operation",
)
@parametrize_idtype
def test_module_node_shuffle(idtype):
transform = dgl.NodeShuffle()
g = dgl.heterograph(
{("A", "r", "B"): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()
)
g.nodes["B"].data["h"] = F.randn((g.num_nodes("B"), 2))
old_nfeat = g.nodes["B"].data["h"]
new_g = transform(g)
new_nfeat = g.nodes["B"].data["h"]
assert F.allclose(old_nfeat, new_nfeat)
@unittest.skipIf(
dgl.backend.backend_name != "pytorch", reason="Only support PyTorch for now"
)
@parametrize_idtype
def test_module_drop_node(idtype):
transform = dgl.DropNode()
g = dgl.heterograph(
{("A", "r", "B"): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()
)
num_nodes_old = g.num_nodes()
new_g = transform(g)
assert new_g.idtype == g.idtype
assert new_g.device == g.device
assert new_g.ntypes == g.ntypes
assert new_g.canonical_etypes == g.canonical_etypes
num_nodes_new = g.num_nodes()
# Ensure that the original graph is not corrupted
assert num_nodes_old == num_nodes_new
@unittest.skipIf(
dgl.backend.backend_name != "pytorch", reason="Only support PyTorch for now"
)
@parametrize_idtype
def test_module_drop_edge(idtype):
transform = dgl.DropEdge()
g = dgl.heterograph(
{
("A", "r1", "B"): ([0, 1], [1, 2]),
("C", "r2", "C"): ([3, 4, 5], [6, 7, 8]),
},
idtype=idtype,
device=F.ctx(),
)
num_edges_old = g.num_edges()
new_g = transform(g)
assert new_g.idtype == g.idtype
assert new_g.device == g.device
assert new_g.ntypes == g.ntypes
assert new_g.canonical_etypes == g.canonical_etypes
num_edges_new = g.num_edges()
# Ensure that the original graph is not corrupted
assert num_edges_old == num_edges_new
@parametrize_idtype
def test_module_add_edge(idtype):
transform = dgl.AddEdge()
g = dgl.heterograph(
{
("A", "r1", "B"): ([0, 1, 2, 3, 4], [1, 2, 3, 4, 5]),
("C", "r2", "C"): ([0, 1, 2, 3, 4], [1, 2, 3, 4, 5]),
},
idtype=idtype,
device=F.ctx(),
)
num_edges_old = g.num_edges()
new_g = transform(g)
assert new_g.num_edges(("A", "r1", "B")) == 6
assert new_g.num_edges(("C", "r2", "C")) == 6
assert new_g.idtype == g.idtype
assert new_g.device == g.device
assert new_g.ntypes == g.ntypes
assert new_g.canonical_etypes == g.canonical_etypes
num_edges_new = g.num_edges()
# Ensure that the original graph is not corrupted
assert num_edges_old == num_edges_new
@parametrize_idtype
def test_module_random_walk_pe(idtype):
transform = dgl.RandomWalkPE(2, "rwpe")
g = dgl.graph(([0, 1, 1], [1, 1, 0]), idtype=idtype, device=F.ctx())
new_g = transform(g)
tgt = F.copy_to(F.tensor([[0.0, 0.5], [0.5, 0.75]]), g.device)
assert F.allclose(new_g.ndata["rwpe"], tgt)
@parametrize_idtype
def test_module_lap_pe(idtype):
g = dgl.graph(
([2, 1, 0, 3, 1, 1], [3, 1, 1, 2, 1, 0]), idtype=idtype, device=F.ctx()
)
tgt_eigval = F.copy_to(
F.repeat(
F.tensor([[1.1534e-17, 1.3333e00, 2.0, np.nan, np.nan]]),
g.num_nodes(),
dim=0,
),
g.device,
)
tgt_pe = F.copy_to(
F.tensor(
[
[0.5, 0.86602539, 0.0, 0.0, 0.0],
[0.86602539, 0.5, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.70710677, 0.0, 0.0],
[0.0, 0.0, 0.70710677, 0.0, 0.0],
]
),
g.device,
)
# without padding (k<n)
transform = dgl.LapPE(2, feat_name="lappe")
new_g = transform(g)
# tensorflow has no abs() api
if dgl.backend.backend_name == "tensorflow":
assert F.allclose(new_g.ndata["lappe"].__abs__(), tgt_pe[:, :2])
# pytorch & mxnet
else:
assert F.allclose(new_g.ndata["lappe"].abs(), tgt_pe[:, :2])
# with padding (k>=n)
transform = dgl.LapPE(5, feat_name="lappe", padding=True)
new_g = transform(g)
# tensorflow has no abs() api
if dgl.backend.backend_name == "tensorflow":
assert F.allclose(new_g.ndata["lappe"].__abs__(), tgt_pe)
# pytorch & mxnet
else:
assert F.allclose(new_g.ndata["lappe"].abs(), tgt_pe)
# with eigenvalues
transform = dgl.LapPE(
5, feat_name="lappe", eigval_name="eigval", padding=True
)
new_g = transform(g)
# tensorflow has no abs() api
if dgl.backend.backend_name == "tensorflow":
assert F.allclose(new_g.ndata["eigval"][:, :3], tgt_eigval[:, :3])
assert F.allclose(new_g.ndata["lappe"].__abs__(), tgt_pe)
# pytorch & mxnet
else:
assert F.allclose(new_g.ndata["eigval"][:, :3], tgt_eigval[:, :3])
assert F.allclose(new_g.ndata["lappe"].abs(), tgt_pe)
@unittest.skipIf(
dgl.backend.backend_name != "pytorch", reason="Only support PyTorch for now"
)
@pytest.mark.parametrize("g", get_cases(["has_scalar_e_feature"]))
def test_module_sign(g):
import torch
atol = 1e-06
ctx = F.ctx()
g = g.to(ctx)
adj = g.adj_external(transpose=True, scipy_fmt="coo").todense()
adj = torch.tensor(adj).float().to(ctx)
weight_adj = (
g.adj_external(transpose=True, scipy_fmt="coo").astype(float).todense()
)
weight_adj = torch.tensor(weight_adj).float().to(ctx)
src, dst = g.edges()
src, dst = src.long(), dst.long()
weight_adj[dst, src] = g.edata["scalar_w"]
# raw
transform = dgl.SIGNDiffusion(k=1, in_feat_name="h", diffuse_op="raw")
g = transform(g)
target = torch.matmul(adj, g.ndata["h"])
assert torch.allclose(g.ndata["out_feat_1"], target, atol=atol)
transform = dgl.SIGNDiffusion(
k=1, in_feat_name="h", eweight_name="scalar_w", diffuse_op="raw"
)
g = transform(g)
target = torch.matmul(weight_adj, g.ndata["h"])
assert torch.allclose(g.ndata["out_feat_1"], target, atol=atol)
# rw
adj_rw = torch.matmul(torch.diag(1 / adj.sum(dim=1)), adj)
transform = dgl.SIGNDiffusion(k=1, in_feat_name="h", diffuse_op="rw")
g = transform(g)
target = torch.matmul(adj_rw, g.ndata["h"])
assert torch.allclose(g.ndata["out_feat_1"], target, atol=atol)
weight_adj_rw = torch.matmul(
torch.diag(1 / weight_adj.sum(dim=1)), weight_adj
)
transform = dgl.SIGNDiffusion(
k=1, in_feat_name="h", eweight_name="scalar_w", diffuse_op="rw"
)
g = transform(g)
target = torch.matmul(weight_adj_rw, g.ndata["h"])
assert torch.allclose(g.ndata["out_feat_1"], target, atol=atol)
# gcn
raw_eweight = g.edata["scalar_w"]
gcn_norm = dgl.GCNNorm()
g = gcn_norm(g)
adj_gcn = adj.clone()
adj_gcn[dst, src] = g.edata.pop("w")
transform = dgl.SIGNDiffusion(k=1, in_feat_name="h", diffuse_op="gcn")
g = transform(g)
target = torch.matmul(adj_gcn, g.ndata["h"])
assert torch.allclose(g.ndata["out_feat_1"], target, atol=atol)
gcn_norm = dgl.GCNNorm("scalar_w")
g = gcn_norm(g)
weight_adj_gcn = weight_adj.clone()
weight_adj_gcn[dst, src] = g.edata["scalar_w"]
g.edata["scalar_w"] = raw_eweight
transform = dgl.SIGNDiffusion(
k=1, in_feat_name="h", eweight_name="scalar_w", diffuse_op="gcn"
)
g = transform(g)
target = torch.matmul(weight_adj_gcn, g.ndata["h"])
assert torch.allclose(g.ndata["out_feat_1"], target, atol=atol)
# ppr
alpha = 0.2
transform = dgl.SIGNDiffusion(
k=1, in_feat_name="h", diffuse_op="ppr", alpha=alpha
)
g = transform(g)
target = (1 - alpha) * torch.matmul(
adj_gcn, g.ndata["h"]
) + alpha * g.ndata["h"]
assert torch.allclose(g.ndata["out_feat_1"], target, atol=atol)
transform = dgl.SIGNDiffusion(
k=1,
in_feat_name="h",
eweight_name="scalar_w",
diffuse_op="ppr",
alpha=alpha,
)
g = transform(g)
target = (1 - alpha) * torch.matmul(
weight_adj_gcn, g.ndata["h"]
) + alpha * g.ndata["h"]
assert torch.allclose(g.ndata["out_feat_1"], target, atol=atol)
@unittest.skipIf(
dgl.backend.backend_name != "pytorch", reason="Only support PyTorch for now"
)
@parametrize_idtype
def test_module_row_feat_normalizer(idtype):
# Case1: Normalize features of a homogeneous graph.
transform = dgl.RowFeatNormalizer(
subtract_min=True, node_feat_names=["h"], edge_feat_names=["w"]
)
g = dgl.rand_graph(5, 5, idtype=idtype, device=F.ctx())
g.ndata["h"] = F.randn((g.num_nodes(), 128))
g.edata["w"] = F.randn((g.num_edges(), 128))
g = transform(g)
assert g.ndata["h"].shape == (g.num_nodes(), 128)
assert g.edata["w"].shape == (g.num_edges(), 128)
assert F.allclose(g.ndata["h"].sum(1), F.tensor([1.0, 1.0, 1.0, 1.0, 1.0]))
assert F.allclose(g.edata["w"].sum(1), F.tensor([1.0, 1.0, 1.0, 1.0, 1.0]))
# Case2: Normalize features of a heterogeneous graph.
transform = dgl.RowFeatNormalizer(
subtract_min=True, node_feat_names=["h", "h2"], edge_feat_names=["w"]
)
g = dgl.heterograph(
{
("user", "follows", "user"): (F.tensor([1, 2]), F.tensor([3, 4])),
("player", "plays", "game"): (F.tensor([2, 2]), F.tensor([1, 1])),
},
idtype=idtype,
device=F.ctx(),
)
g.ndata["h"] = {"game": F.randn((2, 128)), "player": F.randn((3, 128))}
g.ndata["h2"] = {"user": F.randn((5, 128))}
g.edata["w"] = {
("user", "follows", "user"): F.randn((2, 128)),
("player", "plays", "game"): F.randn((2, 128)),
}
g = transform(g)
assert g.ndata["h"]["game"].shape == (2, 128)
assert g.ndata["h"]["player"].shape == (3, 128)
assert g.ndata["h2"]["user"].shape == (5, 128)
assert g.edata["w"][("user", "follows", "user")].shape == (2, 128)
assert g.edata["w"][("player", "plays", "game")].shape == (2, 128)
assert F.allclose(g.ndata["h"]["game"].sum(1), F.tensor([1.0, 1.0]))
assert F.allclose(g.ndata["h"]["player"].sum(1), F.tensor([1.0, 1.0, 1.0]))
assert F.allclose(
g.ndata["h2"]["user"].sum(1), F.tensor([1.0, 1.0, 1.0, 1.0, 1.0])
)
assert F.allclose(
g.edata["w"][("user", "follows", "user")].sum(1), F.tensor([1.0, 1.0])
)
assert F.allclose(
g.edata["w"][("player", "plays", "game")].sum(1), F.tensor([1.0, 1.0])
)
@unittest.skipIf(
dgl.backend.backend_name != "pytorch", reason="Only support PyTorch for now"
)
@parametrize_idtype
def test_module_feat_mask(idtype):
# Case1: Mask node and edge feature tensors of a homogeneous graph.
transform = dgl.FeatMask(node_feat_names=["h"], edge_feat_names=["w"])
g = dgl.rand_graph(5, 20, idtype=idtype, device=F.ctx())
g.ndata["h"] = F.ones((g.num_nodes(), 10))
g.edata["w"] = F.ones((g.num_edges(), 20))
g = transform(g)
assert g.device == g.device
assert g.idtype == g.idtype
assert g.ndata["h"].shape == (g.num_nodes(), 10)
assert g.edata["w"].shape == (g.num_edges(), 20)
# Case2: Mask node and edge feature tensors of a heterogeneous graph.
g = dgl.heterograph(
{
("user", "follows", "user"): (F.tensor([1, 2]), F.tensor([3, 4])),
("player", "plays", "game"): (F.tensor([2, 2]), F.tensor([1, 1])),
},
idtype=idtype,
device=F.ctx(),
)
g.ndata["h"] = {"game": F.randn((2, 5)), "player": F.randn((3, 5))}
g.edata["w"] = {
("user", "follows", "user"): F.randn((2, 5)),
("player", "plays", "game"): F.randn((2, 5)),
}
g = transform(g)
assert g.device == g.device
assert g.idtype == g.idtype
assert g.ndata["h"]["game"].shape == (2, 5)
assert g.ndata["h"]["player"].shape == (3, 5)
assert g.edata["w"][("user", "follows", "user")].shape == (2, 5)
assert g.edata["w"][("player", "plays", "game")].shape == (2, 5)
@parametrize_idtype
def test_shortest_dist(idtype):
g = dgl.graph(([0, 1, 1, 2], [2, 0, 3, 3]), idtype=idtype, device=F.ctx())
# case 1: directed single source
dist = dgl.shortest_dist(g, root=0)
tgt = F.copy_to(F.tensor([0, -1, 1, 2], dtype=F.int64), g.device)
assert F.array_equal(dist, tgt)
# case 2: undirected all pairs
dist, paths = dgl.shortest_dist(g, root=None, return_paths=True)
tgt_dist = F.copy_to(
F.tensor(
[[0, -1, 1, 2], [1, 0, 2, 1], [-1, -1, 0, 1], [-1, -1, -1, 0]],
dtype=F.int64,
),
g.device,
)
tgt_paths = F.copy_to(
F.tensor(
[
[[-1, -1], [-1, -1], [0, -1], [0, 3]],
[[1, -1], [-1, -1], [1, 0], [2, -1]],
[[-1, -1], [-1, -1], [-1, -1], [3, -1]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1]],
],
dtype=F.int64,
),
g.device,
)
assert F.array_equal(dist, tgt_dist)
assert F.array_equal(paths, tgt_paths)
@parametrize_idtype
def test_module_to_levi(idtype):
transform = dgl.ToLevi()
g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 0]), idtype=idtype, device=F.ctx())
g.ndata["h"] = F.randn((g.num_nodes(), 2))
g.edata["w"] = F.randn((g.num_edges(), 2))
lg = transform(g)
assert lg.device == g.device
assert lg.idtype == g.idtype
assert lg.ntypes == ["edge", "node"]
assert lg.canonical_etypes == [
("edge", "e2n", "node"),
("node", "n2e", "edge"),
]
assert lg.num_nodes("node") == g.num_nodes()
assert lg.num_nodes("edge") == g.num_edges()
assert lg.num_edges("n2e") == g.num_edges()
assert lg.num_edges("e2n") == g.num_edges()
src, dst = lg.edges(etype="n2e")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 0), (1, 1), (2, 2), (3, 3)}
src, dst = lg.edges(etype="e2n")
eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
assert eset == {(0, 1), (1, 2), (2, 3), (3, 0)}
assert F.allclose(lg.nodes["node"].data["h"], g.ndata["h"])
assert F.allclose(lg.nodes["edge"].data["w"], g.edata["w"])
@parametrize_idtype
def test_module_svd_pe(idtype):
g = dgl.graph(
(
[0, 0, 1, 1, 2, 2, 2, 2, 3, 3, 4, 4],
[2, 3, 0, 2, 0, 2, 3, 4, 3, 4, 0, 1],
),
idtype=idtype,
device=F.ctx(),
)
# without padding
tgt_pe = F.copy_to(
F.tensor(
[
[0.6669, 0.3068, 0.7979, 0.8477],
[0.6311, 0.6101, 0.1248, 0.5137],
[1.1993, 0.0665, 0.9183, 0.1455],
[0.5682, 0.6766, 0.8952, 0.6449],
[0.3393, 0.8363, 0.6500, 0.4564],
]
),
g.device,
)
transform_1 = dgl.SVDPE(k=2, feat_name="svd_pe")
g1 = transform_1(g)
if dgl.backend.backend_name == "tensorflow":
assert F.allclose(g1.ndata["svd_pe"].__abs__(), tgt_pe)
else:
assert F.allclose(g1.ndata["svd_pe"].abs(), tgt_pe)
# with padding
transform_2 = dgl.SVDPE(k=6, feat_name="svd_pe", padding=True)
g2 = transform_2(g)
assert F.shape(g2.ndata["svd_pe"]) == (5, 12)
if __name__ == "__main__":
test_partition_with_halo()
test_module_heat_kernel(F.int32)
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