chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,538 @@
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import math
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import numbers
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import backend as F
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import dgl
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import networkx as nx
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import numpy as np
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import pytest
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import scipy.sparse as sp
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from dgl import DGLError
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# graph generation: a random graph with 10 nodes
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# and 20 edges.
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# - has self loop
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# - no multi edge
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def edge_pair_input(sort=False):
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if sort:
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src = [0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 4, 4, 5, 5, 6, 7, 7, 7, 9]
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dst = [4, 6, 9, 3, 5, 3, 7, 5, 8, 1, 3, 4, 9, 1, 9, 6, 2, 8, 9, 2]
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return src, dst
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else:
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src = [0, 0, 4, 5, 0, 4, 7, 4, 4, 3, 2, 7, 7, 5, 3, 2, 1, 9, 6, 1]
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dst = [9, 6, 3, 9, 4, 4, 9, 9, 1, 8, 3, 2, 8, 1, 5, 7, 3, 2, 6, 5]
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return src, dst
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def nx_input():
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g = nx.DiGraph()
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src, dst = edge_pair_input()
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for i, e in enumerate(zip(src, dst)):
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g.add_edge(*e, id=i)
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return g
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def elist_input():
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src, dst = edge_pair_input()
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return list(zip(src, dst))
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def scipy_coo_input():
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src, dst = edge_pair_input()
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return sp.coo_matrix((np.ones((20,)), (src, dst)), shape=(10, 10))
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def scipy_csr_input():
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src, dst = edge_pair_input()
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csr = sp.coo_matrix((np.ones((20,)), (src, dst)), shape=(10, 10)).tocsr()
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csr.sort_indices()
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# src = [0 0 0 1 1 2 2 3 3 4 4 4 4 5 5 6 7 7 7 9]
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# dst = [4 6 9 3 5 3 7 5 8 1 3 4 9 1 9 6 2 8 9 2]
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return csr
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def gen_by_mutation():
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g = dgl.graph([])
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src, dst = edge_pair_input()
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g.add_nodes(10)
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g.add_edges(src, dst)
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return g
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def test_query():
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def _test_one(g):
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assert g.num_nodes() == 10
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assert g.num_edges() == 20
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for i in range(10):
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assert g.has_nodes(i)
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assert not g.has_nodes(11)
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assert F.allclose(g.has_nodes([0, 2, 10, 11]), F.tensor([1, 1, 0, 0]))
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src, dst = edge_pair_input()
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for u, v in zip(src, dst):
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assert g.has_edges_between(u, v)
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assert not g.has_edges_between(0, 0)
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assert F.allclose(
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g.has_edges_between([0, 0, 3], [0, 9, 8]), F.tensor([0, 1, 1])
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)
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assert set(F.asnumpy(g.predecessors(9))) == set([0, 5, 7, 4])
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assert set(F.asnumpy(g.successors(2))) == set([7, 3])
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assert g.edge_ids(4, 4) == 5
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assert F.allclose(g.edge_ids([4, 0], [4, 9]), F.tensor([5, 0]))
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src, dst = g.find_edges([3, 6, 5])
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assert F.allclose(src, F.tensor([5, 7, 4]))
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assert F.allclose(dst, F.tensor([9, 9, 4]))
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src, dst, eid = g.in_edges(9, form="all")
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set([(0, 9, 0), (5, 9, 3), (7, 9, 6), (4, 9, 7)])
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src, dst, eid = g.in_edges(
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[9, 0, 8], form="all"
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) # test node#0 has no in edges
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set(
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[(0, 9, 0), (5, 9, 3), (7, 9, 6), (4, 9, 7), (3, 8, 9), (7, 8, 12)]
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)
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src, dst, eid = g.out_edges(0, form="all")
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set([(0, 9, 0), (0, 6, 1), (0, 4, 4)])
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src, dst, eid = g.out_edges(
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[0, 4, 8], form="all"
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) # test node#8 has no out edges
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set(
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[
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(0, 9, 0),
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(0, 6, 1),
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(0, 4, 4),
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(4, 3, 2),
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(4, 4, 5),
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(4, 9, 7),
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(4, 1, 8),
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]
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)
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src, dst, eid = g.edges("all", "eid")
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t_src, t_dst = edge_pair_input()
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t_tup = list(zip(t_src, t_dst, list(range(20))))
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set(t_tup)
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assert list(F.asnumpy(eid)) == list(range(20))
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src, dst, eid = g.edges("all", "srcdst")
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t_src, t_dst = edge_pair_input()
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t_tup = list(zip(t_src, t_dst, list(range(20))))
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set(t_tup)
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assert list(F.asnumpy(src)) == sorted(list(F.asnumpy(src)))
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assert g.in_degrees(0) == 0
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assert g.in_degrees(9) == 4
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assert F.allclose(g.in_degrees([0, 9]), F.tensor([0, 4]))
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assert g.out_degrees(8) == 0
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assert g.out_degrees(9) == 1
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assert F.allclose(g.out_degrees([8, 9]), F.tensor([0, 1]))
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assert np.array_equal(
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F.sparse_to_numpy(g.adj_external(transpose=True)),
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scipy_coo_input().toarray().T,
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)
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assert np.array_equal(
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F.sparse_to_numpy(g.adj_external(transpose=False)),
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scipy_coo_input().toarray(),
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)
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def _test(g):
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# test twice to see whether the cached format works or not
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_test_one(g)
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_test_one(g)
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def _test_csr_one(g):
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assert g.num_nodes() == 10
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assert g.num_edges() == 20
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for i in range(10):
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assert g.has_nodes(i)
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assert not g.has_nodes(11)
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assert F.allclose(g.has_nodes([0, 2, 10, 11]), F.tensor([1, 1, 0, 0]))
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src, dst = edge_pair_input(sort=True)
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for u, v in zip(src, dst):
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assert g.has_edges_between(u, v)
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assert not g.has_edges_between(0, 0)
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assert F.allclose(
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g.has_edges_between([0, 0, 3], [0, 9, 8]), F.tensor([0, 1, 1])
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)
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assert set(F.asnumpy(g.predecessors(9))) == set([0, 5, 7, 4])
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assert set(F.asnumpy(g.successors(2))) == set([7, 3])
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# src = [0 0 0 1 1 2 2 3 3 4 4 4 4 5 5 6 7 7 7 9]
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# dst = [4 6 9 3 5 3 7 5 8 1 3 4 9 1 9 6 2 8 9 2]
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# eid = [0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9]
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assert g.edge_ids(4, 4) == 11
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assert F.allclose(g.edge_ids([4, 0], [4, 9]), F.tensor([11, 2]))
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src, dst = g.find_edges([3, 6, 5])
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assert F.allclose(src, F.tensor([1, 2, 2]))
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assert F.allclose(dst, F.tensor([3, 7, 3]))
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src, dst, eid = g.in_edges(9, form="all")
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set([(0, 9, 2), (5, 9, 14), (7, 9, 18), (4, 9, 12)])
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src, dst, eid = g.in_edges(
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[9, 0, 8], form="all"
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) # test node#0 has no in edges
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set(
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[
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(0, 9, 2),
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(5, 9, 14),
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(7, 9, 18),
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(4, 9, 12),
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(3, 8, 8),
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(7, 8, 17),
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]
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)
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src, dst, eid = g.out_edges(0, form="all")
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set([(0, 9, 2), (0, 6, 1), (0, 4, 0)])
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src, dst, eid = g.out_edges(
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[0, 4, 8], form="all"
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) # test node#8 has no out edges
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set(
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[
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(0, 9, 2),
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(0, 6, 1),
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(0, 4, 0),
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(4, 3, 10),
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(4, 4, 11),
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(4, 9, 12),
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(4, 1, 9),
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]
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)
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src, dst, eid = g.edges("all", "eid")
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t_src, t_dst = edge_pair_input(sort=True)
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t_tup = list(zip(t_src, t_dst, list(range(20))))
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set(t_tup)
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assert list(F.asnumpy(eid)) == list(range(20))
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src, dst, eid = g.edges("all", "srcdst")
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t_src, t_dst = edge_pair_input(sort=True)
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t_tup = list(zip(t_src, t_dst, list(range(20))))
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tup = list(zip(F.asnumpy(src), F.asnumpy(dst), F.asnumpy(eid)))
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assert set(tup) == set(t_tup)
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assert list(F.asnumpy(src)) == sorted(list(F.asnumpy(src)))
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assert g.in_degrees(0) == 0
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assert g.in_degrees(9) == 4
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assert F.allclose(g.in_degrees([0, 9]), F.tensor([0, 4]))
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assert g.out_degrees(8) == 0
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assert g.out_degrees(9) == 1
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assert F.allclose(g.out_degrees([8, 9]), F.tensor([0, 1]))
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assert np.array_equal(
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F.sparse_to_numpy(g.adj_external(transpose=True)),
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scipy_coo_input().toarray().T,
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)
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assert np.array_equal(
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F.sparse_to_numpy(g.adj_external(transpose=False)),
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scipy_coo_input().toarray(),
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)
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def _test_csr(g):
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# test twice to see whether the cached format works or not
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_test_csr_one(g)
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_test_csr_one(g)
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def _test_edge_ids():
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g = gen_by_mutation()
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eids = g.edge_ids([4, 0], [4, 9])
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assert eids.shape[0] == 2
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eid = g.edge_ids(4, 4)
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assert isinstance(eid, numbers.Number)
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with pytest.raises(DGLError):
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eids = g.edge_ids([9, 0], [4, 9])
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with pytest.raises(DGLError):
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eid = g.edge_ids(4, 5)
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g.add_edges(0, 4)
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eids = g.edge_ids([0, 0], [4, 9])
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eid = g.edge_ids(0, 4)
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_test(gen_by_mutation())
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_test(dgl.graph(elist_input()))
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_test(dgl.from_scipy(scipy_coo_input()))
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_test_csr(dgl.from_scipy(scipy_csr_input()))
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_test_edge_ids()
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def test_mutation():
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g = dgl.graph([])
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g = g.to(F.ctx())
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# test add nodes with data
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g.add_nodes(5)
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g.add_nodes(5, {"h": F.ones((5, 2))})
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ans = F.cat([F.zeros((5, 2)), F.ones((5, 2))], 0)
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assert F.allclose(ans, g.ndata["h"])
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g.ndata["w"] = 2 * F.ones((10, 2))
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assert F.allclose(2 * F.ones((10, 2)), g.ndata["w"])
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# test add edges with data
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g.add_edges([2, 3], [3, 4])
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g.add_edges([0, 1], [1, 2], {"m": F.ones((2, 2))})
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ans = F.cat([F.zeros((2, 2)), F.ones((2, 2))], 0)
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assert F.allclose(ans, g.edata["m"])
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def test_scipy_adjmat():
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g = dgl.graph([])
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g.add_nodes(10)
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g.add_edges(range(9), range(1, 10))
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adj_0 = g.adj_external(scipy_fmt="csr")
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adj_1 = g.adj_external(scipy_fmt="coo")
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assert np.array_equal(adj_0.toarray(), adj_1.toarray())
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adj_t0 = g.adj_external(transpose=False, scipy_fmt="csr")
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adj_t_1 = g.adj_external(transpose=False, scipy_fmt="coo")
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assert np.array_equal(adj_0.toarray(), adj_1.toarray())
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def test_incmat():
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g = dgl.graph([])
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g.add_nodes(4)
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g.add_edges(0, 1) # 0
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g.add_edges(0, 2) # 1
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g.add_edges(0, 3) # 2
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g.add_edges(2, 3) # 3
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g.add_edges(1, 1) # 4
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inc_in = F.sparse_to_numpy(g.incidence_matrix("in"))
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inc_out = F.sparse_to_numpy(g.incidence_matrix("out"))
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inc_both = F.sparse_to_numpy(g.incidence_matrix("both"))
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print(inc_in)
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print(inc_out)
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print(inc_both)
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assert np.allclose(
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inc_in,
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np.array(
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[
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[0.0, 0.0, 0.0, 0.0, 0.0],
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[1.0, 0.0, 0.0, 0.0, 1.0],
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[0.0, 1.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 1.0, 1.0, 0.0],
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]
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),
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)
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assert np.allclose(
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inc_out,
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np.array(
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[
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[1.0, 1.0, 1.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 1.0],
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[0.0, 0.0, 0.0, 1.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 0.0],
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]
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),
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)
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assert np.allclose(
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inc_both,
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np.array(
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[
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[-1.0, -1.0, -1.0, 0.0, 0.0],
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[1.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 1.0, 0.0, -1.0, 0.0],
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[0.0, 0.0, 1.0, 1.0, 0.0],
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]
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),
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)
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def test_find_edges():
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g = dgl.graph([])
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g.add_nodes(10)
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g.add_edges(range(9), range(1, 10))
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e = g.find_edges([1, 3, 2, 4])
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assert (
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F.asnumpy(e[0][0]) == 1
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and F.asnumpy(e[0][1]) == 3
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and F.asnumpy(e[0][2]) == 2
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and F.asnumpy(e[0][3]) == 4
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)
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assert (
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F.asnumpy(e[1][0]) == 2
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and F.asnumpy(e[1][1]) == 4
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and F.asnumpy(e[1][2]) == 3
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and F.asnumpy(e[1][3]) == 5
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)
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try:
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g.find_edges([10])
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fail = False
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except DGLError:
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fail = True
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finally:
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assert fail
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def test_ismultigraph():
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g = dgl.graph([])
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g.add_nodes(10)
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assert g.is_multigraph == False
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g.add_edges([0], [0])
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assert g.is_multigraph == False
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g.add_edges([1], [2])
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assert g.is_multigraph == False
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g.add_edges([0, 2], [0, 3])
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assert g.is_multigraph == True
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def test_hypersparse_query():
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g = dgl.graph([])
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g = g.to(F.ctx())
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g.add_nodes(1000001)
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g.add_edges([0], [1])
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for i in range(10):
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assert g.has_nodes(i)
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assert not g.has_nodes(1000002)
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assert g.edge_ids(0, 1) == 0
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src, dst = g.find_edges([0])
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src, dst, eid = g.in_edges(1, form="all")
|
||||
src, dst, eid = g.out_edges(0, form="all")
|
||||
src, dst = g.edges()
|
||||
assert g.in_degrees(0) == 0
|
||||
assert g.in_degrees(1) == 1
|
||||
assert g.out_degrees(0) == 1
|
||||
assert g.out_degrees(1) == 0
|
||||
|
||||
|
||||
def test_empty_data_initialized():
|
||||
g = dgl.graph([])
|
||||
g = g.to(F.ctx())
|
||||
g.ndata["ha"] = F.tensor([])
|
||||
g.add_nodes(1, {"hb": F.tensor([1])})
|
||||
assert "ha" in g.ndata
|
||||
assert len(g.ndata["ha"]) == 1
|
||||
|
||||
|
||||
def test_is_sorted():
|
||||
u_src, u_dst = edge_pair_input(False)
|
||||
s_src, s_dst = edge_pair_input(True)
|
||||
|
||||
u_src = F.tensor(u_src, dtype=F.int32)
|
||||
u_dst = F.tensor(u_dst, dtype=F.int32)
|
||||
s_src = F.tensor(s_src, dtype=F.int32)
|
||||
s_dst = F.tensor(s_dst, dtype=F.int32)
|
||||
|
||||
src_sorted, dst_sorted = dgl.utils.is_sorted_srcdst(u_src, u_dst)
|
||||
assert src_sorted == False
|
||||
assert dst_sorted == False
|
||||
|
||||
src_sorted, dst_sorted = dgl.utils.is_sorted_srcdst(s_src, s_dst)
|
||||
assert src_sorted == True
|
||||
assert dst_sorted == True
|
||||
|
||||
src_sorted, dst_sorted = dgl.utils.is_sorted_srcdst(u_src, u_dst)
|
||||
assert src_sorted == False
|
||||
assert dst_sorted == False
|
||||
|
||||
src_sorted, dst_sorted = dgl.utils.is_sorted_srcdst(s_src, u_dst)
|
||||
assert src_sorted == True
|
||||
assert dst_sorted == False
|
||||
|
||||
|
||||
def test_default_types():
|
||||
dg = dgl.graph([])
|
||||
g = dgl.graph(([], []))
|
||||
assert dg.ntypes == g.ntypes
|
||||
assert dg.etypes == g.etypes
|
||||
|
||||
|
||||
def test_formats():
|
||||
g = dgl.rand_graph(10, 20)
|
||||
# in_degrees works if coo or csc available
|
||||
# out_degrees works if coo or csr available
|
||||
try:
|
||||
g.in_degrees()
|
||||
g.out_degrees()
|
||||
g.formats("coo").in_degrees()
|
||||
g.formats("coo").out_degrees()
|
||||
g.formats("csc").in_degrees()
|
||||
g.formats("csr").out_degrees()
|
||||
fail = False
|
||||
except DGLError:
|
||||
fail = True
|
||||
finally:
|
||||
assert not fail
|
||||
# in_degrees NOT works if csc available only
|
||||
try:
|
||||
g.formats("csc").out_degrees()
|
||||
fail = True
|
||||
except DGLError:
|
||||
fail = False
|
||||
finally:
|
||||
assert not fail
|
||||
# out_degrees NOT works if csr available only
|
||||
try:
|
||||
g.formats("csr").in_degrees()
|
||||
fail = True
|
||||
except DGLError:
|
||||
fail = False
|
||||
finally:
|
||||
assert not fail
|
||||
|
||||
# If the intersection of created formats and allowed formats is
|
||||
# not empty, then retain the intersection.
|
||||
# Case1: intersection is not empty and intersected is equal to
|
||||
# created formats.
|
||||
g = g.formats(["coo", "csr"])
|
||||
g.create_formats_()
|
||||
g = g.formats(["coo", "csr", "csc"])
|
||||
assert sorted(g.formats()["created"]) == sorted(["coo", "csr"])
|
||||
assert sorted(g.formats()["not created"]) == sorted(["csc"])
|
||||
|
||||
# Case2: intersection is not empty and intersected is not equal
|
||||
# to created formats.
|
||||
g = g.formats(["coo", "csr"])
|
||||
g.create_formats_()
|
||||
g = g.formats(["coo", "csc"])
|
||||
assert sorted(g.formats()["created"]) == sorted(["coo"])
|
||||
assert sorted(g.formats()["not created"]) == sorted(["csc"])
|
||||
|
||||
# If the intersection of created formats and allowed formats is
|
||||
# empty, then create a format in the order of `coo` -> `csr` ->
|
||||
# `csc`.
|
||||
# Case1: intersection is empty and just one format is allowed.
|
||||
g = g.formats(["coo", "csr"])
|
||||
g.create_formats_()
|
||||
g = g.formats(["csc"])
|
||||
assert sorted(g.formats()["created"]) == sorted(["csc"])
|
||||
assert sorted(g.formats()["not created"]) == sorted([])
|
||||
|
||||
# Case2: intersection is empty and more than one format is allowed.
|
||||
g = g.formats("csc")
|
||||
g.create_formats_()
|
||||
g = g.formats(["csr", "coo"])
|
||||
assert sorted(g.formats()["created"]) == sorted(["coo"])
|
||||
assert sorted(g.formats()["not created"]) == sorted(["csr"])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_query()
|
||||
test_mutation()
|
||||
test_scipy_adjmat()
|
||||
test_incmat()
|
||||
test_find_edges()
|
||||
test_hypersparse_query()
|
||||
test_is_sorted()
|
||||
test_default_types()
|
||||
test_formats()
|
||||
Reference in New Issue
Block a user