def test_filter():
g = DGLGraph()
g.add_nodes(4)
g.add_edges([0,1,2,3], [1,2,3,0])
n_repr = F.zeros((4, 5))
e_repr = F.zeros((4, 5))
n_repr[[1, 3]] = 1
e_repr[[1, 3]] = 1
g.ndata['a'] = n_repr
g.edata['a'] = e_repr
def predicate(r):
return F.max(r.data['a'], 1) > 0
# full node filter
n_idx = g.filter_nodes(predicate)
assert set(F.zerocopy_to_numpy(n_idx)) == {1, 3}
# partial node filter
n_idx = g.filter_nodes(predicate, [0, 1])
assert set(F.zerocopy_to_numpy(n_idx)) == {1}
# full edge filter
e_idx = g.filter_edges(predicate)
assert set(F.zerocopy_to_numpy(e_idx)) == {1, 3}
# partial edge filter
e_idx = g.filter_edges(predicate, [0, 1])
assert set(F.zerocopy_to_numpy(e_idx)) == {1}
def test_random_walk_with_restart():
edge_list = [(0, 1), (1, 2), (2, 3), (3, 4), (4, 3), (3, 2), (2, 1),
(1, 0)]
seeds = [0, 1]
max_nodes = 10
g = dgl.DGLGraph(edge_list)
# test normal RWR
traces = dgl.contrib.sampling.random_walk_with_restart(
g, seeds, 0.2, max_nodes)
assert len(traces) == len(seeds)
for traces_per_seed in traces:
total_nodes = 0
for t in traces_per_seed:
total_nodes += len(t)
trace_diff = np.diff(F.zerocopy_to_numpy(t), axis=-1)
assert (np.abs(trace_diff) == 1).all()
assert total_nodes >= max_nodes
# test RWR with early stopping
traces = dgl.contrib.sampling.random_walk_with_restart(
g, seeds, 1, 100, max_nodes, 1)
assert len(traces) == len(seeds)
for traces_per_seed in traces:
assert sum(len(t) for t in traces_per_seed) < 100
# test bipartite RWR
traces = dgl.contrib.sampling.bipartite_single_sided_random_walk_with_restart(
g, seeds, 0.2, max_nodes)
assert len(traces) == len(seeds)
for traces_per_seed in traces:
for t in traces_per_seed:
trace_diff = np.diff(F.zerocopy_to_numpy(t), axis=-1)
assert (trace_diff % 2 == 0).all()
def test_send_recv_after_conversion():
# test send and recv after converting from a graph with edges
g = generate_graph()
# nx graph
nxg = g.to_networkx(node_attrs=['h'])
g1 = DGLGraph()
# some random node and edges
g1.add_nodes(4)
g1.add_edges([1, 2], [2, 3])
g1.set_n_initializer(dgl.init.zero_initializer)
g1.from_networkx(nxg, node_attrs=['h'])
# sparse matrix
row, col = g.all_edges()
data = range(len(row))
n = g.number_of_nodes()
a = sp.coo_matrix(
(data, (F.zerocopy_to_numpy(row), F.zerocopy_to_numpy(col))),
shape=(n, n))
g2 = DGLGraph()
# some random node and edges
g2.add_nodes(5)
g2.add_edges([1, 2, 4], [2, 3, 0])
g2.set_n_initializer(dgl.init.zero_initializer)
g2.from_scipy_sparse_matrix(a)
g2.ndata['h'] = g.ndata['h']
# on dgl graph
g.send(message_func=message_func)
g.recv([0, 1, 3, 5],
reduce_func=reduce_func,
apply_node_func=apply_node_func)
g.recv([0, 2, 4, 8],
reduce_func=reduce_func,
apply_node_func=apply_node_func)
# nx
g1.send(message_func=message_func)
g1.recv([0, 1, 3, 5],
reduce_func=reduce_func,
apply_node_func=apply_node_func)
g1.recv([0, 2, 4, 8],
reduce_func=reduce_func,
apply_node_func=apply_node_func)
# sparse matrix
g2.send(message_func=message_func)
g2.recv([0, 1, 3, 5],
reduce_func=reduce_func,
apply_node_func=apply_node_func)
g2.recv([0, 2, 4, 8],
reduce_func=reduce_func,
apply_node_func=apply_node_func)
assert F.allclose(g.ndata['h'], g1.ndata['h'])
assert F.allclose(g.ndata['h'], g2.ndata['h'])
def test_random_walk():
edge_list = [(0, 1), (1, 2), (2, 3), (3, 4), (4, 3), (3, 2), (2, 1),
(1, 0)]
seeds = [0, 1]
n_traces = 3
n_hops = 4
g = dgl.DGLGraph(edge_list, readonly=True)
traces = dgl.contrib.sampling.random_walk(g, seeds, n_traces, n_hops)
traces = F.zerocopy_to_numpy(traces)
assert traces.shape == (len(seeds), n_traces, n_hops + 1)
for i, seed in enumerate(seeds):
assert (traces[i, :, 0] == seeds[i]).all()
trace_diff = np.diff(traces, axis=-1)
# only nodes with adjacent IDs are connected
assert (np.abs(trace_diff) == 1).all()
def test_basics():
g = generate_graph()
h = g.ndata['h']
l = g.edata['l']
nid = [0, 2, 3, 6, 7, 9]
sg = g.subgraph(nid)
eid = {2, 3, 4, 5, 10, 11, 12, 13, 16}
assert set(F.zerocopy_to_numpy(sg.parent_eid)) == eid
eid = sg.parent_eid
# the subgraph is empty initially
assert len(sg.ndata) == 0
assert len(sg.edata) == 0
# the data is copied after explict copy from
sg.copy_from_parent()
assert len(sg.ndata) == 1
assert len(sg.edata) == 1
sh = sg.ndata['h']
assert F.allclose(h[nid], sh)
'''
s, d, eid
0, 1, 0
1, 9, 1
0, 2, 2 1
2, 9, 3 1
0, 3, 4 1
3, 9, 5 1
0, 4, 6
4, 9, 7
0, 5, 8
5, 9, 9 3
0, 6, 10 1
6, 9, 11 1 3
0, 7, 12 1
7, 9, 13 1 3
0, 8, 14
8, 9, 15 3
9, 0, 16 1
'''
assert F.allclose(F.gather_row(l, eid), sg.edata['l'])
# update the node/edge features on the subgraph should NOT
# reflect to the parent graph.
sg.ndata['h'] = F.zeros((6, D))
assert F.allclose(h, g.ndata['h'])
def _pfc(x):
return list(F.zerocopy_to_numpy(x)[:, 0])
def random_block(size):
g = dgl.graph(nx.erdos_renyi_graph(size, 0.1))
return dgl.to_block(g, np.unique(F.zerocopy_to_numpy(g.edges()[1])))
def toset(x):
return set(F.zerocopy_to_numpy(x).tolist())
def toset(x):
# F.zerocopy_to_numpy may return a int
return set(F.zerocopy_to_numpy(x).tolist())