dmlc--dgl
Zihao Ye
724aa0caf0
* fix rgcn tutorial * small fix * upd * findedge/s * upd * upd * upd * upd * add test * remove redundancy * upd * upd * upd * upd * add edge_subgraph * explicit cast * add test immutable subg * reformat * reformat * fix bug * upd
224 行
6.4 KiB
Python
224 行
6.4 KiB
Python
import time
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import math
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import numpy as np
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import scipy.sparse as sp
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import networkx as nx
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import dgl
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import backend as F
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from dgl import DGLError
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def test_graph_creation():
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g = dgl.DGLGraph()
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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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# test clear and add again
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g.clear()
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g.add_nodes(5)
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g.ndata['h'] = 3 * F.ones((5, 2))
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assert F.allclose(3 * F.ones((5, 2)), g.ndata['h'])
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def test_create_from_elist():
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elist = [(2, 1), (1, 0), (2, 0), (3, 0), (0, 2)]
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g = dgl.DGLGraph(elist)
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for i, (u, v) in enumerate(elist):
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assert g.edge_id(u, v) == i
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# immutable graph
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# XXX: not enabled for pytorch
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#g = dgl.DGLGraph(elist, readonly=True)
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#for i, (u, v) in enumerate(elist):
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# assert g.edge_id(u, v) == i
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def test_adjmat_cache():
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n = 1000
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p = 10 * math.log(n) / n
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a = sp.random(n, n, p, data_rvs=lambda n: np.ones(n))
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g = dgl.DGLGraph(a)
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# the first call should contruct the adj
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t0 = time.time()
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adj1 = g.adjacency_matrix()
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dur1 = time.time() - t0
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# the second call should be cached and should be very fast
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t0 = time.time()
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adj2 = g.adjacency_matrix()
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dur2 = time.time() - t0
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print('first time {}, second time {}'.format(dur1, dur2))
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assert dur2 < dur1
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assert id(adj1) == id(adj2)
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# different arg should result in different cache
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adj3 = g.adjacency_matrix(transpose=True)
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assert id(adj3) != id(adj2)
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# manually clear the cache
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g.clear_cache()
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adj35 = g.adjacency_matrix()
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assert id(adj35) != id(adj2)
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# mutating the graph should invalidate the cache
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g.add_nodes(10)
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adj4 = g.adjacency_matrix()
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assert id(adj4) != id(adj35)
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def test_incmat():
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g = dgl.DGLGraph()
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g.add_nodes(4)
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g.add_edge(0, 1) # 0
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g.add_edge(0, 2) # 1
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g.add_edge(0, 3) # 2
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g.add_edge(2, 3) # 3
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g.add_edge(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([[0., 0., 0., 0., 0.],
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[1., 0., 0., 0., 1.],
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[0., 1., 0., 0., 0.],
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[0., 0., 1., 1., 0.]]))
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assert np.allclose(
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inc_out,
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np.array([[1., 1., 1., 0., 0.],
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[0., 0., 0., 0., 1.],
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[0., 0., 0., 1., 0.],
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[0., 0., 0., 0., 0.]]))
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assert np.allclose(
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inc_both,
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np.array([[-1., -1., -1., 0., 0.],
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[1., 0., 0., 0., 0.],
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[0., 1., 0., -1., 0.],
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[0., 0., 1., 1., 0.]]))
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def test_incmat_cache():
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n = 1000
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p = 10 * math.log(n) / n
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a = sp.random(n, n, p, data_rvs=lambda n: np.ones(n))
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g = dgl.DGLGraph(a)
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# the first call should contruct the inc
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t0 = time.time()
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inc1 = g.incidence_matrix("in")
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dur1 = time.time() - t0
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# the second call should be cached and should be very fast
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t0 = time.time()
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inc2 = g.incidence_matrix("in")
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dur2 = time.time() - t0
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print('first time {}, second time {}'.format(dur1, dur2))
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assert dur2 < dur1
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assert id(inc1) == id(inc2)
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# different arg should result in different cache
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inc3 = g.incidence_matrix("both")
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assert id(inc3) != id(inc2)
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# manually clear the cache
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g.clear_cache()
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inc35 = g.incidence_matrix("in")
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assert id(inc35) != id(inc2)
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# mutating the graph should invalidate the cache
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g.add_nodes(10)
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inc4 = g.incidence_matrix("in")
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assert id(inc4) != id(inc35)
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def test_readonly():
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g = dgl.DGLGraph()
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g.add_nodes(5)
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g.add_edges([0, 1, 2, 3], [1, 2, 3, 4])
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g.ndata['x'] = F.zeros((5, 3))
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g.edata['x'] = F.zeros((4, 4))
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g.readonly(False)
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assert g._graph.is_readonly() == False
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assert g.number_of_nodes() == 5
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assert g.number_of_edges() == 4
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g.readonly()
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assert g._graph.is_readonly() == True
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assert g.number_of_nodes() == 5
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assert g.number_of_edges() == 4
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try:
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g.add_nodes(5)
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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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g.readonly()
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assert g._graph.is_readonly() == True
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assert g.number_of_nodes() == 5
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assert g.number_of_edges() == 4
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try:
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g.add_nodes(5)
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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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g.readonly(False)
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assert g._graph.is_readonly() == False
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assert g.number_of_nodes() == 5
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assert g.number_of_edges() == 4
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try:
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g.add_nodes(10)
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g.add_edges([4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
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[5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
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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 not fail
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assert g.number_of_nodes() == 15
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assert F.shape(g.ndata['x']) == (15, 3)
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assert g.number_of_edges() == 14
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assert F.shape(g.edata['x']) == (14, 4)
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def test_find_edges():
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g = dgl.DGLGraph()
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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 e[0][0] == 1 and e[0][1] == 3 and e[0][2] == 2 and e[0][3] == 4
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assert e[1][0] == 2 and e[1][1] == 4 and e[1][2] == 3 and e[1][3] == 5
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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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g.readonly()
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e = g.find_edges([1, 3, 2, 4])
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assert e[0][0] == 1 and e[0][1] == 3 and e[0][2] == 2 and e[0][3] == 4
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assert e[1][0] == 2 and e[1][1] == 4 and e[1][2] == 3 and e[1][3] == 5
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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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if __name__ == '__main__':
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test_graph_creation()
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test_create_from_elist()
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test_adjmat_cache()
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test_incmat()
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test_incmat_cache()
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test_readonly()
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test_find_edges()
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