def test_distinct_random_walk():
# Our test data for a balanced walk
adj = sparse.csr_matrix(np.array([[0, 1, 0, 0],
[1, 0, 1, 0],
[0, 1, 0, 1],
[0, 0, 1, 0]]))
g = graph.CSGraph(adj, 1, 1)
# Gets the entire connected component if the randow walk is long enough
assert batching.distinct_random_walk(g, 0, 4) == {0, 1, 2, 3}
# Gets a subset of the connected component if not
assert batching.distinct_random_walk(g, 0, 3) == {0, 1, 2}
# Test data for biased walks
adj = sparse.csr_matrix(np.array([[0, 1, 1, 0, 0, 0],
[1, 0, 1, 1, 0, 0],
[1, 1, 0, 1, 1, 0],
[0, 1, 1, 0, 1, 1],
[0, 0, 1, 1, 0, 1],
[0, 0, 0, 1, 1, 0]]))
# Favour exclusively non-triad outer links
g = graph.CSGraph(adj, 1, 1e-9)
walks = set()
for _ in range(100):
walks.add(tuple(sorted(batching.distinct_random_walk(g, 0, 4))))
assert walks == {(0, 1, 3, 4), (0, 1, 3, 5), (0, 2, 3, 5), (0, 2, 4, 5)}
# Favour exclusively triad outer links
g = graph.CSGraph(adj, 1e9, 1e9)
walks = set()
for _ in range(100):
walks.add(tuple(sorted(batching.distinct_random_walk(g, 0, 4))))
assert walks == {(0, 1, 2, 3)}
def test_CSGraph_init():
# Works with good arguments
adj = sparse.csr_matrix(np.array([[1, 1], [1, 0]]))
adj.data[0] = 0
g = graph.CSGraph(adj, .5, 4)
# Got the parameters right
assert g._p == .5
assert g._q == 4
# Eliminates zeros in adjacency matrix
assert_array_equal(g.adj.data, [1, 1])
assert_array_equal(g.adj.toarray(), [[0, 1], [1, 0]])
# Computed the edge aliases
assert set(g._edge_aliases.keys()) == {(0, 1), (1, 0)}
assert_alias_equal(g._edge_aliases[(0, 1)], ([0], [1]))
# Rejects non-sparse matrices
with pytest.raises(ValueError):
graph.CSGraph(np.array([[0, 1], [0, 0]]), .5, 4)
# Rejects directed graphs
with pytest.raises(AssertionError):
graph.CSGraph(sparse.csr_matrix(np.array([[0, 1], [0, 0]])), .5, 4)
# Rejects weighted graphs
with pytest.raises(AssertionError):
graph.CSGraph(sparse.csr_matrix(np.array([[0, 1], [2, 0]])), .5, 4)
# Rejects diagonal elements
with pytest.raises(AssertionError):
graph.CSGraph(sparse.csr_matrix(np.array([[1, 1], [1, 0]])), .5, 4)
def test_CSGraph_eq():
# Two graphs initialised with the same value are equal
g1 = graph.CSGraph(
sparse.csr_matrix(np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])), 2, 4)
g2 = graph.CSGraph(
sparse.csr_matrix(np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])), 2, 4)
assert g1 == g2
# Two graphs initialised with different matrices are different
g1 = graph.CSGraph(
sparse.csr_matrix(np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])), 2, 4)
g2 = graph.CSGraph(
sparse.csr_matrix(np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])), 2, 4)
assert g1 != g2
# Two graphs initialised with different p or q are different
g1 = graph.CSGraph(
sparse.csr_matrix(np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])), 2, 4)
g2 = graph.CSGraph(
sparse.csr_matrix(np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])), 3, 4)
assert g1 != g2
# Two graphs initialised with different matrices are different
g1 = graph.CSGraph(
sparse.csr_matrix(np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])), 2, 4)
g2 = graph.CSGraph(
sparse.csr_matrix(np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])), 2, 3)
assert g1 != g2
def test_connected_component_or_none():
# Our test data
adj = sparse.csr_matrix(np.array([[0, 1, 1, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 0]]))
g = graph.CSGraph(adj, 1, 1)
# Works well with good arguments, not maxing out
assert batching.connected_component_or_none(g, 0, 3) == {0, 1, 2}
assert batching.connected_component_or_none(g, 1, 4) == {0, 1, 2}
assert batching.connected_component_or_none(g, 2, 5) == {0, 1, 2}
assert batching.connected_component_or_none(g, 3, 5) == {3}
assert batching.connected_component_or_none(g, 4, 2) == {4, 5}
assert batching.connected_component_or_none(g, 5, 3) == {4, 5}
# Works well with good arguments, maxing out
assert batching.connected_component_or_none(g, 0, 2) is None
assert batching.connected_component_or_none(g, 1, 2) is None
assert batching.connected_component_or_none(g, 2, 2) is None
assert batching.connected_component_or_none(g, 3, 0) is None
assert batching.connected_component_or_none(g, 4, 1) is None
assert batching.connected_component_or_none(g, 5, 1) is None
def test_batch_walks():
# Yields the right number of walks to span the whole graph
adj = sparse.csr_matrix(np.array([[0, 1, 0, 0, 0],
[1, 0, 1, 0, 0],
[0, 1, 0, 1, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0]]))
g = graph.CSGraph(adj, 1, 1)
walks = list(batching.batch_walks(g, 2, 2))
assert len(walks) == 3
def test__collect_maxed_connected_component():
# Our test data
adj = sparse.csr_matrix(np.array([[0, 1, 1, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 0]]))
g = graph.CSGraph(adj, 1, 1)
# Works well with good arguments, not maxing out
collected = set()
assert not batching._collect_maxed_connected_component(g, 0, 3, collected)
assert collected == {0, 1, 2}
collected = set()
assert not batching._collect_maxed_connected_component(g, 1, 4, collected)
assert collected == {0, 1, 2}
collected = set()
assert not batching._collect_maxed_connected_component(g, 2, 5, collected)
assert collected == {0, 1, 2}
collected = set()
assert not batching._collect_maxed_connected_component(g, 3, 5, collected)
assert collected == {3}
collected = set()
assert not batching._collect_maxed_connected_component(g, 4, 2, collected)
assert collected == {4, 5}
collected = set()
assert not batching._collect_maxed_connected_component(g, 5, 3, collected)
assert collected == {4, 5}
# Works well with good arguments, maxing out
assert batching._collect_maxed_connected_component(g, 0, 2, set())
assert batching._collect_maxed_connected_component(g, 1, 2, set())
assert batching._collect_maxed_connected_component(g, 2, 2, set())
assert batching._collect_maxed_connected_component(g, 3, 0, set())
assert batching._collect_maxed_connected_component(g, 4, 1, set())
assert batching._collect_maxed_connected_component(g, 5, 1, set())
# Raises an error if the source node has already been collected
with pytest.raises(AssertionError):
batching._collect_maxed_connected_component(g, 2, None, {2})
def test_epoch_batches(model_depth2):
# Our test data
adj = sparse.csr_matrix(np.array([[0, 1, 0, 0, 0],
[1, 0, 1, 0, 0],
[0, 1, 0, 1, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0]]))
g = graph.CSGraph(adj, 1, 1)
# Works well with good arguments, no sampling
batches = list(batching.epoch_batches(model_depth2, g, 2, 2, None))
assert len(batches) == 3
required_nodes, final_nodes, feeds = batches[0]
assert_array_equal(final_nodes, sorted(final_nodes))
assert set(feeds.keys()) == {'layer1a_adj/indices',
'layer1a_adj/values',
'layer1a_adj/dense_shape',
'layer1a_output_mask',
'layer1b_adj/indices',
'layer1b_adj/values',
'layer1b_adj/dense_shape',
'layer1b_output_mask',
'layer2_adj/indices',
'layer2_adj/values',
'layer2_adj/dense_shape',
'layer2_output_mask'}
# len == 3 if node 4 was in the seeds for batch 0.
# if node 4 is not in the seeds, it can be 2, 3, or 4 depending on the overlap of the walks.
assert len(final_nodes) in [2, 3, 4]
# Make sure we span all possible lengths when iterating enough
lengths = set()
for _ in range(100):
_, final_nodes, _ = next(batching.epoch_batches(model_depth2, g, 2, 2, None))
lengths.add(len(final_nodes))
assert lengths == {2, 3, 4}
# Works well with good arguments, with sampling
batches = list(batching.epoch_batches(model_depth2, g, 2, 2, 1))
assert len(batches) == 3
required_nodes, final_nodes, feeds = batches[0]
assert_array_equal(final_nodes, sorted(final_nodes))
assert set(feeds.keys()) == {'layer1a_adj/indices',
'layer1a_adj/values',
'layer1a_adj/dense_shape',
'layer1a_output_mask',
'layer1b_adj/indices',
'layer1b_adj/values',
'layer1b_adj/dense_shape',
'layer1b_output_mask',
'layer2_adj/indices',
'layer2_adj/values',
'layer2_adj/dense_shape',
'layer2_output_mask'}
# len == 3 if node 4 was in the seeds for batch 0.
# if node 4 is not in the seeds, it can be 2, 3, or 4 depending on the overlap of the walks.
assert len(final_nodes) in [2, 3, 4]
# Make sure we span all possible lengths when iterating enough
lengths = set()
for _ in range(100):
_, final_nodes, _ = next(batching.epoch_batches(model_depth2, g, 2, 2, 1))
lengths.add(len(final_nodes))
assert lengths == {2, 3, 4}
def g():
adj = [[0, 1, 1, 1, 0, 0], [1, 0, 0, 1, 0, 1], [1, 0, 0, 1, 0, 1],
[1, 1, 1, 0, 1, 0], [0, 0, 0, 1, 0, 1], [0, 1, 1, 0, 1, 0]]
return graph.CSGraph(sparse.csr_matrix(np.array(adj)), .5, 4)
