def test_walker_custom(line_graph):
walker = CustomWalker()
sampler = UnsupervisedSampler(line_graph, walker=walker)
batches = sampler.run(2)
assert len(batches) == line_graph.number_of_nodes()
# all positive examples should be self loops, since we defined our custom walker this way
for context_pairs, labels in batches:
for node, neighbour in context_pairs[labels == 1]:
assert node == neighbour
def test_run_batch_sizes(self, line_graph):
batch_size = 4
sampler = UnsupervisedSampler(G=line_graph, length=2, number_of_walks=2)
batches = sampler.run(batch_size)
# check batch sizes
assert len(batches) == np.ceil(len(line_graph.nodes()) * 4 / batch_size)
for ids, labels in batches[:-1]:
assert len(ids) == len(labels) == batch_size
# last batch can be smaller
ids, labels = batches[-1]
assert len(ids) == len(labels)
assert len(ids) <= batch_size
def test_walker_uniform_random(line_graph):
length = 3
number_of_walks = 2
batch_size = 4
walker = UniformRandomWalk(line_graph, n=number_of_walks, length=length)
sampler = UnsupervisedSampler(line_graph, walker=walker)
batches = sampler.run(batch_size)
# batches should match the parameters used to create the walker object, instead of the defaults
# for UnsupervisedSampler
expected_num_batches = np.ceil(line_graph.number_of_nodes() *
number_of_walks * (length - 1) * 2 /
batch_size)
assert len(batches) == expected_num_batches
def test_run_context_pairs(self, line_graph):
batch_size = 4
sampler = UnsupervisedSampler(G=line_graph, length=2, number_of_walks=2)
batches = sampler.run(batch_size)
grouped_by_target = defaultdict(list)
for ids, labels in batches:
for (target, context), label in zip(ids, labels):
grouped_by_target[target].append((context, label))
assert len(grouped_by_target) == len(line_graph.nodes())
for target, sampled in grouped_by_target.items():
# exactly 2 positive and 2 negative context pairs for each target node
assert len(sampled) == 4
# since each walk has length = 2, there must be an edge between each positive context pair
for context, label in sampled:
if label == 1:
assert context in set(line_graph.neighbors(target))
