def test_dont_omit_for_inference():
source = open(filename, 'rb').read().decode('utf-8', 'ignore')
(components, adj) = h.netlist_as_graph(source)
dataset = PrototypeLinkDataset(['LT1001_TA05.net'], resample=False, train=False)
assert len(dataset) == 1
graph = dataset[0]
expected_node_count = len(components) + len(h.component_types)
assert graph.n_nodes == expected_node_count
def test_inference_proto_count():
source = open(filename, 'rb').read().decode('utf-8', 'ignore')
(components, adj) = h.netlist_as_graph(source)
dataset = PrototypeLinkDataset(['LT1001_TA05.net'], resample=False, train=False, normalize=False)
graph = dataset[0]
print(graph.x[:,-1])
proto_count = len(graph.x[:, -1].nonzero()[0])
print('proto_count', proto_count)
assert proto_count == len(h.component_types)
def test_load_graph_adj_shuffle():
source = open(filename, 'rb').read().decode('utf-8', 'ignore')
(components, adj) = h.netlist_as_graph(source)
for omitted_idx in range(len(components)):
graph = OmittedDataset.load_graph(components, adj, omitted_idx, True)
omitted_degree = len(adj[omitted_idx].nonzero()[0]) + len(
adj.transpose()[omitted_idx].nonzero()[0])
assert len(adj[omitted_idx].nonzero()[0]) == len(
adj.transpose()[omitted_idx].nonzero()
[0]), f"Found edge without bidirectional edge: {omitted_idx}"
assert len(adj.nonzero()[0]) - omitted_degree == len(
graph.a.nonzero()[0])
def test_load_graph_adj():
source = open(filename, 'rb').read().decode('utf-8', 'ignore')
(components, adj) = h.netlist_as_graph(source)
for omitted_idx in range(len(components)):
graph = OmittedDataset.load_graph(components, adj, omitted_idx)
omitted_degree = len(adj[omitted_idx].nonzero()[0]) + len(
adj.transpose()[omitted_idx].nonzero()[0])
assert len(adj[omitted_idx].nonzero()[0]) == len(
adj.transpose()[omitted_idx].nonzero()
[0]), f"Found edge without bidirectional edge: {omitted_idx}"
assert len(adj.nonzero()[0]) - omitted_degree == len(
graph.a.nonzero()[0])
# Check that all edges existed in original
all_edges = np.array(adj.nonzero()).transpose().tolist()
edges = np.array(graph.a.nonzero()).transpose().tolist()
for edge_pair in edges:
original_ids = [
e + 1 if e >= omitted_idx else e for e in edge_pair
]
assert original_ids in all_edges
def test_action_nodes_disconnected():
source = open(filename, 'rb').read().decode('utf-8', 'ignore')
(components, adj) = h.netlist_as_graph(source)
for omitted_idx in range(len(components)):
graph = PrototypeLinkDataset.load_graph(components, adj, omitted_idx)
assert len(graph.a[len(components)].nonzero()[0]) == 0
def test_flag_omitted_node():
source = open(filename, 'rb').read().decode('utf-8', 'ignore')
(components, adj) = h.netlist_as_graph(source)
for omitted_idx in range(len(components)):
graph = PrototypeLinkDataset.load_graph(components, adj, omitted_idx)
assert graph.x[omitted_idx][-1] == 1
def test_component_removed():
dataset = datasets.omitted([filename], resample=False)
contents = next(h.valid_netlist_sources([filename]))
(components, adj) = h.netlist_as_graph(contents)
for graph in dataset:
assert graph.x.shape[0] == len(components) - 1