def test_node_types(self):
"""Test the .node_types() method"""
G_epgm = EPGM(self.input_dir)
graph_id = G_epgm.G["graphs"][0]["id"]
# cora has a single 'paper' node type:
node_types = G_epgm.node_types(graph_id)
assert len(node_types) == 1
assert self.node_type in node_types
with pytest.raises(Exception):
G_epgm.node_types("invalid_graph_id")
def test_node_types(self):
"""Test the .node_types() method"""
G_epgm = EPGM(self.input_dir)
graph_id = G_epgm.G["graphs"][0]["id"]
# dataset has multiple node types:
node_types = G_epgm.node_types(graph_id)
assert len(node_types) == 3
assert "person" in node_types
assert "paper" in node_types
assert "venue" in node_types
with pytest.raises(Exception):
G_epgm.node_types("invalid_graph_id")
def load_data(path, dataset_name=None, node_type=None, target_attribute=None):
"""
Loads the node data
:param path: Input filename or directory where graph in EPGM format is stored
:param node_type: For HINs, the node type to consider
:param target_attribute: For EPGM format, the target node attribute
:return: N x 2 numpy arrays where the first column is the node id and the second column is the node label.
"""
if os.path.isdir(path):
g_epgm = EPGM(path)
graphs = g_epgm.G["graphs"]
for g in graphs:
if g["meta"]["label"] == dataset_name:
g_id = g["id"]
g_vertices = g_epgm.G["vertices"] # retrieve all graph vertices
if node_type is None:
node_type = g_epgm.node_types(g_id)
if len(node_type) == 1:
node_type = node_type[0]
else:
raise Exception(
"Multiple node types detected in graph {}: {}.".format(
g_id, node_type
)
)
if target_attribute is None:
target_attribute = g_epgm.node_attributes(g_id, node_type)
if len(target_attribute) == 1:
target_attribute = target_attribute[0]
else:
raise Exception(
"Multiple node attributes detected for nodes of type {} in graph {}: {}.".format(
node_type, g_id, target_attribute
)
)
y = np.array(
get_nodes(
g_vertices, node_type=node_type, target_attribute=target_attribute
)
)
else:
y_df = pd.read_csv(path, delimiter=" ", header=None, dtype=str)
y_df.sort_values(by=[0], inplace=True)
y = y_df.values
return y