def get_graph_data(dataset, isModelDataset=False):
"""
Given a dataset name, loads in the graphs of that dataset.
Creates a specific argument object for that dataset to allow
for different datasets to be created.
return: If isModelDataset: returns args, train, val, test split
else: returgn args, data
"""
args_data = Args()
args_data.change_dataset(dataset)
# Load the graph data - Consider using presaved datasets! with graph load list
graphs = create(args_data)
graphs_len = len(graphs)
random.seed(123)
shuffle(graphs)
# Display some graph stats
graph_node_avg = 0
for graph in graphs:
graph_node_avg += graph.number_of_nodes()
graph_node_avg /= graphs_len
print('Average num nodes', graph_node_avg)
args_data.max_num_node = max(
[graphs[i].number_of_nodes() for i in range(graphs_len)])
max_num_edge = max(
[graphs[i].number_of_edges() for i in range(graphs_len)])
min_num_edge = min(
[graphs[i].number_of_edges() for i in range(graphs_len)])
# show graphs statistics
print('total graph num: {}'.format(graphs_len))
print('max number node: {}'.format(args_data.max_num_node))
print('max/min number edge: {}; {}'.format(max_num_edge, min_num_edge))
print('max previous node: {}'.format(args_data.max_prev_node))
if isModelDataset:
# split datasets
graphs_len = len(graphs)
graphs_test = graphs[int(0.8 * graphs_len):]
graphs_train = graphs[0:int(0.8 * graphs_len)]
graphs_validate = graphs[0:int(0.2 * graphs_len)]
return args_data, graphs_train, graphs_validate, graphs_test
return args_data, graphs
def create_name(name):
arg_temp = Args()
arg_temp.change_dataset(name)
return create(arg_temp)