def main(args):
"""
Characteristic function embedding wrapper.
:param args: Arguments object parsed up.
"""
if args.model_type == "FEATHER":
print("\nFitting a node embedding.\n")
graph = load_graph(args.graph_input)
features = load_features(args.feature_input)
model = FEATHER()
model.fit(graph, features)
elif args.model_type == "FEATHER-G":
print("\nFitting a graph level embedding.\n")
graphs = load_graphs(args.graphs_input)
model = FEATHERG()
model.fit(graphs)
else:
quit()
X = model.get_embedding()
save_embedding(X, args.output)
# exit()
node_count_avg_ref, node_count_avg_pred = [], []
edge_count_avg_ref, edge_count_avg_pred = [], []
degree_mmd, clustering_mmd, orbit_mmd, nspdk_mmd = [], [], [], []
node_label_mmd, edge_label_mmd, node_label_and_degree = [], [], []
print(len(graphs_test_indices))
for i in range(0, len(graphs_pred_indices), eval_args.metric_eval_batch_size):
batch_size = min(eval_args.metric_eval_batch_size,
len(graphs_pred_indices) - i)
graphs_ref_indices = random.sample(graphs_test_indices, batch_size)
graphs_ref = load_graphs(
train_args.current_dataset_path, graphs_ref_indices)
graphs_ref = [patch_graph(g) for g in graphs_ref]
graphs_pred = load_graphs(
eval_args.current_graphs_save_path, graphs_pred_indices[i: i + batch_size])
graphs_pred = [patch_graph(g) for g in graphs_pred]
node_count_avg_ref.append(mean([len(G.nodes()) for G in graphs_ref]))
node_count_avg_pred.append(mean([len(G.nodes()) for G in graphs_pred]))
edge_count_avg_ref.append(mean([len(G.edges()) for G in graphs_ref]))
edge_count_avg_pred.append(mean([len(G.edges()) for G in graphs_pred]))
degree_mmd.append(metrics.stats.degree_stats(graphs_ref, graphs_pred))
))
print(LINE_BREAK)
if __name__ == "__main__":
node_count_avg_ref, node_count_avg_pred = [], []
edge_count_avg_ref, edge_count_avg_pred = [], []
degree_mmd, clustering_mmd, orbit_mmd, nspdk_mmd = [], [], [], []
node_label_mmd, edge_label_mmd, node_label_and_degree = [], [], []
graphs_pred_indices = range(2000)
for i in range(0, len(graphs_pred_indices), 32):
batch_size = min(32, len(graphs_pred_indices) - i)
print(i)
graphs_ref = load_graphs('dat_graphs/lung/', graphs_pred_indices[i:i+batch_size])
graphs_ref = [patch_graph(g) for g in graphs_ref]
graphs_pred = load_graphs('dat_graphs/yeast/', graphs_pred_indices[i:i+batch_size])
graphs_pred = [patch_graph(g) for g in graphs_pred]
node_count_avg_ref.append(mean([len(G.nodes()) for G in graphs_ref]))
node_count_avg_pred.append(mean([len(G.nodes()) for G in graphs_pred]))
edge_count_avg_ref.append(mean([len(G.edges()) for G in graphs_ref]))
edge_count_avg_pred.append(mean([len(G.edges()) for G in graphs_pred]))
# print("Edge count done")
degree_mmd.append(metrics.stats.degree_stats(graphs_ref, graphs_pred))
# print("Degree MMD done")
clustering_mmd.append(
metrics.stats.clustering_stats(graphs_ref, graphs_pred))