def main():
base_path = '/data/infinity-mirror/'
input_path = '/home/dgonza26/infinity-mirror/input'
dataset = 'flights'
model = 'GraphRNN'
output_path = os.path.join(base_path, 'stats', 'lambda')
mkdir_output(output_path)
R = [(root, generation) for root, generation in load_data(os.path.join(base_path, 'cleaned'), dataset)]
R.sort(key=lambda x: x[1])
R = [root for (root, generation) in R]
if dataset == 'clique-ring-500-4':
G = nx.ring_of_cliques(500, 4)
else:
G = init(os.path.join(input_path, f'{dataset}.g'))
# transpose the list
roots = [list(r) for r in zip(*R)]
cols = ['model', 'gen', 'abs']
rows = {col: [] for col in cols}
gs0 = GraphStats(graph=G, run_id=1)
for i, chain in enumerate(roots, 1):
print(f'chain: {i}')
for idx, graph in enumerate(chain, 1):
print(f'\tgen: {idx} ... ', end='', flush=True)
comparator = GraphPairCompare(gs0, GraphStats(graph=graph, run_id=1))
try:
rows['abs'].append(comparator.lambda_dist())
except Exception as e:
print(f'ERROR\n{e}')
else:
rows['model'].append('GraphRNN')
rows['gen'].append(idx)
print('done')
df = pd.DataFrame(rows)
print(df.head())
df.to_csv(f'{output_path}/{dataset}_{model}_lambda.csv', float_format='%.7f', sep='\t', index=False, na_rep='nan')
print(f'wrote {output_path}/{dataset}_{model}_lambda.csv')
return
def main(base_path, dataset, models):
if 'GraphRNN' in models:
#path = os.path.join(base_path, 'GraphRNN')
#for subdir, dirs, files in os.walk(path):
# if dataset == subdir.split('/')[-1].split('_')[0]:
# print(subdir)
# for filename in files:
# print(filename)
models.remove('GraphRNN')
for model in models:
path = os.path.join(base_path, dataset, model)
for subdir, dirs, files in os.walk(path):
for filename in files:
if 'seq' not in filename:
run_id = int(filename.split('.')[0].split('_')[-1])
string = subdir.split('/')[-2:]
file = os.path.join(subdir, filename)
newfile = file.split('.')[0]
if 'rob' in file:
newfile += '_seq_rob.pkl.gz'
else:
newfile += '_seq.pkl.gz'
print(f'starting\t{string[-2]}\t{string[-1]}\t{filename} ... ', end='', flush=True)
root = load_pickle(file)
node = root
try:
node.stats_seq
except AttributeError:
if type(node) is LightTreeNode:
node_graph_stats = GraphStats(run_id=run_id, graph=node.graph)
comparator = GraphPairCompare(GraphStats(graph=root.graph, run_id=run_id), \
GraphStats(graph=root.graph, run_id=run_id))
stats = {}
stats['lambda_dist'] = comparator.lambda_dist()
stats['node_diff'] = comparator.node_diff()
stats['edge_diff'] = comparator.edge_diff()
stats['pgd_pearson'] = comparator.pgd_pearson()
stats['pgd_spearman'] = comparator.pgd_spearman()
stats['deltacon0'] = comparator.deltacon0()
stats['degree_cvm'] = comparator.cvm_degree()
stats['pagerank_cvm'] = comparator.cvm_pagerank()
node = TreeNode(name=node.name, graph=node.graph, stats=stats, stats_seq={}, parent=node.parent, children=node.children)
elif type(node) is TreeNode:
node = TreeNode(name=node.name, graph=node.graph, stats=node.stats, stats_seq={}, parent=node.parent, children=node.children)
else:
print(f'node has unknown type: {type(node)}')
exit()
if node.stats_seq is None or node.stats_seq == {}:
node.stats_seq = {}
while len(node.children) > 0:
child = node.children[0]
try:
child.stats_seq = {}
except AttributeError:
child = TreeNode(name=child.name, graph=child.graph, stats=child.stats, stats_seq={}, parent=child.parent, children=child.children)
comparator = GraphPairCompare(GraphStats(graph=node.graph, run_id=run_id), \
GraphStats(graph=child.graph, run_id=run_id))
child.stats_seq['lambda_dist'] = comparator.lambda_dist()
child.stats_seq['node_diff'] = comparator.node_diff()
child.stats_seq['edge_diff'] = comparator.edge_diff()
child.stats_seq['pgd_pearson'] = comparator.pgd_pearson()
child.stats_seq['pgd_spearman'] = comparator.pgd_spearman()
child.stats_seq['deltacon0'] = comparator.deltacon0()
child.stats_seq['degree_cvm'] = comparator.cvm_degree()
child.stats_seq['pagerank_cvm'] = comparator.cvm_pagerank()
node = child
with open(newfile, 'wb') as f:
pickle.dump(root, f)
print(f'\tdone')