def sequential(graph_stats):
prev = graph_stats[0]
for curr in graph_stats[1:]:
comparator = GraphPairCompare(prev, curr)
dist = comparator.js_distance()
yield dist
prev = curr
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', 'degree')
abs_lambda = []
trials = []
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'))
# add the initial graph and 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.js_distance())
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')
#df.to_csv(f'{output_path}/{dataset}_{model}_lambda.csv', float_format='%.7f', sep='\t', index=False, na_rep='nan')
return
def absolute(graph_stats):
for gs in graph_stats[1:]:
comparator = GraphPairCompare(graph_stats[0], gs)
dist = comparator.js_distance()
yield dist