f = open(features, 'r')
g = open(output_features, 'w')
# skip csv header
if configs.feature_header:
f.readline()
for l in f.readlines():
# convert userID to nodeID
try:
vec = l.strip().split(",")
g.write("{} ".format(nodes[vec[0]]))
g.write(" ".join(vec[1:]))
g.write("\n")
except:
vec = l.strip().split(",")
print(f"node {vec[0]} not in the graph")
g.close()
f.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Parser feature file generator")
parser.add_argument("--json_path",
type=str,
required=True,
help="Path to the json config file")
args = parser.parse_args()
configs = load_json(args.json_path)
main(dict2dotdict(configs))
if v_i == v_j:
continue
adj_mtx_r[v_i, v_j] = self.get_edge_weight(v_i, v_j)
return adj_mtx_r
def get_edge_weight(self, i, j):
return np.dot(self.embedding[i, :], self.embedding[j, :])
dummy_model = _Dummy(embedding)
MAP, prec_curv, err, err_baseline = gr.evaluateStaticGraphReconstruction(graph, dummy_model, embedding, None)
return (MAP, prec_curv)
# Change the name of the dataset
dataset = "pastebin"
json_path = f"./data/{dataset}/config.json"
configs = load_json(json_path)
configs = dict2dotdict(configs)
timespells = configs.timespells
graph_embeddings = {}
reconstruction_performance = {}
reconstruction_performance_curve = {}
for ts in range(1, int(timespells)+1):
f = open(f"{configs.DATA_PATH}/TS{str(ts)}/generated/graphs.pkl", 'rb')
graph, graph_str = pickle.load(f)
f.close()
for model in configs.models:
if model not in graph_embeddings.keys():
graph_embeddings[model] = {}
reconstruction_performance[model] = {}
reconstruction_performance_curve[model] = {}