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facet_unil/facetnet_step.py
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facetnet_step.py
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import argparse
import sys
import time
import numpy as np
import facetnet as fn
import pandas as pd
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("edgelist", help="full path to edge-list file", type=str)
parser.add_argument("alpha", help="alpha cost weight (0.0, 1.0] for gamma = CS*alpha + CT*(1-alpha)", type=float)
parser.add_argument("m_cluster", help="number of clusters", type=int)
parser.add_argument("res_folder", help="path to results folder, where"
"xcap.dat, lcap.dat, idmap.dat, idmap_inv.dat, soft_comm.csv"
"will be saved", type=str)
parser.add_argument("t_step", help="network time step, to save result", type=int)
parser.add_argument("--xcap", help="full path to xcap.dat file", type=str)
parser.add_argument("--lcap", help="full path to lcap.dat file", type=str)
parser.add_argument("--idmap", help="full path to idmap.dat file", type=str)
parser.add_argument("--idmap_inv", help="full path to idmap_inv.dat file", type=str)
parser.add_argument("--show", help="show convergence plot of gamma", type=str)
args = parser.parse_args()
if args.alpha <= 0.0 or args.alpha > 1.0:
print("Alpha value must be in (0.0, 1.0], terminate script")
sys.exit(1)
print("Analyzing network: {}".format(args.edgelist))
print("# time-step: {}, alpha: {}, # communities: {}".format(args.t_step, args.alpha, args.m_cluster))
if args.show is not None:
is_show = True
else:
is_show = False
start = time.time()
if args.xcap is None and args.lcap is None and args.idmap is None and args.idmap_inv is None:
idmap, idmap_inv, xc, lc, qc_s, soft_comm, comm_net, evol_net, m_eff = fn.facetnet_step(
args.edgelist, args.alpha, args.m_cluster, show_plot=is_show)
elif args.xcap is not None and args.lcap is not None and args.idmap is not None and args.idmap_inv is not None:
xc = np.load(args.xcap, allow_pickle=True)
lc = np.load(args.lcap, allow_pickle=True)
idmap = np.ndarray.tolist(np.load(args.idmap, allow_pickle=True))
idmap_inv = np.ndarray.tolist(np.load(args.idmap_inv, allow_pickle=True))
idmap, idmap_inv, xc, lc, qc_s, soft_comm, comm_net, evol_net, m_eff = fn.facetnet_step(
args.edgelist, args.alpha, args.m_cluster,
show_plot=is_show, xc_prev=xc, lc_prev=lc, idmap0=idmap, idmap_inv0=idmap_inv)
else:
print("Some of (xcap, ycap, idmap, idmap_inv) are given, some not")
sys.exit(1)
stop = time.time()
print("Soft modularity Qc = ({})".format(qc_s))
print("Elapsed time: {} sec".format(stop - start))
# Save end state of current step for input to next step
xc.dump("{}/xcap_alpha{}_nw{}.dat".format(args.res_folder, args.alpha, args.t_step))
lc.dump("{}/lcap_alpha{}_nw{}.dat".format(args.res_folder, args.alpha, args.t_step))
np.asarray(idmap).dump("{}/idmap_alpha{}_nw{}.dat".format(args.res_folder, args.alpha, args.t_step))
np.asarray(idmap_inv).dump("{}/idmap_inv_alpha{}_nw{}.dat".format(args.res_folder, args.alpha, args.t_step))
# Save results for analysis
clusters = []
for i in range(m_eff):
clusters.append("cluster_{}".format(i))
if args.xcap is not None and args.lcap is not None and args.idmap is not None and args.idmap_inv is not None:
evol_net_df = pd.DataFrame(data=evol_net, columns=clusters)
evol_net_df.to_csv("{}/evol_net_step_alpha{}_nw{}.csv".format(args.res_folder, args.alpha, args.t_step),
index=False)
soft_comm_df = pd.DataFrame(data=soft_comm, columns=clusters)
soft_comm_df.insert(0, column="id", value=idmap)
soft_comm_df.to_csv("{}/soft_comm_step_alpha{}_nw{}.csv".format(args.res_folder, args.alpha, args.t_step),
index=False)
comm_net_df = pd.DataFrame(data=comm_net, columns=clusters)
comm_net_df.to_csv("{}/comm_net_step_alpha{}_nw{}.csv".format(args.res_folder, args.alpha, args.t_step),
index=False)
lc_df = pd.DataFrame(data=lc, columns=clusters)
lc_df.to_csv("{}/lcap_alpha{}_nw{}.csv".format(args.res_folder, args.alpha, args.t_step), index=False)
qc_s_df = pd.DataFrame(data=qc_s, columns=["Q_s"])
qc_s_df.to_csv("{}/q_alpha{}_nw{}.csv".format(args.res_folder, args.alpha, args.t_step), index=False)
print("Results and state written to: {}".format(args.res_folder))
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