def main(args):
start_start = time.time()
feature_file = open(args.output, "w", newline='')
feature_writer = csv.writer(feature_file)
features_name = ["target_node", "target_neighbors_num", "First_nbr_adopter", "Second_nbr_adopter", \
"Third_nbr_adopter", "past_adoption_exist", "past_adoption_target_num", \
"com_hashtags_max", "com_hashtags_min", "com_hashtags_ave"] + ["outdeg_v1", "num_hashtags_v1", "orig_connections_k", "unique_Ak_num", \
"max_subG", "min_subG", "ave_subG", "views_1k", "subG_edges", "max_AkG_deg", \
"min_AkG_deg", "ave_AkG_deg", "time_ave_k", "time_ave_1_k2", "time_ave_k2_k", \
"speed_exposure", "speed_adoption"] + ["time_1_{}".format(i) for i in range(1, args.k)] + \
["r_node_{}".format(i) for i in range(args.k)] + ["distKV_target_{}".format(i) for i in range(args.k)] + \
["adoption_p", "label"]
feature_writer.writerow(features_name)
# p_nodeToH, p_G = get_G_nodeToHashtag()
# p_G, p_nodeToH = {},{}
count = 0
with open(args.input, 'r', encoding='utf-8') as f:
for line in f:
if count > args.cascades_counts:
break
cas = Cascade(args.k, line)
if cas.isLargerK:
cas.get_cascade_series_subcas()
# print(cas.uniqueAK)
hashtag = cas.cascade_with_unique_node[0]
features = Features(cas.uniqueAK, hashtag)
start = time.time()
Infected_targetNodes = cas.getTargetNodes(flag=0)
with ThreadPoolExecutor(max_workers=cpu_count()) as excutor:
tf_infected = excutor.map(features.Target_Features,
Infected_targetNodes)
targetNodes = cas.getTargetNodes(flag=1)
# partial_target_features = partial(features.Target_Features())
with ThreadPoolExecutor(max_workers=cpu_count()) as excutor:
tf = excutor.map(features.Target_Features, targetNodes)
fff = features.First_Forwarder_Features()
fkff = features.First_K_Forwarders_Features()
sf = features.Structure_Features()
gtf = features.get_Temporal_Features(
cas.uniqueAK_with_time_without_hashtag, cas.k)
# tf = features.Target_Features(0, rwr)
feature_list = fff + fkff + sf + gtf
for tf_list in tf_infected:
feature_list1 = tf_list[:-2 * args.k -
1] + feature_list + tf_list[
-2 * args.k - 1:] + [1]
feature_writer.writerow(feature_list1)
for tf_list in tf:
feature_list1 = tf_list[:-2 * args.k -
1] + feature_list + tf_list[
-2 * args.k - 1:] + [0]
feature_writer.writerow(feature_list1)
# print(fff, fkff, sf, gtf, tf)
print("串行运行时间：", time.time() - start)
# =============================================================================
# start = time.time()
# with ThreadPoolExecutor(max_workers = cpu_count()) as excutor:
# fff = excutor.submit(features.First_Forwarder_Features)
# fkff = excutor.submit(features.First_K_Forwarders_Features)
# sf = excutor.submit(features.Structure_Features)
# gtf = excutor.submit(features.get_Temporal_Features,cas.uniqueAK_with_time_without_hashtag, cas.k)
# tf = excutor.submit(features.Target_Features,0, rwr)
# print(type(fff.result()))
# print(fff.result(),fkff.result(), sf.result(), gtf.result(),tf.result())
# print("并行运行时间：", time.time() - start)
# =============================================================================
count += 1
feature_file.close()
print("总运行时间：", time.time() - start_start)
