def sample_generation(args):
# Preprocessing Step
print("Numpy Version Check")
print(np.__version__)
print("Scipy Version Check")
print(scipy.__version__)
data_dicts = preprocessing(transition_matrix_path=args.transition_matrix,
doc_topics_path=args.document_topic,
user_topic_path=args.user_topic_interest,
query_topic_path=args.query_topic_relation,
search_relevance_path=args.search_relevance)
# GPR, PTSPR, QTSPR construction
if args.pagerank == "gpr":
pr = PageRank(trans_matrix=data_dicts['transition_matrix'],
dampening_factor=args.dampening_factor)
elif args.pagerank == "ptspr" or args.pagerank == "qtspr":
pr = TopicSensitivePageRank(
trans_matrix=data_dicts['transition_matrix'],
topic_matrix=data_dicts['doc_topic_matrix'],
dampening_factor=args.dampening_factor,
topic_factor=args.topic_factor)
pr.converge()
if args.pagerank == "gpr":
np.savetxt("GPR.txt", pr.ranked_vector, delimiter=" ")
elif args.pagerank == "ptspr":
topic_prob = data_dicts['user_topic_probs']["2-1"]
vector = (pr.ranked_matrix * topic_prob.reshape(12, 1)).view(
np.ndarray).squeeze()
np.savetxt("QTSPR-U2Q1.txt", vector, delimiter=" ")
elif args.pagerank == "qtspr":
topic_prob = data_dicts['query_topic_probs']["2-1"]
vector = (pr.ranked_matrix * topic_prob.reshape(12, 1)).view(
np.ndarray).squeeze()
np.savetxt("PTSPR-U2Q1.txt", vector, delimiter=" ")
print("===================== END =====================")
def main(args):
# Preprocessing Step
data_dicts = preprocessing(transition_matrix_path=args.transition_matrix,
doc_topics_path=args.document_topic,
user_topic_path=args.user_topic_interest,
query_topic_path=args.query_topic_relation,
search_relevance_path=args.search_relevance)
# GPR, PTSPR, QTSPR construction
if args.pagerank == "gpr":
pr = PageRank(trans_matrix=data_dicts['transition_matrix'],
dampening_factor=args.dampening_factor)
elif args.pagerank == "ptspr" or args.pagerank == "qtspr":
pr = TopicSensitivePageRank(
trans_matrix=data_dicts['transition_matrix'],
topic_matrix=data_dicts['doc_topic_matrix'],
dampening_factor=args.dampening_factor,
topic_factor=args.topic_factor)
pr_start = time.time()
pr.converge()
pr_end = time.time()
print("Power iteration - {} required time: {:.3f}seconds".format(
args.pagerank, pr_end - pr_start))
pr_result = []
for query_ID in data_dicts['search_relevance_score'].keys():
candidate_indices, retrieval_scores = data_dicts[
'search_relevance_score'][query_ID]
user_topic_prob = data_dicts['user_topic_probs'][query_ID]
query_topic_prob = data_dicts['query_topic_probs'][query_ID]
if args.pagerank == "gpr":
pr_indices, pr_scores = pr.ranking(candidate_indices,
retrieval_scores,
criterion=args.criterion)
elif args.pagerank == "ptspr":
pr_indices, pr_scores = pr.ranking(candidate_indices,
retrieval_scores,
user_topic_prob,
criterion=args.criterion)
elif args.pagerank == "qtspr":
pr_indices, pr_scores = pr.ranking(candidate_indices,
retrieval_scores,
query_topic_prob,
criterion=args.criterion)
for idx in range(len(candidate_indices)):
# Print function
temp = [[]]
temp[0].append(query_ID)
temp[0].append("Q0")
temp[0].append(str(pr_indices[idx] + 1))
temp[0].append(str(idx + 1))
temp[0].append(str(pr_scores[idx]))
temp[0].append(args.cfg)
pr_str = " ".join(temp[0])
pr_result.append(pr_str)
pr_result_text = "\n".join(pr_result)
with open(args.pagerank + "_" + args.cfg + ".txt", "w") as f:
f.write(pr_result_text)
pr_end = time.time()
print("total {} required time : {:.3f}seconds".format(
args.pagerank, pr_end - pr_start))
print("===================== END =====================")