def main():
judgment_path = sys.argv[1]
save_path = sys.argv[2]
# print
qrels = load_qrels_with_subtopic(judgment_path)
new_d = {}
for query_id, docs in qrels.items():
if query_id not in new_d:
new_d[query_id] = {}
for doc_id, subtopic, raw_score in docs:
if raw_score > 0:
score = raw_score
if doc_id in new_d[query_id]:
# other sub-topic is judged, which should be relevant.
assert new_d[query_id][doc_id] > 0
new_d[query_id][doc_id] = raw_score
else:
# If not relevant, no sub-topic is relevant
assert doc_id not in new_d[query_id]
score = 0
new_d[query_id][doc_id] = score
def iter():
for query_id, doc_score_d in new_d.items():
for doc_id in doc_score_d:
score = doc_score_d[doc_id]
yield TrecRelevanceJudgementEntry(query_id, doc_id, score)
write_trec_relevance_judgement(iter(), save_path)
def main():
print("using second score as judgments")
input_path = at_data_dir("clueweb", "2009.prels.1-50")
raw_entries = []
for line in open(input_path, "r"):
query_id, doc_id, s1, s2, s3 = line.split()
maybe_relevance = int(s1)
maybe_relevance2 = int(s2)
some_float = float(s3)
e = TrecRelevanceJudgementEntry(query_id, doc_id, maybe_relevance2)
raw_entries.append(e)
save_path = at_data_dir("clueweb", "2009.qrel_test.2.txt")
write_trec_relevance_judgement(raw_entries, save_path)
def main():
judgment_path = sys.argv[1]
save_path = sys.argv[2]
# print
qrels = load_qrels_flat(judgment_path)
def iter():
for query_id, docs in qrels.items():
for doc_id, raw_score in docs:
if raw_score > 0:
score = raw_score
else:
score = 0
yield TrecRelevanceJudgementEntry(query_id, doc_id, score)
write_trec_relevance_judgement(iter(), save_path)
def main(info_path, input_type, label_dict_path, save_path):
f_handler = get_format_handler(input_type)
info: Dict[str, Dict] = load_combine_info_jsons(info_path, f_handler.get_mapping(), f_handler.drop_kdp())
label_dict: Dict[Tuple[str, str], bool] = load_pickle_from(label_dict_path)
l = []
for entry in info.values():
key = f_handler.get_pair_id(entry)
query_id, candidate_id = key
if key in label_dict:
correctness = label_dict[key]
else:
correctness = False
e = TrecRelevanceJudgementEntry(query_id, candidate_id, int(correctness))
l.append(e)
write_trec_relevance_judgement(l, save_path)
def main():
label_itr = get_labels()
l = get_trec_relevance_judgement(label_itr)
save_path = os.path.join(output_path, "counter_arg", "qrel.txt")
write_trec_relevance_judgement(l, save_path)
def save_only_from_candidate():
l = get_relevance_judgement_only_from_candidate()
save_path = os.path.join(data_path, "perspective", "qrel_sub.txt")
write_trec_relevance_judgement(l, save_path)
def main():
l = get_trec_relevance_judgement()
save_path = os.path.join(data_path, "perspective", "qrel.txt")
write_trec_relevance_judgement(l, save_path)
def main():
label_itr = list(get_labels())
l = get_trec_relevance_judgement(label_itr)
save_path = os.path.join(data_path, "perspective",
"claim_evidence_qrel.txt")
write_trec_relevance_judgement(l, save_path)