def main():
pc_clusters: Iterable[PerspectiveCluster] = enum_perspective_clusters()
tokenizer = TokenizerForGalago()
def get_terms(text: str) -> Counter:
terms = tokenizer.tokenize(text)
return Counter(terms)
# Query = [claim :: avg(perspective)]
claim_text_d: Dict[int, str] = get_all_claim_d()
perspective_text_d: Dict[int, str] = get_perspective_dict()
def cluster_to_query(cluster: PerspectiveCluster) -> DocQuery:
claim_text = claim_text_d[cluster.claim_id]
perspective_text_list = list(
[perspective_text_d[pid] for pid in cluster.perspective_ids])
query_id = get_pc_cluster_query_id(cluster)
claim_tf: Counter = get_terms(claim_text)
pers_tf: Counter = average_counters(
lmap(get_terms, perspective_text_list))
tf = sum_counters([claim_tf, pers_tf])
query: DocQuery = counter_to_galago_query(query_id, tf)
return query
query_list: List[DocQuery] = lmap(cluster_to_query, pc_clusters)
print(len(query_list))
out_path = os.path.join(output_path, "perspective_query",
"pc_query_for_evidence.json")
save_queries_to_file(query_list, out_path)
def main():
claim_text_d: Dict[int, str] = get_all_claim_d()
claim_text_d: Dict[str, str] = dict_key_map(str, claim_text_d)
evi_dict: Dict[str, str] = dict_key_map(str, load_evidence_dict())
evi_gold_dict: Dict[str, List[int]] = evidence_gold_dict_str_qid()
print("V2")
def print_entry(entry):
evidence_text = evi_dict[entry.doc_id]
print("[{}] {}: {}".format(entry.rank, entry.doc_id, evidence_text))
ranked_list_dict = load_ranked_list_grouped(sys.argv[1])
for query, ranked_list in ranked_list_dict.items():
print()
claim_id, perspective_id = query.split("_")
gold_ids: List[str] = lmap(str, evi_gold_dict[query])
if not gold_ids:
print("query {} has no gold".format(query))
continue
assert gold_ids
claim_text = claim_text_d[claim_id]
perspective_text = perspective_getter(int(perspective_id))
pos_entries = []
neg_entries = []
for entry in ranked_list:
label = entry.doc_id in gold_ids
if label:
pos_entries.append(entry)
elif entry.rank < 3:
neg_entries.append(entry)
if not pos_entries:
print("gold not in ranked list")
continue
num_rel = len(pos_entries)
correctness = []
for entry in ranked_list[:num_rel]:
label = entry.doc_id in gold_ids
correctness.append(int(label))
precision = average(correctness)
if precision > 0.99:
print("Good")
continue
print("precision at {}: {}".format(num_rel, precision))
print("Claim: ", claim_text)
print("perspective_text: ", perspective_text)
print(" < GOLD >")
foreach(print_entry, pos_entries)
print(" < False Positive >")
foreach(print_entry, neg_entries)
def main(config):
q_res_path = config['q_res_path']
split = config['split']
query_d: Dict[int, str] = get_all_claim_d()
def worker_gen(out_dir):
qkgen = QKGenFromDB(q_res_path, query_d, out_dir)
return qkgen
num_jobs = d_n_claims_per_split2[split]
runner = JobRunner(job_man_dir, num_jobs, config['job_name'], worker_gen)
runner.auto_runner()
def get_qck_queries_all() -> List[QCKQuery]:
pc_itr = enum_perspective_clusters()
claim_text_d: Dict[int, str] = get_all_claim_d()
query_list = []
for pc in pc_itr:
c_text = claim_text_d[pc.claim_id]
pid = min(pc.perspective_ids)
p_text = perspective_getter(pid)
text = c_text + " " + p_text
query = QCKQuery(get_pc_cluster_query_id(pc), text)
query_list.append(query)
return query_list
def main():
claim_text_d: Dict[int, str] = get_all_claim_d()
evidence_d = load_evidence_dict()
evidence_gold = evidence_gold_dict()
while True:
s = input()
cid, pid = s.split("_")
cid = int(cid)
pid = int(pid)
print("Claim: ", claim_text_d[cid])
print("Perspective: ", perspective_getter(pid))
key = cid, pid
e_ids = evidence_gold[key]
for eid in e_ids:
print("Evidence: ", evidence_d[eid])
def get_qck_queries(split) -> List[QCKQuery]:
claim_ids = set(load_claim_ids_for_split(split))
pc_itr = enum_perspective_clusters_for_split(split)
claim_text_d: Dict[int, str] = get_all_claim_d()
query_list = []
for pc in pc_itr:
if pc.claim_id in claim_ids:
c_text = claim_text_d[pc.claim_id]
pid = min(pc.perspective_ids)
p_text = perspective_getter(pid)
text = c_text + " " + p_text
query = QCKQuery(get_pc_cluster_query_id(pc), text)
query_list.append(query)
return query_list