def transform(
t: Tuple[ArguDataPoint, Passage,
bool]) -> Tuple[QCKQuery, QCKCandidate, bool]:
problem, candidate, is_correct = t
return QCKQuery(problem.text1.id.id, problem.text1.text), \
QCKCandidate(candidate.id.id, candidate.text), \
is_correct
def convert(info):
new_info = {
'query': QCKQuery(str(info['qid']), ""),
'candidate': QCKCandidate(str(info['cid']), ""),
'kdp': KnowledgeDocumentPart("", 0, 0, []),
}
return new_info
def generate(self, query_list, data_id_manager) -> Iterator[ClassificationInstanceWDataID]:
neg_k = self.neg_k
for query_id in query_list:
if query_id not in self.judgement:
continue
qck_query = QCKQuery(query_id, "")
judgement = self.judgement[query_id]
query = self.queries[query_id]
query_tokens = self.tokenizer.tokenize(query)
ranked_list = self.galago_rank[query_id]
ranked_list = ranked_list[:neg_k]
target_docs = set(judgement.keys())
target_docs.update([e.doc_id for e in ranked_list])
print("Total of {} docs".format(len(target_docs)))
for doc_id in target_docs:
tokens = self.data[doc_id]
passage_list = self.encoder.encode(query_tokens, tokens)
label = 1 if doc_id in judgement and judgement[doc_id] > 0 else 0
if not label:
continue
candidate = QCKCandidate(doc_id, "")
for idx, (tokens, seg_ids) in enumerate(passage_list):
info = {
'query': get_light_qckquery(qck_query),
'candidate': get_light_qckcandidate(candidate),
'idx': idx,
}
data_id = data_id_manager.assign(info)
inst = ClassificationInstanceWDataID(tokens, seg_ids, label, data_id)
yield inst
def generate(self, query_list, data_id_manager) -> Iterator[QueryDocInstance]:
neg_k = self.neg_k
for query_id in query_list:
if query_id not in self.judgement:
continue
qck_query = QCKQuery(query_id, "")
judgement = self.judgement[query_id]
query = self.queries[query_id]
query_tokens = self.tokenizer.tokenize(query)
ranked_list = self.galago_rank[query_id]
ranked_list = ranked_list[:neg_k]
target_docs = set(judgement.keys())
target_docs.update([e.doc_id for e in ranked_list])
print("Total of {} docs".format(len(target_docs)))
for doc_id in target_docs:
tokens = self.data[doc_id][:self.doc_max_length]
label = 1 if doc_id in judgement and judgement[doc_id] > 0 else 0
if self.pos_only and not label:
continue
candidate = QCKCandidate(doc_id, "")
info = {
'query': get_light_qckquery(qck_query),
'candidate': get_light_qckcandidate(candidate),
'q_term_len': len(query_tokens),
}
data_id = data_id_manager.assign(info)
inst = QueryDocInstance(query_tokens, tokens, label, data_id)
yield inst
def baseline_eval():
split = "train"
k = 30
c_d: Dict[str, List[QCKCandidate]] = get_candidate_w_score(split, k)
output_ranked_list = []
for qid, ranked_list in c_d.items():
output_ranked_list.append((QCKQuery(qid, ""), ranked_list))
scores = get_precision_recall(output_ranked_list)
print(scores)
def generate(self, data_id_manager, qids):
missing_cnt = 0
success_docs = 0
missing_doc_qid = []
for qid in qids:
if qid not in self.resource.get_doc_for_query_d():
# assert not self.resource.query_in_qrel(qid)
continue
query_text = self.resource.get_query_text(qid)
bert_tokens_d = self.resource.get_bert_tokens_d(qid)
stemmed_tokens_d = self.resource.get_stemmed_tokens_d(qid)
for doc_id in self.resource.get_doc_for_query_d()[qid]:
label = self.resource.get_label(qid, doc_id)
try:
bert_title_tokens, bert_body_tokens_list = bert_tokens_d[
doc_id]
stemmed_title_tokens, stemmed_body_tokens_list = stemmed_tokens_d[
doc_id]
insts: List[Tuple[List, List]]\
= self.encoder.encode(
query_text,
stemmed_title_tokens,
stemmed_body_tokens_list,
bert_title_tokens,
bert_body_tokens_list
)
for passage_idx, passage in enumerate(insts):
tokens_seg, seg_ids = passage
assert type(tokens_seg[0]) == str
assert type(seg_ids[0]) == int
data_id = data_id_manager.assign({
'query':
QCKQuery(qid, ""),
'candidate':
QCKCandidate(doc_id, ""),
'passage_idx':
passage_idx,
})
inst = ClassificationInstanceWDataID(
tokens_seg, seg_ids, label, data_id)
yield inst
success_docs += 1
except KeyError:
missing_cnt += 1
missing_doc_qid.append(qid)
if missing_cnt > 10:
print(missing_doc_qid)
print("success: ", success_docs)
raise KeyError
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 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
def generate(self, data_id_manager, qids):
missing_cnt = 0
success_docs = 0
n_passage = 0
for qid in qids:
if qid not in self.resource.candidate_doc_d:
assert qid not in self.resource.qrel.qrel_d
continue
tokens_d: Dict[str, Tuple[List, List]] = self.resource.get_doc_tokens_d(qid)
q_tokens = self.resource.get_q_tokens(qid)
data_size_maybe = 0
for title_tokens, body_tokens in tokens_d.values():
data_size_maybe += len(title_tokens)
data_size_maybe += len(body_tokens)
for doc_id in self.resource.candidate_doc_d[qid]:
label = self.resource.get_label(qid, doc_id)
try:
title_tokens, body_tokens = tokens_d[doc_id]
insts: List[Tuple[List, List]] = self.doc_encoder.encode(q_tokens, title_tokens, body_tokens)
for passage_idx, passage in enumerate(insts):
tokens_seg, seg_ids = passage
assert type(tokens_seg[0]) == str
assert type(seg_ids[0]) == int
data_id = data_id_manager.assign({
'query': QCKQuery(qid, ""),
'candidate': QCKCandidate(doc_id, ""),
'passage_idx': passage_idx,
})
inst = ClassificationInstanceWDataID(tokens_seg, seg_ids, label, data_id)
n_passage += 1
yield inst
# if n_passage % 1000 == 0:
# tprint("n_passage : {}".format(n_passage))
# tprint('gc.get_count()', gc.get_count())
# tprint('gc.get_stats', gc.get_stats())
success_docs += 1
except KeyError:
missing_cnt += 1
if missing_cnt > 10:
print("success: ", success_docs)
raise KeyError
print(" {} of {} has long title".format(self.doc_encoder.long_title_cnt, self.doc_encoder.total_doc_cnt))
def generate(self, query_list, data_id_manager):
all_insts = []
for query_id in query_list:
if query_id not in self.galago_rank:
continue
query = self.queries[query_id]
qck_query = QCKQuery(query_id, "")
query_tokens = self.tokenizer.tokenize(query)
for doc_id, _, _ in self.galago_rank[query_id][:self.top_k]:
tokens = self.data[doc_id]
passage_list = self.encoder.encode(query_tokens, tokens)
candidate = QCKCandidate(doc_id, "")
for idx, (tokens, seg_ids) in enumerate(passage_list):
info = {
'query': get_light_qckquery(qck_query),
'candidate': get_light_qckcandidate(candidate),
'idx': idx
}
data_id = data_id_manager.assign(info)
inst = Instance(tokens, seg_ids, data_id, 0)
all_insts.append(inst)
return all_insts
def generate(self, query_list,
data_id_manager) -> Iterator[QueryDocInstance]:
neg_k = self.neg_k
for query_id in query_list:
if query_id not in self.judgement:
continue
qck_query = QCKQuery(query_id, "")
judgement = self.judgement[query_id]
ranked_list = self.galago_rank[query_id]
ranked_list = ranked_list[:neg_k]
target_docs = set()
docs_in_ranked_list = [e.doc_id for e in ranked_list]
target_docs.update(docs_in_ranked_list)
if self.include_all_judged:
docs_in_judgements = judgement.keys()
target_docs.update(docs_in_judgements)
print("Total of {} docs".format(len(target_docs)))
for doc_id in target_docs:
for tas in self.encoder.encode(query_id, doc_id):
label = 1 if doc_id in judgement and judgement[
doc_id] > 0 else 0
# if label:
# bprint(" -> Label={}".format(label))
# bflush()
# else:
# bempty()
candidate = QCKCandidate(doc_id, "")
info = {
'query': get_light_qckquery(qck_query),
'candidate': get_light_qckcandidate(candidate),
}
data_id = data_id_manager.assign(info)
inst = ClassificationInstanceWDataID.make_from_tas(
tas, label, data_id)
yield inst
def generate(self, data_id_manager, qids):
missing_cnt = 0
success_docs = 0
missing_doc_qid = []
for qid in qids:
if qid not in self.resource.candidate_doc_d:
assert qid not in self.resource.qrel.qrel_d
continue
tokens_d = self.resource.get_doc_tokens_d(qid)
q_tokens = self.resource.get_q_tokens(qid)
for doc_id in self.resource.get_candidate_doc_d(qid):
label = self.resource.get_label(qid, doc_id)
try:
doc_tokens = tokens_d[doc_id]
insts: List[Tuple[List, List]] = self.encoder.encode(q_tokens, doc_tokens)
for passage_idx, passage in enumerate(insts):
tokens_seg, seg_ids = passage
assert type(tokens_seg[0]) == str
assert type(seg_ids[0]) == int
data_id = data_id_manager.assign({
'query': QCKQuery(qid, ""),
'candidate': QCKCandidate(doc_id, ""),
'passage_idx': passage_idx,
})
inst = ClassificationInstanceWDataID(tokens_seg, seg_ids, label, data_id)
yield inst
success_docs += 1
except KeyError:
missing_cnt += 1
missing_doc_qid.append(qid)
if missing_cnt > 10:
print(missing_doc_qid)
print("success: ", success_docs)
raise KeyError
def claim_to_query(claim: Dict):
return QCKQuery(str(claim['cId']), claim['text'])
def to_qck_queries(queries):
qck_queries = []
for qid, query_text in queries.items():
e = QCKQuery(qid, query_text)
qck_queries.append(e)
return qck_queries
def light_query(obj: QCKQueryWToken):
return QCKQuery(obj.query_id, "")
def problem_to_qckquery(problem: ArguDataPoint):
return QCKQuery(str(problem.text1.id.id), problem.text1.text)
