def save_to_csv():
gold = get_claim_perspective_id_dict()
def routine(claims, out_path):
payloads = predict_by_elastic_search(claims, 50)
head = ['sentence1', 'sentence2', 'gold_label', 'cid', 'pid']
rows = []
for cid, data_list in payloads:
gold_pids = gold[cid]
all_pid_set = set(flatten(gold_pids))
for p_entry in data_list:
c_text = p_entry['claim_text']
p_text = p_entry['perspective_text']
y = 1 if p_entry['pid'] in all_pid_set else 0
row = [c_text, p_text, y, cid, p_entry['pid']]
rows.append(row)
f_out = csv.writer(open(out_path, "w", encoding="utf-8"),
dialect='excel-tab')
f_out.writerows([head] + rows)
claims, val = train_split()
routine(claims, get_file_path('train'))
d_ids: List[int] = list(load_dev_claim_ids())
claims = get_claims_from_ids(d_ids)
routine(claims, get_file_path('dev'))
d_ids: List[int] = list(load_test_claim_ids())
claims = get_claims_from_ids(d_ids)
routine(claims, get_file_path('test'))
def run_bm25_rm():
d_ids: List[int] = list(load_dev_claim_ids())
claims = get_claims_from_ids(d_ids)
rm_info = load_from_pickle("perspective_dev_claim_rm")
top_k = 7
pred = predict_by_bm25_rm(get_bm25_module(), rm_info, claims, top_k)
print(evaluate(pred))
def start_generate_jobs_for_train_val(
generator_functor: Callable[[Dict[int, List[Tuple[List[str], float]]]],
CPPNCGeneratorInterface], writer,
name_prefix):
# claim ids split to train/val
d_ids: List[int] = list(load_train_claim_ids())
claims = get_claims_from_ids(d_ids)
train, val = split_7_3(claims)
data = load_from_pickle("pc_train_a_passages")
entries, all_passages = data
cid_to_passages: Dict[int, List[Tuple[List[str], float]]] = {
claim['cId']: p
for claim, p in entries
}
generator = generator_functor(cid_to_passages)
print("Generate instances : train")
def worker_factory(out_dir):
return CPPNCWorker(train, generator, writer, out_dir)
runner = JobRunner(job_man_dir, 378, name_prefix + "_train",
worker_factory)
runner.start()
print("Generate instances : val")
def worker_factory(out_dir):
return CPPNCWorker(val, generator, writer, out_dir)
runner = JobRunner(job_man_dir, 162, name_prefix + "_val", worker_factory)
runner.start()
def get_perspective_candidates(claim_id):
from arg.perspectives import es_helper
claims = get_claims_from_ids([claim_id])
claim_text = claims[0]['text']
lucene_results = es_helper.get_perspective_from_pool(claim_text, 50)
for _text, _pid, _score in lucene_results:
yield _text, _pid
def claim_language_model_property():
dev_claim_ids = load_dev_claim_ids()
claims = get_claims_from_ids(dev_claim_ids)
all_ranked_list = ClaimRankedList()
all_voca = set()
candidate_k = 50
for claim in claims:
claim_text, perspectives = get_perspective(claim, candidate_k)
print(claim_text)
unigrams = get_relevant_unigrams(perspectives)
ranked_list = all_ranked_list.get(str(claim['cId']))
doc_ids = [t[0] for t in ranked_list]
print("Loading documents")
preload_tf(doc_ids)
docs = lmap(load_and_format_doc, doc_ids)
foreach(lambda doc: all_voca.update(doc['tokens_set']), docs)
# check hypothesis
# check_hypothesis(all_voca, cdf_cont, cdf_ncont, clueweb_cdf, clueweb_ctf, clueweb_df, clueweb_tf, ctf_cont,
# ctf_ncont, df_cont, df_ncont, tf_cont, tf_ncont, unigrams)
print("counting terms stat")
lm_classifier = build_lm(docs, unigrams)
for p_entry in perspectives:
_text, _pid, _score = p_entry
tokens = nltk.word_tokenize(_text)
score = sum(lmap(lm_classifier.per_token_odd, tokens))
print(_text, score)
def main():
docs: Dict[str, List[List[str]]] = load_from_pickle("dev_claim_docs")
_, clue12_13_df = load_clueweb12_B13_termstat()
d_ids: List[int] = list(load_dev_claim_ids())
claims = get_claims_from_ids(d_ids)
r = select_paragraph(docs, clue12_13_df, claims, "topk")
save_to_pickle(r, "dev_claim_paras")
def write(claim_ids, split_name):
claims = get_claims_from_ids(claim_ids)
queries = get_claims_query(claims, True)
out_path = os.path.join(
output_path,
"perspective_{}_claim_query_k0.json".format(split_name))
save_queries_to_file(queries, out_path)
def start_generate_jobs_for_train_val(generator: InstanceGenerator,
name_prefix):
# claim ids split to train/val
print("Loading data ....")
d_ids: List[int] = list(load_train_claim_ids())
claims = get_claims_from_ids(d_ids)
train, val = split_7_3(claims)
train_cids = {str(t['cId']) for t in train}
val_cids = {str(t['cId']) for t in val}
qk_candidate: List[QKUnit] = load_qk_candidate_train()
print("Generate instances : train")
qk_candidate_train: List[QKUnit] = list(
[qk for qk in qk_candidate if qk[0].query_id in train_cids])
qk_candidate_val = list(
[qk for qk in qk_candidate if qk[0].query_id in val_cids])
def worker_factory(out_dir):
return QCKWorker(qk_candidate_train, generator, out_dir)
runner = JobRunner(job_man_dir, 378, name_prefix + "_train",
worker_factory)
runner.start()
print("Generate instances : val")
def worker_factory(out_dir):
return QCKWorker(qk_candidate_val, generator, out_dir)
runner = JobRunner(job_man_dir, 162, name_prefix + "_val", worker_factory)
runner.start()
def submit_jobs_inner(claim_ids, split_name):
claims = get_claims_from_ids(claim_ids)
queries = get_claims_query(claims)
out_root = "/mnt/nfs/work3/youngwookim/data/perspective/{}_claim_rm3".format(
split_name)
exist_or_mkdir(out_root)
submit_rm_jobs(queries, out_root)
def get_eval_candidates(split, top_k=50) -> List[Tuple[int, List[Dict]]]:
# split -> claims
d_ids = load_claim_ids_for_split(split)
claims: List[Dict] = get_claims_from_ids(d_ids)
tokenizer = PCTokenizer()
def get_candidates(c: Dict) -> Tuple[int, List[Dict]]:
cid = c["cId"]
assert type(cid) == int
claim_text = c["text"]
lucene_results = es_helper.get_perspective_from_pool(claim_text, top_k)
candidate_list = []
for rank, (_text, _pid, _score) in enumerate(lucene_results):
rationale = "es_rank={} , es_score={}".format(rank, _score)
p_entry = {
'cid': cid,
'pid': _pid,
'claim_text': claim_text,
'perspective_text': _text,
'p_tokens': tokenizer.tokenize_stem(_text),
'rationale': rationale,
}
candidate_list.append(p_entry)
return cid, candidate_list
candidates: List[Tuple[int, List[Dict]]] = lmap(get_candidates, claims)
return candidates
def sum_random_walk_score(name_class):
d_ids: List[int] = list(load_train_claim_ids())
claims = get_claims_from_ids(d_ids)
claim_d = claims_to_dict(claims)
prob_score_d = load_from_pickle("pc_{}_word_prob_train".format(name_class))
stopwords = load_stopwords()
acc_counter_prob_init = Counter()
for claim_id, prob_scores in prob_score_d.items():
for k, v in prob_scores:
if k not in stopwords:
acc_counter_prob_init[k] += v
rw_score = dict(load_from_pickle("bias_random_walk_train_{}".format(name_class)))
acc_counter = Counter()
for claim_id, qtf in rw_score.items():
for k, v in qtf.items():
acc_counter[k] += v
acc_counter_prob_init = normalize_counter_to_sum1(acc_counter_prob_init)
acc_counter = normalize_counter_to_sum1(acc_counter)
new_counter = Counter()
for k, v in acc_counter.items():
if len(k) > 2:
new_v = v - acc_counter_prob_init[k]
new_counter[k] = new_v
return new_counter
def pc_new_init_prob():
d_ids: List[int] = list(load_dev_claim_ids())
claims = get_claims_from_ids(d_ids)
claim_d = claims_to_dict(claims)
bias_plus_word: Counter = load_from_pickle("bias_plus_words")
tokenizer = PCTokenizer()
base_p = max(bias_plus_word.values())
init_p_score_d = {}
for cid in d_ids:
c_text = claim_d[cid]
tokens = tokenizer.tokenize_stem(c_text)
score_for_cid = Counter()
for t, cnt in Counter(tokens).items():
prob = cnt * base_p
score_for_cid[t] = prob
for t, score in bias_plus_word.items():
score_for_cid[t] += score
score_for_cid = normalize_counter_to_sum1(score_for_cid)
init_p_score_d[cid] = score_for_cid
save_to_pickle(init_p_score_d, "pc_dev_new_init_prob")
def run_reweight():
d_ids: List[int] = list(load_dev_claim_ids())
claims = get_claims_from_ids(d_ids)
top_k = 7
param = {'k1': 0.5}
pred = predict_by_reweighter(get_bm25_module(), claims, top_k, param)
print(evaluate(pred))
def save_dev():
save_name = "pc_dev_a_passages"
q_res_path = FilePath(
"/mnt/nfs/work3/youngwookim/data/perspective/dev_claim/q_res_100")
d_ids = list(load_dev_claim_ids())
claims: List[Dict] = get_claims_from_ids(d_ids)
a_relevant_candidate(save_name, q_res_path, claims)
def work():
d_ids = list(load_train_claim_ids())
claims = get_claims_from_ids(d_ids)
is_train = True
all_data_points = get_candidates(claims, is_train)
all_data_points = all_data_points[:10]
binary_feature_demo(all_data_points)
def write_claim_perspective_pair_as_query():
split = "dev"
assert split in ["train", "dev", "test"]
d_ids = list({
"train": load_train_claim_ids(),
"dev": load_dev_claim_ids(),
"test": load_test_claim_ids()
}[split])
claims = get_claims_from_ids(d_ids)
print(len(claims), " claims")
is_train = split == "train"
all_data_points = get_candidates(claims, is_train)
k = 0
def get_query_entry_from_data_point(x: PerspectiveCandidate) -> DocQuery:
tokens = clean_tokenize_str_to_tokens(x.claim_text + " " + x.p_text)
qid = "{}_{}".format(x.cid, x.pid)
return format_query_bm25(qid, tokens, k)
queries = lmap(get_query_entry_from_data_point, all_data_points)
out_dir = query_dir_format.format(split)
exist_or_mkdir(out_dir)
n_query_per_file = 50
write_queries_to_files(n_query_per_file, out_dir, queries)
def run_eval_with_dict(pickle_name):
d_ids: List[int] = list(load_dev_claim_ids())
claims = get_claims_from_ids(d_ids)
print("targets", len(claims))
top_k = 8
pc_score_d = load_from_pickle(pickle_name)
pred = predict_from_dict(pc_score_d, claims, top_k)
print(evaluate(pred))
def get_qck_queries_from_cids(d_ids: List[int]):
claims: List[Dict] = get_claims_from_ids(d_ids)
def claim_to_query(claim: Dict):
return QCKQuery(str(claim['cId']), claim['text'])
queries: List[QCKQuery] = lmap(claim_to_query, claims)
return queries
def get_claim_lms() -> Dict[str, Counter]:
split = "train"
qids = list(get_qids_for_split(split_name2, split))
cids = lmap(int, qids)
claims = get_claims_from_ids(cids)
claim_lms = build_gold_lms(claims)
claim_lms_dict: Dict[str, Counter] = {str(claim_lm.cid): claim_lm.LM for claim_lm in claim_lms}
return claim_lms_dict
def main():
split = sys.argv[1]
ids = load_claim_ids_for_split(split)
claims = get_claims_from_ids(ids)
for c in claims:
print("Claim {} :\t{}".format(c['cId'], c['text']))
def write_simple_claim_queries():
for split in splits:
claim_ids = load_claim_ids_for_split(split)
claims = get_claims_from_ids(claim_ids)
queries = get_simple_claim_query(claims, True)
out_path = os.path.join(output_path, "perspective_query",
"simple_query_{}.json".format(split))
save_queries_to_file(queries, out_path)
def main():
args = parser.parse_args(sys.argv[1:])
save_name = args.save_name
d_ids = list(load_train_claim_ids())
claims: List[Dict] = get_claims_from_ids(d_ids)
candidate_perspectives: Dict[int, List[Dict]] = dict(
get_eval_candidates_from_pickle("train"))
make_cppnc_dummy_problem(claims, candidate_perspectives, save_name,
encode_two_inputs)
def run_eval_with_two_dict():
d_ids: List[int] = list(load_dev_claim_ids())
claims = get_claims_from_ids(d_ids)
print("targets", len(claims))
top_k = 7
pc_score_d = load_from_pickle("pc_bert_baseline_score_d")
pc_score_d2 = load_from_pickle("pc_random_walk_based_score_d")
pred = predict_from_two_dict(pc_score_d, pc_score_d2, claims, top_k)
print(evaluate(pred))
def save_random_walk_pred():
d_ids: List[int] = list(load_dev_claim_ids())
claims = get_claims_from_ids(d_ids)
top_k = 50
q_tf_replace = dict(load_from_pickle("random_walk_score_100"))
bm25 = get_bm25_module()
pred = pc_predict_from_vector_query(bm25, q_tf_replace, claims, top_k)
score_d = prediction_to_dict(pred)
save_to_pickle(score_d, "pc_random_walk_based_score_d")
def a_relevant():
d_ids = list(load_train_claim_ids())
claims: List[Dict] = get_claims_from_ids(d_ids)
claim_lms = build_gold_lms(claims)
claim_lms_d = {lm.cid: lm for lm in claim_lms}
bg_lm = average_counters(lmap(lambda x: x.LM, claim_lms))
log_bg_lm = get_lm_log(bg_lm)
claims = claims[:10]
top_n = 100
q_res_path = FilePath(
"/mnt/nfs/work3/youngwookim/data/perspective/train_claim/q_res_100")
ranked_list: Dict[
str, List[SimpleRankedListEntry]] = load_galago_ranked_list(q_res_path)
preload_docs(ranked_list, claims, top_n)
stopwords = load_stopwords_for_query()
alpha = 0.7
tokenizer = PCTokenizer()
for c in claims:
q_res: List[SimpleRankedListEntry] = ranked_list[str(c['cId'])]
claim_lm = claim_lms_d[c['cId']]
log_topic_lm = get_lm_log(smooth(claim_lm.LM, bg_lm, alpha))
log_odd: Counter = subtract(log_topic_lm, log_bg_lm)
def get_passage_score(p):
def get_score(t):
if t in stopwords:
return 0
return log_odd[tokenizer.stemmer.stem(t)]
return sum([get_score(t) for t in p]) / len(p) if len(p) > 0 else 0
docs = []
for i in range(top_n):
try:
doc = load_doc(q_res[i].doc_id)
docs.append(doc)
except KeyError:
docs.append(None)
pass
print(c['text'])
rows = []
for rank, doc in enumerate(docs):
if doc is None:
rows.append((rank, "-", "-"))
continue
scores = get_doc_score(doc, get_passage_score)
avg_score = average(scores)
max_score = max(scores)
rows.append((rank, avg_score, max_score))
print_table(rows)
def run_random_walk_score_with_weight():
d_ids: List[int] = list(load_dev_claim_ids())
claims = get_claims_from_ids(d_ids)
top_k = 7
q_tf_replace = dict(load_from_pickle("random_walk_score_100"))
q_tf_replace = dict_key_map(lambda x: int(x), q_tf_replace)
bm25 = get_bm25_module()
pred = pc_predict_vector_query_and_reweight(bm25, q_tf_replace, claims,
top_k, {'k1': 0.5})
print(evaluate(pred))
def run_lm():
d_ids: List[int] = list(load_dev_claim_ids())
claims = get_claims_from_ids(d_ids)
top_k = 5
q_tf_replace = dict(load_from_pickle("pc_dev_par_tf"))
q_tf_replace = dict(load_from_pickle("random_walk_score_100"))
bm25 = get_bm25_module()
ctf = load_collection_tf()
pred = predict_by_lm(q_tf_replace, ctf, bm25, claims, top_k)
print(evaluate(pred))
def run_write_claims_as_plain_query():
for claim_ids, out_name in [
(load_train_claim_ids(), "train_claim_query_raw.txt"),
(load_dev_claim_ids(), "dev_claim_query_raw.txt")
]:
claims = get_claims_from_ids(claim_ids)
q_str_list = get_claims_as_plain_query(claims)
f = open(pjoin(output_path, out_name), "w")
for s in q_str_list:
f.write(s + "\n")
def generate_classification_payload():
claims, val = train_split()
top_k = 50
pred = predict_by_elastic_search(claims, top_k)
save_to_pickle(pred, "perspective_cls_train_X")
d_ids: List[int] = list(load_dev_claim_ids())
claims = get_claims_from_ids(d_ids)
top_k = 50
pred = predict_by_elastic_search(claims, top_k)
save_to_pickle(pred, "perspective_cls_dev_X")
def a_relevant():
d_ids = list(load_train_claim_ids())
claims: List[Dict] = get_claims_from_ids(d_ids)
claims = claims
top_n = 10
q_res_path = FilePath(
"/mnt/nfs/work3/youngwookim/data/perspective/train_claim/q_res_100")
ranked_list: Dict[
str, List[SimpleRankedListEntry]] = load_galago_ranked_list(q_res_path)
preload_docs(ranked_list, claims, top_n)
claim_lms = build_gold_lms(claims)
claim_lms_d = {lm.cid: lm for lm in claim_lms}
bg_lm = average_counters(lmap(lambda x: x.LM, claim_lms))
log_bg_lm = get_lm_log(bg_lm)
stopwords = load_stopwords_for_query()
alpha = 0.3
tokenizer = PCTokenizer()
all_passages = []
entries = []
for c in claims:
q_res: List[SimpleRankedListEntry] = ranked_list[str(c['cId'])]
claim_lm = claim_lms_d[c['cId']]
log_topic_lm = get_lm_log(smooth(claim_lm.LM, bg_lm, alpha))
log_odd: Counter = subtract(log_topic_lm, log_bg_lm)
claim_text = c['text']
claim_tokens = tokenizer.tokenize_stem(claim_text)
scores = []
for t in claim_tokens:
if t in log_odd:
scores.append(log_odd[t])
base = average(scores)
def get_passage_score(p):
def get_score(t):
if t in stopwords:
return 0
return log_odd[tokenizer.stemmer.stem(t)]
return sum([get_score(t) for t in p]) / len(p) if len(p) > 0 else 0
passages = iterate_passages(q_res, top_n, get_passage_score)
all_passages.extend(passages)
a_rel_passages = lfilter(lambda x: x[1] > 0, passages)
entries.append((c, a_rel_passages))
data = entries, all_passages
save_to_pickle(data, "pc_train_a_passages")
