def gen_batch2str(src,
tgt,
generated,
gen_len,
src_vocab,
tgt_vocab,
indices=None,
active_out=None):
generated = generated.cpu().numpy().tolist()
gen_len = gen_len.cpu().numpy().tolist()
src = src.cpu().numpy().tolist()
tgt = tgt.cpu().numpy().tolist()
translated = []
for idx, l in enumerate(generated):
l = l[:gen_len[idx]]
sys_sent = " ".join([tgt_vocab.itos[tok] for tok in l])
src_sent = " ".join([src_vocab.itos[tok] for tok in src[idx]])
ref_sent = " ".join([tgt_vocab.itos[tok] for tok in tgt[idx]])
sys_sent = remove_special_tok(sys_sent)
src_sent = remove_special_tok(src_sent)
ref_sent = remove_special_tok(ref_sent)
translated.append("S: " + src_sent)
translated.append("H: " + sys_sent)
translated.append("T: " + ref_sent)
if indices is not None and active_out is not None:
translated.append("V: " + str(active_out[indices[idx]][3]))
translated.append(active_out[indices[idx]][4])
return translated
def gen_batch2str(src, generated, gen_len, src_vocab, tgt_vocab):
generated = generated.cpu().numpy().tolist()
gen_len = gen_len.cpu().numpy().tolist()
src = src.cpu().numpy().tolist()
translated = []
for i, l in enumerate(generated):
l = l[:gen_len[i]]
sys_sent = " ".join([tgt_vocab.itos[tok] for tok in l])
src_sent = " ".join([src_vocab.itos[tok] for tok in src[i]])
sys_sent = remove_special_tok(remove_bpe(sys_sent))
src_sent = remove_special_tok(remove_bpe(src_sent))
translated.append("S: " + src_sent)
translated.append("H: " + sys_sent)
return translated
def gen_batch2str(generated, gen_len, tgt_vocab):
generated = generated.cpu().numpy().tolist()
gen_len = gen_len.cpu().numpy().tolist()
translated = []
for i, l in enumerate(generated):
l = l[:gen_len[i]]
sys_sent = " ".join([tgt_vocab.itos[tok] for tok in l])
sys_sent = remove_special_tok(sys_sent)
translated.append(sys_sent)
return translated
def translate(args, net, src_vocab, tgt_vocab):
"done"
sentences = [l.split() for l in args.text]
translated = []
for i_s, sentence in enumerate(sentences):
s_trans = translate_sentence(sentence, net, args, src_vocab, tgt_vocab)
s_trans = remove_special_tok(remove_bpe(s_trans))
translated.append(s_trans)
print(translated[-1])
return translated
def translate(args, net, src_vocab, tgt_vocab):
"done"
sentences = [l.split() for l in args.text]
translated = []
if args.greedy:
infer_dataset = ParallelDataset(args.text, args.ref_text, src_vocab,
tgt_vocab)
if args.batch_size is not None:
infer_dataset.BATCH_SIZE = args.batch_size
if args.max_batch_size is not None:
infer_dataset.max_batch_size = args.max_batch_size
if args.tokens_per_batch is not None:
infer_dataset.tokens_per_batch = args.tokens_per_batch
infer_dataiter = iter(infer_dataset.get_iterator(True, True))
num_sents = 0
for raw_batch in infer_dataiter:
src_mask = (raw_batch.src !=
src_vocab.stoi[config.PAD]).unsqueeze(-2)
if args.use_cuda:
src, src_mask = raw_batch.src.cuda(), src_mask.cuda()
generated, gen_len = greedy(args, net, src, src_mask, src_vocab,
tgt_vocab)
new_translations = gen_batch2str(src, raw_batch.tgt, generated,
gen_len, src_vocab, tgt_vocab)
print('src size : {}'.format(src.size()))
'''
for res_sent in new_translations:
print(res_sent)
translated.extend(new_translations)
'''
else:
for i_s, sentence in enumerate(sentences):
s_trans = translate_sentence(sentence, net, args, src_vocab,
tgt_vocab)
s_trans = remove_special_tok(remove_bpe(s_trans))
translated.append(s_trans)
print(translated[-1])
return translated
def back_translation_modify(args):
f = open(args.unlabeled_dataset, 'r')
unlabeled_dataset = f.read().split("\n")[:-1]
f.close()
src_labeled_dataset, tgt_labeled_dataset = args.labeled_dataset.split(",")
labeled_dataset = []
f = open(src_labeled_dataset, 'r')
labeled_dataset.append(f.read().split("\n")[:-1])
f.close()
f = open(tgt_labeled_dataset, 'r')
labeled_dataset.append(f.read().split("\n")[:-1])
f.close()
# Read oracle
f = open(args.oracle, "r")
oracle = f.read().split("\n")[:-1]
f.close()
assert len(oracle) == len(unlabeled_dataset)
# Read active out_1
f = open(args.active_out_1, "r")
active_out_1 = f.read().split("\n")[:-1]
f.close()
# Sort active_out
assert len(active_out_1) % 5 == 0
assert len(active_out_1) / 5 == len(oracle)
active_out_1 = [[
active_out_1[i], active_out_1[i + 1], active_out_1[i + 2],
float(active_out_1[i + 3].split(' ')[-1]), active_out_1[i + 4]
] for i in range(0, len(active_out_1), 5)]
active_out_1 = sorted(active_out_1, key=lambda item: item[3])
# Read active out_2
f = open(args.active_out_2, "r")
active_out_2 = f.read().split("\n")[:-1]
f.close()
# Sort active_out
assert len(active_out_2) % 4 == 0
assert len(active_out_2) / 4 == len(oracle)
active_out_2 = [[
active_out_2[i], "H: ", active_out_2[i + 1],
float(active_out_2[i + 2].split(' ')[-1]), active_out_2[i + 3]
] for i in range(0, len(active_out_2), 4)]
active_out_2 = sorted(active_out_2, key=lambda item: item[3])
active_out = [0] * len(active_out_1)
for i in range(len(active_out_1)):
idx = int(active_out_1[i][4].split()[-1])
active_out[idx] = [
active_out_1[i][0], active_out_1[i][1], active_out_1[i][2], i
]
for i in range(len(active_out_2)):
idx = int(active_out_2[i][4].split()[-1])
assert active_out[idx][0] == active_out_2[i][0]
assert active_out[idx][2] == active_out_2[i][2]
active_out[idx][3] += i
active_out = sorted(active_out, key=lambda item: item[3])
# Change datasets
indices = np.arange(len(active_out))
lengths = np.array([
len(remove_special_tok(remove_bpe(item[0][len("S: "):])).split(' '))
for item in active_out
])
include_oracle = np.cumsum(lengths) <= args.tok_budget
for idx in indices[include_oracle]:
labeled_dataset[0].append(active_out[idx][0][len("S: "):].strip())
labeled_dataset[1].append(active_out[idx][2][len("T: "):].strip())
unlabeled_dataset = []
oracle = []
not_include = (1 - include_oracle).astype('bool')
not_include = indices[not_include]
for idx in not_include:
unlabeled_dataset.append(active_out[idx][0][len("S: "):].strip())
oracle.append(active_out[idx][2][len("T: "):].strip())
combined = list(zip(unlabeled_dataset, oracle))
random.shuffle(combined)
unlabeled_dataset[:], oracle[:] = zip(*combined)
# Store new labeled, unlabeled, oracle dataset
f = open(args.output_unlabeled_dataset, 'w')
f.write("\n".join(unlabeled_dataset) + "\n")
f.close()
output_src_labeled_dataset, output_tgt_labeled_dataset = args.output_labeled_dataset.split(
",")
f = open(output_src_labeled_dataset, 'w')
f.write("\n".join(labeled_dataset[0]) + "\n")
f.close()
f = open(output_tgt_labeled_dataset, 'w')
f.write("\n".join(labeled_dataset[1]) + "\n")
f.close()
f = open(args.output_oracle, 'w')
f.write("\n".join(oracle) + "\n")
f.close()
output_new_queries_src, output_new_queries_tgt = args.output_new_queries.split(
',')
f = open(output_new_queries_src, 'w')
f.write("\n".join([
active_out[idx][0][len("S: "):].strip()
for idx in range(len(include_oracle)) if include_oracle[idx]
]) + '\n')
f.close()
f = open(output_new_queries_tgt, 'w')
f.write("\n".join([
active_out[idx][2][len("T: "):].strip()
for idx in range(len(include_oracle)) if include_oracle[idx]
]) + '\n')
f.close()
output_train_src, output_train_tgt = args.output_train.split(',')
include_pseudo = np.cumsum(lengths) <= (args.tok_budget +
args.back_translation_tok_budget)
include_pseudo = np.logical_xor(include_pseudo, include_oracle)
include_pseudo = indices[include_pseudo]
labeled_dataset[0].extend(
[active_out[idx][0][len("S: "):].strip() for idx in include_pseudo])
labeled_dataset[1].extend(
[active_out[idx][1][len("H: "):].strip() for idx in include_pseudo])
ots = labeled_dataset[0]
ott = labeled_dataset[1]
f = open(output_train_src, 'w')
f.write("\n".join(ots) + "\n")
f.close()
f = open(output_train_tgt, 'w')
f.write("\n".join(ott) + "\n")
f.close()
def supvised_learning_modify(args):
f = open(args.unlabeled_dataset, 'r')
unlabeled_dataset = f.read().split("\n")[:-1]
f.close()
src_labeled_dataset, tgt_labeled_dataset = args.labeled_dataset.split(",")
labeled_dataset = []
f = open(src_labeled_dataset, 'r')
labeled_dataset.append(f.read().split("\n")[:-1])
f.close()
f = open(tgt_labeled_dataset, 'r')
labeled_dataset.append(f.read().split("\n")[:-1])
f.close()
# Read oracle
f = open(args.oracle, "r")
oracle = f.read().split("\n")[:-1]
assert len(oracle) == len(unlabeled_dataset)
# Read active out
f = open(args.active_out, "r")
active_out = f.read().split("\n")[:-1]
# Sort active_out
assert len(active_out) % 4 == 0
assert len(active_out) / 4 == len(oracle)
active_out = [[
active_out[i], active_out[i + 1],
float(active_out[i + 2].split(' ')[-1]), active_out[i + 3]
] for i in range(0, len(active_out), 4)]
random.shuffle(active_out)
active_out = sorted(active_out, key=lambda item: item[2])
# Change datasets
indices = np.arange(len(active_out))
lengths = np.array([
len(remove_special_tok(remove_bpe(item[0][len("S: "):])).split(' '))
for item in active_out
])
include = np.cumsum(lengths) <= args.tok_budget
not_include = (1 - include).astype('bool')
include = indices[include]
not_include = indices[not_include]
for idx in include:
labeled_dataset[0].append(active_out[idx][0][len("S: "):].strip())
labeled_dataset[1].append(active_out[idx][1][len("T: "):].strip())
unlabeled_dataset = []
oracle = []
for idx in not_include:
unlabeled_dataset.append(active_out[idx][0][len("S: "):].strip())
oracle.append(active_out[idx][1][len("T: "):].strip())
combined = list(zip(unlabeled_dataset, oracle))
random.shuffle(combined)
unlabeled_dataset[:], oracle[:] = zip(*combined)
# Store new labeled, unlabeled, oracle dataset
f = open(args.output_unlabeled_dataset, 'w')
f.write("\n".join(unlabeled_dataset) + "\n")
f.close()
output_src_labeled_dataset, output_tgt_labeled_dataset = args.output_labeled_dataset.split(
",")
f = open(output_src_labeled_dataset, 'w')
f.write("\n".join(labeled_dataset[0]) + "\n")
f.close()
f = open(output_tgt_labeled_dataset, 'w')
f.write("\n".join(labeled_dataset[1]) + "\n")
f.close()
f = open(args.output_oracle, 'w')
f.write("\n".join(oracle) + "\n")
f.close()
def query_instances(args,
unlabeled_dataset,
oracle,
active_func="random",
labeled_dataset=None):
# lc stands for least confident
# te stands for token entropy
# tte stands for total token entropy
assert active_func in [
"random", "longest", "shortest", "lc", "margin", "te", "tte", "dden"
]
# lengths represents number of tokens, so BPE should be removed
lengths = np.array([
len(remove_special_tok(remove_bpe(s)).split())
for s in unlabeled_dataset
])
# Preparations before querying instances
# Reloading network parameters
args.use_cuda = (args.no_cuda == False) and torch.cuda.is_available()
net, _ = model.get()
assert os.path.exists(args.checkpoint)
net, src_vocab, tgt_vocab = load_model(args.checkpoint, net)
if args.use_cuda:
net = net.cuda()
# Initialize inference dataset (Unlabeled dataset)
infer_dataset = Dataset(unlabeled_dataset, src_vocab)
if args.batch_size is not None:
infer_dataset.BATCH_SIZE = args.batch_size
if args.max_batch_size is not None:
infer_dataset.max_batch_size = args.max_batch_size
if args.tokens_per_batch is not None:
infer_dataset.tokens_per_batch = args.tokens_per_batch
infer_dataiter = iter(
infer_dataset.get_iterator(shuffle=True,
group_by_size=True,
include_indices=True))
# Start ranking unlabeled dataset
indices = np.arange(len(unlabeled_dataset))
if active_func == "random":
result = get_scores(args, net, active_func, infer_dataiter, src_vocab,
tgt_vocab)
random.shuffle(result)
indices = [item[1] for item in result]
indices = np.array(indices).astype('int')
for idx in indices:
print("S:", unlabeled_dataset[idx])
print("H:", result[idx][2])
print("T:", oracle[idx])
print("V:", result[idx][0])
print("I:", args.input, args.reference,
idx + args.previous_num_sents)
elif active_func == "longest":
result = get_scores(args, net, active_func, infer_dataiter, src_vocab,
tgt_vocab)
result = [(len(
remove_special_tok(remove_bpe(
unlabeled_dataset[item[1]])).split(' ')), item[1], item[2])
for item in result]
result = sorted(result, key=lambda item: -item[0])
indices = [item[1] for item in result]
indices = np.array(indices).astype('int')
for idx in indices:
print("S:", unlabeled_dataset[idx])
print("H:", result[idx][2])
print("T:", oracle[idx])
print("V:", -result[idx][0])
print("I:", args.input, args.reference,
idx + args.previous_num_sents)
elif active_func == "shortest":
result = get_scores(args, net, active_func, infer_dataiter, src_vocab,
tgt_vocab)
result = [(len(
remove_special_tok(remove_bpe(
unlabeled_dataset[item[1]])).split(' ')), item[1], item[2])
for item in result]
result = sorted(result, key=lambda item: item[0])
indices = [item[1] for item in result]
indices = np.array(indices).astype('int')
for idx in indices:
print("S:", unlabeled_dataset[idx])
print("H:", result[idx][2])
print("T:", oracle[idx])
print("V:", result[idx][0])
print("I:", args.input, args.reference,
idx + args.previous_num_sents)
indices = indices[np.argsort(lengths[indices])]
elif active_func in ["lc", "margin", "te", "tte"]:
result = get_scores(args, net, active_func, infer_dataiter, src_vocab,
tgt_vocab)
result = sorted(result, key=lambda item: item[0])
indices = [item[1] for item in result]
indices = np.array(indices).astype('int')
for idx in range(len(result)):
print("S:", unlabeled_dataset[result[idx][1]])
print("H:", result[idx][2])
print("T:", oracle[result[idx][1]])
print("V:", result[idx][0])
print("I:", args.input, args.reference,
result[idx][1] + args.previous_num_sents)
elif active_func == "dden":
punc = [
".", ",", "?", "!", "'", "<", ">", ":", ";", "(", ")", "{", "}",
"[", "]", "-", "..", "...", "...."
]
lamb1 = 1
lamb2 = 1
p_u = {}
unlabeled_dataset_without_bpe = []
labeled_dataset_without_bpe = [[], []]
for s in unlabeled_dataset:
unlabeled_dataset_without_bpe.append(
remove_special_tok(remove_bpe(s)))
for s in labeled_dataset[0]:
labeled_dataset_without_bpe[0].append(
remove_special_tok(remove_bpe(s)))
for s in labeled_dataset[1]:
labeled_dataset_without_bpe[1].append(
remove_special_tok(remove_bpe(s)))
for s in unlabeled_dataset_without_bpe:
sentence = s.split()
for token in sentence:
if token not in punc:
if token in p_u.keys():
p_u[token] += 1
else:
p_u[token] = 1
total_dden = 0
for token in p_u.keys():
p_u[token] = math.log(p_u[token] + 1)
total_dden += p_u[token]
for token in p_u.keys():
p_u[token] /= total_dden
count_l = {}
for s in labeled_dataset_without_bpe[0]:
sentence = s.split()
for token in sentence:
if token not in punc:
if token in count_l.keys():
count_l[token] += 1
else:
count_l[token] = 1
dden = []
for s in unlabeled_dataset_without_bpe:
sentence = s.split()
len_for_sentence = 0
sum_for_sentence = 0
for token in sentence:
if token not in punc:
if token in count_l.keys():
sum_for_sentence += p_u[token] * math.exp(
-lamb1 * count_l[token])
else:
sum_for_sentence += p_u[token]
len_for_sentence += 1
if len_for_sentence != 0:
sum_for_sentence /= len_for_sentence
dden.append(sum_for_sentence)
unlabeled_with_index = []
for i in range((len(unlabeled_dataset))):
unlabeled_with_index.append((dden[i], i))
unlabeled_with_index.sort(key=lambda x: x[0], reverse=True)
count_batch = {}
dden_new = []
for _, i in unlabeled_with_index:
sentence = unlabeled_dataset_without_bpe[i].split()
len_for_sentence = 0
sum_for_sentence = 0
for token in sentence:
if token not in punc:
p_tmp = p_u[token]
if token in count_batch.keys():
p_tmp = 0
p_tmp *= math.exp(-lamb2 * count_batch[token])
if token in count_l.keys():
p_tmp *= math.exp(-lamb1 * count_l[token])
sum_for_sentence += p_tmp
len_for_sentence += 1
for token in sentence:
if token not in punc:
if token in count_batch.keys():
count_batch[token] += 1
else:
count_batch[token] = 1
if len_for_sentence != 0:
sum_for_sentence /= len_for_sentence
dden_new.append((sum_for_sentence, i))
dden_new.sort(key=lambda x: x[1])
dden_sort = []
for dden_num, _ in dden_new:
dden_sort.append(dden_num)
ddens = np.array(dden_sort)
indices = indices[np.argsort(-ddens)]
for idx in indices:
print("S:", unlabeled_dataset[idx])
print("T:", oracle[idx])
print("V:", -ddens[idx])
print("I:", args.input, args.reference, idx)
def main():
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers(help='two modes, score or modify')
# Add argument for score mode
parser_score = subparsers.add_parser(
'score', help='Get active function scores for each unlabeled sentence')
parser_score.add_argument("-a",
"--active_func",
type=str,
help="Active query function type",
required=True)
parser_score.add_argument("-i",
"--input",
type=str,
help="where to read unlabeled data")
parser_score.add_argument("-lb",
"--input_labeled",
type=str,
help="where to read labeled data")
parser_score.add_argument("-ref",
"--reference",
type=str,
help="where to read oracle data")
parser_score.add_argument(
'-ckpt',
'--checkpoint',
type=str,
help="Checkpoint path to reload network parameters")
parser_score.add_argument(
'-max_len',
type=int,
default=250,
help="Maximum length for generating translations")
parser_score.add_argument('-no_cuda',
action="store_true",
help="Use cpu to do translation")
parser_score.add_argument('--batch_size',
type=int,
default=None,
help="Batch size for generating translations")
parser_score.add_argument(
'--max_batch_size',
type=int,
default=None,
help="Maximum batch size if tokens_per_batch is not None")
parser_score.add_argument(
'--tokens_per_batch',
type=int,
default=None,
help="Maximum number of tokens in a batch when generating translations"
)
# Add argument for modify mode
parser_modify = subparsers.add_parser(
'modify',
help=
'Change labeled, unlabeled oracle dataset after activation function values is calculated'
)
parser_modify.add_argument("-U",
"--unlabeled_dataset",
type=str,
help="where to read unlabelded dataset",
required=True)
parser_modify.add_argument(
"-L",
"--labeled_dataset",
type=str,
help="where to read labeled dataset, split by comma, e.g. l.de,l.en",
required=True)
parser_modify.add_argument("--oracle",
type=str,
help="where to read oracle dataset",
required=True)
parser_modify.add_argument("-tb",
"--tok_budget",
type=int,
help="Token budget",
required=True)
parser_modify.add_argument("-OU",
"--output_unlabeled_dataset",
type=str,
help="path to store new unlabeled dataset",
required=True)
parser_modify.add_argument("-OL",
"--output_labeled_dataset",
type=str,
help="path to store new labeled dataset",
required=True)
parser_modify.add_argument("-OO",
"--output_oracle",
type=str,
help="path to oracle",
required=True)
parser_modify.add_argument('-AO',
'--active_out',
type=str,
help="path to active function output")
args = parser.parse_args()
args.mode = "score" if hasattr(args, "active_func") else "modify"
if args.mode == "score":
f = open(args.input, 'r')
text = f.read().split('\n')
if text[-1] == "":
text = text[:-1]
f.close()
f = open(args.reference, 'r')
ref_text = f.read().split('\n')
if ref_text[-1] == "":
ref_text = ref_text[:-1]
f.close()
if hasattr(args, "input_labeled"):
lab_text = []
f = open(args.input_labeled + '.de', 'r')
labeled_text = f.read().split('\n')
if labeled_text[-1] == "":
labeled_text = labeled_text[:-1]
f.close()
lab_text.append(labeled_text)
f = open(args.input_labeled + '.en', 'r')
labeled_text = f.read().split('\n')
if labeled_text[-1] == "":
labeled_text = labeled_text[:-1]
f.close()
lab_text.append(labeled_text)
query_instances(args, text, ref_text, args.active_func, lab_text)
else:
query_instances(args, text, ref_text, args.active_func)
else:
# Read labeled and unlabeled datasets
f = open(args.unlabeled_dataset, 'r')
unlabeled_dataset = f.read().split("\n")[:-1]
f.close()
src_labeled_dataset, tgt_labeled_dataset = args.labeled_dataset.split(
",")
labeled_dataset = []
f = open(src_labeled_dataset, 'r')
labeled_dataset.append(f.read().split("\n")[:-1])
f.close()
f = open(tgt_labeled_dataset, 'r')
labeled_dataset.append(f.read().split("\n")[:-1])
f.close()
# Read oracle
f = open(args.oracle, "r")
oracle = f.read().split("\n")[:-1]
assert len(oracle) == len(unlabeled_dataset)
# Read active out
f = open(args.active_out, "r")
active_out = f.read().split("\n")[:-1]
# Sort active_out
assert len(active_out) % 4 == 0
assert len(active_out) / 4 == len(oracle)
active_out = [[
active_out[i], active_out[i + 1],
float(active_out[i + 2].split(' ')[-1]), active_out[i + 3]
] for i in range(0, len(active_out), 4)]
active_out = sorted(active_out, key=lambda item: item[2])
# Change datasets
indices = np.arange(len(active_out))
lengths = np.array([
len(
remove_special_tok(remove_bpe(
item[0][len("S: "):])).split(' ')) for item in active_out
])
include = np.cumsum(lengths) <= args.tok_budget
not_include = (1 - include).astype('bool')
include = indices[include]
not_include = indices[not_include]
for idx in include:
labeled_dataset[0].append(active_out[idx][0][len("S: "):])
labeled_dataset[1].append(active_out[idx][1][len("T: "):])
unlabeled_dataset = []
oracle = []
for idx in not_include:
unlabeled_dataset.append(active_out[idx][0][len("S: "):])
oracle.append(active_out[idx][1][len("T: "):])
combined = list(zip(unlabeled_dataset, oracle))
random.shuffle(combined)
unlabeled_dataset[:], oracle[:] = zip(*combined)
# Store new labeled, unlabeled, oracle dataset
f = open(args.output_unlabeled_dataset, 'w')
f.write("\n".join(unlabeled_dataset) + "\n")
f.close()
output_src_labeled_dataset, output_tgt_labeled_dataset = args.output_labeled_dataset.split(
",")
f = open(output_src_labeled_dataset, 'w')
f.write("\n".join(labeled_dataset[0]) + "\n")
f.close()
f = open(output_tgt_labeled_dataset, 'w')
f.write("\n".join(labeled_dataset[1]) + "\n")
f.close()
f = open(args.output_oracle, 'w')
f.write("\n".join(oracle) + "\n")
f.close()
def query_instances(args, unlabeled_dataset, oracle, active_func="random"):
# lc stands for least confident
# te stands for token entropy
# tte stands for total token entropy
assert active_func in [
"random", "longest", "shortest", "lc", "margin", "te", "tte"
]
# lengths represents number of tokens, so BPE should be removed
lengths = np.array([
len(remove_special_tok(remove_bpe(s)).split())
for s in unlabeled_dataset
])
# Preparations before querying instances
# Reloading network parameters
args.use_cuda = (args.no_cuda == False) and torch.cuda.is_available()
net, _ = model.get()
assert os.path.exists(args.checkpoint)
net, src_vocab, tgt_vocab = load_model(args.checkpoint, net)
if args.use_cuda:
net = net.cuda()
# Initialize inference dataset (Unlabeled dataset)
infer_dataset = Dataset(unlabeled_dataset, src_vocab)
if args.batch_size is not None:
infer_dataset.BATCH_SIZE = args.batch_size
if args.max_batch_size is not None:
infer_dataset.max_batch_size = args.max_batch_size
if args.tokens_per_batch is not None:
infer_dataset.tokens_per_batch = args.tokens_per_batch
infer_dataiter = iter(
infer_dataset.get_iterator(shuffle=True,
group_by_size=True,
include_indices=True))
# Start ranking unlabeled dataset
indices = np.arange(len(unlabeled_dataset))
if active_func == "random":
result = get_scores(args, net, active_func, infer_dataiter, src_vocab,
tgt_vocab)
random.shuffle(result)
indices = [item[1] for item in result]
indices = np.array(indices).astype('int')
for idx in indices:
print("S:", unlabeled_dataset[idx])
print("H:", result[idx][2])
print("T:", oracle[idx])
print("V:", result[idx][0])
print("I:", args.input, args.reference, idx)
elif active_func == "longest":
result = get_scores(args, net, active_func, infer_dataiter, src_vocab,
tgt_vocab)
result = [(len(
remove_special_tok(remove_bpe(
unlabeled_dataset[item[1]])).split(' ')), item[1], item[2])
for item in result]
result = sorted(result, key=lambda item: -item[0])
indices = [item[1] for item in result]
indices = np.array(indices).astype('int')
for idx in indices:
print("S:", unlabeled_dataset[idx])
print("H:", result[idx][2])
print("T:", oracle[idx])
print("V:", -result[idx][0])
print("I:", args.input, args.reference, idx)
elif active_func == "shortest":
result = get_scores(args, net, active_func, infer_dataiter, src_vocab,
tgt_vocab)
result = [(len(
remove_special_tok(remove_bpe(
unlabeled_dataset[item[1]])).split(' ')), item[1], item[2])
for item in result]
result = sorted(result, key=lambda item: item[0])
indices = [item[1] for item in result]
indices = np.array(indices).astype('int')
for idx in indices:
print("S:", unlabeled_dataset[idx])
print("H:", result[idx][2])
print("T:", oracle[idx])
print("V:", result[idx][0])
print("I:", args.input, args.reference, idx)
indices = indices[np.argsort(lengths[indices])]
elif active_func in ["lc", "margin", "te", "tte"]:
result = get_scores(args, net, active_func, infer_dataiter, src_vocab,
tgt_vocab)
result = sorted(result, key=lambda item: item[0])
indices = [item[1] for item in result]
indices = np.array(indices).astype('int')
for idx in range(len(result)):
print("S:", unlabeled_dataset[result[idx][1]])
print("H:", result[idx][2])
print("T:", oracle[result[idx][1]])
print("V:", result[idx][0])
print("I:", args.input, args.reference, result[idx][1])
def main():
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers(help='two modes, get or translate')
parser_get = subparsers.add_parser(
'get', help='Get texts that needs to be labeled or translated')
parser_get.add_argument(
'-AO',
'--active_out',
type=str,
default=None,
help="Output file generated by active.py score mode")
parser_get.add_argument('-tb',
'--tok_budget',
type=int,
help="Token budget")
parser_get.add_argument('-bttb',
'--back_translation_tok_budget',
type=int,
help="Back translation token budget")
parser_get.add_argument('--sort',
action="store_true",
help="Whether to sort active out by value")
parser_get.add_argument('-o', '--output', type=str, help="Output filepath")
parser_get.add_argument('-on',
'--output_num',
type=int,
default=1,
help="Output filepath")
parser_trans = subparsers.add_parser('translate',
help='Translate sentences')
parser_trans.add_argument('-i', '--input', type=str, help='Input file')
parser_trans.add_argument('-o', '--output', type=str, help="Output file")
parser_trans.add_argument('--ckpt', required=True)
parser_trans.add_argument('--max_len', type=int, default=250)
parser_trans.add_argument('--gen_a', type=float, default=1.3)
parser_trans.add_argument('--gen_b', type=int, default=5)
parser_trans.add_argument('--no_cuda', action='store_true')
parser_trans.add_argument('--batch_size', type=int, default=None)
parser_trans.add_argument('--max_batch_size', type=int, default=None)
parser_trans.add_argument('--tokens_per_batch', type=int, default=None)
args = parser.parse_args()
args.mode = "get" if hasattr(args, 'active_out') else "translate"
if args.mode == 'translate':
args.use_cuda = (args.no_cuda == False) and torch.cuda.is_available()
if args.mode == "get":
f = open(args.active_out, 'r')
lines = f.read().split('\n')[:-1]
f.close()
assert len(lines) % 4 == 0
active_out = [(lines[idx], lines[idx + 1],
float(lines[idx + 2].split(' ')[-1]), lines[idx + 3])
for idx in range(0, len(lines), 4)]
if args.sort:
active_out = sorted(active_out, key=lambda item: item[2])
indices = np.arange(len(active_out))
lengths = np.array([
len(
remove_special_tok(remove_bpe(
item[0][len("S: "):])).split(' ')) for item in active_out
])
include_oracle = np.cumsum(lengths) <= args.tok_budget
include_pseudo = np.cumsum(lengths) <= (
args.tok_budget + args.back_translation_tok_budget)
include_pseudo = np.logical_xor(include_pseudo, include_oracle)
include_pseudo = indices[include_pseudo]
include_oracle = indices[include_oracle]
others = [
idx for idx in indices
if (idx not in include_pseudo) and (idx not in include_oracle)
]
# Output oracle and others
output_oracle = args.output + '_oracle'
f = open(output_oracle, 'w')
out = []
for idx in include_oracle:
item = []
item.append(active_out[idx][0])
item.append('H: ' + active_out[idx][1][len('T: '):])
item.append('T: ' + active_out[idx][1][len('T: '):])
item.append('V: ' + str(active_out[idx][2]))
item.append(active_out[idx][3])
out.extend(item)
f.write('\n'.join(out) + '\n')
f.close()
output_others = args.output + '_others'
f = open(output_others, 'w')
out = []
for idx in others:
item = []
item.append(active_out[idx][0])
item.append('H: ' + active_out[idx][1][len('T: '):])
item.append('T: ' + active_out[idx][1][len('T: '):])
item.append('V: ' + str(active_out[idx][2]))
item.append(active_out[idx][3])
out.extend(item)
f.write('\n'.join(out) + '\n')
f.close()
# Output pseudo
if args.output_num > 1:
n_lines = len(include_pseudo) // args.output_num + 1
for n in range(args.output_num):
output_pseudo = args.output + '_pseudo_' + str(n)
f = open(output_pseudo, 'w')
out = []
for idx in include_pseudo[n * n_lines:(n + 1) * n_lines]:
item = []
item.append(active_out[idx][0])
item.append('H: ' + active_out[idx][1][len('T: '):])
item.append('T: ' + active_out[idx][1][len('T: '):])
item.append('V: ' + str(active_out[idx][2]))
item.append(active_out[idx][3])
out.extend(item)
f.write('\n'.join(out) + '\n')
f.close()
else:
assert args.output_num == 1
output_pseudo = args.output + '_pseudo'
f = open(output_pseudo, 'w')
out = []
for idx in include_pseudo:
item = []
item.append(active_out[idx][0])
item.append('H: ' + active_out[idx][1][len('T: '):])
item.append('T: ' + active_out[idx][1][len('T: '):])
item.append('V: ' + str(active_out[idx][2]))
item.append(active_out[idx][3])
out.extend(item)
f.write('\n'.join(out) + '\n')
f.close()
elif args.mode == 'translate':
assert args.max_len > 10
net, _ = model.get()
net, src_vocab, tgt_vocab = load_model(args.ckpt, net)
if args.use_cuda:
net = net.cuda()
fpath = args.input
try:
lines = open(fpath, 'r').read().split('\n')[:-1]
active_out = [(lines[idx], lines[idx + 1], lines[idx + 2],
float(lines[idx + 3].split(' ')[-1]),
lines[idx + 4]) for idx in range(0, len(lines), 5)]
args.text = [a[0][len('S: '):].strip() for a in active_out]
args.ref_text = [a[2][len('T: '):].strip() for a in active_out]
except:
print("error opening or reading text file")
out = translate(args, net, src_vocab, tgt_vocab, active_out)
f = open(args.output, 'w')
f.write('\n'.join(out) + '\n')
f.close()
def query_instances(args,
unlabeled_dataset,
active_func="random",
tok_budget=None):
# lc stands for least confident
# te stands for token entropy
# tte stands for total token entropy
assert active_func in [
"random", "longest", "shortest", "lc", "margin", "te", "tte"
]
assert isinstance(tok_budget, int)
# lengths represents number of tokens, so BPE should be removed
lengths = np.array([
len(remove_special_tok(remove_bpe(s)).split())
for s in unlabeled_dataset
])
total_num = sum(lengths)
if total_num < tok_budget:
tok_budget = total_num
# Preparations before querying instances
if active_func in ["lc", "margin", "te", "tte"]:
# Reloading network parameters
args.use_cuda = (args.no_cuda == False) and torch.cuda.is_available()
net, _ = model.get()
assert os.path.exists(args.checkpoint)
net, src_vocab, tgt_vocab = load_model(args.checkpoint, net)
if args.use_cuda:
net = net.cuda()
# Initialize inference dataset (Unlabeled dataset)
infer_dataset = Dataset(unlabeled_dataset, src_vocab)
if args.batch_size is not None:
infer_dataset.BATCH_SIZE = args.batch_size
if args.max_batch_size is not None:
infer_dataset.max_batch_size = args.max_batch_size
if args.tokens_per_batch is not None:
infer_dataset.tokens_per_batch = args.tokens_per_batch
infer_dataiter = iter(
infer_dataset.get_iterator(shuffle=True,
group_by_size=True,
include_indices=True))
# Start ranking unlabeled dataset
indices = np.arange(len(unlabeled_dataset))
if active_func == "random":
np.random.shuffle(indices)
elif active_func == "longest":
indices = indices[np.argsort(-lengths[indices])]
elif active_func == "shortest":
indices = indices[np.argsort(lengths[indices])]
elif active_func in ["lc", "margin", "te", "tte"]:
result = get_scores(args, net, active_func, infer_dataiter, src_vocab,
tgt_vocab)
result = sorted(result, key=lambda item: item[0])
indices = [item[1] for item in result]
indices = np.array(indices).astype('int')
include = np.cumsum(lengths[indices]) <= tok_budget
include = indices[include]
return [unlabeled_dataset[idx] for idx in include], include