def decode(args: Dict[str, str]):
"""
performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
"""
print(f"load test source sentences from [{args['TEST_SOURCE_FILE']}]", file=sys.stderr)
test_data_src = read_corpus(args['TEST_SOURCE_FILE'], source='src')
if args['TEST_TARGET_FILE']:
print(f"load test target sentences from [{args['TEST_TARGET_FILE']}]", file=sys.stderr)
test_data_tgt = read_corpus(args['TEST_TARGET_FILE'], source='tgt')
print(f"load model from {args['MODEL_PATH']}", file=sys.stderr)
model = NMT.load(args['MODEL_PATH'])
if args['--cuda']:
model = model.to(torch.device("cuda:0"))
hypotheses = beam_search(model, test_data_src,
beam_size=int(args['--beam-size']),
max_decoding_time_step=int(args['--max-decoding-time-step']))
if args['TEST_TARGET_FILE']:
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt, top_hypotheses)
print(f'Corpus BLEU: {bleu_score}', file=sys.stderr)
with open(args['OUTPUT_FILE'], 'w') as f:
for src_sent, hyps in zip(test_data_src, hypotheses):
top_hyp = hyps[0]
hyp_sent = ' '.join(top_hyp.value)
f.write(hyp_sent + '\n')
def compute_pr(model):
'''Takes in a model to produce tags, then evaluates PR of model'''
print "Evaluating %s" % (model)
(vocab, tf) = utils.read_corpus()
top_words = models.run_model(model, vocab, tf)
average_recall = avg_recall(top_words)
avgerage_precision = avg_precision(top_words)
print "Average Precision: %f" %(avg_precision)
print "Average Precision: %f" %(avg_precision)
def main():
""" Main func.
"""
args = docopt(__doc__)
# Check Python & PyTorch Versions
assert (sys.version_info >= (3, 5)), "Please update your installation of Python to version >= 3.5"
# assert(torch.__version__ == "1.0.0"), "Please update your installation of PyTorch. You have {} and you should have version 1.0.0".format(torch.__version__)
assert(torch.__version__ >= "1.0.0"), "Please update your installation of PyTorch. You have {} and you should have version 1.0.0".format(torch.__version__)
# Seed the Random Number Generators
seed = 1234
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed * 13 // 7)
# Load training data & vocabulary
train_data_src = read_corpus('./sanity_check_en_es_data/train_sanity_check.es', 'src')
train_data_tgt = read_corpus('./sanity_check_en_es_data/train_sanity_check.en', 'tgt')
train_data = list(zip(train_data_src, train_data_tgt))
for src_sents, tgt_sents in batch_iter(train_data, batch_size=BATCH_SIZE, shuffle=True):
src_sents = src_sents
tgt_sents = tgt_sents
break
vocab = Vocab.load('./sanity_check_en_es_data/vocab_sanity_check.json')
# Create NMT Model
model = NMT(
embed_size=EMBED_SIZE,
hidden_size=HIDDEN_SIZE,
dropout_rate=DROPOUT_RATE,
vocab=vocab)
if args['1d']:
question_1d_sanity_check(model, src_sents, tgt_sents, vocab)
elif args['1e']:
question_1e_sanity_check(model, src_sents, tgt_sents, vocab)
elif args['1f']:
# generate_outputs(model, src_sents, tgt_sents, vocab)
question_1f_sanity_check(model, src_sents, tgt_sents, vocab)
else:
raise RuntimeError('invalid run mode')
def build_vocab(args):
if not os.path.exists(args.vocab_path):
src_sents, labels = read_corpus(args.train_data_dir)
labels = {label: idx for idx, label in enumerate(labels)}
vocab = Vocab.build(src_sents, labels, args.max_vocab_size,
args.min_freq)
vocab.save(args.vocab_path)
else:
vocab = Vocab.load(args.vocab_path)
return vocab
def decode(args: Dict[str, str]):
""" Performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
@param args (Dict): args from cmd line
"""
print("load test source sentences from [{}]".format(
args['TEST_SOURCE_FILE']),
file=sys.stderr)
test_data_src = read_corpus(args['TEST_SOURCE_FILE'], source='src')
if args['TEST_TARGET_FILE']:
print("load test target sentences from [{}]".format(
args['TEST_TARGET_FILE']),
file=sys.stderr)
test_data_tgt = read_corpus(args['TEST_TARGET_FILE'], source='tgt')
print("load model from {}".format(args['MODEL_PATH']), file=sys.stderr)
model = NMT.load(args['MODEL_PATH'])
if args['--cuda']:
model = model.to(torch.device("cuda:0"))
hypotheses = beam_search(model,
test_data_src,
beam_size=int(args['--beam-size']),
max_decoding_time_step=int(
args['--max-decoding-time-step']))
if args['TEST_TARGET_FILE']:
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt,
top_hypotheses)
print('Corpus BLEU: {}'.format(bleu_score * 100), file=sys.stderr)
output_file = args['OUTPUT_FILE']
os.makedirs(os.path.dirname(output_file), exist_ok=True)
with open(output_file, 'w+') as f:
for src_sent, hyps in zip(test_data_src, hypotheses):
top_hyp = hyps[0]
hyp_sent = ' '.join(top_hyp.value)
f.write('source sentence: ' + ' '.join(src_sent) + '\n')
f.write('translation: ' + hyp_sent + '\n')
def test(args):
""" Testing the model
Args:
args: dict that contains options in command
"""
sent_vocab = Vocab.load(args.SENT_VOCAB)
tag_vocab = Vocab.load(args.TAG_VOCAB)
sentences, tags = utils.read_corpus(args.TEST)
sentences = utils.words2indices(sentences, sent_vocab)
tags = utils.words2indices(tags, tag_vocab)
test_data = list(zip(sentences, tags))
print('num of test samples: %d' % (len(test_data)))
# device = torch.device('cuda' if args.cuda else 'cpu')
device = torch.device('cuda:0')
model = bilstm_crf.BiLSTMCRF.load(args.MODEL, device)
print('start testing...')
print('using device', device)
start = time.time()
n_iter, num_words = 0, 0
tp, fp, fn = 0, 0, 0
model.eval()
with torch.no_grad():
for sentences, tags in utils.batch_iter(test_data,
batch_size=int(
args.batch_size),
shuffle=False):
sentences, sent_lengths = utils.pad(sentences,
sent_vocab[sent_vocab.PAD],
device)
predicted_tags = model.predict(sentences, sent_lengths)
n_iter += 1
num_words += sum(sent_lengths)
for tag, predicted_tag in zip(tags, predicted_tags):
current_tp, current_fp, current_fn = cal_statistics(
tag, predicted_tag, tag_vocab)
tp += current_tp
fp += current_fp
fn += current_fn
if n_iter % int(args.log_every) == 0:
print(
'log: iter %d, %.1f words/sec, precision %f, recall %f, f1_score %f, time %.1f sec'
% (n_iter, num_words / (time.time() - start), tp /
(tp + fp), tp / (tp + fn),
(2 * tp) / (2 * tp + fp + fn), time.time() - start))
num_words = 0
start = time.time()
print('tp = %d, fp = %d, fn = %d' % (tp, fp, fn))
precision = tp / (tp + fp)
recall = tp / (tp + fn)
f1_score = (2 * tp) / (2 * tp + fp + fn)
print('Precision: %f, Recall: %f, F1 score: %f' %
(precision, recall, f1_score))
def decode(args: Dict[str, str]):
""" Performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
@param args (Dict): args from cmd line
"""
print("load test source sentences from [{}]".format(
args['TEST_SOURCE_FILE']),
file=sys.stderr)
test_data_src = read_corpus(args['TEST_SOURCE_FILE'], source='src')
if args['TEST_TARGET_FILE']:
print("load test target sentences from [{}]".format(
args['TEST_TARGET_FILE']),
file=sys.stderr)
test_data_tgt = read_corpus(args['TEST_TARGET_FILE'], source='tgt')
print("load model from {}".format(args['MODEL_PATH']), file=sys.stderr)
model1 = NMT.load(args['MODEL_PATH'])
model2 = DPPNMT.load(args['MODEL_PATH'])
if args['INDEX']:
index = int(args['INDEX'])
beam_search2(
model1,
model2,
[test_data_src[index]],
5,
70,
[test_data_tgt[index]],
)
else:
beam_search2(
model1,
model2,
test_data_src,
#int(args['--beam-size']),
5,
#int(args['--max-decoding-time-step']),
70,
test_data_tgt,
)
def decode(args: Dict[str, str]):
""" Performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
@param args (Dict): args from cmd line
"""
print("load test source sentences from [{}]".format(args['TEST_SOURCE_FILE']), file=sys.stderr)
test_data_src = read_corpus(args['TEST_SOURCE_FILE'], source='src')
if args['TEST_TARGET_FILE']:
print("load test target sentences from [{}]".format(args['TEST_TARGET_FILE']), file=sys.stderr)
test_data_tgt = read_corpus(args['TEST_TARGET_FILE'], source='tgt')
print("load model from {}".format(args['MODEL_PATH']), file=sys.stderr)
model = NMT.load(args['MODEL_PATH'])
if args['--cuda']:
model = model.to(torch.device("cuda:0"))
hypotheses = beam_search(model, test_data_src,
beam_size=int(args['--beam-size']),
max_decoding_time_step=int(args['--max-decoding-time-step']))
if args['TEST_TARGET_FILE']:
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt, top_hypotheses)
#accuracy (unigrams)
perfectly_correct = 0
for index,hyp in enumerate(top_hypotheses):
if hyp.value[0] == test_data_tgt[index][1]:
perfectly_correct += 1
print('Ignore accuracy for non unigrams')
print('Accuracy: {}'.format(perfectly_correct / len(test_data_tgt)), file=sys.stderr)
print('Corpus BLEU: {}'.format(bleu_score * 100), file=sys.stderr)
with open(args['OUTPUT_FILE'], 'w') as f:
for src_sent, hyps in zip(test_data_src, hypotheses):
top_hyp = hyps[0]
hyp_sent = ' '.join(top_hyp.value)
f.write(hyp_sent + '\n')
def decode(load_from=None):
"""
performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
"""
if gconfig.test:
data_src = read_corpus(paths.test_source, source='src')
data_tgt = read_corpus(paths.test_target, source='tgt')
data_tgt_path = paths.test_target
else:
data_src = read_corpus(paths.dev_source, source='src')
data_tgt = read_corpus(paths.dev_target, source='tgt')
data_tgt_path = paths.dev_target
print(f"load model from {paths.model}", file=sys.stderr)
if load_from is not None:
model_load_path = load_from
else:
model_load_path = paths.model
model = NMTModel.load(model_load_path)
if gconfig.cuda:
model.to_gpu()
model.eval()
max_step = dconfig.max_decoding_time_step
if gconfig.sanity:
max_step = 2
hypotheses = routine.beam_search(model,
data_src,
max_step,
replace=dconfig.replace)
lines = []
for src_sent, hyps in zip(data_src, hypotheses):
top_hyp = hyps[0]
lines.append(top_hyp.value)
write_sents(lines, paths.decode_output)
bleu_command = "perl scripts/multi-bleu.perl " + data_tgt_path + " < " + paths.decode_output
os.system(bleu_command)
def translate(model):
if model.args.en_cn:
test_src, test_tgt = read_en_cn_corpus(
model.args.test_data, source=model.args.source_language)
elif model.args.en_es:
test_src = read_corpus(model.args.test_src_data, False)
test_tgt = read_corpus(model.args.test_tgt_data, True)
else:
print("invalid input")
exit(0)
train_data = list(zip(test_src, test_tgt))
with open(args.translate_result_save_path, 'w') as f:
for src_sent, tgt_sent in batch_iter(train_data, 1, shuffle=False):
tgt_predict = model.translate(src_sent[0])
tgt_predict = ' '.join(tgt_predict[0]['sentence'])
tgt_sent = tgt_sent[0]
tgt = ' '.join(tgt_sent[1:-1])
line = "translate: " + tgt_predict + " " * 5 + "target: " + tgt + '\r\n'
f.write(line)
f.close()
def filter_subs(input_data_path: str, output_data_path: str):
print('Reading data...')
subs = read_corpus(input_data_path)
pattern = re.compile('^(?:[A-z]|[А-я]|[ёЁ\d\s.,!:?\-––\'"%$()`])+$')
print('Filtering...')
filtered = [s for s in tqdm(subs) if pattern.match(s)]
print('Removing too long sentences...')
short = [s for s in tqdm(filtered) if len(s.split()) <= 50 and len(s) <= 250]
print('Saving...')
save_corpus(short, output_data_path)
print('Done!')
def decodeOT(args: Dict[str, str]):
"""
performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
"""
test_data_src = read_corpus(args['TEST_SOURCE_FILE'], source='src')
if args['TEST_TARGET_FILE']:
test_data_tgt = read_corpus(args['TEST_TARGET_FILE'], source='tgt')
print(f"load model from {args['MODEL_PATH']}", file=sys.stderr)
if os.path.exists(args['MODEL_PATH']):
model = NMT.load(args['MODEL_PATH'])
else:
model = NMT(256, 256, pickle.load(open('data/vocab.bin', 'rb')))
# Set models to eval (disables dropout)
model.encoder.eval()
model.decoder.eval()
hypotheses = beam_searchOT(model,
test_data_src,
beam_size=int(args['--beam-size']),
max_decoding_time_step=int(
args['--max-decoding-time-step']))
if args['TEST_TARGET_FILE']:
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt,
top_hypotheses)
print(f'Corpus BLEU: {bleu_score}', file=sys.stderr)
with open(args['OUTPUT_FILE'], 'w') as f:
for src_sent, hyps in zip(test_data_src, hypotheses):
top_hyp = hyps[0]
hyp_sent = ' '.join(top_hyp.value.split()[1:-1])
f.write(hyp_sent + '\n')
# Back to train (not really necessary for now)
model.encoder.train()
model.decoder.train()
def decode(args: Dict[str, str]):
"""
performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
"""
vocab = pickle.load(open(args['--vocab'], 'rb'))
srcEntry = vocab.src # VocabEntry for src
tgtEntry = vocab.tgt # VocabEntry for tgt
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
test_data_src = read_corpus(args['--test-src'], source='src')
test_data = Testset(srcEntry.words2indices(test_data_src))
test_loader = DataLoader(test_data, 1, shuffle=False)
if args['--test-tgt']:
test_data_tgt = read_corpus(args['--test-tgt'], source='tgt')
print(f"load model from {args['--model-path']}", file=sys.stderr)
model = torch.load(args['--model-path'])
hypotheses = beam_search(model,
test_loader,
beam_size=int(args['--beam-size']),
max_decoding_time_step=int(
args['--max-decoding-time-step']),
tgtEntry=tgtEntry,
device=device)
if args['--test-tgt']:
bleu_score = compute_corpus_level_bleu_score(test_data_tgt, hypotheses)
print(f'Corpus BLEU: {bleu_score}', file=sys.stderr)
with open(args['--output-path'], 'w') as f:
for src_sent, top_hyp in zip(test_data_src, hypotheses):
hyp_sent = ' '.join(top_hyp.value)
f.write(hyp_sent + '\n')
def decode(args: Dict[str, str]):
""" Performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
@param args (Dict): args from cmd line
"""
print("load test source sentences from [{}]".format(args['TEST_SOURCE_FILE']), file=sys.stderr)
test_data_src = read_corpus(args['TEST_SOURCE_FILE'], source='src')
if args['TEST_TARGET_FILE']:
print("load test target sentences from [{}]".format(args['TEST_TARGET_FILE']), file=sys.stderr)
test_data_tgt = read_corpus(args['TEST_TARGET_FILE'], source='tgt')
print("load model from {}".format(args['MODEL_PATH']), file=sys.stderr)
if args['--dpp'] and args['--sampling']:
print("Error: cannot specify --dpp and --sampling simultaneously")
elif args['--dpp']:
print("Loading DPP Model")
model = DPPNMT.load(args['MODEL_PATH'])
else:
model = NMT.load(args['MODEL_PATH'])
if args['--sampling']:
model.sampling = True
if args['--cuda']:
model = model.to(torch.device("cuda:0"))
hypotheses = beam_search(model, test_data_src,
beam_size=int(args['--beam-size']),
max_decoding_time_step=int(args['--max-decoding-time-step']))
if args['TEST_TARGET_FILE']:
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt, top_hypotheses)
print('Corpus BLEU: {}'.format(bleu_score * 100), file=sys.stderr)
with open(args['OUTPUT_FILE'], 'w') as f:
for src_sent, hyps in zip(test_data_src, hypotheses):
top_hyp = hyps[0]
hyp_sent = ' '.join(top_hyp.value)
f.write(hyp_sent + '\n')
def detokenize(input_file, output_file):
print('Reading data...')
texts = read_corpus(input_file)
print('Data reading finished!')
print('Detokenizing...')
detokenized = [detokenizer.detokenize(s.split()) for s in tqdm(texts)]
print('Detokenized!')
print('Saving...')
save_corpus(detokenized, output_file)
print('Saved!')
def decode(args: Dict[str, str]):
test_data_src, failed_ids_src = read_corpus(args['TEST_SOURCE_FILE'],
source='src')
test_data_tgt, failed_ids_tgt = read_corpus(args['TEST_TARGET_FILE'],
source='tgt')
total_failed_ids = set(failed_ids_src).union(failed_ids_tgt)
test_data_src = [
test_data_src[i] for i in range(len(test_data_src))
if i not in total_failed_ids
]
test_data_tgt = [
test_data_tgt[i] for i in range(len(test_data_tgt))
if i not in total_failed_ids
]
print(f"load model from {args['MODEL_PATH']}", file=sys.stderr)
model = NMT.load(args['MODEL_PATH'])
if args['--cuda']:
model = model.to(torch.device("cuda:2"))
hypotheses = beam_search(model,
test_data_src,
beam_size=int(args['--beam-size']),
max_decoding_time_step=int(
args['--max-decoding-time-step']))
if args['TEST_TARGET_FILE']:
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt,
top_hypotheses)
print(f'Corpus BLEU: {bleu_score}', file=sys.stderr)
with open(args['OUTPUT_FILE'], 'w') as f:
for src_sent, hyps in zip(test_data_src, hypotheses):
top_hyp = hyps[0]
hyp_sent = ' '.join(top_hyp.value)
f.write(hyp_sent + '\n')
def greedy_test(args):
""" Test function """
# load vocabulary
vocab = torch.load(args.vocab)
# build model
translator = Transformer(args, vocab)
translator.eval()
# load parameters
translator.load_state_dict(torch.load(args.decode_model_path))
if args.cuda:
translator = translator.cuda()
test_data = read_corpus(args.decode_from_file, source="src")
# ['<BOS>', '<PAD>', 'PAD', '<PAD>', '<PAD>']
pred_data = len(test_data) * [[
constants.PAD_WORD if i else constants.BOS_WORD
for i in range(args.decode_max_steps)
]]
output_file = codecs.open(args.decode_output_file, "w", encoding="utf-8")
for test, pred in zip(test_data, pred_data):
pred_output = [constants.PAD_WORD] * args.decode_max_steps
test_var = to_input_variable([test], vocab.src, cuda=args.cuda)
# only need one time
enc_output = translator.encode(test_var[0], test_var[1])
for i in range(args.decode_max_steps):
pred_var = to_input_variable([pred[:i + 1]],
vocab.tgt,
cuda=args.cuda)
scores = translator.translate(enc_output, test_var[0], pred_var)
_, argmax_idxs = torch.max(scores, dim=-1)
one_step_idx = argmax_idxs[-1].item()
pred_output[i] = vocab.tgt.id2word[one_step_idx]
if (one_step_idx
== constants.EOS) or (i == args.decode_max_steps - 1):
print("[Source] %s" % " ".join(test))
print("[Predict] %s" % " ".join(pred_output[:i]))
print()
output_file.write(" ".join(pred_output[:i]) + "\n")
output_file.flush()
break
pred[i + 1] = vocab.tgt.id2word[one_step_idx]
output_file.close()
def decode(args, test_data_src, test_data_tgt=None):
""" Performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
"""
print("load test source sentences from [{}]".format(test_data_src))
test_data_src = read_corpus(test_data_src, source='src')
if test_data_tgt:
print("load test target sentences from [{}]".format(test_data_tgt))
test_data_tgt = read_corpus(test_data_tgt, source='tgt')
print("load model from {}".format(args.model_path))
model = NMT.load(args.model_path)
if args.cuda:
model = model.to(torch.device("cuda:0"))
hypotheses = beam_search(
model,
test_data_src,
beam_size=args.beam_size,
max_decoding_time_step=args.max_decoding_time_step)
if args.test_tgt:
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt,
top_hypotheses)
print('Corpus BLEU: {}'.format(bleu_score * 100))
if args.output_file:
print("Saving predictions to " + args.output_file)
with open(args.output_file, 'w') as f:
for src_sent, hyps in zip(test_data_src, hypotheses):
top_hyp = hyps[0]
hyp_sent = ' '.join(top_hyp.value)
f.write(hyp_sent + '\n')
else:
print("No output_file given, not saving predictions")
def main(args):
if os.path.isfile(os.path.join(args.data_path, args.vocab_file)):
print('vocab file exist in %s.' %
os.path.join(args.data_path, args.vocab_file))
else:
print('Reading in source sentence : %s ...' % args.train_src)
src_sents = read_corpus(os.path.join(args.data_path, args.train_src),
source='src')
print('Reading in target sentence : %s ...' % args.train_tgt)
tgt_sents = read_corpus(os.path.join(args.data_path, args.train_tgt),
source='tgt')
vocab = Vocab.build(src_sents,
tgt_sents,
vocab_size=args.size,
freq_cutoff=args.freq_cutoff)
print('generated vocabulary, source %d words, target %d words' %
(len(vocab.src), len(vocab.tgt)))
vocab.save(os.path.join(args.data_path, args.vocab_file))
print('vocabulary saved to %s' %
os.path.join(args.data_path, args.vocab_file))
def decode(args: Dict[str, str], vocab):
"""
performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
"""
test_data_src = read_corpus(args['TEST_SOURCE_FILE'], source='src')
if args['TEST_TARGET_FILE']:
test_data_tgt = read_corpus(args['TEST_TARGET_FILE'], source='tgt')
print(f"load model from {args['MODEL_PATH']}", file=sys.stderr)
model = NMT(embed_size=int(args['--embed-size']),
hidden_size=int(args['--hidden-size']),
dropout_rate=float(args['--dropout']),
n_layers=int(args['--n_layers']),
vocab=vocab,
tie_weights=int(args['--tie-weights']),
mha=int(args['--mha']))
if torch.cuda.is_available():
model = model.cuda()
model.load(args['MODEL_PATH'])
hypotheses = beam_search(model,
test_data_src,
beam_size=int(args['--beam-size']),
max_decoding_time_step=int(
args['--max-decoding-time-step']))
if args['TEST_TARGET_FILE']:
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt,
top_hypotheses)
print(f'Corpus BLEU: {bleu_score}', file=sys.stderr)
with open(args['OUTPUT_FILE'], 'w') as f:
for src_sent, hyps in zip(test_data_src, hypotheses):
top_hyp = hyps[0]
hyp_sent = ' '.join(top_hyp.value)
f.write(hyp_sent + '\n')
def load_data(xml_file, vectorizer):
texts, labels = [], []
for values, label in utils.read_corpus(xml_file):
texts.append(values[0])
labels.append(label)
feas = vectorizer.fit_transform(texts)
# pdb.set_trace()
labeled_data = {"data": feas, "label": labels}
print "the feature of data:", feas.shape
return labeled_data
def load_data(xml_file, vectorizer):
texts, labels = [], []
for values, label in utils.read_corpus(xml_file):
texts.append(values[0])
labels.append(label)
feas = vectorizer.fit_transform(texts)
# pdb.set_trace()
labeled_data = {"data": feas, "label": labels}
print "the feature of data:", feas.shape
return labeled_data
def main():
args = docopt(__doc__)
sentences, tags = read_corpus(args['TRAIN'])
sent_vocab = Vocab.build(sentences,
int(args['--max-size']),
int(args['--freq-cutoff']),
is_tags=False)
tag_vocab = Vocab.build(tags,
int(args['--max-size']),
int(args['--freq-cutoff']),
is_tags=True)
sent_vocab.save(args['SENT_VOCAB'])
tag_vocab.save(args['TAG_VOCAB'])
def generate_sentiment_words(neg_input_path:str, pos_input_path:str,
neg_output_path:str, pos_output_path:str,
keep_n_most_popular_words:int=3000):
print('Reading data...')
neg_lines = read_corpus(neg_input_path)
pos_lines = read_corpus(pos_input_path)
print('Counting words')
neg_counter = Counter([w.lower() for s in tqdm(neg_lines) for w in s.split()])
pos_counter = Counter([w.lower() for s in tqdm(pos_lines) for w in s.split()])
print('Getting most popular')
neg_top_words = set(w for w, _ in neg_counter.most_common(keep_n_most_popular_words))
pos_top_words = set(w for w, _ in pos_counter.most_common(keep_n_most_popular_words))
only_neg_top_words = neg_top_words - pos_top_words
only_pos_top_words = pos_top_words - neg_top_words
print('Saving')
save_corpus(list(only_neg_top_words), neg_output_path)
save_corpus(list(only_pos_top_words), pos_output_path)
print('Done!')
def decode(args: Dict[str, str]):
"""
performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
"""
print(f"load test source sentences from [{args['TEST_SOURCE_FILE']}]",
file=sys.stderr)
test_data_src = read_corpus(args['TEST_SOURCE_FILE'], source='src')
if args['TEST_TARGET_FILE']:
print(f"load test target sentences from [{args['TEST_TARGET_FILE']}]",
file=sys.stderr)
test_data_tgt = read_corpus(args['TEST_TARGET_FILE'], source='tgt')
print(f"load model from {args['MODEL_PATH']}", file=sys.stderr)
model = NMT.load(args['MODEL_PATH'])
if args['--cuda']:
model = model.to(torch.device("cuda:0"))
hypotheses = beam_search(model,
test_data_src,
beam_size=int(args['--beam-size']),
max_decoding_time_step=int(
args['--max-decoding-time-step']))
if args['TEST_TARGET_FILE']:
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt,
top_hypotheses)
print(f'Corpus BLEU: {bleu_score}', file=sys.stderr)
with open(args['OUTPUT_FILE'], 'w') as f:
for src_sent, hyps in zip(test_data_src, hypotheses):
top_hyp = hyps[0]
hyp_sent = ' '.join(top_hyp.value)
f.write(hyp_sent + '\n')
def main():
head_funcs = {}
pairs = parse_pairs(sys.stdin)
head_np, np_head = parse_NPs(open(sys.argv[1]), pairs)
id_for_sent = []
c = Counter()
for token in read_corpus(open(sys.argv[2])):
if token.id == 'SENT':
for i in id_for_sent:
head_funcs[i]['verbs'] = c['V']
head_funcs[i]['oids'] = c['o']
head_funcs[i]['conjs'] = c['C']
head_funcs[i]['nouns'] = c['N']
id_for_sent = []
c.clear()
continue
add_pos_count(token, c)
if token.id in head_np.keys():
in_pair = _in_pair(token.id, head_np, pairs)
if in_pair:
id_for_sent.append(token.id)
hf = {}
hf['isNomn'], hf['isNpro'], hf['gnc'] = [hhf(token) for hhf in _funcs]
hf['text'] = token.orig_text.encode('utf-8')
hf['id'] = token.id
hf['POS'] = '_'.join([str(_pos_codes[x]) for x in set(token.getPOStags())])
# может быть несколько разборов, это плохой вариант
hf['gnc'] = gnc(token)
hf['gender'], hf['number'], hf['case'] = hf['gnc']
head_funcs[token.id] = hf
if in_pair[0]:
antc = np_head[in_pair[1]]
anph = np_head[in_pair[2]]
hf['c_agr'] = agreement(head_funcs[antc]['case'], head_funcs[anph]['case'])
hf['g_agr'] = agreement(head_funcs[antc]['gender'], head_funcs[anph]['gender'])
hf['n_agr'] = agreement(head_funcs[antc]['number'], head_funcs[anph]['number'])
hf['agreement'] = hf['g_agr'] and hf['c_agr'] and hf['n_agr']
hf['gn_agr'] = hf['g_agr'] and hf['n_agr']
hf['match'] = head_funcs[antc]['text'] == head_funcs[anph]['text']
head_funcs[token.id] = hf
func = 'POS isNomn isNpro g_agr n_agr c_agr agreement verbs oids conjs nouns'.split()
for i, f in enumerate(pairs[0].keys()):
hhf = head_funcs[np_head[f]]
for a in pairs[0][f]:
ana = head_funcs[np_head[a]]
if not i:
print 'id' + '\t' + '\t'.join(str(x[0]) for x in
sorted(filter(lambda x: x[0] in func, hhf.items() + ana.items()), key=lambda x: x[0]))
print str(f) + '_' + str(a) + '\t' + \
'\t'.join(str(x[1]) for x in sorted(filter(lambda x: x[0] in func, hhf.items() + ana.items()), key=lambda x: x[0]))
def cut_long_dialogs(data_path:str, output_path:str, max_len:int=128):
print('Reading data...')
data = read_corpus(data_path)
print('Splitting dialogs...')
dialogs = [d.split('|') for d in data]
print('Cutting data...')
dialogs = [cut_dialog(d, max_len) for d in tqdm(dialogs)]
num_dialogs_before = len(dialogs)
print('Saving data...')
dialogs = list(filter(len, dialogs))
save_corpus(dialogs, output_path)
print('Done! Num dialogs reduced: {} -> {}'.format(num_dialogs_before, len(dialogs)))
def dialogs_from_lines(input_data_path:str, output_data_path:str, n_lines: int, eos:str, n_dialogs:int):
n_lines, n_dialogs = int(n_lines), int(n_dialogs) # TODO: argparse?
print('Reading data...')
lines = read_corpus(input_data_path)
lines = lines[:n_dialogs * n_lines]
print('Generating dialogs')
dialogs = [lines[i:i+n_lines] for i in range(0, len(lines) - n_lines)]
dialogs = [eos.join(d) for d in dialogs]
print('Saving corpus')
save_corpus(dialogs, output_data_path)
print('Done!')
def train(args: Dict):
""" Train the NMT Model.
@param args (Dict): args from cmd line
"""
train_data_src = read_corpus(args['--train-src'], source='src')
train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')
dev_data_src = read_corpus(args['--dev-src'], source='src')
dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')
train_data = list(zip(train_data_src, train_data_tgt))
dev_data = list(zip(dev_data_src, dev_data_tgt))
train_batch_size = int(args['--batch-size'])
clip_grad = float(args['--clip-grad'])
valid_niter = int(args['--valid-niter'])
log_every = int(args['--log-every'])
model_save_path = args['--save-to']
vocab = Vocab.load(args['--vocab'])
model = NMT(embed_size=int(args['--embed-size']),
hidden_size=int(args['--hidden-size']),
dropout_rate=float(args['--dropout']),
vocab=vocab)
model.train()
uniform_init = float(args['--uniform-init'])
if np.abs(uniform_init) > 0.:
print('uniformly initialize parameters [-%f, +%f]' % (uniform_init, uniform_init), file=sys.stderr)
for p in model.parameters():
p.data.uniform_(-uniform_init, uniform_init)
vocab_mask = torch.ones(len(vocab.tgt))
vocab_mask[vocab.tgt['<pad>']] = 0
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
def decode(args: Dict[str, str]):
""" Performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
@param args (Dict): args from cmd line
"""
print("load test source sentences from [{}]".format(args['TEST_SOURCE_FILE']), file=sys.stderr)
test_data_src = read_corpus(args['TEST_SOURCE_FILE'], source='src')
if args['TEST_TARGET_FILE']:
print("load test target sentences from [{}]".format(args['TEST_TARGET_FILE']), file=sys.stderr)
test_data_tgt = read_corpus(args['TEST_TARGET_FILE'], source='tgt')
print("load model from {}".format(args['MODEL_PATH']), file=sys.stderr)
model = NMT.load(args['MODEL_PATH'], no_char_decoder=args['--no-char-decoder'])
if args['--cuda']:
model = model.to(torch.device("cuda:0"))
hypotheses = beam_search(model, test_data_src,
beam_size=int(args['--beam-size']),
max_decoding_time_step=int(args['--max-decoding-time-step']))
if args['TEST_TARGET_FILE']:
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt, top_hypotheses)
print('Corpus BLEU: {}'.format(bleu_score * 100), file=sys.stderr)
with open(args['OUTPUT_FILE'], 'w') as f:
for src_sent, hyps in zip(test_data_src, hypotheses):
top_hyp = hyps[0]
detokenizer = TreebankWordDetokenizer()
detokenizer.DOUBLE_DASHES = (re.compile(r'--'), r'--')
hyp_sent = detokenizer.detokenize(top_hyp.value)
# hyp_sent = ' '.join(top_hyp.value)
f.write(hyp_sent + '\n')
def main():
print('Invoked as:', ' '.join(sys.argv), file=sys.stderr)
parser = argparse.ArgumentParser()
parser.add_argument('corpus')
parser.add_argument('dev_corpus')
parser.add_argument('--layers', type=int, default=1)
parser.add_argument('--emb_dim', type=int, default=128)
parser.add_argument('--hidden_dim', type=int, default=128)
parser.add_argument('--minibatch_size', type=int, default=1)
parser.add_argument('--tied', action='store_true')
parser.add_argument('--autobatch', action='store_true')
parser.add_argument('--dropout', type=float, default=0.0)
parser.add_argument('--output', type=str, default='')
harness.add_optimizer_args(parser)
args = parser.parse_args()
if args.output == '':
args.output = '/tmp/model%d' % random.randint(0, 0xFFFF)
print('Output file:', args.output, file=sys.stderr)
vocab = Vocabulary()
train_corpus = read_corpus(args.corpus, vocab)
dev_corpus = read_corpus(args.dev_corpus, vocab)
print('Vocab size:', len(vocab), file=sys.stderr)
with open(args.output + '.vocab', 'w') as f:
for word in vocab.i2w:
print(word, file=f)
pc = dy.ParameterCollection()
optimizer = harness.make_optimizer(args, pc)
model = RNNLM(pc, args.layers, args.emb_dim, args.hidden_dim, len(vocab),
args.tied)
print('Total parameters:', pc.parameter_count(), file=sys.stderr)
harness.train(model, train_corpus, dev_corpus, optimizer, args)
def multi_parameter_tuning(args):
lrs = [1e-2,1e-3,5e-3,1e-4,5e-4]
hidden_sizes = [128,256,512]
lr_decays = [0.9,0.7,0.5]
iter = 0
valid_metric = {} # 存储各个模型ppl的值
dev_data_src = read_corpus(args['dev_source'], source='src')
dev_data_tgt = read_corpus(args['dev_target'], source='tgt')
dev_data = list(zip(dev_data_src, dev_data_tgt))
for i in lrs:
for j in hidden_sizes:
for k in lr_decays:
print('第%d次测试================================================='%iter)
arg_test = args
arg_test['lr'],arg_test['hidden_size'],arg_test['lr_decay'] = i,j,k
arg_test['save_to'] = 'model_'+'lr_'+str(i)+'hd_size_'+str(j)+'lr_dys_'+str(k)+'.bin'
run.train(arg_test)
model = NMT.load(args['save_to'])
dev_ppl = run.evaluate_ppl(model, dev_data, batch_size=128) # dev batch size can be a bit larger
valid_metric[arg_test['save_to']] = dev_ppl
print(arg_test['save_to'],' validation: iter %d, dev. ppl %f' % (iter, dev_ppl), file=sys.stderr)
iter += 1
model = min(valid_metric,key=valid_metric.get())
print('best_model is %s ,ppl is %f'%(model,valid_metric[model]))
def decode(args: Dict[str, str]):
"""
performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
"""
threshold = 2.0
test_data = read_corpus(args['TEST_SOURCE_FILE'], source='src')
print(f"load model from {args['MODEL_PATH_I']}", file=sys.stderr)
model_I = NMT.load(args['MODEL_PATH_I'])
model_I.encoder.dropout = nn.Dropout(0.)
ces_I = []
with torch.no_grad():
for sent in tqdm(test_data, desc='Decoding', file=sys.stdout):
loss = model_I([sent]).item()
ce = loss / len(sent)
ces_I.append(ce)
print(f"load model from {args['MODEL_PATH_N']}", file=sys.stderr)
model_N = NMT.load(args['MODEL_PATH_N'])
model_N.encoder.dropout = nn.Dropout(0.)
ces_N = []
with torch.no_grad():
for sent in tqdm(test_data, desc='Decoding', file=sys.stdout):
loss = model_N([sent]).item()
ce = loss / len(sent)
ces_N.append(ce)
ces_diff = []
for ce_I, ce_N in zip(ces_I, ces_N):
ces_diff.append(ce_I - ce_N)
selected = 0
with open(args['OUTPUT_FILE'], 'w') as f:
for words, ce in zip(test_data, ces_diff):
if (ce < threshold):
selected += 1
words = words[1:-1:1]
sent = ("".join(words)).replace("▁", " ▁").strip()
# f.write(str(ce) + ' ')
f.write(sent + '\n')
print("%d out of %d sentences selected." % (selected, len(test_data)))
def save(self, file_path):
json.dump(dict(src_word2id=self.src.word2id, tgt_word2id=self.tgt.word2id), open(file_path, 'w'), indent=2)
@staticmethod
def load(file_path):
entry = json.load(open(file_path, 'r'))
src_word2id = entry['src_word2id']
tgt_word2id = entry['tgt_word2id']
return Vocab(VocabEntry(src_word2id), VocabEntry(tgt_word2id))
def __repr__(self):
return 'Vocab(source %d words, target %d words)' % (len(self.src), len(self.tgt))
if __name__ == '__main__':
args = docopt(__doc__)
print('read in source sentences: %s' % args['--train-src'])
print('read in target sentences: %s' % args['--train-tgt'])
src_sents = read_corpus(args['--train-src'], source='src')
tgt_sents = read_corpus(args['--train-tgt'], source='tgt')
vocab = Vocab.build(src_sents, tgt_sents, int(args['--size']), int(args['--freq-cutoff']))
print('generated vocabulary, source %d words, target %d words' % (len(vocab.src), len(vocab.tgt)))
vocab.save(args['VOCAB_FILE'])
print('vocabulary saved to %s' % args['VOCAB_FILE'])
try:
a = stat[i][distance][context]
except KeyError:
continue
bestrule = curr_rule
top_rules[bestrule] = a
return top_rules, bestscore, a
def random_choice(corpus):
for s in corpus:
for token in s:
try:
if token.has_ambig():
token.disambiguate(random.choice(token.getPOStags().split('_')))
except:
pass
if __name__ == '__main__':
inc = read_corpus(sys.stdin)
#s = context_stats(inc, join_context=True)
#print s
#rs = scores(s, [])
#pprint(rs[1:3])
'''for x in rs[1].keys():
print x.display()
print rs[1][x]
print rs[2]'''
r = Rule('ADVB NOUN', 'NOUN', (1, 'PNCT'), 0)
#idf = np.log(float(N)/appCorpus)
weights = np.zeros(tf.shape, dtype='float') # init array for weights
for i in range(0, tf.shape[0]):
lengthD = np.sum(tf[i,:])
weights[i,:] = idf * tf[i,:] * (k1+1) / (tf[i,:] + k1 * (1 - b + b * lengthD/avgD))
return weights
def run_model(model_name, vocab, tf):
speech_info = utils.read_speech_info()
N = len(speech_info) # number of docs in corpus
weights = []
if model_name == "bm25":
weights = bm25(tf)
elif model_name == "tfidf":
weights = tfidf(tf)
top_words = {}
for i in range(N):
gen_tags = utils.get_tags(vocab, weights, i)
top_words[speech_info[i]] = gen_tags
return top_words
if __name__ == '__main__':
(vocab,tf) = utils.read_corpus()
#weights = tfidf(tf)
#topWords = utils.get_tags(vocab, weights, 0) # get top 20 words for Obama's Acceptance Speech
#print topWords
run_model("tfidf", vocab, tf)
#speech_info = utils.read_speech_info()
#for si in speech_info:
#print speech_info[si]
def train(args: Dict):
train_data_src = read_corpus(args['--train-src'], source='src')
train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')
dev_data_src = read_corpus(args['--dev-src'], source='src')
dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')
train_data = list(zip(train_data_src, train_data_tgt))
dev_data = list(zip(dev_data_src, dev_data_tgt))
train_batch_size = int(args['--batch-size'])
clip_grad = float(args['--clip-grad'])
valid_niter = int(args['--valid-niter'])
log_every = int(args['--log-every'])
model_save_path = args['--save-to']
vocab = Vocab.load(args['--vocab'])
model = NMT(embed_size=int(args['--embed-size']),
hidden_size=int(args['--hidden-size']),
dropout_rate=float(args['--dropout']),
input_feed=args['--input-feed'],
label_smoothing=float(args['--label-smoothing']),
vocab=vocab)
model.train()
uniform_init = float(args['--uniform-init'])
if np.abs(uniform_init) > 0.:
print('uniformly initialize parameters [-%f, +%f]' % (uniform_init, uniform_init), file=sys.stderr)
for p in model.parameters():
p.data.uniform_(-uniform_init, uniform_init)
vocab_mask = torch.ones(len(vocab.tgt))
vocab_mask[vocab.tgt['<pad>']] = 0
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
print('use device: %s' % device, file=sys.stderr)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=float(args['--lr']))
num_trial = 0
train_iter = patience = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0
cum_examples = report_examples = epoch = valid_num = 0
hist_valid_scores = []
train_time = begin_time = time.time()
print('begin Maximum Likelihood training')
while True:
epoch += 1
for src_sents, tgt_sents in batch_iter(train_data, batch_size=train_batch_size, shuffle=True):
train_iter += 1
optimizer.zero_grad()
batch_size = len(src_sents)
# (batch_size)
example_losses = -model(src_sents, tgt_sents)
batch_loss = example_losses.sum()
loss = batch_loss / batch_size
loss.backward()
# clip gradient
grad_norm = torch.nn.utils.clip_grad_norm(model.parameters(), clip_grad)
optimizer.step()
batch_losses_val = batch_loss.item()
report_loss += batch_losses_val
cum_loss += batch_losses_val
tgt_words_num_to_predict = sum(len(s[1:]) for s in tgt_sents) # omitting leading `<s>`
report_tgt_words += tgt_words_num_to_predict
cum_tgt_words += tgt_words_num_to_predict
report_examples += batch_size
cum_examples += batch_size
if train_iter % log_every == 0:
print('epoch %d, iter %d, avg. loss %.2f, avg. ppl %.2f ' \
'cum. examples %d, speed %.2f words/sec, time elapsed %.2f sec' % (epoch, train_iter,
report_loss / report_examples,
math.exp(report_loss / report_tgt_words),
cum_examples,
report_tgt_words / (time.time() - train_time),
time.time() - begin_time), file=sys.stderr)
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# perform validation
if train_iter % valid_niter == 0:
print('epoch %d, iter %d, cum. loss %.2f, cum. ppl %.2f cum. examples %d' % (epoch, train_iter,
cum_loss / cum_examples,
np.exp(cum_loss / cum_tgt_words),
cum_examples), file=sys.stderr)
cum_loss = cum_examples = cum_tgt_words = 0.
valid_num += 1
print('begin validation ...', file=sys.stderr)
# compute dev. ppl and bleu
dev_ppl = evaluate_ppl(model, dev_data, batch_size=128) # dev batch size can be a bit larger
valid_metric = -dev_ppl
print('validation: iter %d, dev. ppl %f' % (train_iter, dev_ppl), file=sys.stderr)
is_better = len(hist_valid_scores) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
if is_better:
patience = 0
print('save currently the best model to [%s]' % model_save_path, file=sys.stderr)
model.save(model_save_path)
# also save the optimizers' state
torch.save(optimizer.state_dict(), model_save_path + '.optim')
elif patience < int(args['--patience']):
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if patience == int(args['--patience']):
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == int(args['--max-num-trial']):
print('early stop!', file=sys.stderr)
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * float(args['--lr-decay'])
print('load previously best model and decay learning rate to %f' % lr, file=sys.stderr)
# load model
params = torch.load(model_save_path, map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers', file=sys.stderr)
optimizer.load_state_dict(torch.load(model_save_path + '.optim'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
if epoch == int(args['--max-epoch']):
print('reached maximum number of epochs!', file=sys.stderr)
exit(0)
def t(f):
f = read_corpus(f)
return n(f)[0]
args = p.parse_args()
#name = os.path.split(args.corpus)[1]
name = args.corpus
path = '.'
write = False
n = 0
#out = sys.stdout
copyfile(name, '%s.orig' % name)
out = open('%s.rules' % name, 'w')
i = 0
best_rules = []
best_score = 0
orig = open(args.corpus, 'r')
inc = list(read_corpus(orig))
orig.close()
while True:
context_freq = context_stats(inc, f=args.f)
scores_rule = scores(context_freq, best_rules, f=args.f)
#ss = scores_rule[0]
best_rule = scores_rule[0]
for r in best_rule.keys():
best_rules.append(r)
best_score = scores_rule[1]
applied = scores_rule[2]
if best_score <= 0:
output = open('%s.final' % name, 'w')