def main():
print 'Starting at: {}\n'.format(datetime.now())
s_time = time.time()
df = read_df(args.df_path)
df = df.fillna(u'')
label_tags = pickle.load(open(args.tags_file, 'rb'))
print '\nloaded {} tags'.format(len(label_tags))
raw_corpus = myio.read_corpus(args.corpus_w_tags, with_tags=True)
embedding_layer = create_embedding_layer(
n_d=200,
embs=load_embedding_iterator(args.embeddings),
only_words=False if args.use_embeddings else True,
# only_words will take the words from embedding file and make random initial embeddings
trainable=args.trainable
)
ids_corpus = myio.map_corpus(raw_corpus, embedding_layer, label_tags, max_len=args.max_seq_len)
print("vocab size={}, corpus size={}\n".format(embedding_layer.n_V, len(raw_corpus)))
padding_id = embedding_layer.vocab_map["<padding>"]
if args.reweight:
weights = myio.create_idf_weights(args.corpus_w_tags, embedding_layer, with_tags=True)
if args.layer.lower() == "lstm":
from models import LstmMultiTagsClassifier as Model
elif args.layer.lower() in ["bilstm", "bigru"]:
from models import BiRNNMultiTagsClassifier as Model
elif args.layer.lower() == "cnn":
from models import CnnMultiTagsClassifier as Model
elif args.layer.lower() == "gru":
from models import GruMultiTagsClassifier as Model
else:
raise Exception("no correct layer given")
if args.cross_val:
train, dev, test = myio.create_cross_val_batches(df, ids_corpus, args.batch_size, padding_id)
else:
dev = list(myio.create_batches(
df, ids_corpus, 'dev', args.batch_size, padding_id, N_neg=args.n_neg, samples_file=args.samples_file))
test = list(myio.create_batches(
df, ids_corpus, 'test', args.batch_size, padding_id, N_neg=args.n_neg, samples_file=args.samples_file))
# baselines_eval(train, dev, test)
model = Model(args, embedding_layer, len(label_tags), weights=weights if args.reweight else None)
model.ready()
print 'total (non) trainable params: ', model.num_parameters()
if args.load_pre_trained_part:
# need to remove the old assigns to embeddings
model.init_assign_ops = model.load_pre_trained_part(args.load_pre_trained_part)
print '\nmodel init_assign_ops: {}\n'.format(model.init_assign_ops)
model.train_model(df, ids_corpus, dev=dev, test=test)
print '\nEnded at: {}'.format(datetime.now())
def main(args):
raw_corpus = myio.read_corpus(args.corpus)
embedding_layer = myio.create_embedding_layer(
raw_corpus,
n_d=args.hidden_dim,
embs=load_embedding_iterator(args.embeddings)
if args.embeddings else None)
ids_corpus = myio.map_corpus(raw_corpus,
embedding_layer,
max_len=args.max_seq_len)
say("vocab size={}, corpus size={}\n".format(embedding_layer.n_V,
len(raw_corpus)))
padding_id = embedding_layer.vocab_map["<padding>"]
if args.reweight:
weights = myio.create_idf_weights(args.corpus, embedding_layer)
if args.dev:
dev_raw = myio.read_annotations(args.dev, K_neg=-1, prune_pos_cnt=-1)
dev = myio.create_eval_batches(ids_corpus,
dev_raw,
padding_id,
pad_left=not args.average,
merge=args.merge)
if args.test:
test_raw = myio.read_annotations(args.test, K_neg=-1, prune_pos_cnt=-1)
test = myio.create_eval_batches(ids_corpus,
test_raw,
padding_id,
pad_left=not args.average,
merge=args.merge)
if args.train:
start_time = time.time()
train = myio.read_annotations(args.train)
train_batches = myio.create_batches(ids_corpus,
train,
args.batch_size,
padding_id,
pad_left=not args.average,
merge=args.merge)
say("{} to create batches\n".format(time.time() - start_time))
say("{} batches, {} tokens in total, {} triples in total\n".format(
len(train_batches), sum(len(x[0].ravel()) for x in train_batches),
sum(len(x[1].ravel()) for x in train_batches)))
train_batches = None
model = Model(args,
embedding_layer,
weights=weights if args.reweight else None)
model.ready()
# set parameters using pre-trained network
if args.load_pretrain:
model.encoder.load_pretrained_parameters(args)
model.train(ids_corpus, train, (dev, dev_raw) if args.dev else None,
(test, test_raw) if args.test else None)
def main(args):
raw_corpus = myio.read_corpus(args.corpus)
embedding_layer = myio.create_embedding_layer(
raw_corpus,
n_d = args.hidden_dim,
cut_off = args.cut_off,
embs = load_embedding_iterator(args.embeddings) if args.embeddings else None
)
ids_corpus = myio.map_corpus(raw_corpus, embedding_layer)
say("vocab size={}, corpus size={}\n".format(
embedding_layer.n_V,
len(raw_corpus)
))
padding_id = embedding_layer.vocab_map["<padding>"]
bos_id = embedding_layer.vocab_map["<s>"]
eos_id = embedding_layer.vocab_map["</s>"]
if args.reweight:
weights = myio.create_idf_weights(args.corpus, embedding_layer)
if args.dev:
dev = myio.read_annotations(args.dev, K_neg=20, prune_pos_cnt=-1)
dev = myio.create_eval_batches(ids_corpus, dev, padding_id)
if args.test:
test = myio.read_annotations(args.test, K_neg=20, prune_pos_cnt=-1)
test = myio.create_eval_batches(ids_corpus, test, padding_id)
if args.heldout:
with open(args.heldout) as fin:
heldout_ids = fin.read().split()
heldout_corpus = dict((id, ids_corpus[id]) for id in heldout_ids if id in ids_corpus)
train_corpus = dict((id, ids_corpus[id]) for id in ids_corpus
if id not in heldout_corpus)
heldout = myio.create_batches(heldout_corpus, [ ], args.batch_size,
padding_id, bos_id, eos_id, auto_encode=True)
heldout = [ myio.create_one_batch(b1, t2, padding_id) for t1, b1, t2 in heldout ]
say("heldout examples={}\n".format(len(heldout_corpus)))
if args.train:
model = Model(args, embedding_layer,
weights=weights if args.reweight else None)
start_time = time.time()
train = myio.read_annotations(args.train)
if not args.use_anno: train = [ ]
train_batches = myio.create_batches(ids_corpus, train, args.batch_size,
model.padding_id, model.bos_id, model.eos_id, auto_encode=True)
say("{} to create batches\n".format(time.time()-start_time))
model.ready()
model.train(
ids_corpus if not args.heldout else train_corpus,
train,
dev if args.dev else None,
test if args.test else None,
heldout if args.heldout else None
)
def main(args):
raw_corpus = myio.read_corpus(args.corpus)
print("raw corpus:", args.corpus, "len:", len(raw_corpus))
embedding_layer = myio.create_embedding_layer(
raw_corpus,
n_d = args.hidden_dim,
cut_off = args.cut_off,
embs = None # embs = load_embedding_iterator(args.embeddings) if args.embeddings else None
)
ids_corpus = myio.map_corpus(raw_corpus, embedding_layer, max_len=args.max_seq_len)
myio.say("vocab size={}, corpus size={}\n".format(
embedding_layer.n_V,
len(raw_corpus)
))
padding_id = embedding_layer.vocab_map["<padding>"]
if args.reweight:
weights = myio.create_idf_weights(args.corpus, embedding_layer)
#
# if args.dev:
# dev = myio.read_annotations(args.dev, K_neg=-1, prune_pos_cnt=-1)
# dev = myio.create_eval_batches(ids_corpus, dev, padding_id, pad_left = not args.average)
# if args.test:
# test = myio.read_annotations(args.test, K_neg=-1, prune_pos_cnt=-1)
# test = myio.create_eval_batches(ids_corpus, test, padding_id, pad_left = not args.average)
if args.train:
start_time = time.time()
train = myio.read_annotations(args.train)
print("training data:", args.train, "len:", len(train))
train_batches = myio.create_batches(ids_corpus, train, args.batch_size,
padding_id, pad_left = not args.average)
myio.say("{:.2f} secs to create {} batches of size {}\n".format( (time.time()-start_time), len(train_batches), args.batch_size))
myio.say("{} batches, {} tokens in total, {} triples in total\n".format(
len(train_batches),
sum(len(x[0].ravel())+len(x[1].ravel()) for x in train_batches),
sum(len(x[2].ravel()) for x in train_batches)
))
# train_batches = None
model = Model(args, embedding_layer,
weights=weights if args.reweight else None)
model.ready()
# # set parameters using pre-trained network
# if args.load_pretrain:
# model.load_pretrained_parameters(args)
#
model.train(
ids_corpus,
train,
dev = None, # dev if args.dev else None,
test = None # test if args.test else None
)
def main(args):
raw_corpus = myio.read_corpus(args.corpus)
embedding_layer = myio.create_embedding_layer(
raw_corpus,
n_d = args.hidden_dim,
embs = load_embedding_iterator(args.embeddings) if args.embeddings else None
)
ids_corpus = myio.map_corpus(raw_corpus, embedding_layer, max_len=args.max_seq_len)
say("vocab size={}, corpus size={}\n".format(
embedding_layer.n_V,
len(raw_corpus)
))
padding_id = embedding_layer.vocab_map["<padding>"]
if args.reweight:
weights = myio.create_idf_weights(args.corpus, embedding_layer)
if args.dev:
dev_raw = myio.read_annotations(args.dev, K_neg=-1, prune_pos_cnt=-1)
dev = myio.create_eval_batches(ids_corpus, dev_raw, padding_id,
pad_left=not args.average, merge=args.merge)
if args.test:
test_raw = myio.read_annotations(args.test, K_neg=-1, prune_pos_cnt=-1)
test = myio.create_eval_batches(ids_corpus, test_raw, padding_id,
pad_left=not args.average, merge=args.merge)
if args.train:
start_time = time.time()
train = myio.read_annotations(args.train)
train_batches = myio.create_batches(ids_corpus, train, args.batch_size,
padding_id, pad_left = not args.average, merge=args.merge)
say("{} to create batches\n".format(time.time()-start_time))
say("{} batches, {} tokens in total, {} triples in total\n".format(
len(train_batches),
sum(len(x[0].ravel()) for x in train_batches),
sum(len(x[1].ravel()) for x in train_batches)
))
train_batches = None
model = Model(args, embedding_layer,
weights=weights if args.reweight else None)
model.ready()
# set parameters using pre-trained network
if args.load_pretrain:
model.encoder.load_pretrained_parameters(args)
model.train(
ids_corpus,
train,
(dev, dev_raw) if args.dev else None,
(test, test_raw) if args.test else None
)
def main(args):
raw_corpus = myio.read_corpus(args.corpus)
embedding_layer = myio.create_embedding_layer(
raw_corpus,
n_d=args.hidden_dim,
cut_off=args.cut_off,
embs=load_embedding_iterator(args.embeddings)
if args.embeddings else None)
ids_corpus = myio.map_corpus(raw_corpus, embedding_layer)
say("vocab size={}, corpus size={}\n".format(embedding_layer.n_V,
len(raw_corpus)))
padding_id = embedding_layer.vocab_map["<padding>"]
bos_id = embedding_layer.vocab_map["<s>"]
eos_id = embedding_layer.vocab_map["</s>"]
if args.reweight:
weights = myio.create_idf_weights(args.corpus, embedding_layer)
if args.dev:
dev = myio.read_annotations(args.dev, K_neg=20, prune_pos_cnt=-1)
dev = myio.create_eval_batches(ids_corpus, dev, padding_id)
if args.test:
test = myio.read_annotations(args.test, K_neg=20, prune_pos_cnt=-1)
test = myio.create_eval_batches(ids_corpus, test, padding_id)
if args.heldout:
with open(args.heldout) as fin:
heldout_ids = fin.read().split()
heldout_corpus = dict(
(id, ids_corpus[id]) for id in heldout_ids if id in ids_corpus)
train_corpus = dict((id, ids_corpus[id]) for id in ids_corpus
if id not in heldout_corpus)
heldout = myio.create_batches(heldout_corpus, [],
args.batch_size,
padding_id,
bos_id,
eos_id,
auto_encode=True)
heldout = [
myio.create_one_batch(b1, t2, padding_id) for t1, b1, t2 in heldout
]
say("heldout examples={}\n".format(len(heldout_corpus)))
if args.train:
model = Model(args,
embedding_layer,
weights=weights if args.reweight else None)
start_time = time.time()
train = myio.read_annotations(args.train)
if not args.use_anno: train = []
train_batches = myio.create_batches(ids_corpus,
train,
args.batch_size,
model.padding_id,
model.bos_id,
model.eos_id,
auto_encode=True)
say("{} to create batches\n".format(time.time() - start_time))
model.ready()
model.train(ids_corpus if not args.heldout else train_corpus, train,
dev if args.dev else None, test if args.test else None,
heldout if args.heldout else None)
def main():
print 'Starting at: {}\n'.format(datetime.now())
raw_corpus = myio.read_corpus(args.corpus)
embedding_layer = create_embedding_layer(
n_d=200,
embs=load_embedding_iterator(args.embeddings),
only_words=False if args.use_embeddings else True,
trainable=args.trainable
)
ids_corpus = myio.map_corpus(raw_corpus, embedding_layer, max_len=args.max_seq_len)
print("vocab size={}, corpus size={}\n".format(
embedding_layer.n_V,
len(raw_corpus)
))
padding_id = embedding_layer.vocab_map["<padding>"]
if args.reweight:
weights = myio.create_idf_weights(args.corpus, embedding_layer)
if args.layer.lower() == "lstm":
from models import LstmQR as Model
elif args.layer.lower() in ["bilstm", "bigru"]:
from models import BiRNNQR as Model
elif args.layer.lower() == "cnn":
from models import CnnQR as Model
elif args.layer.lower() == "gru":
from models import GruQR as Model
else:
raise Exception("no correct layer given")
if args.dev:
dev = myio.read_annotations(args.dev, K_neg=-1, prune_pos_cnt=-1)
dev = myio.create_eval_batches(ids_corpus, dev, padding_id, pad_left=False)
if args.test:
test = myio.read_annotations(args.test, K_neg=-1, prune_pos_cnt=-1)
test = myio.create_eval_batches(ids_corpus, test, padding_id, pad_left=False)
model = Model(args, embedding_layer, weights=weights if args.reweight else None)
model.ready()
print 'total (non) trainable params: ', model.num_parameters()
if args.load_pre_trained_part:
# need to remove the old assigns to embeddings
model.init_assign_ops = model.load_pre_trained_part(args.load_pre_trained_part)
print '\nmodel init_assign_ops: {}\n'.format(model.init_assign_ops)
if args.train:
start_time = time.time()
train = myio.read_annotations(args.train)
train_batches = myio.create_batches(
ids_corpus, train, args.batch_size, padding_id, pad_left=False
)
print("{} to create batches\n".format(time.time()-start_time))
print("{} batches, {} tokens in total, {} triples in total\n".format(
len(train_batches),
sum(len(x[0].ravel())+len(x[1].ravel()) for x in train_batches),
sum(len(x[2].ravel()) for x in train_batches)
))
model.train_model(
ids_corpus,
train,
dev=dev if args.dev else None,
test=test if args.test else None
)
print '\nEnded at: {}'.format(datetime.now())
def main(args):
raw_corpus = myio.read_corpus(args.corpus, args.translations or None,
args.translatable_ids or None,
args.generated_questions_train or None)
generated_questions_eval = myio.read_generated_questions(
args.generated_questions)
embedding_layer = None
if args.trainable_embeddings == 1:
embedding_layer = myio.create_embedding_layer(
raw_corpus,
n_d=args.hidden_dim,
cut_off=args.cut_off,
embs=load_embedding_iterator(args.embeddings)
if args.embeddings else None,
fix_init_embs=False)
else:
embedding_layer = myio.create_embedding_layer(
raw_corpus,
n_d=args.hidden_dim,
cut_off=args.cut_off,
embs=load_embedding_iterator(args.embeddings)
if args.embeddings else None)
ids_corpus = myio.map_corpus(raw_corpus,
embedding_layer,
max_len=args.max_seq_len,
generated_questions=generated_questions_eval)
say("vocab size={}, corpus size={}\n".format(embedding_layer.n_V,
len(raw_corpus)))
padding_id = embedding_layer.vocab_map["<padding>"]
if args.reweight:
weights = myio.create_idf_weights(args.corpus, embedding_layer)
if args.dev:
# dev = myio.read_annotations(args.dev, K_neg=-1, prune_pos_cnt=-1)
dev = myio.read_annotations(args.dev,
K_neg=args.dev_pool_size,
prune_pos_cnt=-1)
dev = myio.create_eval_batches(ids_corpus,
dev,
padding_id,
pad_left=not args.average)
if args.test:
test = myio.read_annotations(args.test, K_neg=-1, prune_pos_cnt=-1)
test = myio.create_eval_batches(ids_corpus,
test,
padding_id,
pad_left=not args.average)
if args.train:
start_time = time.time()
train = myio.read_annotations(
args.train, training_data_percent=args.training_data_percent)
train_batches = myio.create_batches(ids_corpus,
train,
args.batch_size,
padding_id,
pad_left=not args.average,
include_generated_questions=True)
say("{} to create batches\n".format(time.time() - start_time))
say("{} batches, {} tokens in total, {} triples in total\n".format(
len(train_batches),
sum(len(x[0].ravel()) + len(x[1].ravel()) for x in train_batches),
sum(len(x[2].ravel()) for x in train_batches)))
train_batches = None
model = Model(args,
embedding_layer,
weights=weights if args.reweight else None)
# print('args.average: '+args.average)
model.ready()
# # # set parameters using pre-trained network
if args.do_train == 1:
if args.load_pretrain:
model.load_pretrained_parameters(args)
model.train(ids_corpus, train, dev if args.dev else None,
test if args.test else None)
# AVERAGE THE PREDICTIONS OBTAINED BY RUNNING THE MODEL 10 TIMES
if args.do_evaluate == 1:
model.load_pretrained_parameters(args)
# model.set_model(model.load_model(args.load_pretrain))
for i in range(1):
r = model.just_eval(dev if args.dev else None,
test if args.test else None)
# ANALYZE the results
if len(args.analyze_file.strip()) > 0:
model.load_pretrained_parameters(args)
file_name = args.analyze_file.strip(
) # 'AskUbuntu.Rcnn_analysis3.gt(es)-gt.txt'
model.analyze(file_name, embedding_layer, dev)
raw_corpus = myio.read_corpus(args.corpus_w_tags, with_tags=True)
embedding_layer = create_embedding_layer(n_d=10,
embs=load_embedding_iterator(
args.embeddings),
only_words=False)
with tf.Session() as sess:
myqrapi = TPAPI(args.model, embedding_layer, sess, len(label_tags),
args.layer)
embedding_layer = myqrapi.model.embedding_layer
ids_corpus = myio.map_corpus(raw_corpus,
embedding_layer,
label_tags,
max_len=args.max_seq_len)
print("vocab size={}, corpus size={}\n".format(embedding_layer.n_V,
len(raw_corpus)))
padding_id = embedding_layer.vocab_map["<padding>"]
say("vocab size={}, corpus size={}\n".format(embedding_layer.n_V,
len(raw_corpus)))
eval_batches = create_batches(df, ids_corpus, 'dev',
myqrapi.model.args.batch_size,
padding_id)
print 'DEV evaluation:'
print '{} batches.'.format(len(eval_batches))
argparser.add_argument("--full_results_file", type=str,
default="") # to write in
argparser.add_argument("--results_file", type=str,
default="") # to write in
argparser.add_argument("--layer", type=str, default="lstm")
args = argparser.parse_args()
print '\n', args, '\n'
with tf.Session() as sess:
myqrapi = QRAPI(args.model, args.corpus, args.embeddings, sess,
args.layer)
raw_corpus = myio.read_corpus(args.corpus)
embedding_layer = myqrapi.model.embedding_layer
ids_corpus = myio.map_corpus(raw_corpus, embedding_layer, max_len=100)
test = myio.read_annotations(args.test_file,
K_neg=-1,
prune_pos_cnt=-1)
test = create_eval_batches(ids_corpus,
test,
myqrapi.model.padding_id,
pad_left=not myqrapi.model.args.average)
testmap, testmrr, testpat1, testpat5, rank_labels, rank_ids, qids, rank_scores = myqrapi.evaluate(
test, sess)
if args.full_results_file:
with open(args.full_results_file, 'w') as f:
for i, (_, _, labels, pid, qids) in enumerate(test):
print_qids_similar = [
