def __init__(self, embedding_method='glove'):
Dataset.__init__(self)
self.max_text_length = 40
self.min_text_length = 3
self.embedding_method = embedding_method
self.character_dim = None
self.name = 'lang8v1'
self.src_vocab_size = 30000
self.tgt_vocab_size = 30000
FLAGS = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
class Config(BaseConfig):
filename = FLAGS.dataset
embed_size = FLAGS.embedding
batch_size = FLAGS.batch_size
l2 = FLAGS.l2
user_count = -1
item_count = -1
optimizer = 'adam'
neg_count = FLAGS.neg
learning_rate = 0.001
config = Config()
dataset = Dataset(config.filename)
config.item_count = dataset.item_count
config.user_count = dataset.user_count
tf.logging.info("\n\n%s\n\n" % config)
model = PairwiseGMF(config)
sv = tf.train.Supervisor(logdir=None, save_model_secs=0, save_summaries_secs=0)
sess = sv.prepare_or_wait_for_session(
config=tf.ConfigProto(gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=0.1,
allow_growth=True)))
for i in range(FLAGS.iters):
if sv.should_stop():
break
progress = tqdm(enumerate(dataset.get_data(FLAGS.batch_size, False, FLAGS.neg)),
class Config(BaseConfig):
filename = FLAGS.dataset
embed_size = FLAGS.embedding
batch_size = FLAGS.batch_size
l2 = FLAGS.l2
user_count = -1
item_count = -1
optimizer = 'adam'
neg_count = FLAGS.neg
learning_rate = 0.001
config = Config()
dataset = Dataset(config.filename)
config.item_count = dataset.item_count
config.user_count = dataset.user_count
tf.logging.info("\n\n%s\n\n" % config)
model = PairwiseGMF(config)
sv = tf.train.Supervisor(logdir=None, save_model_secs=0, save_summaries_secs=0)
sess = sv.prepare_or_wait_for_session(config=tf.ConfigProto(
gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.1,
allow_growth=True)))
for i in range(FLAGS.iters):
if sv.should_stop():
break
progress = tqdm(enumerate(
dataset.get_data(FLAGS.batch_size, False, FLAGS.neg)),
yield t
def store(triples, full_path):
with open(full_path, 'w') as writer:
for t in triples:
s, p, o = t
t_str = s + '\t' + p + '\t' + o + '\n'
writer.write(t_str)
kg = ['WN18RR', 'FB15k-237', 'YAGO3-10']
for i in kg:
data_dir = f'KGs/{i}/'
clean_data_dir = f'KGs/{i}*/'
dataset = Dataset(data_dir=data_dir)
print(data_dir)
clean_valid_set = clean_dataset(
dataset.valid_data,
entities=dataset.get_entities(dataset.train_data),
relations=dataset.get_relations(dataset.train_data))
clean_test_set = clean_dataset(
dataset.test_data,
entities=dataset.get_entities(dataset.train_data),
relations=dataset.get_relations(dataset.train_data))
store(dataset.train_data, clean_data_dir + 'train.txt') # Train set
store(clean_valid_set, clean_data_dir + 'valid.txt') # Cleaned valid set
store(clean_test_set, clean_data_dir + 'test.txt') # Clean test set
# 3. Create mappings.
# 3.1 Entity index mapping.
entity_idxs = {
e: e_idx
for e, e_idx in zip(model.dataset.entity_ids(),
range(len(model.dataset.entity_ids())))
}
# 3.2 Relation index mapping.
relation_idxs = {
r: r_idx
for r, r_idx in zip(model.dataset.relation_ids(),
range(len(model.dataset.relation_ids())))
}
# 2. Load Dataset
dataset = Dataset(data_dir=f'KGs/FB15K-237*/')
# 4. Subject-Predicate to Object mapping and Predicate-Object to Subject mapping. This will be used at computing filtering ranks.
sp_vocab, so_vocab, po_vocab = dataset.get_mappings(
dataset.train_data + dataset.valid_data + dataset.test_data,
entity_idxs=entity_idxs,
relation_idxs=relation_idxs)
ev = Evaluator(entity_idxs=entity_idxs,
relation_idxs=relation_idxs,
sp_vocab=sp_vocab,
so_vocab=so_vocab,
po_vocab=po_vocab)
lp_results = ev.filtered_relation_prediction(dataset.test_data, model)
with open(f'fb15k-237_*_{m}_relation_prediction_results.json',
'w') as file_descriptor:
from util.data import Dataset
kg = ['WN18RR', 'FB15k-237', 'YAGO3-10']
for i in kg:
dataset = Dataset(data_dir=f'KGs/{i}/')
# Get all entities from train set.
entities = set(dataset.get_entities(dataset.train_data))
dataset.describe_oov(dataset.test_data, entities, info=f'{i}-Test set')
dataset.describe_oov(dataset.valid_data, entities, info=f'{i}-Val set')
# Cleaned datasets
kg = ['WN18RR*', 'FB15k-237*', 'YAGO3-10*']
for i in kg:
dataset = Dataset(data_dir=f'KGs/{i}/')
# Get all entities from train set.
entities = set(dataset.get_entities(dataset.train_data))
dataset.describe_oov(dataset.test_data, entities, info=f'{i}-Test set')
dataset.describe_oov(dataset.valid_data, entities, info=f'{i}-Val set')
class Config(BaseConfig):
filename = FLAGS.dataset
embed_size = FLAGS.embedding
batch_size = FLAGS.batch_size
l2 = FLAGS.l2
user_count = -1
item_count = -1
optimizer = 'adam'
neg_count = FLAGS.neg
learning_rate = 0.001
config = Config()
dataset = Dataset(config.filename, limit=limit)
set_parameters(
normalized_popularity=dataset.normalized_popularity,
loss_alpha=loss_alpha,
loss_beta=loss_beta,
loss_scale=loss_scale,
loss_percentile=get_percentile(dataset.normalized_popularity, 45),
metrics_alpha=metrics_alpha,
metrics_beta=metrics_beta,
metrics_gamma=metrics_gamma,
metrics_scale=metrics_scale,
metrics_percentile=metrics_percentile,
loss_type=loss_type,
k=k,
k_trainable=k_trainable,
low_popularity_threshold=dataset.thresholds[0],
def train(env, datasets):
"""
Trains a semantic parser that translates natural
language expressions to program code based on the
language data provided.
:param env: environment including model and
language data.
:param datasets: training and validation datasets.
"""
model = env['model']
lang = env['lang']
# Zero is padding token and no alignment.
crit = nn.NLLLoss(ignore_index=0, reduction='sum')
opt = optim.SGD(
model.parameters(),
lr=args.learning_rate,
momentum=0.9
)
train_data = datasets['train']
train_set = Dataset(
train_data,
model.device,
args.mask_ratio
)
if 'dev' in datasets:
dev_data = datasets['dev']
dev_set = Dataset(
dev_data,
model.device,
args.mask_ratio
)
best_dev_acc = 0
best_epoch = 0
logger['log'].log(
f'[INFO {datetime.now()}] commencing '
f'training for {args.epochs} epochs'
)
# space
print('')
early_stop = 0
for epoch in range(1, args.epochs+1):
since = time.time()
statistics = train_epoch(
env, train_set,
opt, crit, epoch
)
duration = elapsed(since)
loss = statistics.loss
accuracy = statistics.accuracy
gold_acc = statistics.gold_accuracy
logger['log'].log(
f'[INFO {datetime.now()}] EPOCH {epoch} > '
f'{"elapsed time: ":<25}{duration:.3f}s'
)
logger['log'].log(
f'[INFO {datetime.now()}] EPOCH {epoch} > '
f'{"train loss: ":<25}{loss:.5f}'
)
logger['log'].log(
f'[INFO {datetime.now()}] EPOCH {epoch} > '
f'{"train accuracy: ":<25}{accuracy*100:0>6.3f}%'
)
logger['log'].log(
f'[INFO {datetime.now()}] EPOCH {epoch} > '
f'{"train gold acc.: ":<25}{gold_acc*100:0>6.3f}%'
)
if 'dev' in datasets and args.validate:
# Validate model.
statistics = validate(env, dev_set, crit)
dev_loss = statistics.loss
accuracy = statistics.accuracy
gold_acc = statistics.gold_accuracy
logger['log'].log(
f'[INFO {datetime.now()}] EPOCH {epoch} > '
f'{"dev loss: ":<25}{dev_loss:.5f}'
)
logger['log'].log(
f'[INFO {datetime.now()}] EPOCH {epoch} > '
f'{"dev accuracy: ":<25}{accuracy*100:0>6.3f}%'
)
logger['log'].log(
f'[INFO {datetime.now()}] EPOCH {epoch} > '
f'{"dev gold acc.: ":<25}{gold_acc*100:0>6.3f}%'
)
logger['log'].log(
f'[INFO {datetime.now()}] EPOCH {epoch} > '
f'{"best dev accuracy: ":<25}{best_dev_acc*100:0>6.3f}%'
)
# Save model if new best exact match accuracy on
# development set.
if args.best_gold and gold_acc > best_dev_acc:
best_dev_acc = gold_acc
__save_model(model, args, lang, epoch, best_epoch)
best_epoch = epoch
early_stop = 0
logger['log'].log(
f'[INFO {datetime.now()}] EPOCH {epoch} > '
f'new best dev split gold accuracy, saving model'
)
# Save model if new best accuracy on development set.
elif not args.best_gold and accuracy > best_dev_acc:
best_dev_acc = accuracy
__save_model(model, args, lang, epoch, best_epoch)
best_epoch = epoch
early_stop = 0
logger['log'].log(
f'[INFO {datetime.now()}] EPOCH {epoch} > '
f'new best dev split accuracy, saving model'
)
else:
early_stop = early_stop + 1
else: # if not validating
# Save model each epoch if not validating.
__save_model(model, args, lang, epoch, epoch-1)
# space
print('')
if early_stop == args.early_stop:
logger['log'].log(
f'[INFO {datetime.now()}] no dev set improvement '
f'since {args.early_stop} epochs, stop training'
)
break
logger['log'].log(
f'[INFO {datetime.now()}] training concluded'
)
logger['log'].close()
def y_position(x, cutoff):
y = sigmoid(-x * Settings.metrics_gamma / cutoff) + 0.5
return y
def y_custom(popularity, position, cutoff):
y = y_popularity(popularity) * y_position(position, cutoff)
return y
if __name__ == "__main__":
print("Testing settings")
dataset = Dataset('data/pinterest.npz')
set_parameters(
normalized_popularity=dataset.normalized_popularity,
loss_alpha=200,
loss_beta=0.02,
loss_scale=1,
loss_percentile=get_percentile(dataset.normalized_popularity, 45),
metrics_alpha=100,
metrics_beta=0.03,
metrics_gamma=5,
metrics_scale=1 / 15,
metrics_percentile=0.45,
loss_type=2
)
max_neighbors = -1
config = Config()
# print('FLAGS.resume:', FLAGS.resume)
# print('config.logdir:', config.logdir)
if FLAGS.resume:
config.save_directory = config.logdir
config.load()
dictConfig(get_logging_config(config.logdir))
dataset = Dataset(config.filename,
limit=limit,
rebuild=rebuild,
use_preprocess=use_preprocess)
set_parameters(normalized_popularity=dataset.normalized_popularity,
loss_alpha=loss_alpha,
loss_beta=loss_beta,
loss_scale=loss_scale,
loss_percentile=get_percentile(dataset.normalized_popularity,
45),
metrics_alpha=metrics_alpha,
metrics_beta=metrics_beta,
metrics_gamma=metrics_gamma,
metrics_scale=metrics_scale,
metrics_percentile=metrics_percentile,
loss_type=loss_type,
k=k,
k_trainable=k_trainable,
from util.data import Dataset
kg = ['WN18RR', 'FB15K-237'] #, 'YAGO3-10']
for i in kg:
dataset = Dataset(data_dir=f'KGs/{i}/')
dataset.descriptive_statistics(dataset.train_data, info=f'{i}-Train set')
dataset.descriptive_statistics(dataset.valid_data, info=f'{i}-Valid set')
dataset.descriptive_statistics(dataset.test_data, info=f'{i}-Test set')
pretrain = FLAGS.pretrain
max_neighbors = -1
config = Config()
# print('FLAGS.resume:', FLAGS.resume)
# print('config.logdir:', config.logdir)
if FLAGS.resume:
config.save_directory = config.logdir
config.load()
dictConfig(get_logging_config(config.logdir))
dataset = Dataset(config.filename, limit=limit)
set_parameters(
normalized_popularity=dataset.normalized_popularity,
loss_alpha=loss_alpha,
loss_beta=loss_beta,
loss_scale=loss_scale,
loss_percentile=get_percentile(dataset.normalized_popularity, 45),
metrics_alpha=metrics_alpha,
metrics_beta=metrics_beta,
metrics_gamma=metrics_gamma,
metrics_scale=metrics_scale,
metrics_percentile=metrics_percentile,
loss_type=loss_type,
k=k,
k_trainable=k_trainable,
low_popularity_threshold=dataset.thresholds[0],
