def _test_conversion(settings, level='token'):
reader = Reader(settings, settings.input_path)
label_encoder = MultiLabelEncoder.from_settings(settings)
label_encoder.fit_reader(reader)
data = Dataset(settings, reader, label_encoder)
le = label_encoder.tasks['lemma']
for (inp, tasks), (rinp, rtasks) in data.batch_generator(return_raw=True):
# preds
tinp, tlen = tasks['lemma']
preds = [
le.stringify(t, l)
for t, l in zip(tinp.t().tolist(), tlen.tolist())
]
if level == 'token':
preds = [w for line in preds for w in line]
# tokens
tokens = [tok for line in rinp for tok in line]
# trues
trues = [w for line in rtasks for w in line['lemma']]
# check
for pred, token, true in zip(preds, tokens, trues):
rec = le.preprocessor_fn.inverse_transform(pred, token)
assert rec == true, (pred, token, true, rec)
def setUp(self):
settings = settings_from_file(testpath)
settings['batch_size'] = 1
reader = Reader(settings, settings.input_path)
label_encoder = MultiLabelEncoder.from_settings(settings)
insts = label_encoder.fit(line for _, line in reader.readsents())
self.insts = insts
self.num_batches = insts // settings.batch_size
self.data = Dataset(settings, reader, label_encoder)
def test_batch_level(self):
settings = settings_from_file(testpath)
settings['batch_size'] = 20
reader = Reader(settings, settings.input_path)
label_encoder = MultiLabelEncoder.from_settings(settings)
label_encoder.fit(line for _, line in reader.readsents())
data = Dataset(settings, reader, label_encoder)
pre_batches = 0
for batch in data.batch_generator():
pre_batches += 1
self.assertAlmostEqual(pre_batches, self.insts // 20, delta=delta)
devset = data.get_dev_split(self.insts, split=0.05)
post_batches = 0
for batch in data.batch_generator():
post_batches += 1
self.assertAlmostEqual(pre_batches * 0.95, post_batches, delta=delta)
def setUp(self):
settings = settings_from_file(testpath)
reader = Reader(settings, settings.input_path)
label_encoder = MultiLabelEncoder.from_settings(settings)
label_encoder.fit_reader(reader)
self.data = Dataset(settings, reader, label_encoder)
import uuid
import torch
import os
import unittest
from pie.models import SimpleModel
from pie.data import MultiLabelEncoder, Reader, Dataset
from pie.settings import settings_from_file
testpath = os.path.join(os.path.dirname(__file__), 'testconfig.json')
settings = settings_from_file(testpath)
label_encoder = MultiLabelEncoder.from_settings(settings)
reader = Reader(settings, settings.input_path)
label_encoder.fit_reader(reader)
dataset = Dataset(settings, label_encoder, reader)
class TestModelSerialization(unittest.TestCase):
def setUp(self):
emb_dim, hidden_size, num_layers = 64, 100, 1
self.model = SimpleModel(label_encoder, emb_dim, emb_dim, hidden_size,
num_layers)
def test_serialization(self):
model = self.model
fid = '/tmp/{}'.format(str(uuid.uuid1()))
model.save(fid)
model2 = SimpleModel.load(fid)
os.remove('{}.tar'.format(fid))
self.assertEqual(model.label_encoder, model2.label_encoder)
return fname, infix
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('config_path', nargs='?', default='config.json')
args = parser.parse_args()
settings = settings_from_file(args.config_path)
# datasets
reader = Reader(settings, settings.input_path)
tasks = reader.check_tasks(expected=None)
label_encoder = MultiLabelEncoder.from_settings(settings, tasks=tasks)
if settings.verbose:
print("::: Available tasks :::")
print()
for task in tasks:
print("- {}".format(task))
print()
# fit
start = time.time()
if settings.verbose:
print("::: Fitting data :::")
print()
ninsts = label_encoder.fit_reader(reader)
if settings.verbose:
print("Found {} total instances in training set in {:g} secs".format(
def setUp(self):
settings = settings_from_file(testpath)
reader = Reader(settings, settings.input_path)
label_encoder = MultiLabelEncoder.from_settings(settings)
label_encoder.fit(line for _, line in reader.readsents())
self.data = Dataset(settings, reader, label_encoder)
def run(config_path):
now = datetime.now()
seed = now.hour * 10000 + now.minute * 100 + now.second
print("Using seed:", seed)
random.seed(seed)
numpy.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
settings = settings_from_file(config_path)
# check settings
# - check at least and at most one target
has_target = False
for task in settings.tasks:
if len(settings.tasks) == 1:
task['target'] = True
if task.get('target', False):
if has_target:
raise ValueError("Got more than one target task")
has_target = True
if not has_target:
raise ValueError("Needs at least one target task")
# datasets
reader = Reader(settings, settings.input_path)
tasks = reader.check_tasks(expected=None)
if settings.verbose:
print("::: Available tasks :::")
print()
for task in tasks:
print("- {}".format(task))
print()
# label encoder
label_encoder = MultiLabelEncoder.from_settings(settings, tasks=tasks)
if settings.verbose:
print("::: Fitting data :::")
print()
label_encoder.fit_reader(reader)
if settings.verbose:
print()
print("::: Vocabulary :::")
print()
types = '{}/{}={:.2f}'.format(*label_encoder.word.get_type_stats())
tokens = '{}/{}={:.2f}'.format(*label_encoder.word.get_token_stats())
print("- {:<15} types={:<10} tokens={:<10}".format(
"word", types, tokens))
types = '{}/{}={:.2f}'.format(*label_encoder.char.get_type_stats())
tokens = '{}/{}={:.2f}'.format(*label_encoder.char.get_token_stats())
print("- {:<15} types={:<10} tokens={:<10}".format(
"char", types, tokens))
print()
print("::: Tasks :::")
print()
for task, le in label_encoder.tasks.items():
print("- {:<15} target={:<6} level={:<6} vocab={:<6}".format(
task, le.target, le.level, len(le)))
print()
trainset = Dataset(settings, reader, label_encoder)
devset = None
if settings.dev_path:
devset = Dataset(settings, Reader(settings, settings.dev_path),
label_encoder)
else:
logging.warning("No devset: cannot monitor/optimize training")
# model
model = SimpleModel(label_encoder,
settings.tasks,
settings.wemb_dim,
settings.cemb_dim,
settings.hidden_size,
settings.num_layers,
dropout=settings.dropout,
cell=settings.cell,
cemb_type=settings.cemb_type,
cemb_layers=settings.cemb_layers,
custom_cemb_cell=settings.custom_cemb_cell,
linear_layers=settings.linear_layers,
scorer=settings.scorer,
word_dropout=settings.word_dropout,
lm_shared_softmax=settings.lm_shared_softmax,
include_lm=settings.include_lm)
# pretrain(/load pretrained) embeddings
if model.wemb is not None:
if settings.pretrain_embeddings:
print("Pretraining word embeddings")
wemb_reader = Reader(settings, settings.input_path,
settings.dev_path, settings.test_path)
weight = get_pretrained_embeddings(wemb_reader,
label_encoder,
size=settings.wemb_dim,
window=5,
negative=5,
min_count=1)
model.wemb.weight.data = torch.tensor(weight, dtype=torch.float32)
elif settings.load_pretrained_embeddings:
print("Loading pretrained embeddings")
if not os.path.isfile(settings.load_pretrained_embeddings):
print("Couldn't find pretrained eembeddings in: {}".format(
settings.load_pretrained_embeddings))
initialization.init_pretrained_embeddings(
settings.load_pretrained_embeddings, label_encoder.word,
model.wemb)
# load pretrained weights
if settings.load_pretrained_encoder:
model.init_from_encoder(
pie.Encoder.load(settings.load_pretrained_encoder))
# freeze embeddings
if settings.freeze_embeddings:
model.wemb.weight.requires_grad = False
model.to(settings.device)
print("::: Model :::")
print()
print(model)
print()
print("::: Model parameters :::")
print()
trainable = sum(p.nelement() for p in model.parameters()
if p.requires_grad)
total = sum(p.nelement() for p in model.parameters())
print("{}/{} trainable/total".format(trainable, total))
print()
# training
print("Starting training")
running_time = time.time()
trainer = Trainer(settings, model, trainset, reader.get_nsents())
scores = None
try:
scores = trainer.train_epochs(settings.epochs, devset=devset)
except KeyboardInterrupt:
print("Stopping training")
finally:
model.eval()
running_time = time.time() - running_time
if settings.test_path:
print("Evaluating model on test set")
testset = Dataset(settings, Reader(settings, settings.test_path),
label_encoder)
for task in model.evaluate(testset, trainset).values():
task.print_summary()
# save model
fpath, infix = get_fname_infix(settings)
if not settings.run_test:
fpath = model.save(fpath, infix=infix, settings=settings)
print("Saved best model to: [{}]".format(fpath))
if devset is not None and not settings.run_test:
scorers = model.evaluate(devset, trainset)
scores = []
for task in sorted(scorers):
scorer = scorers[task]
result = scorer.get_scores()
for acc in result:
scores.append('{}:{:.6f}'.format(task,
result[acc]['accuracy']))
scores.append('{}-support:{}'.format(task,
result[acc]['support']))
path = '{}.results.{}.csv'.format(settings.modelname,
'-'.join(get_targets(settings)))
with open(path, 'a') as f:
line = [infix, str(seed), str(running_time)]
line += scores
f.write('{}\n'.format('\t'.join(line)))
print("Bye!")
from pie import utils
with utils.shutup(): # avoid pattern warning
from gensim.models import Word2Vec
word2vec = Word2Vec(reader.get_token_iterator(), **kwargs)
weight = np.zeros((len(label_encoder.word), word2vec.wv.vector_size))
found = 0
for w, idx in label_encoder.word.table.items():
try:
weight[idx] = word2vec.wv[w]
found += 1
except KeyError: # reserved symbols are not in training sentences
pass
print("A total of {}/{} word embeddings were pretrained"
.format(found, len(label_encoder.word)))
return weight
if __name__ == '__main__':
from pie.data import Reader, MultiLabelEncoder
from pie.settings import settings_from_file
settings = settings_from_file("config.json")
reader = Reader(settings, settings.input_path)
le = MultiLabelEncoder.from_settings(settings)
le.fit_reader(reader)
get_pretrained_embeddings(reader, le, min_count=1)