def sparse_tensor_batches(batch_size=1000, symmetric=symmetric):
batches = batch_generator2(self.model,
self.sentences_generator(),
batch_size=batch_size)
for batch in batches:
sparse_ppmi_tensor_pair = gatherer.create_pmi_tensor(
batch=batch,
positive=True,
debug=False,
symmetric=symmetric,
log_info=False,
neg_sample_percent=0.0 if nonneg else 0.05,
pmi=True,
shift=shift,
)
yield sparse_ppmi_tensor_pair
def sparse_tensor_batches(batch_size=1000, symmetric=symmetric):
if is_glove:
def grouper(n, iterable):
it = iter(iterable)
while True:
chunk = tuple(itertools.islice(it, n))
if not chunk:
return
yield chunk
(indices, values) = gatherer.create_pmi_tensor(
batch=None,
positive=True,
debug=False,
symmetric=True,
log_info=True,
pmi=False,
)
(indices, values) = (indices, np.log(values))
for i in range(50):
indices_shuffled, values_shuffled = shuffle(
indices, values) # sklearn's shuffle implementation
print('GloVe iteration number {}...'.format(i))
for sampled_indices, sampled_values in zip(
grouper(batch_size, indices_shuffled),
grouper(batch_size, values_shuffled)):
yield (sampled_indices, sampled_values)
else: # not is_glove
batches = batch_generator2(self.model,
self.sentences_generator(),
batch_size=batch_size)
for batch in batches:
sparse_ppmi_tensor_pair = gatherer.create_pmi_tensor(
batch=batch,
positive=True,
debug=False,
symmetric=symmetric,
log_info=False,
neg_sample_percent=neg_sample_percent,
pmi=True,
shift=shift,
)
yield sparse_ppmi_tensor_pair
def sparse_tensor_batches(batch_size=1000):
batches = batch_generator2(
self.model,
self.sentences_generator(num_sents=self.num_sents),
batch_size=batch_size)
for batch in batches:
pairlist = [
gatherer.create_pmi_tensor(
batch=batch,
positive=True,
debug=False,
symmetric=True,
log_info=False,
neg_sample_percent=0.0,
pmi=True,
shift=shift,
) for (shift, gatherer) in zip(shifts, gatherers)
]
yield ([x[0] for x in pairlist], [x[1] for x in pairlist])
def get_pmi_gatherer(self, n):
gatherer = None
if os.path.exists('gatherer_{}_{}_{}.pkl'.format(
self.num_articles, self.min_count, n)):
with open(
'gatherer_{}_{}_{}.pkl'.format(self.num_articles,
self.min_count, n),
'rb') as f:
t = time.time()
import gc
gc.disable()
gatherer = dill.load(f)
gc.enable()
print('Loading gatherer took {} secs'.format(time.time() - t))
else:
# batch_size doesn't matter. But higher is probably better (in terms of threading & speed)
batches = batch_generator2(
self.model,
self.sentences_generator(num_articles=self.num_articles),
batch_size=1000)
gatherer = PMIGatherer(self.model, n=n)
if self.num_articles <= 1e4:
gatherer.populate_counts(batches, huge_vocab=False)
else:
gatherer.populate_counts(batches, huge_vocab=True, min_count=5)
with open(
'gatherer_{}_{}_{}.pkl'.format(self.num_articles,
self.min_count, n),
'wb') as f:
t = time.time()
import gc
gc.disable()
dill.dump(gatherer, f)
gc.enable()
print('Dumping gatherer took {} secs'.format(time.time() - t))
return gatherer
