def train_eval(mode,
model_file,
descriptions_file,
neg_words_mult=2.,
lbda=50,
min_words=50,
eval_lines=5000,
eval_words=10):
model = load_word2vec_model(model_file, mmap='r')
if mode == 'centroid':
entity_model = EntityModelCentroid()
elif mode == 'lr':
bins = np.cumsum(
[model.vocab[word].count for word in model.index2word])
entity_model = EntityModelLR(bins, neg_words_mult, lbda)
else:
raise Exception('unsupported mode %s' % mode)
rng = random.Random(1729)
eval_items = []
def sampled_word_seqs():
for i, (entity, t, word_idxs) in \
enumerate(read_entity_word_seqs(descriptions_file, model, min_words)):
rng.shuffle(word_idxs)
if i < eval_lines:
eval_items.append(
(entity, word_idxs[:eval_words], len(word_idxs)))
yield entity, t, word_idxs[eval_words:]
entity_model.train(model, sampled_word_seqs())
evaluate_retrieval(model, entity_model, eval_items)
def train_eval(mode, model_file, descriptions_file,
neg_words_mult=2., lbda=50, min_words=50,
eval_lines=5000, eval_words=10):
model = load_word2vec_model(model_file, mmap='r')
if mode == 'centroid':
entity_model = EntityModelCentroid()
elif mode == 'lr':
bins = np.cumsum([model.vocab[word].count
for word in model.index2word])
entity_model = EntityModelLR(bins, neg_words_mult, lbda)
else:
raise Exception('unsupported mode %s' % mode)
rng = random.Random(1729)
eval_items = []
def sampled_word_seqs():
for i, (entity, t, word_idxs) in \
enumerate(read_entity_word_seqs(descriptions_file, model, min_words)):
rng.shuffle(word_idxs)
if i < eval_lines:
eval_items.append((entity, word_idxs[:eval_words], len(word_idxs)))
yield entity, t, word_idxs[eval_words:]
entity_model.train(model, sampled_word_seqs())
evaluate_retrieval(model, entity_model, eval_items)
def train(mode, model_file, descriptions_file, output_file=None,
neg_words_mult=2., lbda=50, min_words=1):
model = load_word2vec_model(model_file, mmap='r')
if mode == 'centroid':
entity_model = EntityModelCentroid()
elif mode == 'lr':
bins = np.cumsum([model.vocab[word].count
for word in model.index2word])
entity_model = EntityModelLR(bins, neg_words_mult, lbda)
else:
raise Exception('unsupported mode %s' % mode)
entity_model.train(model,
read_entity_word_seqs(descriptions_file, model, min_words))
if output_file is not None:
entity_model.save(output_file)
def train(mode,
model_file,
descriptions_file,
output_file=None,
neg_words_mult=2.,
lbda=50,
min_words=1):
model = load_word2vec_model(model_file, mmap='r')
if mode == 'centroid':
entity_model = EntityModelCentroid()
elif mode == 'lr':
bins = np.cumsum(
[model.vocab[word].count for word in model.index2word])
entity_model = EntityModelLR(bins, neg_words_mult, lbda)
else:
raise Exception('unsupported mode %s' % mode)
entity_model.train(
model, read_entity_word_seqs(descriptions_file, model, min_words))
if output_file is not None:
entity_model.save(output_file)
