def main(path, min_count):
# Construct vocabulary.
vocab = []
with open(path) as fin:
for line in fin:
words = line.strip().split()
for word in words:
word = process(word)
vocab.append(word)
counter = Counter(vocab)
vocab = dict((word, count) for word, count in counter.most_common() if count >= min_count)
with open('vocab.json', 'w') as fp:
json.dump(vocab, fp, indent=4)
del counter
# Get all neccessary substitutions using vocabulary.
subs = []
with open(path) as fin:
for sent_id, line in enumerate(fin, 1):
words = line.strip().split()
for word_id, word in enumerate(words, 1):
processed = process(word)
if processed not in vocab:
unk = unkify(processed, vocab)
# subs.append(f'{sent_id} {word_id} {UNK}')
subs.append(f'{sent_id} {word_id} {unk}')
elif not processed == word:
subs.append(f'{sent_id} {word_id} {processed}')
print('\n'.join(subs))
def _process_tree(line, words, tags=False):
tokens = line.replace(')', ' )').split()
nonterminals = []
new_tokens = []
pop = False
ind = 0
for token in tokens:
if token.startswith('('): # open paren
new_token = token[1:]
nonterminals.append(new_token)
new_tokens.append(token)
elif token == ')': # close paren
if pop: # preterminal
pop = False
else: # nonterminal
new_token = ')' + nonterminals.pop()
new_tokens.append(new_token)
else: # word
if not tags:
tag = '(' + nonterminals.pop() # pop preterminal
new_tokens.pop()
pop = True
if token.lower() in words:
new_tokens.append(token.lower())
else:
new_tokens.append(unkify(token))
return ' ' + ' '.join(new_tokens[1:-1]) + ' '
def generate(self, num_out=200, prime=None, sample=True):
state = self.sess.run(self.cell.zero_state(1, tf.float32))
if prime is None:
prime = np.random.choice(self.vocab)
else:
prime = unkify(prime, self.vocab)
for word in prime.split():
print word
last_word_i = self.vocab.index(word)
input_i = np.array([[last_word_i]])
feed_dict = {self.inputs: input_i, self.initial_state: state}
state = self.sess.run(self.final_state, feed_dict=feed_dict)
gen_seq = prime
for i in xrange(num_out):
input_i = np.array([[last_word_i]])
feed_dict = {self.inputs: input_i, self.initial_state: state}
probs, state = self.sess.run(
[self.probs, self.final_state], feed_dict=feed_dict)
probs = probs[0]
if sample:
gen_word_i = np.random.choice(np.arange(len(probs)), p=probs)
else:
gen_word_i = np.argmax(probs)
gen_word = self.vocab[gen_word_i]
gen_seq += ' ' + gen_word
last_word_i = gen_word_i
return gen_seq
def ptb_recurse(t, words, forms):
forms.append('(' + t.label)
for child in t.subtrees():
if child.is_preterminal():
token = child.tokens()[0]
if token.lower() not in words:
forms.append(unkify(token))
else:
forms.append(token.lower())
else:
ptb_recurse(child, words, forms)
forms.append(')' + t.label)
def generate(self, num_out=200, prime=None, sample=True):
"""
Generate a sequence of text from the trained model.
@param num_out: The length of the sequence to generate, in num words.
@param prime: The priming sequence for generation. If None, pick a random word from the
vocabulary as prime.
@param sample: Whether to probabalistically sample the next word, rather than take the word
of max probability.
"""
state = self.sess.run(self.cell.zero_state(1, tf.float32))
# if no prime supplied, get a random word. Otherwise, translate all words in prime that
# aren't in dictionary to '*UNK*'
if prime is None:
prime = np.random.choice(self.vocab)
else:
prime = unkify(prime, self.vocab)
# prime the model state
for word in prime.split():
print word
last_word_i = self.vocab.index(word)
input_i = np.array([[last_word_i]])
feed_dict = {self.inputs: input_i, self.initial_state: state}
state = self.sess.run(self.final_state, feed_dict=feed_dict)
# generate the sequence
gen_seq = prime
for i in xrange(num_out):
# generate word probabilities
input_i = np.array([[last_word_i]]) #TODO: use dictionary?
feed_dict = {self.inputs: input_i, self.initial_state: state}
probs, state = self.sess.run([self.probs, self.final_state],
feed_dict=feed_dict)
probs = probs[0]
# select index of new word
if sample:
gen_word_i = np.random.choice(np.arange(len(probs)), p=probs)
else:
gen_word_i = np.argmax(probs)
# append new word to the generated sequence
gen_word = self.vocab[gen_word_i]
gen_seq += ' ' + gen_word
last_word_i = gen_word_i
return gen_seq
def generate(self, num_out=200, prime=None, sample=True):
"""
Generate a sequence of text from the trained model.
@param num_out: The length of the sequence to generate, in num words.
@param prime: The priming sequence for generation. If None, pick a random word from the
vocabulary as prime.
@param sample: Whether to probabalistically sample the next word, rather than take the word
of max probability.
"""
state = self.sess.run(self.cell.zero_state(1, tf.float32))
if prime is None:
prime = np.random.choice(self.vocab)
else:
prime = unkify(prime, self.vocab)
for word in prime.split():
print word
last_word_i = self.vocab.index(word)
input_i = np.array([[last_word_i]])
feed_dict = {self.inputs: input_i, self.initial_state: state}
state = self.sess.run(self.final_state, feed_dict=feed_dict)
gen_seq = prime
for i in xrange(num_out):
input_i = np.array([[last_word_i]])
feed_dict = {self.inputs: input_i, self.initial_state: state}
probs, state = self.sess.run([self.probs, self.final_state],
feed_dict=feed_dict)
probs = probs[0]
if sample:
gen_word_i = np.random.choice(np.arange(len(probs)), p=probs)
else:
gen_word_i = np.argmax(probs)
gen_word = self.vocab[gen_word_i]
gen_seq += ' ' + gen_word
last_word_i = gen_word_i
return gen_seq
