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extract.py
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extract.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from itertools import product
from six.moves import xrange
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1"
import numpy as np
import tensorflow as tf
import utils
tf.flags.DEFINE_string("checkpoint_dir", "",
"Directory containing model checkpoints and meta graph.")
tf.flags.DEFINE_string("extract_dir", "",
"Directory containing the aligned articles to do "
"parallel sentence extraction.")
tf.flags.DEFINE_string("source_vocab_path", "",
"Path to source language vocabulary.")
tf.flags.DEFINE_string("target_vocab_path", "",
"Path to target language vocabulary.")
tf.flags.DEFINE_string("source_output_path", "",
"Path to the file containing the extracted sentences in "
"the source language.")
tf.flags.DEFINE_string("target_output_path", "",
"Path to the file containing the extracted sentences in "
"the target language.")
tf.flags.DEFINE_string("score_output_path", "",
"Path to the file containing the probability scores of "
"the extracted sentence pairs.")
tf.flags.DEFINE_string("source_language", "",
"Source language suffix used as file extension.")
tf.flags.DEFINE_string("target_language", "",
"Target language suffix used as file extension.")
tf.flags.DEFINE_float("decision_threshold", 0.99,
"Decision threshold to predict a positive label.")
tf.flags.DEFINE_integer("batch_size", 500,
"Batch size to use during evaluation.")
tf.flags.DEFINE_integer("max_seq_length", 100,
"Maximum number of tokens per sentence.")
tf.flags.DEFINE_boolean("use_greedy", True,
"Use greedy post-treatment to force one-to-one "
"alignments.")
FLAGS = tf.flags.FLAGS
def read_articles(source_path, target_path):
"""Read the articles in source and target languages."""
with open(source_path, mode="r", encoding="utf-8") as source_file,\
open(target_path, mode="r", encoding="utf-8") as target_file:
source_sentences = [l for l in source_file]
target_sentences = [l for l in target_file]
return source_sentences, target_sentences
def inference(sess, data_iterator, probs_op, placeholders):
"""Get the predicted class {0, 1} of given sentence pairs."""
x_source, source_seq_length,\
x_target, target_seq_length,\
labels = placeholders
num_iter = int(np.ceil(data_iterator.size / FLAGS.batch_size))
probs = []
for step in xrange(num_iter):
source, target, label = data_iterator.next_batch(FLAGS.batch_size)
source_len = utils.sequence_length(source)
target_len = utils.sequence_length(target)
feed_dict = {x_source: source,
x_target: target,
labels: label,
source_seq_length: source_len,
target_seq_length: target_len}
batch_probs = sess.run(probs_op, feed_dict=feed_dict)
probs.extend(batch_probs.tolist())
probs = np.array(probs[:data_iterator.size])
return probs
def extract_pairs(sess, source_sentences, target_sentences,
source_sentences_ids, target_sentences_ids,
probs_op, placeholders):
"""Extract sentence pairs from a pair of articles in source and target languages.
Returns a list of (source sentence, target sentence, probability score) tuples.
"""
pairs = [(i, j) for i, j in product(range(len(source_sentences)),
range(len(target_sentences)))]
data = [(source_sentences_ids[i], target_sentences_ids[j], 1.0)
for i, j in product(range(len(source_sentences)),
range(len(target_sentences)))]
data_iterator = utils.TestingIterator(np.array(data, dtype=object))
y_score = inference(sess, data_iterator, probs_op, placeholders)
y_score = [(score, k) for k, score in enumerate(y_score)]
y_score.sort(reverse=True)
i_aligned = set()
j_aligned = set()
sentence_pairs = []
for score, k in y_score:
i, j = pairs[k]
if score < FLAGS.decision_threshold or i in i_aligned or j in j_aligned:
continue
if FLAGS.use_greedy:
i_aligned.add(i)
j_aligned.add(j)
sentence_pairs.append((source_sentences[i], target_sentences[j], score))
return sentence_pairs
def main(_):
assert FLAGS.checkpoint_dir, "--checkpoint_dir is required."
assert FLAGS.extract_dir, "--extract_dir is required."
assert FLAGS.source_vocab_path, "--source_vocab_path is required."
assert FLAGS.target_vocab_path, "--target_vocab_path is required."
assert FLAGS.source_output_path, "--source_output_path is required."
assert FLAGS.target_output_path, "--target_output_path is required."
assert FLAGS.score_output_path, "--score_output_path is required."
assert FLAGS.source_language, "--source_language is required."
assert FLAGS.target_language, "--target_language is required."
# Read vocabularies.
source_vocab, _ = utils.initialize_vocabulary(FLAGS.source_vocab_path)
target_vocab, _ = utils.initialize_vocabulary(FLAGS.target_vocab_path)
# Read source and target paths for sentence extraction.
source_paths = []
target_paths = []
for file in os.listdir(FLAGS.extract_dir):
if file.endswith(FLAGS.source_language):
source_paths.append(os.path.join(FLAGS.extract_dir, file))
elif file.endswith(FLAGS.target_language):
target_paths.append(os.path.join(FLAGS.extract_dir, file))
source_paths.sort()
target_paths.sort()
utils.reset_graph()
with tf.Session() as sess:
# Restore saved model.
utils.restore_model(sess, FLAGS.checkpoint_dir)
# Recover placeholders and ops for extraction.
x_source = sess.graph.get_tensor_by_name("x_source:0")
source_seq_length = sess.graph.get_tensor_by_name("source_seq_length:0")
x_target = sess.graph.get_tensor_by_name("x_target:0")
target_seq_length = sess.graph.get_tensor_by_name("target_seq_length:0")
labels = sess.graph.get_tensor_by_name("labels:0")
placeholders = [x_source, source_seq_length, x_target, target_seq_length, labels]
probs = sess.graph.get_tensor_by_name("feed_forward/output/probs:0")
with open(FLAGS.source_output_path, mode="w", encoding="utf-8") as source_output_file,\
open(FLAGS.target_output_path, mode="w", encoding="utf-8") as target_output_file,\
open(FLAGS.score_output_path, mode="w", encoding="utf-8") as score_output_file:
for source_path, target_path in zip(source_paths, target_paths):
# Read sentences from articles.
source_sentences, target_sentences = read_articles(source_path, target_path)
# Convert sentences to token ids sequences.
source_sentences_ids = [utils.sentence_to_token_ids(sent, source_vocab, FLAGS.max_seq_length)
for sent in source_sentences]
target_sentences_ids = [utils.sentence_to_token_ids(sent, target_vocab, FLAGS.max_seq_length)
for sent in target_sentences]
# Extract sentence pairs.
pairs = extract_pairs(sess, source_sentences, target_sentences,
source_sentences_ids, target_sentences_ids,
probs, placeholders)
if not pairs:
continue
for source_sentence, target_sentence, score in pairs:
source_output_file.write(source_sentence)
target_output_file.write(target_sentence)
score_output_file.write(str(score) + "\n")
if __name__ == "__main__":
tf.app.run()