def test_load_dataset_from_text():
root_dir = dirname(dirname(dirname(realpath(__file__))))
path_txt = join(root_dir, 'data/small/train/source.txt')
symbol_index = index_table_from_text(path_txt, sos=True, eos=True)
dataset = load_dataset_from_text(join(root_dir, path_txt), symbol_index)
with open(join(root_dir, path_txt), 'r') as f:
num_lines = len(f.read().strip().split('\n'))
assert num_lines == len(dataset)
dataset_with_mark = load_dataset_from_text(join(root_dir, path_txt), symbol_index, sos=True, eos=True)
assert len(dataset[0]) + 2 == len(dataset_with_mark[0])
def train():
# Set the logger
set_logger(os.path.join(params['model_dir'], 'train.log'))
# log params
logging.info(params)
# Load vacabulary
vocab = tf.contrib.lookup.index_table_from_file(vocab_path,
num_oov_buckets=1)
# Create the input data pipeline
logging.info('Creating the datasets...')
train_input_words = load_dataset_from_text(data_dir, train_input_filename,
vocab)
train_context_words = load_dataset_from_text(data_dir,
train_context_filename, vocab)
# Create the iterator over the dataset
train_inputs = input_fn('train', train_input_words, train_context_words,
params)
eval_inputs = input_fn('eval', train_input_words, train_context_words,
params)
logging.info("- done")
# Define the model
logging.info('Creating the model...')
train_model_spec = model_fn('train',
train_inputs,
params,
reuse=tf.AUTO_REUSE)
eval_model_spec = model_fn('eval', eval_inputs, params, reuse=True)
logging.info('- done.')
# Train the model
logging.info('Starting training for {} epochs'.format(
params['num_epochs']))
normalized_embedding_matrix = train_and_evaluate(train_model_spec,
eval_model_spec, params)
save_dict_to_json(params, params['model_dir'] + '/params.json')
pd.DataFrame(normalized_embedding_matrix).to_csv(os.path.join(
params['model_dir'], 'normalized_embedding_matrix.tsv'),
index=False,
header=None,
sep='\t')
# Set the logger
set_logger(os.path.join(args.model_dir, 'evaluate.log'))
# Get paths for vocabularies and dataset
path_vocab = os.path.join(args.data_dir, 'vocab{}'.format(params.min_freq))
params.vocab_path = path_vocab
path_test_queries = os.path.join(args.data_dir, 'dev/queries.txt')
path_test_articles = os.path.join(args.data_dir, 'dev/articles.txt')
# Load Vocabularies
vocab = tf.contrib.lookup.index_table_from_file(
path_vocab, num_oov_buckets=num_oov_buckets, key_column_index=0)
# Create the input data pipeline
logging.info("Creating the dataset...")
test_queries = load_dataset_from_text(path_test_queries, vocab, params)
test_articles = load_dataset_from_text(path_test_articles, vocab, params)
# Specify other parameters for the dataset and the model
params.eval_size = params.test_size
params.id_pad_word = vocab.lookup(tf.constant(params.pad_word))
# Create iterator over the test set
inputs = input_fn('eval', test_queries, test_articles, params)
logging.info("- done.")
# Define the model
logging.info("Creating the model...")
model_spec = model_fn('eval', inputs, params, reuse=False)
logging.info("- done.")
# Set the logger
set_logger(os.path.join(args.model_dir, 'evaluate.log'))
# Get paths for vocabularies and dataset
path_words = os.path.join(args.data_dir, 'words.txt')
path_tags = os.path.join(args.data_dir, 'tags.txt')
path_eval_sentences = os.path.join(args.data_dir, 'dev/sentences.txt')
path_eval_labels = os.path.join(args.data_dir, 'dev/labels.txt')
# Load Vocabularies
words = tf.contrib.lookup.index_table_from_file(path_words, num_oov_buckets=num_oov_buckets)
tags = tf.contrib.lookup.index_table_from_file(path_tags)
# Create the input data pipeline
logging.info("Creating the dataset...")
test_sentences = load_dataset_from_text(path_eval_sentences, words)
test_labels = load_dataset_from_text(path_eval_labels, tags)
# Specify other parameters for the dataset and the model
params.eval_size = params.test_size
params.id_pad_word = words.lookup(tf.constant(params.pad_word))
params.id_pad_tag = tags.lookup(tf.constant(params.pad_tag))
# Create iterator over the test set
inputs = input_fn('eval', test_sentences, test_labels, params)
logging.info("- done.")
# Define the model
logging.info("Creating the model...")
model_spec = model_fn('eval', inputs, params, reuse=False)
logging.info("- done.")
# Get paths for vocabularies and dataset
# path_words = os.path.join(args.data_dir, 'words_small.txt')
path_words = os.path.join(args.data_dir, 'words{}.txt'.format(toy))
path_sentiment_tags = os.path.join(args.data_dir, 'sentiment_tags.txt')
# path_reviews = os.path.join(args.data_dir, 'reviews_small.txt')
path_reviews = os.path.join(args.data_dir, 'reviews{}.txt'.format(toy))
path_sentiments = os.path.join(args.data_dir, 'sentiments{}.txt'.format(toy))
# path_sentiments = os.path.join(args.data_dir, 'sentiments.txt')
# Load vocabularies
words = tf.contrib.lookup.index_table_from_file(path_words, num_oov_buckets=num_oov_buckets)
sentiments = tf.contrib.lookup.index_table_from_file(path_sentiment_tags)
# Create the input data pipeline
reviews = load_dataset_from_text(path_reviews,words)
review_sentiments = load_dataset_from_text(path_sentiments,sentiments, isLabels=True)
# Specify other parameters for the dataset and the model
params_sentiment.id_pad_word = words.lookup(tf.constant(params_sentiment.pad_word))
params_sentiment.id_pad_tag = words.lookup(tf.constant(params_sentiment.pad_tag))
# Create the iterator over the test set
inputs_sentiment = input_fn('eval', reviews, review_sentiments, params_sentiment)
# Define the model
print('Creating sentiment and era models...')
model_spec_sentiment = model_fn('eval', inputs_sentiment, params_sentiment, reuse=False)
print('Done')
# Get paths for vocabularies and dataset
# path_words = os.path.join(args.data_dir, 'words_small.txt')
path_words = os.path.join(args.data_dir, 'words{}.txt'.format(toy))
path_era_tags = os.path.join(args.data_dir, 'era_tags.txt')
# path_reviews = os.path.join(args.data_dir, 'reviews_small.txt')
path_reviews = os.path.join(args.data_dir, 'reviews{}.txt'.format(toy))
path_eras = os.path.join(args.data_dir, 'eras{}.txt'.format(toy))
# path_eras = os.path.join(args.data_dir, 'eras.txt')
# Load vocabularies
words = tf.contrib.lookup.index_table_from_file(
path_words, num_oov_buckets=num_oov_buckets)
eras = tf.contrib.lookup.index_table_from_file(path_era_tags)
# Create the input data pipeline
reviews = load_dataset_from_text(path_reviews, words)
review_eras = load_dataset_from_text(path_eras, eras, isLabels=True)
# Specify other parameters for the dataset and the model
params_era.id_pad_word = words.lookup(tf.constant(params_era.pad_word))
params_era.id_pad_tag = words.lookup(tf.constant(params_era.pad_tag))
# Create the iterator over the test set
inputs_era = input_fn('eval', reviews, review_eras, params_era)
# Define the model
print('Creating era models...')
model_spec_era = model_fn('eval', inputs_era, params_era, reuse=False)
print('Done')
print(era_model_path)
set_logger(os.path.join(args.model_dir, 'evaluate.log'))
# Get paths for vocabularies and dataset
path_words = os.path.join(args.data_dir, 'words.txt')
path_tags = os.path.join(args.data_dir, 'tags.txt')
path_eval_sentences = os.path.join(args.data_dir, 'test/sentences.txt')
path_eval_labels = os.path.join(args.data_dir, 'test/labels.txt')
# Load Vocabularies
words = tf.contrib.lookup.index_table_from_file(
path_words, num_oov_buckets=num_oov_buckets)
tags = tf.contrib.lookup.index_table_from_file(path_tags)
# Create the input data pipeline
logging.info("Creating the dataset...")
test_sentences = load_dataset_from_text(path_eval_sentences, words)
test_labels = load_dataset_from_text(path_eval_labels, split=False)
# Specify other parameters for the dataset and the model
params.eval_size = params.test_size
params.id_pad_word = words.lookup(tf.constant(params.pad_word))
params.id_pad_tag = tags.lookup(tf.constant(params.pad_tag))
# Create iterator over the test set
inputs = input_fn('eval', test_sentences, test_labels, params)
logging.info("- done.")
# Define the model
logging.info("Creating the model...")
model_spec = model_fn('eval', inputs, params, reuse=False)
logging.info("- done.")
# Get paths for vocabularies and dataset
path_words = os.path.join(args.data_dir, 'words.txt')
path_tags = os.path.join(args.data_dir, 'tags.txt')
path_train_sentences = os.path.join(args.data_dir, 'train/sentences.txt')
path_train_labels = os.path.join(args.data_dir, 'train/labels.txt')
path_eval_sentences = os.path.join(args.data_dir, 'dev/sentences.txt')
path_eval_labels = os.path.join(args.data_dir, 'dev/labels.txt')
# Load Vocabularies
words = tf.contrib.lookup.index_table_from_file(
path_words, num_oov_buckets=num_oov_buckets)
tags = tf.contrib.lookup.index_table_from_file(path_tags)
# Create the input data pipeline
logging.info("Creating the datasets...")
train_sentences = load_dataset_from_text(path_train_sentences, words)
train_labels = load_dataset_from_text(path_train_labels, tags)
eval_sentences = load_dataset_from_text(path_eval_sentences, words)
eval_labels = load_dataset_from_text(path_eval_labels, tags)
# Specify other parameters for the dataset and the model
params.eval_size = params.dev_size
params.buffer_size = params.train_size # buffer size for shuffling
params.id_pad_word = words.lookup(tf.constant(params.pad_word))
params.id_pad_tag = tags.lookup(tf.constant(params.pad_tag))
# Create the two iterators over the two datasets
train_inputs = input_fn('train', train_sentences, train_labels, params)
eval_inputs = input_fn('eval', eval_sentences, eval_labels, params)
logging.info("- done.")
# Get paths for vocabularies and dataset
path_words = os.path.join(args.data_dir, 'words.txt')
path_tags = os.path.join(args.data_dir, 'tags.txt')
path_train_sentences = os.path.join(args.data_dir, 'train/sentences.txt')
path_train_labels = os.path.join(args.data_dir, 'train/labels.txt')
path_eval_sentences = os.path.join(args.data_dir, 'dev/sentences.txt')
path_eval_labels = os.path.join(args.data_dir, 'dev/labels.txt')
# Load Vocabularies
words = tf.contrib.lookup.index_table_from_file(
path_words, num_oov_buckets=num_oov_buckets)
tags = tf.contrib.lookup.index_table_from_file(path_tags)
# Create the input data pipeline
logging.info("Creating the datasets...")
train_sentences = load_dataset_from_text(path_train_sentences, words)
train_labels = load_dataset_from_text(path_train_labels, split=False)
# import numpy as np
# with tf.Session() as sess:
# iterator = train_labels.make_one_shot_iterator()
# it = iterator.get_next()
# while True:
# inputs_eval = (sess.run(it))
# print(inputs_eval)
eval_sentences = load_dataset_from_text(path_eval_sentences, words)
eval_labels = load_dataset_from_text(path_eval_labels, split=False)
# Specify other parameters for the dataset and the model
params.eval_size = params.dev_size
if params.model_version == "sif":
vocab_freqs = np.loadtxt(params.vocab_path, usecols=1, comments=None, encoding='utf-8')
vocab_freqs = np.append(vocab_freqs, 0.)
vocab_freqs = tf.constant(vocab_freqs / np.sum(vocab_freqs), dtype=tf.float32) # frequency for OOV token
params.vocab_freqs = vocab_freqs
if params.model_version == "embedtf":
with open(os.path.join(args.data_dir, "tfidfvec.pkl"), "rb") as f:
tfidf = pickle.load(f)
idf = tfidf.idf_.copy()
idf = np.append(idf, idf[0])
params.idf = idf
# Create the input data pipeline
logging.info("Creating the datasets...")
train_queries = load_dataset_from_text(path_train_queries, vocab, params)
train_articles = load_dataset_from_text(path_train_articles, vocab, params)
eval_queries = load_dataset_from_text(path_eval_queries, vocab, params)
eval_articles = load_dataset_from_text(path_eval_articles, vocab, params)
# Specify other parameters for the dataset and the model
params.eval_size = params.dev_size
params.buffer_size = params.train_size # buffer size for shuffling
params.id_pad_word = vocab.lookup(tf.constant(params.pad_word))
# Create the two iterators over the two datasets
train_inputs = input_fn('train', train_queries, train_articles, params)
eval_inputs = input_fn('eval', eval_queries, eval_articles, params)
logging.info("- done.")
# Test input
# with tf.Session() as sess:
# Load the parameters from the dataset, that gives the size etc. into params
json_path = os.path.join(args.data_dir, 'dataset_params.json')
assert os.path.isfile(
json_path), "No json file found at {}, run build.py".format(json_path)
params.update(json_path)
# Set the logger
set_logger(os.path.join(args.model_dir, 'evaluate.log'))
# Get paths for vocabularies and dataset
path_eval_x = os.path.join(args.data_dir, 'dev.x')
path_eval_y = os.path.join(args.data_dir, 'dev.y')
# Create the input data pipeline
logging.info("Creating the dataset...")
test_sentences = load_dataset_from_text(path_eval_x)
test_labels = load_dataset_from_text(path_eval_y)
# Specify other parameters for the dataset and the model
params.eval_size = params.test_size
# Create iterator over the test set
inputs = input_fn('eval', test_sentences, test_labels, params)
logging.info("- done.")
# Define the model
logging.info("Creating the model...")
model_spec = model_fn('eval', inputs, params, reuse=False)
logging.info("- done.")
logging.info("Starting evaluation")
