def feed_dict(self, users, items, ratings=None, prototypes=None, images=None, reviews=None, is_training=False):
fd = {
self.users: users,
self.items: items,
self.is_training: is_training
}
if prototypes is not None:
fd[self.prototypes] = prototypes
if ratings is not None:
fd[self.ratings] = ratings
if images is not None:
fd[self.images] = images
if reviews is not None:
fd[self.reviews] = batch_review_normalize(reviews, self.T)
return fd
def main(_):
vocab = load_vocabulary(FLAGS.data_dir)
if FLAGS.generating:
data_reader = DataReader(FLAGS.data_dir, n_reviews=5, generating=True)
else:
data_reader = DataReader(FLAGS.data_dir)
model = Model(total_users=data_reader.total_users, total_items=data_reader.total_items,
global_rating=data_reader.global_rating, num_factors=FLAGS.num_factors,
img_dims=[196, 512], vocab_size=len(vocab), word_dim=FLAGS.word_dim,
lstm_dim=FLAGS.lstm_dim, max_length=FLAGS.max_length, dropout_rate=FLAGS.dropout_rate)
saver = tf.compat.v1.train.Saver(max_to_keep=10)
log_file = open('log.txt', 'w')
test_step = 0
config = tf.ConfigProto(allow_soft_placement=FLAGS.allow_soft_placement)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
saver.restore(sess, FLAGS.ckpt_dir)
print('Model succesfully restored')
# Testing
review_gen_corpus = defaultdict(list)
review_ref_corpus = defaultdict(list)
photo_bleu_scores = defaultdict(list)
photo_rouge_scores = defaultdict(list)
review_bleu_scores = defaultdict(list)
review_rouge_scores = defaultdict(list)
sess.run(model.init_metrics)
for users, items, ratings in data_reader.read_real_test_set(FLAGS.batch_size, rating_only=True):
test_step += 1
fd = model.feed_dict(users, items, ratings)
sess.run(model.update_metrics, feed_dict=fd)
review_users, review_items, review_ratings, photo_ids, reviews = get_review_data(users, items, ratings,
data_reader.real_test_review)
img_idx = [data_reader.real_test_id2idx[photo_id] for photo_id in photo_ids]
images = data_reader.real_test_img_features[img_idx]
fd = model.feed_dict(users=review_users, items=review_items, images=images)
_reviews, _alphas, _betas = sess.run([model.sampled_reviews, model.alphas, model.betas], feed_dict=fd)
gen_reviews = decode_reviews(_reviews, vocab)
ref_reviews = [decode_reviews(batch_review_normalize(ref), vocab) for ref in reviews]
if FLAGS.generating:
for gen, ref in zip(gen_reviews, ref_reviews):
gen_str = "GENERATED:\n"+" ".join(gen)
ref_str = "REFERENCE:\n"+" ".join([" ".join(sentence) for sentence in ref])+"\n"
log_info(log_file,gen_str)
log_info(log_file,ref_str)
for user, item, gen, refs in zip(review_users, review_items, gen_reviews, ref_reviews):
review_gen_corpus[(user, item)].append(gen)
review_ref_corpus[(user, item)] += refs
bleu_scores = compute_bleu([refs], [gen], max_order=4, smooth=True)
for order, score in bleu_scores.items():
photo_bleu_scores[order].append(score)
rouge_scores = rouge([gen], refs)
for metric, score in rouge_scores.items():
photo_rouge_scores[metric].append(score)
_mae, _rmse = sess.run([model.mae, model.rmse])
log_info(log_file, '\nRating prediction results: MAE={:.3f}, RMSE={:.3f}'.format(_mae, _rmse))
log_info(log_file, '\nReview generation results:')
log_info(log_file, '- Photo level: BLEU-scores = {:.2f}, {:.2f}, {:.2f}, {:.2f}'.format(
np.array(photo_bleu_scores[1]).mean() * 100, np.array(photo_bleu_scores[2]).mean() * 100,
np.array(photo_bleu_scores[3]).mean() * 100, np.array(photo_bleu_scores[4]).mean() * 100))
for user_item, gen_reviews in review_gen_corpus.items():
references = [list(ref) for ref in set(tuple(ref) for ref in review_ref_corpus[user_item])]
user_item_bleu_scores = defaultdict(list)
for gen in gen_reviews:
bleu_scores = compute_bleu([references], [gen], max_order=4, smooth=True)
for order, score in bleu_scores.items():
user_item_bleu_scores[order].append(score)
for order, scores in user_item_bleu_scores.items():
review_bleu_scores[order].append(np.array(scores).mean())
user_item_rouge_scores = defaultdict(list)
for gen in gen_reviews:
rouge_scores = rouge([gen], references)
for metric, score in rouge_scores.items():
user_item_rouge_scores[metric].append(score)
for metric, scores in user_item_rouge_scores.items():
review_rouge_scores[metric].append(np.array(scores).mean())
log_info(log_file, '- Review level: BLEU-scores = {:.2f}, {:.2f}, {:.2f}, {:.2f}'.format(
np.array(review_bleu_scores[1]).mean() * 100, np.array(review_bleu_scores[2]).mean() * 100,
np.array(review_bleu_scores[3]).mean() * 100, np.array(review_bleu_scores[4]).mean() * 100))
for metric in ['rouge_1', 'rouge_2', 'rouge_l']:
log_info(log_file, '- Photo level: {} = {:.2f}, {:.2f}, {:.2f}'.format(
metric,
np.array(photo_rouge_scores['{}/p_score'.format(metric)]).mean() * 100,
np.array(photo_rouge_scores['{}/r_score'.format(metric)]).mean() * 100,
np.array(photo_rouge_scores['{}/f_score'.format(metric)]).mean() * 100))
log_info(log_file, '- Review level: {} = {:.2f}, {:.2f}, {:.2f}'.format(
metric,
np.array(review_rouge_scores['{}/p_score'.format(metric)]).mean() * 100,
np.array(review_rouge_scores['{}/r_score'.format(metric)]).mean() * 100,
np.array(review_rouge_scores['{}/f_score'.format(metric)]).mean() * 100))
def main(_):
vocab = load_vocabulary(FLAGS.data_dir)
data_reader = DataReader(FLAGS.data_dir)
model = Model(total_users=data_reader.total_users, total_items=data_reader.total_items,
global_rating=data_reader.global_rating, num_factors=FLAGS.num_factors,
img_dims=[196, 512], vocab_size=len(vocab), word_dim=FLAGS.word_dim,
lstm_dim=FLAGS.lstm_dim, max_length=FLAGS.max_length, dropout_rate=FLAGS.dropout_rate)
update_rating, update_review, global_step = train_fn(model)
saver = tf.compat.v1.train.Saver(max_to_keep=10)
log_file = open('log.txt', 'w')
test_step = 0
config = tf.ConfigProto(allow_soft_placement=FLAGS.allow_soft_placement)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(1, FLAGS.num_epochs + 1):
log_info(log_file, "\nEpoch: {}/{}".format(epoch, FLAGS.num_epochs))
count = 0
sum_rating_loss = 0
sum_review_loss = 0
# Training
for users, items, ratings in data_reader.read_train_set(FLAGS.batch_size, rating_only=True):
count += 1
fd = model.feed_dict(users=users, items=items, ratings=ratings, is_training=True)
_step, _, _rating_loss = sess.run([global_step, update_rating, model.rating_loss], feed_dict=fd)
sum_rating_loss += _rating_loss
review_users, review_items, _, photo_ids, reviews = get_review_data(users, items, ratings,
data_reader.train_review)
img_idx = [data_reader.train_id2idx[photo_id] for photo_id in photo_ids]
images = data_reader.train_img_features[img_idx]
fd = model.feed_dict(users=review_users, items=review_items, images=images,
reviews=reviews, is_training=True)
_, _review_loss = sess.run([update_review, model.review_loss], feed_dict=fd)
sum_review_loss += _review_loss
if _step % FLAGS.display_step == 0:
data_reader.iter.set_postfix(rating_loss=(sum_rating_loss / count),
review_loss=(sum_review_loss / count))
# Testing
review_gen_corpus = defaultdict(list)
review_ref_corpus = defaultdict(list)
photo_bleu_scores = defaultdict(list)
photo_rouge_scores = defaultdict(list)
review_bleu_scores = defaultdict(list)
review_rouge_scores = defaultdict(list)
sess.run(model.init_metrics)
for users, items, ratings in data_reader.read_test_set(FLAGS.batch_size, rating_only=True):
test_step += 1
fd = model.feed_dict(users, items, ratings)
sess.run(model.update_metrics, feed_dict=fd)
review_users, review_items, review_ratings, photo_ids, reviews = get_review_data(users, items, ratings,
data_reader.test_review)
img_idx = [data_reader.test_id2idx[photo_id] for photo_id in photo_ids]
images = data_reader.test_img_features[img_idx]
fd = model.feed_dict(users=review_users, items=review_items, images=images)
_reviews, _alphas, _betas = sess.run([model.sampled_reviews, model.alphas, model.betas], feed_dict=fd)
gen_reviews = decode_reviews(_reviews, vocab)
ref_reviews = [decode_reviews(batch_review_normalize(ref), vocab) for ref in reviews]
for user, item, gen, refs in zip(review_users, review_items, gen_reviews, ref_reviews):
review_gen_corpus[(user, item)].append(gen)
review_ref_corpus[(user, item)] += refs
bleu_scores = compute_bleu([refs], [gen], max_order=4, smooth=True)
for order, score in bleu_scores.items():
photo_bleu_scores[order].append(score)
rouge_scores = rouge([gen], refs)
for metric, score in rouge_scores.items():
photo_rouge_scores[metric].append(score)
_mae, _rmse = sess.run([model.mae, model.rmse])
log_info(log_file, '\nRating prediction results: MAE={:.3f}, RMSE={:.3f}'.format(_mae, _rmse))
log_info(log_file, '\nReview generation results:')
log_info(log_file, '- Photo level: BLEU-scores = {:.2f}, {:.2f}, {:.2f}, {:.2f}'.format(
np.array(photo_bleu_scores[1]).mean() * 100, np.array(photo_bleu_scores[2]).mean() * 100,
np.array(photo_bleu_scores[3]).mean() * 100, np.array(photo_bleu_scores[4]).mean() * 100))
for user_item, gen_reviews in review_gen_corpus.items():
references = [list(ref) for ref in set(tuple(ref) for ref in review_ref_corpus[user_item])]
user_item_bleu_scores = defaultdict(list)
for gen in gen_reviews:
bleu_scores = compute_bleu([references], [gen], max_order=4, smooth=True)
for order, score in bleu_scores.items():
user_item_bleu_scores[order].append(score)
for order, scores in user_item_bleu_scores.items():
review_bleu_scores[order].append(np.array(scores).mean())
user_item_rouge_scores = defaultdict(list)
for gen in gen_reviews:
rouge_scores = rouge([gen], references)
for metric, score in rouge_scores.items():
user_item_rouge_scores[metric].append(score)
for metric, scores in user_item_rouge_scores.items():
review_rouge_scores[metric].append(np.array(scores).mean())
log_info(log_file, '- Review level: BLEU-scores = {:.2f}, {:.2f}, {:.2f}, {:.2f}'.format(
np.array(review_bleu_scores[1]).mean() * 100, np.array(review_bleu_scores[2]).mean() * 100,
np.array(review_bleu_scores[3]).mean() * 100, np.array(review_bleu_scores[4]).mean() * 100))
for metric in ['rouge_1', 'rouge_2', 'rouge_l']:
log_info(log_file, '- Photo level: {} = {:.2f}, {:.2f}, {:.2f}'.format(
metric,
np.array(photo_rouge_scores['{}/p_score'.format(metric)]).mean() * 100,
np.array(photo_rouge_scores['{}/r_score'.format(metric)]).mean() * 100,
np.array(photo_rouge_scores['{}/f_score'.format(metric)]).mean() * 100))
log_info(log_file, '- Review level: {} = {:.2f}, {:.2f}, {:.2f}'.format(
metric,
np.array(review_rouge_scores['{}/p_score'.format(metric)]).mean() * 100,
np.array(review_rouge_scores['{}/r_score'.format(metric)]).mean() * 100,
np.array(review_rouge_scores['{}/f_score'.format(metric)]).mean() * 100))
save_path = saver.save(sess, f"tmp/model{epoch}.ckpt")
log_info(log_file, '')