business_stats: Dict[str, Business] = Business.load_from_file(
business_stats_file)
print('[ %04ds ] Files loaded' % (time.time() - start_time))
all_user_features = ['NO_FEAT']
all_business_features = Business.collect_business_features(business_stats)
dataset = Dataset()
dataset.fit(User.extract_user_ids(user_stats),
Business.extract_business_ids(business_stats),
user_features=all_user_features,
item_features=all_business_features)
user_features = dataset.build_user_features(
User.build_user_features(user_stats,
User.extract_user_ids(user_stats)), True)
business_features = dataset.build_item_features(
Business.build_business_features(
business_stats, Business.extract_business_ids(business_stats)),
True)
print('[ %04ds ] Dataset initialized' % (time.time() - start_time))
user_avg, user_std = Review.extract_user_average_and_std(training_set)
normalized_training_reviews = Review.normalize_by_user(
training_set, user_avg)
training_interactions = Review.extract_sparse_interaction_matrix(
normalized_training_reviews)
interaction_matrix, interaction_weight = dataset.build_interactions(
print('[ %04ds ] Files loaded' % (time.time() - start_time))
all_user_features = ['NO_FEAT']
all_business_features = Business.collect_business_features(business_stats)
all_user_ids = User.extract_user_ids(user_stats)
all_business_ids = Business.extract_business_ids(business_stats)
dataset = Dataset()
dataset.fit(all_user_ids,
all_business_ids,
user_features=all_user_features,
item_features=all_business_features)
user_features = dataset.build_user_features(
User.build_user_features(user_stats, all_user_ids), True)
business_features = dataset.build_item_features(
Business.build_business_features(business_stats, all_business_ids),
True)
print('[ %04ds ] Dataset initialized' % (time.time() - start_time))
user_avg, user_std = Review.extract_user_average_and_std(training_set)
normalized_training_reviews = Review.normalize_by_user(
training_set, user_avg)
training_interactions = Review.extract_sparse_interaction_matrix(
normalized_training_reviews)
training_user_ids = Review.extract_user_ids(normalized_training_reviews)
training_business_ids = Review.extract_business_ids(
normalized_training_reviews)
