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)