tensorrec_getting_started_mf_cf_results.py

# Create sets of train/test interactions that are only ratings >= 4.0
sparse_train_ratings_4plus = sparse_train_ratings.multiply(sparse_train_ratings >= 4.0)
sparse_test_ratings_4plus = sparse_test_ratings.multiply(sparse_test_ratings >= 4.0)


# This method consumes item ranks for each user and prints out recall@10 train/test metrics
def check_results(ranks):
    train_recall_at_10 = tensorrec.eval.recall_at_k(
        test_interactions=sparse_train_ratings_4plus,
        predicted_ranks=ranks,
        k=10
    ).mean()
    test_recall_at_10 = tensorrec.eval.recall_at_k(
        test_interactions=sparse_test_ratings_4plus,
        predicted_ranks=ranks,
        k=10
    ).mean()
    print("Recall at 10: Train: {:.4f} Test: {:.4f}".format(train_recall_at_10,
                                                            test_recall_at_10))


# Check the results of the MF CF model
print("Matrix factorization collaborative filter:")
predicted_ranks = cf_model.predict_rank(user_features=user_indicator_features,
                                        item_features=item_indicator_features)
check_results(predicted_ranks)