def create_eval_ops(model_input, pred_y, all_triples, eval_triples, n_entity,
                    top_k, idx_1=0, idx_2=1, idx_3=2):
    """ Evaluation operations for any model.

    For given <h,r> predict t, idx_1 = 0, idx_2 = 1, idx_3 = 2
    For given <t,r> predict h, idx_1 = 2, idx_2 = 1, idx_3 = 0

    :param model_input: N by 3 matrix, each row is a h,r,t pair
    :param pred_y: N by ENTITY_VOCAB matrix
    :param all_triples: M by 3 matrix, contains all triples in the KG
    :param eval_triples: M_{eval} by 3 matrix, contains all triples that will be
                         evaluated, this is a subset of all_triples. model_input
                         is a subset of eval_triples where the joint index
                         model_input[idx_1] and model_input[idx_2] is unique in
                         model_input
    :param n_entity: Number of unique entities in the KG
    :param top_k: Parameter of Hits@top_k
    :param idx_1: First index of the <?,r> pair
    :param idx_2: Second index of the <?,r> pair
    :param idx_3: Target index in the h,r,t triple
    :return:
    """

    def get_id_mask(hrt, triples):
        return tf.logical_and(tf.equal(hrt[idx_1], triples[:, idx_1]),
                              tf.equal(hrt[idx_2], triples[:, idx_2]))

    def calculate_metrics(tensors):
        # eval_hrt, a 3 element h,r,t triple
        eval_hrt = tensors

        # find the entity_vocab vector row id of the given h,r pair
        pred_y_mask = get_id_mask(eval_hrt, model_input)
        pred_score = tf.reshape(tf.boolean_mask(pred_y, pred_y_mask), [-1])

        # score of current tail
        target_score = pred_score[eval_hrt[idx_3]]

        triple_mask = get_id_mask(eval_hrt, all_triples)
        # disabling validate_indices will disable duplication check
        entity_mask = tf.sparse_to_dense(tf.boolean_mask(all_triples[:, idx_3], triple_mask),
                                         output_shape=[n_entity],
                                         sparse_values=True,
                                         default_value=False,
                                         validate_indices=False)
        # After masking, [i,j] will equals to min_score - 1e-5 if it is a positive instance
        masked_pred_score = pred_score * tf.cast(tf.logical_not(entity_mask), tf.float32) - \
                            tf.cast(entity_mask, tf.float32) * 1e30

        # Count how many entities has a score larger than target
        def get_rank(score, entity_scores):
            return tf.reduce_sum(tf.cast(tf.greater(score, entity_scores), tf.int32)) + 1

        unfiltered_rank = get_rank(pred_score, target_score)
        filtered_rank = get_rank(masked_pred_score, target_score)

        unfiltered_hit = tf.where(unfiltered_rank <= top_k, 1, 0)
        filtered_hit = tf.where(filtered_rank <= top_k, 1, 0)

        return tf.stack(
            [tf.cast(x, tf.float32) for x in [unfiltered_rank, filtered_rank, unfiltered_hit, filtered_hit]])

    metrics = tf.reduce_mean(
        tf.map_fn(calculate_metrics, eval_triples,
                  dtype=tf.float32, parallel_iterations=20,
                  back_prop=False, swap_memory=True),
        axis=0)

    return metrics