def METHOD_NAME(self, filename):
    """Evaluate the given file and returns some evaluation metrics.
    Args:
        filename (str): A file name that will be evaluated.
    Returns:
        dict: A dictionary containing evaluation metrics.
    """
    load_sess = self.sess
    group_preds = []
    group_labels = []
    for (
        batch_data_input,
        newsid_list,
        data_size,
    ) in self.iterator.load_data_from_file(filename):
        if batch_data_input:
            step_pred, step_labels = self.eval(load_sess, batch_data_input)
            group_preds.extend(step_pred)
            group_labels.extend(step_labels)
    res = cal_metric(group_labels, group_preds, self.hparams.pairwise_metrics)
    return res