utilities.py 文件源码

def predict_snps(y, cut_off_prob=0.5, already_split=False):
    """Predicts which snps are causing epistasis based on one epoch and how many snps to detect.

    Arguments:
        y: the given output tensor
        cut_off_prob: float describing the cutoff probability for a snp to be described as predicted to cause.
                            Recommended Values:
                            0.5 for 2-classifier model
                            0.98 for 1-classifier model

    Returns:
        predicted_snps: a tensor with the indices of the predicted snps
    """
    with tf.name_scope('snp_prediction'):
        if not already_split:
            y_left = get_causing_epi_probs(y)
        else:
            y_left = y
        y_left_t = tf.transpose(y_left, [0, 2, 1])
        top_snps = tf.where(tf.greater_equal(y_left, cut_off_prob))
        _, top_snp_indices, _ = tf.split(1, 3, top_snps, name='split')
        top_snp_indices = tf.reshape(top_snp_indices, [-1])
        top_pred_snps, _, count = tf.unique_with_counts(top_snp_indices)
        return top_pred_snps, count