learning.py 文件源码

def report_metrics(y_dset, y_pred, batch_size, dset='Val'):
    # Print additional metrics involving predictions
    n_rows = (y_dset.shape[0] / batch_size) * batch_size
    y_true = y_dset[0:n_rows, :].flatten()
    y_pred = y_pred.flatten()

    val_ap = average_precision_score(y_true, y_pred)
    val_roc = roc_auc_score(y_true, y_pred)

    n = y_true.size
    n_pos = y_true.sum()
    idx_sorted = np.argsort(-y_pred)
    val_rec = []

    logging.info(dset + "-AP {:.6f}".format(val_ap))
    logging.info(dset + "-ROC {:.6f}".format(val_roc))
    for i, v in enumerate([10, 25, 50, 75, 100]):
        tp = y_true[idx_sorted[:int(v * n / 100)]].sum()
        val_rec.append(tp * 1.0 / n_pos)
        logging.info(dset + "-R{} {:.6f}".format(v, val_rec[i]))
    return val_ap, val_rec[2]


# ############################## Main program #################################