Function top_k_error

tests/python_package_test/test_engine.py:35–39

(y_true, y_pred, k)

def top_k_error(y_true, y_pred, k):
    if k == y_pred.shape[1]:
        return 0
    max_rest = np.max(-np.partition(-y_pred, k)[:, k:], axis=1)
    return 1 - np.mean((y_pred[np.arange(len(y_true)), y_true] > max_rest))


def constant_metric(preds, train_data):