test_gbrt.py 文件源码

def test_gbrt_base_estimator():
    rng = np.random.RandomState(1)
    N = 10000
    X = np.ones((N, 1))
    y = rng.normal(size=N)

    base = RandomForestRegressor()
    rgr = GradientBoostingQuantileRegressor(base_estimator=base)
    assert_raise_message(ValueError, 'type GradientBoostingRegressor',
                         rgr.fit, X, y)

    base = GradientBoostingRegressor()
    rgr = GradientBoostingQuantileRegressor(base_estimator=base)
    assert_raise_message(ValueError, 'quantile loss', rgr.fit, X, y)

    base = GradientBoostingRegressor(loss='quantile', n_estimators=20)
    rgr = GradientBoostingQuantileRegressor(base_estimator=base)
    rgr.fit(X, y)

    estimates = rgr.predict(X, return_quantiles=True)
    assert_almost_equal(stats.norm.ppf(rgr.quantiles),
                        np.mean(estimates, axis=0),
                        decimal=2)