test_ransac.py 文件源码

def test_ransac_residual_metric():
    residual_metric1 = lambda dy: np.sum(np.abs(dy), axis=1)
    residual_metric2 = lambda dy: np.sum(dy ** 2, axis=1)

    yyy = np.column_stack([y, y, y])

    base_estimator = LinearRegression()
    ransac_estimator0 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0)
    ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0,
                                        residual_metric=residual_metric1)
    ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0,
                                        residual_metric=residual_metric2)

    # multi-dimensional
    ransac_estimator0.fit(X, yyy)
    assert_warns(DeprecationWarning, ransac_estimator1.fit, X, yyy)
    assert_warns(DeprecationWarning, ransac_estimator2.fit, X, yyy)
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator1.predict(X))
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator2.predict(X))

    # one-dimensional
    ransac_estimator0.fit(X, y)
    assert_warns(DeprecationWarning, ransac_estimator2.fit, X, y)
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator2.predict(X))