test_ransac.py 文件源码

def test_ransac_residual_loss():
    loss_multi1 = lambda y_true, y_pred: np.sum(np.abs(y_true - y_pred), axis=1)
    loss_multi2 = lambda y_true, y_pred: np.sum((y_true - y_pred) ** 2, axis=1)

    loss_mono = lambda y_true, y_pred : np.abs(y_true - y_pred)
    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,
                                        loss=loss_multi1)
    ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0,
                                        loss=loss_multi2)

    # multi-dimensional
    ransac_estimator0.fit(X, yyy)
    ransac_estimator1.fit(X, yyy)
    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)
    ransac_estimator2.loss = loss_mono
    ransac_estimator2.fit(X, y)
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator2.predict(X))
    ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0,
                                        loss="squared_loss")
    ransac_estimator3.fit(X, y)
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator2.predict(X))