Python linear_model.HuberRegressor类代码示例

def test_huber_equals_lr_for_high_epsilon():
    # Test that Ridge matches LinearRegression for large epsilon
    X, y = make_regression_with_outliers()
    lr = LinearRegression(fit_intercept=True)
    lr.fit(X, y)
    huber = HuberRegressor(fit_intercept=True, epsilon=1e3, alpha=0.0)
    huber.fit(X, y)
    assert_almost_equal(huber.coef_, lr.coef_, 3)
    assert_almost_equal(huber.intercept_, lr.intercept_, 2)

def test_huber_warm_start():
    X, y = make_regression_with_outliers()
    huber_warm = HuberRegressor(
        fit_intercept=True, alpha=1.0, max_iter=10000, warm_start=True, tol=1e-1)
    huber_warm.fit(X, y)
    huber_warm_coef = huber_warm.coef_.copy()
    huber_warm.fit(X, y)

    # SciPy performs the tol check after doing the coef updates, so
    # these would be almost same but not equal.
    assert_array_almost_equal(huber_warm.coef_, huber_warm_coef, 1)

    assert huber_warm.n_iter_ == 0

def test_huber_warm_start():
    X, y = make_regression_with_outliers()
    huber_warm = HuberRegressor(
        fit_intercept=True, alpha=1.0, max_iter=10000, warm_start=True, tol=1e-1)
    huber_warm.fit(X, y)
    huber_warm_coef = huber_warm.coef_.copy()
    huber_warm.fit(X, y)

    # SciPy performs the tol check after doing the coef updates, so
    # these would be almost same but not equal.
    assert_array_almost_equal(huber_warm.coef_, huber_warm_coef, 1)

    # No n_iter_ in old SciPy (<=0.9)
    # And as said above, the first iteration seems to be run anyway.
    if huber_warm.n_iter_ is not None:
        assert_equal(1, huber_warm.n_iter_)

def test_huber_better_r2_score():
    # Test that huber returns a better r2 score than non-outliers"""
    X, y = make_regression_with_outliers()
    huber = HuberRegressor(fit_intercept=True, alpha=0.01, max_iter=100)
    huber.fit(X, y)
    linear_loss = np.dot(X, huber.coef_) + huber.intercept_ - y
    mask = np.abs(linear_loss) < huber.epsilon * huber.scale_
    huber_score = huber.score(X[mask], y[mask])
    huber_outlier_score = huber.score(X[~mask], y[~mask])

    # The Ridge regressor should be influenced by the outliers and hence
    # give a worse score on the non-outliers as compared to the huber regressor.
    ridge = Ridge(fit_intercept=True, alpha=0.01)
    ridge.fit(X, y)
    ridge_score = ridge.score(X[mask], y[mask])
    ridge_outlier_score = ridge.score(X[~mask], y[~mask])
    assert_greater(huber_score, ridge_score)

    # The huber model should also fit poorly on the outliers.
    assert_greater(ridge_outlier_score, huber_outlier_score)

def test_huber_and_sgd_same_results():
    # Test they should converge to same coefficients for same parameters

    X, y = make_regression_with_outliers(n_samples=10, n_features=2)

    # Fit once to find out the scale parameter. Scale down X and y by scale
    # so that the scale parameter is optimized to 1.0
    huber = HuberRegressor(fit_intercept=False, alpha=0.0, max_iter=100,
                           epsilon=1.35)
    huber.fit(X, y)
    X_scale = X / huber.scale_
    y_scale = y / huber.scale_
    huber.fit(X_scale, y_scale)
    assert_almost_equal(huber.scale_, 1.0, 3)

    sgdreg = SGDRegressor(
        alpha=0.0, loss="huber", shuffle=True, random_state=0, max_iter=10000,
        fit_intercept=False, epsilon=1.35, tol=None)
    sgdreg.fit(X_scale, y_scale)
    assert_array_almost_equal(huber.coef_, sgdreg.coef_, 1)

def test_huber_sparse():
    X, y = make_regression_with_outliers()
    huber = HuberRegressor(fit_intercept=True, alpha=0.1)
    huber.fit(X, y)

    X_csr = sparse.csr_matrix(X)
    huber_sparse = HuberRegressor(fit_intercept=True, alpha=0.1)
    huber_sparse.fit(X_csr, y)
    assert_array_almost_equal(huber_sparse.coef_, huber.coef_)

    def get_outliers_by_huber(self, table, column_indexes):
        '''
        Get outliers using huber regression, which outperforms RANSAC, 
        but doesn't scale well when the number of samples are very large. 
        Huber outputs both perfect precision (100%) and recall (100%) in our experiments.
        '''
        X = table[ :, column_indexes[ :-1]].astype(float)
        X = utils.enforce_columns(X)
        y = table[ :, column_indexes[-1]].astype(float)

        # preprocessing could make HUBER fail on some dataset in our experiments 
        #x = preprocessing.minmax_scale(x)
        #y = preprocessing.minmax_scale(y)

        model_huber = HuberRegressor()
        model_huber.fit(X, y)

        outlier_mask = model_huber.outliers_
        outliers = [idx for idx, val in enumerate(outlier_mask) if val]

        residuals = abs(model_huber.predict(X) - y)
        confidences = preprocessing.minmax_scale(residuals[outliers])*0.09+0.9

        return (outliers, confidences)

def test_huber_scaling_invariant():
    # Test that outliers filtering is scaling independent.
    X, y = make_regression_with_outliers()
    huber = HuberRegressor(fit_intercept=False, alpha=0.0, max_iter=100)
    huber.fit(X, y)
    n_outliers_mask_1 = huber.outliers_
    assert_false(np.all(n_outliers_mask_1))

    huber.fit(X, 2. * y)
    n_outliers_mask_2 = huber.outliers_
    assert_array_equal(n_outliers_mask_2, n_outliers_mask_1)

    huber.fit(2. * X, 2. * y)
    n_outliers_mask_3 = huber.outliers_
    assert_array_equal(n_outliers_mask_3, n_outliers_mask_1)

def test_huber_scaling_invariant():
    """Test that outliers filtering is scaling independent."""
    rng = np.random.RandomState(0)
    X, y = make_regression_with_outliers()
    huber = HuberRegressor(fit_intercept=False, alpha=0.0, max_iter=100,
                           epsilon=1.35)
    huber.fit(X, y)
    n_outliers_mask_1 = huber.outliers_

    huber.fit(X, 2. * y)
    n_outliers_mask_2 = huber.outliers_

    huber.fit(2. * X, 2. * y)
    n_outliers_mask_3 = huber.outliers_

    assert_array_equal(n_outliers_mask_2, n_outliers_mask_1)
    assert_array_equal(n_outliers_mask_3, n_outliers_mask_1)

y_outliers = rng.normal(0, 2.0, size=4)
X_outliers[:2, :] += X.max() + X.mean() / 4.
X_outliers[2:, :] += X.min() - X.mean() / 4.
y_outliers[:2] += y.min() - y.mean() / 4.
y_outliers[2:] += y.max() + y.mean() / 4.
X = np.vstack((X, X_outliers))
y = np.concatenate((y, y_outliers))
plt.plot(X, y, 'b.')

# Fit the huber regressor over a series of epsilon values.
colors = ['r-', 'b-', 'y-', 'm-']

x = np.linspace(X.min(), X.max(), 7)
epsilon_values = [1.35, 1.5, 1.75, 1.9]
for k, epsilon in enumerate(epsilon_values):
    huber = HuberRegressor(fit_intercept=True, alpha=0.0, max_iter=100,
                           epsilon=epsilon)
    huber.fit(X, y)
    coef_ = huber.coef_ * x + huber.intercept_
    plt.plot(x, coef_, colors[k], label="huber loss, %s" % epsilon)

# Fit a ridge regressor to compare it to huber regressor.
ridge = Ridge(fit_intercept=True, alpha=0.0, random_state=0, normalize=True)
ridge.fit(X, y)
coef_ridge = ridge.coef_
coef_ = ridge.coef_ * x + ridge.intercept_
plt.plot(x, coef_, 'g-', label="ridge regression")

plt.title("Comparison of HuberRegressor vs Ridge")
plt.xlabel("X")
plt.ylabel("y")
plt.legend(loc=0)

def test_huber_sample_weights():
    # Test sample_weights implementation in HuberRegressor"""

    X, y = make_regression_with_outliers()
    huber = HuberRegressor(fit_intercept=True)
    huber.fit(X, y)
    huber_coef = huber.coef_
    huber_intercept = huber.intercept_

    # Rescale coefs before comparing with assert_array_almost_equal to make sure
    # that the number of decimal places used is somewhat insensitive to the
    # amplitude of the coefficients and therefore to the scale of the data
    # and the regularization parameter
    scale = max(np.mean(np.abs(huber.coef_)),
                np.mean(np.abs(huber.intercept_)))

    huber.fit(X, y, sample_weight=np.ones(y.shape[0]))
    assert_array_almost_equal(huber.coef_ / scale, huber_coef / scale)
    assert_array_almost_equal(huber.intercept_ / scale,
                              huber_intercept / scale)

    X, y = make_regression_with_outliers(n_samples=5, n_features=20)
    X_new = np.vstack((X, np.vstack((X[1], X[1], X[3]))))
    y_new = np.concatenate((y, [y[1]], [y[1]], [y[3]]))
    huber.fit(X_new, y_new)
    huber_coef = huber.coef_
    huber_intercept = huber.intercept_
    sample_weight = np.ones(X.shape[0])
    sample_weight[1] = 3
    sample_weight[3] = 2
    huber.fit(X, y, sample_weight=sample_weight)

    assert_array_almost_equal(huber.coef_ / scale, huber_coef / scale)
    assert_array_almost_equal(huber.intercept_ / scale,
                              huber_intercept / scale)

    # Test sparse implementation with sample weights.
    X_csr = sparse.csr_matrix(X)
    huber_sparse = HuberRegressor(fit_intercept=True)
    huber_sparse.fit(X_csr, y, sample_weight=sample_weight)
    assert_array_almost_equal(huber_sparse.coef_ / scale,
                              huber_coef / scale)

def test_huber_max_iter():
    X, y = make_regression_with_outliers()
    huber = HuberRegressor(max_iter=1)
    huber.fit(X, y)
    assert huber.n_iter_ == huber.max_iter

def test_huber_sample_weights():
    # Test sample_weights implementation in HuberRegressor"""

    X, y = make_regression_with_outliers()
    huber = HuberRegressor(fit_intercept=True, alpha=0.1)
    huber.fit(X, y)
    huber_coef = huber.coef_
    huber_intercept = huber.intercept_

    huber.fit(X, y, sample_weight=np.ones(y.shape[0]))
    assert_array_almost_equal(huber.coef_, huber_coef)
    assert_array_almost_equal(huber.intercept_, huber_intercept)

    X, y = make_regression_with_outliers(n_samples=5, n_features=20)
    X_new = np.vstack((X, np.vstack((X[1], X[1], X[3]))))
    y_new = np.concatenate((y, [y[1]], [y[1]], [y[3]]))
    huber.fit(X_new, y_new)
    huber_coef = huber.coef_
    huber_intercept = huber.intercept_
    huber.fit(X, y, sample_weight=[1, 3, 1, 2, 1])
    assert_array_almost_equal(huber.coef_, huber_coef, 3)
    assert_array_almost_equal(huber.intercept_, huber_intercept, 3)

    # Test sparse implementation with sample weights.
    X_csr = sparse.csr_matrix(X)
    huber_sparse = HuberRegressor(fit_intercept=True, alpha=0.1)
    huber_sparse.fit(X_csr, y, sample_weight=[1, 3, 1, 2, 1])
    assert_array_almost_equal(huber_sparse.coef_, huber_coef, 3)