# Authors: Manoj Kumar mks542@nyu.edu # License: BSD 3 clause import numpy as np from scipy import optimize, sparse from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_false from sklearn.datasets import make_regression from sklearn.linear_model import ( HuberRegressor, LinearRegression, SGDRegressor, Ridge) from sklearn.linear_model.huber import _huber_loss_and_gradient def make_regression_with_outliers(n_samples=50, n_features=20): rng = np.random.RandomState(0) # Generate data with outliers by replacing 10% of the samples with noise. X, y = make_regression( n_samples=n_samples, n_features=n_features, random_state=0, noise=0.05) # Replace 10% of the sample with noise. num_noise = int(0.1 * n_samples) random_samples = rng.randint(0, n_samples, num_noise) X[random_samples, :] = 2.0 * rng.normal(0, 1, (num_noise, X.shape[1])) return X, y 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_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_gradient(): # Test that the gradient calculated by _huber_loss_and_gradient is correct rng = np.random.RandomState(1) X, y = make_regression_with_outliers() sample_weight = rng.randint(1, 3, (y.shape[0])) loss_func = lambda x, *args: _huber_loss_and_gradient(x, *args)[0] grad_func = lambda x, *args: _huber_loss_and_gradient(x, *args)[1] # Check using optimize.check_grad that the gradients are equal. for _ in range(5): # Check for both fit_intercept and otherwise. for n_features in [X.shape[1] + 1, X.shape[1] + 2]: w = rng.randn(n_features) w[-1] = np.abs(w[-1]) grad_same = optimize.check_grad( loss_func, grad_func, w, X, y, 0.01, 0.1, sample_weight) assert_almost_equal(grad_same, 1e-6, 4) 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_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_) assert_array_equal(huber.outliers_, huber_sparse.outliers_) 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_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_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) if huber_warm.n_iter_ is not None: assert_equal(0, 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)