def test_lasso_zero(): # Check that the lasso can handle zero data without crashing X = [[0], [0], [0]] y = [0, 0, 0] clf = Lasso(alpha=0.1).fit(X, y) pred = clf.predict([[1], [2], [3]]) assert_array_almost_equal(clf.coef_, [0]) assert_array_almost_equal(pred, [0, 0, 0]) assert_almost_equal(clf.dual_gap_, 0)
def test_lasso_zero(): # Check that the sparse lasso can handle zero data without crashing X = sp.csc_matrix((3, 1)) y = [0, 0, 0] T = np.array([[1], [2], [3]]) clf = Lasso().fit(X, y) pred = clf.predict(T) assert_array_almost_equal(clf.coef_, [0]) assert_array_almost_equal(pred, [0, 0, 0]) assert_almost_equal(clf.dual_gap_, 0)
def test_lasso_readonly_data(): X = np.array([[-1], [0], [1]]) Y = np.array([-1, 0, 1]) # just a straight line T = np.array([[2], [3], [4]]) # test sample with TempMemmap((X, Y)) as (X, Y): clf = Lasso(alpha=0.5) clf.fit(X, Y) pred = clf.predict(T) assert_array_almost_equal(clf.coef_, [.25]) assert_array_almost_equal(pred, [0.5, 0.75, 1.]) assert_almost_equal(clf.dual_gap_, 0)
def test_sparse_enet_coordinate_descent(): """Test that a warning is issued if model does not converge""" clf = Lasso(max_iter=2) n_samples = 5 n_features = 2 X = sp.csc_matrix((n_samples, n_features)) * 1e50 y = np.ones(n_samples) assert_warns(ConvergenceWarning, clf.fit, X, y)
def test_lasso_positive_constraint(): X = [[-1], [0], [1]] y = [1, 0, -1] # just a straight line with negative slope lasso = Lasso(alpha=0.1, max_iter=1000, positive=True) lasso.fit(X, y) assert min(lasso.coef_) >= 0 lasso = Lasso(alpha=0.1, max_iter=1000, precompute=True, positive=True) lasso.fit(X, y) assert min(lasso.coef_) >= 0
def test_sparse_input_convergence_warning(): X, y, _, _ = build_dataset(n_samples=1000, n_features=500) with pytest.warns(ConvergenceWarning): ElasticNet(max_iter=1, tol=0).fit(sparse.csr_matrix(X, dtype=np.float32), y) # check that the model converges w/o warnings with pytest.warns(None) as record: Lasso(max_iter=1000).fit(sparse.csr_matrix(X, dtype=np.float32), y) assert not record.list
def test_sparse_lasso_not_as_toy_dataset(): n_samples = 100 max_iter = 1000 n_informative = 10 X, y = make_sparse_data(n_samples=n_samples, n_informative=n_informative) X_train, X_test = X[n_samples // 2:], X[:n_samples // 2] y_train, y_test = y[n_samples // 2:], y[:n_samples // 2] s_clf = Lasso(alpha=0.1, fit_intercept=False, max_iter=max_iter, tol=1e-7) s_clf.fit(X_train, y_train) assert_almost_equal(s_clf.dual_gap_, 0, 4) assert s_clf.score(X_test, y_test) > 0.85 # check the convergence is the same as the dense version d_clf = Lasso(alpha=0.1, fit_intercept=False, max_iter=max_iter, tol=1e-7) d_clf.fit(X_train.toarray(), y_train) assert_almost_equal(d_clf.dual_gap_, 0, 4) assert d_clf.score(X_test, y_test) > 0.85 # check that the coefs are sparse assert np.sum(s_clf.coef_ != 0.0) == n_informative
def test_coef_shape_not_zero(): est_no_intercept = Lasso(fit_intercept=False) est_no_intercept.fit(np.c_[np.ones(3)], np.ones(3)) assert est_no_intercept.coef_.shape == (1, )
def test_lasso_toy(): # Test Lasso on a toy example for various values of alpha. # When validating this against glmnet notice that glmnet divides it # against nobs. X = [[-1], [0], [1]] Y = [-1, 0, 1] # just a straight line T = [[2], [3], [4]] # test sample clf = Lasso(alpha=1e-8) clf.fit(X, Y) pred = clf.predict(T) assert_array_almost_equal(clf.coef_, [1]) assert_array_almost_equal(pred, [2, 3, 4]) assert_almost_equal(clf.dual_gap_, 0) clf = Lasso(alpha=0.1) clf.fit(X, Y) pred = clf.predict(T) assert_array_almost_equal(clf.coef_, [.85]) assert_array_almost_equal(pred, [1.7, 2.55, 3.4]) assert_almost_equal(clf.dual_gap_, 0) clf = Lasso(alpha=0.5) clf.fit(X, Y) pred = clf.predict(T) assert_array_almost_equal(clf.coef_, [.25]) assert_array_almost_equal(pred, [0.5, 0.75, 1.]) assert_almost_equal(clf.dual_gap_, 0) clf = Lasso(alpha=1) clf.fit(X, Y) pred = clf.predict(T) assert_array_almost_equal(clf.coef_, [.0]) assert_array_almost_equal(pred, [0, 0, 0]) assert_almost_equal(clf.dual_gap_, 0)
def test_lasso_alpha_warning(): X = [[-1], [0], [1]] Y = [-1, 0, 1] # just a straight line clf = Lasso(alpha=0) assert_warns(UserWarning, clf.fit, X, Y)