def test_enet_toy_list_input(): """Test ElasticNet for various parameters of alpha and rho with list X""" X = np.array([[-1], [0], [1]]) Y = [-1, 0, 1] # just a straight line T = np.array([[2], [3], [4]]) # test sample # this should be the same as unregularized least squares clf = SparseENet(alpha=0, rho=1.0) 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 = SparseENet(alpha=0.5, rho=0.3) clf.fit(X, Y, maxit=1000) pred = clf.predict(T) assert_array_almost_equal(clf.coef_, [0.50819], decimal=3) assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3) assert_almost_equal(clf.dual_gap_, 0) clf = SparseENet(alpha=0.5, rho=0.5) clf.fit(X, Y) pred = clf.predict(T) assert_array_almost_equal(clf.coef_, [0.45454], 3) assert_array_almost_equal(pred, [0.9090, 1.3636, 1.8181], 3) assert_almost_equal(clf.dual_gap_, 0)
def test_sparse_enet_not_as_toy_dataset(): n_samples, n_features, maxit = 100, 100, 1000 n_informative = 10 X, y = make_sparse_data(n_samples, n_features, 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 = SparseENet(alpha=0.1, rho=0.8, fit_intercept=False) s_clf.fit(X_train, y_train, maxit=maxit, tol=1e-7) 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 = DenseENet(alpha=0.1, rho=0.8, fit_intercept=False) d_clf.fit(X_train, y_train, maxit=maxit, tol=1e-7) assert_almost_equal(d_clf.dual_gap_, 0, 4) assert d_clf.score(X_test, y_test) > 0.85 assert_almost_equal(s_clf.coef_, d_clf.coef_, 5) # check that the coefs are sparse assert np.sum(s_clf.coef_ != 0.0) < 2 * n_informative
def test_enet_toy_explicit_sparse_input(): """Test ElasticNet for various parameters of alpha and rho with sparse X""" # training samples X = sparse.lil_matrix((3, 1)) X[0, 0] = -1 # X[1, 0] = 0 X[2, 0] = 1 Y = [-1, 0, 1] # just a straight line (the identity function) # test samples T = sparse.lil_matrix((3, 1)) T[0, 0] = 2 T[1, 0] = 3 T[2, 0] = 4 # this should be the same as lasso clf = SparseENet(alpha=0, rho=1.0) 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 = SparseENet(alpha=0.5, rho=0.3) clf.fit(X, Y, maxit=1000) pred = clf.predict(T) assert_array_almost_equal(clf.coef_, [0.50819], decimal=3) assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3) assert_almost_equal(clf.dual_gap_, 0) clf = SparseENet(alpha=0.5, rho=0.5) clf.fit(X, Y) pred = clf.predict(T) assert_array_almost_equal(clf.coef_, [0.45454], 3) assert_array_almost_equal(pred, [0.9090, 1.3636, 1.8181], 3) assert_almost_equal(clf.dual_gap_, 0)