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, max_iter=1000) clf.fit(X, Y) 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_predict(): """Check that the predict method works with dense coef_ and sparse X""" X = sp.lil_matrix((3, 2)) X[0, 0] = 1 X[0, 1] = 0.5 X[1, 0] = -1 clf = SparseENet() clf._set_coef(np.array([1, -1])) predicted = clf.predict(X) np.testing.assert_array_equal([0.5, -1.0, 0.0], predicted)
def test_enet_toy_explicit_sparse_input(): """Test ElasticNet for various parameters of alpha and rho with sparse X""" # training samples X = sp.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 = sp.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, max_iter=1000) clf.fit(X, Y) 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)