diff options
Diffstat (limited to 'test/test_optim.py')
| -rw-r--r-- | test/test_optim.py | 78 |
1 files changed, 62 insertions, 16 deletions
diff --git a/test/test_optim.py b/test/test_optim.py index 94995d5..4efd9b1 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -8,7 +8,7 @@ import numpy as np import ot -def test_conditional_gradient(): +def test_conditional_gradient(nx): n_bins = 100 # nb bins np.random.seed(0) @@ -29,15 +29,25 @@ def test_conditional_gradient(): def df(G): return G + def fb(G): + return 0.5 * nx.sum(G ** 2) + + ab = nx.from_numpy(a) + bb = nx.from_numpy(b) + Mb = nx.from_numpy(M, type_as=ab) + reg = 1e-1 G, log = ot.optim.cg(a, b, M, reg, f, df, verbose=True, log=True) + Gb, log = ot.optim.cg(ab, bb, Mb, reg, fb, df, verbose=True, log=True) + Gb = nx.to_numpy(Gb) - np.testing.assert_allclose(a, G.sum(1)) - np.testing.assert_allclose(b, G.sum(0)) + np.testing.assert_allclose(Gb, G) + np.testing.assert_allclose(a, Gb.sum(1)) + np.testing.assert_allclose(b, Gb.sum(0)) -def test_conditional_gradient_itermax(): +def test_conditional_gradient_itermax(nx): n = 100 # nb samples mu_s = np.array([0, 0]) @@ -61,16 +71,27 @@ def test_conditional_gradient_itermax(): def df(G): return G + def fb(G): + return 0.5 * nx.sum(G ** 2) + + ab = nx.from_numpy(a) + bb = nx.from_numpy(b) + Mb = nx.from_numpy(M, type_as=ab) + reg = 1e-1 G, log = ot.optim.cg(a, b, M, reg, f, df, numItermaxEmd=10000, verbose=True, log=True) + Gb, log = ot.optim.cg(ab, bb, Mb, reg, fb, df, numItermaxEmd=10000, + verbose=True, log=True) + Gb = nx.to_numpy(Gb) - np.testing.assert_allclose(a, G.sum(1)) - np.testing.assert_allclose(b, G.sum(0)) + np.testing.assert_allclose(Gb, G) + np.testing.assert_allclose(a, Gb.sum(1)) + np.testing.assert_allclose(b, Gb.sum(0)) -def test_generalized_conditional_gradient(): +def test_generalized_conditional_gradient(nx): n_bins = 100 # nb bins np.random.seed(0) @@ -91,13 +112,23 @@ def test_generalized_conditional_gradient(): def df(G): return G + def fb(G): + return 0.5 * nx.sum(G ** 2) + reg1 = 1e-3 reg2 = 1e-1 + ab = nx.from_numpy(a) + bb = nx.from_numpy(b) + Mb = nx.from_numpy(M, type_as=ab) + G, log = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True, log=True) + Gb, log = ot.optim.gcg(ab, bb, Mb, reg1, reg2, fb, df, verbose=True, log=True) + Gb = nx.to_numpy(Gb) - np.testing.assert_allclose(a, G.sum(1), atol=1e-05) - np.testing.assert_allclose(b, G.sum(0), atol=1e-05) + np.testing.assert_allclose(Gb, G) + np.testing.assert_allclose(a, Gb.sum(1), atol=1e-05) + np.testing.assert_allclose(b, Gb.sum(0), atol=1e-05) def test_solve_1d_linesearch_quad_funct(): @@ -106,24 +137,31 @@ def test_solve_1d_linesearch_quad_funct(): np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(-1, 0.5, 0), 1) -def test_line_search_armijo(): +def test_line_search_armijo(nx): xk = np.array([[0.25, 0.25], [0.25, 0.25]]) pk = np.array([[-0.25, 0.25], [0.25, -0.25]]) gfk = np.array([[23.04273441, 23.0449082], [23.04273441, 23.0449082]]) old_fval = -123 # Should not throw an exception and return None for alpha - alpha, _, _ = ot.optim.line_search_armijo(lambda x: 1, xk, pk, gfk, old_fval) + alpha, a, b = ot.optim.line_search_armijo( + lambda x: 1, nx.from_numpy(xk), nx.from_numpy(pk), nx.from_numpy(gfk), old_fval + ) + alpha_np, anp, bnp = ot.optim.line_search_armijo( + lambda x: 1, xk, pk, gfk, old_fval + ) + assert a == anp + assert b == bnp assert alpha is None # check line search armijo def f(x): - return np.sum((x - 5.0) ** 2) + return nx.sum((x - 5.0) ** 2) def grad(x): return 2 * (x - 5.0) - xk = np.array([[[-5.0, -5.0]]]) - pk = np.array([[[100.0, 100.0]]]) + xk = nx.from_numpy(np.array([[[-5.0, -5.0]]])) + pk = nx.from_numpy(np.array([[[100.0, 100.0]]])) gfk = grad(xk) old_fval = f(xk) @@ -132,10 +170,18 @@ def test_line_search_armijo(): np.testing.assert_allclose(alpha, 0.1) # check the case where the direction is not far enough - pk = np.array([[[3.0, 3.0]]]) + pk = nx.from_numpy(np.array([[[3.0, 3.0]]])) alpha, _, _ = ot.optim.line_search_armijo(f, xk, pk, gfk, old_fval, alpha0=1.0) np.testing.assert_allclose(alpha, 1.0) - # check the case where the checking the wrong direction + # check the case where checking the wrong direction alpha, _, _ = ot.optim.line_search_armijo(f, xk, -pk, gfk, old_fval) assert alpha <= 0 + + # check the case where the point is not a vector + xk = nx.from_numpy(np.array(-5.0)) + pk = nx.from_numpy(np.array(100.0)) + gfk = grad(xk) + old_fval = f(xk) + alpha, _, _ = ot.optim.line_search_armijo(f, xk, pk, gfk, old_fval) + np.testing.assert_allclose(alpha, 0.1) |