def _optimise(self, termination_params):
NN = len(self.input_intensities)
dim = self.target.dim
n_sym_ops = len(self.target.get_sym_ops())
coords = flex.random_double(NN * n_sym_ops * dim)
import scitbx.lbfgs
tp = termination_params
termination_params = scitbx.lbfgs.termination_parameters(
traditional_convergence_test=tp.traditional_convergence_test,
traditional_convergence_test_eps=tp.traditional_convergence_test_eps,
drop_convergence_test_n_test_points=tp.drop_convergence_test_n_test_points,
drop_convergence_test_max_drop_eps=tp.drop_convergence_test_max_drop_eps,
drop_convergence_test_iteration_coefficient=tp.drop_convergence_test_iteration_coefficient,
# min_iterations=tp.min_iterations,
max_iterations=tp.max_iterations,
max_calls=tp.max_calls,
)
M = engine.lbfgs_with_curvs(
self.target,
coords,
use_curvatures=self.params.use_curvatures,
termination_params=termination_params,
)
self.minimizer = M
coords = M.x.deep_copy()
coords.reshape(flex.grid(dim, NN * n_sym_ops))
coords.matrix_transpose_in_place()
self.coords = coords
def test_cosym_target(space_group):
datasets, expected_reindexing_ops = generate_test_data(
space_group=sgtbx.space_group_info(symbol=space_group).group(),
sample_size=50)
intensities = datasets[0]
dataset_ids = np.zeros(intensities.size() * len(datasets))
for i, d in enumerate(datasets[1:]):
i += 1
intensities = intensities.concatenate(d,
assert_is_similar_symmetry=False)
dataset_ids[i * d.size():(i + 1) * d.size()] = np.full(d.size(),
i,
dtype=int)
for weights in [None, "count", "standard_error"]:
print(weights)
t = target.Target(intensities, dataset_ids, weights=weights)
m = len(t.sym_ops)
n = len(datasets)
assert t.dim == m
assert t.rij_matrix.shape == (n * m, n * m)
# x = np.random.rand(n * m * t.dim)
x = flex.random_double(n * m * t.dim).as_numpy_array()
f0 = t.compute_functional(x)
g = t.compute_gradients(x)
g_fd = t.compute_gradients_fd(x)
np.testing.assert_allclose(g, g_fd, rtol=2e-3)
c = t.curvatures(x)
c_fd = t.curvatures_fd(x, eps=1e-3)
assert list(c) == pytest.approx(c_fd, rel=0.8e-1)
if weights == "count":
# Absolute upper limit on weights
assert t.wij_matrix.max() <= datasets[0].size()
minimizer = engine.lbfgs_with_curvs(target=t, coords=x)
# check functional has decreased and gradients are approximately zero
f = t.compute_functional(minimizer.coords)
g = t.compute_gradients(minimizer.coords)
g_fd = t.compute_gradients_fd(minimizer.coords)
assert f < f0
assert pytest.approx(g, abs=1e-3) == [0] * len(g)
assert pytest.approx(g_fd, abs=1e-3) == [0] * len(g)
def test_cosym_target(space_group):
datasets, expected_reindexing_ops = generate_test_data(
space_group=sgtbx.space_group_info(symbol=space_group).group(),
sample_size=50)
intensities = datasets[0]
dataset_ids = flex.double(intensities.size(), 0)
for i, d in enumerate(datasets[1:]):
intensities = intensities.concatenate(d,
assert_is_similar_symmetry=False)
dataset_ids.extend(flex.double(d.size(), i + 1))
for weights in [None, "count", "standard_error"]:
print(weights)
t = target.Target(intensities, dataset_ids, weights=weights)
m = len(t.get_sym_ops())
n = len(datasets)
assert t.dim == m
assert t.rij_matrix.all() == (n * m, n * m)
x = flex.random_double(n * m * t.dim)
f0, g = t.compute_functional_and_gradients(x)
g_fd = t.compute_gradients_fd(x)
for n, value in enumerate(zip(g, g_fd)):
assert value[0] == pytest.approx(value[1], rel=2e-3), n
c = t.curvatures(x)
c_fd = t.curvatures_fd(x, eps=1e-3)
assert list(c) == pytest.approx(c_fd, rel=0.8e-1)
assert engine.lbfgs_with_curvs(target=t, coords=x)
t.compute_functional(x)
# check functional has decreased and gradients are approximately zero
f, g = t.compute_functional_and_gradients(x)
g_fd = t.compute_gradients_fd(x)
assert f < f0
assert pytest.approx(g, abs=1e-3) == [0] * len(g)
assert pytest.approx(g_fd, abs=1e-3) == [0] * len(g)
def test_cosym_target(space_group):
datasets, expected_reindexing_ops = generate_test_data(
space_group=sgtbx.space_group_info(symbol=space_group).group())
for weights in [None, 'count', 'standard_error']:
t = target.Target(
datasets,
weights=weights,
)
m = len(t.get_sym_ops())
n = len(datasets)
assert t.dim == m
assert t.rij_matrix.all() == (n * m, n * m)
x = flex.random_double(n * m * t.dim)
x_orig = x.deep_copy()
f0, g = t.compute_functional_and_gradients(x)
g_fd = t.compute_gradients_fd(x)
assert g.all_approx_equal_relatively(g_fd, relative_error=1e-4)
c = t.curvatures(x)
c_fd = t.curvatures_fd(x, eps=1e-3)
assert c.all_approx_equal_relatively(c_fd, relative_error=0.5e-1)
M = engine.lbfgs_with_curvs(
target=t,
coords=x,
verbose=False,
)
t.compute_functional(x)
# check functional has decreased and gradients are approximately zero
f, g = t.compute_functional_and_gradients(x)
g_fd = t.compute_gradients_fd(x)
assert f < f0
assert g.all_approx_equal(0, 1e-3)
assert g_fd.all_approx_equal(0, 1e-3)