def test_valid_derivative_versions(direction, algorithm, derivative,
criterion_and_derivative):
start_params = pd.DataFrame()
start_params["value"] = [1, 2, 3]
if direction == "minimize":
res = minimize(
criterion=sos_dict_criterion,
params=start_params,
algorithm=algorithm,
derivative=derivative,
criterion_and_derivative=criterion_and_derivative,
error_handling="raise",
)
else:
deriv = derivative if derivative is None else switch_sign(derivative)
crit_and_deriv = (criterion_and_derivative
if criterion_and_derivative is None else
switch_sign(criterion_and_derivative))
res = maximize(
criterion=switch_sign(sos_dict_criterion),
params=start_params,
algorithm=algorithm,
derivative=deriv,
criterion_and_derivative=crit_and_deriv,
error_handling="raise",
)
aaae(res["solution_params"]["value"].to_numpy(), np.zeros(3), decimal=4)
def test_invalid_derivative_versions(direction, algorithm, derivative,
criterion_and_derivative):
start_params = pd.DataFrame()
start_params["value"] = [1, 2, 3]
if direction == "minimize":
with pytest.raises(ValueError):
minimize(
criterion=sos_dict_criterion,
params=start_params,
algorithm=algorithm,
derivative=derivative,
criterion_and_derivative=criterion_and_derivative,
)
else:
deriv = derivative if derivative is None else switch_sign(derivative)
crit_and_deriv = (criterion_and_derivative
if criterion_and_derivative is None else
switch_sign(criterion_and_derivative))
with pytest.raises(ValueError):
maximize(
criterion=switch_sign(sos_dict_criterion),
params=start_params,
algorithm=algorithm,
derivative=deriv,
criterion_and_derivative=crit_and_deriv,
)
def test_multistart_minimize_with_sum_of_squares_at_defaults(
criterion, direction, params):
if direction == "minimize":
res = minimize(
criterion=criterion,
params=params,
algorithm="scipy_lbfgsb",
multistart=True,
)
else:
res = maximize(
criterion=switch_sign(sos_dict_criterion),
params=params,
algorithm="scipy_lbfgsb",
multistart=True,
)
assert hasattr(res, "multistart_info")
ms_info = res.multistart_info
assert len(ms_info["exploration_sample"]) == 40
assert len(ms_info["exploration_results"]) == 40
assert all(
isinstance(entry, float) for entry in ms_info["exploration_results"])
assert all(
isinstance(entry, OptimizeResult) for entry in ms_info["local_optima"])
assert all(
isinstance(entry, pd.DataFrame)
for entry in ms_info["start_parameters"])
assert np.allclose(res.criterion, 0)
aaae(res.params["value"], np.zeros(4))
def test_convergence_via_max_discoveries_works(params):
options = {
"convergence_relative_params_tolerance": np.inf,
"convergence_max_discoveries": 2,
}
res = maximize(
criterion=switch_sign(sos_dict_criterion),
params=params,
algorithm="scipy_lbfgsb",
multistart=True,
multistart_options=options,
)
assert len(res.multistart_info["local_optima"]) == 2