def test_epsilon(self, eps, locally_biased):
result = direct(self.styblinski_tang,
self.bounds_stylinski_tang,
eps=eps,
vol_tol=1e-6,
locally_biased=locally_biased)
assert result.status == 4
assert result.success
def test_f_circle_with_args(self, locally_biased):
bounds = 2 * [(-2.0, 2.0)]
res = direct(self.circle_with_args,
bounds,
args=(1, 1),
maxfun=1250,
locally_biased=locally_biased)
assert_allclose(res.x, np.array([1., 1.]), rtol=1e-5)
def test_segmentation_fault(self, locally_biased):
# test that an excessive number of function evaluations
# does not result in segmentation fault
bounds = [(-5., 20.)] * 100
result = direct(self.sphere,
bounds,
maxfun=10000000,
maxiter=1000000,
locally_biased=locally_biased)
assert result is not None
def test_len_tol(self, len_tol, locally_biased):
bounds = 4 * [(-10., 10.)]
res = direct(self.sphere,
bounds=bounds,
len_tol=len_tol,
vol_tol=1e-30,
locally_biased=locally_biased)
assert res.status == 5
assert res.success
assert_allclose(res.x, np.zeros((4, )))
def test_failure_maxiter(self, locally_biased):
# test that if optimization runs for the maximal number of
# iterations, success = False is returned
maxiter = 10
result = direct(self.styblinski_tang,
self.bounds_stylinski_tang,
maxiter=maxiter,
locally_biased=locally_biased)
assert result.success is False
assert result.status == 2
assert result.nit >= maxiter
def test_failure_maxfun(self, locally_biased):
# test that if optimization runs for the maximal number of
# evaluations, success = False is returned
maxfun = 100
result = direct(self.styblinski_tang,
self.bounds_stylinski_tang,
maxfun=maxfun,
locally_biased=locally_biased)
assert result.success is False
assert result.status == 1
assert result.nfev >= maxfun
def test_bounds_variants(self, locally_biased):
# test that new and old bounds yield same result
lb = [-6., 1., -5.]
ub = [-1., 3., 5.]
x_opt = np.array([-1., 1., 0.])
bounds_old = list(zip(lb, ub))
bounds_new = Bounds(lb, ub)
res_old_bounds = direct(self.sphere,
bounds_old,
locally_biased=locally_biased)
res_new_bounds = direct(self.sphere,
bounds_new,
locally_biased=locally_biased)
assert res_new_bounds.nfev == res_old_bounds.nfev
assert res_new_bounds.message == res_old_bounds.message
assert res_new_bounds.success == res_old_bounds.success
assert res_new_bounds.nit == res_old_bounds.nit
assert_allclose(res_new_bounds.x, res_old_bounds.x)
assert_allclose(res_new_bounds.x, x_opt, rtol=1e-2)
def test_f_min(self, f_min_rtol, locally_biased):
# test that desired function value is reached within
# relative tolerance of f_min_rtol
f_min = 1.
bounds = 4 * [(-2., 10.)]
res = direct(self.sphere,
bounds=bounds,
f_min=f_min,
f_min_rtol=f_min_rtol,
locally_biased=locally_biased)
assert res.status == 3
assert res.success
assert res.fun < f_min * (1. + f_min_rtol)
def test_direct_callback(self, locally_biased):
# test that callback does not change the result
res = direct(self.sphere,
self.bounds_sphere,
locally_biased=locally_biased)
def callback(x):
x = 2 * x
dummy = np.square(x)
print("DIRECT minimization algorithm callback test")
return dummy
res_callback = direct(self.sphere,
self.bounds_sphere,
locally_biased=locally_biased,
callback=callback)
assert_allclose(res.x, res_callback.x)
assert res.nit == res_callback.nit
assert res.nfev == res_callback.nfev
assert res.status == res_callback.status
assert res.success == res_callback.success
assert res.fun == res_callback.fun
assert_allclose(res.x, res_callback.x)
assert res.message == res_callback.message
# test accuracy
assert_allclose(res_callback.x,
self.optimum_sphere_pos,
rtol=1e-3,
atol=1e-3)
assert_allclose(res_callback.fun,
self.optimum_sphere,
atol=1e-5,
rtol=1e-5)
def test_direct(self, locally_biased):
res = direct(self.sphere,
self.bounds_sphere,
locally_biased=locally_biased)
# test accuracy
assert_allclose(res.x, self.optimum_sphere_pos, rtol=1e-3, atol=1e-3)
assert_allclose(res.fun, self.optimum_sphere, atol=1e-5, rtol=1e-5)
# test that result lies within bounds
_bounds = np.asarray(self.bounds_sphere)
assert_array_less(_bounds[:, 0], res.x)
assert_array_less(res.x, _bounds[:, 1])
# test number of function evaluations. Original DIRECT overshoots by
# up to 500 evaluations in last iteration
assert res.nfev <= 1000 * (len(self.bounds_sphere) + 1)
# test that number of function evaluations is correct
assert res.nfev == self.fun_calls
# test that number of iterations is below supplied maximum
assert res.nit <= self.maxiter
def test_inf_fun(self, locally_biased):
# test that an objective value of infinity does not crash DIRECT
bounds = [(-5., 5.)] * 2
result = direct(self.inf_fun, bounds, locally_biased=locally_biased)
assert result is not None
def test_nan(self, locally_biased):
bounds = 4 * [(-10, 10)]
direct(self.nan_fun, bounds=bounds, locally_biased=locally_biased)
def test_exception(self, locally_biased):
bounds = 4 * [(-10, 10)]
with pytest.raises(ZeroDivisionError):
direct(self.inv, bounds=bounds, locally_biased=locally_biased)