def test_profiles():
# create the necessary results
result_1 = create_profile_result()
result_2 = create_profile_result()
# test a standard call
visualize.profiles(result_1)
# test plotting of lists
visualize.profiles([result_1, result_2])
def test_profile_with_fixed_parameters():
"""Test using profiles with fixed parameters."""
obj = test_objective.rosen_for_sensi(max_sensi_order=1)['obj']
lb = -2 * np.ones(5)
ub = 2 * np.ones(5)
problem = pypesto.Problem(objective=obj,
lb=lb,
ub=ub,
x_fixed_vals=[0.5, -1.8],
x_fixed_indices=[0, 3])
optimizer = optimize.ScipyOptimizer(options={'maxiter': 50})
result = optimize.minimize(problem=problem,
optimizer=optimizer,
n_starts=2)
for i_method, next_guess_method in enumerate([
'fixed_step', 'adaptive_step_order_0', 'adaptive_step_order_1',
'adaptive_step_regression'
]):
print(next_guess_method)
profile.parameter_profile(problem=problem,
result=result,
optimizer=optimizer,
next_guess_method=next_guess_method)
# standard plotting
axes = visualize.profiles(result, profile_list_ids=i_method)
assert len(axes) == 3
visualize.profile_cis(result, profile_list=i_method)
def test_profiles_with_options():
# create the necessary results
result = create_profile_result()
result.profile_result.list.append([result.profile_result.list[0][1], None])
# alternative figure size and plotting options
(_, _, _, _, ref_point) = create_plotting_options()
alt_fig_size = (9.0, 8.0)
# test a call with some specific options
visualize.profiles(result,
reference=ref_point,
size=alt_fig_size,
profile_list_ids=[0, 1],
legends=['profile list 0', 'profile list 1'],
colors=[[1., .3, .3, .5], [.5, .9, .4, .3]])
def test_default_profiling(self):
# loop over methods for creating new initial guesses
method_list = [
'fixed_step', 'adaptive_step_order_0', 'adaptive_step_order_1',
'adaptive_step_regression'
]
for i_run, method in enumerate(method_list):
# run profiling
result = profile.parameter_profile(problem=self.problem,
result=self.result,
optimizer=self.optimizer,
next_guess_method=method)
# check result
self.assertTrue(
isinstance(result.profile_result.list[i_run][0],
profile.ProfilerResult))
self.assertEqual(len(result.profile_result.list), i_run + 1)
self.assertEqual(len(result.profile_result.list[i_run]), 2)
# check whether profiling needed maybe too many steps
steps = result.profile_result.list[i_run][0]['ratio_path'].size
if method == 'adaptive_step_regression':
self.assertTrue(
steps < 20, 'Profiling with regression based '
'proposal needed too many steps.')
self.assertTrue(
steps > 1, 'Profiling with regression based '
'proposal needed not enough steps.')
elif method == 'adaptive_step_order_1':
self.assertTrue(
steps < 25, 'Profiling with 1st order based '
'proposal needed too many steps.')
self.assertTrue(
steps > 1, 'Profiling with 1st order based '
'proposal needed not enough steps.')
elif method == 'adaptive_step_order_0':
self.assertTrue(
steps < 100, 'Profiling with 0th order based '
'proposal needed too many steps.')
self.assertTrue(
steps > 1, 'Profiling with 0th order based '
'proposal needed not enough steps.')
# standard plotting
visualize.profiles(result, profile_list_ids=i_run)
visualize.profile_cis(result, profile_list=i_run)
