def create_plotting_options():
# create sets of reference points (from tuple, dict and from list)
ref1 = ([1., 1.5], 0.2)
ref2 = ([1.8, 1.9], 0.6)
ref3 = {'x': np.array([1.4, 1.7]), 'fval': 0.4}
ref4 = [ref1, ref2]
ref_point = visualize.create_references(ref4)
return ref1, ref2, ref3, ref4, ref_point
def test_reference_points():
# create the necessary results
(ref1, ref2, ref3, ref4, _) = create_plotting_options()
# Try conversion from different inputs
visualize.create_references(ref1)
visualize.create_references(references=ref2)
ref_list_1 = visualize.create_references(ref3)
ref_list_2 = visualize.create_references(ref4)
# Try to append to a list
ref_list_2.append(ref_list_1[0])
ref_list_2 = visualize.create_references(ref_list_2)
# Try to append one point to a list via the interface
visualize.create_references(references=ref_list_2, x=ref2[0], fval=ref2[1])
# retrieve second optimum
all_x = result1_bfgs.optimize_result.get_for_key('x')
all_fval = result1_bfgs.optimize_result.get_for_key('fval')
x = all_x[19]
fval = all_fval[19]
print('Second optimum at: ' + str(fval))
# create a reference point from it
ref = {
'x': x,
'fval': fval,
'color': [0.2, 0.4, 1., 1.],
'legend': 'second optimum'
}
ref = visualize.create_references(ref)
# new waterfall plot with reference point for second optimum
visualize.waterfall(result1_dogleg,
size=(15, 6),
scale_y='lin',
y_limits=[-1, 101],
reference=ref,
colors=[0., 0., 0., 1.])
###### Visulaize Parameters
visualize.parameters([result1_bfgs, result1_tnc],
legends=['L-BFGS-B', 'TNC'],
balance_alpha=False)
visualize.parameters(result1_dogleg,
legends='dogleg',
# Lucarelli nc = 16 (4* 4)
# Isensee nc = 123 (4*30 + 3)
# Beer nc = 19 (4* 4 + 3)
engine_threads = 3
objective.n_threads = 4
engine = pypesto.engine.MultiThreadEngine(n_threads=engine_threads)
options = optimize.OptimizeOptions(allow_failed_starts=True,
report_sres=False,
report_hess=False)
# do the optimization
ref = visualize.create_references(
x=np.asarray(petab_problem.x_nominal_scaled)[np.asarray(
petab_problem.x_free_indices)],
fval=problem.objective(
np.asarray(petab_problem.x_nominal_scaled)[np.asarray(
petab_problem.x_free_indices)]))
print(f'Reference fval: {ref[0]["fval"]}')
hdf_results_file = os.path.join('results', prefix + '.hdf5')
result = optimize.minimize(
problem=problem,
optimizer=optimizer,
n_starts=N_STARTS,
engine=engine,
options=options,
progress_bar=False,
filename=None,