def test_preeq_guesses():
"""
Test whether optimization with preequilibration guesses works, asserts
that steadystate guesses are written and checks that gradient is still
correct with guesses set
"""
petab_problem = petab.Problem.from_yaml(
folder_base + "Zheng_PNAS2012/Zheng_PNAS2012.yaml")
petab_problem.model_name = "Zheng_PNAS2012"
importer = pypesto.PetabImporter(petab_problem)
obj = importer.create_objective()
problem = importer.create_problem(obj)
optimizer = pypesto.ScipyOptimizer('ls_trf')
result = pypesto.minimize(
problem=problem, optimizer=optimizer, n_starts=2,
)
assert problem.objective.steadystate_guesses['fval'] < np.inf
assert len(obj.steadystate_guesses['data']) == 1
df = obj.check_grad(
result.optimize_result.list[0]['x'],
eps=1e-3,
verbosity=0,
mode=pypesto.objective.constants.MODE_FUN
)
print("relative errors MODE_FUN: ", df.rel_err.values)
print("absolute errors MODE_FUN: ", df.abs_err.values)
assert np.all((df.rel_err.values < RTOL) | (df.abs_err.values < ATOL))
def test_1_compile(self):
for petab_problem in self.petab_problems:
importer = pypesto.PetabImporter(petab_problem, force_compile=True)
self.petab_importers.append(importer)
# check model
model = importer.model
# observable ids
model_obs_ids = list(model.getObservableIds())
problem_obs_ids = list(petab_problem.get_observables().keys())
self.assertEqual(set(model_obs_ids), set(problem_obs_ids))
def test_1_compile(self):
for petab_problem in self.petab_problems:
importer = pypesto.PetabImporter(petab_problem)
self.petab_importers.append(importer)
# check model
model = importer.create_model(force_compile=False)
# observable ids
model_obs_ids = list(model.getObservableIds())
problem_obs_ids = list(petab_problem.get_observable_ids())
self.assertEqual(set(model_obs_ids),
set(problem_obs_ids))
compute_converged_points, \
compute_model_selection_criteria
import LogicleScale
from LogicleScale import logicleInverseTransform, logicleInverseGradient
# IMPORT MODEL__________________________________________________________________________________________________________
folder_base = "models/"
model_name = "Crauste_full_logicle5_1"
petab_problem = petab.Problem.from_folder(folder_base + model_name)
petab_problem.model_name = 'Crauste_full_logicle5_1'
importer = pypesto.PetabImporter(petab_problem)
importer.compile_model()
model = importer.create_model()
print("Model parameters:", list(model.getParameterIds()), '\n')
print('Optimization parameters:',
petab_problem.get_optimization_to_simulation_parameter_mapping(), '\n')
print("Model outputs: ", list(model.getObservableIds()), '\n')
print("Model states: ", list(model.getStateIds()), '\n')
print('sigmas', petab_problem.get_sigmas())
# SOLVER OPTIONS _______________________________________________________________________________________________________
solver = importer.create_solver(model)
cell_line = sys.argv[2]
agent = sys.argv[3]
regpar = np.float(sys.argv[4])
setting = f'{model_name}__{cell_line}__{agent}__lambda{regpar}'
n_threads = int(sys.argv[5])
basedir = os.path.dirname(os.path.realpath(__file__))
petab_problem = petab.Problem.from_yaml(
os.path.join(basedir, 'petab', f'{setting}.yaml')
)
importer = pypesto.PetabImporter(
petab_problem,
output_folder=os.path.join(basedir, 'models', model_name, model_name),
model_name=model_name,
)
obj = importer.create_objective()
obj.n_threads = n_threads
obj.use_amici_petab_simulate = False
problem = importer.create_problem(obj)
optim_options = {
'xtol': 1e-12,
'gtol': 1e-4,
}
optimizer = pypesto.ScipyOptimizer(
method='ls_trf',
