def test_petab():
for engine in [
pypesto.SingleCoreEngine(),
pypesto.MultiProcessEngine(n_procs=2),
pypesto.MultiThreadEngine(n_threads=4)
]:
_test_petab(engine)
def test_basic():
for engine in [
pypesto.SingleCoreEngine(),
pypesto.MultiProcessEngine(n_procs=2),
pypesto.MultiThreadEngine(n_threads=8)
]:
_test_basic(engine)
x_random = np.random.normal(0.5, 0.005, 22)
check_grad_2 = obj.check_grad(x_random)
print(check_grad_2[np.array(['grad', 'fd_c', 'abs_err', 'rel_err'])])
# OPTIMIZATION WITHOUT PRIOR ___________________________________________________________________________________________
optimizer = pypesto.ScipyOptimizer(method='L-BFGS-B')
# play with optimization options
optimizer.options = {'maxiter': 1e5, 'ftol': 1e-10, 'gtol': 1e-10, 'maxls': 80}
# optimizer.options = {'maxcor': 10, 'ftol': 1e-10, 'gtol': 1e-05, 'eps': 1e-08, 'maxfun': 1e5,
# 'maxiter': 1e5, 'maxls': 20}
problem = importer.create_problem(obj)
engine = pypesto.SingleCoreEngine()
n_starts = 10
start = time.time()
result = pypesto.minimize(problem=problem,
optimizer=optimizer,
n_starts=n_starts,
engine=engine)
end = time.time()
print('\nbest parameter: ', result.optimize_result.as_list('x')[0]['x'])
print('best likelihood value: ',
obj(result.optimize_result.as_list('x')[0]['x']))
# calculate computation time
comp_time = end - start
def test_basic(self):
for engine in [pypesto.SingleCoreEngine(),
pypesto.MultiProcessEngine(n_procs=2)]:
self._test_basic(engine)
