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)