def check_minimize(objective, library, solver, allow_failed_starts=False):
options = {'maxiter': 100}
optimizer = None
if library == 'scipy':
optimizer = pypesto.ScipyOptimizer(method=solver, options=options)
elif library == 'dlib':
optimizer = pypesto.DlibOptimizer(method=solver, options=options)
elif library == 'pyswarm':
optimizer = pypesto.PyswarmOptimizer(options=options)
lb = 0 * np.ones((1, 2))
ub = 1 * np.ones((1, 2))
problem = pypesto.Problem(objective, lb, ub)
optimize_options = pypesto.OptimizeOptions(
allow_failed_starts=allow_failed_starts)
result = pypesto.minimize(problem=problem,
optimizer=optimizer,
n_starts=1,
startpoint_method=pypesto.startpoint.uniform,
options=optimize_options)
assert isinstance(result.optimize_result.list[0]['fval'], float)
def parameter_estimation(objective, library, solver, fixed_pars, n_starts):
if re.match(r'(?i)^(ls_)', solver):
options = {'max_nfev': 10}
else:
options = {'maxiter': 10}
if library == 'scipy':
optimizer = pypesto.ScipyOptimizer(method=solver, options=options)
elif library == 'dlib':
optimizer = pypesto.DlibOptimizer(method=solver, options=options)
elif library == 'pyswarm':
optimizer = pypesto.PyswarmOptimizer(options=options)
else:
raise ValueError("This code should not be reached")
optimizer.temp_file = os.path.join('test', 'tmp_{index}.csv')
dim = len(objective.x_ids)
lb = -2 * np.ones((1, dim))
ub = 2 * np.ones((1, dim))
pars = objective.amici_model.getParameters()
problem = pypesto.Problem(objective,
lb,
ub,
x_fixed_indices=fixed_pars,
x_fixed_vals=[pars[idx] for idx in fixed_pars])
optimize_options = pypesto.OptimizeOptions(
allow_failed_starts=False,
startpoint_resample=True,
)
pypesto.minimize(problem, optimizer, n_starts, options=optimize_options)
def parameter_estimation(
objective,
library,
solver,
fixed_pars,
n_starts,
):
options = {
'maxiter': 100
}
if library == 'scipy':
optimizer = pypesto.ScipyOptimizer(method=solver,
options=options)
elif library == 'dlib':
optimizer = pypesto.DlibOptimizer(method=solver,
options=options)
optimizer.temp_file = os.path.join('test', 'tmp_{index}.csv')
lb = -2 * np.ones((1, objective.dim))
ub = 2 * np.ones((1, objective.dim))
pars = objective.amici_model.getParameters()
problem = pypesto.Problem(objective, lb, ub,
x_fixed_indices=fixed_pars,
x_fixed_vals=[pars[idx] for idx in fixed_pars]
)
optimize_options = pypesto.OptimizeOptions(
allow_failed_starts=False,
startpoint_resample=True,
)
results = pypesto.minimize(
problem, optimizer, n_starts, options=optimize_options,
)
results = results.optimize_result.list