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 create_optimization_history():
# create the pypesto problem
problem = create_problem()
# create optimizer
optimizer_options = {'maxiter': 200}
optimizer = pypesto.ScipyOptimizer(method='TNC', options=optimizer_options)
# run optimization
optimize_options = pypesto.OptimizeOptions(allow_failed_starts=True)
result_with_trace = pypesto.minimize(
problem=problem,
optimizer=optimizer,
n_starts=5,
startpoint_method=pypesto.startpoint.uniform,
options=optimize_options)
return result_with_trace
def create_optimization_results(objective):
# create optimizer, pypesto problem and options
options = {'maxiter': 200}
optimizer = pypesto.ScipyOptimizer(method='TNC', options=options)
lb = -2 * np.ones((1, 2))
ub = 2 * np.ones((1, 2))
problem = pypesto.Problem(objective, lb, ub)
optimize_options = pypesto.OptimizeOptions(allow_failed_starts=True)
# run optimization
result = pypesto.minimize(problem=problem,
optimizer=optimizer,
n_starts=5,
startpoint_method=pypesto.startpoint.uniform,
options=optimize_options)
return problem, result, optimizer
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
def check_history(self):
self.problem = pypesto.Problem(self.obj, self.lb, self.ub)
optimize_options = pypesto.OptimizeOptions(
allow_failed_starts=False
)
history_options = pypesto.HistoryOptions(
trace_record=True,
trace_record_hess=False,
trace_save_iter=1,
storage_file='tmp/traces/conversion_example_{id}.csv',
)
result = pypesto.minimize(
problem=self.problem,
optimizer=self.optimizer,
n_starts=1,
startpoint_method=pypesto.startpoint.uniform,
options=optimize_options,
history_options=history_options
)
# disable trace from here on
self.obj.history.options.trace_record = False
for start in result.optimize_result.list:
trace = start.history._trace
it_final = int(trace[('fval', np.NaN)].idxmin())
it_start = int(np.where(np.logical_not(
np.isnan(trace['fval'].values)
))[0][0])
self.assertTrue(np.isclose(
trace['x'].values[0, :], start.x0
).all())
self.assertTrue(np.isclose(
trace['x'].values[it_final, :], start.x
).all())
self.assertTrue(np.isclose(
trace['fval'].values[it_start, 0], start.fval0
))
funs = {
'fval': self.obj.get_fval,
'grad': self.obj.get_grad,
'hess': self.obj.get_hess,
'res': self.obj.get_res,
'sres': self.obj.get_sres,
'chi2': lambda x: res_to_chi2(self.obj.get_res(x)),
'schi2': lambda x: sres_to_schi2(*self.obj(
x,
(0, 1,),
pypesto.objective.constants.MODE_RES
))
}
for var, fun in funs.items():
for it in range(5):
if var in ['fval', 'chi2']:
if not np.isnan(trace[var].values[it, 0]):
self.assertTrue(np.isclose(
trace[var].values[it, 0],
fun(trace['x'].values[it, :])
))
elif var in ['hess', 'sres', 'res']:
if trace[var].values[it, 0] is not None:
self.assertTrue(np.isclose(
trace[var].values[it, 0],
fun(trace['x'].values[it, :])
).all())
elif self.obj.history.options[f'trace_record_{var}'] \
and not \
np.isnan(trace[var].values[it, :]).all():
self.assertTrue(np.isclose(
trace[var].values[it, :],
fun(trace['x'].values[it, :])
).all())