def test_deprecated_alias(self):
# Tests Optimisation()
r = pints.toy.RosenbrockError()
x = np.array([1.1, 1.1])
b = pints.RectangularBoundaries([0.5, 0.5], [1.5, 1.5])
opt = pints.Optimisation(r, x, boundaries=b, method=method)
self.assertIsInstance(opt, pints.OptimisationController)
def test_deprecated_alias(self):
# Tests Optimisation()
r = pints.toy.RosenbrockError()
x = np.array([1.1, 1.1])
b = pints.RectangularBoundaries([0.5, 0.5], [1.5, 1.5])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
opt = pints.Optimisation(r, x, boundaries=b, method=method)
self.assertEqual(len(w), 1)
self.assertIn('deprecated', str(w[-1].message))
self.assertIsInstance(opt, pints.OptimisationController)
def optimise(self, x, parallel=False):
"""
Runs the optimisation, this method:
(1) generates simulated data and adds noise
(2) sets up the optimiser with the method given, trying to
optimise the function f(x) = sum of squared error
(3) runs the optimisation
(4) returns:
- the found parameters x,
- the ratio of f(x) / f(x_0), where x_0 are the real parameters
- time total time taken divided by the time taken to evaluate a
single evaluation of f(x)
"""
the_model = self.model()
print('model = ', the_model)
values = the_model.simulate(self.real_parameters, self.times)
value_range = np.max(values) - np.min(values)
values += np.random.normal(0, self.noise * value_range, values.shape)
problem = pints.MultiOutputProblem(the_model, self.times, values)
score = pints.SumOfSquaresError(problem)
middle = [0.5 * (u + l) for l, u in zip(self.lower, self.upper)]
sigma = [(1.0/6.0)*(u - l) for l, u in zip(self.lower, self.upper)]
print('sigma = ', sigma)
boundaries = pints.RectangularBoundaries(self.lower, self.upper)
optimisation = pints.Optimisation(
score,
middle,
sigma0=sigma,
boundaries=boundaries,
method=self.method
)
optimisation.optimiser().set_hyper_parameters(x)
if parallel:
optimisation.set_parallel(int(os.environ['OMP_NUM_THREADS']))
else:
optimisation.set_parallel(False)
start = timer()
found_parameters, found_value = optimisation.run()
end = timer()
N = 10
start_score = timer()
for i in range(N):
minimum_value = score(self.real_parameters)
end_score = timer()
score_duration = (end_score - start_score) / N
return found_parameters, \
found_value / minimum_value, \
(end - start) / score_duration
else:
x0 = log_prior.sample()
print('Initial guess (untransformed model parameters) = ', x0)
# Create optimiser and log transform parameters
x0 = util.transformer(transform, x0, rate_dict, True)
boundaries = rate_checker._get_boundaries(rate_dict)
Boundaries = pints.RectangularBoundaries(boundaries[0], boundaries[1])
print('Initial guess LogLikelihood = ', log_likelihood(x0))
print('Initial guess LogPrior = ', log_prior(x0))
print('Initial guess LogPosterior = ', log_posterior(x0))
print('Initial guess (transformed optimisation parameters) = ', x0)
opt = pints.Optimisation(log_posterior,
x0,
boundaries=Boundaries,
method=pints.CMAES)
opt.set_max_iterations(None)
opt.set_parallel(True)
log_filename = model_name + '_cell_' + \
str(cell) + '_transform_' + str(transform) + \
'_cmaes_run_' + str(i) + '.log'
opt.set_log_to_file(results_log_folder + '/' + log_filename, csv=True)
# Run optimisation
try:
with np.errstate(all='ignore'): # Tell numpy not to issue warnings
p, s = opt.run()
p = util.transformer(transform, p, rate_dict, False)
params.append(p)