cma.optimization_tools.NoiseHandler

Examples

Minimal example together with fmin on a non-noisy function:

>>> import cma
>>> res = cma.fmin(cma.ff.elli, 7 * [1], 1, noise_handler=cma.NoiseHandler(7))  #doctest: +ELLIPSIS
(4_w,9)-aCMA-ES (mu_w=2.8,...
>>> assert res[1] < 1e-8
>>> res = cma.fmin(cma.ff.elli, 6 * [1], 1, {'AdaptSigma':cma.sigma_adaptation.CMAAdaptSigmaTPA},
...          noise_handler=cma.NoiseHandler(6))  #doctest: +ELLIPSIS
(4_w,...
>>> assert res[1] < 1e-8

in dimension 7 (which needs to be given tice). More verbose example in the optimization loop with a noisy function defined in func:

>>> import cma, numpy as np
>>> func = lambda x: cma.ff.sphere(x) * (1 + 4 * np.random.randn() / len(x))  # cma.ff.noisysphere
>>> es = cma.CMAEvolutionStrategy(np.ones(10), 1)  #doctest: +ELLIPSIS
(5_w,10)-aCMA-ES (mu_w=3.2,...
>>> nh = cma.NoiseHandler(es.N, maxevals=[1, 1, 30])
>>> while not es.stop():
...     X, fit_vals = es.ask_and_eval(func, evaluations=nh.evaluations)
...     es.tell(X, fit_vals)  # prepare for next iteration
...     es.sigma *= nh(X, fit_vals, func, es.ask)  # see method __call__
...     es.countevals += nh.evaluations_just_done  # this is a hack, not important though
...     es.logger.add(more_data = [nh.evaluations, nh.noiseS])  # add a data point
...     es.disp()
...     # nh.maxevals = ...  it might be useful to start with smaller values and then increase
...                # doctest: +ELLIPSIS
Iterat...
>>> print(es.stop())
...                # doctest: +ELLIPSIS
{...
>>> print(es.result[-2])  # take mean value, the best solution is totally off
...                # doctest: +ELLIPSIS
[...
>>> assert sum(es.result[-2]**2) < 1e-9
>>> print(X[np.argmin(fit_vals)])  # not bad, but probably worse than the mean
...                # doctest: +ELLIPSIS
[...

>>> # es.logger.plot()

The command logger.plot() will plot the logged data.

The noise options of fmin` control a NoiseHandler instance similar to this example. The command cma.CMAOptions('noise') lists in effect the parameters of __init__ apart from aggregate.

Details

The parameters reevals, theta, c_s, and alpha_t are set differently than in the original publication, see method __init__. For a very small population size, say popsize <= 5, the measurement technique based on rank changes is likely to fail.

Missing Features

In case no noise is found, self.lam_reeval should be adaptive and get at least as low as 1 (however the possible savings from this are rather limited). Another option might be to decide during the first call by a quantitative analysis of fitness values whether lam_reeval is set to zero. More generally, an automatic noise mode detection might also set the covariance matrix learning rates to smaller values.