def generate(cls, conf):
"""
Generate list of model objects. One for each conf. to be tested.
"""
r = list()
degrees = conf.get("degree", 2)
epsilons = conf.get("epsilon", 0.1)
if "degree" in conf:
del conf["degree"]
if "epsilon" in conf:
del conf["epsilon"]
for degree in expand_parameters(degrees):
for e in expand_parameters(epsilons):
r.append(cls(degree=degree, epsilon=e, **conf))
return r
def prepare(self):
"""
Prepare experiment: Generate configurations to be simulated.
"""
self._lst_max_time_t = expand_parameters(self.conf.get("max_time_t"))
# pmodel
self._lst_pmodel = list()
for pmcls, pmconf in self._pmodel_cls_lst:
self._lst_pmodel += pmcls.generate(pmconf)
# selector(s)
self._lst_selector = list()
for scls, sconf in self._selector_cls_lst:
self._lst_selector += scls.generate(sconf)
# predictor
self._lst_predictor = list()
for pcls, pconf in self._predictor_cls_lst:
self._lst_predictor += pcls.generate(pconf)
# error metrics
self._lst_error = list()
for ecls, econf in self._error_cls_lst:
self._lst_error += ecls.generate(econf)
# plots
self._lst_plot = list()
for pcls, pconf in self._plot_cls_lst:
self._lst_plot += pcls.generate(pconf)
LOG.info("*** Prepared {}x{} configurations to be simulated.".format(
self.n_configs, self.conf.get("repetitions", 1)))
def generate(cls, conf):
"""
Generate list of model objects. One for each conf. to be tested.
"""
r = list()
# extract max_samples parameter because it is the X axis
conf_max_samples = conf.get("max_samples")
del conf["max_samples"]
del conf["name"] # no name in params (implicitly given by class)
# generate one object for each expanded parameter
for max_samples in expand_parameters(conf_max_samples):
r.append(cls(max_samples=max_samples, **conf))
return r