def _create_algorithm(self, base_algorithm, batch_train: bool, **kwargs):
if self._algorithm_name == 'egreedy':
self._algorithm = EpsilonGreedy(
base_algorithm,
nchoices=self._K,
random_state=self._rnd.randint(1000),
explore_prob=kwargs.get('epsilon', 0.1),
batch_train=batch_train
)
elif self._algorithm_name == 'bootstrapped_ucb':
self._algorithm = BootstrappedUCB(
base_algorithm,
nchoices=self._K,
random_state=self._rnd.randint(1000),
batch_train=batch_train
)
else:
sys.exit("no such algorithm: %s" % algorithm)
## The base algorithm is embedded in different metaheuristics
bootstrapped_ucb = BootstrappedUCB(deepcopy(base_algorithm),
nchoices=nchoices,
beta_prior=beta_prior,
batch_train=True)
bootstrapped_ts = BootstrappedTS(deepcopy(base_algorithm),
nchoices=nchoices,
beta_prior=beta_prior,
batch_train=True)
one_vs_rest = SeparateClassifiers(deepcopy(base_algorithm),
nchoices=nchoices,
beta_prior=beta_prior,
batch_train=True)
epsilon_greedy = EpsilonGreedy(deepcopy(base_algorithm),
nchoices=nchoices,
beta_prior=beta_prior,
batch_train=True)
epsilon_greedy_nodecay = EpsilonGreedy(deepcopy(base_algorithm),
nchoices=nchoices,
beta_prior=beta_prior,
decay=None,
batch_train=True)
adaptive_greedy_thr = AdaptiveGreedy(deepcopy(base_algorithm),
nchoices=nchoices,
decay_type='threshold',
batch_train=True)
adaptive_greedy_perc = AdaptiveGreedy(deepcopy(base_algorithm),
nchoices=nchoices,
beta_prior=beta_prior,
decay_type='percentile',
decay=0.9997,
decay_type='percentile',
decay=0.9997,
batch_train=True,
random_state=4444)
active_explorer = ActiveExplorer(deepcopy(base_sgd),
smoothing=None,
nchoices=nchoices,
beta_prior=((3. / nchoices, 4.), 2),
batch_train=True,
refit_buffer=50,
deep_copy_buffer=False,
random_state=5555)
epsilon_greedy_nodecay = EpsilonGreedy(deepcopy(base_ols),
nchoices=nchoices,
smoothing=(1, 2),
beta_prior=None,
decay=None,
batch_train=True,
deep_copy_buffer=False,
random_state=6666)
models = [
linucb, adaptive_active_greedy, softmax_explorer, adaptive_greedy_perc,
active_explorer, epsilon_greedy_nodecay
]
# These lists will keep track of the rewards obtained by each policy
rewards_lucb, rewards_aac, rewards_sft, rewards_agr, \
rewards_ac, rewards_egr = [list() for i in range(len(models))]
lst_rewards = [
rewards_lucb, rewards_aac, rewards_sft, rewards_agr, rewards_ac,
