AdaptiveGreedy(deepcopy(base_algorithm),
nchoices=nchoices,
decay_type='threshold'),
AdaptiveGreedy(deepcopy(base_algorithm),
nchoices=nchoices,
beta_prior=beta_prior,
decay_type='percentile',
decay=0.9997),
AdaptiveGreedy(deepcopy(base_algorithm),
nchoices=nchoices,
beta_prior=beta_prior,
active_choice='weighted',
decay_type='percentile',
decay=0.9997),
BootstrappedTS(deepcopy(base_algorithm),
nchoices=nchoices,
beta_prior=beta_prior),
BootstrappedUCB(deepcopy(base_algorithm),
nchoices=nchoices,
beta_prior=beta_prior),
SoftmaxExplorer(deepcopy(base_algorithm),
nchoices=nchoices,
beta_prior=beta_prior)
]
model_names_opts = [
"adaptive_greedy_thres", "adaptive_greedy_perc",
"adaptive_active_greedy", "bootstrapped_ts", "bootstrapped_ucb",
"softmax"
]
'''
X, y = parse_data("loc_9.txt")
nchoices = y.shape[1]
base_algorithm = SGDClassifier(random_state=123, loss='log')
beta_prior = (
(3, 7), 2
) # until there are at least 2 observations of each class, will use prior Beta(3, 7)
## 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),