def test_EI(self):
opt = SimpleBayesianOptimizer({"initial_random_runs": 3})
exp = Experiment("test", {"x": MinMaxNumericParamDef(0, 1)})
for i in range(10):
cand = opt.get_next_candidates(exp)[0]
assert_true(isinstance(cand, Candidate))
cand.result = 2
exp.add_finished(cand)
cands = opt.get_next_candidates(exp, num_candidates=3)
assert_equal(len(cands), 3)
def test_PoI(self):
opt = SimpleBayesianOptimizer({"initial_random_runs": 3, "acquisition": ProbabilityOfImprovement})
assert_true(isinstance(opt.acquisition_function, ProbabilityOfImprovement))
exp = Experiment("test", {"x": MinMaxNumericParamDef(0, 1)})
for i in range(10):
cand = opt.get_next_candidates(exp)[0]
assert_true(isinstance(cand, Candidate))
cand.result = 2
exp.add_finished(cand)
cands = opt.get_next_candidates(exp, num_candidates=3)
assert_equal(len(cands), 3)
def test_init(self):
#test default parameters
opt = SimpleBayesianOptimizer(None)
assert_equal(opt.initial_random_runs, 10)
assert_is_none(opt.acquisition_hyperparams)
assert_equal(opt.num_gp_restarts, 10)
assert_true(isinstance(opt.acquisition_function, ExpectedImprovement))
assert_dict_equal(opt.kernel_params, {})
assert_equal(opt.kernel, "matern52")
assert_false(opt.mcmc)
assert_equal(opt.num_precomputed, 10)
#test correct initialization
opt_arguments = {
"initial_random_runs": 5,
"acquisition_hyperparams": {},
"num_gp_restarts": 5,
"acquisition": ProbabilityOfImprovement,
"kernel_params": {},
"kernel": "matern52",
"mcmc": True,
"num_precomputed": 5
}
opt = SimpleBayesianOptimizer(opt_arguments)
assert_equal(opt.initial_random_runs, 5)
assert_dict_equal(opt.acquisition_hyperparams, {})
assert_equal(opt.num_gp_restarts, 5)
assert_true(isinstance(opt.acquisition_function, ProbabilityOfImprovement))
assert_dict_equal(opt.kernel_params, {})
assert_equal(opt.kernel, "matern52")
if import_if_exists("pymcmc")[0]:
assert_true(opt.mcmc)
else:
assert_false(opt.mcmc)
assert_equal(opt.num_precomputed, 5)
