def test_bayesopt_batch(parameters, results, transforms):
gpyopt = GPyOpt(max_concurrent=10)
domain = gpyopt._initialize_domain(parameters, transforms)
X, y = GPyOpt._prepare_data_for_bayes_opt(parameters, results, transforms)
batch = gpyopt._generate_bayesopt_batch(domain, X, y, lower_is_better=True)
assert batch.shape == (10, 5)
def test_reverse_format(parameters, results, transforms):
X, y = GPyOpt._prepare_data_for_bayes_opt(parameters, results, transforms)
reversed_X = GPyOpt._reverse_to_sherpa_format(X, transforms, parameters)
assert reversed_X[0] == {
'dropout': 0.1,
'lr': 1e-3,
'activation': 'tanh',
'num_hidden': 111,
'batch_size': 10
}
assert reversed_X[1] == {
'dropout': 0.4,
'lr': 1e-5,
'activation': 'relu',
'num_hidden': 222,
'batch_size': 100
}
assert reversed_X[2] == {
'dropout': 0.33,
'lr': 1e-2,
'activation': 'sigmoid',
'num_hidden': 288,
'batch_size': 1000
}
def test_prepare_data_for_bayes_opt(parameters, results):
X, y, y_var = GPyOpt._prepare_data_for_bayes_opt(parameters, results)
assert numpy.array_equal(
X,
numpy.array([[0.1, -3., 1, 111], [0.4, -5., 0, 222],
[0.33, -2., 2, 288]]))
assert numpy.array_equal(y, numpy.array([[0.1], [0.055], [0.15]]))
def test_bayesopt_batch(parameters, results):
gpyopt = GPyOpt(max_concurrent=10)
gpyopt.domain = gpyopt._initialize_domain(parameters)
gpyopt.lower_is_better = True
X, y, y_var = GPyOpt._prepare_data_for_bayes_opt(parameters, results)
domain = gpyopt._initialize_domain(parameters)
batch = gpyopt._generate_bayesopt_batch(X,
y,
lower_is_better=True,
domain=domain)
assert batch.shape == (10, 4)
def test_reverse_format(parameters, results):
X, y, y_var = GPyOpt._prepare_data_for_bayes_opt(parameters, results)
reversed_X = GPyOpt._reverse_to_sherpa_format(X, parameters)
assert reversed_X[0] == {
'dropout': 0.1,
'lr': 1e-3,
'activation': 'tanh',
'num_hidden': 111
}
assert reversed_X[1] == {
'dropout': 0.4,
'lr': 1e-5,
'activation': 'relu',
'num_hidden': 222
}
assert reversed_X[2] == {
'dropout': 0.33,
'lr': 1e-2,
'activation': 'sigmoid',
'num_hidden': 288
}
