def test_discrete_no_values():
with pytest.raises(ValueError):
hp.DiscreteHyperParameter(None, [0, 1])
with pytest.raises(ValueError):
hp.DiscreteHyperParameter('h1', None)
with pytest.raises(ValueError):
hp.DiscreteHyperParameter('h2', [])
def test_discrete():
h1 = hp.DiscreteHyperParameter('h1', [0, 1])
assert h1.name == 'h1'
assert h1.num_choices == 2
assert 0 in h1.id2param.values()
assert h1.param2id[0] == 0
assert repr(h1)
def test_discrete_sample():
values = [1, 2, 3, 4, 5]
h1 = hp.DiscreteHyperParameter('h1', values)
sample = h1.sample()
assert sample in values
samples = np.array([h1.sample() for _ in range(100)])
_, counts = np.unique(samples, return_counts=True)
assert np.all(counts > 0)
def test_discrete_encode_decode():
values = [10, 11, 12, 13, 14]
h1 = hp.DiscreteHyperParameter('h1', values)
sample = h1.sample()
encoded = h1.encode(sample)
assert encoded == 4
decoded = h1.decode(encoded)
assert decoded == values[encoded]
def test_discrete_encode_decode():
values = [10, 11, 12, 13, 14]
h1 = hp.DiscreteHyperParameter('h1', values, seed=0)
sample = h1.sample()
encoded = h1.encode(sample)
assert encoded == 4
decoded = h1.decode(encoded)
assert decoded == values[encoded]
# Test for None input
values = [None, 1, 2, 3]
h2 = hp.DiscreteHyperParameter('h1', values, seed=0)
sample = None
encoded = h2.encode(sample)
assert encoded == 0
decoded = h2.decode(encoded)
assert decoded == values[encoded]
def test_discrete_serialization_deserialization():
h1 = hp.DiscreteHyperParameter('h1', [0, 1, None])
config = h1.get_config()
assert 'name' in config
assert 'values' in config
values = config['values']
assert len(values) == 3
h2 = hp.DiscreteHyperParameter.load_from_config(config)
config = h2.get_config()
assert 'name' in config
assert 'values' in config
values = config['values']
assert len(values) == 3
def test_evaluate_train_evaluate_failure():
params = [hp.DiscreteHyperParameter('h%d' % i, [0]) for i in range(3)]
h = hp.HyperParameterList(params)
dataset = data.Dataset(h)
# models
clfs = []
# fit samples
num_samples = 16
for i in range(3):
samples = [h.sample() for _ in range(num_samples)]
labels = [np.sum(sample) for sample in samples]
x, y = samples, labels
x, y = dataset.encode_dataset(x, y)
model = xgb_utils.train_single_model(x, y)
clfs.append(model)
# test samples
for model in clfs:
assert model is None
def get_hyperparameter_list():
h1 = hp.DiscreteHyperParameter('h1', [0, 1, 2])
h2 = hp.DiscreteHyperParameter('h2', [3, 4, 5, 6])
h3 = hp.UniformContinuousHyperParameter('h3', 7, 10)
h4 = hp.DiscreteHyperParameter('h4', ['v1', 'v2'])
return [h1, h2, h3, h4]
