def test_categorical_with_weights(self):
rs = np.random.RandomState()
cat_hp_str = CategoricalHyperparameter(
name="param",
choices=["A", "B", "C"],
default_value="A",
weights=[0.1, 0.6, 0.3]
)
for _ in range(1000):
self.assertIn(member=cat_hp_str.sample(rs), container=["A", "B", "C"])
cat_hp_int = CategoricalHyperparameter(
name="param",
choices=[1, 2, 3],
default_value=2,
weights=[0.1, 0.3, 0.6]
)
for _ in range(1000):
self.assertIn(member=cat_hp_int.sample(rs), container=[1, 3, 2])
cat_hp_float = CategoricalHyperparameter(
name="param",
choices=[-0.1, 0.0, 0.3],
default_value=0.3,
weights=[10, 60, 30]
)
for _ in range(1000):
self.assertIn(member=cat_hp_float.sample(rs), container=[-0.1, 0.0, 0.3])
def test_categorical_with_some_zero_weights(self):
# zero weights are okay as long as there is at least one strictly positive weight
rs = np.random.RandomState()
cat_hp_str = CategoricalHyperparameter(name="param",
choices=["A", "B", "C"],
default_value="A",
weights=[0.1, 0.0, 0.3])
for _ in range(1000):
self.assertIn(member=cat_hp_str.sample(rs), container=["A", "C"])
np.testing.assert_almost_equal(actual=cat_hp_str.probabilities,
desired=[0.25, 0., 0.75],
decimal=3)
cat_hp_int = CategoricalHyperparameter(name="param",
choices=[1, 2, 3],
default_value=2,
weights=[0.1, 0.6, 0.0])
for _ in range(1000):
self.assertIn(member=cat_hp_int.sample(rs), container=[1, 2])
np.testing.assert_almost_equal(actual=cat_hp_int.probabilities,
desired=[0.1429, 0.8571, 0.0],
decimal=3)
cat_hp_float = CategoricalHyperparameter(name="param",
choices=[-0.1, 0.0, 0.3],
default_value=0.3,
weights=[0.0, 0.6, 0.3])
for _ in range(1000):
self.assertIn(member=cat_hp_float.sample(rs), container=[0.0, 0.3])
np.testing.assert_almost_equal(actual=cat_hp_float.probabilities,
desired=[0.00, 0.6667, 0.3333],
decimal=3)
