def test_Int():
rg = hp_module.Int("rg", min_value=5, max_value=9, step=1, default=6)
rg = hp_module.Int.from_config(rg.get_config())
assert rg.default == 6
assert 5 <= rg.random_sample() <= 9
assert isinstance(rg.random_sample(), int)
assert rg.random_sample(123) == rg.random_sample(123)
# No default
rg = hp_module.Int("rg", min_value=5, max_value=9, step=1)
assert rg.default == 5
def test_sampling_arg():
f = hp_module.Float("f", 1e-20, 1e10, sampling="log")
f = hp_module.Float.from_config(f.get_config())
assert f.sampling == "log"
i = hp_module.Int("i", 0, 10, sampling="linear")
i = hp_module.Int.from_config(i.get_config())
assert i.sampling == "linear"
with pytest.raises(ValueError, match="`sampling` must be one of"):
hp_module.Int("j", 0, 10, sampling="invalid")
with pytest.raises(
ValueError,
match="`sampling` `min_value` 1 is greater than the `max_value` 0",
):
hp_module.Int("k", 1, 0, sampling="linear")
with pytest.raises(
ValueError,
match="`sampling` `min_value` 1 is greater than the `max_value` 0",
):
hp_module.Int("k", 1, 0, sampling="linear")
def test_int_proto():
hp = hp_module.Int("a", 1, 100, sampling="log")
proto = hp.to_proto()
assert proto.name == "a"
assert proto.min_value == 1
assert proto.max_value == 100
assert proto.sampling == keras_tuner_pb2.Sampling.LOG
# Proto stores the implicit default.
assert proto.default == 1
assert proto.step == 1
new_hp = hp_module.Int.from_proto(proto)
assert new_hp._default == 1
# Pop the implicit default for comparison purposes.
new_hp._default = None
assert new_hp.get_config() == hp.get_config()