def test_Float():
# Test with step arg
linear = hp_module.Float("linear",
min_value=0.5,
max_value=9.5,
step=0.1,
default=9.0)
linear = hp_module.Float.from_config(linear.get_config())
assert linear.default == 9.0
assert 0.5 <= linear.random_sample() <= 9.5
assert isinstance(linear.random_sample(), float)
assert linear.random_sample(123) == linear.random_sample(123)
# Test without step arg
linear = hp_module.Float("linear",
min_value=0.5,
max_value=6.5,
default=2.0)
linear = hp_module.Float.from_config(linear.get_config())
assert linear.default == 2.0
assert 0.5 <= linear.random_sample() < 6.5
assert isinstance(linear.random_sample(), float)
assert linear.random_sample(123) == linear.random_sample(123)
# No default
linear = hp_module.Float("linear", min_value=0.5, max_value=9.5, step=0.1)
assert linear.default == 0.5
def test_sampling_zero_length_intervals():
f = hp_module.Float("f", 2, 2)
rand_sample = f.random_sample()
assert rand_sample == 2
val = 2
prob = hp_module.value_to_cumulative_prob(val, f)
assert prob == 1
def test_float_proto():
hp = hp_module.Float("a", -10, 10, sampling="linear", default=3)
proto = hp.to_proto()
assert proto.name == "a"
assert proto.min_value == -10.0
assert proto.max_value == 10.0
assert proto.sampling == keras_tuner_pb2.Sampling.LINEAR
assert proto.default == 3.0
# Zero is the default, gets converted to `None` in `from_proto`.
assert proto.step == 0.0
new_hp = hp_module.Float.from_proto(proto)
assert new_hp.get_config() == hp.get_config()
def test_reverse_log_sampling_random_state():
f = hp_module.Float("f", 1e-3, 1e3, sampling="reverse_log")
rand_sample = f.random_sample()
assert rand_sample >= f.min_value
assert rand_sample <= f.max_value
val = 1e-3
prob = hp_module.value_to_cumulative_prob(val, f)
assert prob == 0
new_val = hp_module.cumulative_prob_to_value(prob, f)
assert np.isclose(val, new_val)
val = 1
prob = hp_module.value_to_cumulative_prob(val, f)
assert prob > 0 and prob < 1
new_val = hp_module.cumulative_prob_to_value(prob, f)
assert np.isclose(val, new_val)
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")