def __init__(self, transform: tp.Optional[str] = None) -> None:
super().__init__(self._get_pixel_value, p.Instrumentation(p.Scalar(), p.Scalar()).set_name("standard"))
self.register_initialization(transform=transform)
self._image = datasets.get_data("Landscape")
if transform == "gaussian":
variables = list(p.TransitionChoice(list(range(x))) for x in self._image.shape)
self.parametrization = p.Instrumentation(*variables).set_name("gaussian")
elif transform == "square":
stds = (np.array(self._image.shape) - 1.) / 2.
variables2 = list(p.Scalar(init=s).set_mutation(sigma=s) for s in stds)
self.parametrization = p.Instrumentation(*variables2).set_name("square") # maybe buggy, try again?
elif transform is not None:
raise ValueError(f"Unknown transform {transform}")
self._max = float(self._image.max())
testing.printed_assert_equal(args, [0])
testing.printed_assert_equal(kwargs, {"y": 0})
arg_sum, kwarg_sum = 0, 0
for _ in range(24):
args, kwargs = ifunc.parametrization.spawn_child(
).set_standardized_data(data, deterministic=False).value
arg_sum += args[0]
kwarg_sum += kwargs["y"]
assert arg_sum != 0
assert kwarg_sum != 0
@testing.parametrized(
floats=((p.Scalar(), p.Scalar(init=12.0)), True, False),
array_int=((p.Scalar(), p.Array(shape=(1, )).set_integer_casting()), False,
False),
softmax_noisy=((p.Choice(["blue",
"red"]), p.Array(shape=(1, ))), True, True),
softmax_deterministic=((p.Choice(["blue", "red"], deterministic=True),
p.Array(shape=(1, ))), False, False),
ordered_discrete=((p.TransitionChoice([True, False]),
p.Array(shape=(1, ))), False, False),
)
def test_parametrization_continuous_noisy(variables: tp.Tuple[p.Parameter,
...],
continuous: bool,
noisy: bool) -> None:
instru = p.Instrumentation(*variables)
assert instru.descriptors.continuous == continuous
assert instru.descriptors.deterministic != noisy
def test_deterministic_data_setter() -> None:
instru = p.Instrumentation(p.Choice([0, 1, 2, 3]), y=p.Choice([0, 1, 2, 3]))
ifunc = base.ExperimentFunction(_Callable(), instru)
data = [0.01, 0, 0, 0, 0.01, 0, 0, 0]
for _ in range(20):
args, kwargs = ifunc.parametrization.spawn_child().set_standardized_data(data, deterministic=True).value
testing.printed_assert_equal(args, [0])
testing.printed_assert_equal(kwargs, {"y": 0})
arg_sum, kwarg_sum = 0, 0
for _ in range(24):
args, kwargs = ifunc.parametrization.spawn_child().set_standardized_data(data, deterministic=False).value
arg_sum += args[0]
kwarg_sum += kwargs["y"]
assert arg_sum != 0
assert kwarg_sum != 0
@testing.parametrized(
floats=((p.Scalar(), p.Scalar(init=12.0)), True, False),
array_int=((p.Scalar(), p.Array(shape=(1,)).set_integer_casting()), False, False),
softmax_noisy=((p.Choice(["blue", "red"]), p.Array(shape=(1,))), True, True),
softmax_deterministic=((p.Choice(["blue", "red"], deterministic=True), p.Array(shape=(1,))), False, False),
ordered_discrete=((p.TransitionChoice([True, False]), p.Array(shape=(1,))), False, False),
)
def test_parametrization_continuous_noisy(variables: tp.Tuple[p.Parameter, ...], continuous: bool, noisy: bool) -> None:
instru = p.Instrumentation(*variables)
assert instru.descriptors.continuous == continuous
assert instru.descriptors.deterministic != noisy
