def test_experimented_function() -> None:
ifunc = base.ExperimentFunction(
_arg_return,
Instrumentation( # type: ignore
var.SoftmaxCategorical([1, 12]),
"constant",
var.Gaussian(0, 1, [2, 2]),
constkwarg="blublu",
plop=var.SoftmaxCategorical([3, 4]),
))
np.testing.assert_equal(ifunc.dimension, 8)
data = [-100.0, 100, 1, 2, 3, 4, 100, -100]
args0, kwargs0 = ifunc.parametrization.data_to_arguments(np.array(data))
output = ifunc(
*args0, **kwargs0
) # this is very stupid and should be removed when Parameter is in use
args: tp.Any = output[0] # type: ignore
kwargs: tp.Any = output[1] # type: ignore
testing.printed_assert_equal(args, [12, "constant", [[1, 2], [3, 4]]])
testing.printed_assert_equal(kwargs, {"constkwarg": "blublu", "plop": 3})
instru_str = (
"Instrumentation(Tuple(SoftmaxCategorical(choices=Tuple(1,12),"
"weights=Array{(2,)}[recombination=average,sigma=1.0]),constant,G(0,1)),"
"Dict(constkwarg=blublu,plop=SoftmaxCategorical(choices=Tuple(3,4),"
"weights=Array{(2,)}[recombination=average,sigma=1.0])))")
testing.printed_assert_equal(
ifunc.descriptors,
{
"dimension": 8,
"name": "_arg_return",
"function_class": "ExperimentFunction",
"instrumentation": instru_str,
},
)
def test_experimented_function() -> None:
ifunc = base.ExperimentFunction(
_arg_return,
Instrumentation( # type: ignore
var.SoftmaxCategorical([1, 12]),
"constant",
var.Gaussian(0, 1, [2, 2]),
constkwarg="blublu",
plop=var.SoftmaxCategorical([3, 4]),
))
np.testing.assert_equal(ifunc.dimension, 8)
data = [-100.0, 100, 1, 2, 3, 4, 100, -100]
args0, kwargs0 = ifunc.parametrization.data_to_arguments(np.array(data))
output = ifunc(
*args0, **kwargs0
) # this is very stupid and should be removed when Parameter is in use
args: tp.Any = output[0] # type: ignore
kwargs: tp.Any = output[1] # type: ignore
testing.printed_assert_equal(args, [12, "constant", [[1, 2], [3, 4]]])
testing.printed_assert_equal(kwargs, {"constkwarg": "blublu", "plop": 3})
testing.printed_assert_equal(
ifunc.descriptors,
{
"dimension":
8,
"name":
"_arg_return",
"function_class":
"ExperimentFunction",
"instrumentation":
"SC(1,12|0),constant,G(0,1),constkwarg=blublu,plop=SC(3,4|0)",
},
)
def test_instrumented_function_kwarg_order() -> None:
ifunc = base.ExperimentFunction(
_arg_return,
Instrumentation( # type: ignore
kw4=var.SoftmaxCategorical([1, 0]),
kw2="constant",
kw3=var.Array(2, 2),
kw1=var.Gaussian(2, 2)))
np.testing.assert_equal(ifunc.dimension, 7)
data = np.array([-1, 1, 2, 3, 4, 100, -100])
args0, kwargs0 = ifunc.parametrization.data_to_arguments(data)
# this is very stupid and should be removed when Parameter is in use
kwargs: tp.Any = ifunc(*args0, **kwargs0)[1] # type: ignore
testing.printed_assert_equal(kwargs, {
"kw1": 0,
"kw2": "constant",
"kw3": [[1, 2], [3, 4]],
"kw4": 1
})
def test_callable_instrumentation() -> None:
ifunc = base.ExperimentFunction(lambda x: x**2,
var.Gaussian(2, 2)) # type: ignore
np.testing.assert_equal(ifunc.descriptors["name"], "<lambda>")
ifunc = base.ExperimentFunction(_Callable(), var.Gaussian(2, 2))
np.testing.assert_equal(ifunc.descriptors["name"], "_Callable")
args, kwargs = ifunc.parametrization.data_to_arguments(
data, deterministic=True)
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.data_to_arguments(
data, deterministic=False)
arg_sum += args[0]
kwarg_sum += kwargs["y"]
assert arg_sum != 0
assert kwarg_sum != 0
@testing.parametrized(
floats=((var.Gaussian(0, 1), var.Array(1).asscalar()), True, False),
array_int=((var.Gaussian(0, 1), var.Array(1).asscalar(int)), False, False),
softmax_noisy=((var.SoftmaxCategorical(["blue", "red"]), var.Array(1)),
True, True),
softmax_deterministic=((var.SoftmaxCategorical(["blue", "red"],
deterministic=True),
var.Array(1)), False, False),
ordered_discrete=((var.OrderedDiscrete([True, False]), var.Array(1)),
False, False),
)
def test_instrumentation_continuous_noisy(variables: tp.Tuple[var.Variable,
...],
continuous: bool,
noisy: bool) -> None:
instru = Instrumentation(*variables)
assert instru.continuous == continuous
