def setUp(self):
self.search_space = SearchSpace(
parameters=[
RangeParameter(
"x",
lower=1,
upper=3,
parameter_type=ParameterType.FLOAT,
),
RangeParameter("a", lower=1, upper=2, parameter_type=ParameterType.INT),
ChoiceParameter(
"b", parameter_type=ParameterType.STRING, values=["a", "b", "c"]
),
ChoiceParameter(
"c",
parameter_type=ParameterType.BOOL,
values=[True, False],
is_ordered=False,
),
ChoiceParameter(
"d",
parameter_type=ParameterType.FLOAT,
values=[1.0, 10.0, 100.0],
is_ordered=True,
),
],
parameter_constraints=[
ParameterConstraint(constraint_dict={"x": -0.5, "a": 1}, bound=0.5)
],
)
self.t = OneHot(
search_space=self.search_space,
observation_features=None,
observation_data=None,
)
self.t2 = OneHot(
search_space=self.search_space,
observation_features=None,
observation_data=None,
config={"rounding": "randomized"},
)
self.transformed_features = ObservationFeatures(
parameters={
"x": 2.2,
"a": 2,
"b" + OH_PARAM_INFIX + "_0": 0,
"b" + OH_PARAM_INFIX + "_1": 1,
"b" + OH_PARAM_INFIX + "_2": 0,
# Only two choices => one parameter.
"c" + OH_PARAM_INFIX: 0,
"d": 10.0,
}
)
self.observation_features = ObservationFeatures(
parameters={"x": 2.2, "a": 2, "b": "b", "c": False, "d": 10.0}
)
def testTransformSearchSpace(self):
ss2 = deepcopy(self.search_space)
ss2 = self.t.transform_search_space(ss2)
# Parameter type fixed.
self.assertEqual(ss2.parameters["x"].parameter_type,
ParameterType.FLOAT)
self.assertEqual(ss2.parameters["a"].parameter_type, ParameterType.INT)
self.assertEqual(
ss2.parameters["b" + OH_PARAM_INFIX + "_0"].parameter_type,
ParameterType.FLOAT,
)
self.assertEqual(
ss2.parameters["b" + OH_PARAM_INFIX + "_1"].parameter_type,
ParameterType.FLOAT,
)
self.assertEqual(ss2.parameters["c" + OH_PARAM_INFIX].parameter_type,
ParameterType.FLOAT)
self.assertEqual(ss2.parameters["d"].parameter_type,
ParameterType.FLOAT)
# Parameter range fixed to [0,1].
self.assertEqual(ss2.parameters["b" + OH_PARAM_INFIX + "_0"].lower,
0.0)
self.assertEqual(ss2.parameters["b" + OH_PARAM_INFIX + "_1"].upper,
1.0)
self.assertEqual(ss2.parameters["c" + OH_PARAM_INFIX].lower, 0.0)
self.assertEqual(ss2.parameters["c" + OH_PARAM_INFIX].upper, 1.0)
# Ensure we error if we try to transform a fidelity parameter
ss3 = SearchSpace(parameters=[
ChoiceParameter(
"b",
parameter_type=ParameterType.STRING,
values=["a", "b", "c"],
is_fidelity=True,
target_value="c",
)
])
t = OneHot(search_space=ss3,
observation_features=None,
observation_data=None)
with self.assertRaises(ValueError):
t.transform_search_space(ss3)
class OneHotTransformTest(TestCase):
def setUp(self):
self.search_space = SearchSpace(
parameters=[
RangeParameter("x",
lower=1,
upper=3,
parameter_type=ParameterType.FLOAT),
RangeParameter("a",
lower=1,
upper=2,
parameter_type=ParameterType.INT),
ChoiceParameter("b",
parameter_type=ParameterType.STRING,
values=["a", "b", "c"]),
ChoiceParameter("c",
parameter_type=ParameterType.BOOL,
values=[True, False]),
ChoiceParameter(
"d",
parameter_type=ParameterType.FLOAT,
values=[1.0, 10.0, 100.0],
is_ordered=True,
),
],
parameter_constraints=[
ParameterConstraint(constraint_dict={
"x": -0.5,
"a": 1
},
bound=0.5)
],
)
self.t = OneHot(
search_space=self.search_space,
observation_features=None,
observation_data=None,
)
self.t2 = OneHot(
search_space=self.search_space,
observation_features=None,
observation_data=None,
config={"rounding": "randomized"},
)
self.transformed_features = ObservationFeatures(
parameters={
"x": 2.2,
"a": 2,
"b" + OH_PARAM_INFIX + "_0": 0,
"b" + OH_PARAM_INFIX + "_1": 1,
"b" + OH_PARAM_INFIX + "_2": 0,
# Only two choices => one parameter.
"c" + OH_PARAM_INFIX: 0,
"d": 10.0,
})
self.observation_features = ObservationFeatures(parameters={
"x": 2.2,
"a": 2,
"b": "b",
"c": False,
"d": 10.0
})
def testInit(self):
self.assertEqual(list(self.t.encoded_parameters.keys()), ["b", "c"])
self.assertEqual(list(self.t2.encoded_parameters.keys()), ["b", "c"])
def testTransformObservationFeatures(self):
observation_features = [self.observation_features]
obs_ft2 = deepcopy(observation_features)
obs_ft2 = self.t.transform_observation_features(obs_ft2)
self.assertEqual(obs_ft2, [self.transformed_features])
obs_ft2 = self.t.untransform_observation_features(obs_ft2)
self.assertEqual(obs_ft2, observation_features)
# Test partial transform
obs_ft3 = [ObservationFeatures(parameters={"x": 2.2, "b": "b"})]
obs_ft3 = self.t.transform_observation_features(obs_ft3)
self.assertEqual(
obs_ft3[0],
ObservationFeatures(
parameters={
"x": 2.2,
"b" + OH_PARAM_INFIX + "_0": 0,
"b" + OH_PARAM_INFIX + "_1": 1,
"b" + OH_PARAM_INFIX + "_2": 0,
}),
)
obs_ft5 = self.t.transform_observation_features(
[ObservationFeatures({})])
self.assertEqual(obs_ft5[0], ObservationFeatures({}))
def testRandomizedTransform(self):
observation_features = [self.observation_features]
obs_ft2 = deepcopy(observation_features)
obs_ft2 = self.t2.transform_observation_features(obs_ft2)
self.assertEqual(obs_ft2, [self.transformed_features])
obs_ft2 = self.t2.untransform_observation_features(obs_ft2)
self.assertEqual(obs_ft2, observation_features)
def testTransformSearchSpace(self):
ss2 = deepcopy(self.search_space)
ss2 = self.t.transform_search_space(ss2)
# Parameter type fixed.
self.assertEqual(ss2.parameters["x"].parameter_type,
ParameterType.FLOAT)
self.assertEqual(ss2.parameters["a"].parameter_type, ParameterType.INT)
self.assertEqual(
ss2.parameters["b" + OH_PARAM_INFIX + "_0"].parameter_type,
ParameterType.FLOAT,
)
self.assertEqual(
ss2.parameters["b" + OH_PARAM_INFIX + "_1"].parameter_type,
ParameterType.FLOAT,
)
self.assertEqual(ss2.parameters["c" + OH_PARAM_INFIX].parameter_type,
ParameterType.FLOAT)
self.assertEqual(ss2.parameters["d"].parameter_type,
ParameterType.FLOAT)
# Parameter range fixed to [0,1].
self.assertEqual(ss2.parameters["b" + OH_PARAM_INFIX + "_0"].lower,
0.0)
self.assertEqual(ss2.parameters["b" + OH_PARAM_INFIX + "_1"].upper,
1.0)
self.assertEqual(ss2.parameters["c" + OH_PARAM_INFIX].lower, 0.0)
self.assertEqual(ss2.parameters["c" + OH_PARAM_INFIX].upper, 1.0)