def test_transform_callback_log(self, *_):
parameters = [
RangeParameter(
name="x1",
parameter_type=ParameterType.FLOAT,
lower=1,
upper=3,
log_scale=True,
),
RangeParameter(
name="x2",
parameter_type=ParameterType.FLOAT,
lower=1,
upper=3,
log_scale=True,
),
]
search_space = SearchSpace(parameters=parameters)
exp = get_branin_experiment(with_batch=True, search_space=search_space)
gpei = TorchModelBridge(
experiment=exp,
data=exp.fetch_data(),
search_space=search_space,
model=BotorchModel(),
transforms=[Log],
torch_dtype=torch.double,
fit_out_of_design=True,
)
transformed = gpei._transform_callback([1.2, 2.5])
self.assertTrue(np.allclose(transformed, [1.2, 2.5]))
def test_transform_callback_int(self, *_):
exp = get_branin_experiment(with_batch=True)
data = get_branin_data(trial_indices=exp.trials)
parameters = [
RangeParameter(name="x1",
parameter_type=ParameterType.INT,
lower=1,
upper=10),
RangeParameter(name="x2",
parameter_type=ParameterType.INT,
lower=5,
upper=15),
]
gpei = TorchModelBridge(
experiment=exp,
data=data,
search_space=SearchSpace(parameters=parameters),
model=BotorchModel(),
transforms=[IntToFloat],
torch_dtype=torch.double,
fit_out_of_design=True,
)
transformed = gpei._transform_callback([5.4, 7.6])
self.assertTrue(np.allclose(transformed, [5, 8]))
np_mb = ArrayModelBridge(
experiment=exp,
data=exp.fetch_data(),
search_space=SearchSpace(parameters=parameters),
model=NumpyModel(),
transforms=[IntToFloat],
)
transformed = np_mb._transform_callback(np.array([5.4, 7.6]))
self.assertTrue(np.allclose(transformed, [5, 8]))
def test_transform_callback_int_log(self, *_):
exp = get_branin_experiment(with_batch=True)
parameters = [
RangeParameter(
name="x1",
parameter_type=ParameterType.INT,
lower=1,
upper=100,
log_scale=True,
),
RangeParameter(
name="x2",
parameter_type=ParameterType.INT,
lower=1,
upper=100,
log_scale=True,
),
]
gpei = TorchModelBridge(
experiment=exp,
data=exp.fetch_data(),
search_space=SearchSpace(parameters=parameters),
model=BotorchModel(),
transforms=[IntToFloat, Log],
torch_dtype=torch.double,
fit_out_of_design=True,
)
transformed = gpei._transform_callback([0.5, 1.5])
self.assertTrue(np.allclose(transformed, [0.47712, 1.50515]))
def test_transform_callback_unitx(self, *_):
exp = get_branin_experiment(with_batch=True)
parameters = [
RangeParameter(name="x1",
parameter_type=ParameterType.FLOAT,
lower=0,
upper=10),
RangeParameter(name="x2",
parameter_type=ParameterType.FLOAT,
lower=0,
upper=100),
]
gpei = TorchModelBridge(
experiment=exp,
data=exp.fetch_data(),
search_space=SearchSpace(parameters=parameters),
model=BotorchModel(),
transforms=[UnitX],
)
transformed = gpei._transform_callback([0.75, 0.35])
self.assertTrue(np.allclose(transformed, [0.75, 0.35]))
def test_transform_callback_int(self, _):
exp = get_branin_experiment()
parameters = [
RangeParameter(name="x1",
parameter_type=ParameterType.INT,
lower=1,
upper=10),
RangeParameter(name="x2",
parameter_type=ParameterType.INT,
lower=5,
upper=15),
]
gpei = TorchModelBridge(
experiment=exp,
data=exp.fetch_data(),
search_space=SearchSpace(parameters=parameters),
model=BotorchModel(),
transforms=[IntToFloat],
torch_dtype=torch.double,
)
transformed = gpei._transform_callback([5.4, 7.6])
self.assertTrue(np.allclose(transformed, [5, 8]))
