Example for using a synthetic BoTorch test function in a continuous searchspace

Example for using the synthetic test functions in a continuous spaces. All test functions that are available in BoTorch are also available here and wrapped via the botorch_function_wrapper.

This example assumes some basic familiarity with using BayBE. We thus refer to campaign for a basic example. Also, there is a large overlap with other examples with regards to using the test function. We thus refer to discrete_space for details on this aspect.

Necessary imports for this example

from botorch.test_functions import Rastrigin

from baybe import Campaign
from baybe.objectives import SingleTargetObjective
from baybe.parameters import NumericalContinuousParameter
from baybe.searchspace import SearchSpace
from baybe.targets import NumericalTarget
from baybe.utils.botorch_wrapper import botorch_function_wrapper

Defining the test function

See discrete_space for details.

DIMENSION = 4
TestFunctionClass = Rastrigin

if not hasattr(TestFunctionClass, "dim"):
    TestFunction = TestFunctionClass(dim=DIMENSION)
elif TestFunctionClass().dim == DIMENSION:
    TestFunction = TestFunctionClass()
else:
    print(
        f"\nYou choose a dimension of {DIMENSION} for the test function"
        f"{TestFunctionClass}. However, this function can only be used in "
        f"{TestFunctionClass().dim} dimension, so the provided dimension is replaced. "
        "Also, DISC_INDICES and CONT_INDICES will be re-written."
    )
    TestFunction = TestFunctionClass()
    DIMENSION = TestFunctionClass().dim
    DISC_INDICES = list(range(0, (DIMENSION + 1) // 2))
    CONT_INDICES = list(range((DIMENSION + 1) // 2, DIMENSION))

BOUNDS = TestFunction.bounds
WRAPPED_FUNCTION = botorch_function_wrapper(test_function=TestFunction)

Creating the searchspace and the objective

Since the searchspace is continuous, we use NumericalContinuousParameters. We use the data of the test function to deduce bounds and number of parameters.

parameters = [
    NumericalContinuousParameter(
        name=f"x_{k+1}",
        bounds=(BOUNDS[0, k], BOUNDS[1, k]),
    )
    for k in range(DIMENSION)
]

searchspace = SearchSpace.from_product(parameters=parameters)
objective = SingleTargetObjective(target=NumericalTarget(name="Target", mode="MIN"))

Constructing the campaign and performing a recommendation

campaign = Campaign(
    searchspace=searchspace,
    objective=objective,
)

Get a recommendation for a fixed batch size.

BATCH_SIZE = 3
recommendation = campaign.recommend(batch_size=BATCH_SIZE)

Evaluate the test function. Note that we need iterate through the rows of the recommendation. Furthermore, we need to interpret the row as a list.

target_values = []
for index, row in recommendation.iterrows():
    target_values.append(WRAPPED_FUNCTION(*row.to_list()))

We add an additional column with the calculated target values.

recommendation["Target"] = target_values

Here, we inform the campaign about our measurement.

campaign.add_measurements(recommendation)
print("\n\nRecommended experiments with measured values: ")
print(recommendation)

Recommended experiments with measured values: 
        x_1       x_2       x_3       x_4      Target
0 -0.264446 -4.522893  3.674496 -4.295241  110.653259
1  2.178597  0.647430 -4.419142 -2.493019   91.307121
2 -5.090438 -1.109709  2.807990  4.399903   82.765709