Example for using a custom BoTorch test function in a continuous searchspace¶

This example shows how an arbitrary python function can be used as lookup.

This example assumes some basic familiarity with using BayBE. We thus refer to campaign for a basic example.

Necessary imports¶

from baybe import Campaign
from baybe.objective import Objective
from baybe.parameters import NumericalContinuousParameter
from baybe.searchspace import SearchSpace
from baybe.targets import NumericalTarget

Defining the custom test function¶

The function should accept an arbitrary or fixed amount of floats as input. It needs to return either a single float or a tuple of floats. It is assumed that the analytical test function does only perform a single calculation. That is, it is assumed to work in a non-batched-way!

In this example, we implement a simple sum of squares function with a single output.

def sum_of_squares(*x: float) -> float:
    """Calculate the sum of squares."""
    res = 0
    for y in x:
        res += y**2
    return res

TEST_FUNCTION = sum_of_squares

For our actual experiment, we need to specify the number of dimension that we want to use. This is necessary to know for the creation of the parameters. Similarly, it is necessary to state the bounds of the parameters. These should be provided as a list of two-dimensional tuples.

DIMENSION = 4
BOUNDS = [(-2, 2), (-2, 2), (-2, 2), (-2, 2)]

Creating the searchspace and the objective¶

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

searchspace = SearchSpace.from_product(parameters=parameters)

objective = Objective(
    mode="SINGLE", targets=[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(TEST_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  1.827542 -1.966140 -0.510564  0.694022  7.947958
1 -0.544410 -0.030319  0.701851 -0.012472  0.790052
2 -1.826142 -1.407428 -1.116854  1.625259  9.204478