Example for full simulation loop using the multi target mode for custom analytic¶

functions

This example shows how to use a multi target objective for a custom analytic function. It uses a desirability value to handle several targets.

This example assumes basic familiarity with BayBE, custom test functions and multiple targets. For further details, we thus refer to

campaign for a more general and basic example,
custom_analytical for custom test functions, and
desirability for multiple targets.

Necessary imports for this example¶

import os

import numpy as np

from baybe import Campaign
from baybe.objectives import DesirabilityObjective
from baybe.parameters import NumericalDiscreteParameter
from baybe.searchspace import SearchSpace
from baybe.simulation import simulate_scenarios
from baybe.targets import NumericalTarget

Parameters for a full simulation loop¶

For the full simulation, we need to define some additional parameters. These are the number of Monte Carlo runs and the number of experiments to be conducted per run.

SMOKE_TEST = "SMOKE_TEST" in os.environ

N_MC_ITERATIONS = 2 if SMOKE_TEST else 5
N_DOE_ITERATIONS = 2 if SMOKE_TEST else 4
BATCH_SIZE = 1 if SMOKE_TEST else 2
DIMENSION = 4
BOUNDS = [(-2, 2), (-2, 2), (-2, 2), (-2, 2)]
POINTS_PER_DIM = 3 if SMOKE_TEST else 10

Defining the test function¶

See custom_analytical for details.

def sum_of_squares(*x: float) -> tuple[float, float]:
    """Calculate the sum of squares."""
    res = 0
    for y in x:
        res += y**2
    return res, 2 * res**2 - 1

Creating the searchspace¶

In this example, we construct a purely discrete space with 10 points per dimension.

parameters = [
    NumericalDiscreteParameter(
        name=f"x_{k+1}",
        values=list(np.linspace(*BOUNDS[k], POINTS_PER_DIM)),
        tolerance=0.01,
    )
    for k in range(DIMENSION)
]

searchspace = SearchSpace.from_product(parameters=parameters)

Creating multiple target object¶

The multi target mode is handled when creating the objective object. Thus we first need to define the different targets. We use two targets here. The first target is maximized and the second target is minimized during the optimization process.

Target_1 = NumericalTarget(
    name="Target_1", mode="MAX", bounds=(0, 100), transformation="LINEAR"
)
Target_2 = NumericalTarget(
    name="Target_2", mode="MIN", bounds=(0, 100), transformation="LINEAR"
)

Creating the objective object¶

We collect the two targets in a list and use this list to construct the objective.

targets = [Target_1, Target_2]

objective = DesirabilityObjective(
    targets=targets,
    weights=[20, 30],
    scalarizer="MEAN",
)

Constructing a campaign and performing the simulation loop¶

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

We can now use the simulate_scenarios function to simulate a full experiment.

scenarios = {"BayBE": campaign}

results = simulate_scenarios(
    scenarios,
    sum_of_squares,
    batch_size=BATCH_SIZE,
    n_doe_iterations=N_DOE_ITERATIONS,
    n_mc_iterations=N_MC_ITERATIONS,
)

print(results)

  Scenario  Monte_Carlo_Run  Iteration  Num_Experiments Target_1_Measurements  \
  BayBE                0          0                1                [12.0]   
  BayBE                0          1                2                [16.0]   
  BayBE                1          0                1                [16.0]   
  BayBE                1          1                2                [16.0]   

  Target_2_Measurements  Target_1_IterBest  Target_1_CumBest  \
             [287.0]               12.0              12.0   
             [511.0]               16.0              16.0   
             [511.0]               16.0              16.0   
             [511.0]               16.0              16.0   

   Target_2_IterBest  Target_2_CumBest  
            287.0             287.0  
            511.0             287.0  
            511.0             511.0  
            511.0             511.0  