baybe.simulation.scenarios.simulate_scenarios

baybe.simulation.scenarios.simulate_scenarios(scenarios: dict[Any, Campaign], lookup: DataFrame | Callable[[DataFrame], DataFrame] | None = None, /, *, batch_size: int = 1, n_doe_iterations: int | None = None, initial_data: list[DataFrame] | None = None, groupby: list[str] | None = None, n_mc_iterations: int = 1, random_seed: int | None = None, impute_mode: Literal['error', 'worst', 'best', 'mean', 'random', 'ignore'] = 'error', noise_percent: float | None = None)

Simulate multiple Bayesian optimization scenarios.

A wrapper function around baybe.simulation.core.simulate_experiment() that allows to specify multiple simulation settings at once.

Parameters:

• scenarios (dict[Any, Campaign]) – A dictionary mapping scenario identifiers to DOE specifications.

• lookup (Union[DataFrame, Callable[[DataFrame], DataFrame], None]) – See baybe.simulation.core.simulate_experiment().

• batch_size (int) – See baybe.simulation.core.simulate_experiment().

• n_doe_iterations (Optional[int]) – See baybe.simulation.core.simulate_experiment().

• initial_data (Optional[list[DataFrame]]) – A list of initial data sets for which the scenarios should be simulated.

• groupby (Optional[list[str]]) – The names of the parameters to be used to partition the search space. A separate simulation will be conducted for each partition, with the search restricted to that partition.

• n_mc_iterations (int) – The number of Monte Carlo simulations to be used.

• random_seed (Optional[int]) – An optional integer specifying the random seed for the first Monte Carlo run. Each subsequent runs will increase this value by 1. If omitted, the current random seed is used.

• impute_mode (Literal['error', 'worst', 'best', 'mean', 'random', 'ignore']) – See baybe.simulation.core.simulate_experiment().

• noise_percent (Optional[float]) – See baybe.simulation.core.simulate_experiment().

Return type:

DataFrame

Returns:

A dataframe like returned from baybe.simulation.core.simulate_experiment() but with additional columns. See the Note for details.

The following additional columns are contained in the dataframe returned by this function:

• Scenario: Specifies the scenario identifier of the respective simulation.

• Monte_Carlo_Run: Specifies the Monte Carlo repetition of the respective simulation.

• Optional, if random_seed is provided: A column Random_Seed that specifies the random seed used for the respective simulation.

• Optional, if initial_data is provided: A column Initial_Data that specifies the index of the initial data set used for the respective simulation.

• Optional, if groupby is provided: A column for each groupby parameter that specifies the search space partition considered for the respective simulation.