Batchrunner#

batchrunner for running a factorial experiment design over a model.

To take advantage of parallel execution of experiments, batch_run uses multiprocessing if number_processes is larger than 1. It is strongly advised to only run in parallel using a normal python file (so don’t try to do it in a jupyter notebook). Moreover, best practice when using multiprocessing is to put the code inside an if __name__ == '__main__': code black as shown below:

from mesa.batchrunner import batch_run

params = {"width": 10, "height": 10, "N": range(10, 500, 10)}

if __name__ == '__main__':
    results = batch_run(
        MoneyModel,
        parameters=params,
        iterations=5,
        max_steps=100,
        number_processes=None,
        data_collection_period=1,
        display_progress=True,
    )

batch_run(model_cls: type[Model], parameters: Mapping[str, Any | Iterable[Any]], number_processes: int | None = 1, iterations: int = 1, data_collection_period: int = -1, max_steps: int = 1000, display_progress: bool = True) → list[dict[str, Any]][source]#

Batch run a mesa model with a set of parameter values.

Parameters:

model_cls (Type[Model]) – The model class to batch-run
parameters (Mapping[str, Union[Any, Iterable[Any]]]) – Dictionary with model parameters over which to run the model. You can either pass single values or iterables.
number_processes (int, optional) – Number of processes used, by default 1. Set this to None if you want to use all CPUs.
iterations (int, optional) – Number of iterations for each parameter combination, by default 1
data_collection_period (int, optional) – Number of steps after which data gets collected, by default -1 (end of episode)
max_steps (int, optional) – Maximum number of model steps after which the model halts, by default 1000
display_progress (bool, optional) – Display batch run process, by default True

Returns:

List[Dict[str, Any]]

Notes

batch_run assumes the model has a datacollector attribute that has a DataCollector object initialized.

Batchrunner#

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