adaptive.BlockingRunner#

class adaptive.BlockingRunner(learner: LearnerType, goal: Callable[[LearnerType], bool] | None = None, *, loss_goal: float | None = None, npoints_goal: int | None = None, end_time_goal: datetime | None = None, duration_goal: timedelta | int | float | None = None, executor: ExecutorTypes | None = None, ntasks: int | None = None, log: bool = False, shutdown_executor: bool = False, retries: int = 0, raise_if_retries_exceeded: bool = True)[source]#

Bases: BaseRunner

Run a learner synchronously in an executor.

Parameters:

  • learner (BaseLearner instance) –

  • goal (callable, optional) – The end condition for the calculation. This function must take the learner as its sole argument, and return True when we should stop requesting more points.

  • loss_goal (float, optional) – Convenience argument, use instead of goal. The end condition for the calculation. Stop when the loss is smaller than this value.

  • npoints_goal (int, optional) – Convenience argument, use instead of goal. The end condition for the calculation. Stop when the number of points is larger or equal than this value.

  • end_time_goal (datetime, optional) – Convenience argument, use instead of goal. The end condition for the calculation. Stop when the current time is larger or equal than this value.

  • duration_goal (timedelta or number, optional) – Convenience argument, use instead of goal. The end condition for the calculation. Stop when the current time is larger or equal than start_time + duration_goal. duration_goal can be a number indicating the number of seconds.

  • executor (concurrent.futures.Executor, distributed.Client, mpi4py.futures.MPIPoolExecutor, ipyparallel.Client or loky.get_reusable_executor, optional) – The executor in which to evaluate the function to be learned. If not provided, a new loky.get_reusable_executor is used.

  • ntasks (int, optional) – The number of concurrent function evaluations. Defaults to the number of cores available in executor.

  • log (bool, default: False) – If True, record the method calls made to the learner by this runner.

  • shutdown_executor (bool, default: False) – If True, shutdown the executor when the runner has completed. If executor is not provided then the runner uses loky’s reusable executor, which is shared between runners and is therefore not shut down unless this parameter is True.

  • retries (int, default: 0) – Maximum amount of retries of a certain point x in learner.function(x). After retries is reached for x the point is present in runner.failed.

  • raise_if_retries_exceeded (bool, default: True) – Raise the error after a point x failed retries.

learner#

The underlying learner. May be queried for its state.

Type:

BaseLearner instance

log#

Record of the method calls made to the learner, in the format (method_name, *args).

Type:

list or None

to_retry#

List of (point, n_fails). When a point has failed runner.retries times it is removed but will be present in runner.tracebacks.

Type:

list of tuples

tracebacks#

List of of (point, tb) for points that failed.

Type:

list of tuples

pending_points#

A list of tuples with (concurrent.futures.Future, point).

Type:

list of tuples

elapsed_time : callable

A method that returns the time elapsed since the runner was started.

overhead : callable

The overhead in percent of using Adaptive. This includes the overhead of the executor. Essentially, this is 100 * (1 - total_elapsed_function_time / self.elapsed_time()).

elapsed_time() → float[source]#

Return the total time elapsed since the runner was started.