Callbacks API (ng.callbacks)

Callbacks can be registered through the optimizer.register_callback for call on either ask or tell methods. Two of them are available through the ng.callbacks namespace.

class nevergrad.callbacks.EarlyStopping(stopping_criterion: Callable[[Optimizer], bool])

Callback for stopping the minimize method before the budget is fully used.

Parameters:

stopping_criterion (func(optimizer) -> bool) – function that takes the current optimizer as input and returns True if the minimization must be stopped

Note

This callback must be register on the “ask” method only.

Example

In the following code, the minimize method will be stopped at the 4th “ask”

>>> early_stopping = ng.callbacks.EarlyStopping(lambda opt: opt.num_ask > 3)
>>> optimizer.register_callback("ask", early_stopping)
>>> optimizer.minimize(_func, verbosity=2)

A couple other options (equivalent in case of non-noisy optimization) for stopping if the loss is below 12:

>>> early_stopping = ng.callbacks.EarlyStopping(lambda opt: opt.recommend().loss < 12)
>>> early_stopping = ng.callbacks.EarlyStopping(lambda opt: opt.current_bests["minimum"].mean < 12)
classmethod no_improvement_stopper(tolerance_window: int) → EarlyStopping

Early stop when loss didn’t reduce during tolerance_window asks

classmethod timer(max_duration: float) → EarlyStopping

Early stop when max_duration seconds has been reached (from the first ask)

class nevergrad.callbacks.OptimizerDump(filepath: str | Path)

Dumps the optimizer to a pickle file at every call.

Parameters:

filepath (str or Path) – path to the pickle file

class nevergrad.callbacks.ParametersLogger(filepath: str | Path, append: bool = True, order: int = 1)

Logs parameter and run information throughout into a file during optimization.

Parameters:

  • filepath (str or pathlib.Path) – the path to dump data to

  • append (bool) – whether to append the file (otherwise it replaces it)

  • order (int) – order of the internal/model parameters to extract

Example

logger = ParametersLogger(filepath)
optimizer.register_callback("tell",  logger)
optimizer.minimize()
list_of_dict_of_data = logger.load()

Note

Arrays are converted to lists

load() → List[Dict[str, Any]]

Loads data from the log file

load_flattened(max_list_elements: int = 24) → List[Dict[str, Any]]

Loads data from the log file, and splits lists (arrays) into multiple arguments

Parameters:

max_list_elements (int) – Maximum number of elements displayed from the array, each element is given a unique id of type list_name#i0_i1_…

to_hiplot_experiment(max_list_elements: int = 24) → Any

Converts the logs into an hiplot experiment for display.

Parameters:

max_list_elements (int) – maximum number of elements of list/arrays to export (only the first elements are extracted)

Example

exp = logs.to_hiplot_experiment()
exp.display(force_full_width=True)

Note

class nevergrad.callbacks.ProgressBar

Progress bar to register as callback in an optimizer