deephyper.stopper.ConstantStopper

class deephyper.stopper.ConstantStopper(max_steps: int, stop_step: int)

Bases: Stopper

Constant stopping policy which will stop the evaluation of a configuration at a fixed step.

Single-Objective	Multi-Objectives	Failures
✅	❌	✅

Parameters:

• max_steps (int) – The maximum number of steps which should be performed to evaluate the configuration fully.

• stop_step (int) – The step at which to stop the evaluation.

Methods

observe	Observe a new objective value.
stop	Returns True if the evaluation should be stopped and False otherwise.
to_json	Returns a dict version of the stopper which can be saved as JSON.
transform_objective	Replaces currently observed objective by the maximum objective observed from the start.

Attributes

objective	Last observed objective.
observations	Returns copy of the list of observations with 0-index budgets and 1-index objectives.
step	Last observed step.

property objective

Last observed objective.

property observations: list

Returns copy of the list of observations with 0-index budgets and 1-index objectives.

observe(budget: float, objective: float) → None

Observe a new objective value.

Parameters:

• budget (float) – the budget used to obtain the objective (e.g., the number of epochs).

• objective (float) – the objective value to observe (e.g, the accuracy).

property step

Last observed step.

stop() → bool

Returns True if the evaluation should be stopped and False otherwise.

Returns:

(step >= max_steps).

Return type:

bool

to_json()

Returns a dict version of the stopper which can be saved as JSON.

transform_objective(objective: float)

Replaces currently observed objective by the maximum objective observed from the start.

By default the identity transformation is used.

Parameters:

objective (float) – the observed objective value.