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ContextualPolicy

Contextual bandit policy base class.

Parameters

  • reward_obj

    TypeRewardObj | None

    DefaultNone

    The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.

  • reward_scaler

    Typecompose.TargetTransformRegressor | None

    DefaultNone

    A reward scaler used to scale the rewards before they are fed to the reward object. This can be useful to scale the rewards to a (0, 1) range for instance.

  • burn_in

    Default0

    The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.

Attributes

  • ranking

    Return the list of arms in descending order of performance.

Methods

pull

Pull arm(s).

This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call next(policy.pull(arms)).

Parameters

  • arm_ids'list[ArmID]'
  • context'dict' — defaults to None

Returns

ArmID: A single arm.

update

Update an arm's state.

Parameters

  • arm_id
  • context
  • reward_args
  • reward_kwargs