Add loss factor to CostControlBandit and update logic for dynamic subsidy factor #65
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…sidy factor ### Changes: * Added update of subsidy factor on MAB update step. * Added option to allow Beta regime for subsidy factor and added convex combination loss function for determining best action over feasible set. * Edited MultiObjectiveCostControlBandit on strategy.py to generalize CostControlBandit. * Edited Base MAB, sMAB, and cMAB classes in mab.py, smab.py, and cmab.py, respectively to reduce duplicated code. * Added test suite to support the new dynamic CC functionality.
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adarmiento
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Nov 5, 2024
| @@ -39,14 +39,13 @@ class DummyMab(BaseMab): | |||
| epsilon: Optional[Float01] = None | |||
| default_action: Optional[ActionId] = None | |||
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| def update(self, actions: List[ActionId], rewards: Union[List[BinaryReward], List[List[BinaryReward]]]): | |||
| self._validate_update_params(actions=actions, rewards=rewards) | |||
| def _update(self, actions: List[ActionId], rewards: Union[List[BinaryReward], List[List[BinaryReward]]]): | |||
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This type would benefit of being simplified in something like
type BinaryRewards = List[BinaryReward]
def _update(...): List[BinaryReward] | List[BinaryRewards]
| @@ -182,10 +181,27 @@ def update( | |||
| rewards: List[Union[BinaryReward, List[BinaryReward]]] | |||
| The reward for each sample. | |||
| """ | |||
| self._validate_update_params(actions=actions, rewards=rewards) | |||
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This side effect should be a function decorator
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Changes: