def get_action(self, context=None, n_actions=None):
"""Return the action to perform
Parameters
----------
context : {array-like, None}
The context of current state, None if no context available.
n_actions: int (default: None)
Number of actions wanted to recommend users. If None, only return
one action. If -1, get all actions.
Returns
-------
history_id : int
The history id of the action.
recommendations : list of dict
Each dict contains
{Action object, estimated_reward, uncertainty}.
"""
if self._action_storage.count() == 0:
return self._get_action_with_empty_action_storage(context,
n_actions)
probs = self._exp3_probs()
if n_actions == -1:
n_actions = self._action_storage.count()
action_ids = list(six.viewkeys(probs))
prob_array = np.asarray([probs[action_id]
for action_id in action_ids])
recommendation_ids = self.random_state.choice(
action_ids, size=n_actions, p=prob_array, replace=False)
if n_actions is None:
recommendations = self._recommendation_cls(
action=self._action_storage.get(recommendation_ids),
estimated_reward=probs[recommendation_ids],
uncertainty=probs[recommendation_ids],
score=probs[recommendation_ids],
)
else:
recommendations = [] # pylint: disable=redefined-variable-type
for action_id in recommendation_ids:
recommendations.append(self._recommendation_cls(
action=self._action_storage.get(action_id),
estimated_reward=probs[action_id],
uncertainty=probs[action_id],
score=probs[action_id],
))
history_id = self._history_storage.add_history(context, recommendations)
return history_id, recommendations
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