def estimate_interval(
self,
slate_id: np.ndarray,
reward: np.ndarray,
position: np.ndarray,
pscore: np.ndarray,
evaluation_policy_pscore: np.ndarray,
alpha: float = 0.05,
n_bootstrap_samples: int = 10000,
random_state: Optional[int] = None,
**kwargs,
) -> Dict[str, float]:
"""Estimate confidence interval of policy value by nonparametric bootstrap procedure.
Returns
----------
mock_confidence_interval: Dict[str, float]
Dictionary storing the estimated mean and upper-lower confidence bounds.
"""
check_confidence_interval_arguments(
alpha=alpha,
n_bootstrap_samples=n_bootstrap_samples,
random_state=random_state,
)
return {k: v + self.eps for k, v in mock_confidence_interval.items()}
def estimate_interval(
self,
reward: np.ndarray,
action_by_behavior_policy: np.ndarray,
pscore: np.ndarray,
action_by_evaluation_policy: np.ndarray,
alpha: float = 0.05,
n_bootstrap_samples: int = 10000,
random_state: Optional[int] = None,
**kwargs,
) -> Dict[str, float]:
"""Estimate the confidence interval of the policy value using bootstrap.
Returns
----------
mock_confidence_interval: Dict[str, float]
Dictionary storing the estimated mean and upper-lower confidence bounds.
"""
check_confidence_interval_arguments(
alpha=alpha,
n_bootstrap_samples=n_bootstrap_samples,
random_state=random_state,
)
return {k: v + self.eps for k, v in mock_confidence_interval.items()}
def estimate_interval(
self,
position: np.ndarray,
action_dist: np.ndarray,
estimated_rewards_by_reg_model: np.ndarray,
alpha: float = 0.05,
n_bootstrap_samples: int = 10000,
random_state: Optional[int] = None,
**kwargs,
) -> Dict[str, float]:
"""Estimate confidence interval of policy value by nonparametric bootstrap procedure.
Parameters
----------
position: array-like, shape (n_rounds,)
Positions of each round in the given logged bandit feedback.
action_dist: array-like, shape (n_rounds, n_actions, len_list)
Action choice probabilities by the evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`.
estimated_rewards_by_reg_model: array-like, shape (n_rounds, n_actions, len_list)
Expected rewards for each round, action, and position estimated by a regression model, i.e., :math:`\\hat{q}(x_t,a_t)`.
alpha: float, default=0.05
Significant level of confidence intervals.
n_bootstrap_samples: int, default=10000
Number of resampling performed in the bootstrap procedure.
random_state: int, default=None
Controls the random seed in bootstrap sampling.
Returns
----------
mock_confidence_interval: Dict[str, float]
Dictionary storing the estimated mean and upper-lower confidence bounds.
"""
check_confidence_interval_arguments(
alpha=alpha,
n_bootstrap_samples=n_bootstrap_samples,
random_state=random_state,
)
return mock_confidence_interval
def estimate_interval(
self,
position: np.ndarray,
action_dist: np.ndarray,
estimated_rewards_by_reg_model: np.ndarray,
alpha: float = 0.05,
n_bootstrap_samples: int = 10000,
random_state: Optional[int] = None,
**kwargs,
) -> Dict[str, float]:
"""Estimate the confidence interval of the policy value using bootstrap.
Parameters
----------
position: array-like, shape (n_rounds,)
Indices to differentiate positions in a recommendation interface where the actions are presented.
action_dist: array-like, shape (n_rounds, n_actions, len_list)
Action choice probabilities of the evaluation policy (can be deterministic), i.e., :math:`\\pi_e(a_i|x_i)`.
estimated_rewards_by_reg_model: array-like, shape (n_rounds, n_actions, len_list)
Estimated expected rewards given context, action, and position, i.e., :math:`\\hat{q}(x_i,a_i)`.
alpha: float, default=0.05
Significance level.
n_bootstrap_samples: int, default=10000
Number of resampling performed in bootstrap sampling.
random_state: int, default=None
Controls the random seed in bootstrap sampling.
Returns
----------
mock_confidence_interval: Dict[str, float]
Dictionary storing the estimated mean and upper-lower confidence bounds.
"""
check_confidence_interval_arguments(
alpha=alpha,
n_bootstrap_samples=n_bootstrap_samples,
random_state=random_state,
)
return mock_confidence_interval
