def score_cpe(self, metric_name, edp: EvaluationDataPage):
(
direct_method,
inverse_propensity,
doubly_robust,
) = self.doubly_robust_estimator.estimate(edp)
sequential_doubly_robust = self.sequential_doubly_robust_estimator.estimate(edp)
weighted_doubly_robust = (
self.weighted_sequential_doubly_robust_estimator.estimate(
edp, num_j_steps=1, whether_self_normalize_importance_weights=True
)
)
magic = self.weighted_sequential_doubly_robust_estimator.estimate(
edp,
num_j_steps=Evaluator.NUM_J_STEPS_FOR_MAGIC_ESTIMATOR,
whether_self_normalize_importance_weights=True,
)
return CpeEstimateSet(
direct_method=direct_method,
inverse_propensity=inverse_propensity,
doubly_robust=doubly_robust,
sequential_doubly_robust=sequential_doubly_robust,
weighted_doubly_robust=weighted_doubly_robust,
magic=magic,
)
def score_cpe(self, metric_name, edp: EvaluationDataPage):
logger.info("Using OPE adapter")
direct_method = self.ope_dm_estimator.estimate(edp)
inverse_propensity = self.ope_ips_estimator.estimate(edp)
doubly_robust = self.ope_dr_estimator.estimate(edp)
sequential_doubly_robust = self.ope_seq_dr_estimator.estimate(edp)
weighted_doubly_robust = self.ope_seq_weighted_dr_estimator.estimate(
edp)
magic = self.ope_seq_magic_estimator.estimate(edp)
return CpeEstimateSet(
direct_method=direct_method,
inverse_propensity=inverse_propensity,
doubly_robust=doubly_robust,
sequential_doubly_robust=sequential_doubly_robust,
weighted_doubly_robust=weighted_doubly_robust,
magic=magic,
)