reagent.evaluation package

Submodules

reagent.evaluation.cpe module

class reagent.evaluation.cpe.CpeDetails

Bases: object

log()
log_to_tensorboard() → None
class reagent.evaluation.cpe.CpeEstimate(raw, normalized, raw_std_error, normalized_std_error)

Bases: tuple

property normalized

Alias for field number 1

property normalized_std_error

Alias for field number 3

property raw

Alias for field number 0

property raw_std_error

Alias for field number 2

class reagent.evaluation.cpe.CpeEstimateSet(direct_method, inverse_propensity, doubly_robust, sequential_doubly_robust, weighted_doubly_robust, magic)

Bases: tuple

check_estimates_exist()
property direct_method

Alias for field number 0

property doubly_robust

Alias for field number 2

fill_empty_with_zero()
property inverse_propensity

Alias for field number 1

log()
log_to_tensorboard(metric_name: str) → None
property magic

Alias for field number 5

property sequential_doubly_robust

Alias for field number 3

property weighted_doubly_robust

Alias for field number 4

reagent.evaluation.cpe.bootstrapped_std_error_of_mean(data, sample_percent=0.25, num_samples=1000)

Compute bootstrapped standard error of mean of input data.

Parameters
  • data – Input data (1D torch tensor or numpy array).

  • sample_percent – Size of sample to use to calculate bootstrap statistic.

  • num_samples – Number of times to sample.

reagent.evaluation.doubly_robust_estimator module

reagent.evaluation.evaluation_data_page module

reagent.evaluation.evaluator module

reagent.evaluation.ope_adapter module

reagent.evaluation.ranking_listwise_evaluator module

reagent.evaluation.ranking_policy_gradient_evaluator module

reagent.evaluation.reward_net_evaluator module

reagent.evaluation.seq2reward_evaluator module

reagent.evaluation.sequential_doubly_robust_estimator module

reagent.evaluation.weighted_sequential_doubly_robust_estimator module

reagent.evaluation.world_model_evaluator module

Module contents