reagent.ope.test package

Subpackages

• reagent.ope.test.unit_tests package
  • Submodules
  • reagent.ope.test.unit_tests.test_slate_estimators module
  • reagent.ope.test.unit_tests.test_types module
  • reagent.ope.test.unit_tests.test_utils module
  • Module contents

Submodules

reagent.ope.test.envs module

class reagent.ope.test.envs.Environment(max_horizon: int = -1)

Bases: reagent.ope.estimators.sequential_estimators.Model

Environment for RL

abstract close()

property current_state

abstract property observation_space

abstract reset(state: Optional[reagent.ope.estimators.sequential_estimators.State] = None)

abstract property states

step(policy: reagent.ope.estimators.sequential_estimators.RLPolicy)

class reagent.ope.test.envs.PolicyLogGenerator(env: reagent.ope.test.envs.Environment, policy: reagent.ope.estimators.sequential_estimators.RLPolicy)

Bases: object

generate_log(init_state: reagent.ope.estimators.sequential_estimators.State) → Sequence[reagent.ope.estimators.sequential_estimators.Transition]

reagent.ope.test.gridworld module

class reagent.ope.test.gridworld.GridWorld(size: Tuple[int, int], start: Tuple[int, int], goal: Tuple[int, int], max_horizon: int = -1, walls: Iterable[Tuple[int, int]] = ())

Bases: reagent.ope.test.envs.Environment

close()

dump_policy(policy) → str

dump_state_values(state_values) → str

dump_value_func(valfunc: reagent.ope.estimators.sequential_estimators.ValueFunction) → str

classmethod from_grid(grid: Sequence[Sequence[str]], max_horizon: int = -1)

next_state_reward_dist(state: reagent.ope.estimators.sequential_estimators.State, action: reagent.ope.estimators.types.TypeWrapper[typing.Union[int, typing.Tuple[int], float, typing.Tuple[float], numpy.ndarray, torch.Tensor]][Union[int, Tuple[int], float, Tuple[float], numpy.ndarray, torch.Tensor]]) → Mapping[reagent.ope.estimators.sequential_estimators.State, reagent.ope.estimators.sequential_estimators.RewardProbability]

property observation_space

reset(state: Optional[reagent.ope.estimators.sequential_estimators.State] = None)

property states

class reagent.ope.test.gridworld.NoiseGridWorldModel(gridworld: reagent.ope.test.gridworld.GridWorld, action_space: reagent.ope.estimators.types.ActionSpace, epsilon: float = 0.1, max_horizon: int = -1)

Bases: reagent.ope.test.envs.Environment

close()

property current_state

next_state_reward_dist(state: reagent.ope.estimators.sequential_estimators.State, action: reagent.ope.estimators.types.TypeWrapper[typing.Union[int, typing.Tuple[int], float, typing.Tuple[float], numpy.ndarray, torch.Tensor]][Union[int, Tuple[int], float, Tuple[float], numpy.ndarray, torch.Tensor]]) → Mapping[reagent.ope.estimators.sequential_estimators.State, reagent.ope.estimators.sequential_estimators.RewardProbability]

property observation_space

reset(state: Optional[reagent.ope.estimators.sequential_estimators.State] = None)

property states

class reagent.ope.test.gridworld.ThomasGridWorld

Bases: reagent.ope.test.gridworld.GridWorld

GridWorld set up in https://people.cs.umass.edu/~pthomas/papers/Thomas2015c.pdf

reagent.ope.test.mslr_slate module

class reagent.ope.test.mslr_slate.MSLRDatasets(params, num_columns: int, anchor_url_features: List[int], body_features: List[int], dataset_name: str = '', device=None)

Bases: object

property all_features

property anchor_url_features

property body_features

property cache_file

property features

property folder

load()

property name

property queries

property relevances

property sample_weights

save()

property source_file

class reagent.ope.test.mslr_slate.MSLRModel(relevances: torch.Tensor, device=None)

Bases: reagent.ope.estimators.slate_estimators.SlateModel

item_relevances(context: reagent.ope.estimators.slate_estimators.SlateContext) → torch.Tensor

item_rewards(context: reagent.ope.estimators.slate_estimators.SlateContext) → reagent.ope.estimators.slate_estimators.SlateItemValues

Returns each item’s relevance under the context :param context:

Returns

Item relevances

reagent.ope.test.mslr_slate.evaluate(experiments: Iterable[Tuple[Iterable[reagent.ope.estimators.slate_estimators.SlateEstimator], int]], dataset: reagent.ope.test.mslr_slate.MSLRDatasets, slate_size: int, item_size: int, metric_func: str, log_trainer: reagent.ope.estimators.types.Trainer, log_distribution: reagent.ope.estimators.slate_estimators.RewardDistribution, log_features: str, tgt_trainer: reagent.ope.estimators.types.Trainer, tgt_distribution: reagent.ope.estimators.slate_estimators.RewardDistribution, tgt_features: str, dm_features: str, max_num_workers: int, device=None)

reagent.ope.test.mslr_slate.load_dataset(params, num_columns, anchor_url_features, body_features, dataset_name='') → reagent.ope.test.mslr_slate.MSLRDatasets

reagent.ope.test.mslr_slate.train(trainer: reagent.ope.estimators.types.Trainer, train_dataset: reagent.ope.test.mslr_slate.MSLRDatasets, vali_dataset: reagent.ope.test.mslr_slate.MSLRDatasets, prefix: str = '')

reagent.ope.test.mslr_slate.train_all(train_dataset, vali_dataset, prefix: str = '')

reagent.ope.test.mslr_slate.train_models(params)

reagent.ope.test.multiclass_bandits module

class reagent.ope.test.multiclass_bandits.MultiClassContext(query_id: int)

Bases: object

class reagent.ope.test.multiclass_bandits.MultiClassDataRow(feature: torch.Tensor, label: torch.Tensor, one_hot: torch.Tensor)

Bases: object

class reagent.ope.test.multiclass_bandits.MultiClassModel(features: torch.Tensor, rewards: torch.Tensor)

Bases: reagent.ope.estimators.contextual_bandits_estimators.BanditsModel

class reagent.ope.test.multiclass_bandits.MultiClassPolicy(action_space: reagent.ope.estimators.types.ActionSpace, action_distributions: torch.Tensor, epsilon: float, device=None)

Bases: reagent.ope.estimators.types.Policy

class reagent.ope.test.multiclass_bandits.UCIMultiClassDataset(params, device=None)

Bases: object

To load and hold UCI classification datasets: https://archive.ics.uci.edu/ml/datasets.php?task=cla&sort=nameUp&view=table Also to convert it to contextual bandits problems References: https://arxiv.org/abs/1103.4601

property config_file

property features

property labels

property num_actions

property num_features

property one_hots

train_val_test_split(ratios: Tuple[float, float] = (0.8, 0.8), device=None)

reagent.ope.test.multiclass_bandits.evaluate_all(experiments: Iterable[Tuple[Iterable[reagent.ope.estimators.estimator.Estimator], int]], dataset: reagent.ope.test.multiclass_bandits.UCIMultiClassDataset, log_trainer: reagent.ope.estimators.types.Trainer, log_epsilon: float, tgt_trainer: reagent.ope.estimators.types.Trainer, tgt_epsilon: float, max_num_workers: int, device=None)

reagent.ope.test.yandex_web_search module

class reagent.ope.test.yandex_web_search.LoggedQuery(user_id: int, query_id: int, query_terms: Tuple[int], list: Sequence[Tuple[int, int]])

Bases: object

click(url_id: int, dwell_time: int)

property clicks

property list

property position_relevances

property query_id

property query_terms

property url_relevances

property user_id

class reagent.ope.test.yandex_web_search.TrainingDataset(params, device=None)

Bases: object

item_relevances(query_id: int, query_terms: Tuple[int], items: Iterable[Tuple[int, int]]) → reagent.ope.estimators.slate_estimators.SlateItemValues

load_queries(reload=False)

slot_relevances(slots: reagent.ope.estimators.slate_estimators.SlateSlots) → reagent.ope.estimators.slate_estimators.SlateSlotValues

property training_queries

class reagent.ope.test.yandex_web_search.TrainingQuery(query_id: int, query_terms: Tuple[int])

Bases: object

add(query: reagent.ope.test.yandex_web_search.LoggedQuery)

property count

finalize()

merge(other: reagent.ope.test.yandex_web_search.TrainingQuery)

pack()

property position_relevances

property query_id

property query_terms

property url_relevances

class reagent.ope.test.yandex_web_search.YandexSlateModel(dataset: reagent.ope.test.yandex_web_search.TrainingDataset)

Bases: reagent.ope.estimators.slate_estimators.SlateModel

item_rewards(context: reagent.ope.estimators.slate_estimators.SlateContext) → reagent.ope.estimators.slate_estimators.SlateItemValues

Returns each item’s relevance under the context :param context:

Returns

Item relevances

slot_probabilities(context: reagent.ope.estimators.slate_estimators.SlateContext) → reagent.ope.estimators.slate_estimators.SlateSlotValues

Returns each slot/positions’s probability independent of showing item, used in PBM estimator :param context:

Returns:

reagent.ope.test.yandex_web_search.click_to_relevances(clicks: Iterable[Tuple[int, int]], urls: Sequence[Tuple[int, int]]) → Tuple[List[float], Mapping[Tuple[int, int], float]]

reagent.ope.test.yandex_web_search.create_cache(params)

reagent.ope.test.yandex_web_search.evaluate(experiments: Iterable[Tuple[Iterable[reagent.ope.estimators.slate_estimators.SlateEstimator], int]], log_dataset: reagent.ope.test.yandex_web_search.TrainingDataset, log_distribution: reagent.ope.estimators.slate_estimators.RewardDistribution, tgt_dataset: reagent.ope.test.yandex_web_search.TrainingDataset, tgt_distribution: reagent.ope.estimators.slate_estimators.RewardDistribution, log_queries: Sequence[reagent.ope.test.yandex_web_search.TrainingQuery], slate_size: int, item_size: int, metric_func: str, max_num_workers: int, device=None)

reagent.ope.test.yandex_web_search.load_logged_queries(params) → Sequence[reagent.ope.test.yandex_web_search.TrainingQuery]

Module contents