reagent.ope.trainers package
Submodules
reagent.ope.trainers.linear_trainers module
- class reagent.ope.trainers.linear_trainers.DecisionTreeClassifierTrainer
Bases:
reagent.ope.trainers.linear_trainers.LinearTrainer- property name: str
- train(data: reagent.ope.estimators.types.TrainingData, iterations: int = 1, num_samples: int = 0)
- class reagent.ope.trainers.linear_trainers.DecisionTreeTrainer(is_classifier: bool = False)
Bases:
reagent.ope.trainers.linear_trainers.LinearTrainer- property name: str
- train(data: reagent.ope.estimators.types.TrainingData, iterations: int = 1, num_samples: int = 0)
- class reagent.ope.trainers.linear_trainers.LassoTrainer(is_classifier: bool = False)
Bases:
reagent.ope.trainers.linear_trainers.LinearTrainer- property name: str
- train(data: reagent.ope.estimators.types.TrainingData, iterations: int = 1, num_samples: int = 0)
- class reagent.ope.trainers.linear_trainers.LinearNet(D_in: int, H: int, D_out: int, hidden_layers: int = 2, activation=<class 'torch.nn.modules.activation.ReLU'>)
Bases:
torch.nn.modules.module.Module- forward(x: torch.Tensor)
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- training: bool
- class reagent.ope.trainers.linear_trainers.LinearTrainer(is_classifier: bool = False)
Bases:
reagent.ope.estimators.types.Trainer- predict(x: torch.Tensor, device=None) reagent.ope.estimators.types.PredictResults
- score(x: torch.Tensor, y: torch.Tensor, weight: Optional[torch.Tensor] = None) float
- class reagent.ope.trainers.linear_trainers.LogisticRegressionTrainer(solver: str = 'lbfgs')
Bases:
reagent.ope.trainers.linear_trainers.LinearTrainer- property name: str
- train(data: reagent.ope.estimators.types.TrainingData, iterations: int = 1, num_samples: int = 0)
- class reagent.ope.trainers.linear_trainers.NNTrainer(device=None)
Bases:
reagent.ope.estimators.types.Trainer- property name: str
- predict(x: torch.Tensor, device=None) reagent.ope.estimators.types.PredictResults
- score(x: torch.Tensor, y: torch.Tensor, weight: Optional[torch.Tensor] = None) float
- train(data: reagent.ope.estimators.types.TrainingData, iterations: int = 100, epochs: int = 1, num_samples: int = 0)
- class reagent.ope.trainers.linear_trainers.SGDClassifierTrainer(loss: str = 'log', max_iter: int = 1000)
Bases:
reagent.ope.trainers.linear_trainers.LinearTrainer- property name: str
- train(data: reagent.ope.estimators.types.TrainingData, iterations: int = 1, num_samples: int = 0)
reagent.ope.trainers.rl_tabular_trainers module
- class reagent.ope.trainers.rl_tabular_trainers.DPTrainer(env: reagent.ope.test.envs.Environment, policy: reagent.ope.trainers.rl_tabular_trainers.TabularPolicy)
Bases:
object- train(gamma: float = 0.9, threshold: float = 0.0001)
- class reagent.ope.trainers.rl_tabular_trainers.DPValueFunction(policy: reagent.ope.estimators.sequential_estimators.RLPolicy, env: reagent.ope.test.envs.Environment, gamma: float = 0.99, threshold: float = 0.0001)
Bases:
reagent.ope.trainers.rl_tabular_trainers.TabularValueFunction- reset(clear_state_values: bool = False)
- state_value(state: reagent.ope.estimators.sequential_estimators.State, horizon: int = - 1) float
- class reagent.ope.trainers.rl_tabular_trainers.EstimatedStateValueFunction(policy: reagent.ope.estimators.sequential_estimators.RLPolicy, env: reagent.ope.test.envs.Environment, gamma: float, num_episodes: int = 100)
Bases:
reagent.ope.estimators.sequential_estimators.ValueFunction- reset()
- state_action_value(state: reagent.ope.estimators.sequential_estimators.State, action: reagent.ope.estimators.types.TypeWrapper[Union[int, Tuple[int], float, Tuple[float], numpy.ndarray, torch.Tensor]]) float
- state_value(state: reagent.ope.estimators.sequential_estimators.State) float
- class reagent.ope.trainers.rl_tabular_trainers.MonteCarloTrainer(env: reagent.ope.test.envs.Environment, policy: reagent.ope.trainers.rl_tabular_trainers.TabularPolicy)
Bases:
object- train(iterations: int, gamma: float = 0.9, first_visit: bool = True, update_interval: int = 20)
- class reagent.ope.trainers.rl_tabular_trainers.MonteCarloValueFunction(policy: reagent.ope.estimators.sequential_estimators.RLPolicy, env: reagent.ope.test.envs.Environment, gamma: float = 0.99, first_visit: bool = True, count_threshold: int = 100, max_iteration: int = 200)
Bases:
reagent.ope.trainers.rl_tabular_trainers.TabularValueFunction- reset(clear_state_values: bool = False)
- state_value(state: reagent.ope.estimators.sequential_estimators.State) float
- class reagent.ope.trainers.rl_tabular_trainers.TabularPolicy(action_space: reagent.ope.estimators.types.ActionSpace, epsilon: float = 0.0, device=None)
Bases:
reagent.ope.estimators.sequential_estimators.RLPolicy- action_dist(state: reagent.ope.estimators.sequential_estimators.State) reagent.ope.estimators.types.ActionDistribution
- load(path) bool
- save(path) bool
- update(state: reagent.ope.estimators.sequential_estimators.State, actions: Sequence[float]) float
- class reagent.ope.trainers.rl_tabular_trainers.TabularValueFunction(policy: reagent.ope.estimators.sequential_estimators.RLPolicy, model: reagent.ope.estimators.sequential_estimators.Model, gamma=0.99)
Bases:
reagent.ope.estimators.sequential_estimators.ValueFunction- reset(clear_state_values: bool = False)
- state_action_value(state: reagent.ope.estimators.sequential_estimators.State, action: reagent.ope.estimators.types.TypeWrapper[Union[int, Tuple[int], float, Tuple[float], numpy.ndarray, torch.Tensor]]) float
- state_value(state: reagent.ope.estimators.sequential_estimators.State) float