revive.data

class revive.data.dataset.OfflineDataset(data_file: str, config_file: str, revive_config: dict, ignore_check: bool = False, horizon: Optional[int] = None, reward_func=None)

Bases: Dataset

An offline dataset class.

Params:

data_file

The file path where the training dataset is stored.

config_file

The file path where the data description file is stored.

horizon

Length of iteration trajectory.

compute_missing_data(need_truncate)

transition_mode_()

Set the dataset in transition mode. __getitem__ will return a transition.

trajectory_mode_(horizon: Optional[int] = None, fix_sample: bool = False)

Set the dataset in trajectory mode. __getitem__ will return a clip of trajectory.

set_horizon(horizon: int)

Set the horzion for loading data

get_dist_configs(model_config)

Get the config of distributions for each node based on the given model config.

Args:

model_config

The given model config.

Return:

dist_configs

config of distributions for each node.

total_dims

dimensions for each node when it is considered as input and output. (Output dimensions can be different from input dimensions due to the parameterized distribution)

__len__() → int

__getitem__(index: int, raw: bool = False) → Batch

split(ratio: float = 0.5, mode: str = 'outside_traj', recall: bool = False) → Tuple[OfflineDataset, OfflineDataset]

split the dataset into train and validation with the given ratio and mode

Args:

ratio

Ratio to split validate dataset if it is not explicitly given.

mode

Mode of auto splitting training and validation dataset, choose from outside_traj and inside_traj. outside_traj means the split is happened outside the trajectories, one trajectory can only be in one dataset. ‘ + inside_traj means the split is happened inside the trajectories, former part of one trajectory is in training set, later part is in validation set.

Return:

(TrainDataset, ValidateDataset)