pytorch_forecasting.data.data_module._encoder_decoder_data_module.EncoderDecoderTimeSeriesDataModule#

class pytorch_forecasting.data.data_module._encoder_decoder_data_module.EncoderDecoderTimeSeriesDataModule(time_series_dataset: TimeSeries, max_encoder_length: int = 30, min_encoder_length: int | None = None, max_prediction_length: int = 1, min_prediction_length: int | None = None, min_prediction_idx: int | None = None, allow_missing_timesteps: bool = False, add_relative_time_idx: bool = False, add_target_scales: bool = False, add_encoder_length: bool | str = 'auto', target_normalizer: TorchNormalizer | EncoderNormalizer | NaNLabelEncoder | str | list[TorchNormalizer | EncoderNormalizer | NaNLabelEncoder] | tuple[TorchNormalizer | EncoderNormalizer | NaNLabelEncoder] | None = 'auto', categorical_encoders: dict[str, NaNLabelEncoder] | None = None, scalers: dict[str, StandardScaler | RobustScaler | TorchNormalizer | EncoderNormalizer] | None = None, randomize_length: None | tuple[float, float] | bool = False, batch_size: int = 32, num_workers: int = 0, train_val_test_split: tuple = (0.7, 0.15, 0.15))[source]#

Lightning DataModule for processing time series data in an encoder-decoder format.

This module handles preprocessing, splitting, and batching of time series data for use in deep learning models. It supports categorical and continuous features, various scalers, and automatic target normalization.

Parameters:

time_series_dataset (TimeSeries) – The dataset containing time series data.
max_encoder_length (int, default=30) – Maximum length of the encoder input sequence.
min_encoder_length (Optional[int], default=None) – Minimum length of the encoder input sequence. Defaults to max_encoder_length if not specified.
max_prediction_length (int, default=1) – Maximum length of the decoder output sequence.
min_prediction_length (Optional[int], default=None) – Minimum length of the decoder output sequence. Defaults to max_prediction_length if not specified.
min_prediction_idx (Optional[int], default=None) – Minimum index from which predictions start.
allow_missing_timesteps (bool, default=False) – Whether to allow missing timesteps in the dataset.
add_relative_time_idx (bool, default=False) – Whether to add a relative time index feature.
add_target_scales (bool, default=False) – Whether to add target scaling information.
add_encoder_length (Union[bool, str], default="auto") – Whether to include encoder length information.
target_normalizer –

Union[NORMALIZER, str, List[NORMALIZER], Tuple[NORMALIZER], None],
default=”auto”

Normalizer for the target variable. If “auto”, uses RobustScaler.
categorical_encoders (Optional[Dict[str, NaNLabelEncoder]], default=None) – Dictionary of categorical encoders.
scalers
Optional[Dict[str – TorchNormalizer, EncoderNormalizer]]], default=None Dictionary of feature scalers.
Union[StandardScaler – TorchNormalizer, EncoderNormalizer]]], default=None Dictionary of feature scalers.
RobustScaler – TorchNormalizer, EncoderNormalizer]]], default=None Dictionary of feature scalers.

:paramTorchNormalizer, EncoderNormalizer]]], default=None: Dictionary of feature scalers.

Parameters:

randomize_length (Union[None, Tuple[float, float], bool], default=False) – Whether to randomize input sequence length.
batch_size (int, default=32) – Batch size for DataLoader.
num_workers (int, default=0) – Number of workers for DataLoader.
train_val_test_split (tuple, default=(0.7, 0.15, 0.15)) – Proportions for train, validation, and test dataset splits.

prepare_data_per_node#: If True, each LOCAL_RANK=0 will call prepare data. Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data.

allow_zero_length_dataloader_with_multiple_devices#: If True, dataloader with zero length within local rank is allowed. Default value is False.

prepare_data_per_node#: If True, each LOCAL_RANK=0 will call prepare data. Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data.

allow_zero_length_dataloader_with_multiple_devices#: If True, dataloader with zero length within local rank is allowed. Default value is False.

Methods

`__delattr__`(name, /)	Implement delattr(self, name).
`__dir__`()	Default dir() implementation.
`__eq__`(value, /)	Return self==value.
`__format__`(format_spec, /)	Default object formatter.
`__ge__`(value, /)	Return self>=value.
`__getattribute__`(name, /)	Return getattr(self, name).
`__getstate__`()	Helper for pickle.
`__gt__`(value, /)	Return self>value.
`__hash__`()	Return hash(self).
`__init_subclass__`	This method is called when a class is subclassed.
`__le__`(value, /)	Return self<=value.
`__lt__`(value, /)	Return self<value.
`__ne__`(value, /)	Return self!=value.
`__new__`(args, *kwargs)
`__reduce__`()	Helper for pickle.
`__reduce_ex__`(protocol, /)	Helper for pickle.
`__repr__`()	Return repr(self).
`__setattr__`(name, value, /)	Implement setattr(self, name, value).
`__sizeof__`()	Size of object in memory, in bytes.
`__str__`()	Return a string representation of the datasets that are set up.
`__subclasshook__`	Abstract classes can override this to customize issubclass().
`_create_windows`(indices)	Generate sliding windows for training, validation, and testing.
`_prepare_metadata`()	Prepare metadata for model initialisation.
`_preprocess_data`(series_idx)	Preprocess the data before feeding it into _ProcessedEncoderDecoderDataset.
`_set_hparams`(hp)
`_to_hparams_dict`(hp)
`collate_fn`(batch)
`from_datasets`([train_dataset, val_dataset, ...])	Create an instance from torch.utils.data.Dataset.
`load_from_checkpoint`(checkpoint_path[, ...])	Primary way of loading a datamodule from a checkpoint.
`load_state_dict`(state_dict)	Called when loading a checkpoint, implement to reload datamodule state given datamodule state_dict.
`on_after_batch_transfer`(batch, dataloader_idx)	Override to alter or apply batch augmentations to your batch after it is transferred to the device.
`on_before_batch_transfer`(batch, dataloader_idx)	Override to alter or apply batch augmentations to your batch before it is transferred to the device.
`on_exception`(exception)	Called when the trainer execution is interrupted by an exception.
`predict_dataloader`()	An iterable or collection of iterables specifying prediction samples.
`prepare_data`()	Use this to download and prepare data.
`remove_ignored_hparams`(ignore_list)	Remove ignored hyperparameters from the stored state.
`save_hyperparameters`(*args[, ignore, frame, ...])	Save arguments to `hparams` attribute.
`setup`([stage])	Prepare the datasets for training, validation, testing, or prediction.
`state_dict`()	Called when saving a checkpoint, implement to generate and save datamodule state.
`teardown`(stage)	Called at the end of fit (train + validate), validate, test, or predict.
`test_dataloader`()	An iterable or collection of iterables specifying test samples.
`train_dataloader`()	An iterable or collection of iterables specifying training samples.
`transfer_batch_to_device`(batch, device, ...)	Override this hook if your `DataLoader` returns tensors wrapped in a custom data structure.
`val_dataloader`()	An iterable or collection of iterables specifying validation samples.

Attributes

`CHECKPOINT_HYPER_PARAMS_KEY`
`CHECKPOINT_HYPER_PARAMS_NAME`
`CHECKPOINT_HYPER_PARAMS_TYPE`
`__annotations__`
`__dict__`
`__doc__`
`__jit_unused_properties__`
`__module__`
`__weakref__`	list of weak references to the object
`hparams`	The collection of hyperparameters saved with `save_hyperparameters()`.
`hparams_initial`	The collection of hyperparameters saved with `save_hyperparameters()`.
`metadata`	Compute metadata for model initialization.
`name`