gluonts.dataset.loader module#
- class gluonts.dataset.loader.Batch(*, batch_size: int)[source]#
Bases:
gluonts.transform._base.Transformation
,pydantic.main.BaseModel
- batch_size: int#
- gluonts.dataset.loader.InferenceDataLoader(dataset: gluonts.dataset.Dataset, *, transform: gluonts.transform._base.Transformation = <gluonts.transform._base.Identity object>, batch_size: int, stack_fn: typing.Callable)[source]#
Construct an iterator of batches for inference purposes.
- Parameters
dataset – Data to iterate over.
transform – Transformation to be lazily applied as data is being iterated. The transformation is applied in “inference mode” (
is_train=False
).batch_size – Number of entries to include in a batch.
stack_fn – Function to use to stack data entries into batches. This can be used to set a specific array type or computing device the arrays should end up onto (CPU, GPU).
- Returns
An iterable sequence of batches.
- Return type
Iterable[DataBatch]
- gluonts.dataset.loader.TrainDataLoader(dataset: gluonts.dataset.Dataset, *, transform: gluonts.transform._base.Transformation = <gluonts.transform._base.Identity object>, batch_size: int, stack_fn: typing.Callable, num_batches_per_epoch: typing.Optional[int] = None, shuffle_buffer_length: typing.Optional[int] = None)[source]#
Construct an iterator of batches for training purposes.
This function wraps around
DataLoader
to offer training-specific behaviour and options, as follows:1. The provided dataset is iterated cyclically, so that one can go over it multiple times in a single epoch. 2. A transformation must be provided, that is lazily applied as the dataset is being iterated; this is useful e.g. to slice random instances of fixed length out of each time series in the dataset. 3. The resulting batches can be iterated in a pseudo-shuffled order.
The returned object is a stateful iterator, whose length is either
num_batches_per_epoch
(if notNone
) or infinite (otherwise).- Parameters
dataset – Data to iterate over.
transform – Transformation to be lazily applied as data is being iterated. The transformation is applied in “training mode” (
is_train=True
).batch_size – Number of entries to include in a batch.
stack_fn – Function to use to stack data entries into batches. This can be used to set a specific array type or computing device the arrays should end up onto (CPU, GPU).
num_batches_per_epoch – Length of the iterator. If
None
, then the iterator is endless.shuffle_buffer_length – Size of the buffer used for shuffling. Default: None, in which case no shuffling occurs.
- Returns
An iterator of batches.
- Return type
Iterator[DataBatch]
- gluonts.dataset.loader.ValidationDataLoader(dataset: gluonts.dataset.Dataset, *, transform: gluonts.transform._base.Transformation = <gluonts.transform._base.Identity object>, batch_size: int, stack_fn: typing.Callable)[source]#
Construct an iterator of batches for validation purposes.
- Parameters
dataset – Data to iterate over.
transform – Transformation to be lazily applied as data is being iterated. The transformation is applied in “training mode” (
is_train=True
).batch_size – Number of entries to include in a batch.
stack_fn – Function to use to stack data entries into batches. This can be used to set a specific array type or computing device the arrays should end up onto (CPU, GPU).
- Returns
An iterable sequence of batches.
- Return type
Iterable[DataBatch]