pytorch_forecasting.data.samplers.TimeSynchronizedBatchSampler

class pytorch_forecasting.data.samplers.TimeSynchronizedBatchSampler(sampler: Sampler, batch_size: int = 64, shuffle: bool = False, drop_last: bool = False)

Samples mini-batches randomly but in a time-synchronised manner.

Time-synchornisation means that the time index of the first decoder samples are aligned across the batch. This sampler does not support missing values in the dataset.

Initialize.

Parameters:

• sampler (Sampler or Iterable) – Base sampler. Can be any iterable object.

• drop_last (bool) – If True, drop last mini-batch from a group if it is smaller than batch_size. Default is False.

• shuffle (bool) – If True, shuffle dataset. Default is False.

• batch_size (int) – Number of samples in a mini-batch. This is rather the maximum number of samples. Because mini-batches are grouped by prediction time, chances are that there are multiple where batch size will be smaller than the maximum. Default is 64.

__init__(sampler: Sampler, batch_size: int = 64, shuffle: bool = False, drop_last: bool = False)

Initialize.

Parameters:

• sampler (Sampler or Iterable) – Base sampler. Can be any iterable object.

• drop_last (bool) – If True, drop last mini-batch from a group if it is smaller than batch_size. Default is False.

• shuffle (bool) – If True, shuffle dataset. Default is False.

• batch_size (int) – Number of samples in a mini-batch. This is rather the maximum number of samples. Because mini-batches are grouped by prediction time, chances are that there are multiple where batch size will be smaller than the maximum. Default is 64.

Methods

__class_getitem__	Parameterizes a generic class.
__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__	Function to initialize subclasses.
__iter__()
__le__(value, /)	Return self<=value.
__len__()
__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 str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
construct_batch_groups(groups)	Construct index of batches from which can be sampled
get_groups(sampler)	Create the groups which can be sampled.

Attributes

__annotations__
__dict__
__doc__
__module__
__orig_bases__
__parameters__
__weakref__	list of weak references to the object