GroupedSampler#
- class pytorch_forecasting.data.samplers.GroupedSampler(sampler: Sampler, batch_size: int = 64, shuffle: bool = False, drop_last: bool = False)[source]#
Bases:
Sampler
Samples mini-batches randomly but in a grouped manner.
This means that the items from the different groups are always sampled together. This is an abstract class. Implement the
get_groups()
method which creates groups to be sampled from.Initialize.
- Parameters:
sampler (Sampler or Iterable) – Base sampler. Can be any iterable object
drop_last (bool) – if to drop last mini-batch from a group if it is smaller than batch_size. Defaults to False.
shuffle (bool) – if to shuffle dataset. Defaults to False.
batch_size (int, optional) – 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. Defaults to 64.
- Inherited-members:
Methods
construct_batch_groups
(groups)Construct index of batches from which can be sampled
get_groups
(sampler)Create the groups which can be sampled.