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# Changelog | ||
All notable changes to this project will be documented in this file. | ||
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), | ||
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). | ||
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## [Unreleased] | ||
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## [0.1.1] - 2022-03-31 | ||
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### Added | ||
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* Missing data simulation for UEA/UCR data sets | ||
* Support appending missing data masks and time delta channels | ||
* `packed_sequence` collate function | ||
* Documentation now includes a tutorial | ||
* Automated releases using GitHub Actions | ||
* DOI | ||
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### Changed | ||
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* Simplified training/validation/test split approach | ||
* Default file path for PhysioNet2019 data set is now `data/physionet2019` | ||
* Refactored `torchtime.data` to share utility functions across data classes | ||
* Expanded unit tests | ||
* Updated documentation | ||
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## [0.1.0] - 2022-03-28 | ||
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First release to PyPi | ||
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[Unreleased]: https://github.com/philipdarke/torchtime/compare/v0.1.1..HEAD | ||
[0.1.1]: https://github.com/philipdarke/torchtime/compare/v0.1.0..v0.1.1 | ||
[0.1.0]: https://github.com/philipdarke/torchtime/releases/tag/v0.1.0 |
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# API | ||
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## `torchtime.data` | ||
## Time series data sets | ||
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* [PhysioNet2019](torchtime.data.PhysioNet2019) | ||
* [UEA](torchtime.data.UEA) | ||
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```{eval-rst} | ||
.. automodule:: torchtime.data | ||
:members: | ||
:members: | ||
``` | ||
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## `torchtime.collate` | ||
## Custom collate functions | ||
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Data sets of variable length can be efficiently represented in PyTorch using a [`PackedSequence`](https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.PackedSequence.html) object. These are formed using | ||
[`pack_padded_sequence()`](https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pack_padded_sequence.html#torch.nn.utils.rnn.pack_padded_sequence) which by default expects the input batch to be sorted in descending length. This is handled by the [`sort_by_length()`](torchtime.collate.sort_by_length) collate function. Alternatively, a `PackedSequence` object can be formed using the [`packed_sequence()`](torchtime.collate.packed_sequence) collate function. | ||
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Custom collate functions should be passed to the `collate_fn` argument of a [DataLoader](https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader). | ||
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```{eval-rst} | ||
.. automodule:: torchtime.collate | ||
:members: | ||
:members: | ||
``` |
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```{eval-rst} | ||
.. toctree:: | ||
:hidden: | ||
:maxdepth: 2 | ||
tutorial | ||
api | ||
``` |
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