This release contains various additions from the work on three successive release candidates.
It is the official first release distributed along with the publication of the CEBRA paper.
- v0.0.2rc3
- **Add adapt=True in CEBRA.fit() [445](https://github.com/stes/neural_cl/pull/445)**:
Add capability to adapt a trained CEBRA models to new sessions of data, potentially with different input
dimensions.
- **Save/load functionality for sklearn models [408](https://github.com/stes/neural_cl/pull/408)**:
Add a `save/load` function to `cebra.CEBRA` for serialization. Experimental feature for now which will be
refined later on.
- v0.0.2rc2
- **Add cebra.plot package [385](https://github.com/stes/neural_cl/pull/385)**:
Simplify post-hoc analysis of model performance and embeddings by collecting plotting functions for the most common usecases.
- **Multisession API integration [333](https://github.com/stes/neural_cl/pull/333)**:
Add multisession implementation compatibility to the sklearn API.
- v0.0.2rc1
- **Implementation for general dataloading [305](https://github.com/stes/neural_cl/pull/305)**:
Implement `load`, a general function to convert any supported data file types to ``numpy.array``.
- **Add score method [316](https://github.com/stes/neural_cl/pull/316)**:
Add ``score`` method to ``cebra`` to compute the score of the trained model on new data.
- **Add quick testing option [318](https://github.com/stes/neural_cl/pull/318)**:
Add slow marker for longer tests and a quick testing option for pytest and in github workflow.
- **Add CITATION.cff file [339](https://github.com/stes/neural_cl/pull/339)**:
Add CITATION.cff file for easy-to-use citation of the pre-print paper.
- **Update sklearn dependency [317](https://github.com/stes/neural_cl/pull/317)**:
The sklearn dependency was updated to `scikit-learn` as discussed
[in the scikit-learn docs](https://github.com/scikit-learn/sklearn-pypi-package)
- **Increase documentation coverage >90% [265](https://github.com/stes/neural_cl/pull/265)**:
Configure `interrogate` for checking docstring coverage of the codebase. Add docstrings to increase
overall coverage to >90%.
- **Increase documentation coverage >80% [263](https://github.com/stes/neural_cl/pull/263)**:
Configure `interrogate` for checking docstring coverage of the codebase. Add docstrings to increase
overall coverage to >80%.
- **Apply new code and docstring formatting to whole codebase [255](https://github.com/stes/neural_cl/pull/255)**:
Before enforcing google style doc strings with `yapf`, apply `black` for stricter code formatting.
Format docstrings with `docformatter`.
- **Run formatter during workflow run [217](https://github.com/stes/neural_cl/pull/217)**:
This addition checks that `make docs` can be run as part of the tests.
- **Update documentation and enforce working links [198](https://github.com/stes/neural_cl/pull/198)**:
Revision and improvement of the current documentation. "nitpicky" mode is now used in sphinx,
which will check that we dont have any broken links of missing references in the documentation.