Pysipfenn

Major Changes:
- Per [[add] modelExporters](https://github.com/PhasesResearchLab/pySIPFENN/pull/8) by amkrajewski, 3 model exporter classes have been added to pySIPFENN in the `modelExporters` module:
- `ONNXExporter` allowing (1) exporting back to ONNX after models were adjusted to new datasets or new properties (transfer learning), (2) automated simplification of model architecture for improved performance, and (3) adjusting model internal precision to FP16 to reduce its size by half with only minor performance impact.
- `TorchExporter` to export models to `PyTorch` which are used internally by pySIPFENN.
- `CoreMLExporter` to export models to Apple's CoreML format developed for use in their devices, where it provides the most seamless integration with existing apps and can harvest very efficient Neural Engine hardware acceleration. At the same time, it can be used on other platforms as well, such as Linux or Windows. Note that by default, models will be converted to FP16 precision, similar to one of the `ONNXExporter` options.

The above changes are a step in our effort to make advanced use of pySIPFENN easier for end-users. Next items on the roadmap include (1) automation of transfer learning on small datasets and (2) OPTIMADE integrations, which will make pySIPFENN API a one-stop solution for model fine-tuning.

Minor Changes:
- Small improvements in the `Calculator` object printout with the `verbose` option (true by default) added to its initialization.
- Matching tests were added to the CI pipelines.
- `dev` extra dependencies have been established, alongside appropriate documentation, for all future dependencies that are not required for core pySIPFENN functionalities to keep it as light as possible while not limiting advanced users. One of the future directions will be to make core dependencies lighter.
- Model exports and motivations were added to the documentation under [Exporting pySIPFENN Models](https://pysipfenn.org/exportingmodels.html) page.

**Full Changelog**: https://github.com/PhasesResearchLab/pySIPFENN/compare/v0.12.2...v0.13.0

Major Changes:
- This is a minor release that changes the license to [LGPLv3](https://www.gnu.org/licenses/lgpl-3.0.en.html) in order to allow for integration with proprietary software developed by the CALPHAD community (shipping pySIPFENN and then calling it from closed-source tools will be allowed) while supporting the development of new pySIPFENN features for all users, as modifications to pySIPFENN (except for models, see below) itself need to be open.

- This change does not affect the [default pySIPFENN models](https://doi.org/10.5281/zenodo.4006802), which are distributed under [Creative Commons 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) that already comes with no restrictions.

- Many thanks to our colleagues from [GTT-Technologies](https://gtt-technologies.de) and other participants of [50th CALPHAD 2023 conference in Boston](https://calphad.org/calphad-2023) for fruitful discussions.

**Full Changelog**: https://github.com/PhasesResearchLab/pySIPFENN/compare/v0.12.1...v0.12.2

Minor Changes:
- FIX: This update addresses network loading issues, affecting some SIPFENN models (with Dropout), caused by `onnx2torch=1.5.7` recently introducing a new naming schema for the "insides" of ONNX neural networks translated to PyTorch.
- Improved testing, including automated benchmarking of featurizer performance across Python versions and platforms.

**Full Changelog**: https://github.com/PhasesResearchLab/pySIPFENN/compare/v0.12.0...v0.12.1

Major Changes:
- Automated matrix-testing on Linux / Mac / Windows with Python 3.9 / 3.10 / 3.11 through GitHub Actions CLI. Core functions are tested across all of them, and badges in the README indicate test status after every code change
[](https://github.com/PhasesResearchLab/pySIPFENN/actions/workflows/partialTest.yml) [](https://github.com/PhasesResearchLab/pySIPFENN/actions/workflows/fullTest.yml)
- Automated test coverage analysis through GitHub Actions CI and reporting through Codecov service
[](https://codecov.io/gh/PhasesResearchLab/pySIPFENN)
- Many improvements in the testing procedures and additional tests bringing the coverage up from 74% (in v0.11.0) to 86%.
- (affects backward compatibility) The models download and run functions built around MxNet, which have been deprecated for a while since v0.9.0, have been removed.
- (affects backward compatibility) Small change in the behavior of the runModels_dilute() function. Now it expects the _descriptor_ / _feature vector_ input "KS2022" to run the "KS2022_dilute" _descriptor calculator_ / _featurizer_. This change is due to a few new _featurizers_ being in the works, including for approximating random solid solutions and quasicrystals, and all of them will use the "KS2022" _descriptor_, so this will make workflows much more clear.
- Added official Python 3.11 support and tests using it.
- Added small automated benchmarking on Linux using different Python versions so that users can select one that works best. Generally, Python 3.10 is the fastest. Across all 3 featurizers (KS2022, KS2022_dilute, and Ward2017), relative to the Python 3.9 baseline, 3.10 is around 35-40% faster, while 3.11 is 25-30% faster, based on the tests in GitHub Actions CI.

Minor Changes:
- Minor bug fixes, mostly in tests, not the user code.
- The wget dependency has been removed, as we moved to the multi-threaded pySmartDL package for model download.
- Documentation updates.

**Full Changelog**: https://github.com/PhasesResearchLab/pySIPFENN/compare/v0.11.0...v0.12.0

Minor Changes:
- Documentation and README updates.
- Minor bug fixes.

**Full Changelog**: https://github.com/PhasesResearchLab/pySIPFENN/compare/v0.11.0...v0.11.0post1

Major Changes:
- Model download from Zenodo is now multi-threaded. Users should see 15x faster speeds.
- Added an FAQ page to the documentation.
- KS2022 can filter out all structure-independent features to better compare polymorphs.
- Printing the `Calculator` object shows the models' location and relevant current state information.

Minor Changes:
- Minor bug fixes.
- It's now possible to load a single model with `loadModels()`, which is similar to `downloadModels()`.
- Offline documentation is available inside the package.
- Documentation updates.

**Full Changelog**: https://github.com/PhasesResearchLab/pySIPFENN/compare/v0.10.3...v0.11.0