Pytorch-tabular

New Features:

- Added Feature Importance: The library now includes a new method in TabularModel and BaseModel for enabling feature importance. Feature Importance has been enabled for FTTransformer and GATE models. [Commit: dc2a49e]
Enhancements:

- Enabled two more parameters in the GATE model. [Commit: 3680413]
- Included metric_prob_input parameter in the library configuration. This update allows for better control over metrics in the models. [Commit: 0612db5]
- Slight improvements to the GATE model, including changes to defaults for better performance. [Commit: c30a6c3]
- Minor bug fixes and improvements, including accelerator options in the configuration and progress bar enhancements. [Commit: f932230, bdd9adb, f932230]
Dependency Updates:

- Updated dependencies, including docformatter, pyupgrade, and ruff-pre-commit. [Commits: 4aae9a8, b3df4ce, bdd9adb, 55e800c, c6c4679, c01154b, 107cd2f]
Documentation Updates:

- Updated the library's README.md file. [Commits: db8f3b2, cab6bf1, 669faec, 1e6c400, 3097799, 7fabf6b]
Other Improvements:

- Various code optimizations, bug fixes, and CI enhancements. [Commits: 5637020, e5171bf, 812b40f]

For more details, you can refer to the respective commits on the library's GitHub repository.

- Added a new task - Self Supervised Learning (SSL) and a separate training API for it.
- Added new SOTA model - Gated Additive Tree Ensembles (GATE).
- Added one SSL model - Denoising AutoEncoder.
- Added lots of new tutorials and updated entire documentation.
- Improved code documentation and type hints.
- Separated a Model into separate Embedding, Backbone, and Head.
- Refactored all models to separate Backbone as native PyTorch Model(nn.Module).
- Refactored commonly used modules (layers, activations etc. to a common module).
- Changed MixedDensityNetworks completely (breaking change). Now MDN is a head you can use with any model.
- Enabled a low level api for training model.
- Enabled saving and loading of datamodule.
- Added trainer_kwargs to pass any trainer argument PyTorch Lightning supports.
- Added Early Stopping and Model Checkpoint kwargs to use all the arguments in PyTorch Lightining.
- Enabled prediction using GPUs in predict method.
- Added `reset_model` to reset model weights to random.
- Added many save and load functions including ONNX(experimental).
- Added random seed as a parameter.
- Switched over completely to Rich progressbars from tqdm.
- Fixed class-balancing / mu propagation and set default to 1.0.
- Added PyTorch Profiler for debugging performance issues.
- Fixed bugs with FTTransformer and TabTransformer.
- Updated MixedDensityNetworks fixing a bug with lambda_pi.
- Many CI/CD improvements including complete integration with GitHub Actions.
- Upgraded all dependencies, including PyTorch Lightning, pandas, to latest versions and added dependabot to manage it going forward.
- Added pre-commit to ensure code integrity and standardization.

- Added a few more SOTA models - TabTransformer, FTTransformer
- Made improvements in the model save and load capability
- Made installation less restrictive by unfreezing some dependencies.

First Alpha Release