v1.1.1

Release Notes

---

New Features

• Support for Multi-Model Tuning: Added support for tuning multiple models using the tuner functionality, enhancing flexibility in hyperparameter optimization.
  [Commit: 560dec6]

• Multi-Target Classification: Introduced multi-target classification capabilities, expanding the library's support for more complex use cases.
  [Commit: 25691f5]

• dataloader_kwargs in DataConfig: Added support for customizing dataloader_kwargs in the DataConfig module for improved data-loading flexibility.
  [Commit: caa3ea1]

---

Enhancements

• Improved Informative str and repr: Added more informative str and repr methods to enhance debugging and readability of objects.
  [Commit: 495803c]

• Bug Fixes for Categorical Dtype: Fixed issues with Categorical data type handling to ensure smoother model training and predictions.
  [Commit: cf1454a]

• Removed Restrictions on Missing and Unknown Values: Enhanced the framework to handle missing and unknown values more robustly.
  [Commit: fc6060e]

• Protection Against Misuse of MDN Head: Added safeguards to prevent improper usage of the MDN Head in models.
  [Commit: cc3504a]

---

Bug Fixes

• Fixed an SSL finetuning bug to ensure secure operations during model fine-tuning.
  [Commit: 3d978f9]

• Fixed errors in saving and loading custom loss functions to enhance reproducibility and reliability.
  [Commit: 3f0a15c]

• Addressed a bug in cross-validation, ensuring accurate evaluation metrics.
  [Commit: 0d088fc]

• Fixed a KeyError issue with nn.activation in Tab Transformer and FT Transformer models.
  [Commit: 11adefa]

---

Other Improvements

• Multiple pre-commit configuration updates and enhancements for code linting and formatting.
  [Commits: f354b9c, 75b21c4, a890dda]

• Various CI improvements, including dependency bump for gh-action-pypi-publish and caching updates.
  [Commits: cb78a6e, e49a999, da20ed3]

• Fixed typos and minor issues in documentation and code for improved clarity and maintainability.
  [Commits: 7285787, 6586705]

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

New Contributors

---

• @furyhawk made their first contribution in #382
• @HernandoR made their first contribution in #410
• @charitarthchugh made their first contribution in #420
• @abhisharsinha made their first contribution in #455
• @YonyBresler made their first contribution in #441
• @snehilchatterjee made their first contribution in #492

Full Changelog: v1.1.0...v1.1.1

v1.1.0

New Features and Enhancements

• Added DANet Model: Added a new model, DANet, for tabular data.
• Explainability: Integrated Captum for explainability
• Hyperparameter Tuner: Added Grid and Random Search functionality to search through hyperparameters and return best model.
• Model Sweep: Added an easy "Model Sweep" method with which we can sweep a list of models with given data and quickly assess performance.
• Documentation Enhancements: Improved documentation to make it more user-friendly and informative
• Dependency Updates: Updated various dependencies for improved compatibility and security
• Graceful Out-of-Memory Handling: Added graceful out-of-memory handling for tabular models
• GhostBatchNorm: Added GhostBatchNorm to the library

Deprecations

• Deprecations: Handled deprecations and updated the library accordingly
• Entmax Dependency Removed: Removed dependency on entmax

Infrastructure and CI/CD

• Continuous Integration: Improved CI with new actions and labels
• Dependency Management: Updated dependencies and restructured requirements

API Changes

• [BREAKING CHANGE] SSL API Change: Addressed SSL API change, along with documentation and tutorial updates.
• Model Changes: Added is_fitted and other markers to the tabular model.
• Custom Optimizer: Allow custom optimizer in the model config.

Contributors

• Thanks to all the contributors who helped shape this release! (List of Contributors)

Upgrading

• Ensure to check the updated documentation for any breaking changes or new features.
• If you are using SSL, please check the updated API and documentation.

v1.0.2

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.

v1.0.1

• 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.

v0.7.0-alpha

• 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.

v0.5.0-alpha

First Alpha Release