v1.0.0: Major Update for Mambular
This release marks a significant upgrade to Mambular, introducing new models, enhanced functionality, and improved efficiency. Below is an overview of the key changes:
🚀 New Models
- TabM: A cutting-edge tabular deep learning model optimized for performance and flexibility.
- NODE: Incorporates Neural Oblivious Decision Ensembles for more robust handling of tabular data.
- NDTF: Neural Decision Tree Forest, combining decision tree logic with deep learning capabilities.
- TabulaRNN: A recurrent neural network tailored for tabular tasks, with configurable options to use GRU or LSTM cells for sequence modeling.
🎛️ Hyperparameter Optimization
- Integrated support for hyperparameter optimization:
- Built-in Bayesian optimization for more advanced tuning.
- Fully compatible with Scikit-learn's optimization framework, enabling seamless integration for all models.
⚡ Efficiency Improvements
- Leveraging the new mamba-ssm package for a more efficient implementation of the Mamba framework, ensuring faster runtime and reduced memory usage.
🛠️ Enhanced Preprocessing
- Expanded preprocessing options for greater control over feature transformations.
- Improved feature information handling to better accommodate various dataset types and structures.
🧬 Improved Embedding Layers
- New embedding layers, including PLR.
- Customizable activation functions for enhanced flexibility in embedding generation.
This release sets the foundation for continued innovation in tabular deep learning. Feedback and contributions are welcome!