Version 1.0.0

[AnFreTh]

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:

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🚀 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.

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🎛️ 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.

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⚡ Efficiency Improvements

• Leveraging the new mamba-ssm package for a more efficient implementation of the Mamba framework, ensuring faster runtime and reduced memory usage.

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🛠️ Enhanced Preprocessing

• Expanded preprocessing options for greater control over feature transformations.
• Improved feature information handling to better accommodate various dataset types and structures.

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🧬 Improved Embedding Layers

• New embedding layers, including PLR.
• Customizable activation functions for enhanced flexibility in embedding generation.

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This release sets the foundation for continued innovation in tabular deep learning. Feedback and contributions are welcome!