Beta release: v0.1.4

Mambular: Tabular Deep Learning with Mamba Architectures

Introduction

Mambular is a Python package that brings the power of Mamba architectures to tabular data, offering a suite of deep learning models for regression, classification, and distributional regression tasks.

Features

Comprehensive Model Suite: Includes modules for regression (MambularRegressor), classification (MambularClassifier), and distributional regression (MambularLSS), catering to a wide range of tabular data tasks.

• State-of-the-Art Architectures: Leverages the Mamba architecture, known for its effectiveness in handling sequential and time-series data within a state-space modeling framework, adapted here for tabular data.
• Seamless Integration: Designed to work effortlessly with scikit-learn, allowing for easy inclusion in existing machine learning pipelines, cross-validation, and hyperparameter tuning workflows.
• Extensive Preprocessing: Comes with a powerful preprocessing module that supports a broad array of data transformation techniques, ensuring that your data is optimally prepared for model training.
• Sklearn-like API: The familiar scikit-learn fit, predict, and predict_proba methods mean minimal learning curve for those already accustomed to scikit-learn.
• PyTorch Lightning Under the Hood: Built on top of PyTorch Lightning, Mambular models benefit from streamlined training processes, easy customization, and advanced features like distributed training and 16-bit precision.