Release v0.2.4

What's Changed

• sklearn base_modules: Modified conditional checks to use if X_val is not None instead of if X_val in the build_model and fit methods. by @AnFreTh in #142
• mambular/data_utils/datamodule.py: Ensured that keys are converted to strings when constructing cat_key, binned_key, and num_key in the setup and preprocess_test_data methods. by @AnFreTh in #142

Full Changelog: v0.2.3...v0.2.4

Release v0.2.3

• Including Quantile Regression
• Fixing param count bug in sklearnbaselss

v0.2.2

• Fixing SklearnBaseClassifier error
• Fixing TabulaRNNRegressor error

Release v0.2.1

What's Changed

• Included new models (MambaTab, TabulaRNN)
• Added utility functionality to model build
• Improved Embedding layers
• Added AB layernorm and weight decay to Mamba
• Added score function to sklearn base classes

Full Changelog: v0.1.7...v0.2.1

Hotfix Release v0.1.7

What's Changed

• np version fixed by @mkumar73 in #71
• Switch to Numpy <=1.26.4 instead of 2.0.

New Release v0.1.6

New Version Release Highlights:

• Addition of New Models: We've expanded our model suite to include the following architectures:

  • FT-Transformer: Leverages transformer encoders for improved performance on tabular data.
  • MLP (Multi-Layer Perceptron): A classical deep learning model for handling a wide range of tabular data tasks.
  • ResNet: Adapted from the classical ResNet architecture and proven to be a good baseline for tabular tasks.
  • TabTransformer: Utilizes transformer-based models for categorical features.

• Bidirectional and Feature Interaction Capabilities: Mambular now includes bidirectional capabilities and enhanced feature interaction mechanisms, enabling more complex and dynamic data representations and improving model accuracy.

• Architectural Restructuring: The internal architecture has been restructured to facilitate the easy integration of new models. This modular approach simplifies the process of extending Mambular with custom models.

• New Preprocessing Methods: We have introduced new preprocessing techniques to better prepare your data for modeling:

  • Quantile Preprocessing: Transforms numerical features to follow a uniform or normal distribution, improving robustness to outliers.
  • Polynomial Features: Generates polynomial and interaction features to capture more complex relationships within the data.
  • Spline Transformation: Applies piecewise polynomial functions to numerical features, effectively capturing nonlinear relationships.

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.