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Releases: basf/mamba-tabular

Release v0.2.4

21 Oct 15:03
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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

19 Sep 08:40
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  • Including Quantile Regression
  • Fixing param count bug in sklearnbaselss

v0.2.2

13 Aug 16:25
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  • Fixing SklearnBaseClassifier error
  • Fixing TabulaRNNRegressor error

Release v0.2.1

13 Aug 03:01
dad9a12
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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

11 Jul 08:43
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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

01 Jul 13:26
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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

04 Jun 11:28
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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.