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A package that allows for flexible ML pipelines, model validation, and model inspection.

The project started to productize code written for Kaggle competitions and it is mostly designed to facilitate the creation of flexible processing and modeling pipeline. Everything is built around scikit-learn transformers and methods, but it is possible to extend the concept to other ML packages.

The main characteristic that makes the pipeline components of tubesML flexible is the fact that it always preserves the pandas DataFrame structure, hence making it easy to, for example, create a feature within a pipeline and test it in a grid search.

Installation

Dependencies

tubesML requires:

  • Python (>= 3.12)

  • Matplotlib (>= 3.10.0)

  • Pandas (>= 2.3.2)

  • Scikit-Learn (>= 1.6.2)

  • Seaborn (>= 0.13.2)

These requirements are good for a Kaggle notebook, however the package has been developed with the following requirements

  • Python (>= 3.12)

  • Matplotlib (>= 3.10.0)

  • Pandas (>= 2.3.2)

  • Numpy (>= 2.0.0)

  • Scikit-Learn (>= 1.8.0)

  • Seaborn (>= 0.13.2)


User installation

If you already have a working installation of pandas and sklearn, the easiest way to install scikit-learn is using pip

pip install -U tubesml

Release files for tubesml 1.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for tubesml 1.2.0
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tubesml-1.2.0.tar.gz 66.4 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for tubesml 1.2.0
File Interpreter ABI Platform
tubesml-1.2.0-py3-none-any.whl Python 3 none any Details

Total release size: 124.9 kB

Release files / tubesml-1.2.0.tar.gz

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Size 66.4 kB
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Release files / tubesml-1.2.0-py3-none-any.whl

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