Scikit-longitudinal 0.1.0

Scikit-longitudinal is an open-source Python library for longitudinal data analysis, building on Scikit-learn's foundation with tools tailored for repeated measures data.

Scikit-longitudinal

A specialised Python library for longitudinal data analysis built on Scikit-learn

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💡 About The Project

Scikit-longitudinal (Sklong) is a machine learning library designed to analyse longitudinal data (Classification tasks focussed as of today). It offers tools and models for processing, analysing, and predicting longitudinal data, with a user-friendly interface that integrates with the Scikit-learn ecosystem.

For more scientific details, you can refer to our paper published in the Journal of Open Source Software (JOSS).

For more technical details, visit the official documentation.

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🛠️ Installation

[!NOTE] Want to be using Jupyter Notebook, Marimo, Google Colab, or JupyterLab? Head to the Getting Started section of the documentation, we explain it all! 🎉

To install Scikit-longitudinal:

1. ✅ Install the latest version:

   pip install Scikit-longitudinal
   

   To install a specific version:

   pip install Scikit-longitudinal==0.1.0
   

[!CAUTION] Scikit-longitudinal is currently compatible with Python versions 3.9 only. Ensure you have one of these versions installed before proceeding with the installation.

Now, while we understand that this is a limitation, we are tied for the time being because of Deep Forest. Deep Forest is a dependency of Scikit-longitudinal that is not compatible with Python versions greater than 3.9. Deep Forest helps us with the Deep Forest algorithm, to which we have made some modifications to welcome Lexicographical Deep Forest.

To follow up on this discussion, please refer to this github issue.

If you encounter any errors, feel free to explore further the installation section in the Getting Started of the documentation. If it still doesn't work, please open an issue on GitHub.

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🚀 Getting Started

Here's how to analyse longitudinal data with Scikit-longitudinal:

from scikit_longitudinal.data_preparation import LongitudinalDataset
from scikit_longitudinal.estimators.ensemble.lexicographical.lexico_gradient_boosting import LexicoGradientBoostingClassifier

dataset = LongitudinalDataset('./stroke.csv') # Note this is a fictional dataset. Use yours!
dataset.load_data_target_train_test_split(
  target_column="class_stroke_wave_4",
)

# Pre-set or manually set your temporal dependencies 
dataset.setup_features_group(input_data="elsa")

model = LexicoGradientBoostingClassifier(
  features_group=dataset.feature_groups(),
  threshold_gain=0.00015 # Refer to the API for more hyper-parameters and their meaning
)

model.fit(dataset.X_train, dataset.y_train)
y_pred = model.predict(dataset.X_test)

# Classification report
print(classification_report(y_test, y_pred))

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📝 How to Cite

If you use Sklong in your research, please cite our paper:

@article{Provost2025, 
   doi = {10.21105/joss.08481}, 
   url = {https://doi.org/10.21105/joss.08481}, 
   year = {2025}, 
   publisher = {The Open Journal}, 
   volume = {10}, 
   number = {112}, 
   pages = {8481}, 
   author = {Provost, Simon and Freitas, Alex A.}, 
   title = {Scikit-Longitudinal: A Machine Learning Library for Longitudinal Classification in Python}, 
   journal = {Journal of Open Source Software}
}

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🔐 License

Scikit-longitudinal is licensed under the MIT License.