https://github.com/simonprovost/scikit-longitudinal

☂️ Scikit-longitudinal (Sklong) is an open-source Python library & Scikit-Learn API compliant, tailored to longitudinal machine learning classification tasks. It is ideal for researchers, data scientists, and analysts, as it provides specialist tools for dealing with repeated-measures data challenges
https://github.com/simonprovost/scikit-longitudinal

classification longitudinal longitudinal-classification longitudinal-data longitudinal-studies machine-learning repeated-measurements scikit-learn supervised-learning

Last synced: 9 months ago
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☂️ Scikit-longitudinal (Sklong) is an open-source Python library & Scikit-Learn API compliant, tailored to longitudinal machine learning classification tasks. It is ideal for researchers, data scientists, and analysts, as it provides specialist tools for dealing with repeated-measures data challenges

• Host: GitHub
• URL: https://github.com/simonprovost/scikit-longitudinal
• Owner: simonprovost
• License: mit
• Created: 2023-03-27T03:09:51.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2025-05-13T10:35:22.000Z (11 months ago)
• Last Synced: 2025-06-22T02:38:32.715Z (9 months ago)
• Topics: classification, longitudinal, longitudinal-classification, longitudinal-data, longitudinal-studies, machine-learning, repeated-measurements, scikit-learn, supervised-learning
• Language: Python
• Homepage: https://scikit-longitudinal.readthedocs.io/latest/
• Size: 55 MB
• Stars: 38
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.MD
  • License: LICENSE
  • Citation: CITATION.cff

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README

          



   


   



      


      

         

      

      


      Scikit-longitudinal

      


   

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







   





   





   





   











   





   







---

## 💡 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 details, visit the [official documentation](https://scikit-longitudinal.readthedocs.io/latest//).

---

## 🛠️ 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:

   ```bash

   pip install Scikit-longitudinal

   ```

   To install a specific version:

   ```bash

   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](https://github.com/LAMDA-NJU/Deep-Forest/issues/124).

> 

> 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.

---

## 🚀 Getting Started

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

``` py

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))

```

---

## 📝 How to Cite

We are currently cooking a JOSS submission, wait a bit for it! Meanwhile, click on `Cite This Repository` on the top right corner of this page to get a BibTeX entry.

---

## 🔐 License

Scikit-longitudinal is licensed under the [MIT License](./LICENSE).