Scikit-longitudinal 0.1.10

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.

A Scikit-Learn-like Python library for Longitudinal Machine Learning — Paper · Documentation · PyPi Index

About The Project

Scikit-longitudinal (Sklong) is a machine learning library tailored for Longitudinal machine (supervised) learning (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.

Wait, what is Longitudinal Data — In layman's terms?

Longitudinal data is a "time-lapse" snapshot of the same subject, entity, or group tracked over time-periods, similar to checking in on patients to see how they change. For instance, doctors may monitor a patient's blood pressure, weight, and cholesterol every year for a decade to identify health trends or risk factors. This data is more useful for predicting future results than a one-time (cross-sectional) survey because it captures evolution, patterns, and cause-effect throughout time.

See more in the documentation.

Installation

To install Scikit-longitudinal:

pip install Scikit-longitudinal

To install a specific version:

pip install Scikit-longitudinal==0.1.0

[!TIP] Want to use Jupyter Notebook/Lab, Google Colab or want to activate parallelism? Head to the Getting Started section of the documentation, we explain it all! 🎉

Getting Started

Let's run a simple Longitudinal machine learning classification task:

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

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

We would like to personally thank Prof. Lengerich (UW Madison—@blengerich & @AdaptInfer), & Prof. Tahiri (Université de Sherbrooke—@TahiriNadia & @tahiri-lab) for their amazing peer reviews!

@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}
}

License

Scikit-longitudinal is licensed under the MIT License.