Metadata-Version: 2.4
Name: Scikit-longitudinal
Version: 0.1.4
Summary: 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.
Home-page: https://github.com/simonprovost/scikit-longitudinal
Author: Provost Simon, Alex Freitas
Author-email: Provost Simon <simon.gilbert.provost@gmail.com>, Alex Freitas <a.a.freitas@kent.ac.uk>
License: MIT
Project-URL: Homepage, https://github.com/simonprovost/scikit-longitudinal
Project-URL: Documentation, https://scikit-longitudinal.readthedocs.io/latest/
Project-URL: Releases, https://github.com/simonprovost/scikit-longitudinal/releases
Project-URL: Repository, https://github.com/simonprovost/scikit-longitudinal
Project-URL: Issues, https://github.com/simonprovost/scikit-longitudinal/issues/
Project-URL: Scientific Paper, https://joss.theoj.org/papers/10.21105/joss.08481
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.9,<3.10
Description-Content-Type: text/markdown
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Requires-Dist: pandas<2.0.0,>=1.5.3
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Requires-Dist: deep-forest>=0.1.7
Requires-Dist: scikit-lexicographical-trees==0.0.4
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Dynamic: author
Dynamic: home-page
Dynamic: requires-python

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      <br>
      Scikit-longitudinal
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   <h4 align="center">A specialised Python library for longitudinal data analysis built on Scikit-learn</h4>
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---

## <a id="about-the-project"></a>💡 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.

**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 survey because it captures evolution, patterns, and cause-effect throughout
time.

**Not enough?**

* For more scientific details, you can refer to our [paper](https://doi.org/10.21105/joss.08481) published in
  the [Journal of Open Source Software (JOSS)](https://joss.theoj.org/).
* For more technical details, visit the [official documentation](https://scikit-longitudinal.readthedocs.io/latest//).

---

## <a id="installation"></a>🛠️ 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.

---

## <a id="getting-started"></a>🚀 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))
```

---

## <a id="citation"></a>📝 How to Cite

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

```bibtex
@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}
}
```

---

## <a id="license"></a>🔐 License

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