💡 About The Project

Auto-Scikit-Longitudinal (Auto-Sklong) is an Automated Machine Learning (AutoML) library, developed upon the General Machine Learning Assistant (GAMA) framework, introducing a brand-new search space leveraging both Scikit-Longitudinal and Scikit-learn models to tackle the Longitudinal machine learning classification tasks.

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 published by IEEE in the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024 Edition.
• Auto-Sklong comes with various search methods to explore the search space introduced, such as Bayesian Optimisation. For more details, visit the official documentation.

🛠️ Installation

[!NOTE] Want to use Jupyter Notebook, Marimo, Google Colab, or JupyterLab? Head to the Getting Started section of the documentation for full instructions! 🎉

To install Auto-Sklong:

1. ✅ Install the latest version: bash pip install auto-sklong

To install a specific version: bash pip install auto-sklong==0.0.1

[!CAUTION] Auto-Sklong is currently compatible with Python versions 3.9 only. Ensure you have this version installed before proceeding.

This limitation stems from the Deep Forest dependency. Follow updates on this GitHub issue.

If you encounter errors, explore the installation section in the Getting Started of the documentation. If issues persist, open a GitHub issue.

🚀 Getting Started

Here's how to run AutoML on longitudinal data with Auto-Sklong:

```python from sklearn.metrics import classificationreport from scikitlongitudinal.data_preparation import LongitudinalDataset from gama.GamaLongitudinalClassifier import GamaLongitudinalClassifier

Load your dataset (replace 'stroke.csv' with your actual dataset path)

dataset = LongitudinalDataset('./stroke.csv')

Set up the target column and split the data (replace 'classstrokewave_4' with your target)

dataset.loaddatatargettraintestsplit( targetcolumn="classstrokewave_4", )

Set up feature groups (temporal dependencies)

Use a pre-set for ELSA data or define manually (See docs for details)

dataset.setupfeaturesgroup(input_data="elsa")

Initialise the AutoML system

automl = GamaLongitudinalClassifier( featuresgroup=dataset.featuregroups(), nonlongitudinalfeatures=dataset.nonlongitudinalfeatures(), featurelistnames=dataset.data.columns.tolist(), maxtotaltime=3600 # Adjust time as needed (in seconds) )

Fit the AutoML system

automl.fit(dataset.Xtrain, dataset.ytrain)

Make predictions

ypred = automl.predict(dataset.Xtest)

Print the classification report

print(classificationreport(dataset.ytest, y_pred)) ```

More detailed examples and tutorials can be found in the documentation!

📝 How to Cite

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

bibtex @INPROCEEDINGS{10821737, author={Provost, Simon and Freitas, Alex A.}, booktitle={2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, title={Auto-Sklong: A New AutoML System for Longitudinal Classification}, year={2024}, volume={}, number={}, pages={2021-2028}, keywords={Pipelines;Optimization;Predictive models;Classification algorithms;Conferences;Bioinformatics;Biomedical computing;Automated Machine Learning;AutoML;Longitudinal Classification;Scikit-Longitudinal;GAMA}, doi={10.1109/BIBM62325.2024.10821737}}

🚀 What's New Compared to GAMA?

We enhanced @PGijsbers' open-source GAMA initiative by introducing a brand-new search space designed specifically for tackling longitudinal classification problems. This search space is powered by our custom library, Scikit-Longitudinal (Sklong), enabling Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization).

Unlike GAMA or other existing AutoML libraries, Auto-Sklong offers out-of-the-box support for longitudinal classification tasks—a capability not previously available.

Search Space Viz.:

To better understand our proposed search space, refer to the visualisation below (read from left to right, each step being one new component to a final pipeline candidate configuration):

While GAMA offers some configurability for search spaces, we improved its functionality to better suit our needs. You can find the details of our contributions in the following pull requests: - ConfigSpace Technology Integration for Enhanced GAMA Configuration and Management 🥇 - Search Methods Enhancements to Avoid Duplicate Evaluated Pipelines 🥈 - SMAC3 Bayesian Optimisation Integration 🆕

🔐 License

Auto-Sklong is licensed under the MIT License.