💡 About The Project¶

Longitudinal datasets contain information about the same cohort of individuals (instances) over time, with the same set of features (variables) repeatedly measured across different time points (also called waves) [1,2].

Scikit-longitudinal (Sklong) is a machine learning library designed to analyse longitudinal data, also called Panel data in certain fields. Today, Sklong is focussed on the Longitudinal Machine Learning Classification task. It offers tools and models for processing, analysing, and classify longitudinal data, with a user-friendly interface that integrates with the Scikit-learn ecosystem.

For further information, visit the official documentation.

🛠️ Installation¶

To install Sklong, follow these two easy steps:

✅ Install the latest version of Sklong:
```
pip install Scikit-longitudinal
```
Different Versions?

You can also install different versions of the library by specifying the version number, e.g., pip install Scikit-longitudinal==0.0.1. Refer to the Release Notes.
📦 [MANDATORY] Update the required dependencies

Why is this necessary?

See this explanation.

Scikit-longitudinal includes a modified version of Scikit-Learn called Scikit-Lexicographical-Trees, which can be found at this Pypi link.

This revised version ensures compatibility with the unique features of Scikit-longitudinal. However, conflicts may occur with other dependencies that also require Scikit-Learn. Follow these steps to prevent any issues when running your project.
🫵 Simple Setup: Command Line Installation

If you want to try Sklong in a simple environment without a proper pyproject.toml file (such as using Poetry, PDM, etc.), run the following command:
```
pip uninstall scikit-learn scikit-lexicographical-trees && pip install scikit-lexicographical-trees
```
🫵 Project Setup: Using PDM (or any other package manager such as Poetry, etc.)

If you have a project managed by PDM, or any other package manager, the example below demonstrates PDM. The process is similar for Poetry and others. Consult their documentation for instructions on excluding a package.

To prevent dependency conflicts, you can exclude Scikit-Learn by adding the following configuration to your pyproject.toml file:
```
[tool.pdm.resolution]
excludes = ["scikit-learn"]
```
This exclusion ensures Scikit-Lexicographical-Trees (used as Scikit-Learn) is used seamlessly within your project.

💻 Developer Notes¶

For developers looking to contribute, please refer to the Contributing section of the documentation.

🛠️ Supported Operating Systems¶

Scikit-longitudinal is compatible with the following operating systems:

MacOS 
Linux 🐧
Windows via Docker only (Docker uses Linux containers) 🪟

Warning

We haven't tested it on Windows without Docker.

🚀 Getting Started¶

To perform longitudinal machine learning classification using Sklong, start by employing the LongitudinalDataset class to prepare your dataset (i.e, data itself, temporal vector, etc.). To analyse your data, you can utilise for instance the LexicoGradientBoostingClassifier or any other available estimator/preprocessor.

"The LexicoGradientBoostingClassifier in a nutshell: is a variant of Gradient Boosting specifically designed for longitudinal data, using a lexicographical approach that prioritises recent waves over older ones in certain scenarios [1].

Next, you can apply the popular fit, predict, prodict_proba, or transform methods depending on what you previously employed in the same way that Scikit-learn does, as shown in the example below:

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

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

Neural Networks models

Please see the documentation's FAQ tab for a list of similar projects that may offer Neural Network-based models, as this project presently does not. If we are interested in building Neural Network-based models for longitudinal data, we will announce it in due course.

Wants to understand what's the feature_groups? How your temporal dependencies are set via pre-set or manually?

To understand how to set your temporal dependencies, please refer to the Temporal Dependency tab of the documentation.

Wants more to grasp the idea?

To see more examples, please refer to the Examples tab of the documentation.

Wants more control on hyper-parameters?

To see the full API reference, please refer to the API tab.

📚 References¶

[1] Kelloway, E.K. and Francis, L., 2012. Longitudinal research and data analysis. In Research methods in occupational health psychology (pp. 374-394). Routledge.

[2] Ribeiro, C. and Freitas, A.A., 2019. A mini-survey of supervised machine learning approaches for coping with ageing-related longitudinal datasets. In 3^rd Workshop on AI for Aging, Rehabilitation and Independent Assisted Living (ARIAL), held as part of IJCAI-2019 (num. of pages: 5).

[3] Ribeiro, C. and Freitas, A.A., 2024. A lexicographic optimisation approach to promote more recent features on longitudinal decision-tree-based classifiers: applications to the English Longitudinal Study of Ageing. Artificial Intelligence Review, 57(4), p.84