Scikit-longitudinal 0.0.2

Scikit-longitudinal, an open-source Python lib for longitudinal data analysis, builds on Scikit-learn's foundation. It offers specialized tools to tackle challenges of repeated measures data, ideal for researchers, data scientists, & analysts.

Scikit-longitudinal

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

⚙️ Project Status	☎️ Contacts

🌟 Exciting Update: We're delighted to introduce the brand new v0.1 documentation for Scikit-longitudinal! For a deep dive into the library's capabilities and features, please visit here.

💡 About The Project

Scikit-longitudinal 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.

Please for further information, visit the official documentation.

🛠️ Installation

ON-HOLD until the first public release

Note that for developers, you should follow up onto the Contributing tab of the official documentation.

🚀 Getting Started

To perform longitudinal analysis with Scikit-Longitudinal, use the LongitudinalDataset class to prepare the dataset. To analyse your data, use the LexicoGradientBoostingClassifier (i.e. Gradient Boosting variant for Longitudinal Data) or another available estimator/preprocessor.

Following that, you can apply the popular fit, predict, prodict_proba, or transform methods 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_4_years.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
)

model.fit(dataset.X_train, dataset.y_train)
y_pred = model.predict(dataset.X_test)

📝 How to Cite?

Paper's citation information will be added here once published. Currently, it has been submitted to a conference. In the meantime, for the repository, utilise the button top right corner of the repository "How to cite?". Or open the following citation file: CITATION.cff.

🔐 License

MIT License