Scikit-longitudinal 0.0.7

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.

Scikit-longitudinal

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

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📰 Latest News

• Updated Workflow: Now leveraging UV for enhanced project management and dependency resolution.
• Documentation: Dive into Scikit-longitudinal's features and capabilities in our official documentation.
• PyPI Availability: The library is available on PyPI.

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💡 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.

For more details, visit the official documentation.

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🛠️ Installation

To install Scikit-longitudinal:

1. ✅ Install the latest version:

   pip install Scikit-longitudinal
   

   To install a specific version:

   pip install Scikit-longitudinal==0.1.0
   

See further in the Quick Start of the documentation for more details.

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🚀 Getting Started

Here's how to analyse longitudinal data with Scikit-longitudinal:

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))

See further in the Quick Start of the documentation for more details.

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📝 How to Cite

If you find Scikit-longitudinal helpful, please cite us using the CITATION.cff file or via the "Cite this repository" button on GitHub.

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🔐 License

Scikit-longitudinal is licensed under the MIT License.