- 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.
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.
To install Scikit-longitudinal:
-
✅ 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.
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.
If you find Scikit-longitudinal helpful, please cite us using the CITATION.cff file or via the "Cite this repository" button on GitHub.
Scikit-longitudinal is licensed under the MIT License.