scikit-explain

A user-friendly Python module for tabular machine learning explainability. For a comprehensive tutorial, see Flora et al. (2024).

Explainability Methods

Feature Importance

• Single- and Multi-pass Permutation Importance (Breiman et al. 2001; Lakshmanan et al. 2015; McGovern et al. 2019)
• First-order PD/ALE Variance (Greenwell et al. 2018)
• Grouped Permutation Importance (Au et al. 2021)

Feature Effects/Attributions

• Partial Dependence (PD)
• Accumulated Local Effects (ALE)
• Individual Conditional Expectations (ICE)
• SHapley Additive Explanations (SHAP)
• Local Interpretable Model-Agnostic Explanations (LIME)
• TreeInterpreter (tree-based feature contributions)

Feature Interactions

• Second-order PD/ALE
• Interaction Strength (IAS) and Main Effect Complexity (MEC) (Molnar et al. 2019)
• Second-order PD/ALE Variance (Greenwell et al. 2018)
• Second-order Permutation Importance (Oh et al. 2019)
• Friedman H-statistic (Friedman and Popescu 2008)
• Sobol Indices

These methods are discussed in Christoph Molnar's Interpretable Machine Learning. A primary feature of scikit-explain is the built-in plotting methods, designed to be easy to use while producing publication-quality figures. Documentation is available at Read the Docs.

Installation

pip (PyPI):

pip install scikit-explain

conda (conda-forge):

conda install -c conda-forge scikit-explain

Development version (most up-to-date):

git clone https://github.com/monte-flora/scikit-explain.git
cd scikit-explain
pip install -e .

Dependencies

scikit-explain is compatible with Python 3.8 or newer and requires:

numpy, scipy, pandas, scikit-learn, matplotlib, shap>=0.30.0,
xarray>=0.16.0, tqdm, statsmodels, seaborn>=0.11.0

Quick Start

import skexplain

# Load pre-trained models and data
estimators = skexplain.load_models()
X, y = skexplain.load_data()

# Create the explainer
explainer = skexplain.ExplainToolkit(estimators=estimators, X=X, y=y)

# Configure plot display settings once (optional)
explainer.set_plotting_config(
    display_feature_names={"sfc_temp": "$T_{sfc}$", "temp2m": "$T_{2m}$"},
    display_units={"sfc_temp": "$^\\circ$C", "temp2m": "$^\\circ$C"},
)

Permutation Importance

perm_results = explainer.permutation_importance(n_vars=10, evaluation_fn='norm_aupdc')
explainer.plot_importance(data=perm_results, panels=[('multipass', 'Random Forest')])

Accumulated Local Effects

important_vars = explainer.get_important_vars(perm_results, multipass=True, nvars=7)
ale = explainer.ale(features=important_vars, n_bins=20)
explainer.plot_ale(ale=ale)

Feature Attributions

import shap

single_example = X.iloc[[0]]
explainer = skexplain.ExplainToolkit(estimators=estimators, X=single_example)

shap_kws = {
    'masker': shap.maskers.Partition(X, max_samples=100, clustering="correlation"),
    'algorithm': 'auto',
}
attr_results = explainer.local_attributions(
    method=['shap', 'lime', 'tree_interpreter'],
    shap_kws=shap_kws,
)
explainer.plot_contributions(attr_results)

Tutorial Notebooks

Citation

If you use scikit-explain in your research, please cite:

@article{Flora_2024,
  author  = {Flora, Montgomery L. and McGovern, Amy and Handler, Shawn},
  title   = {A Machine Learning Explainability Tutorial for Atmospheric Sciences},
  journal = {Artificial Intelligence for the Earth Systems},
  volume  = {3},
  number  = {1},
  pages   = {e230018},
  year    = {2024},
  doi     = {10.1175/AIES-D-23-0018.1},
}

Acknowledgments

This package includes adapted code from: PyALE, PermutationImportance, ALEPython, SHAP, scikit-learn, LIME, Faster-LIME, treeinterpreter

Contributing

• Issue Tracker: https://github.com/monte-flora/scikit-explain/issues
• Source Code: https://github.com/monte-flora/scikit-explain

License

BSD license.