Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easily customizable.

docs • imodels overview • demo notebooks

Implementations of different popular interpretable models can be easily used and installed:

from imodels import BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier
from imodels import SLIMRegressor, RuleFitRegressor

model = BayesianRuleListClassifier()  # initialize a model
model.fit(X_train, y_train)   # fit model
preds = model.predict(X_test) # discrete predictions: shape is (n_test, 1)
preds_proba = model.predict_proba(X_test) # predicted probabilities: shape is (n_test, n_classes)
print(model) # print the rule-based model

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# if X1 > 5: then 80.5% risk
# else if X2 > 5: then 40% risk
# else: 10% risk

Install with pip install imodels (see here for help). Contains the following models:

Docs 🗂️, Reference code implementation 🔗, Research paper 📄
More models coming soon!

The final form of the above models takes one of the following forms, which aim to be simultaneously simple to understand and highly predictive:

Rule set	Rule list	Rule tree	Algebraic models

Different models and algorithms vary not only in their final form but also in different choices made during modeling. In particular, many models differ in the 3 steps given by the table below.

See the docs for individual models for futher descriptions.

Rule candidate generation	Rule selection	Rule pruning / combination

The code here contains many useful and customizable functions for rule-based learning in the util folder. This includes functions / classes for rule deduplication, rule screening, and converting between trees, rulesets, and neural networks.

Demos are contained in the notebooks folder.

• imodels_demo.ipynb, demos the imodels package. It shows how to fit, predict, and visualize with different interpretable models
• this notebook shows an example of using imodels for deriving a clinical decision rule
• we also include some demos of posthoc analysis, which occurs after fitting models
  • posthoc.ipynb - shows different simple analyses to interpret a trained model
  • uncertainty.ipynb - basic code to get uncertainty estimates for a model

Different models support different machine-learning tasks. Current support for different models is given below:

• Readings
  • Interpretable ML good quick overview: murdoch et al. 2019, pdf
  • Interpretable ML book: molnar 2019, pdf
  • Case for interpretable models rather than post-hoc explanation: rudin 2019, pdf
  • Review on evaluating interpretability: doshi-velez & kim 2017, pdf
• Reference implementations (also linked above): the code here heavily derives from the wonderful work of previous projects. We seek to to extract out, unify, and maintain key parts of these projects.
  • sklearn-expertsys - by @tmadl and @kenben based on original code by Ben Letham
  • rulefit - by @christophM
  • skope-rules - by the skope-rules team (including @ngoix, @floriangardin, @datajms, Bibi Ndiaye, Ronan Gautier)
• Compatible packages
  • sklearn
  • dtreeviz
• Related packages
  • gplearn for symbolic regression/classification
  • pygam for generative additive models
• Updates
  • For updates, star the repo, see this related repo, or follow @csinva_
  • Please make sure to give authors of original methods / base implementations appropriate credit!
  • Pull requests very welcome!