Interpretable machine learning models (imodels) 🔍

Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easily understandable. Pull requests very welcome!

Docs • Popular imodels • Custom imodels • Demo notebooks

Popular interpretable models

Implementations of different interpretable models, all compatible with scikit-learn. The 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)

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

Model	Reference	Description
Rulefit	🗂️, 🔗, 📄	Extracts rules from a decision tree then builds a sparse linear model with them
Skope rules	🗂️, 🔗	Extracts rules from gradient-boosted trees, deduplicates them, then forms a linear combination of them based on their OOB precision
Bayesian rule list	🗂️, 🔗, 📄	Learns a compact rule list by sampling rule lists (rather than using a greedy heuristic)
Greedy rule list	🗂️, 🔗	Uses CART to learn a list (only a single path), rather than a decision tree
Iterative random forest	🗂️, 🔗, 📄	(In progress) Repeatedly fit random forest, giving features with high importance a higher chance of being selected.
Optimal classification tree	🗂️, 🔗, 📄	(In progress) Learns succinct trees using global optimization rather than greedy heuristics
Sparse integer linear model	🗂️, 📄	Forces coefficients to be integers
Rule sets		(Coming soon) Many popular rule sets including SLIPPER, Lightweight Rule Induction, MLRules

Docs 🗂️, Reference code implementation 🔗, Research paper 📄

Custom interpretable models

The code here contains many useful and readable functions for a variety of rule-based models, contained in the util folder. This includes functions and simple classes for rule deduplication, rule screening, converting between trees, rulesets, and pytorch neural nets. The final derived rules easily allows for extending any of the following general classes of models:

Rule set	Rule list	(Decision) Rule tree	Algebraic models

Demo notebooks

Demos are contained in the notebooks folder.

• model_based.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

References

• 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

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!