Interpretable machine learning models (imodels) 🔍
Python package for concise, transparent, and accurate predictive modeling. All sklearncompatible 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 scikitlearn. The interpretable models can be easily used and installed:
from imodels import BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier, IRFClassifier
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 git+https://github.com/csinva/imodels
(see here for help). Contains the following models:
Model  Reference  Description 

Bayesian rule list  🗂️, 🔗, 📄  Learns a compact rule list by sampling rule lists (rather than using a greedy heuristic) 
Rulefit  🗂️, 🔗, 📄  Extracts rules from a decision tree then builds a sparse linear model with them 
Skope rules  🗂️, 🔗  Extracts rules from gradientboosted trees, deduplicates them, then forms a linear combination of them based on their OOB precision 
Sparse integer linear model  🗂️, 📄  Forces coefficients to be integers 
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 
Rule sets  (Coming soon) Many popular rule sets including SLIPPER, Lightweight Rule Induction, MLRules 
Reference includes docs 🗂️, reference code implementation 🔗, and research paper 📄
Custom interpretable models
The code here contains many useful and readable functions for a variety of rulebased 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  After fitting models, we can also do posthoc analysis, following the cheatsheet below
 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 posthoc explanation: (rudin 2019, pdf)  good explanation of why one should use interpretable models
 Review on evaluating interpretability (doshivelez & kim 2017, pdf)
 Reference implementations (also linked above): the code here heavily derives from (and in some case is just a wrapper for) the wonderful work of previous projects. We seek to to extract out, combine, and maintain select relevant parts of these projects.
 sklearnexpertsys  by @tmadl and @kenben based on original code by Ben Letham
 rulefit  by @christophM
 skoperules  by the skoperules team (including @ngoix, @floriangardin, @datajms, Bibi Ndiaye, Ronan Gautier)
 Related packages
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!
Expand source code
"""
.. include:: ../readme.md
"""
# Python `imodels` package for interpretable models compatible with scikitlearn.
# Github repo available [here](https://github.com/csinva/interpretabilityimplementationsdemos).
from .bayesian_rule_list.bayesian_rule_list import BayesianRuleListClassifier
from .greedy_rule_list import GreedyRuleListClassifier
from .iterative_random_forest.iterative_random_forest import IRFClassifier
from .rule_fit import RuleFitRegressor
from .skope_rules import SkopeRulesClassifier
from .slim import SLIMRegressor
CLASSIFIERS = BayesianRuleListClassifier, GreedyRuleListClassifier, IRFClassifier
REGRESSORS = RuleFitRegressor, SkopeRulesClassifier, SLIMRegressor
# from .optimal_classification_tree import OptimalTreeModel
Submodules
bayesian_rule_list

bayesian rule list (based on this implementation)  learn a compact …
greedy_rule_list

greedy rule list (based on this implementation). …
iterative_random_forest

Repeatedly fit random forest, giving features with high importance a higher chance of being selected.
optimal_classification_tree

Still in progress  should optimize globally over all possible splits rather than use greedy 1by1 splits
rule_fit

Linear model of treebased decision rules …
skope_rules

Code adapted with only minor changes from here. Full credit to the authors …
slim

wrapper for sparse, integer linear models …
util

Shared utilities for implementing different interpretable models.