Interpretable machinelearning models (imodels) 🔍
Python package for concise, transparent, and accurate predictive modeling. All sklearncompatible and easily customizable.
docs • imodels overview • demo notebooks
imodels overview
Modern machinelearning models are increasingly complex, often making them difficult to interpret. This package provides a simple interface for fitting and using stateoftheart interpretable models, all compatible with scikitlearn. These models can often replace blackbox models (e.g. random forests) with simpler models (e.g. rule lists) while improving interpretability and computational efficiency, all without sacrificing predictive accuracy! Simply import a classifier or regressor and use the fit
and predict
methods, same as standard scikitlearn models.
from imodels import BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier # see more models below
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 rulebased model

# the model consists of the following 3 rules
# if X1 > 5: then 80.5% risk
# else if X2 > 5: then 40% risk
# else: 10% risk
Installation
Install with pip install imodels
(see here for help).
Supported models
Model  Reference  Description 

Rulefit rule set  🗂️, 🔗, 📄  Extracts rules from a decision tree then builds a sparse linear model with them 
Skope rule set  🗂️, 🔗  Extracts rules from gradientboosted trees, deduplicates them, then forms a linear combination of them based on their OOB precision 
Boosted rule set  🗂️, 🔗, 📄  Uses Adaboost or SLIPPER to sequentially learn a set of rules 
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 
OneR rule list  🗂️, 📄  Learns rule list restricted to only one feature 
Optimal rule tree  🗂️, 🔗, 📄  (In progress) Learns succinct trees using global optimization rather than greedy heuristics 
Iterative random forest  🗂️, 🔗, 📄  (In progress) Repeatedly fit random forest, giving features with high importance a higher chance of being selected. 
Sparse integer linear model  🗂️, 📄  Forces coefficients to be integers 
Rule sets  ⌛  (Coming soon!) Many popular rule sets including Lightweight Rule Induction, MLRules 
Docs 🗂️, Reference code implementation 🔗, Research paper 📄
See also our fast and effective discretizers for data preprocessing.
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.
ex. RuleFit and SkopeRules
RuleFit and SkopeRules differ only in the way they prune rules: RuleFit uses a linear model whereas SkopeRules heuristically deduplicates rules sharing overlap.ex. Bayesian rule lists and greedy rule lists
Bayesian rule lists and greedy rule lists differ in how they select rules; bayesian rule lists perform a global optimization over possible rule lists while Greedy rule lists pick splits sequentially to maximize a given criterion.ex. FPSkope and SkopeRules
FPSkope and SkopeRules differ only in the way they generate candidate rules: FPSkope uses FPgrowth whereas SkopeRules extracts rules from decision trees.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 rulebased learning in the util folder. This includes functions / classes for rule deduplication, rule screening, and converting between trees, rulesets, and neural networks.
Demo notebooks
Demos are contained in the notebooks folder.
imodels demo
Shows how to fit, predict, and visualize with different interpretable modelsimodels colab demo
Shows how to fit, predict, and visualize with different interpretable modelsclinical decision rule notebook
Shows an example of usingimodels
for deriving a clinical decision rule
posthoc analysis
We also include some demos of posthoc analysis, which occurs after fitting models: posthoc.ipynb shows different simple analyses to interpret a trained model and uncertainty.ipynb contains basic code to get uncertainty estimates for a modelSupport for different tasks
Different models support different machinelearning tasks. Current support for different models is given below:
Model  Binary classification  Regression 

Rulefit rule set  ✔️  ✔️ 
Skope rule set  ✔️  
Boosted rule set  ✔️  
Bayesian rule list  ✔️  
Greedy rule list  ✔️  
OneR rule list  ✔️  
Optimal rule tree  
Iterative random forest  
Sparse integer linear model  ✔️  ✔️ 
References
 Readings
 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.
 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
 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!
 Contributing: pull requests very welcome!
If it's useful for you, please cite the package using the below, and make sure to give authors of original methods / base implementations credit:
@software{
imodels2021,
title = {{imodels: a python package for fitting interpretable models}},
journal = {Journal of Open Source Software}
publisher = {The Open Journal},
year = {2021},
author = {Singh, Chandan and Nasseri, Keyan and Tan, Yan Shuo and Tang, Tiffany and Yu, Bin},
volume = {6},
number = {61},
pages = {3192},
doi = {10.21105/joss.03192},
url = {<https://doi.org/10.21105/joss.03192},>
}
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 .tree.iterative_random_forest.iterative_random_forest import IRFClassifier
# from .tree.optimal_classification_tree import OptimalTreeModel
from .algebraic.slim import SLIMRegressor, SLIMClassifier
from .rule_list.bayesian_rule_list.bayesian_rule_list import BayesianRuleListClassifier
from .rule_list.greedy_rule_list import GreedyRuleListClassifier
from .rule_list.one_r import OneRClassifier
from .rule_set.boosted_rules import BoostedRulesClassifier
from .rule_set.fplasso import FPLassoRegressor, FPLassoClassifier
from .rule_set.fpskope import FPSkopeClassifier
from .rule_set.rule_fit import RuleFitRegressor, RuleFitClassifier
from .rule_set.skope_rules import SkopeRulesClassifier
from .rule_set.slipper import SlipperClassifier
CLASSIFIERS = [BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier,
BoostedRulesClassifier, SLIMClassifier, SlipperClassifier] # , IRFClassifier
REGRESSORS = [RuleFitRegressor, SLIMRegressor]
Submodules
algebraic

Generic class for models that take the form of algebraic equations (e.g. linear models).
rule_list

Generic class for models that take the form of a list of rules.
rule_set

Generic class for models that take the form of a set of (potentially overlapping) rules.
tree

Generic class for models that take the form of a tree of rules.
util

Shared utilities for implementing different interpretable models.