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

# 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:
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 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 SLIPPER, Lightweight Rule Induction, MLRules 
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.
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.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
Support 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)
 Compatible packages
 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!
 Pull requests very welcome!
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 .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.rule_fit import RuleFitRegressor, RuleFitClassifier
from .rule_set.fplasso import FPLassoRegressor, FPLassoClassifier
from .rule_set.fpskope import FPSkopeClassifier
from .rule_set.skope_rules import SkopeRulesClassifier
from .rule_set.boosted_rules import BoostedRulesClassifier
# from .tree.iterative_random_forest.iterative_random_forest import IRFClassifier
# from .tree.optimal_classification_tree import OptimalTreeModel
from .algebraic.slim import SLIMRegressor, SLIMClassifier
CLASSIFIERS = BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier, BoostedRulesClassifier #, 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.