Interpretable machine-learning models (imodels) 🔍

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

docs • imodels overview • demo notebooks

imodels overview

Modern machine-learning models are increasingly complex, often making them difficult to interpret. This package provides a simple interface for fitting and using state-of-the-art interpretable models, all compatible with scikit-learn. These models can often replace black-box 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

predict

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 rule-based 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

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 gradient-boosted 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 | | Slipper rule set | 🗂️, 📄 | Uses SLIPPER to sequentially learn a set of rules | | Bayesian rule set | 🗂️, 🔗, 📄 | Uses a Bayesian algorithm to find a concise rule set (slow) | | Corels rule list | 🗂️, 🔗, 📄 | Learns an optimal compact rule list (rather than using a greedy heuristic) | | Bayesian rule list | 🗂️, 🔗, 📄 | Learns a compact rule list distribution by sampling rule lists (slow) | | 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 | | Greedy rule tree | 🗂️, 🔗, 📄 | Greedily learns tree using CART | | 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 | | More models | ⌛ | (Coming soon!) Popular rule sets including Lightweight Rule Induction, MLRules |

Docs 🗂️, Reference code implementation 🔗, Research paper 📄

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.

Demo notebooks

Demos are contained in the notebooks folder.

imodels colab demo

Shows how to fit, predict, and visualize with different interpretable models

Support for different tasks

Different models support different machine-learning tasks. Current support for different models is given below (each of these models can be imported directly from imodels (e.g.

from imodels import RuleFitClassifier

| Model | Binary classification | Regression | | :-------------------------- | :-------------------: | :--------: | | Rulefit rule set | RuleFitClassifier | RuleFitRegressor | | Skope rule set | SkopeRulesClassifier | | | Boosted rule set | BoostedRulesClassifier | | | SLIPPER rule set | SlipperClassifier | | | BOA rule set | BayesianRuleSetClassifier | | | Corels rule list | CorelsRuleListClassifier | | | Bayesian rule list | BayesianRuleListClassifier | | | Greedy rule list | GreedyRuleListClassifier | | | OneR rule list | OneRClassifier | | | Optimal rule tree | | | | Iterative random forest | | | | Sparse integer linear model | SLIMClassifier | SLIMRegressor |

References

If it's useful for you, please star/cite the package, 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},
}