Interpretable machine-learning models (imodels) 🔍

Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use.

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 and predict methods, same as standard scikit-learn models.

from imodels import BoostedRulesClassifier, BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier # see more models below
from imodels import SLIMRegressor, RuleFitRegressor

model = BoostedRulesClassifier()  # 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 (see here for help).

Supported models

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):

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},
}

"""
.. include:: ../readme.md
"""
# Python `imodels` package for interpretable models compatible with scikit-learn.
# Github repo available [here](https://github.com/csinva/interpretability-implementations-demos).

import os

# from .tree.iterative_random_forest.iterative_random_forest import IRFClassifier
# from .tree.optimal_classification_tree import OptimalTreeModel
from .tree.cart_wrapper import GreedyTreeClassifier, GreedyTreeRegressor
from .tree.gosdt.pygosdt import OptimalTreeClassifier
from .tree.c45_tree.c45_tree import C45TreeClassifier
from .tree.saps import SaplingSumRegressor, SaplingSumClassifier
from .algebraic.slim import SLIMRegressor, SLIMClassifier
from .discretization.discretizer import RFDiscretizer, BasicDiscretizer
from .discretization.mdlp import MDLPDiscretizer, BRLDiscretizer
from .rule_list.bayesian_rule_list.bayesian_rule_list import BayesianRuleListClassifier
from .rule_list.corels_wrapper import OptimalRuleListClassifier
from .rule_list.greedy_rule_list import GreedyRuleListClassifier
from .rule_list.one_r import OneRClassifier
from .rule_set import boosted_rules
from .rule_set.boosted_rules import *
from .rule_set.boosted_rules import BoostedRulesClassifier
from .rule_set.brs import BayesianRuleSetClassifier
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

from .util.explain_errors import explain_classification_errors

CLASSIFIERS = [BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier,
               BoostedRulesClassifier, SLIMClassifier, SlipperClassifier, BayesianRuleSetClassifier,
               C45TreeClassifier, OptimalTreeClassifier, OptimalRuleListClassifier, OneRClassifier,
               SlipperClassifier,
               SaplingSumClassifier]  # , IRFClassifier
REGRESSORS = [RuleFitRegressor, SLIMRegressor, GreedyTreeClassifier, SaplingSumRegressor]
DISCRETIZERS = [RFDiscretizer, BasicDiscretizer, MDLPDiscretizer, BRLDiscretizer]

Sub-modules

algebraic

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

discretization

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.

tests

tree

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

util

Shared utilities for implementing different interpretable models.