Python package for concise, transparent, and accurate predictive modeling.
All sklearn-compatible and easy to use.
For interpretability in NLP, check out our new package: imodelsX

📚 docs • 📖 demo notebooks

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 sklearn.model_selection import train_test_split
from imodels import get_clean_dataset, HSTreeClassifierCV # import any imodels model here

# prepare data (a sample clinical dataset)
X, y, feature_names = get_clean_dataset('csi_pecarn_pred')
X_train, X_test, y_train, y_test = train_test_split(
    X, y, random_state=42)

# fit the model
model = HSTreeClassifierCV(max_leaf_nodes=4)  # initialize a tree model and specify only 4 leaf nodes
model.fit(X_train, y_train, feature_names=feature_names)   # 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 model

------------------------------
Decision Tree with Hierarchical Shrinkage
Prediction is made by looking at the value in the appropriate leaf of the tree
------------------------------
|--- FocalNeuroFindings2 <= 0.50
|   |--- HighriskDiving <= 0.50
|   |   |--- Torticollis2 <= 0.50
|   |   |   |--- value: [0.10]
|   |   |--- Torticollis2 >  0.50
|   |   |   |--- value: [0.30]
|   |--- HighriskDiving >  0.50
|   |   |--- value: [0.68]
|--- FocalNeuroFindings2 >  0.50
|   |--- value: [0.42]

Installation

Install with pip install imodels (see here for help).

Supported models

🗂️ Docs   📄 Research paper   🔗 Reference code implementation

Model	Reference	Description
Rulefit rule set	🗂️, 📄, 🔗	Fits a sparse linear model on rules extracted from decision trees
Skope rule set	🗂️, 🔗	Extracts rules from gradient-boosted trees, deduplicates them, then linearly combines them based on their OOB precision
Boosted rule set	🗂️, 📄, 🔗	Sequentially fits a set of rules with Adaboost
Slipper rule set	🗂️, 📄	Sequentially learns a set of rules with SLIPPER
Bayesian rule set	🗂️, 📄, 🔗	Finds concise rule set with Bayesian sampling (slow)
Optimal rule list	🗂️, 📄, 🔗	Fits rule list using global optimization for sparsity (CORELS)
Bayesian rule list	🗂️, 📄, 🔗	Fits compact rule list distribution with Bayesian sampling (slow)
Greedy rule list	🗂️, 🔗	Uses CART to fit a list (only a single path), rather than a tree
OneR rule list	🗂️, 📄	Fits rule list restricted to only one feature
Optimal rule tree	🗂️, 📄, 🔗	Fits succinct tree using global optimization for sparsity (GOSDT)
Greedy rule tree	🗂️, 📄, 🔗	Greedily fits tree using CART
C4.5 rule tree	🗂️, 📄, 🔗	Greedily fits tree using C4.5
TAO rule tree	🗂️, 📄	Fits tree using alternating optimization
Iterative random forest	🗂️, 📄, 🔗	Repeatedly fit random forest, giving features with high importance a higher chance of being selected
Sparse integer linear model	🗂️, 📄	Sparse linear model with integer coefficients
Tree GAM	🗂️, 📄, 🔗	Generalized additive model fit with short boosted trees
Greedy tree sums (FIGS)	🗂️,ㅤ📄	Sum of small trees with very few total rules (FIGS)
Hierarchical shrinkage wrapper	🗂️, 📄	Improve a decision tree, random forest, or gradient-boosting ensemble with ultra-fast, post-hoc regularization
RF+ (MDI+)	🗂️, 📄	Flexible random forest-based feature importance
Distillation wrapper	🗂️	Train a black-box model, then distill it into an interpretable model
AutoML wrapper	🗂️	Automatically fit and select an interpretable model
More models	⌛	(Coming soon!) Lightweight Rule Induction, MLRules, ...

Demo notebooks

Demos are contained in the notebooks folder.

Quickstart demo

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

Autogluon demo

Fit/select an interpretable model automatically using Autogluon AutoML

Quickstart colab demo

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

Clinical decision rule notebook

Shows an example of using imodels 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 model

What's the difference between the models?

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, such as how they generate, select, and postprocess rules:

Rule candidate generation	Rule selection	Rule postprocessing

Ex. RuleFit vs. 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 vs. 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 vs. SkopeRules

FPSkope and SkopeRules differ only in the way they generate candidate rules: FPSkope uses FPgrowth whereas SkopeRules extracts rules from decision trees.

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	Notes
Rulefit rule set	RuleFitClassifier	RuleFitRegressor
Skope rule set	SkopeRulesClassifier
Boosted rule set	BoostedRulesClassifier	BoostedRulesRegressor
SLIPPER rule set	SlipperClassifier
Bayesian rule set	BayesianRuleSetClassifier		Fails for large problems
Optimal rule list (CORELS)	OptimalRuleListClassifier		Requires corels, fails for large problems
Bayesian rule list	BayesianRuleListClassifier
Greedy rule list	GreedyRuleListClassifier
OneR rule list	OneRClassifier
Optimal rule tree (GOSDT)	OptimalTreeClassifier		Requires gosdt, fails for large problems
Greedy rule tree (CART)	GreedyTreeClassifier	GreedyTreeRegressor
C4.5 rule tree	C45TreeClassifier
TAO rule tree	TaoTreeClassifier	TaoTreeRegressor
Iterative random forest	IRFClassifier		Requires irf
Sparse integer linear model	SLIMClassifier	SLIMRegressor	Requires extra dependencies for speed
Tree GAM	TreeGAMClassifier	TreeGAMRegressor
Greedy tree sums (FIGS)	FIGSClassifier	FIGSRegressor
Hierarchical shrinkage	HSTreeClassifierCV	HSTreeRegressorCV	Wraps any sklearn tree-based model
Distillation		DistilledRegressor	Wraps any sklearn-compatible models
AutoML model	AutoInterpretableClassifier️	AutoInterpretableRegressor️

Extras

Data-wrangling functions for working with popular tabular datasets (e.g. compas).

These functions, in conjunction with imodels-data and imodels-experiments, make it simple to download data and run experiments on new models.

Explain classification errors with a simple posthoc function.

Fit an interpretable model to explain a previous model's errors (ex. in this notebook📓).

Fast and effective discretizers for data preprocessing.

Discretizer	Reference	Description
MDLP	🗂️, 🔗, 📄	Discretize using entropy minimization heuristic
Simple	🗂️, 🔗	Simple KBins discretization
Random Forest	🗂️	Discretize into bins based on random forest split popularity

Rule-based utils for customizing models

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.

Our favorite models

After developing and playing with imodels, we developed a few new models to overcome limitations of existing interpretable models.

FIGS: Fast interpretable greedy-tree sums

📄 Paper, 🔗 Post, 📌 Citation

Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models. Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation. The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable. Experiments across a wide array of real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20).

Example FIGS model. FIGS learns a sum of trees with a flexible number of trees; to make its prediction, it sums the result from each tree.

Hierarchical shrinkage: post-hoc regularization for tree-based methods

📄 Paper (ICML 2022), 🔗 Post, 📌 Citation

Hierarchical shrinkage is an extremely fast post-hoc regularization method which works on any decision tree (or tree-based ensemble, such as Random Forest). It does not modify the tree structure, and instead regularizes the tree by shrinking the prediction over each node towards the sample means of its ancestors (using a single regularization parameter). Experiments over a wide variety of datasets show that hierarchical shrinkage substantially increases the predictive performance of individual decision trees and decision-tree ensembles.

HS Example. HS applies post-hoc regularization to any decision tree by shrinking each node towards its parent.

MDI+: Flexible Tree-Based Feature Importance

📄 Paper, 🔗 Post, 📌 Citation

MDI+ is a novel feature importance framework, which generalizes the popular mean decrease in impurity (MDI) importance score for random forests. At its core, MDI+ expands upon a recently discovered connection between linear regression and decision trees. In doing so, MDI+ enables practitioners to (1) tailor the feature importance computation to the data/problem structure and (2) incorporate additional features or knowledge to mitigate known biases of decision trees. In both real data case studies and extensive real-data-inspired simulations, MDI+ outperforms commonly used feature importance measures (e.g., MDI, permutation-based scores, and TreeSHAP) by substantional margins.

References

Readings

• Interpretable ML good quick overview: murdoch et al. 2019, pdf
• Interpretable ML book: molnar 2019, pdf
• Case for interpretable models rather than post-hoc explanation: rudin 2019, pdf
• Review on evaluating interpretability: doshi-velez & kim 2017, pdf

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.

• pycorels - by @fingoldin and the original CORELS team
• sklearn-expertsys - by @tmadl and @kenben based on original code by Ben Letham
• rulefit - by @christophM
• skope-rules - by the skope-rules team (including @ngoix, @floriangardin, @datajms, Bibi Ndiaye, Ronan Gautier)
• boa - by @wangtongada

Related packages

• gplearn: symbolic regression/classification
• pysr: fast symbolic regression
• pygam: generative additive models
• interpretml: boosting-based gam
• h20 ai: gams + glms (and more)
• optbinning: data discretization / scoring models

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!

Please cite the package if you use it in an academic work :)

@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},
}