Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use.
docs • imodels overview • 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 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
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# the model consists of the following 3 rules
# if X1 > 5: then 80.5% risk
# else if X2 > 5: then 40% risk
# else: 10% riskInstall with pip install imodels (see here for help).
| Model | Reference | Description |
|---|---|---|
| Rulefit rule set | 🗂️, 🔗, 📄 | Extracts rules from decision trees then fits 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 | 🗂️, 🔗, 📄 | Sequentially learns 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 | 🗂️, 🔗, 📄 | Learns succinct rule list using global optimization for sparsity (CORELS) |
| Bayesian rule list | 🗂️, 🔗, 📄 | Learns compact rule list distribution with Bayesian sampling (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 | 🗂️, 🔗, 📄 | Learns succinct tree using global optimization for sparsity (GOSDT) |
| Greedy rule tree | 🗂️, 🔗, 📄 | Greedily learns tree using CART |
| C4.5 rule tree | 🗂️, 🔗, 📄 | Greedily learns tree using C4.5 |
| Iterative random forest | 🗂️, 🔗, 📄 | (In progress) 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 |
| Sapling Sums | 🗂️, 📄 | Sum of small trees with very few total rules (SAPS) |
| Shrunk trees | 🗂️, 📄 | Use regularization to improve trees |
| More models | ⌛ | (Coming soon!) Popular rule sets including Lightweight Rule Induction, MLRules |
Docs 🗂️, Reference code implementation 🔗, Research paper 📄
Also see our util functions for downloading 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.Also see our simple function for explaining classification errors.
Fit an interpretable model to explain a previous model's errors (ex. in this notebook📓).Also see our 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 |
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 |
|---|---|---|---|
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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 postprocessing |
|---|---|---|
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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.
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 models
clinical 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 modelDifferent 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 | |
| Bayesian rule set | BayesianRuleSetClassifier | |
| Optimal rule list (CORELS) | OptimalRuleListClassifier | |
| Bayesian rule list | BayesianRuleListClassifier | |
| Greedy rule list | GreedyRuleListClassifier | |
| OneR rule list | OneRClassifier | |
| Optimal rule tree (GOSDT) | OptimalTreeClassifier | |
| Greedy rule tree (CART) | GreedyTreeClassifier | GreedyTreeRegressor |
| C4.5 rule tree | C45TreeClassifier | |
| Iterative random forest | IRFClassifier | |
| Sparse integer linear model | SLIMClassifier | SLIMRegressor |
| Sapling Sums (SAPS) | SaplingSumClassifier | SaplingSumRegressor |
| Shrunk trees | ShrunkTreeClassifierCV | ShrunkTreeRegressorCV |
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.- 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!
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},
}