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Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use.
docs β’ imodels overview β’ 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
-----------------------------
# 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
Install with pip install imodels
(see here for help).
Model | Reference | Description |
---|---|---|
Rulefit rule set | ποΈ, π, π | Fits a sparse linear model on rules extracted from decision trees |
Skope rule set | ποΈ, π | Extracts rules from gradient-boosted trees, deduplicates them, then linearly combines them based on their OOB precision |
Boosted rule set | ποΈ, π, π | Sequentially fits a set of rules with Adaboost |
Slipper rule set | ποΈ, γ €γ € π | Sequentially learns a set of rules with SLIPPER |
Bayesian rule set | ποΈ, π, π | Finds concise rule set with Bayesian sampling (slow) |
Optimal rule list | ποΈ, π, π | Fits rule list using global optimization for sparsity (CORELS) |
Bayesian rule list | ποΈ, π, π | Fits compact rule list distribution with Bayesian sampling (slow) |
Greedy rule list | ποΈ, π | Uses CART to fit a list (only a single path), rather than a tree |
OneR rule list | ποΈ, γ €γ €π | Fits rule list restricted to only one feature |
Optimal rule tree | ποΈ, π, π | Fits succinct tree using global optimization for sparsity (GOSDT) |
Greedy rule tree | ποΈ, π, π | Greedily fits tree using CART |
C4.5 rule tree | ποΈ, π, π | Greedily fits tree using C4.5 |
Iterative random forest |
ποΈ, π, π | Repeatedly fit random forest, giving features with high importance a higher chance of being selected |
Sparse integer linear model |
ποΈ, γ €γ €π | Sparse linear model with integer coefficients |
Sapling Sums | ποΈ, γ €γ €π | Sum of small trees with very few total rules (SAPS) |
Shrunk trees wrapper |
ποΈ, γ €γ €π | Use regularization to improve trees |
Distillation wrapper |
ποΈ, γ €γ €π | Train a black-box model, then distill it into an interpretable model |
More models | β | (Coming soon!) Lightweight Rule Induction, MLRules, ... |
Docs ποΈ, Reference code implementation π, Research paper π
Discretizer | Reference | Description |
---|---|---|
MDLP | ποΈ, π, π | Discretize using entropy minimization heuristic |
Simple | ποΈ, π | Simple KBins discretization |
Random Forest | ποΈ | 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 |
---|---|---|---|
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 postprocessing |
---|---|---|
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
for deriving a clinical decision rule
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 | ||
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 | ||
Iterative random forest | IRFClassifier | Requires irf | |
Sparse integer linear model | SLIMClassifier | SLIMRegressor | Requires extra dependencies for speed |
Sapling Sums (SAPS) | SaplingSumClassifier | SaplingSumRegressor | |
Shrunk trees | ShrunkTreeClassifierCV | ShrunkTreeRegressorCV | Wraps any sklearn tree-based model |
Distillation | DistilledRegressor | Wraps any sklearn-compatible models |
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},
}
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