Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Chandan Singh 004be499f6 add colab demo | 1 day ago | |
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.github | 2 weeks ago | |
docs | 1 day ago | |
experiments | 4 days ago | |
imodels | 5 days ago | |
notebooks | 1 week ago | |
tests | 4 days ago | |
.gitignore | 1 month ago | |
_config.yml | 7 months ago | |
license.md | 7 months ago | |
paper.md | 1 month ago | |
readme.md | 1 day ago | |
setup.py | 1 day ago |
Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easily customizable.
docs • imodels overview • demo notebooks
Implementations of different popular interpretable models can be easily used and installed:
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
-----------------------------
# 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). Contains the following 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 |
Bayesian rule list | 🗂️, 🔗, 📄 | Learns a compact rule list by sampling rule lists (rather than using a greedy heuristic) |
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 |
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 |
Rule sets | ⌛ | (Coming soon) Many popular rule sets including SLIPPER, Lightweight Rule Induction, MLRules |
Docs 🗂️, Reference code implementation 🔗, Research paper 📄
More models coming soon!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.
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:
Model | Binary classification | Regression |
---|---|---|
Rulefit rule set | ✔️ | ✔️ |
Skope rule set | ✔️ | |
Boosted rule set | ✔️ | |
Bayesian rule list | ✔️ | |
Greedy rule list | ✔️ | |
OneR rule list | ✔️ | |
Optimal rule tree | ||
Iterative random forest | ||
Sparse integer linear model | ✔️ | ✔️ |
If it's useful for you, please cite the package using the below, but more importantly make sure to give authors of original methods / base implementations credit:
@software{
imodels2021,
title = {{imodels: a python package for fitting interpretable models}},
publisher = {Zenodo},
year = 2021,
author = {Chandan Singh and Keyan Nasseri and Bin Yu},
version = {v0.2.8},
doi = {10.5281/zenodo.4026886},
url = {https://github.com/csinva/imodels}
}