Interpretable machine-learning models (imodels) 🔍
Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easily customizable.
docs • imodels overview • demo notebooks
imodels overview
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 |
---|---|---|---|
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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 pruning / combination |
---|---|---|
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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.
Demo notebooks
Demos are contained in the notebooks folder.
- imodels_demo.ipynb, demos the imodels package. It shows how to fit, predict, and visualize with different interpretable models
- this notebook shows an example of using
imodels
for deriving a clinical decision rule - we also include some demos of posthoc analysis, which occurs after fitting models
- posthoc.ipynb - shows different simple analyses to interpret a trained model
- uncertainty.ipynb - basic code to get uncertainty estimates for a model
Support for different tasks
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 | ✔️ | ✔️ |
References
- 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.
- 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)
- Compatible packages
- Related packages
- 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!
- Pull requests very welcome!
Expand source code
"""
.. include:: ../readme.md
"""
# Python `imodels` package for interpretable models compatible with scikit-learn.
# Github repo available [here](https://github.com/csinva/interpretability-implementations-demos).
from .rule_list.bayesian_rule_list.bayesian_rule_list import BayesianRuleListClassifier
from .rule_list.greedy_rule_list import GreedyRuleListClassifier
from .rule_list.one_r import OneRClassifier
from .rule_set.rule_fit import RuleFitRegressor, RuleFitClassifier
from .rule_set.fplasso import FPLassoRegressor, FPLassoClassifier
from .rule_set.fpskope import FPSkopeClassifier
from .rule_set.skope_rules import SkopeRulesClassifier
from .rule_set.boosted_rules import BoostedRulesClassifier
# from .tree.iterative_random_forest.iterative_random_forest import IRFClassifier
# from .tree.optimal_classification_tree import OptimalTreeModel
from .algebraic.slim import SLIMRegressor, SLIMClassifier
CLASSIFIERS = BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier, BoostedRulesClassifier #, IRFClassifier
REGRESSORS = RuleFitRegressor, SLIMRegressor
Sub-modules
algebraic
-
Generic class for models that take the form of algebraic equations (e.g. linear models).
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.
tree
-
Generic class for models that take the form of a tree of rules.
util
-
Shared utilities for implementing different interpretable models.