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https://github.com/csinva/imodels

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
https://github.com/csinva/imodels
ai artificial-intelligence bayesian-rule-list data-science explainable-ai explainable-ml imodels interpretability machine-learning ml optimal-classification-tree python rule-learning rulefit rules scikit-learn statistics supervised-learning
Last synced: 26 days ago
JSON representation
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Host: GitHub
URL: https://github.com/csinva/imodels
Owner: csinva
License: mit
Created: 2019-07-04T15:38:48.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2024-09-21T21:59:47.000Z (about 2 months ago)
Last Synced: 2024-09-30T16:03:09.141Z (about 1 month ago)
Topics: ai, artificial-intelligence, bayesian-rule-list, data-science, explainable-ai, explainable-ml, imodels, interpretability, machine-learning, ml, optimal-classification-tree, python, rule-learning, rulefit, rules, scikit-learn, statistics, supervised-learning
Language: Jupyter Notebook
Homepage: https://csinva.io/imodels
Size: 161 MB
Stars: 1,372
Watchers: 22
Forks: 122
Open Issues: 36
Metadata Files:
- Readme: readme.md
- Contributing: docs/contributing.md
- License: license.md
- Citation: citation.cff
Awesome Lists containing this project

AwesomeResponsibleAI - imodels
Awesome-explainable-AI - https://github.com/csinva/imodels
awesome-datascience - imodels
awesome-python-machine-learning-resources - GitHub - 35% open · ⏱️ 25.08.2022): (模型的可解释性)
README

        


	 	 


	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.

```python

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](https://github.com/csinva/imodels/blob/master/docs/troubleshooting.md) for help).

### Supported models



🗂️ Docs   📄 Research paper    🔗 Reference code implementation



| Model 
| :-------------------------- 
| Rulefit rule set 
| Skope rule set 
| Boosted rule set 
| Slipper rule set 
| Bayesian rule set 
| Optimal rule list 
| Bayesian rule list 
| Greedy rule list 
| OneR rule list 
| Optimal rule tree 
| Greedy rule tree 
| C4.5 rule tree 
| TAO rule tree 
| Iterative 
| Sparse 
| Tree GAM 
| Greedy 
| Hierarchical
 
| RF+ 
| Distillation
wrapper 
| AutoML 
| More models

| Reference                                                    | Description                                                  | | ------------------------------------------------------------ | ------------------------------------------------------------ | | [🗂️](https://csinva.io/imodels/rule_set/rule_fit.html), [📄](http://statweb.stanford.edu/~jhf/ftp/RuleFit.pdf), [🔗](https://github.com/christophM/rulefit) | Fits a sparse linear model on rules extracted from decision trees | | [🗂️](https://csinva.io/imodels/rule_set/skope_rules.html#imodels.rule_set.skope_rules.SkopeRulesClassifier), [🔗](https://github.com/scikit-learn-contrib/skope-rules) | Extracts rules from gradient-boosted trees, deduplicates them,
then linearly combines them based on their OOB precision | | [🗂️](https://csinva.io/imodels/rule_set/boosted_rules.html), [📄](https://www.sciencedirect.com/science/article/pii/S002200009791504X), [🔗](https://github.com/jaimeps/adaboost-implementation) | Sequentially fits a set of rules with Adaboost           | | [🗂️](https://csinva.io/imodels/rule_set/slipper.html), [📄](https://www.aaai.org/Papers/AAAI/1999/AAAI99-049.pdf) | Sequentially learns a set of rules with SLIPPER            | | [🗂️](https://csinva.io/imodels/rule_set/brs.html#imodels.rule_set.brs.BayesianRuleSetClassifier), [📄](https://www.jmlr.org/papers/volume18/16-003/16-003.pdf), [🔗](https://github.com/wangtongada/BOA) | Finds concise rule set with Bayesian sampling (slow)  | | [🗂️](https://csinva.io/imodels/rule_list/corels_wrapper.html#imodels.rule_list.corels_wrapper.OptimalRuleListClassifier), [📄](https://www.jmlr.org/papers/volume18/17-716/17-716.pdf), [🔗](https://github.com/corels/pycorels) | Fits rule list using global optimization for sparsity (CORELS) | | [🗂️](https://csinva.io/imodels/rule_list/bayesian_rule_list/bayesian_rule_list.html#imodels.rule_list.bayesian_rule_list.bayesian_rule_list.BayesianRuleListClassifier), [📄](https://arxiv.org/abs/1602.08610), [🔗](https://github.com/tmadl/sklearn-expertsys) | Fits compact rule list distribution with Bayesian sampling (slow) | | [🗂️](https://csinva.io/imodels/rule_list/greedy_rule_list.html), [🔗](https://medium.com/@penggongting/implementing-decision-tree-from-scratch-in-python-c732e7c69aea) | Uses CART to fit a list (only a single path), rather than a tree | | [🗂️](https://csinva.io/imodels/rule_list/one_r.html), [📄](https://link.springer.com/article/10.1023/A:1022631118932) | Fits rule list restricted to only one feature              | | [🗂️](https://csinva.io/imodels/tree/gosdt/pygosdt.html#imodels.tree.gosdt.pygosdt.OptimalTreeClassifier), [📄](https://arxiv.org/abs/2006.08690), [🔗](https://github.com/Jimmy-Lin/GeneralizedOptimalSparseDecisionTrees) | Fits succinct tree using global optimization for sparsity (GOSDT) | | [🗂️](https://csinva.io/imodels/tree/cart_wrapper.html), [📄](https://www.taylorfrancis.com/books/mono/10.1201/9781315139470/classification-regression-trees-leo-breiman-jerome-friedman-richard-olshen-charles-stone), [🔗](https://scikit-learn.org/stable/modules/tree.html)  | Greedily fits tree using CART                              | | [🗂️](https://csinva.io/imodels/tree/c45_tree/c45_tree.html#imodels.tree.c45_tree.c45_tree.C45TreeClassifier), [📄](https://link.springer.com/article/10.1007/BF00993309), [🔗](https://github.com/RaczeQ/scikit-learn-C4.5-tree-classifier) | Greedily fits tree using C4.5                           | | [🗂️](https://csinva.io/imodels/tree/tao.html), [📄](https://proceedings.neurips.cc/paper/2018/hash/185c29dc24325934ee377cfda20e414c-Abstract.html) | Fits tree using alternating optimization                    | random
forest | [🗂️](https://csinva.io/imodels/tree/iterative_random_forest/iterative_random_forest.html), [📄](https://www.pnas.org/content/115/8/1943), [🔗](https://github.com/Yu-Group/iterative-Random-Forest) | Repeatedly fit random forest, giving features with
high importance a higher chance of being selected | integer
linear model | [🗂️](https://csinva.io/imodels/algebraic/slim.html), [📄](https://link.springer.com/article/10.1007/s10994-015-5528-6) | Sparse linear model with integer coefficients                           | | [🗂️](https://csinva.io/imodels/algebraic/tree_gam.html), [📄](https://dl.acm.org/doi/abs/10.1145/2339530.2339556), [🔗](https://github.com/interpretml/interpret) | Generalized additive model fit with short boosted trees                           | treesums (FIGS) | [🗂️](https://csinva.io/imodels/figs.html),ㅤ[📄](https://arxiv.org/abs/2201.11931) | Sum of small trees with very few total rules (FIGS)                          | shrinkage wrapper | [🗂️](https://csinva.io/imodels/shrinkage.html), [📄](https://arxiv.org/abs/2202.00858) | Improve a decision tree, random forest, or
gradient-boosting ensemble with ultra-fast, post-hoc regularization | (MDI+) | [🗂️](https://csinva.io/imodels/mdi_plus.html), [📄](https://arxiv.org/pdf/2307.01932) | Flexible random forest-based feature importance | | [🗂️](https://csinva.io/imodels/util/distillation.html)  | Train a black-box model,
then distill it into an interpretable model | wrapper | [🗂️](https://csinva.io/imodels/util/automl.html)  | Automatically fit and select an interpretable model | | ⌛                                                            | (Coming soon!) Lightweight Rule Induction, MLRules, ... |

## Demo notebooks

Demos are contained in the [notebooks](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](https://csinva.io/imodels/rule_set/rule_fit.html#imodels.rule_set.rule_fit.RuleFitClassifier) | [RuleFitRegressor](https://csinva.io/imodels/rule_set/rule_fit.html#imodels.rule_set.rule_fit.RuleFitRegressor) |  |

| Skope rule set              | [SkopeRulesClassifier](https://csinva.io/imodels/rule_set/skope_rules.html#imodels.rule_set.skope_rules.SkopeRulesClassifier) |                                                              |  |

| Boosted rule set            | [BoostedRulesClassifier](https://csinva.io/imodels/rule_set/boosted_rules.html#imodels.rule_set.boosted_rules.BoostedRulesClassifier) | [BoostedRulesRegressor](https://csinva.io/imodels/rule_set/boosted_rules.html#imodels.rule_set.boosted_rules.BoostedRulesRegressor) |  |

| SLIPPER rule set            | [SlipperClassifier](https://csinva.io/imodels/rule_set/slipper.html#imodels.rule_set.slipper.SlipperClassifier) |                                                              |  |

| Bayesian rule set           | [BayesianRuleSetClassifier](https://csinva.io/imodels/rule_set/brs.html#imodels.rule_set.brs.BayesianRuleSetClassifier) |                                                              | Fails for large problems |

| Optimal rule list (CORELS)  | [OptimalRuleListClassifier](https://csinva.io/imodels/rule_list/corels_wrapper.html#imodels.rule_list.corels_wrapper.OptimalRuleListClassifier) |                                                              | Requires [corels](https://pypi.org/project/corels/), fails for large problems |

| Bayesian rule list          | [BayesianRuleListClassifier](https://csinva.io/imodels/rule_list/bayesian_rule_list/bayesian_rule_list.html#imodels.rule_list.bayesian_rule_list.bayesian_rule_list.BayesianRuleListClassifier) |                                                              |  |

| Greedy rule list            | [GreedyRuleListClassifier](https://csinva.io/imodels/rule_list/greedy_rule_list.html#imodels.rule_list.greedy_rule_list.GreedyRuleListClassifier) |                                                              |  |

| OneR rule list              | [OneRClassifier](https://csinva.io/imodels/rule_list/one_r.html#imodels.rule_list.one_r.OneRClassifier) |                                                              |  |

| Optimal rule tree (GOSDT)   | [OptimalTreeClassifier](https://csinva.io/imodels/tree/gosdt/pygosdt.html#imodels.tree.gosdt.pygosdt.OptimalTreeClassifier) |                                                              | Requires [gosdt](https://pypi.org/project/gosdt/), fails for large problems |

| Greedy rule tree (CART)     | [GreedyTreeClassifier](https://csinva.io/imodels/tree/cart_wrapper.html#imodels.tree.cart_wrapper.GreedyTreeClassifier) |      [GreedyTreeRegressor](https://csinva.io/imodels/tree/cart_wrapper.html#imodels.tree.cart_wrapper.GreedyTreeRegressor)                                                        |  |

| C4.5 rule tree              | [C45TreeClassifier](https://csinva.io/imodels/tree/c45_tree/c45_tree.html#imodels.tree.c45_tree.c45_tree.C45TreeClassifier) |           |  |

| TAO rule tree              | [TaoTreeClassifier](https://csinva.io/imodels/tree/tao.html#imodels.tree.tao.TaoTreeClassifier) |   [TaoTreeRegressor](https://csinva.io/imodels/tree/tao.html#imodels.tree.tao.TaoTreeRegressor)        |  |

| Iterative random forest     | [IRFClassifier](https://csinva.io/imodels/tree/iterative_random_forest/iterative_random_forest.html#imodels.tree.iterative_random_forest.iterative_random_forest.IRFClassifier)                                                             |                                                              | Requires [irf](https://pypi.org/project/irf/) |

| Sparse integer linear model | [SLIMClassifier](https://csinva.io/imodels/algebraic/slim.html#imodels.algebraic.slim.SLIMClassifier) | [SLIMRegressor](https://csinva.io/imodels/algebraic/slim.html#imodels.algebraic.slim.SLIMRegressor) | Requires extra dependencies for speed |

| Tree GAM | [TreeGAMClassifier](https://csinva.io/imodels/algebraic/tree_gam.html) | [TreeGAMRegressor](https://csinva.io/imodels/algebraic/tree_gam.html) | |

| Greedy tree sums (FIGS) | [FIGSClassifier](https://csinva.io/imodels/tree/figs.html#imodels.tree.figs.FIGSClassifier) | [FIGSRegressor](https://csinva.io/imodels/tree/figs.html#imodels.tree.figs.FIGSRegressor) |                                                              |

| Hierarchical shrinkage | [HSTreeClassifierCV](https://csinva.io/imodels/tree/hierarchical_shrinkage.html#imodels.tree.hierarchical_shrinkage.HSTreeClassifierCV) | [HSTreeRegressorCV](https://csinva.io/imodels/tree/hierarchical_shrinkage.html#imodels.tree.hierarchical_shrinkage.HSTreeRegressorCV) | Wraps any sklearn tree-based model |

| Distillation |  | [DistilledRegressor](https://csinva.io/imodels/util/distillation.html#imodels.util.distillation.DistilledRegressor) | Wraps any sklearn-compatible models |

| AutoML model | [AutoInterpretableClassifier️](https://csinva.io/imodels/util/automl.html)  | [AutoInterpretableRegressor️](https://csinva.io/imodels/util/automl.html) | |

### 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](https://arxiv.org/abs/2201.11931), [🔗 Post](https://csinva.io/imodels/figs.html), [📌 Citation](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=fast+interpretable+greedy-tree+sums&oq=fast#d=gs_cit&u=%2Fscholar%3Fq%3Dinfo%3ADnPVL74Rop0J%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D0%26hl%3Den)

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](https://arxiv.org/abs/2202.00858) (ICML 2022), [🔗 Post](https://csinva.io/imodels/shrinkage.html), [📌 Citation](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=hierarchical+shrinkage+singh&btnG=&oq=hierar#d=gs_cit&u=%2Fscholar%3Fq%3Dinfo%3Azc6gtLx-aL4J%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D0%26hl%3Den)

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](https://arxiv.org/pdf/2307.01932.pdf), [🔗 Post](https://csinva.io/imodels/mdi_plus.html), [📌 Citation](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=MDI%2B%3A+A+Flexible+Random+Forest-Based+Feature+Importance+Framework&btnG=#d=gs_cit&t=1690399844081&u=%2Fscholar%3Fq%3Dinfo%3Axc0LcHXE_lUJ%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D0%26hl%3Den)

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 :)

```r

@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},

}

```