Package: imodels
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 notebooksModern 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 thefitandpredictmethods, same as standard scikit-learn models.fromsklearn.model_selectionimporttrain_test_splitfromimodelsimportget_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 modelmodel=HSTreeClassifierCV(max_leaf_nodes=4)# initialize a tree model and specify only 4 leaf nodesmodel.fit(X_train,y_train,feature_names=feature_names)# fit modelpreds=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
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|--- 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]InstallationInstall withpip install imodels(seeherefor help).Supported modelsModelReferenceDescriptionRulefit rule set🗂️,🔗,📄Fits a sparse linear model on rules extracted from decision treesSkope rule set🗂️,🔗Extracts rules from gradient-boosted trees, deduplicates them,then linearly combines them based on their OOB precisionBoosted rule set🗂️,🔗,📄Sequentially fits a set of rules with AdaboostSlipper rule set🗂️,      , ㅤㅤ📄Sequentially learns a set of rules with SLIPPERBayesian rule set🗂️,🔗,📄Finds concise rule set with Bayesian sampling (slow)Optimal rule list🗂️,🔗,📄Fits rule list using global optimization for sparsity (CORELS)Bayesian rule list🗂️,🔗,📄Fits compact rule list distribution with Bayesian sampling (slow)Greedy rule list🗂️,🔗Uses CART to fit a list (only a single path), rather than a treeOneR rule list🗂️,      ,📄Fits rule list restricted to only one featureOptimal rule tree🗂️,🔗,📄Fits succinct tree using global optimization for sparsity (GOSDT)Greedy rule tree🗂️,🔗,📄Greedily fits tree using CARTC4.5 rule tree🗂️,🔗,📄Greedily fits tree using C4.5TAO rule tree🗂️,      , ㅤㅤ📄Fits tree using alternating optimizationIterative randomforest🗂️,🔗,📄Repeatedly fit random forest, giving features withhigh importance a higher chance of being selectedSparse integerlinear model🗂️,      , ㅤㅤ📄Sparse linear model with integer coefficientsTree GAM🗂️,🔗,📄Generalized additive model fit with short boosted treesGreedy tree sums🗂️,      , ㅤㅤ📄Sum of small trees with very few total rules (FIGS)Hierarchicalshrinkage wrapper🗂️,      , ㅤㅤ📄Improve a decision tree, random forest, orgradient-boosting ensemble with ultra-fast, post-hoc regularizationDistillationwrapper🗂️Train a black-box model,then distill it into an interpretable modelAutoML wrapper🗂️Automatically fit and select an interpretable modelMore models⌛(Coming soon!) Lightweight Rule Induction, MLRules, ...Docs🗂️, Reference code implementation 🔗, Research paper 📄Demo notebooksDemos are contained in thenotebooksfolder.Quickstart demoShows how to fit, predict, and visualize with different interpretable modelsAutogluon demoFit/select an interpretable model automatically using Autogluon AutoMLQuickstart colab demoShows how to fit, predict, and visualize with different interpretable modelsClinical decision rule notebookShows an example of usingimodelsfor deriving a clinical decision rulePosthoc analysisWe also include some demos of posthoc analysis, which occurs after fitting models:posthoc.ipynbshows different simple analyses to interpret a trained model anduncertainty.ipynbcontains basic code to get uncertainty estimates for a modelWhat'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 setRule listRule treeAlgebraic modelsDifferent 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 generationRule selectionRule postprocessingEx. RuleFit vs. SkopeRulesRuleFit 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 listsBayesian 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. SkopeRulesFPSkope and SkopeRules differ only in the way they generate candidate rules: FPSkope uses FPgrowth whereas SkopeRules extracts rules from decision trees.Support for different tasksDifferent 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):ModelBinary classificationRegressionNotesRulefit rule setRuleFitClassifierRuleFitRegressorSkope rule setSkopeRulesClassifierBoosted rule setBoostedRulesClassifierBoostedRulesRegressorSLIPPER rule setSlipperClassifierBayesian rule setBayesianRuleSetClassifierFails for large problemsOptimal rule list (CORELS)OptimalRuleListClassifierRequirescorels, fails for large problemsBayesian rule listBayesianRuleListClassifierGreedy rule listGreedyRuleListClassifierOneR rule listOneRClassifierOptimal rule tree (GOSDT)OptimalTreeClassifierRequiresgosdt, fails for large problemsGreedy rule tree (CART)GreedyTreeClassifierGreedyTreeRegressorC4.5 rule treeC45TreeClassifierTAO rule treeTaoTreeClassifierTaoTreeRegressorIterative random forestIRFClassifierRequiresirfSparse integer linear modelSLIMClassifierSLIMRegressorRequires extra dependencies for speedTree GAMTreeGAMClassifierTreeGAMRegressorGreedy tree sums (FIGS)FIGSClassifierFIGSRegressorHierarchical shrinkageHSTreeClassifierCVHSTreeRegressorCVWraps any sklearn tree-based modelDistillationDistilledRegressorWraps any sklearn-compatible modelsAutoML modelAutoInterpretableClassifier️AutoInterpretableRegressor️ExtrasData-wrangling functionsfor working with popular tabular datasets (e.g. compas).These functions, in conjunction withimodels-dataandimodels-experiments, make it simple to download data and run experiments on new models.Explain classification errorswith a simple posthoc function.Fit an interpretable model to explain a previous model's errors (ex. inthis notebook📓).Fast and effective discretizersfor data preprocessing.DiscretizerReferenceDescriptionMDLP🗂️,🔗,📄Discretize using entropy minimization heuristicSimple🗂️,🔗Simple KBins discretizationRandom Forest🗂️Discretize into bins based on random forest split popularityRule-based utilsfor customizing modelsThe code here contains many useful and customizable functions for rule-based learning in theutil folder. This includes functions / classes for rule deduplication, rule screening, and converting between trees, rulesets, and neural networks.Our favorite modelsAfter developing and playing withimodels, we developed a few new models to overcome limitations of existing interpretable models.FIGS: Fast interpretable greedy-tree sums📄 Paper,🔗 Post,📌 CitationFast 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(ICML 2022),🔗 Post,📌 CitationHierarchical 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,📌 CitationMDI+ 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.ReferencesReadingsInterpretable ML good quick overview: murdoch et al. 2019,pdfInterpretable ML book: molnar 2019,pdfCase for interpretable models rather than post-hoc explanation: rudin 2019,pdfReview on evaluating interpretability: doshi-velez & kim 2017,pdfReference 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@fingoldinand theoriginal CORELS teamsklearn-expertsys- by@tmadland@kenbenbased on original code byBen Lethamrulefit- by@christophMskope-rules- by theskope-rules team(including@ngoix,@floriangardin,@datajms,Bibi Ndiaye,Ronan Gautier)boa- by@wangtongadaRelated packagesgplearn: symbolic regression/classificationpysr: fast symbolic regressionpygam: generative additive modelsinterpretml: boosting-based gamh20 ai: gams + glms (and more)optbinning: data discretization / scoring modelsUpdatesFor 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 requestsvery 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:apythonpackageforfittinginterpretablemodels},journal={JournalofOpenSourceSoftware},publisher={TheOpenJournal},year={2021},author={Singh,ChandanandNasseri,KeyanandTan,YanShuoandTang,TiffanyandYu,Bin},volume={6},number={61},pages={3192},doi={10.21105/joss.03192},url={https://doi.org/10.21105/joss.03192},}