deprecate and remove optimalrulelist (corels)

csinva · csinva · commit ca90ceb8e3df · 2025-08-25T09:41:38.000-07:00
diff --git a/imodels/__init__.py b/imodels/__init__.py
@@ -13,7 +13,7 @@
 from .discretization.mdlp import MDLPDiscretizer, BRLDiscretizer
 from .experimental.bartpy import BART
 from .rule_list.bayesian_rule_list.bayesian_rule_list import BayesianRuleListClassifier
-from .rule_list.corels_wrapper import OptimalRuleListClassifier
+# from .experimental.corels_wrapper import OptimalRuleListClassifier
 from .rule_list.greedy_rule_list import GreedyRuleListClassifier
 from .rule_list.one_r import OneRClassifier
 from .rule_set import boosted_rules
@@ -65,7 +65,6 @@
     BayesianRuleSetClassifier,
     C45TreeClassifier,
     OptimalTreeClassifier,
-    OptimalRuleListClassifier,
     OneRClassifier,
     SlipperClassifier,
     RuleFitClassifier,
diff --git a/imodels/experimental/corels_wrapper.py b/imodels/experimental/corels_wrapper.py
diff --git a/pyproject.toml b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "imodels"
-version = "2.0.1"
+version = "2.0.2"
 description = "Implementations of various interpretable models"
 readme = "readme.md"
 requires-python = ">=3.9"
@@ -62,7 +62,7 @@ dev = [
 # Optional algorithm deps mentioned in setup.py comments
 [project.optional-dependencies]
 optional = [
-  "cvxpy", # for OptimalRuleListClassifier
+  "cvxpy", # for slim
   "gosdt-deprecated", # for OptimalTreeClassifier
   "irf", # for IterativeRandomForestClassifier
   "statsmodels", # for bartpy
diff --git a/readme.md b/readme.md
@@ -76,7 +76,6 @@ Install with `pip install imodels` (see [here](https://github.com/csinva/imodels
 | Boosted rule set            | [🗂️](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           |
 | Slipper rule set            | [🗂️](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            |
 | Bayesian rule set           | [🗂️](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)  |
-| Optimal rule list           | [🗂️](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) |
 | Bayesian rule list          | [🗂️](https://csinva.io/imodels/rule_list/bayesian_rule_list/bayesian_rule_list.html#imodels.rule_list.bayesian_rule_list.bayesian_rule_list.BayesianRuleListClassifier), [📄](https://projecteuclid.org/journals/annals-of-applied-statistics/volume-9/issue-3/Interpretable-classifiers-using-rules-and-Bayesian-analysis--Building-a/10.1214/15-AOAS848.full), [🔗](https://github.com/tmadl/sklearn-expertsys) | Fits compact rule list distribution with Bayesian sampling (slow) |
 | Greedy rule list            | [🗂️](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 |
 | OneR rule list              | [🗂️](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              |
@@ -165,7 +164,6 @@ Different models support different machine-learning tasks. Current support for d
 | 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) |                                                              |  |
@@ -282,7 +280,6 @@ MDI+ is a novel feature importance framework, which generalizes the popular mean
 <summary>Reference implementations (also linked above)</summary>
 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.
 <ul>
-  <li><a href="https://github.com/corels/pycorels">pycorels</a> - by <a href="https://github.com/fingoldin">@fingoldin</a> and the <a href="https://github.com/corels/corels">original CORELS team</a>
   <li><a href="https://github.com/tmadl/sklearn-expertsys">sklearn-expertsys</a> - by <a href="https://github.com/tmadl">@tmadl</a> and <a href="https://github.com/kenben">@kenben</a> based on original code by <a href="http://lethalletham.com/">Ben Letham</a></li>
   <li><a href="https://github.com/christophM/rulefit">rulefit</a> - by <a href="https://github.com/christophM">@christophM</a></li>
   <li><a href="https://github.com/scikit-learn-contrib/skope-rules">skope-rules</a> - by the <a href="https://github.com/scikit-learn-contrib/skope-rules/blob/master/AUTHORS.rst">skope-rules team</a> (including <a href="https://github.com/ngoix">@ngoix</a>, <a href="https://github.com/floriangardin">@floriangardin</a>, <a href="https://github.com/datajms">@datajms</a>, <a href="">Bibi Ndiaye</a>, <a href="">Ronan Gautier</a>)</li>
diff --git a/tests/classification_binary_inputs_test.py b/tests/classification_binary_inputs_test.py
@@ -3,7 +3,6 @@
 import numpy as np
 
 from imodels import (
-    OptimalRuleListClassifier,
     OptimalTreeClassifier,
     FPLassoClassifier,
     FPSkopeClassifier,
@@ -34,7 +33,6 @@ def setup_method(self):
     def test_classification_binary(self):
         """Test imodels on basic binary classification task"""
         for model_type in [
-            OptimalRuleListClassifier,
             OptimalTreeClassifier,
             FPLassoClassifier,
             FPSkopeClassifier,
@@ -55,7 +53,7 @@ def test_classification_binary(self):
             assert preds.size == self.n, "predict() yields right size"
 
             # test preds_proba()
-            if model_type not in {OptimalRuleListClassifier, OptimalTreeClassifier}:
+            if model_type not in {OptimalTreeClassifier}:
                 preds_proba = m.predict_proba(X)
                 assert len(preds_proba.shape) == 2, "preds_proba has 2 columns"
                 assert preds_proba.shape[1] == 2, "preds_proba has 2 columns"
diff --git a/tests/classification_continuous_inputs_test.py b/tests/classification_continuous_inputs_test.py
@@ -56,7 +56,7 @@ def test_classification_binary(self):
             assert preds.size == self.n, 'predict() yields right size'
 
             # test preds_proba()
-            if model_type not in {OptimalRuleListClassifier, OptimalTreeClassifier}:
+            if model_type not in {OptimalTreeClassifier}:
                 preds_proba = m.predict_proba(X)
                 assert len(preds_proba.shape) == 2, 'preds_proba has 2 columns'
                 assert preds_proba.shape[1] == 2, 'preds_proba has 2 columns'
diff --git a/uv.lock b/uv.lock
