change EBM implementation used from the interpret repo directly to using the autogluon implementation of EBMs

Author: paulbkoch

tabarena/pyproject.toml

@@ -43,7 +43,7 @@ tabpfn = [
   "tabpfn-extensions[many_class] @ git+https://github.com/PriorLabs/tabpfn-extensions.git",
 ]
 tabicl = ["tabicl>=2.0.0"]
-ebm = ["interpret-core>=0.7.3"]
+ebm = ["autogluon.tabular[interpret]>=1.5,<1.6"]
 search_spaces = ["configspace>=1.2,<2.0"]
 realmlp = ["pytabkit>=1.5.0,<2.0"]
 tabdpt = [
@@ -62,7 +62,7 @@ benchmark = [
   "tabpfn>=7.0.0",
   "tabpfn-extensions[many_class] @ git+https://github.com/PriorLabs/tabpfn-extensions.git",
   "tabicl>=2.0.0",
-  "interpret-core>=0.7.3",
+  "autogluon.tabular[interpret]>=1.5,<1.6",
   "configspace>=1.2,<2.0",
   "pytabkit>=1.5.0,<2.0",
   "tabdpt>=1.1.10",

tabarena/tabarena/benchmark/models/ag/__init__.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-from tabarena.benchmark.models.ag.ebm.ebm_model import ExplainableBoostingMachineModel
 from tabarena.benchmark.models.ag.knn_new.knn_model import KNNNewModel
 from tabarena.benchmark.models.ag.modernnca.modernnca_model import ModernNCAModel
 from tabarena.benchmark.models.ag.perpetual_booster.perpetual_booster_model import (
@@ -19,7 +18,6 @@
 from tabarena.benchmark.models.ag.xrfm.xrfm_model import XRFMModel
 
 __all__ = [
-    "ExplainableBoostingMachineModel",
     "KNNNewModel",
     "ModernNCAModel",
     "PerpetualBoosterModel",

tabarena/tabarena/benchmark/models/ag/ebm/__init__.py

@@ -5,7 +5,6 @@
 from autogluon.tabular.registry import ModelRegistry, ag_model_registry
 
 from tabarena.benchmark.models.ag import (
-    ExplainableBoostingMachineModel,
     KNNNewModel,
     ModernNCAModel,
     PerpetualBoosterModel,
@@ -24,7 +23,6 @@
 tabarena_model_registry: ModelRegistry = copy.deepcopy(ag_model_registry)
 
 _models_to_add = [
-    ExplainableBoostingMachineModel,
     RealMLPModel,
     TabICLModel,
     TabDPTModel,

tabarena/tabarena/benchmark/models/ag/ebm/ebm_model.py

@@ -1,12 +1,11 @@
-from __future__ import annotations
-
 from autogluon.common.space import Categorical, Int, Real
-
-from tabarena.benchmark.models.ag.ebm.ebm_model import ExplainableBoostingMachineModel
+from autogluon.tabular.models import EBMModel
 from tabarena.utils.config_utils import ConfigGenerator
 
 name = "EBM"
-manual_configs = []
+manual_configs = [
+    {},
+]
 search_space = {
     "max_leaves": Int(2, 3, default=2),
     "smoothing_rounds": Int(0, 1000, default=200),
@@ -42,9 +41,9 @@
 }
 
 gen_ebm = ConfigGenerator(
-    model_cls=ExplainableBoostingMachineModel,
+    model_cls=EBMModel,
     search_space=search_space,
-    manual_configs=[{}],
+    manual_configs=manual_configs,
 )
 
 

tabarena/tabarena/benchmark/models/model_registry.py

@@ -6,8 +6,8 @@ def test_ebm():
 
     try:
         from autogluon.tabular.testing import FitHelper
-        from tabarena.benchmark.models.ag.ebm.ebm_model import ExplainableBoostingMachineModel
-        model_cls = ExplainableBoostingMachineModel
+        from autogluon.tabular.models import EBMModel
+        model_cls = EBMModel
         FitHelper.verify_model(model_cls=model_cls, model_hyperparameters=model_hyperparameters)
     except ImportError as err:
         pytest.skip(