adaboost: adapt to scikit-learn's 1.4 deprecation of base_estimator

Orange/ensembles/ada_boost.py

@@ -1,3 +1,5 @@
+import warnings
+
 import sklearn.ensemble as skl_ensemble
 
 from Orange.base import SklLearner
@@ -7,6 +9,8 @@
 from Orange.regression.base_regression import (
     SklLearnerRegression, SklModelRegression
 )
+from Orange.util import OrangeDeprecationWarning
+
 
 __all__ = ['SklAdaBoostClassificationLearner', 'SklAdaBoostRegressionLearner']
 
@@ -15,21 +19,32 @@ class SklAdaBoostClassifier(SklModelClassification):
     pass
 
 
+def base_estimator_deprecation():
+    warnings.warn(
+        "`base_estimator` is deprecated: use `estimator` instead.",
+        OrangeDeprecationWarning, stacklevel=3)
+
+
 class SklAdaBoostClassificationLearner(SklLearnerClassification):
     __wraps__ = skl_ensemble.AdaBoostClassifier
     __returns__ = SklAdaBoostClassifier
     supports_weights = True
 
-    def __init__(self, base_estimator=None, n_estimators=50, learning_rate=1.,
-                 algorithm='SAMME.R', random_state=None, preprocessors=None):
+    def __init__(self, estimator=None, n_estimators=50, learning_rate=1.,
+                 algorithm='SAMME.R', random_state=None, preprocessors=None,
+                 base_estimator="deprecated"):
+        if base_estimator != "deprecated":
+            base_estimator_deprecation()
+            estimator = base_estimator
+        del base_estimator
         from Orange.modelling import Fitter
         # If fitter, get the appropriate Learner instance
-        if isinstance(base_estimator, Fitter):
-            base_estimator = base_estimator.get_learner(
-                base_estimator.CLASSIFICATION)
+        if isinstance(estimator, Fitter):
+            estimator = estimator.get_learner(
+                estimator.CLASSIFICATION)
         # If sklearn learner, get the underlying sklearn representation
-        if isinstance(base_estimator, SklLearner):
-            base_estimator = base_estimator.__wraps__(**base_estimator.params)
+        if isinstance(estimator, SklLearner):
+            estimator = estimator.__wraps__(**estimator.params)
         super().__init__(preprocessors=preprocessors)
         self.params = vars()
 
@@ -43,15 +58,20 @@ class SklAdaBoostRegressionLearner(SklLearnerRegression):
     __returns__ = SklAdaBoostRegressor
     supports_weights = True
 
-    def __init__(self, base_estimator=None, n_estimators=50, learning_rate=1.,
-                 loss='linear', random_state=None, preprocessors=None):
+    def __init__(self, estimator=None, n_estimators=50, learning_rate=1.,
+                 loss='linear', random_state=None, preprocessors=None,
+                 base_estimator="deprecated"):
+        if base_estimator != "deprecated":
+            base_estimator_deprecation()
+            estimator = base_estimator
+        del base_estimator
         from Orange.modelling import Fitter
         # If fitter, get the appropriate Learner instance
-        if isinstance(base_estimator, Fitter):
-            base_estimator = base_estimator.get_learner(
-                base_estimator.REGRESSION)
+        if isinstance(estimator, Fitter):
+            estimator = estimator.get_learner(
+                estimator.REGRESSION)
         # If sklearn learner, get the underlying sklearn representation
-        if isinstance(base_estimator, SklLearner):
-            base_estimator = base_estimator.__wraps__(**base_estimator.params)
+        if isinstance(estimator, SklLearner):
+            estimator = estimator.__wraps__(**estimator.params)
         super().__init__(preprocessors=preprocessors)
         self.params = vars()

Orange/tests/test_ada_boost.py

@@ -2,7 +2,11 @@
 # pylint: disable=missing-docstring
 
 import unittest
+from distutils.version import LooseVersion
+
 import numpy as np
+
+import Orange
 from Orange.data import Table
 from Orange.classification import SklTreeLearner
 from Orange.regression import SklTreeRegressionLearner
@@ -27,14 +31,14 @@ def test_adaboost(self):
         self.assertGreater(ca, 0.9)
         self.assertLess(ca, 0.99)
 
-    def test_adaboost_base_estimator(self):
+    def test_adaboost_estimator(self):
         np.random.seed(0)
         stump_estimator = SklTreeLearner(max_depth=1)
         tree_estimator = SklTreeLearner()
         stump = SklAdaBoostClassificationLearner(
-            base_estimator=stump_estimator, n_estimators=5)
+            estimator=stump_estimator, n_estimators=5)
         tree = SklAdaBoostClassificationLearner(
-            base_estimator=tree_estimator, n_estimators=5)
+            estimator=tree_estimator, n_estimators=5)
         cv = CrossValidation(k=4)
         results = cv(self.iris, [stump, tree])
         ca = CA(results)
@@ -68,12 +72,12 @@ def test_adaboost_reg(self):
         results = cv(self.housing, [learn])
         _ = RMSE(results)
 
-    def test_adaboost_reg_base_estimator(self):
+    def test_adaboost_reg_estimator(self):
         np.random.seed(0)
         stump_estimator = SklTreeRegressionLearner(max_depth=1)
         tree_estimator = SklTreeRegressionLearner()
-        stump = SklAdaBoostRegressionLearner(base_estimator=stump_estimator)
-        tree = SklAdaBoostRegressionLearner(base_estimator=tree_estimator)
+        stump = SklAdaBoostRegressionLearner(estimator=stump_estimator)
+        tree = SklAdaBoostRegressionLearner(estimator=tree_estimator)
         cv = CrossValidation(k=3)
         results = cv(self.housing, [stump, tree])
         rmse = RMSE(results)
@@ -103,3 +107,10 @@ def test_predict_numpy_reg(self):
     def test_adaboost_adequacy_reg(self):
         learner = SklAdaBoostRegressionLearner()
         self.assertRaises(ValueError, learner, self.iris)
+
+    def test_remove_deprecation(self):
+        if LooseVersion(Orange.__version__) >= LooseVersion("3.39"):
+            self.fail(
+                "`base_estimator` was deprecated in "
+                "version 3.37. Please remove everything related to it."
+            )

Orange/widgets/model/owadaboost.py

@@ -80,7 +80,7 @@ def create_learner(self):
         if self.base_estimator is None:
             return None
         return self.LEARNER(
-            base_estimator=self.base_estimator,
+            estimator=self.base_estimator,
             n_estimators=self.n_estimators,
             learning_rate=self.learning_rate,
             random_state=self.random_seed,