From 4df22bda10ab7d539bfb80ac901552542e5b09dd Mon Sep 17 00:00:00 2001
From: Vesna Tanko <tankovesna@hotmail.com>
Date: Mon, 6 Jan 2025 09:26:02 +0100
Subject: [PATCH] test_explainer: Adapt to latest Orange

---
 orangecontrib/explain/inspection.py           |  2 +-
 orangecontrib/explain/tests/test_explainer.py | 15 +++++++++------
 2 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/orangecontrib/explain/inspection.py b/orangecontrib/explain/inspection.py
index 931af07..3af0263 100644
--- a/orangecontrib/explain/inspection.py
+++ b/orangecontrib/explain/inspection.py
@@ -203,7 +203,7 @@ def dummy_fit(*_, **__):
     model.fit_ = dummy_fit
     if model.domain.class_var.is_discrete:
         model._estimator_type = "classifier"
-        model.classes_ = model.domain.class_var.values
+        model.classes_ = np.array(model.domain.class_var.values)
     else:
         model._estimator_type = "regressor"
 
diff --git a/orangecontrib/explain/tests/test_explainer.py b/orangecontrib/explain/tests/test_explainer.py
index 77c8700..5968625 100644
--- a/orangecontrib/explain/tests/test_explainer.py
+++ b/orangecontrib/explain/tests/test_explainer.py
@@ -2,7 +2,6 @@
 import unittest
 
 import numpy as np
-from numpy.testing import assert_array_equal
 from Orange.classification import (
     LogisticRegressionLearner,
     RandomForestLearner,
@@ -11,6 +10,10 @@
     TreeLearner,
     ThresholdLearner,
 )
+try:
+    from Orange.classification import ScoringSheetLearner
+except :
+    ScoringSheetLearner = None
 from Orange.data import Table, Domain, ContinuousVariable
 from Orange.regression import LinearRegressionLearner, CurveFitLearner
 from Orange.tests import test_regression, test_classification
@@ -206,7 +209,7 @@ def test_gradient_boosting_shape(self):
         )
         self.assertEqual(len(shap_values), 2)
         self.assertEqual(len(base_value), 2)
-        assert_array_equal(-shap_values[0], shap_values[1])
+        np.testing.assert_array_almost_equal(-shap_values[0], shap_values[1])
 
         learner = GBLearner()
         model = learner(self.hearth_disease)
@@ -215,7 +218,7 @@ def test_gradient_boosting_shape(self):
         )
         self.assertEqual(len(shap_values), 2)
         self.assertEqual(len(base_value), 2)
-        assert_array_equal(-shap_values[0], shap_values[1])
+        np.testing.assert_array_almost_equal(-shap_values[0], shap_values[1])
 
         learner = XGBRFLearner()
         model = learner(self.hearth_disease)
@@ -224,7 +227,7 @@ def test_gradient_boosting_shape(self):
         )
         self.assertEqual(len(shap_values), 2)
         self.assertEqual(len(base_value), 2)
-        assert_array_equal(-shap_values[0], shap_values[1])
+        np.testing.assert_array_almost_equal(-shap_values[0], shap_values[1])
 
     @unittest.skipIf(XGBLearner is None, "Missing 'xgboost' package")
     def test_remove_workaround(self):
@@ -253,8 +256,8 @@ def test_all_classifiers(self):
         """ Test explanation for all classifiers """
         for learner in test_classification.all_learners():
             with self.subTest(learner):
-                if learner == ThresholdLearner:
-                    # ThresholdLearner require binary class
+                if learner in (ThresholdLearner, ScoringSheetLearner):
+                    # ThresholdLearner and ScoringSheetLearner require binary class
                     continue
                 kwargs = {}
                 if "base_learner" in inspect.signature(learner).parameters: