Handle stringified binary predict_proba labels

src/pyrecest/utils/association_features.py

@@ -205,14 +205,23 @@ def _predict_proba_positive_class_index(self) -> int:
         if len(classes) != 2:
             return 1
         for class_index, class_label in enumerate(classes):
-            try:
-                if bool(class_label == 1):
-                    return class_index
-            except (TypeError, ValueError):
-                continue
+            if _is_positive_binary_label(class_label):
+                return class_index
         return 1
 
 
+def _is_positive_binary_label(class_label: Any) -> bool:
+    try:
+        if bool(class_label == 1):
+            return True
+    except (TypeError, ValueError):
+        pass
+    if isinstance(class_label, str):
+        normalized_label = class_label.strip().casefold()
+        return normalized_label in {"1", "true"}
+    return False
+
+
 def _class_labels_to_list(classes: Any) -> list[Any]:
     if classes is None:
         return []

tests/test_calibrated_association_predict_proba_string_labels.py

@@ -0,0 +1,29 @@
+import unittest
+
+import numpy.testing as npt
+
+from pyrecest.backend import array
+from pyrecest.utils import CalibratedPairwiseAssociationModel
+
+
+class TestCalibratedAssociationPredictProbaStringLabels(unittest.TestCase):
+    def test_predict_proba_respects_stringified_binary_class_order(self):
+        class StringBinaryPredictProbaModel:
+            classes_ = ["1", "0"]
+
+            def predict_proba(self, features):
+                del features
+                return array([[0.8, 0.2], [0.3, 0.7]])
+
+        calibrated_model = CalibratedPairwiseAssociationModel(
+            StringBinaryPredictProbaModel(), feature_names=("distance", "similarity")
+        )
+        features = array([[0.1, 0.9], [2.0, 0.1]])
+
+        probabilities = calibrated_model.predict_match_probability(features)
+
+        npt.assert_allclose(probabilities, array([0.8, 0.3]))
+
+
+if __name__ == "__main__":
+    unittest.main()