Fix delete_topics crash when custom labels are set

bertopic/_bertopic.py

@@ -2188,6 +2188,7 @@ def delete_topics(
         """
         check_is_fitted(self)
 
+        original_unique_topics = sorted(set(self.topics_))
         topics_df = pd.DataFrame({"Topic": self.topics_})
 
         # Check if -1 exists in the current topics
@@ -2215,10 +2216,6 @@ def delete_topics(
                 outlier_image = np.zeros((1, self.representative_images_.shape[1]))
                 self.representative_images_ = np.vstack([outlier_image, self.representative_images_])
 
-            # Initialize custom labels for -1 topic if they exist
-            if hasattr(self, "custom_labels_") and self.custom_labels_ is not None:
-                self.custom_labels_[-1] = ""
-
             # Initialize ctfidf model diagonal for -1 topic (ones) if it exists
             if hasattr(self, "ctfidf_model") and self.ctfidf_model is not None:
                 n_features = self.ctfidf_model._idf_diag.shape[1]
@@ -2268,14 +2265,23 @@ def delete_topics(
             }
             self.topic_aspects_ = new_aspects
 
-        # Update custom labels if they exist
+        # Update custom labels if they exist. `.custom_labels_` is a list aligned with
+        # sorted unique topic IDs (see `set_topic_labels`), not a dict.
         if hasattr(self, "custom_labels_") and self.custom_labels_ is not None:
-            new_labels = {
-                (final_mapping[old_topic] if old_topic != -1 else -1): label
-                for old_topic, label in self.custom_labels_.items()
-                if old_topic not in topics_to_delete
-            }
-            self.custom_labels_ = new_labels
+            if isinstance(self.custom_labels_, dict):
+                labels_by_topic = dict(self.custom_labels_)
+            else:
+                labels_by_topic = dict(zip(original_unique_topics, self.custom_labels_))
+
+            remapped_labels = {}
+            for old_topic, label in labels_by_topic.items():
+                if old_topic in topics_to_delete:
+                    continue
+                new_topic = -1 if old_topic == -1 else final_mapping[old_topic]
+                remapped_labels[new_topic] = label
+
+            new_unique_topics = sorted(set(self.topics_))
+            self.custom_labels_ = [remapped_labels.get(topic, "") for topic in new_unique_topics]
 
         # Update topic representations
         new_representations = {

tests/test_reduction/test_delete.py

@@ -57,3 +57,26 @@ def test_delete(model, request):
         assert mapped_labels == topic_model.topics_[950:]
     else:
         assert mapped_labels == topic_model.topics_
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        ("kmeans_pca_topic_model"),
+        ("base_topic_model"),
+        ("custom_topic_model"),
+    ],
+)
+def test_delete_keeps_custom_labels(model, request):
+    topic_model = copy.deepcopy(request.getfixturevalue(model))
+    labels = topic_model.generate_topic_labels()
+    topic_model.set_topic_labels(labels)
+
+    remaining = [topic for topic in sorted(set(topic_model.topics_)) if topic != -1]
+    topic_to_delete = remaining[0]
+
+    topic_model.delete_topics([topic_to_delete])
+
+    unique_topics = sorted(set(topic_model.topics_))
+    assert isinstance(topic_model.custom_labels_, list)
+    assert len(topic_model.custom_labels_) == len(unique_topics)