Adjust _topic_id_to_zeroshot_topic_idx when adding mapping to `Topi…

…cMapper` (#7) (#2120)

bertopic/_bertopic.py

@@ -2143,7 +2143,7 @@ def merge_topics(
 
         # Update topics
         documents.Topic = documents.Topic.map(mapping)
-        self.topic_mapper_.add_mappings(mapping)
+        self.topic_mapper_.add_mappings(mapping, topic_model=self)
         documents = self._sort_mappings_by_frequency(documents)
         self._extract_topics(documents, mappings=mappings)
         self._update_topic_size(documents)
@@ -4402,50 +4402,12 @@ def _reduce_to_n_topics(self, documents: pd.DataFrame, use_ctfidf: bool = False)
         # Map topics
         documents.Topic = new_topics
         self._update_topic_size(documents)
-        self.topic_mapper_.add_mappings(mapped_topics)
+        self.topic_mapper_.add_mappings(mapped_topics, topic_model=self)
 
         # Update representations
         documents = self._sort_mappings_by_frequency(documents)
         self._extract_topics(documents, mappings=mappings)
 
-        # When zero-shot topic(s) are present in the topics to merge,
-        # determine whether to take one of the zero-shot topic labels
-        # or use a calculated representation.
-        if self._is_zeroshot():
-            new_topic_id_to_zeroshot_topic_idx = {}
-            topics_to_map = {
-                topic_mapping[0]: topic_mapping[1] for topic_mapping in np.array(self.topic_mapper_.mappings_)[:, -2:]
-            }
-
-            for topic_to, topics_from in basic_mappings.items():
-                # When extracting topics, the reduced topics were reordered.
-                # Must get the updated topic_to.
-                topic_to = topics_to_map[topic_to]
-
-                # which of the original topics are zero-shot
-                zeroshot_topic_ids = [
-                    topic_id for topic_id in topics_from if topic_id in self._topic_id_to_zeroshot_topic_idx
-                ]
-                if len(zeroshot_topic_ids) == 0:
-                    continue
-
-                # If any of the original topics are zero-shot, take the best fitting zero-shot label
-                # if the cosine similarity with the new topic exceeds the zero-shot threshold
-                zeroshot_labels = [
-                    self.zeroshot_topic_list[self._topic_id_to_zeroshot_topic_idx[topic_id]]
-                    for topic_id in zeroshot_topic_ids
-                ]
-                zeroshot_embeddings = self._extract_embeddings(zeroshot_labels)
-                cosine_similarities = cosine_similarity(
-                    zeroshot_embeddings, [self.topic_embeddings_[topic_to]]
-                ).flatten()
-                best_zeroshot_topic_idx = np.argmax(cosine_similarities)
-                best_cosine_similarity = cosine_similarities[best_zeroshot_topic_idx]
-                if best_cosine_similarity >= self.zeroshot_min_similarity:
-                    new_topic_id_to_zeroshot_topic_idx[topic_to] = zeroshot_topic_ids[best_zeroshot_topic_idx]
-
-            self._topic_id_to_zeroshot_topic_idx = new_topic_id_to_zeroshot_topic_idx
-
         self._update_topic_size(documents)
         return documents
 
@@ -4498,7 +4460,7 @@ def _auto_reduce_topics(self, documents: pd.DataFrame, use_ctfidf: bool = False)
         }
 
         # Update documents and topics
-        self.topic_mapper_.add_mappings(mapped_topics)
+        self.topic_mapper_.add_mappings(mapped_topics, topic_model=self)
         documents = self._sort_mappings_by_frequency(documents)
         self._extract_topics(documents, mappings=mappings)
         self._update_topic_size(documents)
@@ -4538,7 +4500,7 @@ def _sort_mappings_by_frequency(self, documents: pd.DataFrame) -> pd.DataFrame:
         df = pd.DataFrame(self.topic_sizes_.items(), columns=["Old_Topic", "Size"]).sort_values("Size", ascending=False)
         df = df[df.Old_Topic != -1]
         sorted_topics = {**{-1: -1}, **dict(zip(df.Old_Topic, range(len(df))))}
-        self.topic_mapper_.add_mappings(sorted_topics)
+        self.topic_mapper_.add_mappings(sorted_topics, topic_model=self)
 
         # Map documents
         documents.Topic = documents.Topic.map(sorted_topics).fillna(documents.Topic).astype(int)
@@ -4728,11 +4690,12 @@ def get_mappings(self, original_topics: bool = True) -> Mapping[int, int]:
             mappings = dict(zip(mappings[:, 0], mappings[:, 1]))
         return mappings
 
-    def add_mappings(self, mappings: Mapping[int, int]):
+    def add_mappings(self, mappings: Mapping[int, int], topic_model: BERTopic):
         """Add new column(s) of topic mappings.
 
         Arguments:
             mappings: The mappings to add
+            topic_model: The topic model this TopicMapper belongs to
         """
         for topics in self.mappings_:
             topic = topics[-1]
@@ -4741,6 +4704,50 @@ def add_mappings(self, mappings: Mapping[int, int]):
             else:
                 topics.append(-1)
 
+        # When zero-shot topic(s) are present in the topics to merge,
+        # determine whether to take one of the zero-shot topic labels
+        # or use a calculated representation.
+        if topic_model._is_zeroshot() and len(topic_model._topic_id_to_zeroshot_topic_idx) > 0:
+            new_topic_id_to_zeroshot_topic_idx = {}
+            topics_to_map = {
+                topic_mapping[0]: topic_mapping[1]
+                for topic_mapping in np.array(topic_model.topic_mapper_.mappings_)[:, -2:]
+            }
+
+            # Map topic_to to topics_from
+            mapping = defaultdict(list)
+            for key, value in topics_to_map.items():
+                mapping[value].append(key)
+
+            for topic_to, topics_from in mapping.items():
+                # which of the original topics are zero-shot
+                zeroshot_topic_ids = [
+                    topic_id for topic_id in topics_from if topic_id in topic_model._topic_id_to_zeroshot_topic_idx
+                ]
+                if len(zeroshot_topic_ids) == 0:
+                    continue
+
+                # If any of the original topics are zero-shot, take the best fitting zero-shot label
+                # if the cosine similarity with the new topic exceeds the zero-shot threshold
+                zeroshot_labels = [
+                    topic_model.zeroshot_topic_list[topic_model._topic_id_to_zeroshot_topic_idx[topic_id]]
+                    for topic_id in zeroshot_topic_ids
+                ]
+                zeroshot_embeddings = topic_model._extract_embeddings(zeroshot_labels)
+                cosine_similarities = cosine_similarity(
+                    zeroshot_embeddings, [topic_model.topic_embeddings_[topic_to]]
+                ).flatten()
+                best_zeroshot_topic_idx = np.argmax(cosine_similarities)
+                best_cosine_similarity = cosine_similarities[best_zeroshot_topic_idx]
+
+                if best_cosine_similarity >= topic_model.zeroshot_min_similarity:
+                    # Using the topic ID from before mapping, get the idx into the zeroshot topic list
+                    new_topic_id_to_zeroshot_topic_idx[topic_to] = topic_model._topic_id_to_zeroshot_topic_idx[
+                        zeroshot_topic_ids[best_zeroshot_topic_idx]
+                    ]
+
+            topic_model._topic_id_to_zeroshot_topic_idx = new_topic_id_to_zeroshot_topic_idx
+
     def add_new_topics(self, mappings: Mapping[int, int]):
         """Add new row(s) of topic mappings.