Fixed issue #2144

bertopic/_bertopic.py

@@ -478,21 +478,20 @@ def fit_transform(
         if documents.Document.values[0] is None:
             custom_documents = self._images_to_text(documents, embeddings)
 
-            # Extract topics by calculating c-TF-IDF
-            self._extract_topics(custom_documents, embeddings=embeddings)
-            self._create_topic_vectors(documents=documents, embeddings=embeddings)
-
-            # Reduce topics
+            # Extract topics by calculating c-TF-IDF, reduce topics if needed, and get representations.
+            self._extract_topics(custom_documents, embeddings=embeddings, calculate_representation=not self.nr_topics)
             if self.nr_topics:
                 custom_documents = self._reduce_topics(custom_documents)
+            self._create_topic_vectors(documents=documents, embeddings=embeddings)
 
             # Save the top 3 most representative documents per topic
             self._save_representative_docs(custom_documents)
-        else:
-            # Extract topics by calculating c-TF-IDF
-            self._extract_topics(documents, embeddings=embeddings, verbose=self.verbose)
 
-            # Reduce topics
+        else:
+            # Extract topics by calculating c-TF-IDF, reduce topics if needed, and get representations.
+            self._extract_topics(
+                documents, embeddings=embeddings, verbose=self.verbose, calculate_representation=not self.nr_topics
+            )
             if self.nr_topics:
                 documents = self._reduce_topics(documents)
 
@@ -3972,6 +3971,7 @@ def _extract_topics(
         embeddings: np.ndarray = None,
         mappings=None,
         verbose: bool = False,
+        calculate_representation: bool = True,
     ):
         """Extract topics from the clusters using a class-based TF-IDF.
 
@@ -3980,18 +3980,29 @@ def _extract_topics(
             embeddings: The document embeddings
             mappings: The mappings from topic to word
             verbose: Whether to log the process of extracting topics
+            calculate_representation: Whether to extract the topic representations
 
         Returns:
             c_tf_idf: The resulting matrix giving a value (importance score) for each word per topic
         """
         if verbose:
-            logger.info("Representation - Extracting topics from clusters using representation models.")
+            action = "Representation" if calculate_representation else "Topics"
+            logger.info(
+                f"{action} - Extracting topics from clusters{' using representation models' if calculate_representation else ''}."
+            )
+
         documents_per_topic = documents.groupby(["Topic"], as_index=False).agg({"Document": " ".join})
         self.c_tf_idf_, words = self._c_tf_idf(documents_per_topic)
-        self.topic_representations_ = self._extract_words_per_topic(words, documents)
+        self.topic_representations_ = self._extract_words_per_topic(
+            words,
+            documents,
+            calculate_representation=calculate_representation,
+            calculate_aspects=calculate_representation,
+        )
         self._create_topic_vectors(documents=documents, embeddings=embeddings, mappings=mappings)
+
         if verbose:
-            logger.info("Representation - Completed \u2713")
+            logger.info(f"{action} - Completed \u2713")
 
     def _save_representative_docs(self, documents: pd.DataFrame):
         """Save the 3 most representative docs per topic.
@@ -4245,6 +4256,7 @@ def _extract_words_per_topic(
         words: List[str],
         documents: pd.DataFrame,
         c_tf_idf: csr_matrix = None,
+        calculate_representation: bool = True,
         calculate_aspects: bool = True,
     ) -> Mapping[str, List[Tuple[str, float]]]:
         """Based on tf_idf scores per topic, extract the top n words per topic.
@@ -4258,6 +4270,7 @@ def _extract_words_per_topic(
             words: List of all words (sorted according to tf_idf matrix position)
             documents: DataFrame with documents and their topic IDs
             c_tf_idf: A c-TF-IDF matrix from which to calculate the top words
+            calculate_representation: Whether to calculate the topic representations
             calculate_aspects: Whether to calculate additional topic aspects
 
         Returns:
@@ -4288,15 +4301,15 @@ def _extract_words_per_topic(
 
         # Fine-tune the topic representations
         topics = base_topics.copy()
-        if not self.representation_model:
+        if not self.representation_model or not calculate_representation:
             # Default representation: c_tf_idf + top_n_words
             topics = {label: values[: self.top_n_words] for label, values in topics.items()}
-        elif isinstance(self.representation_model, list):
+        elif calculate_representation and isinstance(self.representation_model, list):
             for tuner in self.representation_model:
                 topics = tuner.extract_topics(self, documents, c_tf_idf, topics)
-        elif isinstance(self.representation_model, BaseRepresentation):
+        elif calculate_representation and isinstance(self.representation_model, BaseRepresentation):
             topics = self.representation_model.extract_topics(self, documents, c_tf_idf, topics)
-        elif isinstance(self.representation_model, dict):
+        elif calculate_representation and isinstance(self.representation_model, dict):
             if self.representation_model.get("Main"):
                 main_model = self.representation_model["Main"]
                 if isinstance(main_model, BaseRepresentation):
@@ -4350,6 +4363,13 @@ def _reduce_topics(self, documents: pd.DataFrame, use_ctfidf: bool = False) -> p
         if isinstance(self.nr_topics, int):
             if self.nr_topics < initial_nr_topics:
                 documents = self._reduce_to_n_topics(documents, use_ctfidf)
+            else:
+                logger.info(
+                    f"Topic reduction - Number of topics ({self.nr_topics}) is equal or higher than the clustered topics({len(self.get_topics())})."
+                )
+                documents = self._sort_mappings_by_frequency(documents)
+                self._extract_topics(documents, verbose=self.verbose)
+                return documents
         elif isinstance(self.nr_topics, str):
             documents = self._auto_reduce_topics(documents, use_ctfidf)
         else:
@@ -4412,7 +4432,7 @@ def _reduce_to_n_topics(self, documents: pd.DataFrame, use_ctfidf: bool = False)
 
         # Update representations
         documents = self._sort_mappings_by_frequency(documents)
-        self._extract_topics(documents, mappings=mappings)
+        self._extract_topics(documents, mappings=mappings, verbose=self.verbose)
 
         self._update_topic_size(documents)
         return documents
@@ -4468,7 +4488,7 @@ def _auto_reduce_topics(self, documents: pd.DataFrame, use_ctfidf: bool = False)
         # Update documents and 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._extract_topics(documents, mappings=mappings, verbose=self.verbose)
         self._update_topic_size(documents)
         return documents
 

tests/test_representation/test_representations.py

@@ -153,6 +153,7 @@ def test_topic_reduction_edge_cases(model, documents, request):
     topics = np.random.randint(-1, nr_topics - 1, len(documents))
     old_documents = pd.DataFrame({"Document": documents, "ID": range(len(documents)), "Topic": topics})
     topic_model._update_topic_size(old_documents)
+    old_documents = topic_model._sort_mappings_by_frequency(old_documents)
     topic_model._extract_topics(old_documents)
     old_freq = topic_model.get_topic_freq()