Method reduce_topics

bertopic/_bertopic.py:2313–2378

Reduce the number of topics to a fixed number of topics or automatically. If nr_topics is an integer, then the number of topics is reduced to nr_topics using `AgglomerativeClustering` on the cosine distance matrix of the topic c-TF-IDF or semantic embeddings.

(
        self,
        docs: List[str],
        nr_topics: Union[int, str] = 20,
        images: List[str] | None = None,
        use_ctfidf: bool = False,
    )

            self.ctfidf_model._idf_diag = self.ctfidf_model._idf_diag[mask]

    def reduce_topics(
        self,
        docs: List[str],
        nr_topics: Union[int, str] = 20,
        images: List[str] | None = None,
        use_ctfidf: bool = False,
    ) -> None:
        """Reduce the number of topics to a fixed number of topics
        or automatically.

        If nr_topics is an integer, then the number of topics is reduced
        to nr_topics using `AgglomerativeClustering` on the cosine distance matrix
        of the topic c-TF-IDF or semantic embeddings.

        If nr_topics is `"auto"`, then HDBSCAN is used to automatically
        reduce the number of topics by running it on the topic embeddings.

        The topics, their sizes, and representations are updated.

        Arguments:
            docs: The docs you used when calling either `fit` or `fit_transform`
            nr_topics: The number of topics you want reduced to
            images: A list of paths to the images used when calling either
                    `fit` or `fit_transform`
            use_ctfidf: Whether to calculate distances between topics based on c-TF-IDF embeddings. If False, the
                        embeddings from the embedding model are used.

        Updates:
            topics_ : Assigns topics to their merged representations.
            probabilities_ : Assigns probabilities to their merged representations.

        Examples:
        You can further reduce the topics by passing the documents with their
        topics and probabilities (if they were calculated):

        ```python
        topic_model.reduce_topics(docs, nr_topics=30)
        ```

        You can then access the updated topics and probabilities with:

        ```python
        topics = topic_model.topics_
        probabilities = topic_model.probabilities_
        ```
        """
        check_is_fitted(self)
        check_documents_type(docs)

        self.nr_topics = nr_topics
        documents = pd.DataFrame(
            {
                "Document": docs,
                "Topic": self.topics_,
                "Image": images,
                "ID": range(len(docs)),
            }
        )