Function visualize_documents

bertopic/plotting/_documents.py:8–263

Visualize documents and their topics in 2D. Arguments: topic_model: A fitted BERTopic instance. docs: The documents you used when calling either `fit` or `fit_transform` topics: A selection of topics to visualize. Not to be confused with the topics that y

(
    topic_model,
    docs: List[str],
    topics: List[int] | None = None,
    embeddings: np.ndarray = None,
    reduced_embeddings: np.ndarray = None,
    sample: float | None = None,
    hide_annotations: bool = False,
    hide_document_hover: bool = False,
    custom_labels: Union[bool, str] = False,
    title: str = "<b>Documents and Topics</b>",
    width: int = 1200,
    height: int = 750,
)

def visualize_documents(
    topic_model,
    docs: List[str],
    topics: List[int] | None = None,
    embeddings: np.ndarray = None,
    reduced_embeddings: np.ndarray = None,
    sample: float | None = None,
    hide_annotations: bool = False,
    hide_document_hover: bool = False,
    custom_labels: Union[bool, str] = False,
    title: str = "<b>Documents and Topics</b>",
    width: int = 1200,
    height: int = 750,
):
    """Visualize documents and their topics in 2D.

    Arguments:
        topic_model: A fitted BERTopic instance.
        docs: The documents you used when calling either `fit` or `fit_transform`
        topics: A selection of topics to visualize.
                Not to be confused with the topics that you get from `.fit_transform`.
                For example, if you want to visualize only topics 1 through 5:
                `topics = [1, 2, 3, 4, 5]`.
        embeddings: The embeddings of all documents in `docs`.
        reduced_embeddings: The 2D reduced embeddings of all documents in `docs`.
        sample: The percentage of documents in each topic that you would like to keep.
                Value can be between 0 and 1. Setting this value to, for example,
                0.1 (10% of documents in each topic) makes it easier to visualize
                millions of documents as a subset is chosen.
        hide_annotations: Hide the names of the traces on top of each cluster.
        hide_document_hover: Hide the content of the documents when hovering over
                             specific points. Helps to speed up generation of visualization.
        custom_labels: If bool, whether to use custom topic labels that were defined using
                       `topic_model.set_topic_labels`.
                       If `str`, it uses labels from other aspects, e.g., "Aspect1".
        title: Title of the plot.
        width: The width of the figure.
        height: The height of the figure.

    Examples:
    To visualize the topics simply run:

    ```python
    topic_model.visualize_documents(docs)
    ```

    Do note that this re-calculates the embeddings and reduces them to 2D.
    The advised and preferred pipeline for using this function is as follows:

    ```python
    from sklearn.datasets import fetch_20newsgroups
    from sentence_transformers import SentenceTransformer
    from bertopic import BERTopic
    from umap import UMAP

    # Prepare embeddings
    docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
    sentence_model = SentenceTransformer("all-MiniLM-L6-v2")