Class TopicMapper

bertopic/_bertopic.py:4887–5015

Keep track of Topic Mappings. The number of topics can be reduced by merging them together. This mapping needs to be tracked in BERTopic as new predictions need to be mapped to the new topics. These mappings are tracked in the `self.mappings_` attribute where each set o

class TopicMapper:
    """Keep track of Topic Mappings.

    The number of topics can be reduced
    by merging them together. This mapping
    needs to be tracked in BERTopic as new
    predictions need to be mapped to the new
    topics.

    These mappings are tracked in the `self.mappings_`
    attribute where each set of topic is stacked horizontally.
    For example, the most recent topics can be found in the
    last column. To get a mapping, simply take two columns
    of topics.

    In other words, it is represented as graph:
    Topic 1 --> Topic 11 --> Topic 4 --> etc.

    Attributes:
        self.mappings_ (np.ndarray) : A  matrix indicating the mappings from one topic
                                      to another. The columns represent a collection of topics
                                      at any time. The last column represents the current state
                                      of topics and the first column represents the initial state
                                      of topics.
    """

    def __init__(self, topics: List[int]):
        """Initialization of Topic Mapper.

        Arguments:
            topics: A list of topics per document
        """
        base_topics = np.array(sorted(set(topics)))
        topics = base_topics.copy().reshape(-1, 1)
        self.mappings_ = np.hstack([topics.copy(), topics.copy()]).tolist()

    def get_mappings(self, original_topics: bool = True) -> Mapping[int, int]:
        """Get mappings from either the original topics or
        the second-most recent topics to the current topics.

        Arguments:
            original_topics: Whether we want to map from the
                             original topics to the most recent topics
                             or from the second-most recent topics.

        Returns:
            mappings: The mappings from old topics to new topics

        Examples:
        To get mappings, simply call:
        ```python
        mapper = TopicMapper(topics)
        mappings = mapper.get_mappings(original_topics=False)
        ```
        """
        if original_topics:
            mappings = np.array(self.mappings_)[:, [0, -1]]
            mappings = dict(zip(mappings[:, 0], mappings[:, 1]))