lightopic._lightopic
====================

.. py:module:: lightopic._lightopic


Classes
-------

.. autoapisummary::

   lightopic._lightopic.Lightopic


Module Contents
---------------

.. py:class:: Lightopic

   The Lightopic class is to be used with a trained topic model
   that has been serialised appropriately (see docs for
   `lightopic.LightBERTopic.save_lightopic`). Methods will transform
   new data by reducing the embeddings or putting them into topics.


   .. py:method:: load(model_directory: str) -> None


   .. py:method:: reduce_embeddings(embeddings: numpy.ndarray, output_type: str = 'reduced') -> numpy.ndarray

      Reduce the dimensions of an input array of embeddings.

      :param embeddings: An array of embeddings.
      :type embeddings: numpy.ndarray
      :param output_type: Set the dimensions of the
                          output. Defaults to "reduced", which will reduce to the
                          dimensions used for clustering. (If you used the
                          BERTopic defaults this will be 5.) Setting this to "2d"
                          will output 2-dimensional vectors that you can use for
                          plotting.
      :type output_type: str, optional

      :raises ValueError: if `output_type` is not either `"reduced"` or
          `"2d"`.
      :raises ValueError: the dimension of the `embeddings` must be
          compatible with the Lightopic model, i.e. it must match
          either the full dimension of the original embeddings
          (`self.len_full_embedding`) or that of the reduced
          embeddings (`self.len_umap_embedding`).

      :returns: The reduced embeddings.
      :rtype: numpy.ndarray



   .. py:method:: transform(embeddings: numpy.ndarray, calculate_probabilities: bool = True) -> tuple[numpy.ndarray, numpy.ndarray]

      Transform new documents with the model.

      :param embeddings: Array of embeddings
      :type embeddings: numpy.ndarray
      :param calculate_probabilities: Return
                                      probabilities for all topics? Defaults to True.
      :type calculate_probabilities: bool, optional

      :raises ValueError: the dimension of the `embeddings` must be
          compatible with the Lightopic model, i.e. it must match
          either the full dimension of the original embeddings
          (`self.len_full_embedding`) or that of the reduced
          embeddings (`self.len_umap_embedding`).

      :returns:

                The topic assignments
                    and probabilities.
      :rtype: tuple[numpy.ndarray, numpy.ndarray]