zen_mapper.adapters
===================

.. py:module:: zen_mapper.adapters

.. autoapi-nested-parse::

   Adapters for converting between zen-mapper types and 3rd party types



Functions
---------

.. autoapisummary::

   zen_mapper.adapters.to_networkx
   zen_mapper.adapters.sk_learn


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

.. py:function:: to_networkx(komplex: zen_mapper.types.Komplex)

   Converts a zen-mapper komplex to a networkx graph

   This function takes a `Komplex` object, which represents a simplicial complex,
   and converts it into a `networkx.Graph` object. The vertices of the `Komplex`
   become the nodes in the `networkx` graph, and the 1-simplices (edges) of
   the `Komplex` become the edges in the `networkx` graph.

   :param komplex: The `Komplex` object to convert. This object is expected to
                   have a `vertices` attribute and support indexing for its
                   simplices (e.g., `komplex[1]` for 1-simplices).
   :type komplex: Komplex

   :returns: A `networkx.Graph` object representing the 0- and 1-dimensional
             structure of the input `Komplex`.
   :rtype: networkx.Graph

   :raises ImportError: If the `networkx` library is not installed.


.. py:function:: sk_learn(base_clusterer: C) -> zen_mapper.types.Clusterer[C]

   Wraps a scikit-learn clusterer for use with zen-mapper.

   This function acts as an adapter, allowing scikit-learn's clustering
   algorithms to be integrated into the zen-mapper pipeline. Note: any
   datapoints which are considered noise by the base clusterer are ignored.

   :param base_clusterer: An instance of a scikit-learn compatible clustering algorithm.
                          This object should have a `fit_predict` method and a `labels_`
                          attribute after fitting, which is standard for scikit-learn
                          clusterers.
   :type base_clusterer: C

   :returns: An object conforming to the zen-mapper `Clusterer` protocol, which
             wraps the provided `clusterer`. This allows zen-mapper to use the
             scikit-learn clusterer's `fit_predict` methods within its pipeline. A
             copy of the fitted base clusterer is also returned as metadata allowing
             for inspection of the fitted model (e.g., centroids, parameters,
             dendrograms) after the mapper pipeline.
   :rtype: Clusterer[C]