jpt.learning.dependency.xi
==========================

.. py:module:: jpt.learning.dependency.xi

.. autoapi-nested-parse::

   Xi-correlation-based dependency discovery.

   Uses Chatterjee's xi coefficient to determine
   which feature-target pairs exhibit statistically
   significant functional dependence.



Classes
-------

.. autoapisummary::

   jpt.learning.dependency.xi.XiDependencyDiscovery


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

.. py:class:: XiDependencyDiscovery(alpha: float = 0.05)

   Bases: :py:obj:`jpt.learning.dependency.base.DependencyDiscovery`


   Dependency discovery via Chatterjee's xi.

   Computes the xi correlation between all
   feature-target pairs and retains only those
   where the correlation is statistically
   significant under the asymptotic null
   distribution of xi.

   Under H0 (independence with continuous Y),
   sqrt(n) * xi ~ N(0, 2/5).

   :param alpha: significance level for the
                 independence test


   .. py:attribute:: alpha
      :type:  float
      :value: 0.05



   .. py:method:: __call__(data: numpy.ndarray, features: list[jpt.variables.Variable], targets: list[jpt.variables.Variable], variables: list[jpt.variables.Variable]) -> dict[jpt.variables.Variable, list[jpt.variables.Variable]]

      Discover dependencies via xi correlation.

      :param data:      data array (n x d)
      :param features:  list of feature Variables
      :param targets:   list of target Variables
      :param variables: list of all Variables
      :returns:         dict mapping features to
                        their dependent targets



   .. py:method:: to_json() -> dict[str, Any]

      Serialize configuration.

      :returns: JSON-serializable dict



   .. py:method:: from_json(data: dict[str, Any]) -> XiDependencyDiscovery
      :classmethod:


      Restore from JSON.

      :param data: dict from ``to_json()``
      :returns:    XiDependencyDiscovery instance