lenskit.metrics.predict.PredictMetric
=====================================

.. py:class:: lenskit.metrics.predict.PredictMetric(missing_scores = 'error', missing_truth = 'error')
   

   Bases: :py:obj:`lenskit.metrics._base.Metric`


   Extension to the metric function interface for prediction metrics.

   In addition to the general metric interface, predict metrics can be called
   with a single item list (or item list collection) that has both ``scores``
   and a ``rating`` field.

   :param missing_scores: The action to take when a test item has not been scored.  The
                          default throws an exception, avoiding situations where non-scored
                          items are silently excluded from overall statistics.
   :param missing_truth: The action to take when no test items are available for a scored
                         item. The default is to also to fail; if you are scoring a superset
                         of the test items for computational efficiency, set this to
                         ``"ignore"``.

   :Stability: Caller


   .. py:attribute:: default
      :value: None



   .. py:attribute:: missing_scores
      :type:  MissingDisposition


   .. py:attribute:: missing_truth
      :type:  MissingDisposition


   .. py:method:: align_scores(predictions, truth = None)

      Align prediction scores and rating values, applying the configured
      missing dispositions.  The result is two Pandas series, predictions and
      truth, that are aligned and checked for missing data in accordance with
      the configured options.