:orphan:

:py:mod:`traccuracy.metrics._base`
==================================

.. py:module:: traccuracy.metrics._base


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

Classes
~~~~~~~

.. autoapisummary::

   traccuracy.metrics._base.Metric




Attributes
~~~~~~~~~~

.. autoapisummary::

   traccuracy.metrics._base.MATCHING_TYPES


.. py:data:: MATCHING_TYPES
   :value: ['one-to-one', 'one-to-many', 'many-to-one', 'many-to-many']

   

.. py:class:: Metric(valid_matches: list)




   The base class for Metrics

   Data should be passed directly into the compute method
   Kwargs should be specified in the constructor

   .. py:property:: info
      :type: dict[str, Any]

      Dictionary with Metric name and any parameters

   .. py:method:: compute(matched: traccuracy.matchers._matched.Matched, override_matcher: bool = False, relax_skips_gt: bool = False, relax_skips_pred: bool = False) -> traccuracy.metrics._results.Results

      The compute methods of Metric objects return a Results object populated with results
      and associated metadata

      :param matched: Matched data object to compute metrics on
      :type matched: traccuracy.matchers.Matched
      :param override_matcher: If True, the metric will not validate the matcher type
      :type override_matcher: bool
      :param relax_skips_gt: If True, the metric will check if skips in the ground truth
                             graph have an equivalent multi-edge path in predicted graph
      :type relax_skips_gt: bool
      :param relax_skips_pred: If True, the metric will check if skips in the predicted
                               graph have an equivalent multi-edge path in ground truth graph
      :type relax_skips_pred: bool

      :returns:

                Object containing metric results
                    and associated pipeline metadata
      :rtype: traccuracy.metrics._results.Results