:orphan: :py:mod:`traccuracy.metrics._basic` =================================== .. py:module:: traccuracy.metrics._basic Module Contents --------------- Classes ~~~~~~~ .. autoapisummary:: traccuracy.metrics._basic.BasicMetrics .. py:class:: BasicMetrics Generates basic statistics describing node and edge errors If `relax_skips_gt` or `relax_skips_pred` is True, we can match skip edges in the prediction to a series of edges in the gt, or vice versa. The total number of skip TPs/FNs/FPs will be reported and these counts will be incorporated in the calculation of precision/recall/F1. These metrics are written assuming that the ground truth annotations are dense. If that is not the case, interpret the numbers carefully. Consider eliminating metrics that use the number of false positives. .. 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