traccuracy.metrics._cca

Module Contents

Classes

CellCycleAccuracy

The CCA metric captures the ability of a method to identify a distribution of cell
class traccuracy.metrics._cca.CellCycleAccuracy[source]

The CCA metric captures the ability of a method to identify a distribution of cell cycle lengths that matches the distribution present in the ground truth. The evaluation is done on distributions and therefore does not require a matching of solution to the ground truth. It ranges from [0,1] with higher values indicating better performance.

This metric is part of the biologically inspired metrics introduced by the CTC and defined in Ulman 2017.

property info: dict[str, Any]

Dictionary with Metric name and any parameters

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

Parameters:

  • matched (traccuracy.matchers.Matched) – Matched data object to compute metrics on

  • override_matcher (bool) – If True, the metric will not validate the matcher type

  • relax_skips_gt (bool) – If True, the metric will check if skips in the ground truth graph have an equivalent multi-edge path in predicted graph

  • relax_skips_pred (bool) – If True, the metric will check if skips in the predicted graph have an equivalent multi-edge path in ground truth graph

Returns:

Object containing metric results

and associated pipeline metadata

Return type:

traccuracy.metrics._results.Results