rail.interactive.evaluation.evaluator module

rail.interactive.evaluation.evaluator.old_evaluator(**kwargs)

Evaluate the performance of a photo-Z estimator

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Evaluate the performance of an estimator

This will attach the input data and truth to this Evaluator (for introspection and provenance tracking). Then it will call the run() and finalize() methods, which need to be implemented by the sub-classes. The run() method will need to register the data that it creates to this Estimator by using self.add_data(‘output’, output_data).

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This function was generated from the function rail.evaluation.evaluator.OldEvaluator.evaluate

Parameters:

  • data (qp.Ensemble, required) – The sample to evaluate

  • truth (Any, required) – Table with the truth information

  • zmin (float, optional) – The minimum redshift of the z grid or sample Default: 0.0

  • zmax (float, optional) – The maximum redshift of the z grid or sample Default: 3.0

  • nzbins (int, optional) – The number of gridpoints in the z grid Default: 301

  • pit_metrics (str, optional) – PIT-based metrics to include Default: all

  • point_metrics (str, optional) – Point-estimate metrics to include Default: all

  • hdf5_groupname (str, optional) – name of hdf5 group for data, if None, then set to ‘’ Default:

  • do_cde (bool, optional) – Evaluate CDE Metric Default: True

  • redshift_col (str, optional) – name of redshift column Default: redshift

Returns:

The evaluation metrics

Return type:

dict