rail.evaluation.evaluator module

” Abstract base class defining an Evaluator

The key feature is that the evaluate method.

class rail.evaluation.evaluator.Evaluator(args, comm=None)

Bases: RailStage

Evaluate the performance of a photo-Z estimator

config_options = {'do_cde': <ceci.config.StageParameter object>, 'nzbins': <ceci.config.StageParameter object>, 'output_mode': <ceci.config.StageParameter object>, 'pit_metrics': <ceci.config.StageParameter object>, 'point_metrics': <ceci.config.StageParameter object>, 'zmax': <ceci.config.StageParameter object>, 'zmin': <ceci.config.StageParameter object>}

evaluate(data, truth)

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).

Parameters:

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

• truth (Table-like) – Table with the truth information

Returns:

output – The evaluation metrics

Return type:

Table-like

inputs = [('input', <class 'rail.core.data.QPHandle'>), ('truth', <class 'rail.core.data.Hdf5Handle'>)]

name = 'Evaluator'

outputs = [('output', <class 'rail.core.data.Hdf5Handle'>)]

run()

Run method

Evaluate all the metrics and put them into a table

Notes

Get the input data from the data store under this stages ‘input’ tag Get the truth data from the data store under this stages ‘truth’ tag Puts the data into the data store under this stages ‘output’ tag