rail.interactive.evaluation.point_to_point_evaluator module

rail.interactive.evaluation.point_to_point_evaluator.point_to_point_binned_evaluator(**kwargs)

Evaluate the performance of a photo-z estimator against reference point estimate

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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.point_to_point_evaluator.PointToPointBinnedEvaluator.evaluate

Parameters:

data (qp.Ensemble, required) – The sample to evaluate
truth (Any, required) – Table with the truth information
metrics (list, optional) – The metrics you want to evaluate. Default: []
exclude_metrics (list, optional) – List of metrics to exclude Default: []
metric_config (dict, optional) – configuration of individual_metrics Default: {}
chunk_size (int, optional) – Number of objects per chunk for parallel processing or to evalute per loop in single node processing Default: 10000
seed (float, optional) – Random seed value to use for reproducible results. Default: None
hdf5_groupname (str, optional) – name of hdf5 group for data, if None, then set to ‘’ Default: photometry
reference_dictionary_key (str, optional) – The key in the truth dictionary where the redshift data is stored. Default: redshift
point_estimate_key (str, optional) – The key in the point estimate table. Default: zmode
bin_col (str, optional) – The column metrics are binned by Default: redshift
bin_min (float, optional) – The mininum value of the binning edge Default: 0.0
bin_max (float, optional) – The maximum value of the binning edge Default: 3.0
nbin (int, optional) – The mininum value of the binning edge Default: 10

Returns:

The evaluation metrics

Return type:

dict[str, DataHandle]

rail.interactive.evaluation.point_to_point_evaluator.point_to_point_evaluator(**kwargs)