rail.evaluation.dist_to_dist_evaluator module

class rail.evaluation.dist_to_dist_evaluator.DistToDistEvaluator

Evaluate the performance of a photo-z estimator against reference PDFs

Parameters:

output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.
metrics ([list] default=[]) – The metrics you want to evaluate.
exclude_metrics ([list] default=[]) – List of metrics to exclude
metric_config ([dict] default={}) – configuration of individual_metrics
chunk_size ([int] default=10000) – Number of objects per chunk for parallel processing or to evalute per loop in single node processing
seed ([float] default=None) – Random seed value to use for reproducible results.
force_exact ([bool] default=False) – Force the exact calculation. This will not allow parallelization
metric_integration_limits ([list] default=[0.0, 3.0]) – The default end points for calculating metrics on a grid.
dx ([float] default=0.01) – The default step size when calculating metrics on a grid.
n_samples ([int] default=100) – The number of random samples to select for certain metrics.
input (QPHandle (INPUT))
truth (QPHandle (INPUT))
output (Hdf5Handle (OUTPUT))
summary (Hdf5Handle (OUTPUT))
single_distribution_summary (QPDictHandle (OUTPUT))

inputs: list[tuple[str, type[DataHandle]]] = [('input', <class 'rail.core.data.QPHandle'>), ('truth', <class 'rail.core.data.QPHandle'>)]