rail.evaluation.point_to_point_evaluator module

class rail.evaluation.point_to_point_evaluator.PointToPointBinnedEvaluator

Bases: Evaluator

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

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=True) – Force the exact calculation. This will not allow parallelization

  • hdf5_groupname ([str] default=photometry) – name of hdf5 group for data, if None, then set to ‘’

  • reference_dictionary_key ([str] default=redshift) – The key in the truth dictionary where the redshift data is stored.

  • point_estimate_key ([str] default=zmode) – The key in the point estimate table.

  • bin_col ([str] default=redshift) – The column metrics are binned by

  • bin_min ([float] default=0.0) – The mininum value of the binning edge

  • bin_max ([float] default=3.0) – The maximum value of the binning edge

  • nbin ([int] default=10) – The mininum value of the binning edge

  • input (QPHandle (INPUT))

  • truth (TableHandle (INPUT))

  • output (Hdf5Handle (OUTPUT))

  • summary (Hdf5Handle (OUTPUT))

  • single_distribution_summary (QPDictHandle (OUTPUT))

entrypoint_function: str | None = 'evaluate'
inputs: list[tuple[str, type[DataHandle]]] = [('input', <class 'rail.core.data.QPHandle'>), ('truth', <class 'rail.core.data.TableHandle'>)]
interactive_function: str | None = 'point_to_point_binned_evaluator'
metric_base_class

alias of PointToPointMetric

name = 'PointToPointBinnedEvaluator'
run()

Run the stage and return the execution status.

Subclasses must implemented this method.

Return type:

None

class rail.evaluation.point_to_point_evaluator.PointToPointEvaluator

Bases: Evaluator

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

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

  • hdf5_groupname ([str] default=photometry) – name of hdf5 group for data, if None, then set to ‘’

  • reference_dictionary_key ([str] default=redshift) – The key in the truth dictionary where the redshift data is stored.

  • point_estimate_key ([str] default=zmode) – The key in the point estimate table.

  • input (QPHandle (INPUT))

  • truth (TableHandle (INPUT))

  • output (Hdf5Handle (OUTPUT))

  • summary (Hdf5Handle (OUTPUT))

  • single_distribution_summary (QPDictHandle (OUTPUT))

entrypoint_function: str | None = 'evaluate'
inputs: list[tuple[str, type[DataHandle]]] = [('input', <class 'rail.core.data.QPHandle'>), ('truth', <class 'rail.core.data.TableHandle'>)]
interactive_function: str | None = 'point_to_point_evaluator'
metric_base_class

alias of PointToPointMetric

name = 'PointToPointEvaluator'