rail.estimation.algos.equal_count module

A classifier that uses pz point estimate to assign tomographic bins with uniform binning.

class rail.estimation.algos.equal_count.EqualCountClassifier

Bases: PZClassifier

Classifier that simply assign tomographic bins based on point estimate according to SRD

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.

  • chunk_size ([int] default=10000) – Number of objects per chunk for parallel processing or to evalute per loop in single node processing

  • object_id_col ([str] default=) – name of object id column

  • point_estimate_key ([str] default=zmode) – Which point estimate to use

  • zmin ([float] default=0.0) – The minimum redshift of the z grid or sample

  • zmax ([float] default=3.0) – The maximum redshift of the z grid or sample

  • n_tom_bins ([int] default=5) – Number of tomographic bins

  • no_assign ([int] default=-99) – Value for no assignment flag

  • input (QPHandle (INPUT))

  • output (Hdf5Handle (OUTPUT))

entrypoint_function: str | None = 'classify'
interactive_function: str | None = 'equal_count_classifier'
name = 'EqualCountClassifier'
outputs = [('output', <class 'rail.core.data.Hdf5Handle'>)]
run()

Processes the input data in chunks and performs classification.

This method iterates over chunks of the input data, calling the _process_chunk method for each chunk to perform the actual classification.

The _process_chunk method should be implemented by subclasses to define the specific classification logic.

Return type:

None