rail.interactive.estimation.algos.equal_count module
- rail.interactive.estimation.algos.equal_count.equal_count_classifier(**kwargs)
Classifier that simply assign tomographic bins based on point estimate according to SRD
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The main run method for the classifier, should be implemented in the specific subclass.
This will attach the input_data to this PZClassifier (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 Classifier by using self.add_data(‘output’, output_data).
The run() method relies on the _process_chunk() method, which should be implemented by subclasses to perform the actual classification on each chunk of data. The results from each chunk are then combined in the _finalize_run() method. (Alternatively, override run() in a subclass to perform the classification without parallelization.)
Finally, this will return a TableHandle providing access to that output data.
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This function was generated from the function rail.estimation.algos.equal_count.EqualCountClassifier.classify
- Parameters:
input_data (qp.Ensemble, required) – Per-galaxy p(z), and any ancilary data associated with it
chunk_size (int, optional) – Number of objects per chunk for parallel processing or to evalute per loop in single node processing Default: 10000
object_id_col (str, optional) – name of object id column Default:
point_estimate_key (str, optional) – Which point estimate to use Default: zmode
zmin (float, optional) – The minimum redshift of the z grid or sample Default: 0.0
zmax (float, optional) – The maximum redshift of the z grid or sample Default: 3.0
n_tom_bins (int, optional) – Number of tomographic bins Default: 5
no_assign (int, optional) – Value for no assignment flag Default: -99
- Returns:
Class assignment for each galaxy, typically in the form of a dictionary with IDs and class labels.
- Return type:
A tablesio-compatible table