rail.interactive.estimation.algos.point_est_hist module

rail.interactive.estimation.algos.point_est_hist.point_est_hist_informer(**kwargs)

Placeholder Informer

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The main interface method for Informers

This will attach the input_data to this Informer (for introspection and provenance tracking).

Then it will call the run(), validate() and finalize() methods, which need to be implemented by the sub-classes.

The run() method will need to register the model that it creates to this Estimator by using self.add_data(‘model’, model).

Finally, this will return a ModelHandle providing access to the trained model.

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This function was generated from the function rail.estimation.algos.point_est_hist.PointEstHistInformer.inform

Parameters:

  • training_data (qp.Ensemble | str, required) – Per-galaxy p(z), and any ancilary data associated with it, by default “None”

  • truth_data (TableLike | str, required) – Table with the true redshifts, by default “None”

  • hdf5_groupname (str, optional) – name of hdf5 group for data, if None, then set to ‘’ Default: photometry

  • chunk_size (int, optional) – Number of objects per chunk for parallel processing or to evalute per loop in single node processing Default: 10000

Returns:

Handle providing access to trained model

Return type:

dict[str, ModelHandle]

rail.interactive.estimation.algos.point_est_hist.point_est_hist_masked_summarizer(**kwargs)

Summarizer which simply histograms a point estimate

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Override the Summarizer.summarize() method to take tomo bins as an additional input

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This function was generated from the function rail.estimation.algos.point_est_hist.PointEstHistMaskedSummarizer.summarize

Parameters:

  • input_data (qp.Ensemble, required) – Per-galaxy p(z), and any ancilary data associated with it

  • tomography_bins (A tablesio-compatible table, required)

  • tomo_bins (TableLike | None, optional) – Tomographic bins file, by default None

  • chunk_size (int, optional) – Number of objects per chunk for parallel processing or to evalute per loop in single node processing Default: 10000

  • 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

  • nzbins (int, optional) – The number of gridpoints in the z grid Default: 301

  • seed (int, optional) – random seed Default: 87

  • point_estimate_key (str, optional) – Which point estimate to use Default: zmode

  • n_samples (int, optional) – Number of sample distributions to return Default: 1000

  • selected_bin (int, optional) – bin to use Default: -1

Returns:

Ensemble with n(z), and any ancilary data

Return type:

qp.core.ensemble.Ensemble

rail.interactive.estimation.algos.point_est_hist.point_est_hist_summarizer(**kwargs)

Summarizer which simply histograms a point estimate

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The main run method for the summarization, should be implemented in the specific subclass.

This will attach the input_data to this PZtoNZSummarizer (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).

Finally, this will return a QPHandle providing access to that output data.

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This function was generated from the function rail.estimation.algos.point_est_hist.PointEstHistSummarizer.summarize

Parameters:

  • input_data (qp.Ensemble, required) – Per-galaxy p(z), and any ancillary 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

  • 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

  • nzbins (int, optional) – The number of gridpoints in the z grid Default: 301

  • seed (int, optional) – random seed Default: 87

  • point_estimate_key (str, optional) – Which point estimate to use Default: zmode

  • n_samples (int, optional) – Number of sample distributions to return Default: 1000

Returns:

Ensemble with n(z), and any ancillary data

Return type:

qp.core.ensemble.Ensemble