rail.interactive.estimation.algos.dnf module

rail.interactive.estimation.algos.dnf.dnf_estimator(**kwargs)

A class for estimating photometric redshifts using the DNF method.

This class extends CatEstimator and predicts redshifts based on photometric. It supports multiple selection modes for redshift estimation, processes missing data, and generates probability density functions (PDFs) for photometric redshifts.

Metrics (selection_mode): - ENF (1): Euclidean neighbourhood. It’s a common distance metric used in kNN (k-Nearest Neighbors) for photometric redshift prediction. - ANF (2): uses normalized inner product for more accurate photo-z predictions. It is particularly recommended when working with datasets containing more than four filters. - DNF (3): combines Euclidean and angular metrics, improving accuracy, especially for larger neighborhoods, and maintaining proportionality in observable content.

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The main interface method for the photo-z estimation

This will attach the input data (defined in inputs as “input”) to this Estimator (for introspection and provenance tracking). Then call the run(), validate(), and finalize() methods.

The run method will call _process_chunk(), which needs to be implemented in the subclass, to process input data in batches. See RandomGaussEstimator for a simple example.

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

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This function was generated from the function rail.estimation.algos.dnf.DNFEstimator.estimate

Parameters:

  • input_data (TableLike, required) – A dictionary of all input data

  • model (numpy.ndarray, required)

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

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

  • 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

  • id_col (str, optional) – name of the object ID column Default: object_id

  • redshift_col (str, optional) – name of redshift column Default: redshift

  • calc_summary_stats (bool, optional) – Compute summary statistics Default: False

  • calculated_point_estimates (list, optional) – List of strings defining which point estimates to automatically calculate using qp.Ensemble.Options include, ‘mean’, ‘mode’, ‘median’. Default: []

  • recompute_point_estimates (bool, optional) – Force recomputation of point estimates Default: False

  • bands (list, optional) – Names of columns for magnitude by filter band Default: [‘mag_u_lsst’, ‘mag_g_lsst’, ‘mag_r_lsst’, ‘mag_i_lsst’,…]

  • err_bands (list, optional) – Names of columns for magnitude errors by filter band Default: [‘mag_err_u_lsst’, ‘mag_err_g_lsst’, ‘mag_err_r_lsst’,…]

  • nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

  • mag_limits (dict, optional) – Limiting magnitudes by filter Default: {‘mag_u_lsst’: 27.79, ‘mag_g_lsst’: 29.04, ‘mag_r_lsst’: 29.06,…}

  • selection_mode (int, optional) – select which mode to choose the redshift estimate:0: ENF, 1: ANF, 2: DNF Default: 1

Returns:

Handle providing access to QP ensemble with output data

Return type:

qp.core.ensemble.Ensemble

rail.interactive.estimation.algos.dnf.dnf_informer(**kwargs)

A class for photometric redshift estimation.

This class extends CatInformer and processes photometric data to train for estimating redshifts. It handles missing data by replacing non-detections with predefined magnitude limits and assigns errors accordingly.

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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.dnf.DNFInformer.inform

Parameters:

  • training_data (TableLike, required) – dictionary of all input data, or a TableHandle providing access to it

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

  • bands (list, optional) – Names of columns for magnitude by filter band Default: [‘mag_u_lsst’, ‘mag_g_lsst’, ‘mag_r_lsst’, ‘mag_i_lsst’,…]

  • err_bands (list, optional) – Names of columns for magnitude errors by filter band Default: [‘mag_err_u_lsst’, ‘mag_err_g_lsst’, ‘mag_err_r_lsst’,…]

  • redshift_col (str, optional) – name of redshift column Default: redshift

  • mag_limits (dict, optional) – Limiting magnitudes by filter Default: {‘mag_u_lsst’: 27.79, ‘mag_g_lsst’: 29.04, ‘mag_r_lsst’: 29.06,…}

  • nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

Returns:

Handle providing access to trained model

Return type:

numpy.ndarray