rail.interactive.estimation.algos.nz_dir module

rail.interactive.estimation.algos.nz_dir.nz_dir_informer(**kwargs)

Quick implementation of an NZ Estimator that creates weights for each input object using sklearn’s NearestNeighbors. Very basic, we can probably create a more sophisticated SOM-based DIR method in the future. This inform stage just creates a nearneigh model of the spec-z data and some distances to N-th neighbor that will be used in the estimate stage.

This will create model a dictionary of the nearest neighbor model and params used by estimate

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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.nz_dir.NZDirInformer.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

  • usecols (list, optional) – columns from sz_data for Neighbor calculation Default: [‘mag_u_lsst’, ‘mag_g_lsst’, ‘mag_r_lsst’, ‘mag_i_lsst’,…]

  • n_neigh (int, optional) – number of neighbors to use Default: 10

  • kalgo (str, optional) – Neighbor algorithm to use Default: kd_tree

  • kmetric (str, optional) – Knn metric to use Default: euclidean

  • sz_name (str, optional) – name of specz column in sz_data Default: redshift

  • szweightcol (str, optional) – name of sz weight column Default:

  • distance_delta (float, optional) – padding for distance calculation Default: 1e-06

Returns:

Handle providing access to trained model

Return type:

numpy.ndarray

rail.interactive.estimation.algos.nz_dir.nz_dir_summarizer(**kwargs)

Quick implementation of a summarizer that creates weights for each input object using sklearn’s NearestNeighbors. Very basic, we can probably create a more sophisticated SOM-based DIR method in the future

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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.nz_dir.NZDirSummarizer.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

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

  • usecols (list, optional) – columns from sz_data for Neighbor calculation Default: [‘mag_u_lsst’, ‘mag_g_lsst’, ‘mag_r_lsst’, ‘mag_i_lsst’,…]

  • leafsize (int, optional) – leaf size for testdata KDTree Default: 40

  • phot_weightcol (str, optional) – name of photometry weight, if present Default:

  • n_samples (int, optional) – number of bootstrap samples to generate Default: 20

Returns:

Handle providing access to QP ensemble with output data

Return type:

qp.core.ensemble.Ensemble