rail.interactive.estimation.algos.sklearn_neurnet module

rail.interactive.estimation.algos.sklearn_neurnet.skl_neur_net_estimator(**kwargs)

Subclass to implement a simple point estimate Neural Net photoz rather than actually predict PDF, for now just predict point zb and then put an error of width*(1+zb). We’ll do a “real” NN photo-z later.

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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.sklearn_neurnet.SklNeurNetEstimator.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

  • width (float, optional) – The ad hoc base width of the PDFs Default: 0.05

  • ref_band (str, optional) – band to use in addition to colors Default: mag_i_lsst

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

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

Returns:

Handle providing access to QP ensemble with output data

Return type:

qp.core.ensemble.Ensemble

rail.interactive.estimation.algos.sklearn_neurnet.skl_neur_net_informer(**kwargs)

Subclass to train a simple point estimate Neural Net photoz rather than actually predict PDF, for now just predict point zb and then put an error of width*(1+zb). We’ll do a “real” NN photo-z later.

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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.sklearn_neurnet.SklNeurNetInformer.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

  • 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

  • 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,…}

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

  • ref_band (str, optional) – band to use in addition to colors Default: mag_i_lsst

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

  • width (float, optional) – The ad hoc base width of the PDFs Default: 0.05

  • max_iter (int, optional) – max number of iterations while training the neural net. Too low a value will cause an error to be printed (though the code will still work, justnot optimally) Default: 500

Returns:

Handle providing access to trained model

Return type:

numpy.ndarray