rail.estimation.algos.sklearn_neurnet module

Example code that implements a simple Neural Net predictor for z_mode, and Gaussian centered at z_mode with base_width read in fromfile and pdf width set to base_width*(1+zmode).

class rail.estimation.algos.sklearn_neurnet.SklNeurNetEstimator

Bases: CatEstimator

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.

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • chunk_size ([int] default=10000) – Number of objects per chunk for parallel processing or to evalute per loop in single node processing

  • hdf5_groupname ([str] default=photometry) – name of hdf5 group for data, if None, then set to ‘’

  • zmin ([float] default=0.0) – The minimum redshift of the z grid or sample

  • zmax ([float] default=3.0) – The maximum redshift of the z grid or sample

  • nzbins ([int] default=301) – The number of gridpoints in the z grid

  • id_col ([str] default=object_id) – name of the object ID column

  • redshift_col ([str] default=redshift) – name of redshift column

  • calc_summary_stats ([bool] default=False) – Compute summary statistics

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

  • recompute_point_estimates ([bool] default=False) – Force recomputation of point estimates

  • width ([float] default=0.05) – The ad hoc base width of the PDFs

  • ref_band (str] (default=mag_i_lsst))

  • nondetect_val (float] (default=99.0))

  • bands (list] (default=['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst']))

  • model (ModelHandle (INPUT))

  • input (TableHandle (INPUT))

  • output (QPHandle (OUTPUT))

__init__(args, **kwargs)

Constructor: Do CatEstimator specific initialization

entrypoint_function: str | None = 'estimate'
interactive_function: str | None = 'skl_neur_net_estimator'
name = 'SklNeurNetEstimator'
class rail.estimation.algos.sklearn_neurnet.SklNeurNetInformer

Bases: CatInformer

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.

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • hdf5_groupname (str] (default=photometry))

  • zmin (float] (default=0.0))

  • zmax (float] (default=3.0))

  • nzbins (int] (default=301))

  • nondetect_val (float] (default=99.0))

  • mag_limits (dict] (default={'mag_u_lsst': 27.79, 'mag_g_lsst': 29.04, 'mag_r_lsst': 29.06, 'mag_i_lsst': 28.62, 'mag_z_lsst': 27.98, 'mag_y_lsst': 27.05}))

  • bands (list] (default=['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst']))

  • ref_band (str] (default=mag_i_lsst))

  • redshift_col (str] (default=redshift))

  • width ([float] default=0.05) – The ad hoc base width of the PDFs

  • max_iter ([int] default=500) – 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)

  • input (TableHandle (INPUT))

  • model (ModelHandle (OUTPUT))

__init__(args, **kwargs)

Constructor: Do CatInformer specific initialization

entrypoint_function: str | None = 'inform'
interactive_function: str | None = 'skl_neur_net_informer'
name = 'SklNeurNetInformer'
run()

Train the NN model

rail.estimation.algos.sklearn_neurnet.make_color_data(data_dict, bands, ref_band, nondet_val)

make a dataset consisting of the i-band mag and the five colors

Returns:

input_data

Return type:

ndarray array of imag and 5 colors

rail.estimation.algos.sklearn_neurnet.regularize_data(data)

Utility function to prepare data for sklearn