rail.estimation.algos.knnpz module

quick implementation of k nearest neighbor estimator First pass will ignore photometric errors and just do things in terms of magnitudes, we will expand in a future update

class rail.estimation.algos.knnpz.Inform_KNearNeighPDF(args, comm=None)

Bases: CatInformer

Train a KNN-based estimator

config_options = {'bands': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'hdf5_groupname': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'leaf_size': <ceci.config.StageParameter object>, 'mag_limits': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'ngrid_sigma': <ceci.config.StageParameter object>, 'nneigh_max': <ceci.config.StageParameter object>, 'nneigh_min': <ceci.config.StageParameter object>, 'nondetect_val': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'nzbins': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'output_mode': <ceci.config.StageParameter object>, 'redshift_col': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'ref_band': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'save_train': True, 'seed': <ceci.config.StageParameter object>, 'sigma_grid_max': <ceci.config.StageParameter object>, 'sigma_grid_min': <ceci.config.StageParameter object>, 'trainfrac': <ceci.config.StageParameter object>, 'zmax': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'zmin': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}}

name = 'Inform_KNearNeighPDF'

run()

train a KDTree on a fraction of the training data

class rail.estimation.algos.knnpz.KNearNeighPDF(args, comm=None)

Bases: CatEstimator

KNN-based estimator

config_options = {'bands': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'chunk_size': 10000, 'hdf5_groupname': <class 'str'>, 'mag_limits': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'nondetect_val': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'nzbins': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'output_mode': <ceci.config.StageParameter object>, 'redshift_col': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'ref_band': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'zmax': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}, 'zmin': {'bands': ['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst'], 'dz': 0.01, 'err_bands': ['mag_err_u_lsst', 'mag_err_g_lsst', 'mag_err_r_lsst', 'mag_err_i_lsst', 'mag_err_z_lsst', 'mag_err_y_lsst'], 'hdf5_groupname': 'photometry', 'mag_limits': {'mag_g_lsst': 29.04, 'mag_i_lsst': 28.62, 'mag_r_lsst': 29.06, 'mag_u_lsst': 27.79, 'mag_y_lsst': 27.05, 'mag_z_lsst': 27.98}, 'nondetect_val': 99.0, 'nzbins': 301, 'redshift_col': 'redshift', 'ref_band': 'mag_i_lsst', 'zmax': 3.0, 'zmin': 0.0}}

name = 'KNearNeighPDF'

open_model(**kwargs)

Load the mode and/or attach it to this Estimator

Parameters:

model (object, str or ModelHandle) – Either an object with a trained model, a path pointing to a file that can be read to obtain the trained model, or a ModelHandle providing access to the trained model.

Returns:

self.model – The object encapsulating the trained model.

Return type:

object