TrainParticleClassifier

class ctapipe.tools.train_particle_classifier.TrainParticleClassifier(**kwargs: Any)

Bases: Tool

Tool to train a ParticleClassifier on dl1b/dl2 data.

The tool first performs a cross validation to give an initial estimate on the quality of the estimation and then finally trains one model per telescope type on the full dataset.

Attributes Summary

aliases
chunk_size	How many subarray events to load at once before training on n_events (or all available) events.
classes
description
examples
input_url_background	Input dl1b/dl2 file for the background class.
input_url_signal	Input dl1b/dl2 file for the signal class.
n_events	Total number of events to be used for training.
n_jobs	Number of threads to use for the reconstruction.
name
output_path	Output file for the trained reconstructor.
random_seed	Random seed for sampling training events.
signal_fraction	Fraction of signal events in all events to be used for training.

Methods Summary

finish()	Write-out trained models and cross-validation results.
setup()	Initialize components from config.
start()	Train models per telescope type.

Attributes Documentation

aliases: StrDict = {'background': 'TrainParticleClassifier.input_url_background', 'cv-output': 'CrossValidator.output_path', 'n-events': 'TrainParticleClassifier.n_events', 'n-jobs': 'ParticleClassifier.n_jobs', 'signal': 'TrainParticleClassifier.input_url_signal', 'signal-fraction': 'TrainParticleClassifier.signal_fraction', ('o', 'output'): 'TrainParticleClassifier.output_path'}

chunk_size

How many subarray events to load at once before training on n_events (or all available) events.

classes: ClassesType = [<class 'ctapipe.io.tableloader.TableLoader'>, <class 'ctapipe.reco.sklearn.ParticleClassifier'>, <class 'ctapipe.reco.sklearn.CrossValidator'>]

description: str | Unicode[str, str | bytes] = '\n    Tool to train a `~ctapipe.reco.ParticleClassifier` on dl1b/dl2 data.\n\n    The tool first performs a cross validation to give an initial estimate\n    on the quality of the estimation and then finally trains one model\n    per telescope type on the full dataset.\n    '

examples: str | Unicode[str, str | bytes] = '\n    ctapipe-train-particle-classifier \\\n        -c train_particle_classifier.yaml \\\n        --signal gamma.dl2.h5 \\\n        --background proton.dl2.h5 \\\n        -o particle_classifier.pkl\n    '

input_url_background

Input dl1b/dl2 file for the background class.

input_url_signal

Input dl1b/dl2 file for the signal class.

n_events

Total number of events to be used for training. If not given, all available events will be used (considering signal_fraction).

n_jobs

Number of threads to use for the reconstruction. This overwrites the values in the config of each reconstructor.

name: str | Unicode[str, str | bytes] = 'ctapipe-train-particle-classifier'

output_path

Output file for the trained reconstructor. At the moment, pickle is the only supported format.

random_seed

Random seed for sampling training events.

signal_fraction

Fraction of signal events in all events to be used for training. signal_fraction = n_signal / (n_signal + n_background)

Methods Documentation

finish()

Write-out trained models and cross-validation results.

setup()

Initialize components from config.

start()

Train models per telescope type.