def TuningExpertParser():
tuningExpertParser = ArgumentParser(
add_help=False,
description='Tune expert discriminator for a specific TuningTool data.',
conflict_handler='resolve',
parents=[TuningJobParser()])
tuningExpertParser.delete_arguments('data')
tuningExpertParser.suppress_arguments(core='keras')
tuneExpDataArgs = tuningExpertParser.add_argument_group(
"required arguments", "")
tuneExpDataArgs.add_argument(
'-dc',
'--data-calo',
action='store',
metavar='data_calo',
required=True,
help=
"The calorimeter data file that will be used to tune the discriminators"
)
tuneExpDataArgs.add_argument(
'-dt',
'--data-track',
action='store',
metavar='data_track',
required=True,
help=
"The tracking data file that will be used to tune the discriminators")
tuneExpDataArgs.add_argument(
'-nc',
'--network-calo',
action='store',
metavar='nn_calo',
required=True,
help=
"""List of files of the calorimeter neural networks performance analysis.
There must be one file per bin and they must be ordered from the first
et bin to the last, and for each et the eta bins must also be ordered.
Example:
For et-bins 0 and 1 and eta-bins 0, 1 and 2 the files must be in the
order: Et0 Eta0, Et0 Eta1, Et0 Eta2, Et1 Eta0, Et1 Eta1, Et1 Eta2.
In order to obtain such files, it is necessary to run the executable
crossValidStatAnalysis.py.""")
tuneExpDataArgs.add_argument(
'-nt',
'--network-track',
action='store',
metavar='nn_track',
required=True,
help=
"""List of files of the tracking neural networks performance analysis.
For more information see explanation of network_calo argument."""
)
return tuningExpertParser
help="""Tune classifier using P_D, P_F and
SP when set to True. Uses only SP when set to False.""")
else:
tuningJobParser.set_defaults(seed=NotSet, do_multi_stop=NotSet)
################################################################################
# Create tuningExpert file related objects
################################################################################
tuningExpertParser = ArgumentParser(
add_help=False,
description='Tune expert discriminator for a specific TuningTool data.',
conflict_handler='resolve',
parents=[tuningJobParser])
tuningExpertParser.delete_arguments('data')
tuningExpertParser.suppress_arguments(core='keras')
tuneExpDataArgs = tuningExpertParser.add_argument_group(
"required arguments", "")
tuneExpDataArgs.add_argument(
'-dc',
'--data-calo',
action='store',
metavar='data_calo',
required=True,
help=
"The calorimeter data file that will be used to tune the discriminators")
tuneExpDataArgs.add_argument(
'-dt',
'--data-track',
action='store',