#backgrounds = [ TTZtoLLNuNu ]
samples = backgrounds + [signal]
for sample in samples:
sample.setSelectionString(selectionString)
if args.small:
sample.reduceFiles(to=1)
mvas = [bdt1, bdt2, bdt3, bdt4, mlp1, mlp2, mlp3]
## TMVA Trainer instance
trainer = Trainer(
signal=signal,
backgrounds=backgrounds,
output_directory=mva_directory,
plot_directory=plot_directory,
mva_variables=mva_variables,
label="Test",
fractionTraining=args.trainingFraction,
)
weightString = "(1)"
trainer.createTestAndTrainingSample(
read_variables=read_variables,
sequence=sequence,
weightString=weightString,
overwrite=args.overwrite,
)
#trainer.addMethod(method = default_methods["BDT"])
trainer.addMethod(method=default_methods["MLP"])
samples = backgrounds + [signal]
for sample in samples:
sample.setSelectionString(selectionString)
if args.small:
sample.reduceFiles(to=1)
mvas = [
all_mlp_ncnc1s0c3e0c5
] #, all_mlp_np5s0c3e0c5 ] #, all_mlp_ncnc1s0c5e0c8, all_mlp_ncnp5c1s0c5e0c8, all_mlp_np40, all_mlp_np7s0c5e1 ]
## TMVA Trainer instance
trainer = Trainer(
signal=signal,
backgrounds=backgrounds,
output_directory=mva_directory_twz_wz,
mva_variables=mva_variables,
label="TWZ_3l",
fractionTraining=args.trainingFraction,
)
weightString = "(1)"
trainer.createTestAndTrainingSample(read_variables=read_variables,
sequence=sequence,
weightString=weightString,
overwrite=args.overwrite,
mva_variables=all_mva_variables)
#trainer.addMethod(method = default_methods["BDT"])
#trainer.addMethod(method = default_methods["MLP"])
for mva in mvas: