def cross_validate(self, nfolds, outputfile):
'''Run cross validation with the given number of folds.'''
crossval = TMVA.CrossValidation(self.dataloader.dataloader)
crossval.SetNumFolds(nfolds)
methods = self.book_methods(crossval, self.dataloader.dataloader)
crossval.Evaluate()
results = crossval.GetResults()
return crossval, results
signal = f1.Get('NOMINAL')
background = f2.Get('NOMINAL')
# In[5]:
loader.AddSignalTree(signal, 1.0)
loader.AddBackgroundTree(background, 1.0)
loader.PrepareTrainingAndTestTree(
TCut(""),
"nTrain_Signal=86203 :nTrain_Background=412573 :SplitMode=Random:NormMode=NumEvents:!V"
)
# In[6]:
cv = TMVA.CrossValidation(
"TMVACrossValidation", loader, outputFile,
"!V:!Silent:ModelPersistence:AnalysisType=Classification:NumFolds=3:SplitExpr="
)
# In[7]:
cv.BookMethod(
TMVA.Types.kMLP, "MLP",
"H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+1:TestRate=5:!UseRegulator"
)
# In[8]:
cv.Evaluate()
# In[9]: