from ROOT import TMVA, TFile, TTree, TCut
from subprocess import call
from os.path import isfile
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras.optimizers import SGD
output =
TFile.Open(
'TMVA_Regression_Keras.root',
'RECREATE')
'!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=Regression')
if not isfile('tmva_reg_example.root'):
call(['curl', '-L', '-O', 'http://root.cern.ch/files/tmva_reg_example.root'])
tree = data.Get('TreeR')
for branch in tree.GetListOfBranches():
name = branch.GetName()
if name != 'fvalue':
dataloader.AddVariable(name)
dataloader.AddTarget('fvalue')
dataloader.AddRegressionTree(tree, 1.0)
dataloader.PrepareTrainingAndTestTree(
TCut(
''),
'nTrain_Regression=1000:SplitMode=Random:NormMode=NumEvents:!V')
model = Sequential()
model.add(Dense(64, activation='tanh', input_dim=2))
model.add(Dense(1, activation='linear'))
model.compile(loss='mean_squared_error', optimizer=SGD(learning_rate=0.01), weighted_metrics=[])
model.save('modelRegression.h5')
model.summary()
factory.BookMethod(dataloader, TMVA.Types.kPyKeras, 'PyKeras',
'H:!V:VarTransform=D,G:FilenameModel=modelRegression.h5:FilenameTrainedModel=trainedModelRegression.h5:NumEpochs=20:BatchSize=32')
factory.BookMethod(dataloader, TMVA.Types.kBDT, 'BDTG',
'!H:!V:VarTransform=D,G:NTrees=1000:BoostType=Grad:Shrinkage=0.1:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=4')
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
A specialized string object used for TTree selections.
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
This is the main MVA steering class.
static void PyInitialize()
Initialize Python interpreter.