ApplicationClassificationKeras.py

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#!/usr/bin/env python
## \file
## \ingroup tutorial_tmva_keras
## \notebook -nodraw
## This tutorial shows how to apply a trained model to new data.
##
## \macro_code
##
## \date 2017
## \author TMVA Team
 
from ROOT import TMVA, TFile, TString
from array import array
from subprocess import call
from os.path import isfile
 
# Setup TMVA
TMVA.Tools.Instance()
TMVA.PyMethodBase.PyInitialize()
reader = TMVA.Reader("Color:!Silent")
 
# Load data
if not isfile('tmva_class_example.root'):
    call(['curl', '-O', 'http://root.cern.ch/files/tmva_class_example.root'])
 
data = TFile.Open('tmva_class_example.root')
signal = data.Get('TreeS')
background = data.Get('TreeB')
 
branches = {}
for branch in signal.GetListOfBranches():
    branchName = branch.GetName()
    branches[branchName] = array('f', [-999])
    reader.AddVariable(branchName, branches[branchName])
    signal.SetBranchAddress(branchName, branches[branchName])
    background.SetBranchAddress(branchName, branches[branchName])
 
# Book methods
reader.BookMVA('PyKeras', TString('dataset/weights/TMVAClassification_PyKeras.weights.xml'))
 
# Print some example classifications
print('Some signal example classifications:')
for i in range(20):
    signal.GetEntry(i)
    print(reader.EvaluateMVA('PyKeras'))
print('')
 
print('Some background example classifications:')
for i in range(20):
    background.GetEntry(i)
    print(reader.EvaluateMVA('PyKeras'))