TMVA_SOFIE_Inference.py File Reference

Detailed Description

This macro provides an example of using a trained model with Keras and make inference using SOFIE directly from Numpy This macro uses as input a Keras model generated with the TMVA_Higgs_Classification.C tutorial You need to run that macro before this one.

In this case we are parsing the input file and then run the inference in the same macro making use of the ROOT JITing capability

 
import ROOT
import numpy as np
 
 
ROOT.TMVA.PyMethodBase.PyInitialize()
 
 
# check if the input file exists
modelFile = "Higgs_trained_model.h5"
if (ROOT.gSystem.AccessPathName(modelFile)) :
    ROOT.Info("TMVA_SOFIE_RDataFrame","You need to run TMVA_Higgs_Classification.C to generate the Keras trained model")
    exit()
 
 
# parse the input Keras model into RModel object
model = ROOT.TMVA.Experimental.SOFIE.PyKeras.Parse(modelFile)
 
generatedHeaderFile = modelFile.replace(".h5",".hxx")
print("Generating inference code for the Keras model from ",modelFile,"in the header ", generatedHeaderFile)
#Generating inference code
model.Generate()
model.OutputGenerated(generatedHeaderFile)
model.PrintGenerated()
 
# now compile using ROOT JIT trained model
modelName = modelFile.replace(".h5","")
print("compiling SOFIE model ", modelName)
ROOT.gInterpreter.Declare('#include "' + generatedHeaderFile + '"')
 
 
generatedHeaderFile = modelFile.replace(".h5",".hxx")
print("Generating inference code for the Keras model from ",modelFile,"in the header ", generatedHeaderFile)
#Generating inference
 
inputFileName = "Higgs_data.root"
inputFile = str(ROOT.gROOT.GetTutorialDir()) + "/machine_learning/data/" + inputFileName
 
 
 
 
 
# make SOFIE inference on signal data
 
df1 = ROOT.RDataFrame("sig_tree", inputFile)
sigData = df1.AsNumpy(columns=['m_jj', 'm_jjj', 'm_lv', 'm_jlv', 'm_bb', 'm_wbb', 'm_wwbb'])
#print(sigData)
 
# stack all the 7 numpy array in a single array (nevents x nvars)
xsig = np.column_stack(list(sigData.values()))
dataset_size = xsig.shape[0]
print("size of data", dataset_size)
 
#instantiate SOFIE session class
session = ROOT.TMVA_SOFIE_Higgs_trained_model.Session()
 
hs = ROOT.TH1D("hs","Signal result",100,0,1)
for i in range(0,dataset_size):
    result = session.infer(xsig[i,:])
    hs.Fill(result[0])
 
 
# make SOFIE inference on background data
df2 = ROOT.RDataFrame("bkg_tree", inputFile)
bkgData = df2.AsNumpy(columns=['m_jj', 'm_jjj', 'm_lv', 'm_jlv', 'm_bb', 'm_wbb', 'm_wwbb'])
 
xbkg = np.column_stack(list(bkgData.values()))
dataset_size = xbkg.shape[0]
 
hb = ROOT.TH1D("hb","Background result",100,0,1)
for i in range(0,dataset_size):
    result = session.infer(xbkg[i,:])
    hb.Fill(result[0])
 
 
c1 = ROOT.TCanvas()
ROOT.gStyle.SetOptStat(0)
hs.SetLineColor("kRed")
hs.Draw()
hb.SetLineColor("kBlue")
hb.Draw("SAME")
c1.BuildLegend()
c1.Draw()
 
 
print("Number of signal entries",hs.GetEntries())
print("Number of background entries",hb.GetEntries())
 

Author

Lorenzo Moneta

Definition in file TMVA_SOFIE_Inference.py.