13.7.6. Macro “modelerClassificationNeuralNetworks.py”

13.7.6.1. Objective

The objective of this macro is to build a surrogate model (an Artificial Neural Network) from a database for a “Classification” Problem. From a first database loaded from the ASCII file problem2Classes_001000.dat, which defines three variables \((x,y) \in [-1., 1]^{2}\) and \(rc01 \in \{0, 1\}\) on 1000 patterns,

../../_images/modelerClassificationNeuralNetworks_App.png

two ASCII files are created, one with the 800st patterns (_problem2Classes_app_.dat), the other with the 200 last patterns (_problem2Classes_val_.dat). The surrogate model is built with the database extracted from the first of the two files, the second allowing to perform calculations with a function.

13.7.6.2. Macro Uranie

"""
Example of classification problem with neural network
"""
from URANIE import DataServer, Launcher, Modeler
import ROOT

nH1 = 15
nH2 = 10
nH3 = 5

sX = "x:y"
sY = "rc01"
sYhat = sY+"_"+str(nH1)+"_"+str(nH2)+"_"+str(nH3)

# Load a database in an ASCII file
tdsu = DataServer.TDataServer("tdsu", "tds u")
tdsu.fileDataRead("problem2Classes_001000.dat")

# Split into 2 datasets for learning and testing
Niter = tdsu.getIteratorName()
tdsu.exportData("_problem2Classes_app_.dat", "", Niter+"<=800")
tdsu.exportData("_problem2Classes_val_.dat", "", Niter+">800")

tds = DataServer.TDataServer("tdsApp", "tds App for problem2Classes")
tds.fileDataRead("_problem2Classes_app_.dat")

tann = Modeler.TANNModeler(tds, sX+", %i, %i, %i,@" % (nH1, nH2, nH3)+sY)
# tann.setLog()
tann.setNormalization(Modeler.TANNModeler.kMinusOneOne)
tann.setFcnTol(1e-6)
tann.train(2, 2, "test", False)

tann.exportFunction("c++", "uranie_ann_problem2Classes", "ANNproblem2Classes")

ROOT.gROOT.LoadMacro("uranie_ann_problem2Classes.C")

tdsv = DataServer.TDataServer()
tdsv.fileDataRead("_problem2Classes_val_.dat")

# evaluate the surrogate model on the database
tlf = Launcher.TLauncherFunction(tdsv, "ANNproblem2Classes", sX, sYhat)
tlf.run()

c = ROOT.TCanvas("c1", "Graph for the Macro modeler", 5, 64, 1270, 667)
# Buils a surrogate model (Artificial Neural Networks) from the DataBase
tds.getTuple().SetMarkerStyle(8)
tds.getTuple().SetMarkerSize(1.0)
tds.getTuple().SetMarkerColor(ROOT.kGreen)
tds.draw(sY+":"+sX)

tdsv.getTuple().SetMarkerStyle(8)
tdsv.getTuple().SetMarkerSize(0.75)
tdsv.getTuple().SetMarkerColor(ROOT.kRed)
tdsv.Draw(sYhat+":"+sX, "", "same")

The main TDataServer loads the main ASCII data file problem2Classes_001000.dat

tdsu = DataServer.TDataServer("tdsu", "tds u")
tdsu.fileDataRead("problem2Classes_001000.dat")

The database is split with the internal iterator attribute in two parts by exporting the 800st patterns in a file and the remaining 200 in another one

tdsu.exportData("_problem2Classes_app_.dat", "", Niter+"<=800")
tdsu.exportData("_problem2Classes_val_.dat", "", Niter+">800")

A second TDataServer loads _problem2Classes_app_.dat and builds the surrogate model over all the variables with 3 hidden layers, a Hyperbolic Tangent (TanH) activation function (normalization) in the 3 hidden layers and set the function tolerance to 1e-.6. The “@” character behind the output name defines a classification problem.

tds = DataServer.TDataServer("tdsApp", "tds App for problem2Classes")
tds.fileDataRead("_problem2Classes_app_.dat")
tann = Modeler.TANNModeler(tds, sX+", %i, %i, %i,@" % (nH1, nH2, nH3)+sY)
tann.setNormalization(Modeler.TANNModeler.kMinusOneOne)
tann.setFcnTol(1e-6)
tann.train(2, 2, "test", False)

The model is exported in an external file in C++ language "uranie_ann_problem2Classes.C" where the function name is ANNproblem2Classes

tann.exportFunction("c++", "uranie_ann_problem2Classes", "ANNproblem2Classes")

The model is loaded from the macro uranie_ann_problem2Classes.C and applied on the second database with the function ANNproblem2Classes.

ROOT.gROOT.LoadMacro("uranie_ann_problem2Classes.C")
tdsv = DataServer.TDataServer()
tdsv.fileDataRead("_problem2Classes_val_.dat")
tlf = Launcher.TLauncherFunction(tdsv, "ANNproblem2Classes", sX, sYhat)
tlf.run()

We draw on a 3D graph, the learning database (in green) and the estimations by the Artificial Neural Network with red points.

c = ROOT.TCanvas("c1", "Graph for the Macro modeler", 5, 64, 1270, 667)
tds.getTuple().SetMarkerStyle(8)
tds.getTuple().SetMarkerSize(1.0)
tds.getTuple().SetMarkerColor(ROOT.kGreen)
tds.draw(sY+":"+sX)

tdsv.getTuple().SetMarkerStyle(8)
tdsv.getTuple().SetMarkerSize(0.75)
tdsv.getTuple().SetMarkerColor(ROOT.kRed)
tdsv.Draw(sYhat+":"+sX, "", "same")

13.7.6.3. Graph

../../_images/modelerClassificationNeuralNetworks.png — Figure 13.42 Graph of the macro **“modelerClassificationNeuralNetworks.py”**

13.7.6.4. Console

--- Uranie v4.11/0 --- Developed with ROOT (6.36.06)
                      Copyright (C) 2013-2026 CEA/DES 
                      Contact: support-uranie@cea.fr 
                      Date: Thu Feb 12, 2026

 <URANIE::WARNING> 
 <URANIE::WARNING> *** URANIE WARNING ***
 <URANIE::WARNING> *** File[${SOURCEDIR}/dataSERVER/souRCE/TDataServer.cxx] Line[760]
 <URANIE::WARNING> TDataServer::fileDataRead: Expected iterator tdsu__n__iter__ not found but _tds___n__iter__ looks like an URANIE iterator => Will be used as so.
 <URANIE::WARNING> *** END of URANIE WARNING ***
 <URANIE::WARNING> 
 <URANIE::WARNING> 
 <URANIE::WARNING> *** URANIE WARNING ***
 <URANIE::WARNING> *** File[${SOURCEDIR}/dataSERVER/souRCE/TDataServer.cxx] Line[760]
 <URANIE::WARNING> TDataServer::fileDataRead: Expected iterator tdsApp__n__iter__ not found but _tds___n__iter__ looks like an URANIE iterator => Will be used as so.
 <URANIE::WARNING> *** END of URANIE WARNING ***
 <URANIE::WARNING> 
 ** TANNModeler::train niter[2] ninit[2]
 ** init the ANN
 ** Input  (1) Name[x] Min[-0.999842] Max[0.998315]
 ** Input  (2) Name[y] Min[-0.999434] Max[0.998071]
 ** Output  (1) Name[rc01] Min[0] Max[1]
 ** Tolerance  (1e-06)
 ** sHidden    (15,10,5) (30)
 ** Nb Weights (266)
 ** ActivationFunction[TanH]
 **

 ** iter[1/2] : ** : mse_min[0.00813999]
 ** iter[2/2] : ** : mse_min[0.00636923]

 ** solutions : 2
 ** isol[1] iter[0] learn[0.00405863] test[0.00813999] *
 ** isol[2] iter[1] learn[0.00560685] test[0.00636923] *
 ** CPU training finished. Total elapsed time: 19 sec

*******************************
*** TModeler::exportFunction lang[c++] file[uranie_ann_problem2Classes] name[ANNproblem2Classes] soption[]

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*******************************
*** exportFunction lang[c++] file[uranie_ann_problem2Classes] name[ANNproblem2Classes]
*** End Of exportFunction
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