// @(#)root/mlp:$Name: $:$Id: TMLPAnalyzer.cxx,v 1.6 2004/12/16 21:20:47 brun Exp $
// Author: Christophe.Delaere@cern.ch 25/04/04
/*************************************************************************
* Copyright (C) 1995-2003, Rene Brun and Fons Rademakers. *
* All rights reserved. *
* *
* For the licensing terms see $ROOTSYS/LICENSE. *
* For the list of contributors see $ROOTSYS/README/CREDITS. *
*************************************************************************/
///////////////////////////////////////////////////////////////////////////
//
// TMLPAnalyzer
//
// This utility class contains a set of tests usefull when developing
// a neural network.
// It allows you to check for unneeded variables, and to control
// the network structure.
//
///////////////////////////////////////////////////////////////////////////
#include "TSynapse.h"
#include "TNeuron.h"
#include "TMultiLayerPerceptron.h"
#include "TMLPAnalyzer.h"
#include "TTree.h"
#include "TTreeFormula.h"
#include "TEventList.h"
#include "TH1D.h"
#include "TProfile.h"
#include "THStack.h"
#include "TLegend.h"
#include "TPad.h"
#include "Riostream.h"
ClassImp(TMLPAnalyzer)
//______________________________________________________________________________
TMLPAnalyzer::~TMLPAnalyzer() { if(fAnalysisTree) delete fAnalysisTree; }
//______________________________________________________________________________
Int_t TMLPAnalyzer::GetLayers()
{
// Returns the number of layers.
TString fStructure = fNetwork->GetStructure();
return fStructure.CountChar(':')+1;
}
//______________________________________________________________________________
Int_t TMLPAnalyzer::GetNeurons(Int_t layer)
{
// Returns the number of neurons in given layer.
if(layer==1) {
TString fStructure = fNetwork->GetStructure();
TString input = TString(fStructure(0, fStructure.First(':')));
return input.CountChar(',')+1;
}
else if(layer==GetLayers()) {
TString fStructure = fNetwork->GetStructure();
TString output = TString(fStructure(fStructure.Last(':') + 1,
fStructure.Length() - fStructure.Last(':')));
return output.CountChar(',')+1;
}
else {
Int_t cnt=1;
TString fStructure = fNetwork->GetStructure();
TString hidden = TString(fStructure(fStructure.First(':') + 1,
fStructure.Last(':') - fStructure.First(':') - 1));
Int_t beg = 0;
Int_t end = hidden.Index(":", beg + 1);
Int_t num = 0;
while (end != -1) {
num = atoi(TString(hidden(beg, end - beg)).Data());
cnt++;
beg = end + 1;
end = hidden.Index(":", beg + 1);
if(layer==cnt) return num;
}
num = atoi(TString(hidden(beg, hidden.Length() - beg)).Data());
cnt++;
if(layer==cnt) return num;
}
return -1;
}
//______________________________________________________________________________
TString TMLPAnalyzer::GetNeuronFormula(Int_t idx)
{
// Returns the formula used as input for neuron (idx) in
// the first layer.
TString fStructure = fNetwork->GetStructure();
TString input = TString(fStructure(0, fStructure.First(':')));
Int_t beg = 0;
Int_t end = input.Index(",", beg + 1);
TString brName;
Int_t cnt = 0;
while (end != -1) {
brName = TString(input(beg, end - beg));
beg = end + 1;
end = input.Index(",", beg + 1);
if(cnt==idx) return brName;
cnt++;
}
brName = TString(input(beg, input.Length() - beg));
return brName;
}
//______________________________________________________________________________
void TMLPAnalyzer::CheckNetwork()
{
// Gives some information about the network in the terminal.
TString fStructure = fNetwork->GetStructure();
cout << "Network with structure: " << fStructure.Data() << endl;
cout << "an input with lower values may not be needed" << endl;
// Checks if some input variable is not needed
char var[64], sel[64];
for (Int_t i = 0; i < GetNeurons(1); i++) {
sprintf(var,"Diff>>tmp%d",i);
sprintf(sel,"InNeuron==%d",i);
fAnalysisTree->Draw(var, sel, "goff");
TH1F* tmp = (TH1F*)gDirectory->Get(Form("tmp%d",i));
cout << ((TNeuron*)fNetwork->fFirstLayer[i])->GetName()
<< " -> " << tmp->GetMean()
<< " +/- " << tmp->GetRMS() << endl;
}
}
//______________________________________________________________________________
void TMLPAnalyzer::GatherInformations()
{
// Collect informations about what is usefull in the network.
// This method has to be called first when analyzing a network.
// Fills the two analysis trees.
Double_t shift = 0.1;
TTree* data = fNetwork->fData;
TEventList* test = fNetwork->fTest;
Int_t nEvents = test->GetN();
Int_t NN = GetNeurons(1);
Double_t* params = new Double_t[NN];
Double_t* rms = new Double_t[NN];
TTreeFormula** formulas = new TTreeFormula*[NN];
TString formula;
Int_t i(0), j(0), k(0), l(0);
for(i=0; i<NN; i++){
formula = GetNeuronFormula(i);
formulas[i] = new TTreeFormula(Form("NF%d",this),formula,data);
TH1D tmp("tmpb", "tmpb", 1, -FLT_MAX, FLT_MAX);
data->Draw(Form("%s>>tmpb",formula.Data()),"","goff");
rms[i] = tmp.GetRMS();
}
Int_t InNeuron = 0;
Double_t Diff = 0.;
if(fAnalysisTree) delete fAnalysisTree;
fAnalysisTree = new TTree("result","analysis");
fAnalysisTree->SetDirectory(0);
fAnalysisTree->Branch("InNeuron",&InNeuron,"InNeuron/I");
fAnalysisTree->Branch("Diff",&Diff,"Diff/D");
Int_t numOutNodes=GetNeurons(GetLayers());
Double_t *outVal=new Double_t[numOutNodes];
Double_t *trueVal=new Double_t[numOutNodes];
delete fIOTree;
fIOTree=new TTree("MLP_iotree","MLP_iotree");
fIOTree->SetDirectory(0);
TString leaflist;
for (i=0; i<NN; i++)
leaflist+=Form("In%d/D:",i);
leaflist.Remove(leaflist.Length()-1);
fIOTree->Branch("In", params, leaflist);
leaflist="";
for (i=0; i<numOutNodes; i++)
leaflist+=Form("Out%d/D:",i);
leaflist.Remove(leaflist.Length()-1);
fIOTree->Branch("Out", outVal, leaflist);
leaflist="";
for (i=0; i<numOutNodes; i++)
leaflist+=Form("True%d/D:",i);
leaflist.Remove(leaflist.Length()-1);
fIOTree->Branch("True", trueVal, leaflist);
Double_t v1 = 0.;
Double_t v2 = 0.;
// Loop on the events in the test sample
for(j=0; j< nEvents; j++) {
fNetwork->GetEntry(test->GetEntry(j));
// Loop on the neurons to evaluate
for(k=0; k<GetNeurons(1); k++)
params[k] = formulas[k]->EvalInstance();
for(k=0; k<GetNeurons(GetLayers()); k++) {
outVal[k] = fNetwork->Evaluate(k,params);
trueVal[k] = ((TNeuron*)fNetwork->fLastLayer[k])->GetBranch();
}
fIOTree->Fill();
// Loop on the input neurons
for (i = 0; i < GetNeurons(1); i++) {
InNeuron = i;
Diff = 0;
// Loop on the neurons in the output layer
for(l=0; l<GetNeurons(GetLayers()); l++){
params[i] += shift*rms[i];
v1 = fNetwork->Evaluate(l,params);
params[i] -= 2*shift*rms[i];
v2 = fNetwork->Evaluate(l,params);
Diff += (v1-v2)*(v1-v2);
// reset to original vealue
params[i] += shift*rms[i];
}
Diff = TMath::Sqrt(Diff);
fAnalysisTree->Fill();
}
}
delete[] params;
delete[] rms;
delete[] outVal;
for(i=0; i<GetNeurons(1); i++) delete formulas[i]; delete [] formulas;
fAnalysisTree->ResetBranchAddresses();
fIOTree->ResetBranchAddresses();
}
//______________________________________________________________________________
void TMLPAnalyzer::DrawDInput(Int_t i)
{
// Draws the distribution (on the test sample) of the
// impact on the network output of a small variation of
// the ith input.
char sel[64];
sprintf(sel, "InNeuron==%d", i);
fAnalysisTree->Draw("Diff", sel);
}
//______________________________________________________________________________
void TMLPAnalyzer::DrawDInputs()
{
// Draws the distribution (on the test sample) of the
// impact on the network output of a small variation of
// each input.
THStack* stack = new THStack("differences","differences");
TLegend* legend = new TLegend(0.75,0.75,0.95,0.95);
TH1F* tmp = NULL;
char var[64], sel[64];
for(Int_t i = 0; i < GetNeurons(1); i++) {
sprintf(var, "Diff>>tmp%d", i);
sprintf(sel, "InNeuron==%d", i);
fAnalysisTree->Draw(var, sel, "goff");
tmp = (TH1F*)gDirectory->Get(Form("tmp%d",i));
tmp->SetDirectory(0);
tmp->SetLineColor(i+1);
stack->Add(tmp);
legend->AddEntry(tmp,((TNeuron*)fNetwork->fFirstLayer[i])->GetName(),"l");
}
stack->Draw("nostack");
legend->Draw();
gPad->SetLogy();
}
//______________________________________________________________________________
void TMLPAnalyzer::DrawNetwork(Int_t neuron, const char* signal, const char* bg)
{
// Draws the distribution of the neural network (using ith neuron).
// Two distributions are drawn, for events passing respectively the "signal"
// and "background" cuts. Only the test sample is used.
TTree* data = fNetwork->fData;
TEventList* test = fNetwork->fTest;
TEventList* current = data->GetEventList();
data->SetEventList(test);
THStack* stack = new THStack("__NNout_TMLPA",Form("Neural net output (neuron %d)",neuron));
TH1F *bgh = new TH1F("__bgh_TMLPA", "NN output", 50, 0, -1);
TH1F *sigh = new TH1F("__sigh_TMLPA", "NN output", 50, 0, -1);
bgh->SetDirectory(0);
sigh->SetDirectory(0);
Int_t nEvents = 0;
Int_t j=0;
// build event lists for signal and background
TEventList* signal_list = new TEventList("__tmpSig_MLPA");
TEventList* bg_list = new TEventList("__tmpBkg_MLPA");
data->Draw(">>__tmpSig_MLPA",signal,"goff");
data->Draw(">>__tmpBkg_MLPA",bg,"goff");
// fill the background
nEvents = bg_list->GetN();
for(j=0; j< nEvents; j++) {
bgh->Fill(fNetwork->Result(bg_list->GetEntry(j),neuron));
}
// fill the signal
nEvents = signal_list->GetN();
for(j=0; j< nEvents; j++) {
sigh->Fill(fNetwork->Result(signal_list->GetEntry(j),neuron));
}
// draws the result
bgh->SetLineColor(kBlue);
bgh->SetFillStyle(3008);
bgh->SetFillColor(kBlue);
sigh->SetLineColor(kRed);
sigh->SetFillStyle(3003);
sigh->SetFillColor(kRed);
bgh->SetStats(0);
sigh->SetStats(0);
stack->Add(bgh);
stack->Add(sigh);
TLegend *legend = new TLegend(.75, .80, .95, .95);
legend->AddEntry(bgh, "Background");
legend->AddEntry(sigh,"Signal");
stack->Draw("nostack");
legend->Draw();
// restore the default event list
data->SetEventList(current);
delete signal_list;
delete bg_list;
}
//______________________________________________________________________________
TProfile* TMLPAnalyzer::DrawTruthDeviation(Int_t i, Option_t *option /*=""*/)
{
// Draws a profile of the difference of the MLP output minus the
// true value for a given output i, vs the true i, for all test data events.
// Options are passed to TProfile::Draw
TString pipehist=Form("MLP_truthdev_%d",i);
TString drawline;
drawline.Form("Out.Out%d/True.True%d-1.:True.True%d>>", i,i,i);
fIOTree->Draw(drawline+pipehist, "", "goff prof");
TProfile* h=(TProfile*)gROOT->FindObject(pipehist);
const char* title=((TNeuron*)fNetwork->fLastLayer[i])->GetName();
if (title && h) {
h->GetXaxis()->SetTitle(title);
h->GetYaxis()->SetTitle(Form("Deviation from %s", title));
}
h->Draw(option);
return h;
}
//______________________________________________________________________________
THStack* TMLPAnalyzer::DrawTruthDeviations(Option_t *option /*=""*/)
{
// Draws a profile of the difference of the MLP output minus the
// true value vs the true value, stacked for all outputs, for all
// test data events.
// Options are passed to TProfile::Draw
THStack *hs=new THStack("MLP_TruthDeviation",
"Deviation of MLP output from truth");
TLegend *leg=new TLegend(.7,.7,.95,.95,"MLP output");
for (Int_t o=0; o<GetNeurons(GetLayers()); o++) {
TProfile* h=DrawTruthDeviation(o, "goff");
h->SetLineColor(1+o);
hs->Add(h, Form("node %d",o));
}
if (!option || !strstr(option,"goff")) {
hs->Draw();
leg->Draw();
}
return hs;
}
//______________________________________________________________________________
TProfile* TMLPAnalyzer::DrawTruthDeviationInOut(Int_t i, Int_t o,
Option_t *option /*=""*/)
{
// Draws a profile of the difference of the MLP output o minus the
// true value of o vs the input value i, for all test data events.
// Options are passed to TProfile::Draw
TString pipehist=Form("MLP_truthdev_i%d_o%d",i,o);
TString drawline;
drawline.Form("Out.Out%d/True.True%d-1.:In.In%d>>", o,o,i);
fIOTree->Draw(drawline+pipehist, "", "goff prof");
TProfile* h=(TProfile*)gROOT->FindObject(pipehist);
const char* title=((TNeuron*)fNetwork->fFirstLayer[i])->GetName();
if (title && h)
h->GetXaxis()->SetTitle(title);
title=((TNeuron*)fNetwork->fLastLayer[o])->GetName();
if (title && h)
h->GetYaxis()->SetTitle(Form("Deviation from output %s", title));
h->Draw(option);
return h;
}
//______________________________________________________________________________
THStack* TMLPAnalyzer::DrawTruthDeviationInsOut(Int_t o, Option_t *option /*=""*/)
{
// Draws a profile of the difference of the MLP output o minus the
// true value of o vs the input value, stacked for all inputs, for
// all test data events.
// Options are passed to TProfile::Draw
TString sName;
sName.Form("MLP_TruthDeviationIO_%d", o);
THStack *hs=new THStack(sName,
Form("Deviation of MLP output %o from truth"));
TLegend *leg=new TLegend(.7,.7,.95,.95,"MLP output");
for (Int_t o=0; o<GetNeurons(GetLayers()); o++) {
TProfile* h=DrawTruthDeviation(o, "goff");
h->SetLineColor(1+o);
hs->Add(h, Form("node %d",o));
}
if (!option || !strstr(option,"goff")) {
hs->Draw();
leg->Draw();
}
return hs;
}
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