/*
The following shows how to apply a PCA transformation to a subset
of input variables.
You need to prepare tmva_subset_root.pat. Copy tmva_root.pat and
remove var2 on the Leaves: line. This will force the reader to read
in only 3 variables: var1, var3, and var4.
After you plot the results, you can see that only variables
var1, var3 and var4 have been decorrelated. Variable var2 shows
substantial correlation with others.
*/
int spr_transform_2()
{
// load lib
gSystem->Load("/afs/cern.ch/user/n/narsky/w0/CMSSW_1_8_X_2007-11-09-0200/lib/slc4_ia32_gcc345/libPhysicsToolsStatPatternRecognition.so");
// create main SPR object
SprRootAdapter spr;
// load training data
spr.loadDataFromRoot("tmva_subset_root.pat","train");
// split data into train/test
spr.split(0.5,true);
// choose classes
spr.chooseClasses("0:1");
// show how much data we have
const int nClasses = spr.nClasses();
char classes[nClasses][200];
int events [nClasses];
double weights [nClasses];
spr.showDataInClasses(classes,events,weights,"train");
plotClasses("SPR_Class_1","train",nClasses,classes,events,weights);
spr.showDataInClasses(classes,events,weights,"test");
plotClasses("SPR_Class_2","test",nClasses,classes,events,weights);
// perform PCA
int verbose = 0;
spr.trainVarTransformer("PCA",verbose);
spr.saveVarTransformer("pca.spr");
// reload training and test data, now with all variables included
spr.loadDataFromRoot("tmva_root.pat","train");
spr.split(0.5,true);
spr.chooseClasses("0:1");
// compute correlations between variables before PCA transform
const unsigned dim = spr.dim();
double corr [dim*dim];
char vars[dim][200];
spr.vars(vars);
spr.correlation(0,corr,"train");// background
plotCorrelation("SPR_1B","background",dim,vars,corr);
spr.correlation(1,corr,"train");// signal
plotCorrelation("SPR_1S","signal",dim,vars,corr);
// apply PCA transform
spr.transform();
// Plot correlations after PCA transform.
spr.vars(vars);
spr.correlation(0,corr,"train");// background
plotCorrelation("SPR_2B","decorrelated background",dim,vars,corr);
spr.correlation(1,corr,"train");// signal
plotCorrelation("SPR_2S","decorrelated signal",dim,vars,corr);
// exit
return 0;
}
int spr_transform_1()
{
// load lib
gSystem->Load("/afs/cern.ch/user/n/narsky/w0/CMSSW_1_8_X_2007-11-29-1600/lib/slc4_ia32_gcc345/libPhysicsToolsStatPatternRecognition.so");
// create main SPR object
SprRootAdapter spr;
// load training data
spr.loadDataFromRoot("tmva_root.pat","train");
// split data into train/test
spr.split(0.5,true);
// choose classes
spr.chooseClasses("0:1");
// show how much data we have
const int nClasses = spr.nClasses();
char classes[nClasses][200];
int events [nClasses];
double weights [nClasses];
spr.showDataInClasses(classes,events,weights,"train");
plotClasses("SPR_Class_1","train",nClasses,classes,events,weights);
spr.showDataInClasses(classes,events,weights,"test");
plotClasses("SPR_Class_2","test",nClasses,classes,events,weights);
// compute correlations between variables
const unsigned dim = spr.dim();
double corr [dim*dim];
char vars[dim][200];
spr.vars(vars);
spr.correlation(0,corr,"train");// background
plotCorrelation("SPR_1B","background",dim,vars,corr);
spr.correlation(1,corr,"train");// signal
plotCorrelation("SPR_1S","signal",dim,vars,corr);
// save original test data
spr.saveTestData("test_orig.root");
// perform PCA
int verbose = 0;
spr.trainVarTransformer("PCA",verbose);
spr.transform();
// Plot correlations after PCA transform.
// Note that PCA transform does not change dimensionality.
// But it changes variable names!
spr.vars(vars);
spr.correlation(0,corr,"train");// background
plotCorrelation("SPR_2B","decorrelated background",dim,vars,corr);
spr.correlation(1,corr,"train");// signal
plotCorrelation("SPR_2S","decorrelated signal",dim,vars,corr);
// save transformer to a file for future reference
spr.saveVarTransformer("pca.spr");
// select classifiers
spr.addBoostedBinarySplits("splits",100,20);
// train
spr.train(verbose);
// test
spr.test();
// get signal-vs-background curve for classifiers
const int npts = 9;
double signalEff [npts] = { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 };
const int ntrained = spr.nTrained();
double bgrndEff [npts*ntrained];
double bgrndErr [npts*ntrained];
double fom [npts*ntrained];
char classifiers[ntrained][200];
spr.allEffCurves(npts,signalEff,classifiers,bgrndEff,bgrndErr,fom);
plotEffCurveMulti("SPR_3",ntrained,npts,signalEff,
classifiers,bgrndEff,bgrndErr,0);
// save transformed test data with classifier responses
spr.saveTestData("test_transformed.root");
// exit
return 0;
}
