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Reference Guide
mvas.cxx
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1#include "TMVA/mvas.h"
2#include "TMVA/Types.h"
3#include "TLegend.h"
4#include "TText.h"
5#include "TH2.h"
6
7
8
9// this macro plots the resulting MVA distributions (Signal and
10// Background overlayed) of different MVA methods run in TMVA
11// (e.g. running TMVAnalysis.C).
12
13
14// input: - Input file (result from TMVA)
15// - use of TMVA plotting TStyle
16void TMVA::mvas(TString dataset, TString fin, HistType htype, Bool_t useTMVAStyle )
17{
18 // set style and remove existing canvas'
19 TMVAGlob::Initialize( useTMVAStyle );
20
21 // switches
22 const Bool_t Save_Images = kTRUE;
23
24 // checks if file with name "fin" is already open, and if not opens one
25 TFile* file = TMVAGlob::OpenFile( fin );
26
27 // define Canvas layout here!
28 const Int_t width = 600; // size of canvas
29
30 // this defines how many canvases we need
31 TCanvas *c = 0;
32
33 // counter variables
34 Int_t countCanvas = 0;
35
36 // search for the right histograms in full list of keys
37 TIter next(file->GetDirectory(dataset.Data())->GetListOfKeys());
38 TKey *key(0);
39 while ((key = (TKey*)next())) {
40
41 if (!TString(key->GetName()).BeginsWith("Method_")) continue;
42 if (!gROOT->GetClass(key->GetClassName())->InheritsFrom("TDirectory")) continue;
43
44 TString methodName;
45 TMVAGlob::GetMethodName(methodName,key);
46
47 TDirectory* mDir = (TDirectory*)key->ReadObj();
48
49 TIter keyIt(mDir->GetListOfKeys());
50 TKey *titkey;
51 while ((titkey = (TKey*)keyIt())) {
52
53 if (!gROOT->GetClass(titkey->GetClassName())->InheritsFrom("TDirectory")) continue;
54
55 TDirectory *titDir = (TDirectory *)titkey->ReadObj();
56 TString methodTitle;
57 TMVAGlob::GetMethodTitle(methodTitle,titDir);
58
59 std::cout << "--- Found directory for method: " << methodName << "::" << methodTitle << std::flush;
60 TString hname = "MVA_" + methodTitle;
61 if (htype == kProbaType ) hname += "_Proba";
62 else if (htype == kRarityType ) hname += "_Rarity";
63 TH1* sig = dynamic_cast<TH1*>(titDir->Get( hname + "_S" ));
64 TH1* bgd = dynamic_cast<TH1*>(titDir->Get( hname + "_B" ));
65
66 if (sig==0 || bgd==0) {
67 if (htype == kMVAType)
68 cout << ":\t mva distribution not available (this is normal for Cut classifier)" << endl;
69 else if(htype == kProbaType)
70 cout << ":\t probability distribution not available" << endl;
71 else if(htype == kRarityType)
72 cout << ":\t rarity distribution not available" << endl;
73 else if(htype == kCompareType)
74 cout << ":\t overtraining check not available" << endl;
75 else cout << endl;
76 continue;
77 }
78
79 cout << " containing " << hname << "_S/_B" << endl;
80 // chop off useless stuff
81 sig->SetTitle( Form("TMVA response for classifier: %s", methodTitle.Data()) );
82 if (htype == kProbaType)
83 sig->SetTitle( Form("TMVA probability for classifier: %s", methodTitle.Data()) );
84 else if (htype == kRarityType)
85 sig->SetTitle( Form("TMVA Rarity for classifier: %s", methodTitle.Data()) );
86 else if (htype == kCompareType)
87 sig->SetTitle( Form("TMVA overtraining check for classifier: %s", methodTitle.Data()) );
88
89 // create new canvas
90 TString ctitle = ((htype == kMVAType) ?
91 Form("TMVA response %s",methodTitle.Data()) :
92 (htype == kProbaType) ?
93 Form("TMVA probability %s",methodTitle.Data()) :
94 (htype == kCompareType) ?
95 Form("TMVA comparison %s",methodTitle.Data()) :
96 Form("TMVA Rarity %s",methodTitle.Data()));
97
98 c = new TCanvas( Form("canvas%d", countCanvas+1), ctitle,
99 countCanvas*50+200, countCanvas*20, width, (Int_t)width*0.78 );
100
101 // set the histogram style
103
104 // normalise both signal and background
105 TMVAGlob::NormalizeHists( sig, bgd );
106
107 // frame limits (choose judicuous x range)
108 Float_t nrms = 10;
109 cout << "--- Mean and RMS (S): " << sig->GetMean() << ", " << sig->GetRMS() << endl;
110 cout << "--- Mean and RMS (B): " << bgd->GetMean() << ", " << bgd->GetRMS() << endl;
111 Float_t xmin = TMath::Max( TMath::Min(sig->GetMean() - nrms*sig->GetRMS(),
112 bgd->GetMean() - nrms*bgd->GetRMS() ),
113 sig->GetXaxis()->GetXmin() );
114 Float_t xmax = TMath::Min( TMath::Max(sig->GetMean() + nrms*sig->GetRMS(),
115 bgd->GetMean() + nrms*bgd->GetRMS() ),
116 sig->GetXaxis()->GetXmax() );
117 Float_t ymin = 0;
118 Float_t maxMult = (htype == kCompareType) ? 1.3 : 1.2;
119 Float_t ymax = TMath::Max( sig->GetMaximum(), bgd->GetMaximum() )*maxMult;
120
121 // build a frame
122 Int_t nb = 500;
123 TString hFrameName(TString("frame") + methodTitle);
124 TObject *o = gROOT->FindObject(hFrameName);
125 if(o) delete o;
126 TH2F* frame = new TH2F( hFrameName, sig->GetTitle(),
127 nb, xmin, xmax, nb, ymin, ymax );
128 frame->GetXaxis()->SetTitle( methodTitle + ((htype == kMVAType || htype == kCompareType) ? " response" : "") );
129 if (htype == kProbaType ) frame->GetXaxis()->SetTitle( "Signal probability" );
130 else if (htype == kRarityType ) frame->GetXaxis()->SetTitle( "Signal rarity" );
131 frame->GetYaxis()->SetTitle("(1/N) dN^{ }/^{ }dx");
133
134 // eventually: draw the frame
135 frame->Draw();
136
137 c->GetPad(0)->SetLeftMargin( 0.105 );
138 frame->GetYaxis()->SetTitleOffset( 1.2 );
139
140 // Draw legend
141 TLegend *legend= new TLegend( c->GetLeftMargin(), 1 - c->GetTopMargin() - 0.12,
142 c->GetLeftMargin() + (htype == kCompareType ? 0.40 : 0.3), 1 - c->GetTopMargin() );
143 legend->SetFillStyle( 1 );
144 legend->AddEntry(sig,TString("Signal") + ((htype == kCompareType) ? " (test sample)" : ""), "F");
145 legend->AddEntry(bgd,TString("Background") + ((htype == kCompareType) ? " (test sample)" : ""), "F");
146 legend->SetBorderSize(1);
147 legend->SetMargin( (htype == kCompareType ? 0.2 : 0.3) );
148 legend->Draw("same");
149
150 // overlay signal and background histograms
151 sig->Draw("samehist");
152 bgd->Draw("samehist");
153
154 if (htype == kCompareType) {
155 // if overtraining check, load additional histograms
156 TH1* sigOv = 0;
157 TH1* bgdOv = 0;
158
159 TString ovname = hname += "_Train";
160 sigOv = dynamic_cast<TH1*>(titDir->Get( ovname + "_S" ));
161 bgdOv = dynamic_cast<TH1*>(titDir->Get( ovname + "_B" ));
162
163 if (sigOv == 0 || bgdOv == 0) {
164 cout << "+++ Problem in \"mvas.C\": overtraining check histograms do not exist" << endl;
165 }
166 else {
167 cout << "--- Found comparison histograms for overtraining check" << endl;
168
169 TLegend *legend2= new TLegend( 1 - c->GetRightMargin() - 0.42, 1 - c->GetTopMargin() - 0.12,
170 1 - c->GetRightMargin(), 1 - c->GetTopMargin() );
171 legend2->SetFillStyle( 1 );
172 legend2->SetBorderSize(1);
173 legend2->AddEntry(sigOv,"Signal (training sample)","P");
174 legend2->AddEntry(bgdOv,"Background (training sample)","P");
175 legend2->SetMargin( 0.1 );
176 legend2->Draw("same");
177 }
178 // normalise both signal and background
179 TMVAGlob::NormalizeHists( sigOv, bgdOv );
180
181 Int_t col = sig->GetLineColor();
182 sigOv->SetMarkerColor( col );
183 sigOv->SetMarkerSize( 0.7 );
184 sigOv->SetMarkerStyle( 20 );
185 sigOv->SetLineWidth( 1 );
186 sigOv->SetLineColor( col );
187 sigOv->Draw("e1same");
188
189 col = bgd->GetLineColor();
190 bgdOv->SetMarkerColor( col );
191 bgdOv->SetMarkerSize( 0.7 );
192 bgdOv->SetMarkerStyle( 20 );
193 bgdOv->SetLineWidth( 1 );
194 bgdOv->SetLineColor( col );
195 bgdOv->Draw("e1same");
196
197 ymax = TMath::Max( ymax, float(TMath::Max( sigOv->GetMaximum(), bgdOv->GetMaximum() )*maxMult ));
198 frame->GetYaxis()->SetLimits( 0, ymax );
199
200 // for better visibility, plot thinner lines
201 sig->SetLineWidth( 1 );
202 bgd->SetLineWidth( 1 );
203
204 // perform K-S test
205 cout << "--- Perform Kolmogorov-Smirnov tests" << endl;
206 Double_t kolS = sig->KolmogorovTest( sigOv, "X" );
207 Double_t kolB = bgd->KolmogorovTest( bgdOv, "X" );
208 cout << "--- Goodness of signal (background) consistency: " << kolS << " (" << kolB << ")" << endl;
209
210 TString probatext = Form( "Kolmogorov-Smirnov test: signal (background) probability = %5.3g (%5.3g)", kolS, kolB );
211 TText* tt = new TText( 0.12, 0.74, probatext );
212 tt->SetNDC(); tt->SetTextSize( 0.032 ); tt->AppendPad();
213 }
214
215 // redraw axes
216 frame->Draw("sameaxis");
217
218 // text for overflows
219 Int_t nbin = sig->GetNbinsX();
220 Double_t dxu = sig->GetBinWidth(0);
221 Double_t dxo = sig->GetBinWidth(nbin+1);
222 TString uoflow = Form( "U/O-flow (S,B): (%.1f, %.1f)%% / (%.1f, %.1f)%%",
223 sig->GetBinContent(0)*dxu*100, bgd->GetBinContent(0)*dxu*100,
224 sig->GetBinContent(nbin+1)*dxo*100, bgd->GetBinContent(nbin+1)*dxo*100 );
225 TText* t = new TText( 0.975, 0.115, uoflow );
226 t->SetNDC();
227 t->SetTextSize( 0.030 );
228 t->SetTextAngle( 90 );
229 t->AppendPad();
230
231 // update canvas
232 c->Update();
233
234 // save canvas to file
235
236 TMVAGlob::plot_logo(1.058);
237 if (Save_Images) {
238 if (htype == kMVAType) TMVAGlob::imgconv( c, Form("%s/plots/mva_%s",dataset.Data(), methodTitle.Data()) );
239 else if (htype == kProbaType) TMVAGlob::imgconv( c, Form("%s/plots/proba_%s",dataset.Data(), methodTitle.Data()) );
240 else if (htype == kCompareType) TMVAGlob::imgconv( c, Form("%s/plots/overtrain_%s",dataset.Data(), methodTitle.Data()) );
241 else TMVAGlob::imgconv( c, Form("%s/plots/rarity_%s",dataset.Data(), methodTitle.Data()) );
242 }
243 countCanvas++;
244
245 }
246 cout << "";
247 }
248}
249
#define c(i)
Definition: RSha256.hxx:101
bool Bool_t
Definition: RtypesCore.h:63
int Int_t
Definition: RtypesCore.h:45
float Float_t
Definition: RtypesCore.h:57
double Double_t
Definition: RtypesCore.h:59
const Bool_t kTRUE
Definition: RtypesCore.h:100
Option_t Option_t width
float xmin
Definition: THbookFile.cxx:95
float ymin
Definition: THbookFile.cxx:95
float xmax
Definition: THbookFile.cxx:95
float ymax
Definition: THbookFile.cxx:95
#define gROOT
Definition: TROOT.h:404
char * Form(const char *fmt,...)
Formats a string in a circular formatting buffer.
Definition: TString.cxx:2447
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
Definition: TAttAxis.cxx:301
virtual void SetFillStyle(Style_t fstyle)
Set the fill area style.
Definition: TAttFill.h:39
virtual Color_t GetLineColor() const
Return the line color.
Definition: TAttLine.h:33
virtual void SetLineWidth(Width_t lwidth)
Set the line width.
Definition: TAttLine.h:43
virtual void SetLineColor(Color_t lcolor)
Set the line color.
Definition: TAttLine.h:40
virtual void SetMarkerColor(Color_t mcolor=1)
Set the marker color.
Definition: TAttMarker.h:38
virtual void SetMarkerStyle(Style_t mstyle=1)
Set the marker style.
Definition: TAttMarker.h:40
virtual void SetMarkerSize(Size_t msize=1)
Set the marker size.
Definition: TAttMarker.h:41
virtual void SetTextAngle(Float_t tangle=0)
Set the text angle.
Definition: TAttText.h:43
virtual void SetTextSize(Float_t tsize=1)
Set the text size.
Definition: TAttText.h:47
Double_t GetXmax() const
Definition: TAxis.h:135
virtual void SetLimits(Double_t xmin, Double_t xmax)
Definition: TAxis.h:155
Double_t GetXmin() const
Definition: TAxis.h:134
The Canvas class.
Definition: TCanvas.h:23
Describe directory structure in memory.
Definition: TDirectory.h:45
virtual TObject * Get(const char *namecycle)
Return pointer to object identified by namecycle.
Definition: TDirectory.cxx:814
virtual TList * GetListOfKeys() const
Definition: TDirectory.h:214
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:54
TH1 is the base class of all histogram classes in ROOT.
Definition: TH1.h:58
void SetTitle(const char *title) override
See GetStatOverflows for more information.
Definition: TH1.cxx:6710
virtual Double_t GetMean(Int_t axis=1) const
For axis = 1,2 or 3 returns the mean value of the histogram along X,Y or Z axis.
Definition: TH1.cxx:7456
TAxis * GetXaxis()
Get the behaviour adopted by the object about the statoverflows. See EStatOverflows for more informat...
Definition: TH1.h:319
virtual Double_t GetMaximum(Double_t maxval=FLT_MAX) const
Return maximum value smaller than maxval of bins in the range, unless the value has been overridden b...
Definition: TH1.cxx:8422
virtual Int_t GetNbinsX() const
Definition: TH1.h:295
TAxis * GetYaxis()
Definition: TH1.h:320
void Draw(Option_t *option="") override
Draw this histogram with options.
Definition: TH1.cxx:3070
Double_t GetRMS(Int_t axis=1) const
Definition: TH1.h:314
virtual Double_t GetBinContent(Int_t bin) const
Return content of bin number bin.
Definition: TH1.cxx:5035
virtual Double_t GetBinWidth(Int_t bin) const
Return bin width for 1D histogram.
Definition: TH1.cxx:9040
virtual Double_t KolmogorovTest(const TH1 *h2, Option_t *option="") const
Statistical test of compatibility in shape between this histogram and h2, using Kolmogorov test.
Definition: TH1.cxx:8097
2-D histogram with a float per channel (see TH1 documentation)}
Definition: TH2.h:257
Book space in a file, create I/O buffers, to fill them, (un)compress them.
Definition: TKey.h:28
virtual const char * GetClassName() const
Definition: TKey.h:75
virtual TObject * ReadObj()
To read a TObject* from the file.
Definition: TKey.cxx:750
This class displays a legend box (TPaveText) containing several legend entries.
Definition: TLegend.h:23
TLegendEntry * AddEntry(const TObject *obj, const char *label="", Option_t *option="lpf")
Add a new entry to this legend.
Definition: TLegend.cxx:331
void Draw(Option_t *option="") override
Draw this legend with its current attributes.
Definition: TLegend.cxx:424
void SetMargin(Float_t margin)
Definition: TLegend.h:69
@ kRarityType
Definition: Types.h:66
@ kCompareType
Definition: Types.h:66
@ kProbaType
Definition: Types.h:66
@ kMVAType
Definition: Types.h:66
virtual void SetTitle(const char *title="")
Set the title of the TNamed.
Definition: TNamed.cxx:164
const char * GetTitle() const override
Returns title of object.
Definition: TNamed.h:48
Mother of all ROOT objects.
Definition: TObject.h:37
virtual void AppendPad(Option_t *option="")
Append graphics object to current pad.
Definition: TObject.cxx:107
virtual void SetBorderSize(Int_t bordersize=4)
Definition: TPave.h:73
Basic string class.
Definition: TString.h:136
const char * Data() const
Definition: TString.h:369
Bool_t BeginsWith(const char *s, ECaseCompare cmp=kExact) const
Definition: TString.h:615
Base class for several text objects.
Definition: TText.h:22
virtual void SetNDC(Bool_t isNDC=kTRUE)
Set NDC mode on if isNDC = kTRUE, off otherwise.
Definition: TText.cxx:813
void Initialize(Bool_t useTMVAStyle=kTRUE)
Definition: tmvaglob.cxx:176
void NormalizeHists(TH1 *sig, TH1 *bkg=0)
Definition: tmvaglob.cxx:324
void GetMethodTitle(TString &name, TKey *ikey)
Definition: tmvaglob.cxx:348
void plot_logo(Float_t v_scale=1.0, Float_t skew=1.0)
Definition: tmvaglob.cxx:270
TFile * OpenFile(const TString &fin)
Definition: tmvaglob.cxx:192
void SetFrameStyle(TH1 *frame, Float_t scale=1.0)
Definition: tmvaglob.cxx:77
void GetMethodName(TString &name, TKey *mkey)
Definition: tmvaglob.cxx:342
void SetSignalAndBackgroundStyle(TH1 *sig, TH1 *bkg, TH1 *all=0)
Definition: tmvaglob.cxx:8
void imgconv(TCanvas *c, const TString &fname)
Definition: tmvaglob.cxx:212
void mvas(TString dataset, TString fin="TMVA.root", HistType htype=kMVAType, Bool_t useTMVAStyle=kTRUE)
Short_t Max(Short_t a, Short_t b)
Returns the largest of a and b.
Definition: TMathBase.h:250
Short_t Min(Short_t a, Short_t b)
Returns the smallest of a and b.
Definition: TMathBase.h:198
Definition: file.py:1
auto * tt
Definition: textangle.C:16