source: trunk/Mars/mjtrain/MJTrainSeparation.cc@ 19011

Last change on this file since 19011 was 19007, checked in by tbretz, 7 years ago
Replaced some unicode characters by proper string representations.
File size: 31.3 KB
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1/* ======================================================================== *\
2!
3! *
4! * This file is part of MARS, the MAGIC Analysis and Reconstruction
5! * Software. It is distributed to you in the hope that it can be a useful
6! * and timesaving tool in analysing Data of imaging Cerenkov telescopes.
7! * It is distributed WITHOUT ANY WARRANTY.
8! *
9! * Permission to use, copy, modify and distribute this software and its
10! * documentation for any purpose is hereby granted without fee,
11! * provided that the above copyright notice appear in all copies and
12! * that both that copyright notice and this permission notice appear
13! * in supporting documentation. It is provided "as is" without express
14! * or implied warranty.
15! *
16!
17!
18! Author(s): Thomas Bretz 11/2005 <mailto:tbretz@astro.uni-wuerzburg.de>
19!
20! Copyright: MAGIC Software Development, 2006
21!
22!
23\* ======================================================================== */
24
25/////////////////////////////////////////////////////////////////////////////
26//
27// MJTrainSeparation
28//
29////////////////////////////////////////////////////////////////////////////
30#include "MJTrainSeparation.h"
31
32#include <TF1.h>
33#include <TH2.h>
34#include <TChain.h>
35#include <TGraph.h>
36#include <TMarker.h>
37#include <TCanvas.h>
38#include <TStopwatch.h>
39#include <TVirtualPad.h>
40
41#include "MHMatrix.h"
42
43#include "MLog.h"
44#include "MLogManip.h"
45
46// tools
47#include "MMath.h"
48#include "MDataSet.h"
49#include "MTFillMatrix.h"
50#include "MStatusDisplay.h"
51
52// eventloop
53#include "MParList.h"
54#include "MTaskList.h"
55#include "MEvtLoop.h"
56
57// tasks
58#include "MReadMarsFile.h"
59#include "MReadReports.h"
60#include "MContinue.h"
61#include "MFillH.h"
62#include "MSrcPosRndm.h"
63#include "MHillasCalc.h"
64#include "MRanForestCalc.h"
65#include "MParameterCalc.h"
66
67// container
68#include "MMcEvt.hxx"
69#include "MParameters.h"
70
71// histograms
72#include "MBinning.h"
73#include "MH3.h"
74#include "MHHadronness.h"
75
76// filter
77#include "MF.h"
78#include "MFEventSelector.h"
79#include "MFilterList.h"
80
81// wobble
82#include "MPointingPos.h"
83#include "MPointingDevCalc.h"
84#include "../mastro/MObservatory.h"
85#include "MSrcPosCalc.h"
86#include "MSrcPosCorrect.h"
87#include "../mimage/MHillasCalc.h"
88
89
90ClassImp(MJTrainSeparation);
91
92using namespace std;
93
94void MJTrainSeparation::DisplayResult(MH3 &h31, MH3 &h32, Float_t ontime)
95{
96 TH2D &g = (TH2D&)h32.GetHist();
97 TH2D &h = (TH2D&)h31.GetHist();
98
99 h.SetMarkerColor(kRed);
100 g.SetMarkerColor(kBlue);
101
102 TH2D res1(g);
103 TH2D res2(g);
104
105 h.SetTitle("Hadronness-Distribution vs. Size");
106 res1.SetTitle("Significance Li/Ma");
107 res1.SetXTitle("Size [phe]");
108 res1.SetYTitle("Hadronness");
109 res2.SetTitle("Significance-Distribution");
110 res2.SetXTitle("Size-Cut [phe]");
111 res2.SetYTitle("Hadronness-Cut");
112 res1.SetContour(50);
113 res2.SetContour(50);
114
115 const Int_t nx = h.GetNbinsX();
116 const Int_t ny = h.GetNbinsY();
117
118 gROOT->SetSelectedPad(NULL);
119
120
121 Double_t Stot = 0;
122 Double_t Btot = 0;
123
124 Double_t max2 = -1;
125
126 TGraph gr1;
127 TGraph gr2;
128 for (int x=nx-1; x>=0; x--)
129 {
130 TH1 *hx = h.ProjectionY("H_py", x+1, x+1);
131 TH1 *gx = g.ProjectionY("G_py", x+1, x+1);
132
133 Double_t S = 0;
134 Double_t B = 0;
135
136 Double_t max1 = -1;
137 Int_t maxy1 = 0;
138 Int_t maxy2 = 0;
139 for (int y=ny-1; y>=0; y--)
140 {
141 const Float_t s = gx->Integral(1, y+1);
142 const Float_t b = hx->Integral(1, y+1);
143 const Float_t sig1 = MMath::SignificanceLiMa(s+b, b);
144 const Float_t sig2 = MMath::SignificanceLiMa(s+Stot+b+Btot, b+Btot)*TMath::Log10(s+Stot+1);
145 if (sig1>max1)
146 {
147 maxy1 = y;
148 max1 = sig1;
149 }
150 if (sig2>max2)
151 {
152 maxy2 = y;
153 max2 = sig2;
154
155 S=s;
156 B=b;
157 }
158
159 res1.SetBinContent(x+1, y+1, sig1);
160 }
161
162 Stot += S;
163 Btot += B;
164
165 gr1.SetPoint(x, h.GetXaxis()->GetBinCenter(x+1), h.GetYaxis()->GetBinCenter(maxy1+1));
166 gr2.SetPoint(x, h.GetXaxis()->GetBinCenter(x+1), h.GetYaxis()->GetBinCenter(maxy2+1));
167
168 delete hx;
169 delete gx;
170 }
171
172 //cout << "--> " << MMath::SignificanceLiMa(Stot+Btot, Btot) << " ";
173 //cout << Stot << " " << Btot << endl;
174
175
176 Int_t mx1=0;
177 Int_t my1=0;
178 Int_t mx2=0;
179 Int_t my2=0;
180 Int_t s1=0;
181 Int_t b1=0;
182 Int_t s2=0;
183 Int_t b2=0;
184 Double_t sig1=-1;
185 Double_t sig2=-1;
186 for (int x=0; x<nx; x++)
187 {
188 TH1 *hx = h.ProjectionY("H_py", x+1);
189 TH1 *gx = g.ProjectionY("G_py", x+1);
190 for (int y=0; y<ny; y++)
191 {
192 const Float_t s = gx->Integral(1, y+1);
193 const Float_t b = hx->Integral(1, y+1);
194 const Float_t sig = MMath::SignificanceLiMa(s+b, b);
195 res2.SetBinContent(x+1, y+1, sig);
196
197 // Search for top-rightmost maximum
198 if (sig>=sig1)
199 {
200 mx1=x+1;
201 my1=y+1;
202 s1 = TMath::Nint(s);
203 b1 = TMath::Nint(b);
204 sig1=sig;
205 }
206 if (TMath::Log10(s)*sig>=sig2)
207 {
208 mx2=x+1;
209 my2=y+1;
210 s2 = TMath::Nint(s);
211 b2 = TMath::Nint(b);
212 sig2=TMath::Log10(s)*sig;
213 }
214 }
215 delete hx;
216 delete gx;
217 }
218
219 TGraph gr3;
220 TGraph gr4;
221 gr4.SetTitle("Significance Li/Ma vs. Hadronness-cut");
222
223 TH1 *hx = h.ProjectionY("H_py");
224 TH1 *gx = g.ProjectionY("G_py");
225 for (int y=0; y<ny; y++)
226 {
227 const Float_t s = gx->Integral(1, y+1);
228 const Float_t b = hx->Integral(1, y+1);
229 const Float_t sg1 = MMath::SignificanceLiMa(s+b, b);
230 const Float_t sg2 = s<1 ? 0 : MMath::SignificanceLiMa(s+b, b)*TMath::Log10(s);
231
232 gr3.SetPoint(y, h.GetYaxis()->GetBinLowEdge(y+2), sg1);
233 gr4.SetPoint(y, h.GetYaxis()->GetBinLowEdge(y+2), sg2);
234 }
235 delete hx;
236 delete gx;
237
238 if (fDisplay)
239 {
240 TCanvas &c = fDisplay->AddTab("OptCut");
241 c.SetBorderMode(0);
242 c.Divide(2,2);
243
244 c.cd(1);
245 gPad->SetBorderMode(0);
246 gPad->SetFrameBorderMode(0);
247 gPad->SetLogx();
248 gPad->SetGridx();
249 gPad->SetGridy();
250 h.DrawCopy();
251 g.DrawCopy("same");
252 gr1.SetMarkerStyle(kFullDotMedium);
253 gr1.DrawClone("LP")->SetBit(kCanDelete);
254 gr2.SetLineColor(kBlue);
255 gr2.SetMarkerStyle(kFullDotMedium);
256 gr2.DrawClone("LP")->SetBit(kCanDelete);
257
258 c.cd(3);
259 gPad->SetBorderMode(0);
260 gPad->SetFrameBorderMode(0);
261 gPad->SetGridx();
262 gPad->SetGridy();
263 gr4.SetMinimum(0);
264 gr4.SetMarkerStyle(kFullDotMedium);
265 gr4.DrawClone("ALP")->SetBit(kCanDelete);
266 gr3.SetLineColor(kBlue);
267 gr3.SetMarkerStyle(kFullDotMedium);
268 gr3.DrawClone("LP")->SetBit(kCanDelete);
269
270 c.cd(2);
271 gPad->SetBorderMode(0);
272 gPad->SetFrameBorderMode(0);
273 gPad->SetLogx();
274 gPad->SetGridx();
275 gPad->SetGridy();
276 gPad->AddExec("color", "gStyle->SetPalette(1, 0);");
277 res1.SetMaximum(7);
278 res1.DrawCopy("colz");
279
280 c.cd(4);
281 gPad->SetBorderMode(0);
282 gPad->SetFrameBorderMode(0);
283 gPad->SetLogx();
284 gPad->SetGridx();
285 gPad->SetGridy();
286 gPad->AddExec("color", "gStyle->SetPalette(1, 0);");
287 res2.SetMaximum(res2.GetMaximum()*1.05);
288 res2.DrawCopy("colz");
289
290 // Int_t mx, my, mz;
291 // res2.GetMaximumBin(mx, my, mz);
292
293 TMarker m;
294 m.SetMarkerStyle(kStar);
295 m.DrawMarker(res2.GetXaxis()->GetBinCenter(mx1), res2.GetYaxis()->GetBinCenter(my1));
296 m.SetMarkerStyle(kPlus);
297 m.DrawMarker(res2.GetXaxis()->GetBinCenter(mx2), res2.GetYaxis()->GetBinCenter(my2));
298 }
299
300 if (ontime>0)
301 *fLog << all << "Observation Time: " << TMath::Nint(ontime/60) << "min" << endl;
302 *fLog << "Maximum Significance: " << Form("%.1f", sig1);
303 if (ontime>0)
304 *fLog << Form(" [%.1f/sqrt(h)]", sig1/TMath::Sqrt(ontime/3600));
305 *fLog << endl;
306
307 *fLog << "Significance: S=" << Form("%.1f", sig1) << " E=" << s1 << " B=" << b1 << " h<";
308 *fLog << Form("%.2f", res2.GetYaxis()->GetBinCenter(my1)) << " s>";
309 *fLog << Form("%3d", TMath::Nint(res2.GetXaxis()->GetBinCenter(mx1))) << endl;
310 *fLog << "Significance*LogE: S=" << Form("%.1f", sig2/TMath::Log10(s2)) << " E=" << s2 << " B=" << b2 << " h<";
311 *fLog << Form("%.2f", res2.GetYaxis()->GetBinCenter(my2)) << " s>";
312 *fLog << Form("%3d", TMath::Nint(res2.GetXaxis()->GetBinCenter(mx2))) << endl;
313 *fLog << endl;
314}
315
316/*
317Bool_t MJSpectrum::InitWeighting(const MDataSet &set, MMcSpectrumWeight &w) const
318{
319 fLog->Separator("Initialize energy weighting");
320
321 if (!CheckEnv(w))
322 {
323 *fLog << err << "ERROR - Reading resources for MMcSpectrumWeight failed." << endl;
324 return kFALSE;
325 }
326
327 TChain chain("RunHeaders");
328 set.AddFilesOn(chain);
329
330 MMcCorsikaRunHeader *h=0;
331 chain.SetBranchAddress("MMcCorsikaRunHeader.", &h);
332 chain.GetEntry(1);
333
334 if (!h)
335 {
336 *fLog << err << "ERROR - Couldn't read MMcCorsikaRunHeader from DataSet." << endl;
337 return kFALSE;
338 }
339
340 if (!w.Set(*h))
341 {
342 *fLog << err << "ERROR - Initializing MMcSpectrumWeight failed." << endl;
343 return kFALSE;
344 }
345
346 w.Print();
347 return kTRUE;
348}
349
350Bool_t MJSpectrum::ReadOrigMCDistribution(const MDataSet &set, TH1 &h, MMcSpectrumWeight &weight) const
351{
352 // Some debug output
353 fLog->Separator("Compiling original MC distribution");
354
355 weight.SetNameMcEvt("MMcEvtBasic");
356 const TString w(weight.GetFormulaWeights());
357 weight.SetNameMcEvt();
358
359 *fLog << inf << "Using weights: " << w << endl;
360 *fLog << "Please stand by, this may take a while..." << flush;
361
362 if (fDisplay)
363 fDisplay->SetStatusLine1("Compiling MC distribution...");
364
365 // Create chain
366 TChain chain("OriginalMC");
367 set.AddFilesOn(chain);
368
369 // Prepare histogram
370 h.Reset();
371
372 // Fill histogram from chain
373 h.SetDirectory(gROOT);
374 if (h.InheritsFrom(TH2::Class()))
375 {
376 h.SetNameTitle("ThetaEMC", "Event-Distribution vs Theta and Energy for MC (produced)");
377 h.SetXTitle("\\Theta [\\circ]");
378 h.SetYTitle("E [GeV]");
379 h.SetZTitle("Counts");
380 chain.Draw("MMcEvtBasic.fEnergy:MMcEvtBasic.fTelescopeTheta*TMath::RadToDeg()>>ThetaEMC", w, "goff");
381 }
382 else
383 {
384 h.SetNameTitle("ThetaMC", "Event-Distribution vs Theta for MC (produced)");
385 h.SetXTitle("\\Theta [\\circ]");
386 h.SetYTitle("Counts");
387 chain.Draw("MMcEvtBasic.fTelescopeTheta*TMath::RadToDeg()>>ThetaMC", w, "goff");
388 }
389 h.SetDirectory(0);
390
391 *fLog << "done." << endl;
392 if (fDisplay)
393 fDisplay->SetStatusLine2("done.");
394
395 if (h.GetEntries()>0)
396 return kTRUE;
397
398 *fLog << err << "ERROR - Histogram with original MC distribution empty..." << endl;
399
400 return h.GetEntries()>0;
401}
402*/
403
404Bool_t MJTrainSeparation::GetEventsProduced(MDataSet &set, Double_t &num, Double_t &min, Double_t &max) const
405{
406 TChain chain("OriginalMC");
407 if (!set.AddFilesOn(chain))
408 return kFALSE;
409
410 min = chain.GetMinimum("MMcEvtBasic.fEnergy");
411 max = chain.GetMaximum("MMcEvtBasic.fEnergy");
412
413 num = chain.GetEntries();
414
415 if (num<100)
416 *fLog << err << "ERROR - Less than 100 entries in OriginalMC-Tree of MC-Train-Data found." << endl;
417
418 return num>=100;
419}
420
421Double_t MJTrainSeparation::GetDataRate(MDataSet &set, Double_t &num) const
422{
423 TChain chain1("Events");
424 if (!set.AddFilesOff(chain1))
425 return kFALSE;
426
427 num = chain1.GetEntries();
428 if (num<100)
429 {
430 *fLog << err << "ERROR - Less than 100 entries in Events-Tree of Train-Data found." << endl;
431 return -1;
432 }
433
434 TChain chain("EffectiveOnTime");
435 if (!set.AddFilesOff(chain))
436 return kFALSE;
437
438 chain.Draw("MEffectiveOnTime.fVal", "MEffectiveOnTime.fVal", "goff");
439
440 TH1 *h = dynamic_cast<TH1*>(gROOT->FindObject("htemp"));
441 if (!h)
442 {
443 *fLog << err << "ERROR - Weird things are happening (htemp not found)!" << endl;
444 return -1;
445 }
446
447 const Double_t ontime = h->Integral();
448 delete h;
449
450 if (ontime<1)
451 {
452 *fLog << err << "ERROR - Less than 1s of effective observation time found in Train-Data." << endl;
453 return -1;
454 }
455
456 return num/ontime;
457}
458
459Double_t MJTrainSeparation::GetNumMC(MDataSet &set) const
460{
461 TChain chain1("Events");
462 if (!set.AddFilesOn(chain1))
463 return kFALSE;
464
465 const Double_t num = chain1.GetEntries();
466 if (num<100)
467 {
468 *fLog << err << "ERROR - Less than 100 entries in Events-Tree of Train-Data found." << endl;
469 return -1;
470 }
471
472 return num;
473}
474
475Float_t MJTrainSeparation::AutoTrain(MDataSet &set, Type_t typon, Type_t typof, Float_t flux)
476{
477 Double_t num, min, max;
478 if (!GetEventsProduced(set, num, min, max))
479 return -1;
480
481 *fLog << inf << "Using build-in radius of 300m to calculate collection area!" << endl;
482
483 // Target spectrum
484 TF1 flx("Flux", "[0]/1000*(x/1000)^(-2.6)", min, max);
485 flx.SetParameter(0, flux);
486
487 // Number n0 of events this spectrum would produce per s and m^2
488 const Double_t n0 = flx.Integral(min, max); //[#]
489
490 // Area produced in MC
491 const Double_t A = TMath::Pi()*300*300; //[m²]
492
493 // Rate R of events this spectrum would produce per s
494 const Double_t R = n0*A; //[Hz]
495
496 *fLog << "Source Spectrum: " << flux << " * (E/TeV)^(-2.6) * TeV*m^2*s" << endl;
497
498 *fLog << "Gamma rate from the source inside the MC production area: " << R << "Hz" << endl;
499
500 // Number N of events produced (in trainings sample)
501 const Double_t N = num; //[#]
502
503 *fLog << "Events produced by MC inside the production area: " << TMath::Nint(num) << endl;
504
505 // This correponds to an observation time T [s]
506 const Double_t T = N/R; //[s]
507
508 *fLog << "Total time produced by the Monte Carlo: " << T << "s" << endl;
509
510 // With an average data rate after star of
511 Double_t data=0;
512 const Double_t r = GetDataRate(set, data); //[Hz]
513 Double_t ontime = data/r;
514
515 *fLog << "Events measured per second effective on time: " << r << "Hz" << endl;
516 *fLog << "Total effective on time: " << ontime << "s" << endl;
517
518 const Double_t ratio = T/ontime;
519 *fLog << "Ratio of Monte Carlo to data observation time: " << ratio << endl;
520
521 // 3570.5/43440.2 = 0.082
522
523
524 // this yields a number of n events to be read for training
525 const Double_t n = r*T; //[#]
526
527 *fLog << "Events to be read from the data sample: " << TMath::Nint(n) << endl;
528 *fLog << "Events available in data sample: " << data << endl;
529
530 if (r<0)
531 return -1;
532
533 Double_t nummc = GetNumMC(set);
534
535 *fLog << "Events available in MC sample: " << nummc << endl;
536
537// *fLog << "MC read probability: " << data/n << endl;
538
539 // more data requested than available => Scale down num MC events
540 Double_t on, off;
541 if (data<n)
542 {
543 on = TMath::Nint(nummc*data/n);
544 off = TMath::Nint(data);
545 *fLog << warn;
546 *fLog << "Not enough data events available... scaling MC to data by " << data/n << endl;
547 *fLog << inf;
548 }
549 else
550 {
551 on = TMath::Nint(nummc);
552 off = TMath::Nint(n);
553 }
554
555 if (fNum[typon]>0 && fNum[typon]<on)
556 {
557 fNum[typof] = TMath::Nint(off*fNum[typon]/on);
558 ontime *= fNum[typon]/on;
559 *fLog << warn << "Less MC events requested... scaling data to MC by " << fNum[typon]/on << endl;
560 }
561 else
562 {
563 fNum[typon] = TMath::Nint(on);
564 fNum[typof] = TMath::Nint(off);
565 }
566
567 *fLog << inf;
568 *fLog << "Target number of MC events: " << fNum[typon] << endl;
569 *fLog << "Target number of data events: " << fNum[typof] << endl;
570
571 /*
572 An event rate dependent selection?
573 ----------------------------------
574 Total average data rate: R
575 Goal number of events: N
576 Number of data events: N0
577 Rate assigned to single evt: r
578
579 Selection probability: N/N0 * r/R
580
581 f := N/N0 * r
582
583 MF f("f * MEventRate.fRate < rand");
584 */
585
586 return ontime;
587}
588
589Bool_t MJTrainSeparation::Train(const char *out)
590{
591 if (fDisplay)
592 fDisplay->SetTitle(out);
593
594 if (!fDataSetTrain.IsValid())
595 {
596 *fLog << err << "ERROR - DataSet for training invalid!" << endl;
597 return kFALSE;
598 }
599 if (!fDataSetTest.IsValid())
600 {
601 *fLog << err << "ERROR - DataSet for testing invalid!" << endl;
602 return kFALSE;
603 }
604
605 if (fDataSetTrain.IsWobbleMode()!=fDataSetTest.IsWobbleMode())
606 {
607 *fLog << err << "ERROR - Train- and Test-DataSet have different observation modes!" << endl;
608 return kFALSE;
609 }
610
611 if (!HasWritePermission(out))
612 return kFALSE;
613
614 TStopwatch clock;
615 clock.Start();
616
617 // ----------------------- Auto Train? ----------------------
618
619 Float_t ontime = -1;
620 if (fAutoTrain)
621 {
622 fLog->Separator("Auto-Training -- Train-Data");
623 if (AutoTrain(fDataSetTrain, kTrainOn, kTrainOff, fFluxTrain)<0)
624 return kFALSE;
625 fLog->Separator("Auto-Training -- Test-Data");
626 ontime = AutoTrain(fDataSetTest, kTestOn, kTestOff, fFluxTest);
627 if (ontime<0)
628 return kFALSE;
629 }
630
631 // --------------------- Setup files --------------------
632 MReadReports read1;//("Events");
633 MReadMarsFile read2("Events");
634 MReadMarsFile read3("Events");
635 MReadReports read4;//("Events");
636 //read1.DisableAutoScheme();
637 read2.DisableAutoScheme();
638 read3.DisableAutoScheme();
639 //read4.DisableAutoScheme();
640
641 read1.AddTree("Events", "MTime.", MReadReports::kMaster);
642 read4.AddTree("Events", "MTime.", MReadReports::kMaster);
643 read1.AddTree("Drive", MReadReports::kRequired);
644 read4.AddTree("Drive", MReadReports::kRequired);
645 read1.AddTree("Starguider", MReadReports::kRequired);
646 read4.AddTree("Starguider", MReadReports::kRequired);
647
648 // Setup four reading tasks with the on- and off-data of the two datasets
649 if (!fDataSetTrain.AddFilesOn(read1))
650 return kFALSE;
651 const Bool_t setrc1 = fDataSetTrain.IsWobbleMode() ?
652 fDataSetTrain.AddFilesOn(read3) : fDataSetTrain.AddFilesOff(read3);
653 const Bool_t setrc2 = fDataSetTest.IsWobbleMode() ?
654 fDataSetTest.AddFilesOn(read2) : fDataSetTest.AddFilesOff(read2);
655 if (!setrc1 || !setrc2)
656 return kFALSE;
657 if (!fDataSetTest.AddFilesOn(read4))
658 return kFALSE;
659
660 // ----------------------- Setup RF Matrix ----------------------
661 MHMatrix train("Train");
662 train.AddColumns(fRules);
663 if (fEnableWeights[kTrainOn] || fEnableWeights[kTrainOff])
664 train.AddColumn("MWeight.fVal");
665 train.AddColumn("MHadronness.fVal");
666
667 // ----------------------- Fill Matrix RF ----------------------
668
669 // Setup the hadronness container identifying gammas and off-data
670 // and setup a container for the weights
671 MParameterD had("MHadronness");
672 MParameterD wgt("MWeight");
673
674 // Add them to the parameter list
675 MParList plistx;
676 plistx.AddToList(this); // take care of fDisplay!
677 plistx.AddToList(&had);
678 plistx.AddToList(&wgt);
679
680 // Setup the tool class to fill the matrix
681 MTFillMatrix fill;
682 fill.SetLogStream(fLog);
683 fill.SetDisplay(fDisplay);
684 fill.AddPreCuts(fPreCuts);
685 fill.AddPreCuts(fTrainCuts);
686
687 // Set classifier for gammas
688 had.SetVal(0);
689 wgt.SetVal(1);
690
691 // How to get source position from off- and on-data?
692 MPointingPos source("MSourcePos");
693 MObservatory obs;
694 MSrcPosCalc scalc;
695 MSrcPosCorrect scor;
696 MHillasCalc hcalcw;
697 MHillasCalc hcalcw2;
698 MPointingDevCalc devcalc;
699 scalc.SetMode(MSrcPosCalc::kDefault); // kWobble for off-source
700 hcalcw.SetFlags(MHillasCalc::kCalcHillasSrc);
701 hcalcw2.SetFlags(MHillasCalc::kCalcHillasSrc);
702 hcalcw2.SetNameHillasSrc("MHillasSrcAnti");
703 hcalcw2.SetNameSrcPosCam("MSrcPosAnti");
704 if (fDataSetTrain.IsWobbleMode())
705 {
706 // *******************************************************************
707 // Possible source position (eg. Wobble Mode)
708 if (fDataSetTrain.HasSource())
709 {
710 if (!fDataSetTrain.GetSourcePos(source))
711 return -1;
712 *fLog << all;
713 source.Print("RaDec");
714 }
715 else
716 *fLog << all << "No source position applied..." << endl;
717
718 // La Palma Magic1
719 plistx.AddToList(&obs);
720 plistx.AddToList(&source);
721
722 TList tlist2;
723 tlist2.Add(&scalc);
724 tlist2.Add(&scor);
725 tlist2.Add(&hcalcw);
726 tlist2.Add(&hcalcw2);
727
728 devcalc.SetStreamId("Starguider");
729 tlist2.Add(&devcalc);
730
731 fill.AddPreTasks(tlist2);
732 // *******************************************************************
733 }
734
735 // Setup the tool class to read the gammas and read them
736 fill.SetName("FillGammas");
737 fill.SetDestMatrix1(&train, fNum[kTrainOn]);
738 fill.SetReader(&read1);
739 fill.AddPreTasks(fPreTasksSet[kTrainOn]);
740 fill.AddPreTasks(fPreTasks);
741 fill.AddPostTasks(fPostTasksSet[kTrainOn]);
742 fill.AddPostTasks(fPostTasks);
743 if (!fill.Process(plistx))
744 return kFALSE;
745
746 // Check the number or read events
747 const Int_t numgammastrn = train.GetNumRows();
748 if (numgammastrn==0)
749 {
750 *fLog << err << "ERROR - No gammas available for training... aborting." << endl;
751 return kFALSE;
752 }
753
754 // Remove possible post tasks
755 fill.ClearPreTasks();
756 fill.ClearPostTasks();
757
758 // Set classifier for background
759 had.SetVal(1);
760 wgt.SetVal(1);
761
762 // In case of wobble mode we have to do something special
763 MSrcPosRndm srcrndm;
764 srcrndm.SetDistOfSource(0.4);
765
766 MHillasCalc hcalc;
767 MHillasCalc hcalc2;
768 hcalc.SetFlags(MHillasCalc::kCalcHillasSrc);
769 hcalc2.SetFlags(MHillasCalc::kCalcHillasSrc);
770 hcalc2.SetNameHillasSrc("MHillasSrcAnti");
771 hcalc2.SetNameSrcPosCam("MSrcPosAnti");
772
773 if (fDataSetTrain.IsWobbleMode())
774 {
775 scalc.SetMode(MSrcPosCalc::kWobble); // kWobble for off-source
776 fPreTasksSet[kTrainOff].AddFirst(&hcalc2);
777 fPreTasksSet[kTrainOff].AddFirst(&hcalc);
778 //fPreTasksSet[kTrainOff].AddFirst(&srcrndm);
779 fPreTasksSet[kTrainOff].AddFirst(&scor);
780 fPreTasksSet[kTrainOff].AddFirst(&scalc);
781 }
782
783 // Setup the tool class to read the background and read them
784 fill.SetName("FillBackground");
785 fill.SetDestMatrix1(&train, fNum[kTrainOff]);
786 fill.SetReader(&read3);
787 fill.AddPreTasks(fPreTasksSet[kTrainOff]);
788 fill.AddPreTasks(fPreTasks);
789 fill.AddPostTasks(fPostTasksSet[kTrainOff]);
790 fill.AddPostTasks(fPostTasks);
791 if (!fill.Process(plistx))
792 return kFALSE;
793
794 // Check the number or read events
795 const Int_t numbackgrndtrn = train.GetNumRows()-numgammastrn;
796 if (numbackgrndtrn==0)
797 {
798 *fLog << err << "ERROR - No background available for training... aborting." << endl;
799 return kFALSE;
800 }
801
802 // ------------------------ Train RF --------------------------
803
804 MRanForestCalc rf("TrainSeparation", fTitle);
805 rf.SetNumTrees(fNumTrees);
806 rf.SetNdSize(fNdSize);
807 rf.SetNumTry(fNumTry);
808 rf.SetNumObsoleteVariables(1);
809 rf.SetLastDataColumnHasWeights(fEnableWeights[kTrainOn] || fEnableWeights[kTrainOff]);
810 rf.SetDebug(fDebug>1);
811 rf.SetDisplay(fDisplay);
812 rf.SetLogStream(fLog);
813 rf.SetFileName(out);
814 rf.SetNameOutput("MHadronness");
815
816 // Train the random forest either by classification or regression
817 if (fUseRegression)
818 {
819 if (!rf.TrainRegression(train)) // regression
820 return kFALSE;
821 }
822 else
823 {
824 if (!rf.TrainSingleRF(train)) // classification
825 return kFALSE;
826 }
827
828 // Output information about what was going on so far.
829 *fLog << all;
830 fLog->Separator("The forest was trained with...");
831
832 *fLog << "Training method:" << endl;
833 *fLog << " * " << (fUseRegression?"regression":"classification") << endl;
834 if (fEnableWeights[kTrainOn])
835 *fLog << " * weights for on-data" << endl;
836 if (fEnableWeights[kTrainOff])
837 *fLog << " * weights for off-data" << endl;
838 if (fDataSetTrain.IsWobbleMode())
839 *fLog << " * random source position in a distance of 0.4\u00b0" << endl;
840 *fLog << endl;
841 *fLog << "Events used for training:" << endl;
842 *fLog << " * Gammas: " << numgammastrn << endl;
843 *fLog << " * Background: " << numbackgrndtrn << endl;
844 *fLog << endl;
845 *fLog << "Gamma/Background ratio:" << endl;
846 *fLog << " * Requested: " << (float)fNum[kTrainOn]/fNum[kTrainOff] << endl;
847 *fLog << " * Result: " << (float)numgammastrn/numbackgrndtrn << endl;
848 *fLog << endl;
849 *fLog << "Run-Time: " << Form("%.1f", clock.RealTime()/60) << "min (CPU: ";
850 *fLog << Form("%.1f", clock.CpuTime()/60) << "min)" << endl;
851 *fLog << endl;
852 *fLog << "Output file name: " << out << endl;
853
854 // Chekc if testing is requested
855 if (!fDataSetTest.IsValid())
856 return kTRUE;
857
858 // --------------------- Display result ----------------------
859 fLog->Separator("Test");
860
861 clock.Continue();
862
863 // Setup parlist and tasklist for testing
864 MParList plist;
865 MTaskList tlist;
866 plist.AddToList(this); // Take care of display
867 plist.AddToList(&tlist);
868
869 MMcEvt mcevt;
870 plist.AddToList(&mcevt);
871
872 plist.AddToList(&wgt);
873
874 // ----- Setup histograms -----
875 MBinning binsy(50, 0 , 1, "BinningMH3Y", "lin");
876 MBinning binsx(40, 10, 100000, "BinningMH3X", "log");
877
878 plist.AddToList(&binsx);
879 plist.AddToList(&binsy);
880
881 MH3 h31("MHillas.fSize", "MHadronness.fVal");
882 MH3 h32("MHillas.fSize", "MHadronness.fVal");
883 MH3 h40("MMcEvt.fEnergy", "MHadronness.fVal");
884 h31.SetTitle("Background probability vs. Size:Size [phe]:Hadronness h");
885 h32.SetTitle("Background probability vs. Size:Size [phe]:Hadronness h");
886 h40.SetTitle("Background probability vs. Energy:Energy [GeV]:Hadronness h");
887
888 MHHadronness hist;
889
890 // ----- Setup tasks -----
891 MFillH fillh0(&hist, "", "FillHadronness");
892 MFillH fillh1(&h31);
893 MFillH fillh2(&h32);
894 MFillH fillh4(&h40);
895 fillh0.SetWeight("MWeight");
896 fillh1.SetWeight("MWeight");
897 fillh2.SetWeight("MWeight");
898 fillh4.SetWeight("MWeight");
899 fillh1.SetDrawOption("colz profy");
900 fillh2.SetDrawOption("colz profy");
901 fillh4.SetDrawOption("colz profy");
902 fillh1.SetNameTab("Background");
903 fillh2.SetNameTab("GammasH");
904 fillh4.SetNameTab("GammasE");
905 fillh0.SetBit(MFillH::kDoNotDisplay);
906
907 // ----- Setup filter -----
908 MFilterList precuts;
909 precuts.AddToList(fPreCuts);
910 precuts.AddToList(fTestCuts);
911
912 MContinue c0(&precuts);
913 c0.SetName("PreCuts");
914 c0.SetInverted();
915
916 MFEventSelector sel; // FIXME: USING IT (WITH PROB?) in READ will by much faster!!!
917 sel.SetNumSelectEvts(fNum[kTestOff]);
918
919 MContinue c1(&sel);
920 c1.SetInverted();
921
922 // ----- Setup tasklist -----
923 tlist.AddToList(&read2);
924 if (fDataSetTest.IsWobbleMode())
925 {
926 tlist.AddToList(&srcrndm);
927 tlist.AddToList(&hcalc);
928 }
929 tlist.AddToList(&c1);
930 tlist.AddToList(fPreTasksSet[kTestOff]);
931 tlist.AddToList(fPreTasks);
932 tlist.AddToList(&c0);
933 tlist.AddToList(&rf);
934 tlist.AddToList(fPostTasksSet[kTestOff]);
935 tlist.AddToList(fPostTasks);
936 tlist.AddToList(&fillh0);
937 tlist.AddToList(&fillh1);
938
939 // Enable Acceleration
940 tlist.SetAccelerator(MTask::kAccDontReset|MTask::kAccDontTime);
941
942 // ----- Run eventloop on background -----
943 MEvtLoop loop;
944 loop.SetDisplay(fDisplay);
945 loop.SetLogStream(fLog);
946 loop.SetParList(&plist);
947 //if (!SetupEnv(loop))
948 // return kFALSE;
949
950 wgt.SetVal(1);
951 if (!loop.Eventloop())
952 return kFALSE;
953 /*
954 if (!loop.GetDisplay())
955 {
956 gLog << warn << "Display closed by user... execution aborted." << endl << endl;
957 return kFALSE;
958 }
959 */
960 // ----- Setup and run eventloop on gammas -----
961 sel.SetNumSelectEvts(fNum[kTestOn]);
962 fillh0.ResetBit(MFillH::kDoNotDisplay);
963
964 // Remove PreTasksOff and PostTasksOff from the list
965 tlist.RemoveFromList(fPreTasksSet[kTestOff]);
966 tlist.RemoveFromList(fPostTasksSet[kTestOff]);
967
968 // replace the reading task by a new one
969 tlist.Replace(&read4);
970
971 if (fDataSetTest.IsWobbleMode())
972 {
973 // *******************************************************************
974 // Possible source position (eg. Wobble Mode)
975 if (fDataSetTest.HasSource())
976 {
977 if (!fDataSetTest.GetSourcePos(source))
978 return -1;
979 *fLog << all;
980 source.Print("RaDec");
981 }
982 else
983 *fLog << all << "No source position applied..." << endl;
984
985 // La Palma Magic1
986 plist.AddToList(&obs);
987 plist.AddToList(&source);
988
989 // How to get source position from off- and on-data?
990 tlist.AddToListAfter(&scalc, &read4);
991 tlist.AddToListAfter(&scor, &scalc);
992 tlist.AddToListAfter(&hcalcw, &scor);
993
994 tlist.AddToList(&devcalc, "Starguider");
995 // *******************************************************************
996 }
997
998 // Add the PreTasksOn directly after the reading task
999 tlist.AddToListAfter(fPreTasksSet[kTestOn], &c1);
1000
1001 // Add the PostTasksOn after rf
1002 tlist.AddToListAfter(fPostTasksSet[kTestOn], &rf);
1003
1004 // Replace fillh1 by fillh2
1005 tlist.Replace(&fillh2);
1006
1007 // Add fillh4 after the new fillh2
1008 tlist.AddToListAfter(&fillh4, &fillh2);
1009
1010 // Enable Acceleration
1011 tlist.SetAccelerator(MTask::kAccDontReset|MTask::kAccDontTime);
1012
1013 wgt.SetVal(1);
1014 if (!loop.Eventloop())
1015 return kFALSE;
1016
1017 // Show what was going on in the testing
1018 const Double_t numgammastst = h32.GetHist().GetEntries();
1019 const Double_t numbackgrndtst = h31.GetHist().GetEntries();
1020
1021 *fLog << all;
1022 fLog->Separator("The forest was tested with...");
1023 *fLog << "Test method:" << endl;
1024 *fLog << " * Random Forest: " << out << endl;
1025 if (fEnableWeights[kTestOn])
1026 *fLog << " * weights for on-data" << endl;
1027 if (fEnableWeights[kTestOff])
1028 *fLog << " * weights for off-data" << endl;
1029 if (fDataSetTrain.IsWobbleMode())
1030 *fLog << " * random source position in a distance of 0.4\u00b0" << endl;
1031 *fLog << endl;
1032 *fLog << "Events used for test:" << endl;
1033 *fLog << " * Gammas: " << numgammastst << endl;
1034 *fLog << " * Background: " << numbackgrndtst << endl;
1035 *fLog << endl;
1036 *fLog << "Gamma/Background ratio:" << endl;
1037 *fLog << " * Requested: " << (float)fNum[kTestOn]/fNum[kTestOff] << endl;
1038 *fLog << " * Result: " << (float)numgammastst/numbackgrndtst << endl;
1039 *fLog << endl;
1040
1041 // Display the result plots
1042 DisplayResult(h31, h32, ontime);
1043
1044 *fLog << "Total Run-Time: " << Form("%.1f", clock.RealTime()/60) << "min (CPU: ";
1045 *fLog << Form("%.1f", clock.CpuTime()/60) << "min)" << endl;
1046 fLog->Separator();
1047
1048 fDataSetTrain.SetName("DataSetTrain");
1049 fDataSetTest.SetName("DataSetTest");
1050
1051 // Write the display
1052 TObjArray arr;
1053 arr.Add(const_cast<MDataSet*>(&fDataSetTrain));
1054 arr.Add(const_cast<MDataSet*>(&fDataSetTest));
1055 if (fDisplay)
1056 arr.Add(fDisplay);
1057
1058 SetPathOut(out);
1059 return WriteContainer(arr, 0, "UPDATE");
1060}
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