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Reference Guide
TMVAClassification_LikelihoodPCA.class.C
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1 // Class: ReadLikelihoodPCA
2 // Automatically generated by MethodBase::MakeClass
3 //
4 
5 /* configuration options =====================================================
6 
7 #GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-
8 
9 Method : Likelihood::LikelihoodPCA
10 TMVA Release : 4.2.1 [262657]
11 ROOT Release : 6.08/07 [395271]
12 Creator : sftnight
13 Date : Thu May 31 21:35:40 2018
14 Host : Linux SFT-ubuntu-1710-1 4.13.0-31-generic #34-Ubuntu SMP Fri Jan 19 16:34:46 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux
15 Dir : /mnt/build/workspace/root-makedoc-v608/rootspi/rdoc/src/v6-08-00-patches/documentation/doxygen
16 Training events: 2000
17 Analysis type : [Classification]
18 
19 
20 #OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-
21 
22 # Set by User:
23 V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
24 VarTransform: "PCA" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
25 H: "False" [Print method-specific help message]
26 TransformOutput: "False" [Transform likelihood output by inverse sigmoid function]
27 # Default:
28 VerbosityLevel: "Default" [Verbosity level]
29 CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
30 IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
31 ##
32 
33 
34 #VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-
35 
36 NVar 4
37 var1+var2 myvar1 myvar1 myvar1 'F' [-8.14423561096,7.26972866058]
38 var1-var2 myvar2 myvar2 Expression 2 'F' [-3.96643972397,4.0258936882]
39 var3 var3 var3 Variable 3 units 'F' [-5.03730010986,4.27845287323]
40 var4 var4 var4 Variable 4 units 'F' [-5.95050764084,4.64035463333]
41 NSpec 2
42 var1*2 spec1 spec1 Spectator 1 units 'F' [-9.91655540466,8.67800140381]
43 var1*3 spec2 spec2 Spectator 2 units 'F' [-14.874833107,13.0170021057]
44 
45 
46 ============================================================================ */
47 
48 #include <vector>
49 #include <cmath>
50 #include <string>
51 #include <iostream>
52 
53 #include <math.h>
54 #include <cstdlib>
55 #ifndef IClassifierReader__def
56 #define IClassifierReader__def
57 
58 class IClassifierReader {
59 
60  public:
61 
62  // constructor
63  IClassifierReader() : fStatusIsClean( true ) {}
64  virtual ~IClassifierReader() {}
65 
66  // return classifier response
67  virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
68 
69  // returns classifier status
70  bool IsStatusClean() const { return fStatusIsClean; }
71 
72  protected:
73 
74  bool fStatusIsClean;
75 };
76 
77 #endif
78 
79 class ReadLikelihoodPCA : public IClassifierReader {
80 
81  public:
82 
83  // constructor
84  ReadLikelihoodPCA( std::vector<std::string>& theInputVars )
85  : IClassifierReader(),
86  fClassName( "ReadLikelihoodPCA" ),
87  fNvars( 4 ),
88  fIsNormalised( false )
89  {
90  // the training input variables
91  const char* inputVars[] = { "var1+var2", "var1-var2", "var3", "var4" };
92 
93  // sanity checks
94  if (theInputVars.size() <= 0) {
95  std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
96  fStatusIsClean = false;
97  }
98 
99  if (theInputVars.size() != fNvars) {
100  std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
101  << theInputVars.size() << " != " << fNvars << std::endl;
102  fStatusIsClean = false;
103  }
104 
105  // validate input variables
106  for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
107  if (theInputVars[ivar] != inputVars[ivar]) {
108  std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
109  << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
110  fStatusIsClean = false;
111  }
112  }
113 
114  // initialize min and max vectors (for normalisation)
115  fVmin[0] = -11.1978998184204;
116  fVmax[0] = 8.98580265045166;
117  fVmin[1] = -4.05419635772705;
118  fVmax[1] = 3.94173216819763;
119  fVmin[2] = -2.01150608062744;
120  fVmax[2] = 1.96753799915314;
121  fVmin[3] = -0.997752070426941;
122  fVmax[3] = 1.02176344394684;
123 
124  // initialize input variable types
125  fType[0] = 'F';
126  fType[1] = 'F';
127  fType[2] = 'F';
128  fType[3] = 'F';
129 
130  // initialize constants
131  Initialize();
132 
133  // initialize transformation
134  InitTransform();
135  }
136 
137  // destructor
138  virtual ~ReadLikelihoodPCA() {
139  Clear(); // method-specific
140  }
141 
142  // the classifier response
143  // "inputValues" is a vector of input values in the same order as the
144  // variables given to the constructor
145  double GetMvaValue( const std::vector<double>& inputValues ) const;
146 
147  private:
148 
149  // method-specific destructor
150  void Clear();
151 
152  // input variable transformation
153 
154  void X2P_1( const double*, double*, int ) const;
155  double fMeanValues_1[3][4];
156  double fEigenVectors_1[3][4][4];
157 
158  void InitTransform_1();
159  void Transform_1( std::vector<double> & iv, int sigOrBgd ) const;
160  void InitTransform();
161  void Transform( std::vector<double> & iv, int sigOrBgd ) const;
162 
163  // common member variables
164  const char* fClassName;
165 
166  const size_t fNvars;
167  size_t GetNvar() const { return fNvars; }
168  char GetType( int ivar ) const { return fType[ivar]; }
169 
170  // normalisation of input variables
171  const bool fIsNormalised;
172  bool IsNormalised() const { return fIsNormalised; }
173  double fVmin[4];
174  double fVmax[4];
175  double NormVariable( double x, double xmin, double xmax ) const {
176  // normalise to output range: [-1, 1]
177  return 2*(x - xmin)/(xmax - xmin) - 1.0;
178  }
179 
180  // type of input variable: 'F' or 'I'
181  char fType[4];
182 
183  // initialize internal variables
184  void Initialize();
185  double GetMvaValue__( const std::vector<double>& inputValues ) const;
186 
187  // private members (method specific)
188  double fEpsilon;
189  static float fRefS[][10000]; // signal reference vector [nvars][max_nbins]
190  static float fRefB[][10000]; // backgr reference vector [nvars][max_nbins]
191 
192 // if a variable has its PDF encoded as a spline0 --> treat it like an Integer valued one
193  bool fHasDiscretPDF[4];
194  int fNbin[4]; // number of bins (discrete variables may have less bins)
195  double fHistMin[4];
196  double fHistMax[4];
197  double TransformLikelihoodOutput( double, double ) const;
198 };
199 
200 inline void ReadLikelihoodPCA::Initialize()
201 {
202  fEpsilon = 2.2250738585072e-305;
203  fNbin[0] = 10000;
204  fHistMin[0] = -11.8069553375244;
205  fHistMax[0] = 9.62459087371826;
206  fNbin[1] = 10000;
207  fHistMin[1] = -4.05161714553833;
208  fHistMax[1] = 4.07033824920654;
209  fNbin[2] = 10000;
210  fHistMin[2] = -2.12039041519165;
211  fHistMax[2] = 2.23738646507263;
212  fNbin[3] = 10000;
213  fHistMin[3] = -1.23021447658539;
214  fHistMax[3] = 1.54229879379272;
215  fHasDiscretPDF[0] = false;
216  fHasDiscretPDF[1] = false;
217  fHasDiscretPDF[2] = false;
218  fHasDiscretPDF[3] = false;
219 }
220 
221 inline double ReadLikelihoodPCA::GetMvaValue__( const std::vector<double>& inputValues ) const
222 {
223  double ps(1), pb(1);
224  std::vector<double> inputValuesSig = inputValues;
225  std::vector<double> inputValuesBgd = inputValues;
226  Transform(inputValuesSig,0);
227  Transform(inputValuesBgd,1);
228  for (size_t ivar = 0; ivar < GetNvar(); ivar++) {
229 
230  // dummy at present... will be used for variable transforms
231  double x[2] = { inputValuesSig[ivar], inputValuesBgd[ivar] };
232 
233  for (int itype=0; itype < 2; itype++) {
234 
235  // interpolate linearly between adjacent bins
236  // this is not useful for discrete variables (or forced Spline0)
237  int bin = int((x[itype] - fHistMin[ivar])/(fHistMax[ivar] - fHistMin[ivar])*fNbin[ivar]) + 0;
238 
239  // since the test data sample is in general different from the training sample
240  // it can happen that the min/max of the training sample are trespassed --> correct this
241  if (bin < 0) {
242  bin = 0;
243  x[itype] = fHistMin[ivar];
244  }
245  else if (bin >= fNbin[ivar]) {
246  bin = fNbin[ivar]-1;
247  x[itype] = fHistMax[ivar];
248  }
249 
250  // find corresponding histogram from cached indices
251  float ref = (itype == 0) ? fRefS[ivar][bin] : fRefB[ivar][bin];
252 
253  // sanity check
254  if (ref < 0) {
255  std::cout << "Fatal error in ReadLikelihoodPCA: bin entry < 0 ==> abort" << std::endl;
256  std::exit(1);
257  }
258 
259  double p = ref;
260 
261  if (GetType(ivar) != 'I' && !fHasDiscretPDF[ivar]) {
262  float bincenter = (bin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
263  int nextbin = bin;
264  if ((x[itype] > bincenter && bin != fNbin[ivar]-1) || bin == 0)
265  nextbin++;
266  else
267  nextbin--;
268 
269  double refnext = (itype == 0) ? fRefS[ivar][nextbin] : fRefB[ivar][nextbin];
270  float nextbincenter = (nextbin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
271 
272  double dx = bincenter - nextbincenter;
273  double dy = ref - refnext;
274  p += (x[itype] - bincenter) * dy/dx;
275  }
276 
277  if (p < fEpsilon) p = fEpsilon; // avoid zero response
278 
279  if (itype == 0) ps *= p;
280  else pb *= p;
281  }
282  }
283 
284  // the likelihood ratio (transform it ?)
285  return TransformLikelihoodOutput( ps, pb );
286 }
287 
288 inline double ReadLikelihoodPCA::TransformLikelihoodOutput( double ps, double pb ) const
289 {
290  // returns transformed or non-transformed output
291  if (ps < fEpsilon) ps = fEpsilon;
292  if (pb < fEpsilon) pb = fEpsilon;
293  double r = ps/(ps + pb);
294  if (r >= 1.0) r = 1. - 1.e-15;
295 
296  if (false) {
297  // inverse Fermi function
298 
299  // sanity check
300  if (r <= 0.0) r = fEpsilon;
301  else if (r >= 1.0) r = 1. - 1.e-15;
302 
303  double tau = 15.0;
304  r = - log(1.0/r - 1.0)/tau;
305  }
306 
307  return r;
308 }
309 
310 // Clean up
311 inline void ReadLikelihoodPCA::Clear()
312 {
313  // nothing to clear
314 }
315 
316 // signal map
317 float ReadLikelihoodPCA::fRefS[][10000] =
318 {
319  { 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 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7.34183086504789e-15, 7.34183086504789e-15, 7.34183086504789e-15, 7.34183086504789e-15 },
323 };
324 
325 // background map
326 float ReadLikelihoodPCA::fRefB[][10000] =
327 {
328  { 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 9.4545436e-16, 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332 };
333 
334  inline double ReadLikelihoodPCA::GetMvaValue( const std::vector<double>& inputValues ) const
335  {
336  // classifier response value
337  double retval = 0;
338 
339  // classifier response, sanity check first
340  if (!IsStatusClean()) {
341  std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
342  << " because status is dirty" << std::endl;
343  retval = 0;
344  }
345  else {
346  if (IsNormalised()) {
347  // normalise variables
348  std::vector<double> iV;
349  iV.reserve(inputValues.size());
350  int ivar = 0;
351  for (std::vector<double>::const_iterator varIt = inputValues.begin();
352  varIt != inputValues.end(); varIt++, ivar++) {
353  iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));
354  }
355  retval = GetMvaValue__( iV );
356  }
357  else {
358  retval = GetMvaValue__( inputValues );
359  }
360  }
361 
362  return retval;
363  }
364 
365 //_______________________________________________________________________
366 inline void ReadLikelihoodPCA::X2P_1( const double* x, double* p, int index ) const
367 {
368  // Calculate the principal components from the original data vector
369  // x, and return it in p (function extracted from TPrincipal::X2P)
370  // It's the users responsibility to make sure that both x and p are
371  // of the right size (i.e., memory must be allocated for p).
372  const int nVar = 4;
373 
374  for (int i = 0; i < nVar; i++) {
375  p[i] = 0;
376  for (int j = 0; j < nVar; j++) p[i] += (x[j] - fMeanValues_1[index][j]) * fEigenVectors_1[index][j][i];
377  }
378 }
379 
380 //_______________________________________________________________________
381 inline void ReadLikelihoodPCA::InitTransform_1()
382 {
383  // PCA transformation, initialisation
384  // initialise vector of mean values
385  fMeanValues_1[0][0] = 0.501171251774;
386  fMeanValues_1[0][1] = -0.0994528753422;
387  fMeanValues_1[0][2] = 0.404633918432;
388  fMeanValues_1[0][3] = 0.801902735948;
389  fMeanValues_1[1][0] = -0.13187061622;
390  fMeanValues_1[1][1] = 0.0861246706024;
391  fMeanValues_1[1][2] = -0.193617885351;
392  fMeanValues_1[1][3] = -0.245611558282;
393  fMeanValues_1[2][0] = 0.184650317777;
394  fMeanValues_1[2][1] = -0.00666410236992;
395  fMeanValues_1[2][2] = 0.105508016541;
396  fMeanValues_1[2][3] = 0.278145588833;
397 
398  // initialise matrix of eigenvectors
399  fEigenVectors_1[0][0][0] = 0.769850496029;
400  fEigenVectors_1[0][0][1] = 0.0214560882009;
401  fEigenVectors_1[0][0][2] = 0.53366216298;
402  fEigenVectors_1[0][0][3] = 0.349391679706;
403  fEigenVectors_1[0][1][0] = -0.0261975664757;
404  fEigenVectors_1[0][1][1] = 0.993735389308;
405  fEigenVectors_1[0][1][2] = -0.0610151208007;
406  fEigenVectors_1[0][1][3] = 0.0898933733973;
407  fEigenVectors_1[0][2][0] = 0.406874796009;
408  fEigenVectors_1[0][2][1] = -0.07414675704;
409  fEigenVectors_1[0][2][2] = -0.829585758536;
410  fEigenVectors_1[0][2][3] = 0.375156804586;
411  fEigenVectors_1[0][3][0] = 0.491016091027;
412  fEigenVectors_1[0][3][1] = 0.0808199897049;
413  fEigenVectors_1[0][3][2] = -0.152542846675;
414  fEigenVectors_1[0][3][3] = -0.853874702485;
415  fEigenVectors_1[1][0][0] = 0.747072878467;
416  fEigenVectors_1[1][0][1] = -0.0309636701488;
417  fEigenVectors_1[1][0][2] = 0.550364978004;
418  fEigenVectors_1[1][0][3] = 0.371512794364;
419  fEigenVectors_1[1][1][0] = 0.0365121539727;
420  fEigenVectors_1[1][1][1] = 0.990509185752;
421  fEigenVectors_1[1][1][2] = -0.0698472228143;
422  fEigenVectors_1[1][1][3] = 0.112604533739;
423  fEigenVectors_1[1][2][0] = 0.409718983323;
424  fEigenVectors_1[1][2][1] = -0.116337090967;
425  fEigenVectors_1[1][2][2] = -0.820328626956;
426  fEigenVectors_1[1][2][3] = 0.381650337045;
427  fEigenVectors_1[1][3][0] = 0.522187065691;
428  fEigenVectors_1[1][3][1] = 0.0663210776248;
429  fEigenVectors_1[1][3][2] = -0.138854961196;
430  fEigenVectors_1[1][3][3] = -0.838833405891;
431  fEigenVectors_1[2][0][0] = 0.744343580161;
432  fEigenVectors_1[2][0][1] = 0.00738265903333;
433  fEigenVectors_1[2][0][2] = -0.597855936426;
434  fEigenVectors_1[2][0][3] = 0.297433034983;
435  fEigenVectors_1[2][1][0] = 0.0249331241684;
436  fEigenVectors_1[2][1][1] = -0.993034846946;
437  fEigenVectors_1[2][1][2] = 0.0657955964205;
438  fEigenVectors_1[2][1][3] = 0.0945043467932;
439  fEigenVectors_1[2][2][0] = 0.411076013343;
440  fEigenVectors_1[2][2][1] = 0.107578409358;
441  fEigenVectors_1[2][2][2] = 0.758746350422;
442  fEigenVectors_1[2][2][3] = 0.493707780793;
443  fEigenVectors_1[2][3][0] = 0.525687630868;
444  fEigenVectors_1[2][3][1] = -0.047478152212;
445  fEigenVectors_1[2][3][2] = 0.250086374066;
446  fEigenVectors_1[2][3][3] = -0.811698925293;
447 }
448 
449 //_______________________________________________________________________
450 inline void ReadLikelihoodPCA::Transform_1( std::vector<double>& iv, int cls ) const
451 {
452  // PCA transformation
453  const int nVar = 4;
454  double *dv = new double[nVar];
455  double *rv = new double[nVar];
456  if (cls < 0 || cls > 2) {
457  if (2 > 1 ) cls = 2;
458  else cls = 2;
459  }
460 
461  // define the indices of the variables which are transformed by this transformation
462  static std::vector<int> indicesGet;
463  static std::vector<int> indicesPut;
464 
465  if ( indicesGet.empty() ) {
466  indicesGet.reserve(fNvars);
467  indicesGet.push_back( 0);
468  indicesGet.push_back( 1);
469  indicesGet.push_back( 2);
470  indicesGet.push_back( 3);
471  }
472  if ( indicesPut.empty() ) {
473  indicesPut.reserve(fNvars);
474  indicesPut.push_back( 0);
475  indicesPut.push_back( 1);
476  indicesPut.push_back( 2);
477  indicesPut.push_back( 3);
478  }
479 
480  for (int ivar=0; ivar<nVar; ivar++) dv[ivar] = iv[indicesGet.at(ivar)];
481 
482  // Perform PCA and put it into PCAed events tree
483  this->X2P_1( dv, rv, cls );
484  for (int ivar=0; ivar<nVar; ivar++) iv[indicesPut.at(ivar)] = rv[ivar];
485 
486  delete [] dv;
487  delete [] rv;
488 }
489 
490 //_______________________________________________________________________
491 inline void ReadLikelihoodPCA::InitTransform()
492 {
493  InitTransform_1();
494 }
495 
496 //_______________________________________________________________________
497 inline void ReadLikelihoodPCA::Transform( std::vector<double>& iv, int sigOrBgd ) const
498 {
499  Transform_1( iv, sigOrBgd );
500 }
float xmin
Definition: THbookFile.cxx:93
Type GetType(const std::string &Name)
Definition: Systematics.cxx:34
Double_t x[n]
Definition: legend1.C:17
you should not use this method at all Int_t Int_t Double_t Double_t Double_t Int_t Double_t Double_t Double_t tau
Definition: TRolke.cxx:630
TRandom2 r(17)
void Initialize(Bool_t useTMVAStyle=kTRUE)
Definition: tmvaglob.cxx:176
float xmax
Definition: THbookFile.cxx:93
PyObject * fType
double log(double)