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Reference Guide
TMVAClassification_Likelihood.class.C
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1 // Class: ReadLikelihood
2 // Automatically generated by MethodBase::MakeClass
3 //
4 
5 /* configuration options =====================================================
6 
7 #GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-
8 
9 Method : Likelihood::Likelihood
10 TMVA Release : 4.2.1 [262657]
11 ROOT Release : 6.10/09 [395785]
12 Creator : sftnight
13 Date : Thu May 31 12:04:22 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-v610/rootspi/rdoc/src/v6-10-00-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 H: "True" [Print method-specific help message]
25 TransformOutput: "True" [Transform likelihood output by inverse sigmoid function]
26 # Default:
27 VerbosityLevel: "Default" [Verbosity level]
28 VarTransform: "None" [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)"]
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 ReadLikelihood : public IClassifierReader {
80 
81  public:
82 
83  // constructor
84  ReadLikelihood( std::vector<std::string>& theInputVars )
85  : IClassifierReader(),
86  fClassName( "ReadLikelihood" ),
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] = 0;
116  fVmax[0] = 0;
117  fVmin[1] = 0;
118  fVmax[1] = 0;
119  fVmin[2] = 0;
120  fVmax[2] = 0;
121  fVmin[3] = 0;
122  fVmax[3] = 0;
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  }
134 
135  // destructor
136  virtual ~ReadLikelihood() {
137  Clear(); // method-specific
138  }
139 
140  // the classifier response
141  // "inputValues" is a vector of input values in the same order as the
142  // variables given to the constructor
143  double GetMvaValue( const std::vector<double>& inputValues ) const;
144 
145  private:
146 
147  // method-specific destructor
148  void Clear();
149 
150  // common member variables
151  const char* fClassName;
152 
153  const size_t fNvars;
154  size_t GetNvar() const { return fNvars; }
155  char GetType( int ivar ) const { return fType[ivar]; }
156 
157  // normalisation of input variables
158  const bool fIsNormalised;
159  bool IsNormalised() const { return fIsNormalised; }
160  double fVmin[4];
161  double fVmax[4];
162  double NormVariable( double x, double xmin, double xmax ) const {
163  // normalise to output range: [-1, 1]
164  return 2*(x - xmin)/(xmax - xmin) - 1.0;
165  }
166 
167  // type of input variable: 'F' or 'I'
168  char fType[4];
169 
170  // initialize internal variables
171  void Initialize();
172  double GetMvaValue__( const std::vector<double>& inputValues ) const;
173 
174  // private members (method specific)
175  double fEpsilon;
176  static float fRefS[][10000]; // signal reference vector [nvars][max_nbins]
177  static float fRefB[][10000]; // backgr reference vector [nvars][max_nbins]
178 
179 // if a variable has its PDF encoded as a spline0 --> treat it like an Integer valued one
180  bool fHasDiscretPDF[4];
181  int fNbin[4]; // number of bins (discrete variables may have less bins)
182  double fHistMin[4];
183  double fHistMax[4];
184  double TransformLikelihoodOutput( double, double ) const;
185 };
186 
187 inline void ReadLikelihood::Initialize()
188 {
189  fEpsilon = 2.2250738585072e-305;
190  fNbin[0] = 10000;
191  fHistMin[0] = -8.14423561096191;
192  fHistMax[0] = 7.2697286605835;
193  fNbin[1] = 10000;
194  fHistMin[1] = -3.96643972396851;
195  fHistMax[1] = 4.0258936882019;
196  fNbin[2] = 10000;
197  fHistMin[2] = -5.03730010986328;
198  fHistMax[2] = 4.27845287322998;
199  fNbin[3] = 10000;
200  fHistMin[3] = -5.95050764083862;
201  fHistMax[3] = 4.6403546333313;
202  fHasDiscretPDF[0] = false;
203  fHasDiscretPDF[1] = false;
204  fHasDiscretPDF[2] = false;
205  fHasDiscretPDF[3] = false;
206 }
207 
208 inline double ReadLikelihood::GetMvaValue__( const std::vector<double>& inputValues ) const
209 {
210  double ps(1), pb(1);
211  std::vector<double> inputValuesSig = inputValues;
212  std::vector<double> inputValuesBgd = inputValues;
213  for (size_t ivar = 0; ivar < GetNvar(); ivar++) {
214 
215  // dummy at present... will be used for variable transforms
216  double x[2] = { inputValuesSig[ivar], inputValuesBgd[ivar] };
217 
218  for (int itype=0; itype < 2; itype++) {
219 
220  // interpolate linearly between adjacent bins
221  // this is not useful for discrete variables (or forced Spline0)
222  int bin = int((x[itype] - fHistMin[ivar])/(fHistMax[ivar] - fHistMin[ivar])*fNbin[ivar]) + 0;
223 
224  // since the test data sample is in general different from the training sample
225  // it can happen that the min/max of the training sample are trespassed --> correct this
226  if (bin < 0) {
227  bin = 0;
228  x[itype] = fHistMin[ivar];
229  }
230  else if (bin >= fNbin[ivar]) {
231  bin = fNbin[ivar]-1;
232  x[itype] = fHistMax[ivar];
233  }
234 
235  // find corresponding histogram from cached indices
236  float ref = (itype == 0) ? fRefS[ivar][bin] : fRefB[ivar][bin];
237 
238  // sanity check
239  if (ref < 0) {
240  std::cout << "Fatal error in ReadLikelihood: bin entry < 0 ==> abort" << std::endl;
241  std::exit(1);
242  }
243 
244  double p = ref;
245 
246  if (GetType(ivar) != 'I' && !fHasDiscretPDF[ivar]) {
247  float bincenter = (bin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
248  int nextbin = bin;
249  if ((x[itype] > bincenter && bin != fNbin[ivar]-1) || bin == 0)
250  nextbin++;
251  else
252  nextbin--;
253 
254  double refnext = (itype == 0) ? fRefS[ivar][nextbin] : fRefB[ivar][nextbin];
255  float nextbincenter = (nextbin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
256 
257  double dx = bincenter - nextbincenter;
258  double dy = ref - refnext;
259  p += (x[itype] - bincenter) * dy/dx;
260  }
261 
262  if (p < fEpsilon) p = fEpsilon; // avoid zero response
263 
264  if (itype == 0) ps *= p;
265  else pb *= p;
266  }
267  }
268 
269  // the likelihood ratio (transform it ?)
270  return TransformLikelihoodOutput( ps, pb );
271 }
272 
273 inline double ReadLikelihood::TransformLikelihoodOutput( double ps, double pb ) const
274 {
275  // returns transformed or non-transformed output
276  if (ps < fEpsilon) ps = fEpsilon;
277  if (pb < fEpsilon) pb = fEpsilon;
278  double r = ps/(ps + pb);
279  if (r >= 1.0) r = 1. - 1.e-15;
280 
281  if (true) {
282  // inverse Fermi function
283 
284  // sanity check
285  if (r <= 0.0) r = fEpsilon;
286  else if (r >= 1.0) r = 1. - 1.e-15;
287 
288  double tau = 15.0;
289  r = - log(1.0/r - 1.0)/tau;
290  }
291 
292  return r;
293 }
294 
295 // Clean up
296 inline void ReadLikelihood::Clear()
297 {
298  // nothing to clear
299 }
300 
301 // signal map
302 float ReadLikelihood::fRefS[][10000] =
303 {
304  { 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 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308 };
309 
310 // background map
311 float ReadLikelihood::fRefB[][10000] =
312 {
313  { 1.3629563e-05, 1.3600491e-05, 1.3571419e-05, 1.3542392e-05, 1.3513407e-05, 1.3484465e-05, 1.3455481e-05, 1.3426627e-05, 1.3397728e-05, 1.3368916e-05, 1.3340062e-05, 1.3311295e-05, 1.3282527e-05, 1.3253803e-05, 1.3225079e-05, 1.3196441e-05, 1.3167803e-05, 1.3139166e-05, 1.3110571e-05, 1.308202e-05, 1.3053513e-05, 1.3025006e-05, 1.2996542e-05, 1.296812e-05, 1.29397e-05, 1.2911323e-05, 1.2883032e-05, 1.2854655e-05, 1.2826365e-05, 1.2798117e-05, 1.276987e-05, 1.2741667e-05, 1.2713463e-05, 1.2685347e-05, 1.2657229e-05, 1.2629155e-05, 1.2601083e-05, 1.2573052e-05, 1.2545022e-05, 1.2517035e-05, 1.2489135e-05, 1.2461192e-05, 1.2433336e-05, 1.2405436e-05, 1.2377666e-05, 1.2349809e-05, 1.2322083e-05, 1.2294314e-05, 1.2266631e-05, 1.2238947e-05, 1.2211307e-05, 1.2183667e-05, 1.2156072e-05, 1.2128519e-05, 1.2100922e-05, 1.20735e-05, 1.2045991e-05, 1.2018568e-05, 1.1991146e-05, 1.1963765e-05, 1.1936387e-05, 1.1909094e-05, 1.1881759e-05, 1.1854509e-05, 1.182726e-05, 1.1800011e-05, 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317 };
318 
319  inline double ReadLikelihood::GetMvaValue( const std::vector<double>& inputValues ) const
320  {
321  // classifier response value
322  double retval = 0;
323 
324  // classifier response, sanity check first
325  if (!IsStatusClean()) {
326  std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
327  << " because status is dirty" << std::endl;
328  retval = 0;
329  }
330  else {
331  if (IsNormalised()) {
332  // normalise variables
333  std::vector<double> iV;
334  iV.reserve(inputValues.size());
335  int ivar = 0;
336  for (std::vector<double>::const_iterator varIt = inputValues.begin();
337  varIt != inputValues.end(); varIt++, ivar++) {
338  iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));
339  }
340  retval = GetMvaValue__( iV );
341  }
342  else {
343  retval = GetMvaValue__( inputValues );
344  }
345  }
346 
347  return retval;
348  }
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)