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Reference Guide
TMVAClassificationCategory_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
9Method : Likelihood::Likelihood
10TMVA Release : 4.2.1 [262657]
11ROOT Release : 6.16/01 [397313]
12Creator : sftnight
13Date : Sun Dec 19 22:14:01 2021
14Host : Linux root-ubuntu-2004-3 5.4.0-73-generic #82-Ubuntu SMP Wed Apr 14 17:39:42 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
15Dir : /home/sftnight/build/workspace/root-makedoc-v616/rootspi/rdoc/src/v6-16-00-patches/documentation/doxygen
16Training events: 10000
17Analysis type : [Classification]
18
19
20#OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-
21
22# Set by User:
23V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
24H: "False" [Print method-specific help message]
25TransformOutput: "True" [Transform likelihood output by inverse sigmoid function]
26# Default:
27VerbosityLevel: "Default" [Verbosity level]
28VarTransform: "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)"]
29CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
30IgnoreNegWeightsInTraining: "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
36NVar 4
37var1 var1 var1 var1 'F' [-4.16552305222,4.20716428757]
38var2 var2 var2 var2 'F' [-4.220682621,4.51537322998]
39var3 var3 var3 var3 'F' [-4.52968788147,4.82019376755]
40var4 var4 var4 var4 'F' [-4.64965724945,5.14146757126]
41NSpec 5
42eta eta eta eta 'F' [-2.49952101707,2.49992322922]
43abs(eta)<=1.3 FisherCat_cat1 FisherCat_cat1 FisherCat:Category_Fisher_1 pass 'C' [0,1]
44abs(eta)>1.3 FisherCat_cat2 FisherCat_cat2 FisherCat:Category_Fisher_2 pass 'C' [0,1]
45abs(eta)<=1.3 LikelihoodCat_cat1 LikelihoodCat_cat1 LikelihoodCat:Category_Likelihood_1pass 'C' [0,1]
46abs(eta)>1.3 LikelihoodCat_cat2 LikelihoodCat_cat2 LikelihoodCat:Category_Likelihood_2pass 'C' [0,1]
47
48
49============================================================================ */
50
51#include <array>
52#include <vector>
53#include <cmath>
54#include <string>
55#include <iostream>
56
57#include <math.h>
58#include <cstdlib>
59#ifndef IClassifierReader__def
60#define IClassifierReader__def
61
62class IClassifierReader {
63
64 public:
65
66 // constructor
67 IClassifierReader() : fStatusIsClean( true ) {}
68 virtual ~IClassifierReader() {}
69
70 // return classifier response
71 virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
72
73 // returns classifier status
74 bool IsStatusClean() const { return fStatusIsClean; }
75
76 protected:
77
78 bool fStatusIsClean;
79};
80
81#endif
82
83class ReadLikelihood : public IClassifierReader {
84
85 public:
86
87 // constructor
88 ReadLikelihood( std::vector<std::string>& theInputVars )
89 : IClassifierReader(),
90 fClassName( "ReadLikelihood" ),
91 fNvars( 4 )
92 {
93 // the training input variables
94 const char* inputVars[] = { "var1", "var2", "var3", "var4" };
95
96 // sanity checks
97 if (theInputVars.size() <= 0) {
98 std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
99 fStatusIsClean = false;
100 }
101
102 if (theInputVars.size() != fNvars) {
103 std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
104 << theInputVars.size() << " != " << fNvars << std::endl;
105 fStatusIsClean = false;
106 }
107
108 // validate input variables
109 for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
110 if (theInputVars[ivar] != inputVars[ivar]) {
111 std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
112 << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
113 fStatusIsClean = false;
114 }
115 }
116
117 // initialize min and max vectors (for normalisation)
118 fVmin[0] = 0;
119 fVmax[0] = 0;
120 fVmin[1] = 0;
121 fVmax[1] = 0;
122 fVmin[2] = 0;
123 fVmax[2] = 0;
124 fVmin[3] = 0;
125 fVmax[3] = 0;
126
127 // initialize input variable types
128 fType[0] = 'F';
129 fType[1] = 'F';
130 fType[2] = 'F';
131 fType[3] = 'F';
132
133 // initialize constants
134 Initialize();
135
136 }
137
138 // destructor
139 virtual ~ReadLikelihood() {
140 Clear(); // method-specific
141 }
142
143 // the classifier response
144 // "inputValues" is a vector of input values in the same order as the
145 // variables given to the constructor
146 double GetMvaValue( const std::vector<double>& inputValues ) const override;
147
148 private:
149
150 // method-specific destructor
151 void Clear();
152
153 // common member variables
154 const char* fClassName;
155
156 const size_t fNvars;
157 size_t GetNvar() const { return fNvars; }
158 char GetType( int ivar ) const { return fType[ivar]; }
159
160 // normalisation of input variables
161 double fVmin[4];
162 double fVmax[4];
163 double NormVariable( double x, double xmin, double xmax ) const {
164 // normalise to output range: [-1, 1]
165 return 2*(x - xmin)/(xmax - xmin) - 1.0;
166 }
167
168 // type of input variable: 'F' or 'I'
169 char fType[4];
170
171 // initialize internal variables
172 void Initialize();
173 double GetMvaValue__( const std::vector<double>& inputValues ) const;
174
175 // private members (method specific)
176 double fEpsilon;
177 static float fRefS[][10000]; // signal reference vector [nvars][max_nbins]
178 static float fRefB[][10000]; // backgr reference vector [nvars][max_nbins]
179
180// if a variable has its PDF encoded as a spline0 --> treat it like an Integer valued one
181 bool fHasDiscretPDF[4];
182 int fNbin[4]; // number of bins (discrete variables may have less bins)
183 double fHistMin[4];
184 double fHistMax[4];
185 double TransformLikelihoodOutput( double, double ) const;
186};
187
188inline void ReadLikelihood::Initialize()
189{
190 fEpsilon = 2.2250738585072e-305;
191 fNbin[0] = 10000;
192 fHistMin[0] = -4.16552305221558;
193 fHistMax[0] = 4.20716428756714;
194 fNbin[1] = 10000;
195 fHistMin[1] = -4.2206826210022;
196 fHistMax[1] = 4.51537322998047;
197 fNbin[2] = 10000;
198 fHistMin[2] = -4.52968788146973;
199 fHistMax[2] = 4.82019376754761;
200 fNbin[3] = 10000;
201 fHistMin[3] = -4.64965724945068;
202 fHistMax[3] = 5.14146757125854;
203 fHasDiscretPDF[0] = false;
204 fHasDiscretPDF[1] = false;
205 fHasDiscretPDF[2] = false;
206 fHasDiscretPDF[3] = false;
207}
208
209inline double ReadLikelihood::GetMvaValue__( const std::vector<double>& inputValues ) const
210{
211 double ps(1), pb(1);
212 std::vector<double> inputValuesSig = inputValues;
213 std::vector<double> inputValuesBgd = inputValues;
214 for (size_t ivar = 0; ivar < GetNvar(); ivar++) {
215
216 // dummy at present... will be used for variable transforms
217 double x[2] = { inputValuesSig[ivar], inputValuesBgd[ivar] };
218
219 for (int itype=0; itype < 2; itype++) {
220
221 // interpolate linearly between adjacent bins
222 // this is not useful for discrete variables (or forced Spline0)
223 int bin = int((x[itype] - fHistMin[ivar])/(fHistMax[ivar] - fHistMin[ivar])*fNbin[ivar]) + 0;
224
225 // since the test data sample is in general different from the training sample
226 // it can happen that the min/max of the training sample are trespassed --> correct this
227 if (bin < 0) {
228 bin = 0;
229 x[itype] = fHistMin[ivar];
230 }
231 else if (bin >= fNbin[ivar]) {
232 bin = fNbin[ivar]-1;
233 x[itype] = fHistMax[ivar];
234 }
235
236 // find corresponding histogram from cached indices
237 float ref = (itype == 0) ? fRefS[ivar][bin] : fRefB[ivar][bin];
238
239 // sanity check
240 if (ref < 0) {
241 std::cout << "Fatal error in ReadLikelihood: bin entry < 0 ==> abort" << std::endl;
242 std::exit(1);
243 }
244
245 double p = ref;
246
247 if (GetType(ivar) != 'I' && !fHasDiscretPDF[ivar]) {
248 float bincenter = (bin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
249 int nextbin = bin;
250 if ((x[itype] > bincenter && bin != fNbin[ivar]-1) || bin == 0)
251 nextbin++;
252 else
253 nextbin--;
254
255 double refnext = (itype == 0) ? fRefS[ivar][nextbin] : fRefB[ivar][nextbin];
256 float nextbincenter = (nextbin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
257
258 double dx = bincenter - nextbincenter;
259 double dy = ref - refnext;
260 p += (x[itype] - bincenter) * dy/dx;
261 }
262
263 if (p < fEpsilon) p = fEpsilon; // avoid zero response
264
265 if (itype == 0) ps *= p;
266 else pb *= p;
267 }
268 }
269
270 // the likelihood ratio (transform it ?)
271 return TransformLikelihoodOutput( ps, pb );
272}
273
274inline double ReadLikelihood::TransformLikelihoodOutput( double ps, double pb ) const
275{
276 // returns transformed or non-transformed output
277 if (ps < fEpsilon) ps = fEpsilon;
278 if (pb < fEpsilon) pb = fEpsilon;
279 double r = ps/(ps + pb);
280 if (r >= 1.0) r = 1. - 1.e-15;
281
282 if (true) {
283 // inverse Fermi function
284
285 // sanity check
286 if (r <= 0.0) r = fEpsilon;
287 else if (r >= 1.0) r = 1. - 1.e-15;
288
289 double tau = 15.0;
290 r = - log(1.0/r - 1.0)/tau;
291 }
292
293 return r;
294}
295
296// Clean up
297inline void ReadLikelihood::Clear()
298{
299 // nothing to clear
300}
301
302// signal map
303float ReadLikelihood::fRefS[][10000] =
304{
305 { 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 2.39841804762205e-15, 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309};
310
311// background map
312float ReadLikelihood::fRefB[][10000] =
313{
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318};
319
320 inline double ReadLikelihood::GetMvaValue( const std::vector<double>& inputValues ) const
321 {
322 // classifier response value
323 double retval = 0;
324
325 // classifier response, sanity check first
326 if (!IsStatusClean()) {
327 std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
328 << " because status is dirty" << std::endl;
329 retval = 0;
330 }
331 else {
332 retval = GetMvaValue__( inputValues );
333 }
334
335 return retval;
336 }
fType
PyObject * fType
Definition: MethodProxy.cxx:120
r
ROOT::R::TRInterface & r
Definition: Object.C:4
xmin
float xmin
Definition: THbookFile.cxx:93
xmax
float xmax
Definition: THbookFile.cxx:93
log
double log(double)
x
Double_t x[n]
Definition: legend1.C:17
RooStats::HistFactory::Constraint::GetType
Type GetType(const std::string &Name)
Definition: Systematics.cxx:34
TGeoUnit::ps
static constexpr double ps
Definition: TGeoSystemOfUnits.h:165
TMVA::TMVAGlob::Initialize
void Initialize(Bool_t useTMVAStyle=kTRUE)
Definition: tmvaglob.cxx:176