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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
9Method : Likelihood::Likelihood
10TMVA Release : 4.2.1 [262657]
11ROOT Release : 6.16/01 [397313]
12Creator : sftnight
13Date : Sun Dec 19 22:13:31 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: 2000
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: "True" [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+var2 myvar1 myvar1 myvar1 'F' [-9.33803939819,7.69307804108]
38var1-var2 myvar2 myvar2 Expression 2 'F' [-3.25508260727,4.02912044525]
39var3 var3 var3 Variable 3 units 'F' [-5.2777428627,4.64297914505]
40var4 var4 var4 Variable 4 units 'F' [-5.6007027626,4.67435789108]
41NSpec 2
42var1*2 spec1 spec1 Spectator 1 units 'F' [-9.91655540466,8.7030172348]
43var1*3 spec2 spec2 Spectator 2 units 'F' [-14.874833107,13.0545253754]
44
45
46============================================================================ */
47
48#include <array>
49#include <vector>
50#include <cmath>
51#include <string>
52#include <iostream>
53
54#include <math.h>
55#include <cstdlib>
56#ifndef IClassifierReader__def
57#define IClassifierReader__def
58
59class IClassifierReader {
60
61 public:
62
63 // constructor
64 IClassifierReader() : fStatusIsClean( true ) {}
65 virtual ~IClassifierReader() {}
66
67 // return classifier response
68 virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
69
70 // returns classifier status
71 bool IsStatusClean() const { return fStatusIsClean; }
72
73 protected:
74
75 bool fStatusIsClean;
76};
77
78#endif
79
80class ReadLikelihood : public IClassifierReader {
81
82 public:
83
84 // constructor
85 ReadLikelihood( std::vector<std::string>& theInputVars )
86 : IClassifierReader(),
87 fClassName( "ReadLikelihood" ),
88 fNvars( 4 )
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 override;
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 double fVmin[4];
159 double fVmax[4];
160 double NormVariable( double x, double xmin, double xmax ) const {
161 // normalise to output range: [-1, 1]
162 return 2*(x - xmin)/(xmax - xmin) - 1.0;
163 }
164
165 // type of input variable: 'F' or 'I'
166 char fType[4];
167
168 // initialize internal variables
169 void Initialize();
170 double GetMvaValue__( const std::vector<double>& inputValues ) const;
171
172 // private members (method specific)
173 double fEpsilon;
174 static float fRefS[][10000]; // signal reference vector [nvars][max_nbins]
175 static float fRefB[][10000]; // backgr reference vector [nvars][max_nbins]
176
177// if a variable has its PDF encoded as a spline0 --> treat it like an Integer valued one
178 bool fHasDiscretPDF[4];
179 int fNbin[4]; // number of bins (discrete variables may have less bins)
180 double fHistMin[4];
181 double fHistMax[4];
182 double TransformLikelihoodOutput( double, double ) const;
183};
184
185inline void ReadLikelihood::Initialize()
186{
187 fEpsilon = 2.2250738585072e-305;
188 fNbin[0] = 10000;
189 fHistMin[0] = -9.33803939819336;
190 fHistMax[0] = 7.69307804107666;
191 fNbin[1] = 10000;
192 fHistMin[1] = -3.25508260726929;
193 fHistMax[1] = 4.02912044525146;
194 fNbin[2] = 10000;
195 fHistMin[2] = -5.27774286270142;
196 fHistMax[2] = 4.64297914505005;
197 fNbin[3] = 10000;
198 fHistMin[3] = -5.60070276260376;
199 fHistMax[3] = 4.67435789108276;
200 fHasDiscretPDF[0] = false;
201 fHasDiscretPDF[1] = false;
202 fHasDiscretPDF[2] = false;
203 fHasDiscretPDF[3] = false;
204}
205
206inline double ReadLikelihood::GetMvaValue__( const std::vector<double>& inputValues ) const
207{
208 double ps(1), pb(1);
209 std::vector<double> inputValuesSig = inputValues;
210 std::vector<double> inputValuesBgd = inputValues;
211 for (size_t ivar = 0; ivar < GetNvar(); ivar++) {
212
213 // dummy at present... will be used for variable transforms
214 double x[2] = { inputValuesSig[ivar], inputValuesBgd[ivar] };
215
216 for (int itype=0; itype < 2; itype++) {
217
218 // interpolate linearly between adjacent bins
219 // this is not useful for discrete variables (or forced Spline0)
220 int bin = int((x[itype] - fHistMin[ivar])/(fHistMax[ivar] - fHistMin[ivar])*fNbin[ivar]) + 0;
221
222 // since the test data sample is in general different from the training sample
223 // it can happen that the min/max of the training sample are trespassed --> correct this
224 if (bin < 0) {
225 bin = 0;
226 x[itype] = fHistMin[ivar];
227 }
228 else if (bin >= fNbin[ivar]) {
229 bin = fNbin[ivar]-1;
230 x[itype] = fHistMax[ivar];
231 }
232
233 // find corresponding histogram from cached indices
234 float ref = (itype == 0) ? fRefS[ivar][bin] : fRefB[ivar][bin];
235
236 // sanity check
237 if (ref < 0) {
238 std::cout << "Fatal error in ReadLikelihood: bin entry < 0 ==> abort" << std::endl;
239 std::exit(1);
240 }
241
242 double p = ref;
243
244 if (GetType(ivar) != 'I' && !fHasDiscretPDF[ivar]) {
245 float bincenter = (bin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
246 int nextbin = bin;
247 if ((x[itype] > bincenter && bin != fNbin[ivar]-1) || bin == 0)
248 nextbin++;
249 else
250 nextbin--;
251
252 double refnext = (itype == 0) ? fRefS[ivar][nextbin] : fRefB[ivar][nextbin];
253 float nextbincenter = (nextbin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
254
255 double dx = bincenter - nextbincenter;
256 double dy = ref - refnext;
257 p += (x[itype] - bincenter) * dy/dx;
258 }
259
260 if (p < fEpsilon) p = fEpsilon; // avoid zero response
261
262 if (itype == 0) ps *= p;
263 else pb *= p;
264 }
265 }
266
267 // the likelihood ratio (transform it ?)
268 return TransformLikelihoodOutput( ps, pb );
269}
270
271inline double ReadLikelihood::TransformLikelihoodOutput( double ps, double pb ) const
272{
273 // returns transformed or non-transformed output
274 if (ps < fEpsilon) ps = fEpsilon;
275 if (pb < fEpsilon) pb = fEpsilon;
276 double r = ps/(ps + pb);
277 if (r >= 1.0) r = 1. - 1.e-15;
278
279 if (true) {
280 // inverse Fermi function
281
282 // sanity check
283 if (r <= 0.0) r = fEpsilon;
284 else if (r >= 1.0) r = 1. - 1.e-15;
285
286 double tau = 15.0;
287 r = - log(1.0/r - 1.0)/tau;
288 }
289
290 return r;
291}
292
293// Clean up
294inline void ReadLikelihood::Clear()
295{
296 // nothing to clear
297}
298
299// signal map
300float ReadLikelihood::fRefS[][10000] =
301{
302 { 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 1.15499242165669e-15, 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0.00240148743614554, 0.00240668770857155 },
306};
307
308// background map
309float ReadLikelihood::fRefB[][10000] =
310{
311 { 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 1.1473213e-15, 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0.00019586485, 0.00019693754, 0.00019801542, 0.00019909158, 0.00020017465, 0.00020125597, 0.00020234249, 0.00020342729, 0.00020451898, 0.00020561068, 0.00020670584, 0.00020780099 },
315};
316
317 inline double ReadLikelihood::GetMvaValue( const std::vector<double>& inputValues ) const
318 {
319 // classifier response value
320 double retval = 0;
321
322 // classifier response, sanity check first
323 if (!IsStatusClean()) {
324 std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
325 << " because status is dirty" << std::endl;
326 retval = 0;
327 }
328 else {
329 retval = GetMvaValue__( inputValues );
330 }
331
332 return retval;
333 }
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