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1 Dear TMVA User,
2
3 this README contains three sections
4
5 + Overview
6 + How to run the example
7 + The TMVA GUI
8
9 Overview
10 ========
11
12 This directory contains two files that illustrate the usage of the
13 TMVA package to perform a MultiVariate Analysis. It also contains a
14 GUI for easy access to the training result.
15
16 An MV analysis is executed in two steps, the training of the MVA
17 methods, and the application of the best suited MVA method to your
18 dataset. These two steps are shown in the files TMVAnalysis.C and
19 TMVApplication.C, respectively.
20
21 The example uses some toy data (tmva_example.root) that comes with
22 the sourceforge distribution or is available on the ROOT web. The
23 example files contains a signal and a background tree with four
24 variables.
25
26
27 TMVAnalysis.C
28 -------------
29
30 TMVAnalysis.C explains the usage of the TMVA::Factory class, which
31 is the framework for the whole training process. The following steps
32 are taken:
33
34 1st: The factory is created (a name for the project is specified,
35 which will help with the bookkeeping of different training cycles)
36
37 2nd: The input data trees are made available to the factory ( one
38 can also use text files as input, also shown in this example ). The
39 sizes of the training and the testing data samples are also specified.
40
41 3rd: The variables that are selected to be used in the MVA are
42 declared to the factory
43
44 4th: The factory needs to transfer the input data into the local
45 data structure used for the training and testing
46
47 5th: Methods are than booked with the factory. For each method
48 number of options can be set that steer the training behavior. The
49 options are described in greater detail in the method classes and
50 can be looked at in the CVS web-viewer
51
52 6th: The training of all methods on the training data sample is run
53 by the factory. The training results for each method are stored in
54 the directory weights/
55
56 7th: The testing of all methods on the testing data sample is run by
57 the factory. The test results are stored in a root file (filename
58 specified in the Factory constructor)
59
60 8th: The performance of all methods is analyzed by the factory. The
61 performance results are stored in the root file (see last step)
62
63
64 TMVApplication.C
65 ----------------
66
67 TMVApplication.C explains the usage of the TMVA::Reader class, which
68 is to be used to evaluate your data with the trained MVA
69 methods. The following steps are taken:
70
71 1st: The reader is created.
72
73 2nd: A set of local variables is created and declared to the reader.
74
75 3rd: The method(s) - which means the name of the weight files
76 created in the training process - are booked with the reader.
77
78 4th: Your event loop:
79 - The values of the local variables are set (either by assignment
80 of, as in the example, by setting the BranchAddresses of your
81 datatree)
82 - the reader is asked to evaluate the MVA, the result of which is
83 used to separate signal from background
84
85
86 How to run the example
87 ======================
88
89 First, TMVAnalysis.C needs to be run to perform the training. You
90 can edit TMVAnalysis.C (boolean flags at the head of the file) to
91 enable (disable) the methods you would (not) like to use. Then run
92
93 > root TMVAnalysis.C
94
95 Add the end a file TMVA.root is written, and a GUI opens. Once
96 TMVA.root exists, the GUI can also be started via
97
98 > root TMVAGui.C
99
100 You can also run
101
102 > root TMVApplication.C
103
104 which evaluates the methods on the signal data from the toy
105 tmva_example.root and writes the result to TMVApp.root. However,
106 TMVApplication is more of a pedagogical example for the usage of
107 TMVA inside your own analysis framework.
108
109
110 The TMVA GUI
111 ============
112
113 The GUI provides easy access to a large number of macros that plot
114 various distributions and correlations of the input data, the MVA
115 method output distributions and the performance comparison plot
116 (background rejection versus efficiency). It can be used once the
117 training is run, and is started with
118
119 > root TMVAGui.C
120
121 or
122
123 > root TMVA\(\"MyTMVA.root\"\)
124
125 in case you had directed your training output to a different root
126 file (2nd argument of the TMVA::Factory constructor)
127