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Reference Guide
 
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TMVA tutorials

Example code which illustrates how to use the TMVA toolkit.

Modules

 Envelope Tutorials
 
 TMVA Keras tutorials
 Example code which illustrates how to use keras with the python interface of TMVA.
 
 TMVA PyTorch tutorials
 Example code which illustrates how to use pytorch with the python interface of TMVA.
 

Files

file  createData.C
 Plot the variables.
 
file  RBatchGenerator_NumPy.py
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file  RBatchGenerator_PyTorch.py
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file  RBatchGenerator_TensorFlow.py
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file  tmva001_RTensor.C
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This tutorial illustrates the basic features of the RTensor class, RTensor is a std::vector-like container with additional shape information.
 
file  tmva002_RDataFrameAsTensor.C
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This tutorial shows how the content of an RDataFrame can be converted to an RTensor object.
 
file  tmva003_RReader.C
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This tutorial shows how to apply with the modern interfaces models saved in TMVA XML files.
 
file  tmva004_RStandardScaler.C
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This tutorial illustrates the usage of the standard scaler as preprocessing method.
 
file  tmva100_DataPreparation.py
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This tutorial illustrates how to prepare ROOT datasets to be nicely readable by most machine learning methods.
 
file  tmva101_Training.py
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This tutorial show how you can train a machine learning model with any package reading the training data directly from ROOT files.
 
file  tmva102_Testing.py
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This tutorial illustrates how you can test a trained BDT model using the fast tree inference engine offered by TMVA and external tools such as scikit-learn.
 
file  tmva103_Application.C
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This tutorial illustrates how you can conveniently apply BDTs in C++ using the fast tree inference engine offered by TMVA.
 
file  TMVA_CNN_Classification.C
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TMVA Classification Example Using a Convolutional Neural Network
 
file  TMVA_CNN_Classification.py
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TMVA Classification Example Using a Convolutional Neural Network
 
file  TMVA_Higgs_Classification.C
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Classification example of TMVA based on public Higgs UCI dataset
 
file  TMVA_Higgs_Classification.py
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Classification example of TMVA based on public Higgs UCI dataset
 
file  TMVA_RNN_Classification.C
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TMVA Classification Example Using a Recurrent Neural Network
 
file  TMVA_RNN_Classification.py
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TMVA Classification Example Using a Recurrent Neural Network
 
file  TMVA_SOFIE_Inference.py
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This macro provides an example of using a trained model with Keras and make inference using SOFIE directly from Numpy This macro uses as input a Keras model generated with the TMVA_Higgs_Classification.C tutorial You need to run that macro before this one.
 
file  TMVA_SOFIE_Keras.C
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This macro provides a simple example for the parsing of Keras .h5 file into RModel object and further generating the .hxx header files for inference.
 
file  TMVA_SOFIE_Keras_HiggsModel.C
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This macro run the SOFIE parser on the Keras model obtaining running TMVA_Higgs_Classification.C You need to run that macro before this one
 
file  TMVA_SOFIE_Models.py
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Example of inference with SOFIE using a set of models trained with Keras.
 
file  TMVA_SOFIE_ONNX.C
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This macro provides a simple example for the parsing of ONNX files into RModel object and further generating the .hxx header files for inference.
 
file  TMVA_SOFIE_PyTorch.C
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This macro provides a simple example for the parsing of PyTorch .pt file into RModel object and further generating the .hxx header files for inference.
 
file  TMVA_SOFIE_RDataFrame.C
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This macro provides an example of using a trained model with Keras and make inference using SOFIE and RDataFrame This macro uses as input a Keras model generated with the TMVA_Higgs_Classification.C tutorial You need to run that macro before to generate the trained Keras model Then you need to run the macro TMVA_SOFIE_Keras_HiggsModel.C to generate the corresponding header file using SOFIE.
 
file  TMVA_SOFIE_RDataFrame.py
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Example of inference with SOFIE and RDataFrame, of a model trained with Keras.
 
file  TMVA_SOFIE_RDataFrame_JIT.C
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This macro provides an example of using a trained model with Keras and make inference using SOFIE and RDataFrame This macro uses as input a Keras model generated with the TMVA_Higgs_Classification.C tutorial You need to run that macro before this one.
 
file  TMVA_SOFIE_RSofieReader.C
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This macro provides an example of using a trained model with Keras and make inference using SOFIE with the RSofieReader class This macro uses as input a Keras model generated with the TMVA_Higgs_Classification.C tutorial You need to run that macro before to generate the trained Keras model
 
file  TMVAClassification.C
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This macro provides examples for the training and testing of the TMVA classifiers.
 
file  TMVAClassificationApplication.C
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This macro provides a simple example on how to use the trained classifiers within an analysis module
 
file  TMVAClassificationCategory.C
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This macro provides examples for the training and testing of the TMVA classifiers in categorisation mode.
 
file  TMVAClassificationCategoryApplication.C
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This macro provides a simple example on how to use the trained classifiers (with categories) within an analysis module
 
file  TMVACrossValidation.C
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This macro provides an example of how to use TMVA for k-folds cross evaluation.
 
file  TMVACrossValidationApplication.C
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This macro provides an example of how to use TMVA for k-folds cross evaluation in application.
 
file  TMVACrossValidationRegression.C
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This macro provides an example of how to use TMVA for k-folds cross evaluation.
 
file  TMVAGAexample.C
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This executable gives an example of a very simple use of the genetic algorithm of TMVA
 
file  TMVAGAexample2.C
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This executable gives an example of a very simple use of the genetic algorithm of TMVA.
 
file  TMVAMinimalClassification.C
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Minimal self-contained example for setting up TMVA with binary classification.
 
file  TMVAMulticlass.C
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This macro provides a simple example for the training and testing of the TMVA multiclass classification
 
file  TMVAMulticlassApplication.C
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This macro provides a simple example on how to use the trained multiclass classifiers within an analysis module
 
file  TMVAMultipleBackgroundExample.C
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This example shows the training of signal with three different backgrounds Then in the application a tree is created with all signal and background events where the true class ID and the three classifier outputs are added finally with the application tree, the significance is maximized with the help of the TMVA genetic algorithm.
 
file  TMVARegression.C
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This macro provides examples for the training and testing of the TMVA classifiers.
 
file  TMVARegressionApplication.C
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This macro provides a simple example on how to use the trained regression MVAs within an analysis module