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[tmva] Fix usage of TFile in Python tutorials
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,70 +1,81 @@ | ||
| #!/usr/bin/env python | ||
| ## \file | ||
| ## \ingroup tutorial_tmva_keras | ||
| ## \notebook -nodraw | ||
| ## This tutorial shows how to do classification in TMVA with neural networks | ||
| ## trained with keras. | ||
| ## | ||
| ## \macro_code | ||
| ## | ||
| ## \date 2017 | ||
| ## \author TMVA Team | ||
|
|
||
| from ROOT import TMVA, TFile, TTree, TCut | ||
| # \file | ||
| # \ingroup tutorial_tmva_keras | ||
| # \notebook -nodraw | ||
| # This tutorial shows how to do classification in TMVA with neural networks | ||
| # trained with keras. | ||
| # | ||
| # \macro_code | ||
| # | ||
| # \date 2017 | ||
| # \author TMVA Team | ||
|
|
||
| from ROOT import TMVA, TFile, TCut | ||
| from subprocess import call | ||
| from os.path import isfile | ||
|
|
||
| from tensorflow.keras.models import Sequential | ||
| from tensorflow.keras.layers import Dense, Activation | ||
| from tensorflow.keras.layers import Dense | ||
| from tensorflow.keras.optimizers import SGD | ||
|
|
||
| # Setup TMVA | ||
| TMVA.Tools.Instance() | ||
| TMVA.PyMethodBase.PyInitialize() | ||
|
|
||
| output = TFile.Open('TMVA_Classification_Keras.root', 'RECREATE') | ||
| factory = TMVA.Factory('TMVAClassification', output, | ||
| '!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=Classification') | ||
| def create_model(): | ||
| # Generate model | ||
|
|
||
| # Define model | ||
| model = Sequential() | ||
| model.add(Dense(64, activation='relu', input_dim=4)) | ||
| model.add(Dense(2, activation='softmax')) | ||
|
|
||
| # Set loss and optimizer | ||
| model.compile(loss='categorical_crossentropy', | ||
| optimizer=SGD(learning_rate=0.01), weighted_metrics=['accuracy', ]) | ||
|
|
||
| # Store model to file | ||
| model.save('modelClassification.h5') | ||
| model.summary() | ||
|
|
||
|
|
||
| def run(): | ||
| with TFile.Open('TMVA_Classification_Keras.root', 'RECREATE') as output, TFile.Open('tmva_class_example.root') as data: | ||
| factory = TMVA.Factory('TMVAClassification', output, | ||
| '!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=Classification') | ||
|
|
||
| # Load data | ||
| if not isfile('tmva_class_example.root'): | ||
| call(['curl', '-L', '-O', 'http://root.cern/files/tmva_class_example.root']) | ||
| signal = data.Get('TreeS') | ||
| background = data.Get('TreeB') | ||
|
|
||
| data = TFile.Open('tmva_class_example.root') | ||
| signal = data.Get('TreeS') | ||
| background = data.Get('TreeB') | ||
| dataloader = TMVA.DataLoader('dataset') | ||
| for branch in signal.GetListOfBranches(): | ||
| dataloader.AddVariable(branch.GetName()) | ||
|
|
||
| dataloader = TMVA.DataLoader('dataset') | ||
| for branch in signal.GetListOfBranches(): | ||
| dataloader.AddVariable(branch.GetName()) | ||
| dataloader.AddSignalTree(signal, 1.0) | ||
| dataloader.AddBackgroundTree(background, 1.0) | ||
| dataloader.PrepareTrainingAndTestTree(TCut(''), | ||
| 'nTrain_Signal=4000:nTrain_Background=4000:SplitMode=Random:NormMode=NumEvents:!V') | ||
|
|
||
| dataloader.AddSignalTree(signal, 1.0) | ||
| dataloader.AddBackgroundTree(background, 1.0) | ||
| dataloader.PrepareTrainingAndTestTree(TCut(''), | ||
| 'nTrain_Signal=4000:nTrain_Background=4000:SplitMode=Random:NormMode=NumEvents:!V') | ||
| # Book methods | ||
| factory.BookMethod(dataloader, TMVA.Types.kFisher, 'Fisher', | ||
| '!H:!V:Fisher:VarTransform=D,G') | ||
| factory.BookMethod(dataloader, TMVA.Types.kPyKeras, 'PyKeras', | ||
| 'H:!V:VarTransform=D,G:FilenameModel=modelClassification.h5:FilenameTrainedModel=trainedModelClassification.h5:NumEpochs=20:BatchSize=32') | ||
|
|
||
| # Generate model | ||
| # Run training, test and evaluation | ||
| factory.TrainAllMethods() | ||
| factory.TestAllMethods() | ||
| factory.EvaluateAllMethods() | ||
|
|
||
| # Define model | ||
| model = Sequential() | ||
| model.add(Dense(64, activation='relu', input_dim=4)) | ||
| model.add(Dense(2, activation='softmax')) | ||
|
|
||
| # Set loss and optimizer | ||
| model.compile(loss='categorical_crossentropy', | ||
| optimizer=SGD(learning_rate=0.01), weighted_metrics=['accuracy', ]) | ||
| if __name__ == "__main__": | ||
| # Setup TMVA | ||
| TMVA.Tools.Instance() | ||
| TMVA.PyMethodBase.PyInitialize() | ||
|
|
||
| # Store model to file | ||
| model.save('modelClassification.h5') | ||
| model.summary() | ||
| # Create and store the ML model | ||
| create_model() | ||
|
|
||
| # Book methods | ||
| factory.BookMethod(dataloader, TMVA.Types.kFisher, 'Fisher', | ||
| '!H:!V:Fisher:VarTransform=D,G') | ||
| factory.BookMethod(dataloader, TMVA.Types.kPyKeras, 'PyKeras', | ||
| 'H:!V:VarTransform=D,G:FilenameModel=modelClassification.h5:FilenameTrainedModel=trainedModelClassification.h5:NumEpochs=20:BatchSize=32') | ||
| # Load data | ||
| if not isfile('tmva_class_example.root'): | ||
| call(['curl', '-L', '-O', 'http://root.cern/files/tmva_class_example.root']) | ||
|
|
||
| # Run training, test and evaluation | ||
| factory.TrainAllMethods() | ||
| factory.TestAllMethods() | ||
| factory.EvaluateAllMethods() | ||
| # Run TMVA | ||
| run() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,75 +1,85 @@ | ||
| #!/usr/bin/env python | ||
| ## \file | ||
| ## \ingroup tutorial_tmva_keras | ||
| ## \notebook -nodraw | ||
| ## This tutorial shows how to do multiclass classification in TMVA with neural | ||
| ## networks trained with keras. | ||
| ## | ||
| ## \macro_code | ||
| ## | ||
| ## \date 2017 | ||
| ## \author TMVA Team | ||
|
|
||
| from ROOT import TMVA, TFile, TTree, TCut, gROOT | ||
| # \file | ||
| # \ingroup tutorial_tmva_keras | ||
| # \notebook -nodraw | ||
| # This tutorial shows how to do multiclass classification in TMVA with neural | ||
| # networks trained with keras. | ||
| # | ||
| # \macro_code | ||
| # | ||
| # \date 2017 | ||
| # \author TMVA Team | ||
|
|
||
| from ROOT import TMVA, TFile, TCut, gROOT | ||
| from os.path import isfile | ||
|
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||
| from tensorflow.keras.models import Sequential | ||
| from tensorflow.keras.layers import Dense, Activation | ||
| from tensorflow.keras.layers import Dense | ||
| from tensorflow.keras.optimizers import SGD | ||
|
|
||
| # Setup TMVA | ||
| TMVA.Tools.Instance() | ||
| TMVA.PyMethodBase.PyInitialize() | ||
|
|
||
| output = TFile.Open('TMVA.root', 'RECREATE') | ||
| factory = TMVA.Factory('TMVAClassification', output, | ||
| '!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=multiclass') | ||
|
|
||
| # Load data | ||
| if not isfile('tmva_example_multiple_background.root'): | ||
| createDataMacro = str(gROOT.GetTutorialDir()) + '/tmva/createData.C' | ||
| print(createDataMacro) | ||
| gROOT.ProcessLine('.L {}'.format(createDataMacro)) | ||
| gROOT.ProcessLine('create_MultipleBackground(4000)') | ||
|
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||
| data = TFile.Open('tmva_example_multiple_background.root') | ||
| signal = data.Get('TreeS') | ||
| background0 = data.Get('TreeB0') | ||
| background1 = data.Get('TreeB1') | ||
| background2 = data.Get('TreeB2') | ||
|
|
||
| dataloader = TMVA.DataLoader('dataset') | ||
| for branch in signal.GetListOfBranches(): | ||
| dataloader.AddVariable(branch.GetName()) | ||
|
|
||
| dataloader.AddTree(signal, 'Signal') | ||
| dataloader.AddTree(background0, 'Background_0') | ||
| dataloader.AddTree(background1, 'Background_1') | ||
| dataloader.AddTree(background2, 'Background_2') | ||
| dataloader.PrepareTrainingAndTestTree(TCut(''), | ||
| 'SplitMode=Random:NormMode=NumEvents:!V') | ||
|
|
||
| # Generate model | ||
|
|
||
| # Define model | ||
| model = Sequential() | ||
| model.add(Dense(32, activation='relu', input_dim=4)) | ||
| model.add(Dense(4, activation='softmax')) | ||
|
|
||
| # Set loss and optimizer | ||
| model.compile(loss='categorical_crossentropy', optimizer=SGD(learning_rate=0.01), weighted_metrics=['accuracy',]) | ||
|
|
||
| # Store model to file | ||
| model.save('modelMultiClass.h5') | ||
| model.summary() | ||
|
|
||
| # Book methods | ||
| factory.BookMethod(dataloader, TMVA.Types.kFisher, 'Fisher', | ||
| '!H:!V:Fisher:VarTransform=D,G') | ||
| factory.BookMethod(dataloader, TMVA.Types.kPyKeras, 'PyKeras', | ||
| 'H:!V:VarTransform=D,G:FilenameModel=modelMultiClass.h5:FilenameTrainedModel=trainedModelMultiClass.h5:NumEpochs=20:BatchSize=32') | ||
|
|
||
| # Run TMVA | ||
| factory.TrainAllMethods() | ||
| factory.TestAllMethods() | ||
| factory.EvaluateAllMethods() | ||
|
|
||
| def create_model(): | ||
| # Define model | ||
| model = Sequential() | ||
| model.add(Dense(32, activation='relu', input_dim=4)) | ||
| model.add(Dense(4, activation='softmax')) | ||
|
|
||
| # Set loss and optimizer | ||
| model.compile(loss='categorical_crossentropy', optimizer=SGD( | ||
| learning_rate=0.01), weighted_metrics=['accuracy',]) | ||
|
|
||
| # Store model to file | ||
| model.save('modelMultiClass.h5') | ||
| model.summary() | ||
|
|
||
|
|
||
| def run(): | ||
| with TFile.Open('TMVA.root', 'RECREATE') as output, TFile.Open('tmva_example_multiple_background.root') as data: | ||
| factory = TMVA.Factory('TMVAClassification', output, | ||
| '!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=multiclass') | ||
|
|
||
| signal = data.Get('TreeS') | ||
| background0 = data.Get('TreeB0') | ||
| background1 = data.Get('TreeB1') | ||
| background2 = data.Get('TreeB2') | ||
|
|
||
| dataloader = TMVA.DataLoader('dataset') | ||
| for branch in signal.GetListOfBranches(): | ||
| dataloader.AddVariable(branch.GetName()) | ||
|
|
||
| dataloader.AddTree(signal, 'Signal') | ||
| dataloader.AddTree(background0, 'Background_0') | ||
| dataloader.AddTree(background1, 'Background_1') | ||
| dataloader.AddTree(background2, 'Background_2') | ||
| dataloader.PrepareTrainingAndTestTree(TCut(''), | ||
| 'SplitMode=Random:NormMode=NumEvents:!V') | ||
|
|
||
| # Book methods | ||
| factory.BookMethod(dataloader, TMVA.Types.kFisher, 'Fisher', | ||
| '!H:!V:Fisher:VarTransform=D,G') | ||
| factory.BookMethod(dataloader, TMVA.Types.kPyKeras, 'PyKeras', | ||
| 'H:!V:VarTransform=D,G:FilenameModel=modelMultiClass.h5:FilenameTrainedModel=trainedModelMultiClass.h5:NumEpochs=20:BatchSize=32') | ||
|
|
||
| # Run TMVA | ||
| factory.TrainAllMethods() | ||
| factory.TestAllMethods() | ||
| factory.EvaluateAllMethods() | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| # Generate model | ||
| create_model() | ||
|
|
||
| # Setup TMVA | ||
| TMVA.Tools.Instance() | ||
| TMVA.PyMethodBase.PyInitialize() | ||
|
|
||
| # Load data | ||
| if not isfile('tmva_example_multiple_background.root'): | ||
| createDataMacro = str(gROOT.GetTutorialDir()) + '/tmva/createData.C' | ||
| print(createDataMacro) | ||
| gROOT.ProcessLine('.L {}'.format(createDataMacro)) | ||
| gROOT.ProcessLine('create_MultipleBackground(4000)') | ||
|
|
||
| # Run TMVA | ||
| run() |
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