TMVA_CNN_Classification.py File Reference

Detailed Description

TMVA Classification Example Using a Convolutional Neural Network

This is an example of using a CNN in TMVA. We do classification using a toy image data set that is generated when running the example macro

Running with nthreads  = 4
DataSetInfo              : [dataset] : Added class "Signal"
                         : Add Tree sig_tree of type Signal with 1000 events
DataSetInfo              : [dataset] : Added class "Background"
                         : Add Tree bkg_tree of type Background with 1000 events
Factory                  : Booking method: ␛[1mBDT␛[0m
                         : 
                         : Rebuilding Dataset dataset
                         : Building event vectors for type 2 Signal
                         : Dataset[dataset] :  create input formulas for tree sig_tree
                         : Using variable vars[0] from array expression vars of size 256
                         : Building event vectors for type 2 Background
                         : Dataset[dataset] :  create input formulas for tree bkg_tree
                         : Using variable vars[0] from array expression vars of size 256
DataSetFactory           : [dataset] : Number of events in input trees
                         : 
                         : 
                         : Number of training and testing events
                         : ---------------------------------------------------------------------------
                         : Signal     -- training events            : 800
                         : Signal     -- testing events             : 200
                         : Signal     -- training and testing events: 1000
                         : Background -- training events            : 800
                         : Background -- testing events             : 200
                         : Background -- training and testing events: 1000
                         : 
Factory                  : ␛[1mTrain all methods␛[0m
Factory                  : Train method: BDT for Classification
                         : 
BDT                      : #events: (reweighted) sig: 800 bkg: 800
                         : #events: (unweighted) sig: 800 bkg: 800
                         : Training 400 Decision Trees ... patience please
                         : Elapsed time for training with 1600 events: 1.32 sec         
BDT                      : [dataset] : Evaluation of BDT on training sample (1600 events)
                         : Elapsed time for evaluation of 1600 events: 0.014 sec       
                         : Creating xml weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_BDT.weights.xml␛[0m
                         : Creating standalone class: ␛[0;36mdataset/weights/TMVA_CNN_Classification_BDT.class.C␛[0m
                         : TMVA_CNN_ClassificationOutput.root:/dataset/Method_BDT/BDT
Factory                  : Training finished
                         : 
                         : Ranking input variables (method specific)...
BDT                      : Ranking result (top variable is best ranked)
                         : --------------------------------------
                         : Rank : Variable  : Variable Importance
                         : --------------------------------------
                         :    1 : vars      : 9.259e-03
                         :    2 : vars      : 9.226e-03
                         :    3 : vars      : 9.089e-03
                         :    4 : vars      : 8.319e-03
                         :    5 : vars      : 8.294e-03
                         :    6 : vars      : 8.048e-03
                         :    7 : vars      : 7.851e-03
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                         : --------------------------------------
Factory                  : === Destroy and recreate all methods via weight files for testing ===
                         : 
                         : Reading weight file: ␛[0;36mdataset/weights/TMVA_CNN_Classification_BDT.weights.xml␛[0m
Factory                  : ␛[1mTest all methods␛[0m
Factory                  : Test method: BDT for Classification performance
                         : 
BDT                      : [dataset] : Evaluation of BDT on testing sample (400 events)
                         : Elapsed time for evaluation of 400 events: 0.00348 sec       
Factory                  : ␛[1mEvaluate all methods␛[0m
Factory                  : Evaluate classifier: BDT
                         : 
BDT                      : [dataset] : Loop over test events and fill histograms with classifier response...
                         : 
                         : Dataset[dataset] :  variable plots are not produces ! The number of variables is 256 , it is larger than 200
                         : 
                         : Evaluation results ranked by best signal efficiency and purity (area)
                         : -------------------------------------------------------------------------------------------------------------------
                         : DataSet       MVA                       
                         : Name:         Method:          ROC-integ
                         : dataset       BDT            : 0.779
                         : -------------------------------------------------------------------------------------------------------------------
                         : 
                         : Testing efficiency compared to training efficiency (overtraining check)
                         : -------------------------------------------------------------------------------------------------------------------
                         : DataSet              MVA              Signal efficiency: from test sample (from training sample) 
                         : Name:                Method:          @B=0.01             @B=0.10            @B=0.30   
                         : -------------------------------------------------------------------------------------------------------------------
                         : dataset              BDT            : 0.095 (0.415)       0.408 (0.709)      0.675 (0.862)
                         : -------------------------------------------------------------------------------------------------------------------
                         : 
Dataset:dataset          : Created tree 'TestTree' with 400 events
                         : 
Dataset:dataset          : Created tree 'TrainTree' with 1600 events
                         : 
Factory                  : ␛[1mThank you for using TMVA!␛[0m
                         : ␛[1mFor citation information, please visit: http://tmva.sf.net/citeTMVA.html␛[0m

 
 
# TMVA Classification Example Using a Convolutional Neural Network
 
 
## Helper function to create input images data
## we create a signal and background 2D histograms from 2d gaussians
## with a location (means in X and Y)  different for each event
## The difference between signal and background is in the gaussian width.
## The width for the background gaussian is slightly larger than the signal width by few % values
 
import ROOT
 
import os
import importlib.util
 
opt = [1, 1, 1, 1, 1]
useTMVACNN = opt[0] if len(opt) > 0  else False
useKerasCNN = opt[1] if len(opt) > 1 else False
useTMVADNN = opt[2] if len(opt) > 2 else False
useTMVABDT = opt[3] if len(opt) > 3 else False
usePyTorchCNN = opt[4] if len(opt) > 4 else False
 
TMVA = ROOT.TMVA
TFile = ROOT.TFile
 
TMVA.Tools.Instance()
 
def MakeImagesTree(n, nh, nw):
    # image size (nh x nw)
    ntot = nh * nw
    fileOutName = "images_data_16x16.root"
    nRndmEvts = 10000  # number of events we use to fill each image
    delta_sigma = 0.1  # 5% difference in the sigma
    pixelNoise = 5
 
    sX1 = 3
    sY1 = 3
    sX2 = sX1 + delta_sigma
    sY2 = sY1 - delta_sigma
    h1 = ROOT.TH2D("h1", "h1", nh, 0, 10, nw, 0, 10)
    h2 = ROOT.TH2D("h2", "h2", nh, 0, 10, nw, 0, 10)
    f1 = ROOT.TF2("f1", "xygaus")
    f2 = ROOT.TF2("f2", "xygaus")
    sgn = ROOT.TTree("sig_tree", "signal_tree")
    bkg = ROOT.TTree("bkg_tree", "background_tree")
 
    f = TFile(fileOutName, "RECREATE")
    x1 = ROOT.std.vector["float"](ntot)
    x2 = ROOT.std.vector["float"](ntot)
 
    # create signal and background trees with a single branch
    # an std::vector<float> of size nh x nw containing the image data
    bkg.Branch("vars", "std::vector<float>", x1)
    sgn.Branch("vars", "std::vector<float>", x2)
 
    sgn.SetDirectory(f)
    bkg.SetDirectory(f)
 
    f1.SetParameters(1, 5, sX1, 5, sY1)
    f2.SetParameters(1, 5, sX2, 5, sY2)
    ROOT.gRandom.SetSeed(0)
    ROOT.Info("TMVA_CNN_Classification", "Filling ROOT tree \n")
    for i in range(n):
        if i % 1000 == 0:
            print("Generating image event ...", i)
 
        h1.Reset()
        h2.Reset()
        # generate random means in range [3,7] to be not too much on the border
        f1.SetParameter(1, ROOT.gRandom.Uniform(3, 7))
        f1.SetParameter(3, ROOT.gRandom.Uniform(3, 7))
        f2.SetParameter(1, ROOT.gRandom.Uniform(3, 7))
        f2.SetParameter(3, ROOT.gRandom.Uniform(3, 7))
 
        h1.FillRandom("f1", nRndmEvts)
        h2.FillRandom("f2", nRndmEvts)
 
        for k in range(nh):
            for l in range(nw):
                m = k * nw + l
                # add some noise in each bin
                x1[m] = h1.GetBinContent(k + 1, l + 1) + ROOT.gRandom.Gaus(0, pixelNoise)
                x2[m] = h2.GetBinContent(k + 1, l + 1) + ROOT.gRandom.Gaus(0, pixelNoise)
 
        sgn.Fill()
        bkg.Fill()
 
    sgn.Write()
    bkg.Write()
 
    print("Signal and background tree with images data written to the file %s", f.GetName())
    sgn.Print()
    bkg.Print()
    f.Close()
 
hasGPU = "tmva-gpu" in ROOT.gROOT.GetConfigFeatures()
hasCPU = "tmva-cpu" in ROOT.gROOT.GetConfigFeatures()
 
nevt = 1000    # use a larger value to get better results
 
if (not hasCPU and not hasGPU) :
    ROOT.Warning("TMVA_CNN_Classificaton","ROOT is not supporting tmva-cpu and tmva-gpu skip using TMVA-DNN and TMVA-CNN")
    useTMVACNN = False
    useTMVADNN = False
 
if not "tmva-pymva" in ROOT.gROOT.GetConfigFeatures():
    useKerasCNN = False
    usePyTorchCNN = False
else:
    TMVA.PyMethodBase.PyInitialize()
 
if not useTMVACNN:
    ROOT.Warning(
        "TMVA_CNN_Classificaton",
        "TMVA is not build with GPU or CPU multi-thread support. Cannot use TMVA Deep Learning for CNN",
    )
 
writeOutputFile = True
 
num_threads = 4  # use max 4 threads
max_epochs = 10  # maximum number of epochs used for training
 
 
# do enable MT running
if "imt" in ROOT.gROOT.GetConfigFeatures():
    ROOT.EnableImplicitMT(num_threads)
    ROOT.gSystem.Setenv("OMP_NUM_THREADS", "1")  # switch OFF MT in OpenBLAS
    print("Running with nthreads  = {}".format(ROOT.GetThreadPoolSize()))
else:
    print("Running in serial mode since ROOT does not support MT")
 
 
 
 
outputFile = None
if writeOutputFile:
    outputFile = TFile.Open("TMVA_CNN_ClassificationOutput.root", "RECREATE")
 
 
## Create TMVA Factory
 
# Create the Factory class. Later you can choose the methods
# whose performance you'd like to investigate.
 
# The factory is the major TMVA object you have to interact with. Here is the list of parameters you need to pass
 
# - The first argument is the base of the name of all the output
#   weight files in the directory weight/ that will be created with the
#    method parameters
 
# - The second argument is the output file for the training results
 
# - The third argument is a string option defining some general configuration for the TMVA session.
# For example all TMVA output can be suppressed by removing the "!" (not) in front of the "Silent" argument in the
# option string
 
# - note that we disable any pre-transformation of the input variables and we avoid computing correlations between
# input variables
 
 
factory = TMVA.Factory(
    "TMVA_CNN_Classification",
    outputFile,
    V=False,
    ROC=True,
    Silent=False,
    Color=True,
    AnalysisType="Classification",
    Transformations=None,
    Correlations=False,
)
 
 
## Declare DataLoader(s)
 
# The next step is to declare the DataLoader class that deals with input variables
 
# Define the input variables that shall be used for the MVA training
# note that you may also use variable expressions, which can be parsed by TTree::Draw( "expression" )]
 
# In this case the input data consists of an image of 16x16 pixels. Each single pixel is a branch in a ROOT TTree
 
loader = TMVA.DataLoader("dataset")
 
 
## Setup Dataset(s)
 
# Define input data file and signal and background trees
 
 
imgSize = 16 * 16
inputFileName = "images_data_16x16.root"
 
# if the input file does not exist create it
if ROOT.gSystem.AccessPathName(inputFileName):
    MakeImagesTree(nevt, 16, 16)
 
inputFile = TFile.Open(inputFileName)
if inputFile is None:
    ROOT.Warning("TMVA_CNN_Classification", "Error opening input file %s - exit", inputFileName.Data())
 
 
# inputFileName = "tmva_class_example.root"
 
 
# --- Register the training and test trees
 
signalTree = inputFile.Get("sig_tree")
backgroundTree = inputFile.Get("bkg_tree")
 
nEventsSig = signalTree.GetEntries()
nEventsBkg = backgroundTree.GetEntries()
 
# global event weights per tree (see below for setting event-wise weights)
signalWeight = 1.0
backgroundWeight = 1.0
 
# You can add an arbitrary number of signal or background trees
loader.AddSignalTree(signalTree, signalWeight)
loader.AddBackgroundTree(backgroundTree, backgroundWeight)
 
## add event variables (image)
## use new method (from ROOT 6.20 to add a variable array for all image data)
loader.AddVariablesArray("vars", imgSize)
 
# Set individual event weights (the variables must exist in the original TTree)
#    for signal    : factory->SetSignalWeightExpression    ("weight1*weight2");
#    for background: factory->SetBackgroundWeightExpression("weight1*weight2");
# loader->SetBackgroundWeightExpression( "weight" );
 
# Apply additional cuts on the signal and background samples (can be different)
mycuts = ""  # for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
mycutb = ""  # for example: TCut mycutb = "abs(var1)<0.5";
 
# Tell the factory how to use the training and testing events
# If no numbers of events are given, half of the events in the tree are used
# for training, and the other half for testing:
#    loader.PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
# It is possible also to specify the number of training and testing events,
# note we disable the computation of the correlation matrix of the input variables
 
nTrainSig = 0.8 * nEventsSig
nTrainBkg = 0.8 * nEventsBkg
 
# build the string options for DataLoader::PrepareTrainingAndTestTree
 
loader.PrepareTrainingAndTestTree(
    mycuts,
    mycutb,
    nTrain_Signal=nTrainSig,
    nTrain_Background=nTrainBkg,
    SplitMode="Random",
    SplitSeed=100,
    NormMode="NumEvents",
    V=False,
    CalcCorrelations=False,
)
 
 
# DataSetInfo              : [dataset] : Added class "Signal"
#    : Add Tree sig_tree of type Signal with 10000 events
#    DataSetInfo              : [dataset] : Added class "Background"
#        : Add Tree bkg_tree of type Background with 10000 events
 
# signalTree.Print();
 
# Booking Methods
 
# Here we book the TMVA methods. We book a Boosted Decision Tree method (BDT)
 
 
# Boosted Decision Trees
if useTMVABDT:
    factory.BookMethod(
        loader,
        TMVA.Types.kBDT,
        "BDT",
        V=False,
        NTrees=400,
        MinNodeSize="2.5%",
        MaxDepth=2,
        BoostType="AdaBoost",
        AdaBoostBeta=0.5,
        UseBaggedBoost=True,
        BaggedSampleFraction=0.5,
        SeparationType="GiniIndex",
        nCuts=20,
    )
 
 
#### Booking Deep Neural Network
 
# Here we book the DNN of TMVA. See the example TMVA_Higgs_Classification.C for a detailed description of the
# options
 
if useTMVADNN:
    layoutString = ROOT.TString(
        "DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,BNORM,DENSE|100|RELU,DENSE|1|LINEAR"
    )
 
    # Training strategies
    # one can catenate several training strings with different parameters (e.g. learning rates or regularizations
    # parameters) The training string must be concatenated with the `|` delimiter
    trainingString1 = ROOT.TString(
        "LearningRate=1e-3,Momentum=0.9,Repetitions=1,"
        "ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,"
        "WeightDecay=1e-4,Regularization=None,"
        "Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0."
    )  # + "|" + trainingString2 + ...
    trainingString1 += ",MaxEpochs=" + str(max_epochs)
 
    # Build now the full DNN Option string
    dnnMethodName = "TMVA_DNN_CPU"
 
    # use GPU if available
    dnnOptions = "CPU"
    if hasGPU :
        dnnOptions = "GPU"
        dnnMethodName = "TMVA_DNN_GPU"
 
    factory.BookMethod(
        loader,
        TMVA.Types.kDL,
        dnnMethodName,
        H=False,
        V=True,
        ErrorStrategy="CROSSENTROPY",
        VarTransform=None,
        WeightInitialization="XAVIER",
        Layout=layoutString,
        TrainingStrategy=trainingString1,
        Architecture=dnnOptions
    )
 
 
### Book Convolutional Neural Network in TMVA
 
# For building a CNN one needs to define
 
# -  Input Layout :  number of channels (in this case = 1)  | image height | image width
# -  Batch Layout :  batch size | number of channels | image size = (height*width)
 
# Then one add Convolutional layers and MaxPool layers.
 
# -  For Convolutional layer the option string has to be:
# - CONV | number of units | filter height | filter width | stride height | stride width | padding height | paddig
# width | activation function
 
# - note in this case we are using a filer 3x3 and padding=1 and stride=1 so we get the output dimension of the
# conv layer equal to the input
 
# - note we use after the first convolutional layer a batch normalization layer. This seems to help significantly the
# convergence
 
# - For the MaxPool layer:
# - MAXPOOL  | pool height | pool width | stride height | stride width
 
# The RESHAPE layer is needed to flatten the output before the Dense layer
 
# Note that to run the CNN is required to have CPU  or GPU support
 
 
if useTMVACNN:
    # Training strategies.
    trainingString1 = ROOT.TString(
        "LearningRate=1e-3,Momentum=0.9,Repetitions=1,"
        "ConvergenceSteps=5,BatchSize=100,TestRepetitions=1,"
        "WeightDecay=1e-4,Regularization=None,"
        "Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0"
    )
    trainingString1 += ",MaxEpochs=" + str(max_epochs)
 
    ## New DL (CNN)
    cnnMethodName = "TMVA_CNN_CPU"
    cnnOptions = "CPU"
    # use GPU if available
    if hasGPU:
        cnnOptions = "GPU"
        cnnMethodName = "TMVA_CNN_GPU"
 
    factory.BookMethod(
        loader,
        TMVA.Types.kDL,
        cnnMethodName,
        H=False,
        V=True,
        ErrorStrategy="CROSSENTROPY",
        VarTransform=None,
        WeightInitialization="XAVIER",
        InputLayout="1|16|16",
        Layout="CONV|10|3|3|1|1|1|1|RELU,BNORM,CONV|10|3|3|1|1|1|1|RELU,MAXPOOL|2|2|1|1,RESHAPE|FLAT,DENSE|100|RELU,DENSE|1|LINEAR",
        TrainingStrategy=trainingString1,
        Architecture=cnnOptions,
    )
 
 
### Book Convolutional Neural Network in Keras using a generated model
 
 
if usePyTorchCNN:
    ROOT.Info("TMVA_CNN_Classification", "Using Convolutional PyTorch Model")
    pyTorchFileName = str(ROOT.gROOT.GetTutorialDir())
    pyTorchFileName += "/tmva/PyTorch_Generate_CNN_Model.py"
    # check that pytorch can be imported and file defining the model exists
    torch_spec = importlib.util.find_spec("torch")
    if torch_spec is not None and os.path.exists(pyTorchFileName):
        #cmd = str(ROOT.TMVA.Python_Executable()) + "  " + pyTorchFileName
        #os.system(cmd)
        #import PyTorch_Generate_CNN_Model
        ROOT.Info("TMVA_CNN_Classification", "Booking PyTorch CNN model")
        factory.BookMethod(
            loader,
            TMVA.Types.kPyTorch,
            "PyTorch",
            H=True,
            V=False,
            VarTransform=None,
            FilenameModel="PyTorchModelCNN.pt",
            FilenameTrainedModel="PyTorchTrainedModelCNN.pt",
            NumEpochs=max_epochs,
            BatchSize=100,
            UserCode=str(pyTorchFileName)
        )
    else:
        ROOT.Warning(
            "TMVA_CNN_Classification",
            "PyTorch is not installed or model building file is not existing - skip using PyTorch",
        )
 
if useKerasCNN:
    ROOT.Info("TMVA_CNN_Classification", "Building convolutional keras model")
    # create python script which can be executed
    # create 2 conv2d layer + maxpool + dense
    import tensorflow
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.optimizers import Adam
 
    # from keras.initializers import TruncatedNormal
    # from keras import initializations
    from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Reshape
 
    # from keras.callbacks import ReduceLROnPlateau
    model = Sequential()
    model.add(Reshape((16, 16, 1), input_shape=(256,)))
    model.add(Conv2D(10, kernel_size=(3, 3), kernel_initializer="TruncatedNormal", activation="relu", padding="same"))
    model.add(Conv2D(10, kernel_size=(3, 3), kernel_initializer="TruncatedNormal", activation="relu", padding="same"))
    # stride for maxpool is equal to pool size
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(64, activation="tanh"))
    # model.add(Dropout(0.2))
    model.add(Dense(2, activation="sigmoid"))
    model.compile(loss="binary_crossentropy", optimizer=Adam(learning_rate=0.001), weighted_metrics=["accuracy"])
    model.save("model_cnn.h5")
    model.summary()
 
    if not os.path.exists("model_cnn.h5"):
        raise FileNotFoundError("Error creating Keras model file - skip using Keras")
    else:
        # book PyKeras method only if Keras model could be created
        ROOT.Info("TMVA_CNN_Classification", "Booking convolutional keras model")
        factory.BookMethod(
            loader,
            TMVA.Types.kPyKeras,
            "PyKeras",
            H=True,
            V=False,
            VarTransform=None,
            FilenameModel="model_cnn.h5",
            FilenameTrainedModel="trained_model_cnn.h5",
            NumEpochs=max_epochs,
            BatchSize=100,
            GpuOptions="allow_growth=True",
        )  # needed for RTX NVidia card and to avoid TF allocates all GPU memory
 
 
 
## Train Methods
 
factory.TrainAllMethods()
 
## Test and Evaluate Methods
 
factory.TestAllMethods()
 
factory.EvaluateAllMethods()
 
## Plot ROC Curve
 
c1 = factory.GetROCCurve(loader)
c1.Draw()
 
# close outputfile to save output file
outputFile.Close()

Author

Harshal Shende

Definition in file TMVA_CNN_Classification.py.