TMVA_RNN_Classification.py

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## \file
## \ingroup tutorial_tmva
## \notebook
##  TMVA Classification Example Using a Recurrent Neural Network
##
## This is an example of using a RNN in TMVA. We do classification using a toy time dependent data set
## that is generated when running this example macro
##
## \macro_image
## \macro_output
## \macro_code
##
## \author Harshal Shende
 
 
# TMVA Classification Example Using a Recurrent Neural Network
 
# This is an example of using a RNN in TMVA.
# We do the classification using a toy data set containing a time series of data sample ntimes
# and with dimension ndim that is generated when running the provided function `MakeTimeData (nevents, ntime, ndim)`
 
 
import ROOT
 
num_threads = 4  # use max 4 threads
# do enable MT running
if "imt" in ROOT.gROOT.GetConfigFeatures():
    ROOT.EnableImplicitMT(num_threads)
    # switch off MT in OpenBLAS to avoid conflict with tbb
    ROOT.gSystem.Setenv("OMP_NUM_THREADS", "1")
    print("Running with nthreads  = {}".format(ROOT.GetThreadPoolSize()))
else:
    print("Running in serial mode since ROOT does not support MT")
 
 
TMVA = ROOT.TMVA
TFile = ROOT.TFile
 
import os
import importlib
 
 
TMVA.Tools.Instance()
TMVA.Config.Instance()
 
 
##  Helper function to generate the time data set
##  make some time data but not of fixed length.
##  use a poisson with mu = 5 and truncated at 10
 
 
def MakeTimeData(n, ntime, ndim):
    # ntime = 10;
    # ndim = 30; // number of dim/time
 
    fname = "time_data_t" + str(ntime) + "_d" + str(ndim) + ".root"
    v1 = []
    v2 = []
 
    for i in range(ntime):
        v1.append(ROOT.TH1D("h1_" + str(i), "h1", ndim, 0, 10))
        v2.append(ROOT.TH1D("h2_" + str(i), "h2", ndim, 0, 10))
 
    f1 = ROOT.TF1("f1", "gaus")
    f2 = ROOT.TF1("f2", "gaus")
 
    sgn = ROOT.TTree("sgn", "sgn")
    bkg = ROOT.TTree("bkg", "bkg")
    f = TFile(fname, "RECREATE")
 
    x1 = []
    x2 = []
 
    for i in range(ntime):
        x1.append(ROOT.std.vector["float"](ndim))
        x2.append(ROOT.std.vector["float"](ndim))
 
    for i in range(ntime):
        bkg.Branch("vars_time" + str(i), "std::vector<float>", x1[i])
        sgn.Branch("vars_time" + str(i), "std::vector<float>", x2[i])
 
    sgn.SetDirectory(f)
    bkg.SetDirectory(f)
    ROOT.gRandom.SetSeed(0)
 
    mean1 = ROOT.std.vector["double"](ntime)
    mean2 = ROOT.std.vector["double"](ntime)
    sigma1 = ROOT.std.vector["double"](ntime)
    sigma2 = ROOT.std.vector["double"](ntime)
 
    for j in range(ntime):
        mean1[j] = 5.0 + 0.2 * ROOT.TMath.Sin(ROOT.TMath.Pi() * j / float(ntime))
        mean2[j] = 5.0 + 0.2 * ROOT.TMath.Cos(ROOT.TMath.Pi() * j / float(ntime))
        sigma1[j] = 4 + 0.3 * ROOT.TMath.Sin(ROOT.TMath.Pi() * j / float(ntime))
        sigma2[j] = 4 + 0.3 * ROOT.TMath.Cos(ROOT.TMath.Pi() * j / float(ntime))
 
    for i in range(n):
        if i % 1000 == 0:
            print("Generating  event ... %d", i)
 
        for j in range(ntime):
            h1 = v1[j]
            h2 = v2[j]
            h1.Reset()
            h2.Reset()
 
            f1.SetParameters(1, mean1[j], sigma1[j])
            f2.SetParameters(1, mean2[j], sigma2[j])
 
            h1.FillRandom("f1", 1000)
            h2.FillRandom("f2", 1000)
 
            for k in range(ntime):
                # std::cout << j*10+k << "   ";
                x1[j][k] = h1.GetBinContent(k + 1) + ROOT.gRandom.Gaus(0, 10)
                x2[j][k] = h2.GetBinContent(k + 1) + ROOT.gRandom.Gaus(0, 10)
 
        sgn.Fill()
        bkg.Fill()
 
        if n == 1:
            c1 = ROOT.TCanvas()
            c1.Divide(ntime, 2)
            for j in range(ntime):
                c1.cd(j + 1)
                v1[j].Draw()
            for j in range(ntime):
                c1.cd(ntime + j + 1)
                v2[j].Draw()
 
            ROOT.gPad.Update()
 
    if n > 1:
        sgn.Write()
        bkg.Write()
        sgn.Print()
        bkg.Print()
        f.Close()
 
 
## macro for performing a classification using a Recurrent Neural Network
## @param use_type
##    use_type = 0    use Simple RNN network
##    use_type = 1    use LSTM network
##    use_type = 2    use GRU
##    use_type = 3    build 3 different networks with RNN, LSTM and GRU
 
 
use_type = 1
ninput = 30
ntime = 10
batchSize = 100
maxepochs = 10
 
nTotEvts = 2000  # total events to be generated for signal or background
 
useKeras = False
 
useTMVA_RNN = True
useTMVA_DNN = True
useTMVA_BDT = False
 
if ROOT.gSystem.GetFromPipe("root-config --has-tmva-pymva") == "yes":
    useKeras = True
 
 
rnn_types = ["RNN", "LSTM", "GRU"]
use_rnn_type = [1, 1, 1]
 
if 0 <= use_type < 3:
    use_rnn_type = [0, 0, 0]
    use_rnn_type[use_type] = 1
 
useGPU = True  # use GPU for TMVA if available
 
useGPU = "tmva-gpu" in ROOT.gROOT.GetConfigFeatures()
useTMVA_RNN = ("tmva-cpu" in ROOT.gROOT.GetConfigFeatures()) or useGPU
 
if useTMVA_RNN:
    ROOT.Warning(
        "TMVA_RNN_Classification",
        "TMVA is not build with GPU or CPU multi-thread support. Cannot use TMVA Deep Learning for RNN",
    )
 
archString = "GPU" if useGPU else "CPU"
 
writeOutputFile = True
 
rnn_type = "RNN"
 
if "tmva-pymva" in ROOT.gROOT.GetConfigFeatures():
    TMVA.PyMethodBase.PyInitialize()
else:
    useKeras = False
 
 
 
inputFileName = "time_data_t10_d30.root"
 
fileDoesNotExist = ROOT.gSystem.AccessPathName(inputFileName)
 
# if file does not exists create it
if fileDoesNotExist:
    MakeTimeData(nTotEvts, ntime, ninput)
 
 
inputFile = TFile.Open(inputFileName)
if inputFile is None:
    raise ROOT.Error("Error opening input file %s - exit", inputFileName.Data())
 
 
print("--- RNNClassification  : Using input file: {}".format(inputFile.GetName()))
 
# Create a ROOT output file where TMVA will store ntuples, histograms, etc.
outfileName = "data_RNN_" + archString + ".root"
outputFile = None
 
 
if writeOutputFile:
    outputFile = TFile.Open(outfileName, "RECREATE")
 
 
## Declare 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
# weightfiles 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
 
 
# // Creating the factory object
factory = TMVA.Factory(
    "TMVAClassification",
    outputFile,
    V=False,
    Silent=False,
    Color=True,
    DrawProgressBar=True,
    Transformations=None,
    Correlations=False,
    AnalysisType="Classification",
    ModelPersistence=True,
)
dataloader = TMVA.DataLoader("dataset")
 
signalTree = inputFile.Get("sgn")
background = inputFile.Get("bkg")
 
nvar = ninput * ntime
 
## add variables - use new AddVariablesArray function
for i in range(ntime):
    dataloader.AddVariablesArray("vars_time" + str(i), ninput)
 
 
dataloader.AddSignalTree(signalTree, 1.0)
dataloader.AddBackgroundTree(background, 1.0)
 
# check given input
datainfo = dataloader.GetDataSetInfo()
vars = datainfo.GetListOfVariables()
print("number of variables is {}".format(vars.size()))
 
 
for v in vars:
    print(v)
 
nTrainSig = 0.8 * nTotEvts
nTrainBkg = 0.8 * nTotEvts
 
# 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 = ""
 
# build the string options for DataLoader::PrepareTrainingAndTestTree
dataloader.PrepareTrainingAndTestTree(
    mycuts,
    mycutb,
    nTrain_Signal=nTrainSig,
    nTrain_Background=nTrainBkg,
    SplitMode="Random",
    SplitSeed=100,
    NormMode="NumEvents",
    V=False,
    CalcCorrelations=False,
)
 
print("prepared DATA LOADER ")
 
 
## Book TMVA  recurrent models
 
# Book the different types of recurrent models in TMVA  (SimpleRNN, LSTM or GRU)
 
 
if useTMVA_RNN:
    for i in range(3):
        if not use_rnn_type[i]:
            continue
 
        rnn_type = rnn_types[i]
 
        ## Define RNN layer layout
        ##  it should be   LayerType (RNN or LSTM or GRU) |  number of units | number of inputs | time steps | remember output (typically no=0 | return full sequence
        rnnLayout = str(rnn_type) + "|10|" + str(ninput) + "|" + str(ntime) + "|0|1,RESHAPE|FLAT,DENSE|64|TANH,LINEAR"
 
        ## Defining Training strategies. Different training strings can be concatenate. Use however only one
        trainingString1 = "LearningRate=1e-3,Momentum=0.0,Repetitions=1,ConvergenceSteps=5,BatchSize=" + str(batchSize)
        trainingString1 += ",TestRepetitions=1,WeightDecay=1e-2,Regularization=None,MaxEpochs=" + str(maxepochs)
        trainingString1 += "Optimizer=ADAM,DropConfig=0.0+0.+0.+0."
 
        ## define the inputlayout string for RNN
        ## the input data should be organize as   following:
        ##/ input layout for RNN:    time x ndim
        ## add after RNN a reshape layer (needed top flatten the output) and a dense layer with 64 units and a last one
        ## Note the last layer is linear because  when using Crossentropy a Sigmoid is applied already
        ## Define the full RNN Noption string adding the final options for all network
        rnnName = "TMVA_" + str(rnn_type)
        factory.BookMethod(
            dataloader,
            TMVA.Types.kDL,
            rnnName,
            H=False,
            V=True,
            ErrorStrategy="CROSSENTROPY",
            VarTransform=None,
            WeightInitialization="XAVIERUNIFORM",
            ValidationSize=0.2,
            RandomSeed=1234,
            InputLayout=str(ntime) + "|" + str(ninput),
            Layout=rnnLayout,
            TrainingStrategy=trainingString1,
            Architecture=archString
        )
 
 
## Book TMVA  fully connected dense layer  models
if useTMVA_DNN:
    # Method DL with Dense Layer
    # Training strategies.
    trainingString1 = ROOT.TString(
        "LearningRate=1e-3,Momentum=0.0,Repetitions=1,"
        "ConvergenceSteps=10,BatchSize=256,TestRepetitions=1,"
        "WeightDecay=1e-4,Regularization=None,MaxEpochs=20"
        "DropConfig=0.0+0.+0.+0.,Optimizer=ADAM:"
    )  # + "|" + trainingString2
    # General Options.
    trainingString1.Append(archString)
    dnnName = "TMVA_DNN"
    factory.BookMethod(
        dataloader,
        TMVA.Types.kDL,
        dnnName,
        H=False,
        V=True,
        ErrorStrategy="CROSSENTROPY",
        VarTransform=None,
        WeightInitialization="XAVIER",
        RandomSeed=0,
        InputLayout="1|1|" + str(ntime * ninput),
        Layout="DENSE|64|TANH,DENSE|TANH|64,DENSE|TANH|64,LINEAR",
        TrainingStrategy=trainingString1
    )
 
 
## Book Keras recurrent models
 
# Book the different types of recurrent models in Keras  (SimpleRNN, LSTM or GRU)
 
 
if useKeras:
    for i in range(3):
        if use_rnn_type[i]:
            modelName = "model_" + rnn_types[i] + ".h5"
            trainedModelName = "trained_" + modelName
            print("Building recurrent keras model using a", rnn_types[i], "layer")
            # create python script which can be executed
            # create 2 conv2d layer + maxpool + dense
            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, SimpleRNN, GRU, LSTM, Reshape, BatchNormalization
 
            model = Sequential()
            model.add(Reshape((10, 30), input_shape=(10 * 30,)))
            # add recurrent neural network depending on type / Use option to return the full output
            if rnn_types[i] == "LSTM":
                model.add(LSTM(units=10, return_sequences=True))
            elif rnn_types[i] == "GRU":
                model.add(GRU(units=10, return_sequences=True))
            else:
                model.add(SimpleRNN(units=10, return_sequences=True))
            # m.AddLine("model.add(BatchNormalization())");
            model.add(Flatten())  # needed if returning the full time output sequence
            model.add(Dense(64, activation="tanh"))
            model.add(Dense(2, activation="sigmoid"))
            model.compile(loss="binary_crossentropy", optimizer=Adam(learning_rate=0.001), weighted_metrics=["accuracy"])
            model.save(modelName)
            model.summary()
            print("saved recurrent model", modelName)
 
            if not os.path.exists(modelName):
                useKeras = False
                print("Error creating Keras recurrent model file - Skip using Keras")
            else:
                # book PyKeras method only if Keras model could be created
                print("Booking Keras  model ", rnn_types[i])
                factory.BookMethod(
                    dataloader,
                    TMVA.Types.kPyKeras,
                    "PyKeras_" + rnn_types[i],
                    H=True,
                    V=False,
                    VarTransform=None,
                    FilenameModel=modelName,
                    FilenameTrainedModel="trained_" + modelName,
                    NumEpochs=maxepochs,
                    BatchSize=batchSize,
                    GpuOptions="allow_growth=True",
                )
 
 
# use BDT in case not using Keras or TMVA DL
if not useKeras or not useTMVA_BDT:
    useTMVA_BDT = True
 
 
## Book TMVA BDT
 
 
if useTMVA_BDT:
    factory.BookMethod(
        dataloader,
        TMVA.Types.kBDT,
        "BDTG",
        H=True,
        V=False,
        NTrees=100,
        MinNodeSize="2.5%",
        BoostType="Grad",
        Shrinkage=0.10,
        UseBaggedBoost=True,
        BaggedSampleFraction=0.5,
        nCuts=20,
        MaxDepth=2,
    )
 
 
## Train all methods
factory.TrainAllMethods()
 
print("nthreads  = {}".format(ROOT.GetThreadPoolSize()))
 
# ---- Evaluate all MVAs using the set of test events
factory.TestAllMethods()
 
# ----- Evaluate and compare performance of all configured MVAs
factory.EvaluateAllMethods()
 
# check method
 
# plot ROC curve
c1 = factory.GetROCCurve(dataloader)
c1.Draw()
 
if outputFile:
    outputFile.Close()