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261 lines
10 KiB
261 lines
10 KiB
import os
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import argparse
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import ROOT
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from ROOT import TMVA
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def train_default_forward_ghost_mlp(
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input_file: str = "data/ghost_data.root",
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tree_name: str = "PrForwardTrackingVelo.PrMCDebugForwardTool/MVAInput",
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exclude_electrons: bool = False,
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n_train_signal: int = 300e3,
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n_train_bkg: int = 300e3,
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n_test_signal: int = 50e3,
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n_test_bkg: int = 50e3,
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prepare_data: bool = False,
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):
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"""Trains an MLP to classify track candidates from PrForwardTrackingVelo as ghost or Long Track.
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Args:
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input_file (str, optional): Defaults to "data/ghost_data.root".
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tree_name (str, optional): Defaults to "PrForwardTrackingVelo.PrMCDebugForwardTool/MVAInput".
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exclude_electrons (bool, optional): Defaults to False.
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n_train_signal (int, optional): Number of true tracks to train on. Defaults to 750e3.
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n_train_bkg (int, optional): Number of fake tracks to train on. Defaults to 750e3.
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n_test_signal (int, optional): Number of true tracks to test on. Defaults to 50e3.
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n_test_bkg (int, optional): umber of fake tracks to test on. Defaults to 50e3.
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prepare_data (bool, optional): Split data into signal and background file. Defaults to False.
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"""
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if prepare_data:
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rdf = ROOT.RDataFrame(tree_name, input_file)
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if exclude_electrons:
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rdf_signal = rdf.Filter(
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"label == 1",
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"Signal is defined as one label (excluding electrons)",
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)
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rdf_bkg = rdf.Filter("label == 0", "Ghosts are defined as zero label")
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else:
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rdf_signal = rdf.Filter("label > 0", "Signal is defined as non-zero label")
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rdf_bkg = rdf.Filter("label == 0", "Ghosts are defined as zero label")
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rdf_signal.Snapshot(
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"Signal",
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input_file.strip(".root") + "_forward_signal.root",
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)
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rdf_bkg.Snapshot("Bkg", input_file.strip(".root") + "_forward_bkg.root")
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signal_file = ROOT.TFile.Open(
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input_file.strip(".root") + "_forward_signal.root",
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"READ",
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)
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signal_tree = signal_file.Get("Signal")
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bkg_file = ROOT.TFile.Open(input_file.strip(".root") + "_forward_bkg.root")
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bkg_tree = bkg_file.Get("Bkg")
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os.chdir("neural_net_training/result")
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output = ROOT.TFile(
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"default_forward_ghost_mlp_training.root",
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"RECREATE",
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)
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factory = TMVA.Factory(
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"TMVAClassification",
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output,
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"V:!Silent:Color:DrawProgressBar:AnalysisType=Classification",
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)
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factory.SetVerbose(True)
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dataloader = TMVA.DataLoader("GhostNNDataSet")
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dataloader.AddVariable("redChi2", "F")
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dataloader.AddVariable(
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"distXMatch := abs((x + ( zMagMatch - 770.0 ) * tx) - (xEndT + ( zMagMatch - 9410.0 ) * txEndT))",
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"F",
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)
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dataloader.AddVariable(
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"distY := abs(ySeedMatch - yEndT)",
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"F",
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)
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dataloader.AddVariable("abs(yParam0Final-yParam0Init)", "F")
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dataloader.AddVariable("abs(yParam1Final-yParam1Init)", "F")
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dataloader.AddVariable("abs(ty)", "F")
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dataloader.AddVariable("abs(qop)", "F")
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dataloader.AddVariable("abs(tx)", "F")
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dataloader.AddVariable("abs(xParam1Final-xParam1Init)", "F")
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dataloader.AddSignalTree(signal_tree, 1.0)
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dataloader.AddBackgroundTree(bkg_tree, 1.0)
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preselectionCuts = ROOT.TCut(
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"redChi2 < 8 && abs((x + ( zMagMatch - 770.0 ) * tx) - (xEndT + ( zMagMatch - 9410.0 ) * txEndT)) < 140 && abs(ySeedMatch - yEndT) < 500 && abs(yParam0Final-yParam0Init) < 140 && abs(yParam1Final-yParam1Init) < 0.055",
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)
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dataloader.PrepareTrainingAndTestTree(
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preselectionCuts,
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f"NormMode=NumEvents:SplitMode=random:V:nTrain_Signal={n_train_signal}:nTrain_Background={n_train_bkg}:nTest_Signal={n_test_signal}:nTest_Background={n_test_bkg}",
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)
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factory.BookMethod(
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dataloader,
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TMVA.Types.kMLP,
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"default_forward_ghost_mlp",
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"!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=750:HiddenLayers=N+4,N+2:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.005:!UseRegulator",
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)
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factory.TrainAllMethods()
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factory.TestAllMethods()
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factory.EvaluateAllMethods()
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output.Close()
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def train_veloUT_forward_ghost_mlp(
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input_file: str = "data/ghost_data.root",
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tree_name: str = "PrForwardTracking.PrMCDebugForwardTool/MVAInput",
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exclude_electrons: bool = False,
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n_train_signal: int = 300e3,
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n_train_bkg: int = 300e3,
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n_test_signal: int = 50e3,
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n_test_bkg: int = 50e3,
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prepare_data: bool = False,
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):
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"""Trains an MLP to classify track candidates from PrForwardTracking as ghost or Long Track.
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Args:
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input_file (str, optional): Defaults to "data/ghost_data.root".
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tree_name (str, optional): Defaults to "PrForwardTracking.PrMCDebugForwardTool/MVAInput".
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exclude_electrons (bool, optional): Defaults to False.
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n_train_signal (int, optional): Number of true tracks to train on. Defaults to 750e3.
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n_train_bkg (int, optional): Number of fake tracks to train on. Defaults to 750e3.
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n_test_signal (int, optional): Number of true tracks to test on. Defaults to 50e3.
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n_test_bkg (int, optional): umber of fake tracks to test on. Defaults to 50e3.
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prepare_data (bool, optional): Split data into signal and background file. Defaults to False.
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"""
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if prepare_data:
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rdf = ROOT.RDataFrame(tree_name, input_file)
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if exclude_electrons:
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rdf_signal = rdf.Filter(
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"label == 1",
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"Signal is defined as one label (excluding electrons)",
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)
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rdf_bkg = rdf.Filter("label == 0", "Ghosts are defined as zero label")
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else:
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rdf_signal = rdf.Filter("label > 0", "Signal is defined as non-zero label")
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rdf_bkg = rdf.Filter("label == 0", "Ghosts are defined as zero label")
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rdf_signal.Snapshot(
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"Signal",
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input_file.strip(".root") + "_forward_velout_signal.root",
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)
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rdf_bkg.Snapshot("Bkg", input_file.strip(".root") + "_forward_velout_bkg.root")
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signal_file = ROOT.TFile.Open(
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input_file.strip(".root") + "_forward_velout_signal.root",
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"READ",
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)
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signal_tree = signal_file.Get("Signal")
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bkg_file = ROOT.TFile.Open(input_file.strip(".root") + "_forward_velout_bkg.root")
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bkg_tree = bkg_file.Get("Bkg")
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os.chdir("neural_net_training/result")
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output = ROOT.TFile(
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"veloUT_forward_ghost_mlp_training.root",
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"RECREATE",
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)
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factory = TMVA.Factory(
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"TMVAClassification",
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output,
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"V:!Silent:Color:DrawProgressBar:AnalysisType=Classification",
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)
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factory.SetVerbose(True)
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dataloader = TMVA.DataLoader("GhostNNDataSet")
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dataloader.AddVariable("dMom := log(abs((1.0/qop) - (1.0/qopUT) ))", "F")
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dataloader.AddVariable("redChi2", "F")
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dataloader.AddVariable(
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"distXMatch := abs((x + ( zMagMatch - 770.0 ) * tx) - (xEndT + ( zMagMatch - 9410.0 ) * txEndT))",
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"F",
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)
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dataloader.AddVariable(
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"distY := abs(ySeedMatch - yEndT)",
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"F",
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)
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dataloader.AddVariable("abs(yParam0Final-yParam0Init)", "F")
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dataloader.AddVariable("abs(yParam1Final-yParam1Init)", "F")
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dataloader.AddVariable("abs(ty)", "F")
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dataloader.AddVariable("abs(qop)", "F")
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dataloader.AddVariable("abs(tx)", "F")
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dataloader.AddVariable("abs(xParam1Final-xParam1Init)", "F")
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dataloader.AddSignalTree(signal_tree, 1.0)
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dataloader.AddBackgroundTree(bkg_tree, 1.0)
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preselectionCuts = ROOT.TCut(
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"redChi2 < 8 && abs((x + ( zMagMatch - 770.0 ) * tx) - (xEndT + ( zMagMatch - 9410.0 ) * txEndT)) < 140 && abs(ySeedMatch - yEndT) < 500 && abs(yParam0Final-yParam0Init) < 140 && abs(yParam1Final-yParam1Init) < 0.055",
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)
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dataloader.PrepareTrainingAndTestTree(
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preselectionCuts,
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f"NormMode=NumEvents:SplitMode=random:V:nTrain_Signal={n_train_signal}:nTrain_Background={n_train_bkg}:nTest_Signal={n_test_signal}:nTest_Background={n_test_bkg}",
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)
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factory.BookMethod(
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dataloader,
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TMVA.Types.kMLP,
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"veloUT_forward_ghost_mlp",
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"!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=550:HiddenLayers=N+2:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.005:!UseRegulator",
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)
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factory.TrainAllMethods()
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factory.TestAllMethods()
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factory.EvaluateAllMethods()
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output.Close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--input-file",
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type=str,
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help="Path to the input file",
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required=False,
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)
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parser.add_argument(
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"--exclude_electrons",
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action="store_true",
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help="Excludes electrons from training.",
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required=False,
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)
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parser.add_argument(
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"--n-train-signal",
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type=int,
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help="Number of training tracks for signal.",
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required=False,
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)
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parser.add_argument(
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"--n-train-bkg",
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type=int,
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help="Number of training tracks for bkg.",
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required=False,
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)
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parser.add_argument(
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"--n-test-signal",
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type=int,
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help="Number of testing tracks for signal.",
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required=False,
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)
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parser.add_argument(
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"--n-test-bkg",
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type=int,
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help="Number of testing tracks for bkg.",
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required=False,
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)
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parser.add_argument(
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"--veloUT",
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action="store_true",
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help="Toggle whether the NN for upstream tracks input is trained.",
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)
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parser.add_argument(
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"--all",
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action="store_true",
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help="Toggle whether both NNs are trained, for VELO and VeloUT input.",
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)
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args = parser.parse_args()
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args_dict = {
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arg: val
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for arg, val in vars(args).items()
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if val is not None and arg not in ["veloUT", "all"]
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}
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if args.all:
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train_default_forward_ghost_mlp(**args_dict)
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train_veloUT_forward_ghost_mlp(**args_dict)
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elif args.veloUT:
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train_veloUT_forward_ghost_mlp(**args_dict)
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else:
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train_default_forward_ghost_mlp(**args_dict)
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