MulticlassPyTorch.py File Reference

Namespaces
namespace	MulticlassPyTorch

Detailed Description

This tutorial shows how to do multiclass classification in TMVA with neural networks trained with PyTorch.

 
 
from ROOT import TMVA, TFile, TTree, TCut, gROOT
from os.path import isfile
 
import torch
from torch import nn
 
 
# 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)')
 
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 = nn.Sequential()
model.add_module('linear_1', nn.Linear(in_features=4, out_features=32))
model.add_module('relu', nn.ReLU())
model.add_module('linear_2', nn.Linear(in_features=32, out_features=4))
model.add_module('softmax', nn.Softmax(dim=1))
 
 
# Set loss and optimizer
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD
 
 
# Define train function
def train(model, train_loader, val_loader, num_epochs, batch_size, optimizer, criterion, save_best, scheduler):
    trainer = optimizer(model.parameters(), lr=0.01)
    schedule, schedulerSteps = scheduler
    best_val = None
 
    for epoch in range(num_epochs):
        # Training Loop
        # Set to train mode
        model.train()
        running_train_loss = 0.0
        running_val_loss = 0.0
        for i, (X, y) in enumerate(train_loader):
            trainer.zero_grad()
            output = model(X)
            target = torch.max(y, 1)[1]
            train_loss = criterion(output, target)
            train_loss.backward()
            trainer.step()
 
            # print train statistics
            running_train_loss += train_loss.item()
            if i % 32 == 31:    # print every 32 mini-batches
                print("[{}, {}] train loss: {:.3f}".format(epoch+1, i+1, running_train_loss / 32))
                running_train_loss = 0.0
 
        if schedule:
            schedule(optimizer, epoch, schedulerSteps)
 
        # Validation Loop
        # Set to eval mode
        model.eval()
        with torch.no_grad():
            for i, (X, y) in enumerate(val_loader):
                output = model(X)
                target = torch.max(y, 1)[1]
                val_loss = criterion(output, target)
                running_val_loss += val_loss.item()
 
            curr_val = running_val_loss / len(val_loader)
            if save_best:
               if best_val==None:
                   best_val = curr_val
               best_val = save_best(model, curr_val, best_val)
 
            # print val statistics per epoch
            print("[{}] val loss: {:.3f}".format(epoch+1, curr_val))
            running_val_loss = 0.0
 
    print("Finished Training on {} Epochs!".format(epoch+1))
 
    return model
 
 
# Define predict function
def predict(model, test_X, batch_size=32):
    # Set to eval mode
    model.eval()
 
    test_dataset = torch.utils.data.TensorDataset(torch.Tensor(test_X))
    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
 
    predictions = []
    with torch.no_grad():
        for i, data in enumerate(test_loader):
            X = data[0]
            outputs = model(X)
            predictions.append(outputs)
        preds = torch.cat(predictions)
 
    return preds.numpy()
 
 
load_model_custom_objects = {"optimizer": optimizer, "criterion": loss, "train_func": train, "predict_func": predict}
 
 
# Store model to file
# Convert the model to torchscript before saving
m = torch.jit.script(model)
torch.jit.save(m, "modelMultiClass.pt")
print(m)
 
 
# Book methods
factory.BookMethod(dataloader, TMVA.Types.kFisher, 'Fisher',
        '!H:!V:Fisher:VarTransform=D,G')
factory.BookMethod(dataloader, TMVA.Types.kPyTorch, "PyTorch",
        'H:!V:VarTransform=D,G:FilenameModel=modelMultiClass.pt:FilenameTrainedModel=trainedModelMultiClass.pt:NumEpochs=20:BatchSize=32')
 
 
# Run TMVA
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
 
# Plot ROC Curves
roc = factory.GetROCCurve(dataloader)
roc.SaveAs('ROC_MulticlassPyTorch.png')

Date

2020

Author

Anirudh Dagar aniru.nosp@m.dhda.nosp@m.gar6@.nosp@m.gmai.nosp@m.l.com - IIT, Roorkee

Definition in file MulticlassPyTorch.py.