ClassificationPyTorch.py File Reference

Namespaces
namespace	ClassificationPyTorch

Detailed Description

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

 
 
# PyTorch has to be imported before ROOT to avoid crashes because of clashing
# std::regexp symbols that are exported by cppyy.
# See also: https://github.com/wlav/cppyy/issues/227
import torch
from torch import nn
 
from ROOT import TMVA, TFile, TTree, TCut
from subprocess import call
from os.path import isfile
 
 
# Setup TMVA
TMVA.Tools.Instance()
TMVA.PyMethodBase.PyInitialize()
 
# create factory without output file since it is not needed
factory = TMVA.Factory('TMVAClassification',
                       '!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=Classification')
 
 
# Load data
TFile.SetCacheFileDir(".")
data = TFile.Open("http://root.cern.ch/files/tmva_class_example.root", "CACHEREAD")
if data is None:
    raise FileNotFoundError("Input file cannot be downloaded - exit")
 
signal = data.Get('TreeS')
background = data.Get('TreeB')
 
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')
 
 
# Generate model
 
# Define model
model = nn.Sequential()
model.add_module('linear_1', nn.Linear(in_features=4, out_features=64))
model.add_module('relu', nn.ReLU())
model.add_module('linear_2', nn.Linear(in_features=64, out_features=2))
model.add_module('softmax', nn.Softmax(dim=1))
 
 
# Construct loss function and Optimizer.
loss = torch.nn.MSELoss()
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)
            train_loss = criterion(output, y)
            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)
                val_loss = criterion(output, y)
                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, "modelClassification.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=modelClassification.pt:FilenameTrainedModel=trainedModelClassification.pt:NumEpochs=20:BatchSize=32')
 
 
# Run training, test and evaluation
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
 
 
# Plot ROC Curves
roc = factory.GetROCCurve(dataloader)
roc.SaveAs('ROC_ClassificationPyTorch.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 ClassificationPyTorch.py.