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ClassificationPyTorch.py File Reference

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namespace  ClassificationPyTorch
 

Detailed Description

View in nbviewer Open in SWAN This tutorial shows how to do classification in TMVA with neural networks trained with PyTorch.

from ROOT import TMVA, TFile, TTree, TCut
from subprocess import call
from os.path import isfile
import torch
from torch import nn
# Setup TMVA
output = TFile.Open('TMVA.root', 'RECREATE')
factory = TMVA.Factory('TMVAClassification', output,
'!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=Classification')
# Load data
if not isfile('tmva_class_example.root'):
call(['curl', '-L', '-O', 'http://root.cern.ch/files/tmva_class_example.root'])
data = TFile.Open('tmva_class_example.root')
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, "model.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=model.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')
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t UChar_t len
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t format
A specialized string object used for TTree selections.
Definition: TCut.h:25
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
Definition: TFile.cxx:4019
This is the main MVA steering class.
Definition: Factory.h:80
static void PyInitialize()
Initialize Python interpreter.
static Tools & Instance()
Definition: Tools.cxx:71
def predict(model, test_X, batch_size=100)
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.