ApplicationRegressionPyTorch.py

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## \file
## \ingroup tutorial_tmva_pytorch
## \notebook -nodraw
## This tutorial shows how to apply a trained model to new data (regression).
##
## \macro_code
##
## \date 2020
## \author Anirudh Dagar <anirudhdagar6@gmail.com> - IIT, Roorkee
 
 
# 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 ROOT import TMVA, TFile, TString, gROOT
from array import array
from subprocess import call
from os.path import isfile
 
 
# Setup TMVA
TMVA.Tools.Instance()
TMVA.PyMethodBase.PyInitialize()
reader = TMVA.Reader("Color:!Silent")
 
 
# Load data
data = TFile.Open(str(gROOT.GetTutorialDir()) + "/machine_learning/data/tmva_reg_example.root")
tree = data.Get('TreeR')
 
branches = {}
for branch in tree.GetListOfBranches():
    branchName = branch.GetName()
    branches[branchName] = array('f', [-999])
    tree.SetBranchAddress(branchName, branches[branchName])
    if branchName != 'fvalue':
        reader.AddVariable(branchName, branches[branchName])
 
 
# Book methods
reader.BookMVA('PyTorch', TString('dataset/weights/TMVARegression_PyTorch.weights.xml'))
 
 
# 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": None, "criterion": None, "train_func": None, "predict_func": predict}
 
 
# Print some example regressions
print('Some example regressions:')
for i in range(20):
    tree.GetEntry(i)
    print('True/MVA value: {}/{}'.format(branches['fvalue'][0],reader.EvaluateMVA('PyTorch')))