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Reference Guide
ApplicationRegressionPyTorch.py File Reference


namespace  ApplicationRegressionPyTorch

Detailed Description

View in nbviewer Open in SWAN This tutorial shows how to apply a trained model to new data (regression).

from ROOT import TMVA, TFile, TString
from array import array
from subprocess import call
from os.path import isfile
# Setup TMVA
reader = TMVA.Reader("Color:!Silent")
# Load data
if not isfile('tmva_reg_example.root'):
call(['curl', '-L', '-O', 'http://root.cern.ch/files/tmva_reg_example.root'])
data = TFile.Open('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
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)
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):
print('True/MVA value: {}/{}'.format(branches['fvalue'][0],reader.EvaluateMVA('PyTorch')))
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
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
static void PyInitialize()
Initialize Python interpreter.
The Reader class serves to use the MVAs in a specific analysis context.
Definition: Reader.h:64
static Tools & Instance()
Definition: Tools.cxx:71
Basic string class.
Definition: TString.h:136
def predict(model, test_X, batch_size=100)
Anirudh Dagar aniru.nosp@m.dhda.nosp@m.gar6@.nosp@m.gmai.nosp@m.l.com - IIT, Roorkee

Definition in file ApplicationRegressionPyTorch.py.