doc/v632/ApplicationRegressionPyTorch_8py.html

# 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()) + "/tmva/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')))

TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358

format
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
Definition TGWin32VirtualXProxy.cxx:249

ROOT::Detail::TRangeCast
Definition TCollection.h:311

TMVA::Reader
The Reader class serves to use the MVAs in a specific analysis context.
Definition Reader.h:64

TString
Basic string class.
Definition TString.h:139