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tmva101_Training.py File Reference


namespace  tmva101_Training

Detailed Description

View in nbviewer Open in SWAN This tutorial show how you can train a machine learning model with any package reading the training data directly from ROOT files.

Using XGBoost, we illustrate how you can convert an externally trained model in a format serializable and readable with the fast tree inference engine offered by TMVA.

import ROOT
import numpy as np
import pickle
from tmva100_DataPreparation import variables
def load_data(signal_filename, background_filename):
# Read data from ROOT files
data_sig = ROOT.RDataFrame("Events", signal_filename).AsNumpy()
data_bkg = ROOT.RDataFrame("Events", background_filename).AsNumpy()
# Convert inputs to format readable by machine learning tools
x_sig = np.vstack([data_sig[var] for var in variables]).T
x_bkg = np.vstack([data_bkg[var] for var in variables]).T
x = np.vstack([x_sig, x_bkg])
# Create labels
num_sig = x_sig.shape[0]
num_bkg = x_bkg.shape[0]
y = np.hstack([np.ones(num_sig), np.zeros(num_bkg)])
# Compute weights balancing both classes
num_all = num_sig + num_bkg
w = np.hstack([np.ones(num_sig) * num_all / num_sig, np.ones(num_bkg) * num_all / num_bkg])
return x, y, w
if __name__ == "__main__":
# Load data
x, y, w = load_data("train_signal.root", "train_background.root")
# Fit xgboost model
from xgboost import XGBClassifier
bdt = XGBClassifier(max_depth=3, n_estimators=500)
bdt.fit(x, y, w)
# Save model in TMVA format
ROOT.TMVA.Experimental.SaveXGBoost(bdt, "myBDT", "tmva101.root")
ROOT's RDataFrame offers a high level interface for analyses of data stored in TTrees,...
August 2019
Stefan Wunsch

Definition in file tmva101_Training.py.