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.
Author: Stefan Wunsch
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Sunday, February 05, 2023 at 11:19 AM.
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 num_bkg = x_bkg.shape 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, sample_weight=w) # Save model in TMVA format print("Training done on ",x.shape,"events. Saving model in tmva101.root") ROOT.TMVA.Experimental.SaveXGBoost(bdt, "myBDT", "tmva101.root", num_inputs=x.shape)