VXDQE_teacher.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
 
 
 
 
 
 
import sys
import subprocess
import argparse
 
import tracking.utilities as utilities
import tracking.root_utils as root_utils
 
import logging
 
 
def main():
    argument_parser = utilities.DefaultHelpArgumentParser()
 
    argument_parser.add_argument(
        "records_file_path",
        help="ROOT file containing the TTree of records on which to train a boosted decision tree.",
    )
 
    argument_parser.add_argument(
        "-r",
        "--treename",
        default="tree",
        help="Name of the input TTree in the ROOT file",
    )
 
    argument_parser.add_argument(
        "-i",
        "--identifier",
        default=argparse.SUPPRESS,
        help="Database identifier or name of weight file to be generated",
    )
 
    argument_parser.add_argument(
        "-t",
        "--truth",
        type=str,
        default="truth",
        help="Name of the column containing the truth information."
    )
 
    argument_parser.add_argument(
        "--variables",
        default=None,
        nargs='+',
        help="Name of the column containing the variables to be used."
    )
 
    argument_parser.add_argument(
        "-x",
        "--variable_excludes",
        default=None,
        nargs='+',
        help="Variables to be excluded"
    )
 
    argument_parser.add_argument(
        "--method",
        type=str,
        default="FastBDT",
        help="MVA Method [FastBDT], not implemented: [NeuroBayes|TMVA|XGBoost|Theano|Tensorflow|FANN|SKLearn]"
    )
 
    argument_parser.add_argument(
        "-e",
        "--evaluate",
        action="store_true",
        help="Evaluate the method after the training is finished"
    )
 
    argument_parser.add_argument(
        "-n",
        "--fillnan",
        action="store_true",
        help="Fill nan and inf values with actual numbers in evaluation"
    )
 
    arguments = argument_parser.parse_args()
 
    records_file_path = arguments.records_file_path
    treename = arguments.treename
    feature_names = arguments.variables
 
    excludes = arguments.variable_excludes
    if excludes is None:
        excludes = ["truth"]
    elif "truth" not in excludes:
        excludes.append("truth")
 
    print('excludes: ', excludes)
 
    # Figure out feature variables
    if feature_names is None:
        with root_utils.root_open(records_file_path) as records_tfile:
            input_tree = records_tfile.Get(treename)
            feature_names = [leave.GetName() for leave in input_tree.GetListOfLeaves()]
 
    truth = arguments.truth
    method = arguments.method
 
    identifier = vars(arguments).get("identifier", method + ".weights.xml")
 
    # Remove the variables that have Monte Carlo truth information unless explicitly selected
    truth_free_variable_names = [name for name
                                 in feature_names
                                 if name not in excludes]
 
    weight_variable = ""
    if "weight" in truth_free_variable_names:
        truth_free_variable_names.remove("weight")
        weight_variable = "weight"
 
    elif "__weight__" in truth_free_variable_names:
        truth_free_variable_names.remove("__weight__")
        weight_variable = "__weight__"
 
    else:
        weight_variable = ""
 
    cmd = [
        "basf2_mva_teacher",
        "--datafiles", records_file_path,
        "--treename", treename,
        "--identifier", identifier,
        "--target_variable", truth,
        "--method", method,
        "--variables", *truth_free_variable_names,
        "--weight_variable", weight_variable,
    ]
 
    print(cmd)
    subprocess.call(cmd)
 
    if arguments.evaluate:
        evaluation_pdf = identifier.rsplit(".", 1)[0] + ".pdf"
        cmd = [
            "basf2_mva_evaluate.py",
            "--identifier", identifier,
            "-d", records_file_path,
            "--treename", treename,
            "-o", evaluation_pdf
        ]
        if arguments.fillnan:
            cmd.append("-n")
        print(cmd)
        subprocess.call(cmd)
 
 
if __name__ == "__main__":
    logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(levelname)s:%(message)s')
    main()