train_K_L0_classifier.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
 
 
 
import sys
import os
 
# USE THIS TO TRAIN AND UPLOAD A NEW CLASSIFIER FOR THE KLONG ID
# USE THE OTHER SCRIPT TO GENERATE DATA. THEN TRAIN AND UPLOAD WITH &THIS.
# only thing to set is the path were you have the data to train the classifier on.
 
try:
    data_path = sys.argv[1]
except BaseException:
    data_path = "root_files/training/*.root"
try:
    identifier = sys.argv[2]
except BaseException:
    # the default name used in KLMExpert
    identifier = "KLM_fBDT_10xbkg100k.xml"
 
ex_b = 0   # experiment begin, 0 for all of them
ex_e = -1   # experiment end, -1 for all of them
run_b = 0   # run begin, 0 for all
run_e = -1   # run end, -1 for all of them
 
training_string = ('basf2_mva_teacher --datafiles {data_path} --treename KLMdata --identifier {identifier} ' +
                   '--variable "KLMnLayer" "KLMnInnermostlayer" "KLMglobalZ" "KLMtime" "KLMdistToNextCl" ' +
                   '"KLMenergy" "KLMTrackSepDist" "KLMInitialtrackSepAngle" "KLMTrackRotationAngle" ' +
                   '"KLMTrackSepAngle" "KLMhitDepth" "KLMECLenergy" "KLMECLE9oE25" "KLMECLtiming" ' +
                   '"KLMECLmintrackDist" "KLMECLZMVA" "KLMECLZ40" "KLMECLZ51" --target_variable isSignal ' +
                   '--method FastBDT --nTrees 400 --nCutLevels 8 --nLevels 4').format(data_path=data_path, identifier=identifier)
 
os.system(training_string)
 
# "upload" to localdb
os.system(
    ('basf2_mva_upload --identifier {identifier} --db_identifier {identifier_db} ' +
     '--begin_experiment {ex_b} --end_experiment {ex_e} --begin_run {run_b} --end_run {run_e}').format(
        identifier=identifier,
        identifier_db=identifier.split(".xml")[0],
        ex_b=ex_b,
        ex_e=ex_e,
        run_b=run_b,
        run_e=run_e))