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draw_overtraining.py
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draw_overtraining.py
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#!/usr/bin/env python
"""Overtraining tests for the semi-parametric MVAs.
"""
# python-2 compatibility
from __future__ import division # 1/2 = 0.5, not 0
from __future__ import print_function # print() syntax from python-3
import os
import pickle
import fnmatch
import ROOT
# for keeping drawed ROOT objects in memory
saves = []
def main():
"""Steering function.
"""
ROOT.gStyle.SetOptStat(0)
ROOT.TH1.AddDirectory(False)
# ntuples to process
infiles = fnmatch.filter(os.listdir('input'), '*.root')
infiles = sorted('input/' + f for f in infiles)
# names of ntuples
fnames = [f[f.rfind('/') + 1:].replace('.root', '') for f in infiles]
# make output directories
for d in ['output', 'output/cache', 'output/plots']:
if not os.access(d, os.X_OK):
os.mkdir(d)
for det in ['EB', 'EE']:
for (infile, fname) in zip(infiles, fnames):
r = make_histos(infile, det)
cname = 'overtraining_{0}_{1}'.format(fname, det)
combine(r, cname, det)
# save all open canvases as images
canvases = ROOT.gROOT.GetListOfCanvases()
for i in range(canvases.GetEntries()):
c = canvases.At(i)
c.SaveAs('output/plots/{0}.png'.format(c.GetTitle()))
def make_histos(infile, det):
"""Fills energy resolution histograms for train and test trees.
Results are cached into file.
"""
# return cached results, if any
fname = os.path.basename(infile).replace('.root', '')
cachefile = 'output/cache/draw_overtraining_{0}_{1}.pkl'.format(fname, det)
if os.access(cachefile, os.R_OK):
with open(cachefile, 'rb') as f:
return pickle.load(f)
hTrain = ROOT.TH1D('h', '', 500, 0., 1.2)
hTest = ROOT.TH1D('h', '', 500, 0., 1.2)
hOrig = ROOT.TH1D('h', '', 500, 0., 1.2)
# get TTree with PFClusters
fi = ROOT.TFile(infile)
tree = fi.Get('ntuplizer/PFClusterTree')
if not tree:
raise Exception('TTree not found')
# add branches with outputs from MVAs
tree.AddFriend('ntuplizer/PFClusterTree', 'output/friend_{0}.root'.format(fname))
# fill histograms
for ev in range(tree.GetEntriesFast()):
if tree.GetEntry(ev) <= 0:
raise Exception
# barrel vs endcaps
if det == 'EB':
if abs(tree.pfEta) > 1.479:
continue
else:
if abs(tree.pfEta) < 1.479:
continue
#if tree.pfSize5x5_ZS != 2:
#continue
orig = tree.pfE/tree.mcE
corr = getattr(tree, 'mva_mean_' + fname)
if ev % 2 == 0:
hTrain.Fill(corr * orig)
else:
hTest.Fill(corr * orig)
hOrig.Fill(orig)
# normalization
hOrig.Scale(0.5)
result = (hTrain, hTest, hOrig)
# save cache
with open(cachefile, 'wb') as f:
pickle.dump(result, f)
return result
def combine(histos, cname, det):
"""Visualization of train/test/original distributions on single canvas.
"""
c = ROOT.TCanvas(cname, cname, 700, 700)
saves.append(c)
c.SetLeftMargin(0.14)
c.SetRightMargin(0.08)
c.SetTopMargin(0.06)
c.SetBottomMargin(0.1)
c.SetGridx()
c.SetGridy()
# draw empty histogram
xmin = histos[0].GetXaxis().GetXmin()
xmax = histos[0].GetXaxis().GetXmax()
ymax = max(h.GetMaximum() for h in histos)
frame = c.DrawFrame(xmin, 0, xmax, ymax * 1.1)
frame.SetTitle('Overtraining test, ' + det)
frame.SetXTitle('correction * E^{PF}/E^{gen}')
frame.SetYTitle('Entries')
frame.SetTitleOffset(1.2, 'X')
frame.SetTitleOffset(1.95, 'Y')
frame.Draw()
# legend
leg = ROOT.TLegend(0.6, 0.79, 0.91, 0.89)
clrs = [ROOT.kBlack, ROOT.kBlue, ROOT.kOrange]
txts = ['Corrections from train', 'Corrections from test',
'No corrections, test+train']
for (h, clr, txt) in zip(histos, clrs, txts):
h.SetLineColor(clr)
h.Draw('same')
leg.AddEntry(h, txt, 'l')
leg.SetFillColor(0)
leg.Draw('same')
saves.append((frame, histos, leg))
c.Update()
if __name__ == '__main__':
main()