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crossSectionCheck.py
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crossSectionCheck.py
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#!/usr/bin/env python
__author__ = "Pavez J. <juan.pavezs@alumnos.usm.cl>"
'''
This code is used to build morhphed histograms
for single features.
'''
import ROOT
import numpy as np
from os import listdir
from os.path import isfile, join
import sys
import os.path
import pdb
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from utils import printMultiFrame, printFrame, saveFig, loadData,\
makeROC, makeSigBkg, makePlotName, getWeights
from xgboost_wrapper import XGBoostClassifier
from pyMorphWrapper import MorphingWrapper
def findOutliers(x):
q5, q95 = np.percentile(x, [5,95])
iqr = 2.0*(q95 - q5)
outliers = (x <= q95 + iqr) & (x >= q5 - iqr)
return outliers
def evalDist(x,f0,val):
iter = x.createIterator()
v = iter.Next()
i = 0
while v:
v.setVal(val[i])
v = iter.Next()
i = i+1
return f0.getVal(x)
def checkCrossSection(c1,cross_section,samples,target,dir,c1_g,model_g,feature=0,targetdata=None,samplesdata=None):
'''
Build morphed histograms for a feature
'''
w = ROOT.RooWorkspace('w')
normalizer_abs = (np.abs(np.multiply(c1,cross_section))).sum()
normalizer = (np.multiply(c1,cross_section)).sum()
n_eff = normalizer / normalizer_abs
print 'n_eff_ratio: {0}, n_tot: {0}'.format(n_eff,normalizer_abs)
#normalizer = cross_section.sum()
data_file = 'data'
targetdata = targetdata[:2500,feature]
fulldata = targetdata[:]
targetdata = targetdata[targetdata <> -999.]
targetdata = targetdata[findOutliers(targetdata)]
bins = 300
minimum = targetdata.min()
maximum = targetdata.max()
low = minimum - ((maximum - minimum) / bins)*10
high = maximum + ((maximum - minimum) / bins)*10
w.factory('score[{0},{1}]'.format(low,high))
s = w.var('score')
target_hist = ROOT.TH1F('targethist','targethist',bins,low,high)
for val in targetdata:
target_hist.Fill(val)
norm = 1./target_hist.Integral()
target_hist.Scale(norm)
# Creating samples histograms
samples_hists = []
sum_hist = ROOT.TH1F('sampleshistsum','sampleshistsum',bins,low,high)
for i,sample in enumerate(samples):
samples_hist = ROOT.TH1F('sampleshist{0}'.format(i),'sampleshist',bins,low,high)
testdata = samplesdata[i]
testdata = testdata[:2500,feature]
testdata = testdata[testdata <> -999.]
weight = (c1[i] * cross_section[i])/normalizer
for val in testdata:
samples_hist.Fill(val)
#samples_hist.Fill(val,weight)
norm = 1./samples_hist.Integral()
samples_hist.Scale(norm)
samples_hists.append(samples_hist)
sum_hist.Add(samples_hist,weight)
target_datahist = ROOT.RooDataHist('{0}datahist'.format('target'),'histtarget',
ROOT.RooArgList(s),target_hist)
target_histpdf = ROOT.RooHistFunc('{0}histpdf'.format('target'),'histtarget',
ROOT.RooArgSet(s), target_datahist, 0)
samples_datahist = ROOT.RooDataHist('{0}datahist'.format('samples'),'histsamples',
ROOT.RooArgList(s),sum_hist)
samples_histpdf = ROOT.RooHistFunc('{0}histpdf'.format('samples'),'histsamples',
ROOT.RooArgSet(s), samples_datahist, 0)
#printFrame(w,['score'],[target_histpdf,samples_histpdf],'check_cross_section_{0}'.format(feature),['real','weighted'],
# dir=dir, model_g=model_g,title='cross section check',x_text='x',y_text='dN')
score = ROOT.RooArgSet(w.var('score'))
# Now compute likelihood
evalValues = np.array([evalDist(score,samples_histpdf,[xs]) for xs in fulldata])
n_zeros = evalValues[evalValues <= 0.].shape[0]
evalValues = evalValues[evalValues > 0.]
print evalValues.shape
likelihood = -np.log(evalValues).sum()
print likelihood
return likelihood,n_eff,n_zeros
def fullCrossSectionCheck(dir,c1_g,model_g,data_files,f1_dist,accept_list,c_min,c_max,npoints,n_eval):
'''
Likelihood of morphed distributions plots for all features
'''
csarray = np.linspace(c_min,c_max,npoints)
# Loading morphed indexes, couplings and cross section
all_indexes = np.loadtxt('2indexes_{0:.2f}_{1:.2f}_{2}.dat'.format(c_min,c_max,npoints))
all_indexes = np.array([int(x) for x in all_indexes])
#all_indexes = np.array([[int(x) for x in rows] for rows in all_indexes])
all_couplings = np.loadtxt('2couplings_{0:.2f}_{1:.2f}_{2}.dat'.format(c_min,c_max,npoints))
all_cross_sections = np.loadtxt('2crosssection_{0:.2f}_{1:.2f}_{2}.dat'.format(c_min,c_max,npoints))
features = accept_list
n_effs = np.zeros((len(features),all_couplings.shape[0]))
n_zeros = np.zeros((len(features),all_couplings.shape[0]))
likelihoods = []
data_file='data'
# Loading target and samples data
targetdata = np.loadtxt('{0}/data/{1}/{2}/{3}_{4}.dat'.format(dir,'mlp',c1_g,data_file,f1_dist))
basis_files = [data_files[i] for i in all_indexes]
samplesdata = []
data_file='data'
for i,sample in enumerate(basis_files):
samplesdata.append(np.loadtxt('{0}/data/{1}/{2}/{3}_{4}.dat'.format(dir,'mlp',c1_g,data_file,sample)))
for k,couplings in enumerate(all_couplings):
#samplesdata = []
likelihoods.append([])
for i,f in enumerate(features):
likelihood,n_eff,n_zero = checkCrossSection(couplings,all_cross_sections[k],basis_files,f1_dist,
dir,c1_g,model_g,feature=f,targetdata=targetdata,samplesdata=samplesdata)
likelihoods[-1].append(likelihood)
n_effs[i,k] = n_eff
n_zeros[i,k] = n_zero
print likelihoods
likelihoods = np.array(likelihoods)
likelihoods = likelihoods - np.abs(likelihoods.min(axis=0))
likelihoods = likelihoods/likelihoods.max(axis=0)
n_zeros_max = n_zeros.max(axis=1)
n_zeros = n_zeros.transpose()/n_zeros.max(axis=1)
n_zeros = n_zeros.transpose()
for k,feat in enumerate(features):
fig, ax1 = plt.subplots(1, 1, figsize=(8, 12))
ax1.plot(csarray, likelihoods[:,k])
ax1.plot(csarray, n_effs[k])
ax1.plot(csarray, n_zeros[k])
plt.legend(['Likelihood','n_eff','n_zeros/{0}'.format(n_zeros_max[k])])
fig.savefig('{0}/plots/{1}/{2}.png'.format(dir,model_g,'features_likelihood_g1_{0}'.format(feat)))
def CrossSectionCheck2D(dir,c1_g,model_g,data_files,f1_dist,accept_list,c_min,c_max,npoints,n_eval,feature):
'''
2D likelihood plots for a single feature
'''
# 2D version
csarray = np.linspace(c_min[0],c_max[0],npoints)
csarray2 = np.linspace(c_min[1], c_max[1], npoints)
all_indexes = np.loadtxt('3indexes_{0:.2f}_{1:.2f}_{2:.2f}_{3:.2f}_{4}.dat'.format(c_min[0],c_min[1],c_max[0],c_max[1],npoints))
all_indexes = np.array([int(x) for x in all_indexes])
all_couplings = np.loadtxt('3couplings_{0:.2f}_{1:.2f}_{2:.2f}_{3:.2f}_{4}.dat'.format(c_min[0],c_min[1],c_max[0],c_max[1],npoints))
all_cross_sections = np.loadtxt('3crosssection_{0:.2f}_{1:.2f}_{2:.2f}_{3:.2f}_{4}.dat'.format(c_min[0],c_min[1],c_max[0],c_max[1],npoints))
basis_files = [data_files[i] for i in all_indexes]
samplesdata = []
data_file='data'
for i,sample in enumerate(basis_files):
samplesdata.append(np.loadtxt('{0}/data/{1}/{2}/{3}_{4}.dat'.format(dir,'mlp',c1_g,data_file,sample)))
print all_indexes
targetdata = np.loadtxt('{0}/data/{1}/{2}/{3}_{4}.dat'.format(dir,'mlp',c1_g,data_file,f1_dist))
likelihoods = np.zeros((npoints,npoints))
n_effs = np.zeros((npoints,npoints))
n_zeros = np.zeros((npoints,npoints))
for k,cs in enumerate(csarray):
for j,cs2 in enumerate(csarray2):
likelihood,n_eff,n_zero = checkCrossSection(all_couplings[k*npoints+j],all_cross_sections[k*npoints + j],basis_files,f1_dist,
dir,c1_g,model_g,feature=feature,targetdata=targetdata,samplesdata=samplesdata)
likelihoods[k,j] = likelihood
n_effs[k,j] = n_eff
n_zeros[k,j] = n_zero
#print likelihoods
saveFig(csarray,[csarray2,likelihoods],makePlotName('feature{0}'.format(25),'train',type='pixel_g1g2'),labels=['composed'],pixel=True,marker=True,dir=dir,model_g=model_g,marker_value=(1.0,0.5),print_pdf=True,contour=True,title='Feature for g1,g2')