basePath = 'samples'
sgnInputFile = 'sample.sgn.root'
bkgInputFile = 'sample.bkg.root'
outputFile = 'tuningData_citest1'
treePath = 'HLT/Egamma/Ntuple/Ntuple/HLT_e24_lhmedium_nod0_iloose'
crossValPath = 'data/crossValid_citest0.pic.gz'
etBins = [0, 100000]
etaBins = [0, 2.5]
from TuningTools.CrossValid import CrossValidArchieve
with CrossValidArchieve(crossValPath) as CVArchieve:
crossVal = CVArchieve
del CVArchieve
from TuningTools import createData
from TuningTools import Reference, RingerOperation
from RingerCore import expandFolders
createData(basePath + '/' + sgnInputFile,
basePath + '/' + bkgInputFile,
RingerOperation.EFCalo,
referenceSgn=Reference.Off_Likelihood,
referenceBkg=Reference.Truth,
treePath=treePath,
pattern_oFile=outputFile,
l1EmClusCut=1,
l2EtCut=1,
efEtCut=1,
etBins=etBins,
etaBins=etaBins,
crossVal=crossVal,
nInitsPerJob=1,
nSortsPerJob=2,
compress=True)
from TuningTools.CrossValid import CrossValid, CrossValidArchieve
crossValid = CrossValid(
nSorts=nSorts,
nBoxes=5,
nTrain=3,
nValid=2,
#nTest=args.nTest,
#seed=args.seed,
#level=args.output_level
)
place = CrossValidArchieve(
'crossValid_citest0',
crossValid=crossValid,
).save(True)
from TuningTools.PreProc import *
#ppCol = PreProcCollection( PreProcChain( MapStd() ) )
ppCol = PreProcChain(Norm1())
from TuningTools import fixPPCol
ppCol = fixPPCol(ppCol)
place = PreProcArchieve('ppFile_citest0', ppCol=ppCol).save()
from TuningTools import SubsetGeneratorArchieve, fixSubsetCol, SubsetGeneratorPatterns, Cluster, GMMCluster
from TuningTools import Norm1, PreProcChain, Projection
ppCross = [[[None for k in range(nSorts)] for j in range(nEta)]
for i in range(nEt)]
import numpy as np
from TuningTools.CreateTuningJobFiles import createTuningJobFiles
createTuningJobFiles(outputFolder='config.n5to20.s10.i100.100IPerSPerN',
neuronBounds=[5, 20],
sortBounds=10,
nInits=100,
nNeuronsPerJob=1,
nInitsPerJob=100,
nSortsPerJob=1,
compress=True)
from TuningTools.CrossValid import CrossValid, CrossValidArchieve
crossValid = CrossValid(
nSorts=50,
nBoxes=10,
nTrain=6,
nValid=4,
#nTest=args.nTest,
#seed=args.seed,
#level=args.output_level
)
place = CrossValidArchieve(
'crossValid',
crossValid=crossValid,
).save(True)
from TuningTools.PreProc import *
#ppCol = PreProcCollection( PreProcChain( MapStd() ) )
ppCol = PreProcChain(Norm1())
place = PreProcArchieve('ppFile', ppCol=ppCol).save()
#!/usr/bin/env python
from TuningTools.CreateData import TuningDataArchieve
filePath = '/afs/cern.ch/work/j/jodafons/public/Tuning2016/TuningConfig/mc14_13TeV.147406.129160.sgn.offLikelihood.bkg.truth.trig.e24_lhmedium_nod0_l1etcut20_l2etcut19_efetcut24_binned.pic.npz'
TDArchieve = TuningDataArchieve(filePath)
nEtBins = TDArchieve.nEtBins()
nEtaBins = TDArchieve.nEtaBins()
from TuningTools.PreProc import PreProcArchieve
with PreProcArchieve(
'/afs/cern.ch/work/w/wsfreund/public/Online/Trigger_PCA_ICA_20_7_3_6/pre_proc_pca_T.pic'
) as ppCol:
pass
from TuningTools.CrossValid import CrossValid, CrossValidArchieve
crossValidFile = '/afs/cern.ch/work/j/jodafons/public/Tuning2016/TuningConfig/crossVal_50sorts_20160324.pic.gz'
with CrossValidArchieve(crossValidFile) as CVArchieve:
crossValid = CVArchieve
del CVArchieve
import numpy as np
#np.set_printoptions(threshold=np.nan)
from itertools import product
for etBinIdx, etaBinIdx in product(
range(nEtBins if nEtBins is not None else 1),
range(nEtaBins if nEtaBins is not None else 1)):
with TuningDataArchieve(
filePath,
et_bin=etBinIdx if nEtBins is not None else None,
eta_bin=etaBinIdx if nEtaBins is not None else None) as data:
for sort in range(50):