def check_retrieve(self, filename, md5sum, dlurl):
filename = os.path.expandvars(filename)
basefile = os.path.basename(filename)
dirname = os.path.dirname( filename )
from RingerCore.FileIO import checkFile
if not checkFile(filename, md5sum):
self._logger.info('Downloading %s to avoid doing it on server side.', basefile)
import urllib
if not os.path.isdir( dirname ):
from RingerCore import mkdir_p
mkdir_p( dirname )
urllib.urlretrieve(dlurl, filename=filename)
else:
self._logger.info('%s already downloaded.',filename)
def check_retrieve(self, filename, md5sum, dlurl):
filename = os.path.expandvars(filename)
basefile = os.path.basename(filename)
dirname = os.path.dirname(filename)
from RingerCore.FileIO import checkFile
if not checkFile(filename, md5sum):
self._info('Downloading %s to avoid doing it on server side.',
basefile)
import urllib
if not os.path.isdir(dirname):
from RingerCore import mkdir_p
mkdir_p(dirname)
urllib.urlretrieve(dlurl, filename=filename)
else:
self._debug('%s already downloaded.', filename)
RingerOperation.EFCalo,
ringConfig = 100,
#referenceSgn = Reference.AcceptAll,
referenceSgn = Reference.Off_Likelihood,
#referenceBkg = Reference.Truth,
referenceBkg = Reference.Off_Likelihood,
treePath = treePath,
pattern_oFile = outputFile,
l1EmClusCut = 20,
l2EtCut = 19,
efEtCut = 24,
etBins = etBins,
etaBins = etaBins,
crossVal = crossVal,
nClusters = 5000,
#efficiencyValues = [97.0, 2.0],
toMatlab = True)
from RingerCore import mkdir_p
mkdir_p(outputFile)
import os
os.system(('mv %s.* %s/') % (outputFile, outputFile) )
os.system(('mv *.pdf %s/') % (outputFile) )
if not args.subsetDS is None:
args.append_to_job_submission_option(
'secondaryDSs',
SecondaryDataset(key="SUBSET",
nFilesPerJob=1,
container=args.subsetDS[0],
reusable=True))
subsetStr = '%SUBSET'
elif clusterManagerConf() in (ClusterManager.PBS, ClusterManager.LSF):
#if args.core_framework is TuningToolCores.keras:
# Keras run single-threaded
#args.set_job_submission_option('ncpus', SubOptionRetrieve( option = '-l', suboption = 'ncpus', value=1 ) )
# Make sure we have permision to create the directory:
if not args.dry_run:
mkdir_p(args.outputDir)
rootcorebin = os.environ.get('ROOTCOREBIN')
#setrootcore = os.path.join(rootcorebin,'../setrootcore.sh')
setrootcore = ''
# TODO Add to setrootcore the number of cores in the job
# TODO Set the OMP_NUM_CLUSTER environment to the same value as the one in the job.
#setrootcore_opts = '--ncpus=%d' % args.get_job_submission_option('ncpus')
setrootcore_opts = ''
expandArg = lambda x: ' '.join(x) if x else ''
tuningJob = os.path.join(
rootcorebin, 'user_scripts/TuningTools/standalone/runTuning.py')
dataStr, configStr, ppStr, crossFileStr, refStr, subsetStr, expertNetworksStr = \
expandArg(args.data), '{CONFIG_FILES}', args.ppFile, args.crossFile, args.refFile, args.clusterFile, expandArg(args.expert_networks)
configFileDir = os.path.abspath(args.configFileDir)
if os.path.isdir(configFileDir):
configFiles = getFiles(configFileDir)
def __call__(self, **kw):
"""
Create a collection of tuning job configuration files at the output
folder.
"""
# Cross validation configuration
outputFolder = retrieve_kw( kw, 'outputFolder', 'jobConfig' )
neuronBounds = retrieve_kw( kw, 'neuronBounds', SeqLoopingBounds(5, 20) )
sortBounds = retrieve_kw( kw, 'sortBounds', PythonLoopingBounds(50) )
nInits = retrieve_kw( kw, 'nInits', 100 )
# Output configuration
nNeuronsPerJob = retrieve_kw( kw, 'nNeuronsPerJob', 1 )
nSortsPerJob = retrieve_kw( kw, 'nSortsPerJob', 1 )
nInitsPerJob = retrieve_kw( kw, 'nInitsPerJob', 100 )
compress = retrieve_kw( kw, 'compress', True )
prefix = retrieve_kw( kw, 'prefix' , 'job' )
if 'level' in kw: self.level = kw.pop('level')
# Make sure that bounds variables are LoopingBounds objects:
if not isinstance( neuronBounds, SeqLoopingBounds ):
neuronBounds = SeqLoopingBounds(neuronBounds)
if not isinstance( sortBounds, SeqLoopingBounds ):
sortBounds = PythonLoopingBounds(sortBounds)
# and delete it to avoid mistakes:
checkForUnusedVars( kw, self._warning )
del kw
if nInits < 1:
self._fatal(("Cannot require zero or negative initialization "
"number."), ValueError)
# Do some checking in the arguments:
nNeurons = len(neuronBounds)
nSorts = len(sortBounds)
if not nSorts:
self._fatal("Sort bounds is empty.")
if nNeuronsPerJob > nNeurons:
self._warning(("The number of neurons per job (%d) is "
"greater then the total number of neurons (%d), changing it "
"into the maximum possible value."), nNeuronsPerJob, nNeurons )
nNeuronsPerJob = nNeurons
if nSortsPerJob > nSorts:
self._warning(("The number of sorts per job (%d) is "
"greater then the total number of sorts (%d), changing it "
"into the maximum possible value."), nSortsPerJob, nSorts )
nSortsPerJob = nSorts
# Create the output folder:
mkdir_p(outputFolder)
# Create the windows in which each job will loop upon:
neuronJobsWindowList = \
CreateTuningJobFiles._retrieveJobLoopingBoundsCol( neuronBounds,
nNeuronsPerJob )
sortJobsWindowList = \
CreateTuningJobFiles._retrieveJobLoopingBoundsCol( sortBounds,
nSortsPerJob )
initJobsWindowList = \
CreateTuningJobFiles._retrieveJobLoopingBoundsCol( \
PythonLoopingBounds( nInits ), \
nInitsPerJob )
# Loop over windows and create the job configuration
for neuronWindowBounds in neuronJobsWindowList():
for sortWindowBounds in sortJobsWindowList():
for initWindowBounds in initJobsWindowList():
self._debug(('Retrieved following job configuration '
'(bounds.vec) : '
'[ neuronBounds=%s, sortBounds=%s, initBounds=%s]'),
neuronWindowBounds.formattedString('hn'),
sortWindowBounds.formattedString('s'),
initWindowBounds.formattedString('i'))
fulloutput = '{outputFolder}/{prefix}.{neuronStr}.{sortStr}.{initStr}'.format(
outputFolder = outputFolder,
prefix = prefix,
neuronStr = neuronWindowBounds.formattedString('hn'),
sortStr = sortWindowBounds.formattedString('s'),
initStr = initWindowBounds.formattedString('i') )
savedFile = TuningJobConfigArchieve( fulloutput,
neuronBounds = neuronWindowBounds,
sortBounds = sortWindowBounds,
initBounds = initWindowBounds ).save( compress )
self._info('Saved job option configuration at path: %s',
savedFile )
parser = ArgumentParser( description = 'Retrieve performance information from the Cross-Validation method.'
, parents = [crossValStatsJobParser, loggerParser] )
parser.make_adjustments()
emptyArgumentsPrintHelp( parser )
## Retrieve parser args:
args = parser.parse_args( )
mainLogger = Logger.getModuleLogger(__name__)
mainLogger.level = args.output_level
# Overwrite tempfile in the beginning of the job:
if args.tmpFolder:
args.tmpFolder = expandPath( args.tmpFolder )
mkdir_p( args.tmpFolder )
import tempfile
tempfile.tempdir = args.tmpFolder
if mainLogger.isEnabledFor( LoggingLevel.DEBUG ):
import cProfile, pstats, StringIO
pr = cProfile.Profile()
pr.enable()
## Treat special arguments
# Check if binFilters is a class
if args.binFilters is not NotSet:
try:
args.binFilters = str_to_class( "TuningTools.CrossValidStat", args.binFilters )
except (TypeError, AttributeError,):
args.binFilters = csvStr2List( args.binFilters )
def __call__(self, **kw):
"""
Create a collection of tuning job configuration files at the output
folder.
"""
# Cross validation configuration
outputFolder = retrieve_kw( kw, 'outputFolder', 'jobConfig' )
neuronBounds = retrieve_kw( kw, 'neuronBounds', SeqLoopingBounds(5, 20) )
sortBounds = retrieve_kw( kw, 'sortBounds', PythonLoopingBounds(50) )
nInits = retrieve_kw( kw, 'nInits', 100 )
# Output configuration
nNeuronsPerJob = retrieve_kw( kw, 'nNeuronsPerJob', 1 )
nSortsPerJob = retrieve_kw( kw, 'nSortsPerJob', 1 )
nInitsPerJob = retrieve_kw( kw, 'nInitsPerJob', 100 )
compress = retrieve_kw( kw, 'compress', True )
if 'level' in kw: self.level = kw.pop('level')
# Make sure that bounds variables are LoopingBounds objects:
if not isinstance( neuronBounds, SeqLoopingBounds ):
neuronBounds = SeqLoopingBounds(neuronBounds)
if not isinstance( sortBounds, SeqLoopingBounds ):
sortBounds = PythonLoopingBounds(sortBounds)
# and delete it to avoid mistakes:
checkForUnusedVars( kw, self._logger.warning )
del kw
if nInits < 1:
self._logger.fatal(("Cannot require zero or negative initialization "
"number."), ValueError)
# Do some checking in the arguments:
nNeurons = len(neuronBounds)
nSorts = len(sortBounds)
if not nSorts:
self._logger.fatal("Sort bounds is empty.")
if nNeuronsPerJob > nNeurons:
self._logger.warning(("The number of neurons per job (%d) is "
"greater then the total number of neurons (%d), changing it "
"into the maximum possible value."), nNeuronsPerJob, nNeurons )
nNeuronsPerJob = nNeurons
if nSortsPerJob > nSorts:
self._logger.warning(("The number of sorts per job (%d) is "
"greater then the total number of sorts (%d), changing it "
"into the maximum possible value."), nSortsPerJob, nSorts )
nSortsPerJob = nSorts
# Create the output folder:
mkdir_p(outputFolder)
# Create the windows in which each job will loop upon:
neuronJobsWindowList = \
CreateTuningJobFiles._retrieveJobLoopingBoundsCol( neuronBounds,
nNeuronsPerJob )
sortJobsWindowList = \
CreateTuningJobFiles._retrieveJobLoopingBoundsCol( sortBounds,
nSortsPerJob )
initJobsWindowList = \
CreateTuningJobFiles._retrieveJobLoopingBoundsCol( \
PythonLoopingBounds( nInits ), \
nInitsPerJob )
# Loop over windows and create the job configuration
for neuronWindowBounds in neuronJobsWindowList():
for sortWindowBounds in sortJobsWindowList():
for initWindowBounds in initJobsWindowList():
self._logger.debug(('Retrieved following job configuration '
'(bounds.vec) : '
'[ neuronBounds=%s, sortBounds=%s, initBounds=%s]'),
neuronWindowBounds.formattedString('hn'),
sortWindowBounds.formattedString('s'),
initWindowBounds.formattedString('i'))
fulloutput = '{outputFolder}/job.{neuronStr}.{sortStr}.{initStr}'.format(
outputFolder = outputFolder,
neuronStr = neuronWindowBounds.formattedString('hn'),
sortStr = sortWindowBounds.formattedString('s'),
initStr = initWindowBounds.formattedString('i') )
savedFile = TuningJobConfigArchieve( fulloutput,
neuronBounds = neuronWindowBounds,
sortBounds = sortWindowBounds,
initBounds = initWindowBounds ).save( compress )
self._logger.info('Saved job option configuration at path: %s',
savedFile )
caloLayers = [RingerLayer.PS,
RingerLayer.EM1,
RingerLayer.EM2,
RingerLayer.EM3,
RingerLayer.HAD1,
RingerLayer.HAD2,
RingerLayer.HAD3,]
from RingerCore import load,save
from RingerCore import changeExtension, ensureExtension, appendToFileName, progressbar, mkdir_p
from itertools import product
import numpy as np
if args.outputPath is None:
args.outputPath = os.path.dirname(args.inputFile)
if not os.path.isdir( args.outputPath ): mkdir_p( args.outputPath )
f = load(args.inputFile)
# Copy all metada information
baseDict = { k : f[k] for k in f.keys() if not '_etBin_' in k and not '_etaBin_' in k }
nEtBins = f['nEtBins'].item()
nEtaBins = f['nEtaBins'].item()
for etIdx, etaIdx in progressbar( product(xrange(nEtBins), xrange(nEtaBins))
, nEtBins*nEtaBins
, logger = mainLogger
, prefix = 'Juicing file '):
binDict= {k:f[k] for k in f.keys() if 'etBin_%d_etaBin_%d'%(etIdx,etaIdx) in k}
binDict.update(baseDict)
from copy import deepcopy
for layer in caloLayers:
pp=PreProcChain([RingerLayerSegmentation(layer=layer)])
parser.add_argument("--triggerList", nargs="+", default=defaultTrigList)
parser.add_argument("--numberOfSamplesPerPackage", type=int, default=50)
args = parser.parse_args()
mainLogger = Logger.getModuleLogger(__name__, LoggingLevel.INFO)
if os.path.exists("dq2_ls.txt"):
os.system("rm dq2_ls.txt")
if args.inDS[-1] != "/":
args.inDS += "/"
if args.outFolder[-1] != "/":
args.outFolder += "/"
mkdir_p(args.outFolder)
mkdir_p("tmpDir")
os.system("dq2-ls -fH " + args.inDS + " >& dq2_ls.txt")
with open("dq2_ls.txt", "r") as f:
lines = f.readlines()
samples = []
dataset = ""
fileLine = re.compile("\[ \]\s+(\S+)\s+\S+\s+\S+\s+\S+\s+\S+")
for s in lines:
m = fileLine.match(s)
if m:
samples.append(m.group(1))
package = []
def loop(self, **kw):
import gc
output = kw.pop('output' , 'Mon' )
tuningReport = kw.pop('tuningReport', 'tuningReport' )
doBeamer = kw.pop('doBeamer' , True )
shortSlides = kw.pop('shortSlides' , False )
debug = kw.pop('debug' , False )
overwrite = kw.pop('overwrite' , False )
basepath=output
basepath+=('_et%d_eta%d')%(self._infoObjs[0].etbin(),self._infoObjs[0].etabin())
if not overwrite and os.path.isdir( basepath ):
self._logger.warning("Monitoring output path already exists!")
return
if shortSlides:
self._logger.warning('Short slides enabled! Doing only tables...')
if debug:
self._logger.warning('Debug mode activated!')
wantedPlotNames = {'allBestTstSorts','allBestOpSorts','allWorstTstSorts', 'allWorstOpSorts',\
'allBestTstNeurons','allBestOpNeurons', 'allWorstTstNeurons', 'allWorstOpNeurons'}
perfBenchmarks = dict()
pathBenchmarks = dict()
from PlotHolder import PlotHolder
from PlotHelper import plot_4c, plot_rocs, plot_nnoutput
from TuningMonitoringInfo import MonitoringPerfInfo
#Loop over benchmarks
for infoObj in self._infoObjs:
#Initialize all plos
plotObjects = dict()
perfObjects = dict()
infoObjects = dict()
pathObjects = dict()
#Init PlotsHolder
for plotname in wantedPlotNames:
if 'Sorts' in plotname:
plotObjects[plotname] = PlotHolder(label = 'Sort')
else:
plotObjects[plotname] = PlotHolder(label = 'Neuron')
#Retrieve benchmark name
benchmarkName = infoObj.name()
#Retrieve reference name
reference = infoObj.reference()
#summary
csummary = infoObj.summary()
#benchmark object
cbenchmark = infoObj.rawBenchmark()
# reference value
refVal = infoObj.rawBenchmark()['refVal']
#Eta bin
etabin = infoObj.etabin()
#Et bin
etbin = infoObj.etbin()
self._logger.info(('Start loop over the benchmark: %s and etaBin = %d etBin = %d')%(benchmarkName,etabin, etbin) )
import copy
args = dict()
args['reference'] = reference
args['refVal'] = refVal
args['eps'] = cbenchmark['eps']
self._logger.info('Creating plots...')
# Creating plots
for neuron in progressbar(infoObj.neuronBounds(), len(infoObj.neuronBounds()), 'Loading : ', 60, False, logger=self._logger):
# Figure path location
currentPath = ('%s/figures/%s/%s') % (basepath,benchmarkName,'neuron_'+str(neuron))
neuronName = 'config_'+str(neuron).zfill(3)
# Create folder to store all plot objects
mkdir_p(currentPath)
#Clear all hold plots stored
plotObjects['allBestTstSorts'].clear()
plotObjects['allBestOpSorts'].clear()
infoObjects['allInfoOpBest_'+neuronName] = list()
#plotObjects['allWorstTstSorts'].clear()
#plotObjects['allWorstOpSorts'].clear()
for sort in infoObj.sortBounds(neuron):
sortName = 'sort_'+str(sort).zfill(3)
#Init bounds
initBounds = infoObj.initBounds(neuron,sort)
#Create path list from initBound list
initPaths = [('%s/%s/%s/init_%s')%(benchmarkName,neuronName,sortName,init) for init in initBounds]
self._logger.debug('Creating init plots into the path: %s, (neuron_%s,sort_%s)', \
benchmarkName, neuron, sort)
obj = PlotHolder(label = 'Init')
try: #Create plots holder class (Helper), store all inits
obj.retrieve(self._rootObj, initPaths)
except RuntimeError:
self._logger.fatal('Can not create plot holder object')
#Hold all inits from current sort
obj.set_index_correction(initBounds)
obj.set_best_index( csummary[neuronName][sortName]['infoTstBest']['init'] )
obj.set_worst_index( csummary[neuronName][sortName]['infoTstWorst']['init'] )
plotObjects['allBestTstSorts'].append( copy.deepcopy(obj.get_best() ) )
obj.set_best_index( csummary[neuronName][sortName]['infoOpBest']['init'] )
obj.set_worst_index( csummary[neuronName][sortName]['infoOpWorst']['init'] )
plotObjects['allBestOpSorts'].append( copy.deepcopy(obj.get_best() ) )
#plotObjects['allWorstTstSorts'].append( copy.deepcopy(tstObj.getBest() )
#plotObjects['allWorstOpSorts'].append( copy.deepcopy(opObj.getBest() )
infoObjects['allInfoOpBest_'+neuronName].append( copy.deepcopy(csummary[neuronName][sortName]['infoOpBest']) )
#Release memory
del obj
#Loop over sorts
gc.collect()
plotObjects['allBestTstSorts'].set_index_correction( infoObj.sortBounds(neuron) )
plotObjects['allBestOpSorts'].set_index_correction( infoObj.sortBounds(neuron) )
#plotObjects['allWorstTstSorts'].setIdxCorrection( infoObj.sortBounds(neuron) )
#plotObjects['allWorstOpSorts'].setIdxCorrection( infoObj.sortBounds(neuron) )
# Best and worst sorts for this neuron configuration
plotObjects['allBestTstSorts'].set_best_index( csummary[neuronName]['infoTstBest']['sort'] )
plotObjects['allBestTstSorts'].set_worst_index( csummary[neuronName]['infoTstWorst']['sort'] )
plotObjects['allBestOpSorts'].set_best_index( csummary[neuronName]['infoOpBest']['sort'] )
plotObjects['allBestOpSorts'].set_worst_index( csummary[neuronName]['infoOpWorst']['sort'] )
# Hold the information from the best and worst discriminator for this neuron
infoObjects['infoOpBest_'+neuronName] = copy.deepcopy(csummary[neuronName]['infoOpBest'])
infoObjects['infoOpWorst_'+neuronName] = copy.deepcopy(csummary[neuronName]['infoOpWorst'])
# Best and worst neuron sort for this configuration
plotObjects['allBestTstNeurons'].append( copy.deepcopy(plotObjects['allBestTstSorts'].get_best() ))
plotObjects['allBestOpNeurons'].append( copy.deepcopy(plotObjects['allBestOpSorts'].get_best() ))
plotObjects['allWorstTstNeurons'].append(copy.deepcopy(plotObjects['allBestTstSorts'].get_worst() ))
plotObjects['allWorstOpNeurons'].append( copy.deepcopy(plotObjects['allBestOpSorts'].get_worst() ))
# Create perf (tables) Objects for test and operation (Table)
perfObjects[neuronName] = MonitoringPerfInfo(benchmarkName, reference,
csummary[neuronName]['summaryInfoTst'],
csummary[neuronName]['infoOpBest'],
cbenchmark)
# Debug information
self._logger.debug(('Crossval indexs: (bestSort = %d, bestInit = %d) (worstSort = %d, bestInit = %d)')%\
(plotObjects['allBestTstSorts'].best, plotObjects['allBestTstSorts'].get_best()['bestInit'],
plotObjects['allBestTstSorts'].worst, plotObjects['allBestTstSorts'].get_worst()['bestInit']))
self._logger.debug(('Operation indexs: (bestSort = %d, bestInit = %d) (worstSort = %d, bestInit = %d)')%\
(plotObjects['allBestOpSorts'].best, plotObjects['allBestOpSorts'].get_best()['bestInit'],
plotObjects['allBestOpSorts'].worst, plotObjects['allBestOpSorts'].get_worst()['bestInit']))
# Figure 1: Plot all validation/test curves for all crossval sorts tested during
# the training. The best sort will be painted with black and the worst sort will
# be on red color. There is a label that will be draw into the figure to show
# the current location (neuron, sort, init) of the best and the worst network.
args['label'] = ('#splitline{#splitline{Total sorts: %d}{etaBin: %d, etBin: %d}}'+\
'{#splitline{sBestIdx: %d iBestIdx: %d}{sWorstIdx: %d iBestIdx: %d}}') % \
(plotObjects['allBestTstSorts'].size(),etabin, etbin, plotObjects['allBestTstSorts'].best, \
plotObjects['allBestTstSorts'].get_best()['bestInit'], plotObjects['allBestTstSorts'].worst,\
plotObjects['allBestTstSorts'].get_worst()['bestInit'])
args['cname'] = ('%s/plot_%s_neuron_%s_sorts_val')%(currentPath,benchmarkName,neuron)
args['set'] = 'val'
args['operation'] = False
args['paintListIdx'] = [plotObjects['allBestTstSorts'].best, plotObjects['allBestTstSorts'].worst]
pname1 = plot_4c(plotObjects['allBestTstSorts'], args)
# Figure 2: Plot all validation/test curves for all crossval sorts tested during
# the training. The best sort will be painted with black and the worst sort will
# be on red color. But, here the painted curves represented the best and the worst
# curve from the operation dataset. In other words, we pass all events into the
# network and get the efficiencis than we choose the best operation and the worst
# operation network and paint the validation curve who represent these sorts.
# There is a label that will be draw into the figure to show
# the current location (neuron, sort, init) of the best and the worst network.
args['label'] = ('#splitline{#splitline{Total sorts: %d (operation)}{etaBin: %d, etBin: %d}}'+\
'{#splitline{sBestIdx: %d iBestIdx: %d}{sWorstIdx: %d iBestIdx: %d}}') % \
(plotObjects['allBestOpSorts'].size(),etabin, etbin, plotObjects['allBestOpSorts'].best, \
plotObjects['allBestOpSorts'].get_best()['bestInit'], plotObjects['allBestOpSorts'].worst,\
plotObjects['allBestOpSorts'].get_worst()['bestInit'])
args['cname'] = ('%s/plot_%s_neuron_%s_sorts_op')%(currentPath,benchmarkName,neuron)
args['set'] = 'val'
args['operation'] = True
args['paintListIdx'] = [plotObjects['allBestOpSorts'].best, plotObjects['allBestOpSorts'].worst]
pname2 = plot_4c(plotObjects['allBestOpSorts'], args)
# Figure 3: This figure show us in deteails the best operation network for the current hidden
# layer and benchmark analysis. Depend on the benchmark, we draw lines who represents the
# stops for each curve. The current neuron will be the last position of the plotObjects
splotObject = PlotHolder()
args['label'] = ('#splitline{#splitline{Best network neuron: %d}{etaBin: %d, etBin: %d}}'+\
'{#splitline{sBestIdx: %d iBestIdx: %d}{}}') % \
(neuron,etabin, etbin, plotObjects['allBestOpSorts'].best, plotObjects['allBestOpSorts'].get_best()['bestInit'])
args['cname'] = ('%s/plot_%s_neuron_%s_best_op')%(currentPath,benchmarkName,neuron)
args['set'] = 'val'
args['operation'] = True
splotObject.append( plotObjects['allBestOpNeurons'][-1] )
pname3 = plot_4c(splotObject, args)
# Figure 4: Here, we have a plot of the discriminator output for all dataset. Black histogram
# represents the signal and the red onces represent the background. TODO: Apply this outputs
# using the feedfoward manual method to generate the network outputs and create the histograms.
args['cname'] = ('%s/plot_%s_neuron_%s_best_op_output')%(currentPath,benchmarkName,neuron)
args['nsignal'] = self._data[0].shape[0]
args['nbackground'] = self._data[1].shape[0]
sbest = plotObjects['allBestOpNeurons'][-1]['bestSort']
args['cut'] = csummary[neuronName]['sort_'+str(sbest).zfill(3)]['infoOpBest']['cut']
args['rocname'] = 'roc_op'
pname4 = plot_nnoutput(splotObject,args)
# Figure 5: The receive operation test curve for all sorts using the test dataset as base.
# Here, we will draw the current tunnel and ref value used to set the discriminator threshold
# when the bechmark are Pd or Pf case. When we use the SP case, this tunnel will not be ploted.
# The black curve represents the best sort and the red onces the worst sort. TODO: Put the SP
# point for the best and worst when the benchmark case is SP.
args['cname'] = ('%s/plot_%s_neuron_%s_sorts_roc_tst')%(currentPath,benchmarkName,neuron)
args['set'] = 'tst'
args['paintListIdx'] = [plotObjects['allBestTstSorts'].best, plotObjects['allBestTstSorts'].worst]
pname5 = plot_rocs(plotObjects['allBestTstSorts'], args)
# Figure 6: The receive operation curve for all sorts using the operation dataset (train+test) as base.
# Here, we will draw the current tunnel and ref value used to set the discriminator threshold
# when the bechmark are Pd or Pf case. When we use the SP case, this tunnel will not be ploted.
# The black curve represents the best sort and the red onces the worst sort. TODO: Put the SP
# point for the best and worst when the benchmark case is SP.
args['cname'] = ('%s/plot_%s_neuron_%s_sorts_roc_op')%(currentPath,benchmarkName,neuron)
args['set'] = 'op'
args['paintListIdx'] = [plotObjects['allBestOpSorts'].best, plotObjects['allBestOpSorts'].worst]
pname6 = plot_rocs(plotObjects['allBestOpSorts'], args)
# Map names for beamer, if you add a plot, you must add into
# the path objects holder
pathObjects['neuron_'+str(neuron)+'_sorts_val'] = pname1
pathObjects['neuron_'+str(neuron)+'_sort_op'] = pname2
pathObjects['neuron_'+str(neuron)+'_best_op'] = pname3
pathObjects['neuron_'+str(neuron)+'_best_op_output'] = pname4
pathObjects['neuron_'+str(neuron)+'_sorts_roc_tst'] = pname5
pathObjects['neuron_'+str(neuron)+'_sorts_roc_op'] = pname6
if debug: break
#Loop over neurons
#External
pathBenchmarks[benchmarkName] = pathObjects
perfBenchmarks[benchmarkName] = perfObjects
#Release memory
for xname in plotObjects.keys():
del plotObjects[xname]
gc.collect()
#if debug: break
#Loop over benchmark
#Start beamer presentation
if doBeamer:
from BeamerMonReport import BeamerMonReport
from BeamerTemplates import BeamerPerfTables, BeamerFigure, BeamerBlocks
#Eta bin
etabin = self._infoObjs[0].etabin()
#Et bin
etbin = self._infoObjs[0].etbin()
#Create the beamer manager
reportname = ('%s_et%d_eta%d')%(output,etbin,etabin)
beamer = BeamerMonReport(basepath+'/'+reportname, title = ('Tuning Report (et=%d, eta=%d)')%(etbin,etabin) )
neuronBounds = self._infoObjs[0].neuronBounds()
for neuron in neuronBounds:
#Make the tables for crossvalidation
ptableCross = BeamerPerfTables(frametitle= ['Neuron '+str(neuron)+': Cross Validation Performance',
'Neuron '+str(neuron)+": Operation Best Network"],
caption=['Efficiencies from each benchmark.',
'Efficiencies for the best operation network'])
block = BeamerBlocks('Neuron '+str(neuron)+' Analysis', [('All sorts (validation)','All sorts evolution are ploted, each sort represents the best init;'),
('All sorts (operation)', 'All sorts evolution only for operation set;'),
('Best operation', 'Detailed analysis from the best sort discriminator.'),
('Tables','Cross validation performance')])
if not shortSlides: block.tolatex( beamer.file() )
for info in self._infoObjs:
#If we produce a short presentation, we do not draw all plots
if not shortSlides:
bname = info.name().replace('OperationPoint_','')
fig1 = BeamerFigure( pathBenchmarks[info.name()]['neuron_'+str(neuron)+'_sorts_val'].replace(basepath+'/',''), 0.7,
frametitle=bname+', Neuron '+str(neuron)+': All sorts (validation)')
fig2 = BeamerFigure( pathBenchmarks[info.name()]['neuron_'+str(neuron)+'_sorts_roc_tst'].replace(basepath+'/',''), 0.8,
frametitle=bname+', Neuron '+str(neuron)+': All ROC sorts (validation)')
fig3 = BeamerFigure( pathBenchmarks[info.name()]['neuron_'+str(neuron)+'_sort_op'].replace(basepath+'/',''), 0.7,
frametitle=bname+', Neuron '+str(neuron)+': All sorts (operation)')
fig4 = BeamerFigure( pathBenchmarks[info.name()]['neuron_'+str(neuron)+'_sorts_roc_op'].replace(basepath+'/',''), 0.8,
frametitle=bname+', Neuron '+str(neuron)+': All ROC sorts (operation)')
fig5 = BeamerFigure( pathBenchmarks[info.name()]['neuron_'+str(neuron)+'_best_op'].replace(basepath+'/',''), 0.7,
frametitle=bname+', Neuron '+str(neuron)+': Best Network')
fig6 = BeamerFigure( pathBenchmarks[info.name()]['neuron_'+str(neuron)+'_best_op_output'].replace(basepath+'/',''), 0.8,
frametitle=bname+', Neuron '+str(neuron)+': Best Network output')
#Draw figures into the tex file
fig1.tolatex( beamer.file() )
fig2.tolatex( beamer.file() )
fig3.tolatex( beamer.file() )
fig4.tolatex( beamer.file() )
fig5.tolatex( beamer.file() )
fig6.tolatex( beamer.file() )
#Concatenate performance table, each line will be a benchmark
#e.g: det, sp and fa
ptableCross.add( perfBenchmarks[info.name()]['config_'+str(neuron).zfill(3)] )
#if debug: break
ptableCross.tolatex( beamer.file() )# internal switch is false to true: test
ptableCross.tolatex( beamer.file() )# internal swotch is true to false: operation
if debug: break
beamer.close()
self._logger.info('Done! ')
def loop(self, **kw):
from scipy.io import loadmat
import gc
output = kw.pop('output', 'Mon')
tuningReport = kw.pop('tuningReport', 'tuningReport')
doBeamer = kw.pop('doBeamer', True)
shortSlides = kw.pop('shortSlides', False)
debug = kw.pop('debug', False)
overwrite = kw.pop('overwrite', False)
choicesfile = kw.pop('choicesfile', None)
basepath = output
basepath += ('_et%d_eta%d') % (self._infoObjs[0].etbinidx(),
self._infoObjs[0].etabinidx())
if choicesfile: choices = loadmat(choicesfile)
if not overwrite and os.path.isdir(basepath):
self._logger.warning("Monitoring output path already exists!")
return
if shortSlides:
self._logger.warning('Short slides enabled! Doing only tables...')
if debug:
self._logger.warning('Debug mode activated!')
wantedPlotNames = {'allBestTstSorts','allBestOpSorts','allWorstTstSorts', 'allWorstOpSorts',\
'allBestTstNeurons','allBestOpNeurons', 'allWorstTstNeurons', 'allWorstOpNeurons'}
perfBenchmarks = dict()
pathBenchmarks = dict()
from PlotHolder import PlotHolder
from PlotHelper import plot_4c, plot_rocs, plot_nnoutput
from TuningMonitoringInfo import MonitoringPerfInfo
#Loop over benchmarks
for infoObj in self._infoObjs:
#Initialize all plos
plotObjects = dict()
perfObjects = dict()
infoObjects = dict()
pathObjects = dict()
#Init PlotsHolder
for plotname in wantedPlotNames:
if 'Sorts' in plotname:
plotObjects[plotname] = PlotHolder(label='Sort')
else:
plotObjects[plotname] = PlotHolder(label='Neuron')
# keyboard()
#Retrieve benchmark name
benchmarkName = infoObj.name()
#Retrieve reference name
reference = infoObj.reference()
#summary
csummary = infoObj.summary()
#benchmark object
cbenchmark = infoObj.rawBenchmark()
#
etBin = infoObj.etbin()
# reference value
refVal = infoObj.rawBenchmark()['refVal']
#Eta bin
etabinidx = infoObj.etabinidx()
#Et bin
etbinidx = infoObj.etbinidx()
#Eta bin
etabin = infoObj.etabin()
#Et bin
etbin = infoObj.etbin()
self._logger.info(
('Start loop over the benchmark: %s and etaBin = %d etBin = %d'
) % (benchmarkName, etabinidx, etbinidx))
import copy
args = dict()
args['reference'] = reference
args['refVal'] = refVal
args['eps'] = cbenchmark['eps']
self._logger.info('Creating plots...')
# Creating plots
for neuron in progressbar(infoObj.neuronBounds(),
len(infoObj.neuronBounds()),
'Loading : ',
60,
False,
logger=self._logger):
if choicesfile:
neuron = choices['choices'][infoObj.name().split(
'_')[-1]][0][0][etbinidx][etabinidx]
# Figure path location
currentPath = ('%s/figures/%s/%s') % (basepath, benchmarkName,
'neuron_' + str(neuron))
neuronName = 'config_' + str(neuron).zfill(3)
# Create folder to store all plot objects
mkdir_p(currentPath)
#Clear all hold plots stored
plotObjects['allBestTstSorts'].clear()
plotObjects['allBestOpSorts'].clear()
infoObjects['allInfoOpBest_' + neuronName] = list()
#plotObjects['allWorstTstSorts'].clear()
#plotObjects['allWorstOpSorts'].clear()
for sort in infoObj.sortBounds(neuron):
sortName = 'sort_' + str(sort).zfill(3)
#Init bounds
initBounds = infoObj.initBounds(neuron, sort)
#Create path list from initBound list
initPaths = [('%s/%s/%s/init_%s') %
(benchmarkName, neuronName, sortName, init)
for init in initBounds]
self._logger.debug('Creating init plots into the path: %s, (neuron_%s,sort_%s)', \
benchmarkName, neuron, sort)
obj = PlotHolder(label='Init')
try: #Create plots holder class (Helper), store all inits
obj.retrieve(self._rootObj, initPaths)
except RuntimeError:
self._logger.fatal('Can not create plot holder object')
#Hold all inits from current sort
obj.set_index_correction(initBounds)
obj.set_best_index(
csummary[neuronName][sortName]['infoTstBest']['init'])
obj.set_worst_index(
csummary[neuronName][sortName]['infoTstWorst']['init'])
plotObjects['allBestTstSorts'].append(
copy.deepcopy(obj.get_best()))
obj.set_best_index(
csummary[neuronName][sortName]['infoOpBest']['init'])
obj.set_worst_index(
csummary[neuronName][sortName]['infoOpWorst']['init'])
plotObjects['allBestOpSorts'].append(
copy.deepcopy(obj.get_best()))
#plotObjects['allWorstTstSorts'].append( copy.deepcopy(tstObj.getBest() )
#plotObjects['allWorstOpSorts'].append( copy.deepcopy(opObj.getBest() )
infoObjects['allInfoOpBest_' + neuronName].append(
copy.deepcopy(
csummary[neuronName][sortName]['infoOpBest']))
#Release memory
del obj
#Loop over sorts
gc.collect()
plotObjects['allBestTstSorts'].set_index_correction(
infoObj.sortBounds(neuron))
plotObjects['allBestOpSorts'].set_index_correction(
infoObj.sortBounds(neuron))
#plotObjects['allWorstTstSorts'].setIdxCorrection( infoObj.sortBounds(neuron) )
#plotObjects['allWorstOpSorts'].setIdxCorrection( infoObj.sortBounds(neuron) )
# Best and worst sorts for this neuron configuration
plotObjects['allBestTstSorts'].set_best_index(
csummary[neuronName]['infoTstBest']['sort'])
plotObjects['allBestTstSorts'].set_worst_index(
csummary[neuronName]['infoTstWorst']['sort'])
plotObjects['allBestOpSorts'].set_best_index(
csummary[neuronName]['infoOpBest']['sort'])
plotObjects['allBestOpSorts'].set_worst_index(
csummary[neuronName]['infoOpWorst']['sort'])
# Hold the information from the best and worst discriminator for this neuron
infoObjects['infoOpBest_' + neuronName] = copy.deepcopy(
csummary[neuronName]['infoOpBest'])
infoObjects['infoOpWorst_' + neuronName] = copy.deepcopy(
csummary[neuronName]['infoOpWorst'])
# Best and worst neuron sort for this configuration
plotObjects['allBestTstNeurons'].append(
copy.deepcopy(plotObjects['allBestTstSorts'].get_best()))
plotObjects['allBestOpNeurons'].append(
copy.deepcopy(plotObjects['allBestOpSorts'].get_best()))
plotObjects['allWorstTstNeurons'].append(
copy.deepcopy(plotObjects['allBestTstSorts'].get_worst()))
plotObjects['allWorstOpNeurons'].append(
copy.deepcopy(plotObjects['allBestOpSorts'].get_worst()))
# Create perf (tables) Objects for test and operation (Table)
perfObjects[neuronName] = MonitoringPerfInfo(
benchmarkName, reference,
csummary[neuronName]['summaryInfoTst'],
csummary[neuronName]['infoOpBest'], cbenchmark)
# Debug information
self._logger.debug(('Crossval indexs: (bestSort = %d, bestInit = %d) (worstSort = %d, bestInit = %d)')%\
(plotObjects['allBestTstSorts'].best, plotObjects['allBestTstSorts'].get_best()['bestInit'],
plotObjects['allBestTstSorts'].worst, plotObjects['allBestTstSorts'].get_worst()['bestInit']))
self._logger.debug(('Operation indexs: (bestSort = %d, bestInit = %d) (worstSort = %d, bestInit = %d)')%\
(plotObjects['allBestOpSorts'].best, plotObjects['allBestOpSorts'].get_best()['bestInit'],
plotObjects['allBestOpSorts'].worst, plotObjects['allBestOpSorts'].get_worst()['bestInit']))
# Figure 1: Plot all validation/test curves for all crossval sorts tested during
# the training. The best sort will be painted with black and the worst sort will
# be on red color. There is a label that will be draw into the figure to show
# the current location (neuron, sort, init) of the best and the worst network.
args['label'] = ('#splitline{#splitline{Total sorts: %d}{etaBin: %d, etBin: %d}}'+\
'{#splitline{sBestIdx: %d iBestIdx: %d}{sWorstIdx: %d iBestIdx: %d}}') % \
(plotObjects['allBestTstSorts'].size(),etabinidx, etbinidx, plotObjects['allBestTstSorts'].best, \
plotObjects['allBestTstSorts'].get_best()['bestInit'], plotObjects['allBestTstSorts'].worst,\
plotObjects['allBestTstSorts'].get_worst()['bestInit'])
args['cname'] = ('%s/plot_%s_neuron_%s_sorts_val') % (
currentPath, benchmarkName, neuron)
args['set'] = 'val'
args['operation'] = False
args['paintListIdx'] = [
plotObjects['allBestTstSorts'].best,
plotObjects['allBestTstSorts'].worst
]
pname1 = plot_4c(plotObjects['allBestTstSorts'], args)
# Figure 2: Plot all validation/test curves for all crossval sorts tested during
# the training. The best sort will be painted with black and the worst sort will
# be on red color. But, here the painted curves represented the best and the worst
# curve from the operation dataset. In other words, we pass all events into the
# network and get the efficiencis than we choose the best operation and the worst
# operation network and paint the validation curve who represent these sorts.
# There is a label that will be draw into the figure to show
# the current location (neuron, sort, init) of the best and the worst network.
args['label'] = ('#splitline{#splitline{Total sorts: %d (operation)}{etaBin: %d, etBin: %d}}'+\
'{#splitline{sBestIdx: %d iBestIdx: %d}{sWorstIdx: %d iBestIdx: %d}}') % \
(plotObjects['allBestOpSorts'].size(),etabinidx, etbinidx, plotObjects['allBestOpSorts'].best, \
plotObjects['allBestOpSorts'].get_best()['bestInit'], plotObjects['allBestOpSorts'].worst,\
plotObjects['allBestOpSorts'].get_worst()['bestInit'])
args['cname'] = ('%s/plot_%s_neuron_%s_sorts_op') % (
currentPath, benchmarkName, neuron)
args['set'] = 'val'
args['operation'] = True
args['paintListIdx'] = [
plotObjects['allBestOpSorts'].best,
plotObjects['allBestOpSorts'].worst
]
pname2 = plot_4c(plotObjects['allBestOpSorts'], args)
# Figure 3: This figure show us in deteails the best operation network for the current hidden
# layer and benchmark analysis. Depend on the benchmark, we draw lines who represents the
# stops for each curve. The current neuron will be the last position of the plotObjects
splotObject = PlotHolder()
args['label'] = ('#splitline{#splitline{Best network neuron: %d}{etaBin: %d, etBin: %d}}'+\
'{#splitline{sBestIdx: %d iBestIdx: %d}{}}') % \
(neuron,etabinidx, etbinidx, plotObjects['allBestOpSorts'].best, plotObjects['allBestOpSorts'].get_best()['bestInit'])
args['cname'] = ('%s/plot_%s_neuron_%s_best_op') % (
currentPath, benchmarkName, neuron)
args['set'] = 'val'
args['operation'] = True
splotObject.append(plotObjects['allBestOpNeurons'][-1])
pname3 = plot_4c(splotObject, args)
# Figure 4: Here, we have a plot of the discriminator output for all dataset. Black histogram
# represents the signal and the red onces represent the background. TODO: Apply this outputs
# using the feedfoward manual method to generate the network outputs and create the histograms.
args['cname'] = ('%s/plot_%s_neuron_%s_best_op_output') % (
currentPath, benchmarkName, neuron)
args['nsignal'] = self._data[0].shape[0]
args['nbackground'] = self._data[1].shape[0]
sbest = plotObjects['allBestOpNeurons'][-1]['bestSort']
args['cut'] = csummary[neuronName][
'sort_' + str(sbest).zfill(3)]['infoOpBest']['cut']
args['rocname'] = 'roc_operation'
pname4 = plot_nnoutput(splotObject, args)
# Figure 5: The receive operation test curve for all sorts using the test dataset as base.
# Here, we will draw the current tunnel and ref value used to set the discriminator threshold
# when the bechmark are Pd or Pf case. When we use the SP case, this tunnel will not be ploted.
# The black curve represents the best sort and the red onces the worst sort. TODO: Put the SP
# point for the best and worst when the benchmark case is SP.
args['cname'] = ('%s/plot_%s_neuron_%s_sorts_roc_tst') % (
currentPath, benchmarkName, neuron)
args['set'] = 'tst'
args['paintListIdx'] = [
plotObjects['allBestTstSorts'].best,
plotObjects['allBestTstSorts'].worst
]
pname5 = plot_rocs(plotObjects['allBestTstSorts'], args)
# Figure 6: The receive operation curve for all sorts using the operation dataset (train+test) as base.
# Here, we will draw the current tunnel and ref value used to set the discriminator threshold
# when the bechmark are Pd or Pf case. When we use the SP case, this tunnel will not be ploted.
# The black curve represents the best sort and the red onces the worst sort. TODO: Put the SP
# point for the best and worst when the benchmark case is SP.
args['cname'] = ('%s/plot_%s_neuron_%s_sorts_roc_op') % (
currentPath, benchmarkName, neuron)
args['set'] = 'operation'
args['paintListIdx'] = [
plotObjects['allBestOpSorts'].best,
plotObjects['allBestOpSorts'].worst
]
pname6 = plot_rocs(plotObjects['allBestOpSorts'], args)
# Map names for beamer, if you add a plot, you must add into
# the path objects holder
pathObjects['neuron_' + str(neuron) + '_sorts_val'] = pname1
pathObjects['neuron_' + str(neuron) + '_sort_op'] = pname2
pathObjects['neuron_' + str(neuron) + '_best_op'] = pname3
pathObjects['neuron_' + str(neuron) +
'_best_op_output'] = pname4
pathObjects['neuron_' + str(neuron) +
'_sorts_roc_tst'] = pname5
pathObjects['neuron_' + str(neuron) + '_sorts_roc_op'] = pname6
if choicesfile: break
#Loop over neurons
#External
pathBenchmarks[benchmarkName] = pathObjects
perfBenchmarks[benchmarkName] = perfObjects
#Release memory
for xname in plotObjects.keys():
del plotObjects[xname]
gc.collect()
#if debug: break
#Loop over benchmark
#Eta bin
# etabinidx = self._infoObjs[0].etabinidx()
#Et bin
binBounds = dict()
if len(etbin) > 0:
binBounds['etbinstr'] = r'$%d < E_{T} \text{[Gev]}<%d$' % etbin
else:
binBounds['etbinstr'] = r'\text{etBin[%d]}' % etbinidx
if len(etabin) > 0:
binBounds['etabinstr'] = r'$%.2f<\eta<%.2f$' % etabin
else:
binBounds['etabinstr'] = r'\text{etaBin[%d]}' % etabinidx
perfBounds = dict()
perfBounds['bounds'] = binBounds
perfBounds['perf'] = perfBenchmarks
fname = basepath + '/' + 'perfBounds'
save(perfBounds, fname)
#Start beamer presentation
if doBeamer:
from BeamerTemplates import BeamerReport, BeamerTables, BeamerFigure, BeamerBlocks
#Eta bin
etabin = self._infoObjs[0].etabin()
etabinidx = self._infoObjs[0].etabinidx()
#Et bin
etbin = self._infoObjs[0].etbin()
etbinidx = self._infoObjs[0].etbinidx()
#Create the beamer manager
reportname = ('%s_et%d_eta%d') % (output, etbinidx, etabinidx)
beamer = BeamerReport(basepath + '/' + reportname,
title=('Tuning Report (et=%d, eta=%d)') %
(etbinidx, etabinidx))
neuronBounds = self._infoObjs[0].neuronBounds()
for neuron in neuronBounds:
#Make the tables for crossvalidation
ptableCross = BeamerTables(
frametitle=[
'Neuron ' + str(neuron) +
': Cross Validation Performance',
'Neuron ' + str(neuron) + ": Operation Best Network"
],
caption=[
'Efficiencies from each benchmark.',
'Efficiencies for the best operation network'
])
block = BeamerBlocks('Neuron ' + str(neuron) + ' Analysis', [
('All sorts (validation)',
'All sorts evolution are ploted, each sort represents the best init;'
),
('All sorts (operation)',
'All sorts evolution only for operation set;'),
('Best operation',
'Detailed analysis from the best sort discriminator.'),
('Tables', 'Cross validation performance')
])
if not shortSlides: block.tolatex(beamer.file())
for info in self._infoObjs:
#If we produce a short presentation, we do not draw all plots
if not shortSlides:
bname = info.name().replace('OperationPoint_', '')
fig1 = BeamerFigure(
pathBenchmarks[info.name()]['neuron_' +
str(neuron) +
'_sorts_val'].replace(
basepath + '/',
''),
0.7,
frametitle=bname + ', Neuron ' + str(neuron) +
': All sorts (validation)')
fig2 = BeamerFigure(pathBenchmarks[info.name()][
'neuron_' + str(neuron) +
'_sorts_roc_tst'].replace(basepath + '/', ''),
0.8,
frametitle=bname + ', Neuron ' +
str(neuron) +
': All ROC sorts (validation)')
fig3 = BeamerFigure(
pathBenchmarks[info.name()]['neuron_' +
str(neuron) +
'_sort_op'].replace(
basepath + '/',
''),
0.7,
frametitle=bname + ', Neuron ' + str(neuron) +
': All sorts (operation)')
fig4 = BeamerFigure(pathBenchmarks[info.name()][
'neuron_' + str(neuron) + '_sorts_roc_op'].replace(
basepath + '/', ''),
0.8,
frametitle=bname + ', Neuron ' +
str(neuron) +
': All ROC sorts (operation)')
fig5 = BeamerFigure(
pathBenchmarks[info.name()]['neuron_' +
str(neuron) +
'_best_op'].replace(
basepath + '/',
''),
0.7,
frametitle=bname + ', Neuron ' + str(neuron) +
': Best Network')
fig6 = BeamerFigure(pathBenchmarks[info.name()][
'neuron_' + str(neuron) +
'_best_op_output'].replace(basepath + '/', ''),
0.8,
frametitle=bname + ', Neuron ' +
str(neuron) +
': Best Network output')
#Draw figures into the tex file
fig1.tolatex(beamer.file())
fig2.tolatex(beamer.file())
fig3.tolatex(beamer.file())
fig4.tolatex(beamer.file())
fig5.tolatex(beamer.file())
fig6.tolatex(beamer.file())
#Concatenate performance table, each line will be a benchmark
#e.g: det, sp and fa
ptableCross.add(
perfBenchmarks[info.name()]['config_' +
str(neuron).zfill(3)])
#if debug: break
ptableCross.tolatex(
beamer.file()) # internal switch is false to true: test
ptableCross.tolatex(beamer.file(
)) # internal swotch is true to false: operation
if debug: break
beamer.close()
self._logger.info('Done! ')
]
pidnames = [
['Medium', 'VeryLoose'],
['Medium'],
['Medium', 'VeryLoose'],
['Tight', 'Medium', 'Loose', 'VeryLoose'],
['Medium', 'VeryLoose'],
]
####################### Extract Ringer Configuration #########################
for idx, cv in enumerate(crossval):
tpath = os.getcwd() + '/' + tuningdirs[idx]
mkdir_p(tpath)
for jdx, pid in enumerate(pidnames[idx]):
files = expandFolders(basepath + '/' + cv[jdx])
crossValGrid = []
for path in files:
if path.endswith('.pic.gz'):
crossValGrid.append(path)
d = CrossValidStatAnalysis.exportDiscrFilesToOnlineFormat(
crossValGrid,
refBenchCol=ref,
discrFilename='%s/ElectronRinger%sConstants' % (tpath, pid),
thresFilename='%s/ElectronRinger%sThresholds' % (tpath, pid),
version=4,
parser.add_argument('--triggerList', nargs='+', default=defaultTrigList)
parser.add_argument('--numberOfSamplesPerPackage', type=int, default=50)
args = parser.parse_args()
mainLogger = Logger.getModuleLogger(__name__, LoggingLevel.INFO)
if os.path.exists('dq2_ls.txt'):
os.system('rm dq2_ls.txt')
if args.inDS[-1] != '/':
args.inDS += '/'
if args.outFolder[-1] != '/':
args.outFolder += '/'
mkdir_p(args.outFolder)
mkdir_p('tmpDir')
os.system('dq2-ls -fH ' + args.inDS + ' >& dq2_ls.txt')
with open('dq2_ls.txt', 'r') as f:
lines = f.readlines()
samples = []
dataset = ''
fileLine = re.compile('\[ \]\s+(\S+)\s+\S+\s+\S+\s+\S+\s+\S+')
for s in lines:
m = fileLine.match(s)
if m:
samples.append(m.group(1))
package = []