Esempio n. 1
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 def save(self, compress=True):
     rawData = self.getData()
     from RingerCore import save
     try:
         save(rawData, self._filePath, protocol='mat')
     except ImportError:
         self._warning(
             ("Cannot save matlab file, it seems that scipy is not "
              "available."))
     return save(rawData, self._filePath, compress=compress)
Esempio n. 2
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 def save(self, compress=True):
     rawData = self.getData()
     try:
         import scipy.io
         scipy.io.savemat(ensureExtension(self._filePath, '.mat'), rawData)
     except ImportError:
         self._warning(
             ("Cannot save matlab file, it seems that scipy is not "
              "available."))
     return save(rawData, self._filePath, compress=compress)
Esempio n. 3
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parser.add_argument('-d',
                    '--data',
                    action='store',
                    dest='data',
                    required=True,
                    nargs='+',
                    help="The input tuning files.")

import sys, os
if len(sys.argv) == 1:
    parser.print_help()
    sys.exit(1)
args = parser.parse_args()

# Take all files
paths = csvStr2List(args.data)
paths = expandFolders(paths)

from RingerCore import load, save, appendToFileName
for f in paths:
    ff = load(f)
    for k in ff.keys():
        if 'SP' in k:
            etBin = ff[k]['etBinIdx']
            etaBin = ff[k]['etaBinIdx']
    print 'etBin = ', etBin, ', etaBin = ', etaBin
    outname = f.split('/')[len(f.split('/')) - 2]
    cOutputName = appendToFileName(outname, ('et%d_eta%d') % (etBin, etaBin))
    save(ff, cOutputName, compress=True)
Esempio n. 4
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 def save(self, compress = True):
   return save( self.getData(), self._filePath, compress = compress )
Esempio n. 5
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    def create_weights(self, discrList, filename):

        self._logger.info('Getting weights...')

        from TuningTools import TuningToolCores
        modelDict = {}
        modelDict['__version__'] = self._version
        modelDict['__type__'] = 'Fex'
        modelDict['__core__'] = TuningToolCores.FastNet
        modelDict['metadata'] = {
            'UseEtaVar': self._useEtaVar,
            'UseLumiVar': self._useLumiVar,
        }
        modelDict['tuning'] = []

        def tolist(a):
            if isinstance(a, list): return a
            else: return a.tolist()

        for model in discrList:
            #etBinIdx = model['etBinIdx']
            #etaBinIdx = model['etaBinIdx']
            ## Discriminator configuration
            discrData = {}
            discrData['etBin'] = model['etBin'].tolist()
            if discrData['etBin'][0] == 0 and discrData['etBin'][1] == 20.0:
                discrData['etBin'] = [15.0, 20.0]
            discrData['etaBin'] = model['etaBin'].tolist()

            discrData['nodes'] = tolist(model['discriminator']['nodes'])
            discrData['bias'] = tolist(model['discriminator']['bias'])
            discrData['weights'] = tolist(model['discriminator']['weights'])
            modelDict['tuning'].append({'discriminator': discrData})

        if self._toPickle:
            self._logger.info('Export weights to pickle format...')
            modelDict['__version__'] = self._version
            modelDict['__core__'] = TuningToolCores.keras
            from RingerCore import save
            save(modelDict, filename)

        from ROOT import TFile, TTree
        from ROOT import std
        self._logger.info('Export weights to root format...')
        ### Create the discriminator root object
        fdiscr = TFile(appendToFileName(filename, '.root', separator=''),
                       'recreate')
        self.__createRootParameter('int', '__version__', self._version).Write()
        self.__createRootParameter('int', '__core__',
                                   TuningToolCores.FastNet).Write()
        fdiscr.mkdir('tuning')
        fdiscr.cd('tuning')
        tdiscr = TTree('discriminators', '')

        for idx, b in enumerate(self._discrBranches):
            b[2] = std.vector(b[0])()
            tdiscr.Branch(b[1], 'vector<%s>' % b[0], b[2])

        for t in modelDict['tuning']:
            for idx, b in enumerate(self._discrBranches):
                self.__attachToVector(t['discriminator'][b[1]], b[2])
            tdiscr.Fill()

        tdiscr.Write()

        ### Create the thresholds root object
        fdiscr.mkdir('metadata')
        fdiscr.cd('metadata')
        for key, value in modelDict['metadata'].iteritems():
            self._logger.info('Saving metadata %s as %s', key, value)
            self.__createRootParameter('int' if type(value) is int else 'bool',
                                       key, value).Write()

        fdiscr.Close()
Esempio n. 6
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    def create_thresholds(self, discrList, filename):

        self._logger.info('Getting thresholds...')

        from TuningTools import TuningToolCores
        modelDict = {}
        modelDict['__version__'] = self._version
        modelDict['__type__'] = 'Hypo'
        modelDict['__core__'] = TuningToolCores.FastNet
        modelDict['metadata'] = {
            'UseNoActivationFunctionInTheLastLayer':
            self._removeOutputTansigTF,
            'DoPileupCorrection': self._doPileupCorrection,
            'LumiCut': self._maxPileupLinearCorrectionValue,
        }

        modelDict['tuning'] = []

        for model in discrList:
            thresData = {}
            #etBinIdx                = model['etBinIdx']
            #etaBinIdx               = model['etaBinIdx']
            thresData['etBin'] = model['etBin'].tolist()
            thresData['etaBin'] = model['etaBin'].tolist()
            thresData['thresholds'] = model['threshold']
            modelDict['tuning'].append(thresData)

        if self._toPickle:
            self._logger.info('Export Thresholds to pickle format...')
            modelDict['__version__'] = self._version
            modelDict['__core__'] = TuningToolCores.keras
            from RingerCore import save
            save(modelDict, filename)

        from ROOT import TFile, TTree
        from ROOT import std

        self._logger.info('Export Thresholds to root format...')
        ### Create the thresholds root object
        fthres = TFile(appendToFileName(filename, '.root', separator=''),
                       'recreate')
        self.__createRootParameter('int', '__version__', self._version).Write()
        self.__createRootParameter('int', '__core__',
                                   TuningToolCores.FastNet).Write()
        fthres.mkdir('tuning')
        fthres.cd('tuning')
        tthres = TTree('thresholds', '')

        for idx, b in enumerate(self._thresBranches):
            b[2] = std.vector(b[0])()
            tthres.Branch(b[1], 'vector<%s>' % b[0], b[2])

        for t in modelDict['tuning']:
            for idx, b in enumerate(self._thresBranches):
                self.__attachToVector(t[b[1]], b[2])
            tthres.Fill()

        tthres.Write()

        fthres.mkdir('metadata')
        fthres.cd('metadata')
        for key, value in modelDict['metadata'].iteritems():
            self._logger.info('Saving metadata %s as %s', key, value)
            self.__createRootParameter('int' if type(value) is int else 'bool',
                                       key, value).Write()

        fthres.Close()
Esempio n. 7
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                os.path.basename(cOutputName))
        f = ROOT.TFile(inFile, 'r')
        data = {}
        for keyName, obj in getall(f):
            mainLogger.debug("Reading key: %s", keyName)
            shortKey = keyName.split('/')[-1]
            if not issubclass(type(obj), ROOT.TH1):
                mainLogger.verbose("Ignoring key: %s", shortKey)
                continue
            hist = obj
            result = [exclude in shortKey for exclude in args.exclude_keys]
            if result and all(result):
                mainLogger.debug("key <%s> does not match any filter",
                                 shortKey)
                continue
            if result and all(result):
                mainLogger.debug("key <%s> matches exclude pattern", shortKey)
                continue
            if not hist:
                mainLogger.warning("Couldn't retrieve histogram with key: %s",
                                   shortKey)
            data[shortKey] = rnp.hist2array(hist,
                                            include_overflow=True,
                                            return_edges=True)
        savedPath = save(data, cOutputName, protocol='savez_compressed')
        mainLogger.info("Successfully created numpy file: %s", cOutputName)
    else:
        mainLogger.error("Cannot transform file '%s' to numpy format." %
                         inFile)
# end of (for fileCollection)
    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! ')
 def save(self, compress = True):
   return save( self.getData(), self._filePath, compress = compress )
Esempio n. 10
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    changePassed(key, mat)
    changeTotal(key, mat)


from RingerCore import load, save

a = load(
    'mc15_13TeV.361106.423300.sgn.trigegprobes.bkg.vetotruth.trig.l2calo.eg.std.grid.medium.npz'
)
d = dict(a)

changeOp(d, 'signal_efficiencies', mergeEffTable(veryloose20160701))
changeOp(d, 'background_efficiencies', pfrefs_vloose)
save(
    d,
    'mc15_13TeV.361106.423300.sgn.trigegprobes.bkg.vetotruth.trig.l2calo.eg.std.grid.veryloose',
    protocol='savez_compressed')

d = dict(a)
changeOp(d, 'signal_efficiencies', mergeEffTable(loose20160701))
changeOp(d, 'background_efficiencies', pfrefs_loose)
save(
    d,
    'mc15_13TeV.361106.423300.sgn.trigegprobes.bkg.vetotruth.trig.l2calo.eg.std.grid.loose',
    protocol='savez_compressed')

d = dict(a)
changeOp(d, 'signal_efficiencies', mergeEffTable(tight20160701))
changeOp(d, 'background_efficiencies', pfrefs_tight)
save(
    d,
from RingerCore import Logger, LoggingLevel, save, load, expandFolders, traverse
import numpy as np
from TuningTools.coreDef import retrieve_npConstants
npCurrent, _ = retrieve_npConstants()
npCurrent.level = args.output_level
logger = Logger.getModuleLogger( __name__, args.output_level )

files = expandFolders( args.inputs ) # FIXME *.npz

from zipfile import BadZipfile
for f in files:
  logger.info("Changing representation of file '%s'...", f)
  try:
    data = dict(load(f))
  except BadZipfile, e:
    logger.warning("Couldn't load file '%s'. Reason:\n%s", f, str(e))
    continue
  logger.debug("Finished loading file '%s'...", f)
  for key in data:
    if key == 'W':
      for obj, idx,  parent, _, _ in traverse(data[key],
                                              tree_types = (np.ndarray,),
                                              max_depth = 3):
        parent[idx] = obj.T
    elif type(data[key]) is np.ndarray:
      logger.debug("Checking key '%s'...", key)
      data[key] = npCurrent.toRepr(data[key])
  path = save(data, f, protocol = 'savez_compressed')
  logger.info("Overwritten file '%s'",f)
Esempio n. 12
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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)])
    tmpBinDict = deepcopy(binDict)
    for key in binDict.keys():
      if 'Patterns' in key:
        tmpBinDict[key] = pp(binDict[key])
    outFile = os.path.join( args.outputPath, os.path.basename( appendToFileName(args.inputFile.replace('calo','calo'+RingerLayer.tostring(layer)), 
      'et%d_eta%d' % (etIdx, etaIdx) ) ) )
    save(tmpBinDict, outFile, protocol = 'savez_compressed' )





Esempio n. 13
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  if checkExtension( inFile, "root" ):
    cOutputName = changeExtension( inFile, '.npz' )
    if args.change_output_folder:
      import os.path
      cOutputName = os.path.join( os.path.abspath(args.change_output_folder) , os.path.basename(cOutputName) )
    f = ROOT.TFile( inFile, 'r' )
    data = {}
    for keyName, obj in getall(f):
      mainLogger.debug("Reading key: %s", keyName)
      shortKey = keyName.split('/')[-1]
      if not issubclass(type(obj), ROOT.TH1): 
        mainLogger.verbose("Ignoring key: %s", shortKey )
        continue
      hist = obj
      if all( [wanted not in shortKey for wanted in args.filter_keys] ):
        mainLogger.debug("key <%s> does not match any filter", shortKey )
        continue
      if all( [exclude in shortKey for exclude in args.exclude_keys] ):
        mainLogger.debug("key <%s> matches exclude pattern", shortKey )
        continue
      if not hist:
        mainLogger.warning("Couldn't retrieve histogram with key: %s", shortKey )
      data[ shortKey ] = rnp.hist2array( hist, include_overflow=True, return_edges=True )
    savedPath = save(data, cOutputName, protocol = 'savez_compressed')
    mainLogger.info("Successfully created numpy file: %s", cOutputName)
  else:
    mainLogger.error("Cannot transform file '%s' to numpy format." % inFile)
# end of (for fileCollection)


Esempio n. 14
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args = parser.parse_args(namespace=LoggerNamespace())

from RingerCore import Logger, LoggingLevel, save, load, expandFolders, traverse
import numpy as np
from TuningTools.coreDef import npCurrent
npCurrent.level = args.output_level
logger = Logger.getModuleLogger(__name__, args.output_level)

files = expandFolders(args.inputs)  # FIXME *.npz

from zipfile import BadZipfile
for f in files:
    logger.info("Changing representation of file '%s'...", f)
    try:
        data = dict(load(f))
    except BadZipfile, e:
        logger.warning("Couldn't load file '%s'. Reason:\n%s", f, str(e))
        continue
    logger.debug("Finished loading file '%s'...", f)
    for key in data:
        if key == 'W':
            for obj, idx, parent, _, _ in traverse(data[key],
                                                   tree_types=(np.ndarray, ),
                                                   max_depth=3):
                parent[idx] = obj.T
        elif type(data[key]) is np.ndarray:
            logger.debug("Checking key '%s'...", key)
            data[key] = npCurrent.toRepr(data[key])
    path = save(data, f, protocol='savez_compressed')
    logger.info("Overwritten file '%s'", f)
Esempio n. 15
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    def create_weights(self, discrList, filename):

        self._logger.info('Getting weights...')

        from TuningTools import TuningToolCores
        modelDict = {}
        modelDict['__version__'] = self._version
        modelDict['__type__'] = 'Fex'
        modelDict['__core__'] = TuningToolCores.FastNet
        modelDict['metadata'] = {
            'UseEtaVar': self._useEtaVar,
            'UseLumiVar': self._useLumiVar,
            'UseCaloRings': True,
            'UseTrack': False,
            'UseShowerShape': False,
            'RemoveOutputTansigTF': self._removeOutputTansigTF,
        }

        modelDict['tuning'] = []

        def tolist(a):
            if isinstance(a, list): return a
            elif isinstance(a, tuple): return a
            else: return a.tolist()

        from keras.models import model_from_json
        from keras.layers import Conv2D, Dense, Activation
        import json
        from copy import deepcopy

        for model in discrList:

            etBinIdx = model['etBinIdx']
            etaBinIdx = model['etaBinIdx']
            ## Discriminator configuration
            discrData = {}
            discrData['etBin'] = tolist(model['etBin'])
            discrData['etaBin'] = tolist(model['etaBin'])
            discrData['muBin'] = tolist(model['muBin'])

            if self._toPickle:
                discrData['model'] = deepcopy(model['discriminator']['model'])
                discrData['weights'] = deepcopy(
                    model['discriminator']['weights'])

            else:
                keras_model = model_from_json(
                    json.dumps(model['discriminator']['model'],
                               separators=(',', ':')))
                keras_model.set_weights(model['discriminator']['weights'])

                ### Extract and reshape Keras weights
                dense_weights = []
                dense_bias = []
                dense_nodes = []
                dense_tfnames = []
                conv_kernel = []
                conv_kernel_i = []
                conv_kernel_j = []
                conv_bias = []
                conv_tfnames = []
                conv_nodes = []

                useConvLayer = False
                ### Loop over layers
                for idx, obj in enumerate(keras_model.layers):

                    dobj = model['discriminator']['model']['config'][idx][
                        'config']

                    if type(obj) is Conv2D:
                        useConvLayer = True
                        conv_nodes.append(dobj['filters'])
                        conv_tfnames.append(str(dobj['activation']))
                        w, b = obj.get_weights()

                        for wn in w.T:
                            for wu in wn:
                                conv_kernel.extend(
                                    wu.T.reshape(
                                        (dobj['kernel_size'][0] *
                                         dobj['kernel_size'][1], )).tolist())

                        conv_bias.extend(b.tolist())
                        conv_kernel_i.append(dobj['kernel_size'][0])
                        conv_kernel_j.append(dobj['kernel_size'][1])

                    elif type(obj) is Dense:
                        dense_nodes.append(dobj['units'])
                        dense_tfnames.append(str(dobj['activation']))
                        w, b = obj.get_weights()
                        dense_weights.extend(w.reshape(-1, order='F'))
                        dense_bias.extend(b.reshape(-1, order='F'))

                    # TODO: Need to implement something smart to tread this case
                    elif type(obj) is Activation:
                        dense_tfnames.pop()
                        dense_tfnames.append(str(dobj['activation']))

                    else:
                        continue

                discrData['dense_nodes'] = tolist(dense_nodes)
                discrData['dense_bias'] = tolist(dense_bias)
                discrData['dense_weights'] = tolist(dense_weights)
                discrData['dense_tfnames'] = tolist(dense_tfnames)

                discrData['useConvLayer'] = [useConvLayer]
                # Convolutional neural network
                if useConvLayer:
                    discrData['conv_nodes'] = tolist(conv_nodes)
                    discrData['conv_tfnames'] = tolist(conv_tfnames)
                    discrData['conv_kernel_i'] = tolist(conv_kernel_i)
                    discrData['conv_kernel_j'] = tolist(conv_kernel_j)
                    discrData['conv_kernel'] = tolist(conv_kernel)
                    discrData['conv_bias'] = tolist(conv_bias)
                    discrData['conv_input_i'] = [
                        model['discriminator']['model']['config'][0]['config']
                        ['batch_input_shape'][1]
                    ]
                    discrData['conv_input_j'] = [
                        model['discriminator']['model']['config'][0]['config']
                        ['batch_input_shape'][2]
                    ]

                    i = discrData['conv_input_i'][0] - (sum(conv_kernel_i) -
                                                        len(conv_kernel_i))
                    j = discrData['conv_input_j'][0] - (sum(conv_kernel_j) -
                                                        len(conv_kernel_j))
                    input_layer = i * j * discrData['conv_nodes'][-1]
                    discrData['dense_nodes'] = [input_layer
                                                ] + discrData['dense_nodes']

            ### Attach
            modelDict['tuning'].append({'discriminator': discrData})

        if self._toPickle:
            self._logger.info('Export weights to pickle format...')
            modelDict['__version__'] = self._version
            modelDict['__core__'] = TuningToolCores.keras
            from RingerCore import save
            save(modelDict, filename)

        from ROOT import TFile, TTree
        from ROOT import std
        self._logger.info('Export weights to root format...')
        ### Create the discriminator root object
        fdiscr = TFile(appendToFileName(filename, '.root', separator=''),
                       'recreate')
        self.__createRootParameter('int', '__version__', self._version).Write()
        self.__createRootParameter('int', '__core__',
                                   TuningToolCores.FastNet).Write()
        fdiscr.mkdir('tuning')
        fdiscr.cd('tuning')
        tdiscr = TTree('discriminators', '')

        for idx, b in enumerate(self._discrBranches):
            b[2] = std.vector(b[0])()
            tdiscr.Branch(b[1], 'vector<%s>' % b[0], b[2])

        for t in modelDict['tuning']:
            for idx, b in enumerate(self._discrBranches):
                self.__attachToVector(t['discriminator'][b[1]], b[2])
            tdiscr.Fill()

        tdiscr.Write()

        ### Create the thresholds root object
        fdiscr.mkdir('metadata')
        fdiscr.cd('metadata')
        for key, value in modelDict['metadata'].iteritems():
            self._logger.info('Saving metadata %s as %s', key, value)
            self.__createRootParameter('int' if type(value) is int else 'bool',
                                       key, value).Write()

        fdiscr.Close()
Esempio n. 16
0
    )
    c = monitoring(dirname=args.output, doBeamer=args.doBeamer)
    if c['etBinIdx'] > etBinMax: etBinMax = c['etBinIdx']
    if c['etaBinIdx'] > etaBinMax: etaBinMax = c['etaBinIdx']
    csummaryList.append(c)
    del monitoring

### list to binned grid

summary = [[None for _ in range(etaBinMax + 1)] for __ in range(etBinMax + 1)]
for c in csummaryList:
    summary[c['etBinIdx']][c['etaBinIdx']] = c

print 'saving summary...'
from RingerCore import save
### Save this for backup
save(summary, args.output + '/summary')
print 'loading...'
### Built the final table
MonitoringTool.ttReport(args.output + '/summary.pic.gz',
                        args.dataPath,
                        outname=args.output + '/' + args.output,
                        title=args.output,
                        toPDF=False)
MonitoringTool.ttReport(args.output + '/summary.pic.gz',
                        args.dataPath,
                        outname=args.output + '/' + args.output,
                        title=args.output,
                        toPDF=True)
#MonitoringTool.ttReport( args.output+'_summary.pic.gz', args.dataPath, args.output, toPDF=True )
Esempio n. 17
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            import os.path
            cOutputName = os.path.join(
                os.path.abspath(args.change_output_folder),
                os.path.basename(cOutputName))
        f = ROOT.TFile(inFile, 'r')
        mainLogger.debug("Reading key: %s", args.treePath)
        tree = f.Get(args.treePath)
        if not isinstance(tree, ROOT.TTree):
            mainLogger.error("Path %s does not contain a TTree object",
                             args.treePath)
            continue
        shortKey = args.treePath.split('/')[-1]
        # TODO Save each numpy object key instead of the collection tree:
        data = rnp.tree2array(tree,
                              branches=args.branches,
                              selection=args.selection)

        toSave = {
            key:
            (data[key] if key != 'elCand2_ringer_rings' else np.concatenate(
                data['elCand2_ringer_rings']).reshape(-1, 100))
            for key in data.dtype.names
        }
        #toSave = { shortKey : rnp.tree2array( tree, branches=args.branches, selection=args.selection ) }
        savedPath = save(toSave, cOutputName, protocol='savez_compressed')
        mainLogger.info("Successfully created numpy file: %s", cOutputName)
    else:
        mainLogger.error("Cannot transform file '%s' to numpy format." %
                         inFile)
# end of (for fileCollection)
Esempio n. 18
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  def changePassed(key, mat):
    for lkey in d[key].item():
      for rowIdx, array1 in enumerate( d[key].item()[lkey]):
        for columnIdx, rawdict in enumerate(array1):
            rawdict['_passed'] = mat[rowIdx][columnIdx]
  def changeTotal(key, mat):
    for lkey in d[key].item():
      for rowIdx, array1 in enumerate( d[key].item()[lkey]):
        for columnIdx, rawdict in enumerate(array1):
            rawdict['_count'] = 100.
  changePassed( key, mat )
  changeTotal( key, mat )

from RingerCore import load, save
a = load('mc15_13TeV.361106.423300.sgn.trigegprobes.bkg.vetotruth.trig.l2calo.eg.std.grid.medium.npz')
d = dict(a)

changeOp( d, 'signal_efficiencies', mergeEffTable( veryloose20160701 ) )
changeOp( d, 'background_efficiencies', pfrefs_vloose )
save(d, 'mc15_13TeV.361106.423300.sgn.trigegprobes.bkg.vetotruth.trig.l2calo.eg.std.grid.veryloose', protocol = 'savez_compressed')

d = dict(a)
changeOp( d, 'signal_efficiencies', mergeEffTable( loose20160701 ) )
changeOp( d, 'background_efficiencies', pfrefs_loose )
save(d, 'mc15_13TeV.361106.423300.sgn.trigegprobes.bkg.vetotruth.trig.l2calo.eg.std.grid.loose', protocol = 'savez_compressed')

d = dict(a)
changeOp( d, 'signal_efficiencies', mergeEffTable( tight20160701 ) )
changeOp( d, 'background_efficiencies', pfrefs_tight )
save(d, 'mc15_13TeV.361106.423300.sgn.trigegprobes.bkg.vetotruth.trig.l2calo.eg.std.grid.tight', protocol = 'savez_compressed')