#compDatasets = [sampleDatasets_pf_cpf_sv,["db","sv"],["db","sv"],["db","sv"],["db","sv"]]
#compTrainDataCollections = [trainDataCollection_pf_cpf_sv,trainDataCollection_sv,trainDataCollection_sv,trainDataCollection_sv,trainDataCollection_sv]
#compTestDataCollections = [testDataCollection_pf_cpf_sv, testDataCollection_sv,testDataCollection_sv,testDataCollection_sv,testDataCollection_sv]
from DataCollection import DataCollection
if os.path.isdir(compareDir):
raise Exception('output directory: %s must not exists yet' % compareDir)
else:
os.mkdir(compareDir)
models = []
testds = []
for i in range(len(compModels)):
curModel = loadModel(compTrainDirs[i], compTrainDataCollections[i],
compModels[i], compLoadModels[i], compDatasets[i],
compRemovals[i])
testd = DataCollection()
testd.readFromFile(compTestDataCollections[i])
models.append(curModel)
testds.append(testd)
#print testd
#print testds
#makeComparisonPlots(testd,curModel,compNames,compareDir)
print testds
print models
print compNames
print compareDir
makeComparisonPlots(testds, models, compNames, compareDir)
inputDataset = sampleDatasets_cpf_sv
trainDir = opts.d
inputTrainDataCollection = opts.i
inputTestDataCollection = opts.t
LoadModel = False
removedVars = None
forceNClasses = False
signals = [1]
sigNames = ['Hbb']
backgrounds = [0]
backNames = ['QCD']
NClasses = len(signals) + len(backgrounds)
if True:
evalModel = loadModel(trainDir, inputTrainDataCollection, trainingModel,
LoadModel, forceNClasses, NClasses, inputDataset,
removedVars)
evalDir = opts.o
from DeepJetCore.DataCollection import DataCollection
testd = DataCollection()
testd.readFromFile(inputTestDataCollection)
if os.path.isdir(evalDir):
raise Exception('output directory: %s must not exists yet' % evalDir)
else:
os.mkdir(evalDir)
df, features_val = makePlots(testd, evalModel, evalDir)
makeLossPlot(trainDir, evalDir)
trainDataCollection = '/cms-sc17/convert_20180401_ak8_deepDoubleB_db_cpf_sv_reduced_6label_train_val/dataCollection.dc'
testDataCollection = '/cms-sc17/convert_20180401_ak8_deepDoubleB_db_cpf_sv_reduced_6label_test/dataCollection.dc'
datasets = ["db", "cpf", "SV"]
removedVars = None
LoadModel = True
from DeepJet_models_final import conv_model_final as trainingModel
from eval_funcs import loadModel, _byteify, makeLossPlot
trainDir = "train_2018Samples_60Track_fixed/"
#trainDir_massPen = "train_2018Samples_60Track_fixed_BNfixed/"
trainDir_massPen = "../independence/example/train_2018Samples_60Track_fixed_lossFunc/"
evalModel = loadModel(trainDir, trainDataCollection, trainingModel, LoadModel,
datasets)
evalModel_massPen = loadModel(trainDir_massPen, trainDataCollection,
trainingModel, LoadModel, datasets)
#outputDir = "Plots/2018Samples_6Label/"
outputDir = "Plots/2018Samples_fixed_31May/"
if os.path.isdir(outputDir):
shutil.rmtree(outputDir)
os.mkdir(outputDir)
else:
os.mkdir(outputDir)
shutil.copy2('plots.py', outputDir)
#### READ IN DATA ###