def main(args):
root = os.path.dirname(os.path.realpath(__file__))
numCPUs = int(args.numCPUs) if args.numCPUs is not None else None
dataDir = os.path.join(root, args.dataDir)
labelFile = os.path.join(root, args.labelFile)
resultsDir = os.path.join(root, args.resultsDir)
profilesFile = os.path.join(root, args.profilesFile)
thresholdsFile = os.path.join(root, args.thresholdsFile)
runner = Runner(dataDir=dataDir,
labelPath=labelFile,
resultsDir=resultsDir,
profilesPath=profilesFile,
thresholdPath=thresholdsFile,
numCPUs=numCPUs)
runner.initialize()
if args.detect:
detectorConstructors = getDetectorClassConstructors(args.detectors)
runner.detect(detectorConstructors)
if args.optimize:
runner.optimize(args.detectors)
if args.score:
with open(args.thresholdsFile) as thresholdConfigFile:
detectorThresholds = json.load(thresholdConfigFile)
runner.score(args.detectors, detectorThresholds)
def main(args):
root = os.path.dirname(os.path.realpath(__file__))
numCPUs = int(args.numCPUs) if args.numCPUs is not None else None
dataDir = os.path.join(root, args.dataDir)
windowsFile = os.path.join(root, args.windowsFile)
resultsDir = os.path.join(root, args.resultsDir)
profilesFile = os.path.join(root, args.profilesFile)
thresholdsFile = os.path.join(root, args.thresholdsFile)
runner = Runner(dataDir=dataDir,
labelPath=windowsFile,
resultsDir=resultsDir,
profilesPath=profilesFile,
thresholdPath=thresholdsFile,
numCPUs=numCPUs)
runner.initialize()
if args.detect:
detectorConstructors = getDetectorClassConstructors(args.detectors)
print("Starting Evaluation!!\n")
start_time = datetime.datetime.now()
runner.detect(detectorConstructors)
delta = datetime.datetime.now() - start_time
print("Total time for classification in milliseconds " +
str(int(delta.total_seconds() * 1000)) + '\n')
#print("Total time for classification: " + str(delta) + '\n')
if args.optimize:
runner.optimize(args.detectors)
if args.score:
with open(args.thresholdsFile) as thresholdConfigFile:
detectorThresholds = json.load(thresholdConfigFile)
runner.score(args.detectors, detectorThresholds)
if args.normalize:
try:
runner.normalize()
except AttributeError("Error: you must run the scoring step with the "
"normalization step."):
return
def main(args):
if not args.detect and not args.score and not args.optimize:
args.detect = True
args.optimize = True
args.score = True
detectors = args.detectors
numCPUs = int(args.numCPUs) if args.numCPUs is not None else None
probationaryPercent = float(args.probationaryPercent)
dataDir = os.path.join(root, args.dataDir)
labelDir = os.path.join(root, args.labelDir)
resultsDir = os.path.join(root, args.resultsDir)
profilesPath = os.path.join(root, args.profilesPath)
thresholdPath = os.path.join(root, args.thresholdPath)
runner = Runner(dataDir=dataDir,
labelDir=labelDir,
resultsDir=resultsDir,
profilesPath=profilesPath,
thresholdPath=thresholdPath,
probationaryPercent=probationaryPercent,
numCPUs=numCPUs)
runner.initialize()
if args.detect:
detectorConstructors = getDetectorClassConstructors(args.detectors)
runner.detect(detectorConstructors)
if args.optimize:
runner.optimize(args.detectors)
if args.score:
with open(args.thresholdPath) as thresholdConfigFile:
detectorThresholds = yaml.load(thresholdConfigFile)
runner.score(args.detectors, detectorThresholds)
def main(args):
root = os.path.dirname(os.path.realpath(__file__))
numCPUs = int(args.numCPUs) if args.numCPUs is not None else None
dataDir = os.path.join(root, args.dataDir)
labelFile = os.path.join(root, args.labelFile)
resultsDir = os.path.join(root, args.resultsDir)
profilesFile = os.path.join(root, args.profilesFile)
thresholdsFile = os.path.join(root, args.thresholdsFile)
runner = Runner(dataDir=dataDir,
labelPath=labelFile,
resultsDir=resultsDir,
profilesPath=profilesFile,
thresholdPath=thresholdsFile,
numCPUs=numCPUs)
runner.initialize()
if args.detect:
detectorConstructors = getDetectorClassConstructors(
args.detectors[0].split(','))
runner.detect(detectorConstructors)
if args.optimize:
runner.optimize(args.detectors)
if args.score:
with open(args.thresholdsFile) as thresholdConfigFile:
detectorThresholds = json.load(thresholdConfigFile)
runner.score(args.detectors, detectorThresholds)
if args.normalize:
try:
runner.normalize()
except AttributeError("Error: you must run the scoring step with the "
"normalization step."):
return
def main(args):
numCPUs = int(args.numCPUs) if args.numCPUs is not None else None
dataDir = args.dataDir
windowsFile = args.windowsFile
resultsDir = args.resultsDir
profilesFile = args.profilesFile
thresholdsFile = args.thresholdsFile
runner = Runner(dataDir=dataDir,
labelPath=windowsFile,
resultsDir=resultsDir,
profilesPath=profilesFile,
thresholdPath=thresholdsFile,
numCPUs=numCPUs)
runner.initialize()
if args.detect:
detectorConstructors = getDetectorClassConstructors(args.detectors)
runner.detect(detectorConstructors)
if args.optimize:
runner.optimize(args.detectors)
if args.score:
with open(args.thresholdsFile) as thresholdConfigFile:
detectorThresholds = json.load(thresholdConfigFile)
runner.score(args.detectors, detectorThresholds)
if args.normalize:
try:
runner.normalize()
except AttributeError("Error: you must run the scoring step with the "
"normalization step."):
return