def main(args):
if not args.absolutePaths:
dataDir = os.path.join(root, args.dataDir)
labelDir = os.path.join(root, args.labelDir)
else:
dataDir = args.dataDir
labelDir = args.labelDir
# The following params are used in NAB scoring, but defined here because they
# impact the labeling process -- i.e. windows cannot exist in the probationary
# period.
windowSize = 0.10
probationaryPercent = 0.15
print "Getting Corpus"
corpus = Corpus(dataDir)
print "Creating LabelCombiner"
labelCombiner = LabelCombiner(labelDir, corpus,
args.threshold, windowSize,
probationaryPercent, args.verbosity)
print "Combining Labels"
labelCombiner.combine()
print "Writing combined labels files"
labelCombiner.write(args.combinedLabelsPath, args.combinedWindowsPath)
print "Attempting to load objects as a test"
corpusLabel = CorpusLabel(args.combinedWindowsPath, corpus)
corpusLabel.validateLabels()
print "Successfully combined labels!"
print "Resulting windows stored in:", args.combinedWindowsPath
def main(args):
if not args.absolutePaths:
args.labelDir = os.path.join(root, args.labelDir)
args.dataDir = os.path.join(root, args.dataDir)
args.destDir = os.path.join(root, args.destDir)
if not checkInputs(args):
return
corpus = Corpus(args.dataDir)
corpusLabel = CorpusLabel(args.labelDir, corpus=corpus)
corpusLabel.getEverything()
columnData = {}
for relativePath in corpusLabel.labels.keys():
columnData[relativePath] = pandas.Series(
corpusLabel.labels[relativePath]["label"])
corpus.addColumn("label", columnData)
corpus.copy(newRoot=args.destDir)
print "Done adding labels!"
def main(args): dataDir = os.path.join(root, args.dataDir) destDir = args.destDir labelDir = args.labelDir threshold = 1 labelCombiner = LabelCombiner(labelDir, dataDir, threshold) print "Combining Labels" labelCombiner.combine() print "Writing combined labels" labelCombiner.write(destDir) print "Attempting to load objects as a test" corpusLabel = CorpusLabel(destDir, dataDir) corpusLabel.initialize() print "Success!"
def main(args):
if not args.absolutePaths:
args.labelDir = os.path.join(root, args.labelDir)
args.dataDir = os.path.join(root, args.dataDir)
args.destDir = os.path.join(root, args.destDir)
if not checkInputs(args):
return
corpus = Corpus(args.dataDir)
corpusLabel = CorpusLabel(args.labelDir, corpus=corpus)
corpusLabel.getEverything()
columnData = {}
for relativePath in corpusLabel.labels.keys():
columnData[relativePath] = pandas.Series(
corpusLabel.labels[relativePath]["label"])
corpus.addColumn("label", columnData)
corpus.copy(newRoot=args.destDir)
print "Done adding labels!"
def initialize(self):
"""Initialize all the relevant objects for the run."""
self.corpus = Corpus(self.dataDir)
self.corpusLabel = CorpusLabel(path=self.labelPath, corpus=self.corpus)
with open(self.profilesPath) as p:
self.profiles = json.load(p)
def initialize(self):
"""Initialize all the relevant objects for the run."""
self.corpus = Corpus(self.dataDir)
self.corpusLabel = CorpusLabel(self.labelDir, None, self.corpus)
self.corpusLabel.initialize()
with open(self.profilesPath) as p:
self.profiles = yaml.load(p)
def main(args):
if args.create:
createApplication()
if args.start:
startApplication()
if args.stop:
stopApplication()
if args.file:
corpus = Corpus(args.data)
labels = CorpusLabel(path=args.labels, corpus=corpus)
streamFile(corpus, labels, args.results, args.file)
if args.stream:
corpus = Corpus(args.data)
labels = CorpusLabel(path=args.labels, corpus=corpus)
streamAll(corpus, labels, args.results)
if args.delete:
deleteApplication()
class Runner(object):
"""Class to run a configured nab benchmark."""
def __init__(self,
dataDir,
labelDir,
resultsDir,
profilesPath,
thresholdPath,
probationaryPercent=0.15,
numCPUs=None):
"""
@param dataDir (string) Directory where all the raw datasets exist.
@param labelDir (string) Directory where the labels of the datasets
exist.
@param resultsDir (string) Directory where the detector anomaly scores
will be scored.
@param profilesPath (string) Path to user profiles prescribing the
username and the cost matrix.
@param thresholdPath (string) Path to thresholds dictionary containing the
best thresholds (and their corresponding
score) for a combination of detector and
user profile.
@probationaryPercent (float) Percent of each dataset which will be
ignored during the scoring process.
@param numCPUs (int) Number of CPUs to be used for calls to
multiprocessing.pool.map
"""
self.dataDir = dataDir
self.labelDir = labelDir
self.resultsDir = resultsDir
self.profilesPath = profilesPath
self.thresholdPath = thresholdPath
self.probationaryPercent = probationaryPercent
self.pool = multiprocessing.Pool(numCPUs)
self.corpus = None
self.corpusLabel = None
self.profiles = None
def initialize(self):
"""Initialize all the relevant objects for the run."""
self.corpus = Corpus(self.dataDir)
self.corpusLabel = CorpusLabel(self.labelDir, None, self.corpus)
self.corpusLabel.initialize()
with open(self.profilesPath) as p:
self.profiles = yaml.load(p)
def detect(self, detectors):
"""Generate results file given a dictionary of detector classes
Function that takes a set of detectors and a corpus of data and creates a
set of files storing the alerts and anomaly scores given by the detectors
@param detectors (dict) Dictionary with key value pairs of a
detector name and its corresponding
class constructor.
"""
print "\nObtaining detections"
count = 0
args = []
for detectorName, detectorConstructor in detectors.iteritems():
for relativePath, dataSet in self.corpus.dataSets.iteritems():
args.append(
(
count,
detectorConstructor(
dataSet=dataSet,
probationaryPercent=self.probationaryPercent),
detectorName,
self.corpusLabel.labels[relativePath]["label"],
self.resultsDir,
relativePath
)
)
count += 1
print "calling multiprocessing pool"
self.pool.map(detectDataSet, args)
def optimize(self, detectorNames):
"""Optimize the threshold for each combination of detector and profile.
@param detectorNames (list) List of detector names.
@return thresholds (dict) Dictionary of dictionaries with detector names
then usernames as keys followed by another
dictionary containing the score and the
threshold used to obtained that score.
"""
print "\nOptimizing anomaly Scores"
thresholds = dict()
for detector in detectorNames:
resultsDetectorDir = os.path.join(self.resultsDir, detector)
resultsCorpus = Corpus(resultsDetectorDir)
thresholds[detector] = dict()
for username, profile in self.profiles.iteritems():
costMatrix = profile["CostMatrix"]
thresholds[detector][username] = optimizeThreshold(
(self.pool,
detector,
username,
costMatrix,
resultsCorpus,
self.corpusLabel,
self.probationaryPercent))
updateThresholds(thresholds, self.thresholdPath)
return thresholds
def score(self, detectors, thresholds):
"""Score the performance of the detectors.
Function that must be called only after detection result files have been
generated and thresholds have been optimized. This looks at the result files
and scores the performance of each detector specified and stores these
results in a csv file.
@param detectorNames (list) List of detector names.
@param thresholds (dict) Dictionary of dictionaries with detector
names then usernames as keys followed by
another dictionary containing the score and
the threshold used to obtained that score.
"""
print "\nObtaining Scores"
for detector in detectors:
ans = pandas.DataFrame(columns=("Detector", "Username", "File", \
"Threshold", "Score", "tp", "tn", "fp", "fn", "Total_Count"))
resultsDetectorDir = os.path.join(self.resultsDir, detector)
resultsCorpus = Corpus(resultsDetectorDir)
for username, profile in self.profiles.iteritems():
costMatrix = profile["CostMatrix"]
threshold = thresholds[detector][username]["threshold"]
results = scoreCorpus(threshold,
(self.pool,
detector,
username,
costMatrix,
resultsCorpus,
self.corpusLabel,
self.probationaryPercent))
for row in results:
ans.loc[len(ans)] = row
scorePath = os.path.join(resultsDetectorDir, detector + "_scores.csv")
ans.to_csv(scorePath, index=False)
