def testSerialization(self):
""" test to ensure serialization preserves the state of the region
correctly. """
anomalyLikelihoodRegion1 = AnomalyLikelihoodRegion()
inputs = AnomalyLikelihoodRegion.getSpec()['inputs']
outputs = AnomalyLikelihoodRegion.getSpec()['outputs']
for _ in range(0, 6):
inputs['rawAnomalyScore'] = numpy.array([random.random()])
inputs['metricValue'] = numpy.array([random.random()])
anomalyLikelihoodRegion1.compute(inputs, outputs)
score1 = outputs['anomalyLikelihood'][0]
proto1 = AnomalyLikelihoodRegionProto.new_message()
anomalyLikelihoodRegion1.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = AnomalyLikelihoodRegionProto.read(f)
# # Load the deserialized proto
anomalyLikelihoodRegion2 = AnomalyLikelihoodRegion.read(proto2)
self.assertEqual(anomalyLikelihoodRegion1, anomalyLikelihoodRegion2)
for _ in range(6, 500):
inputs['rawAnomalyScore'] = numpy.array([random.random()])
inputs['metricValue'] = numpy.array([random.random()])
anomalyLikelihoodRegion1.compute(inputs, outputs)
score1 = outputs['anomalyLikelihood'][0]
anomalyLikelihoodRegion2.compute(inputs, outputs)
score2 = outputs['anomalyLikelihood'][0]
self.assertEqual(score1, score2)
def testParamterError(self):
""" ensure historicWindowSize is greater than estimationSamples """
try:
anomalyLikelihoodRegion = AnomalyLikelihoodRegion(
estimationSamples=100, historicWindowSize=99)
self.assertEqual(False, True, "Should have failed with ValueError")
except ValueError:
pass
def testSerialization(self):
""" test to ensure serialization preserves the state of the region
correctly. """
anomalyLikelihoodRegion1 = AnomalyLikelihoodRegion()
inputs = AnomalyLikelihoodRegion.getSpec()['inputs']
outputs = AnomalyLikelihoodRegion.getSpec()['outputs']
parameters = AnomalyLikelihoodRegion.getSpec()['parameters']
# Make sure to calculate distribution by passing the probation period
learningPeriod = parameters['learningPeriod']['defaultValue']
reestimationPeriod = parameters['reestimationPeriod']['defaultValue']
probation = learningPeriod + reestimationPeriod
for _ in xrange(0, probation + 1):
inputs['rawAnomalyScore'] = numpy.array([random.random()])
inputs['metricValue'] = numpy.array([random.random()])
anomalyLikelihoodRegion1.compute(inputs, outputs)
score1 = outputs['anomalyLikelihood'][0]
proto1 = AnomalyLikelihoodRegionProto.new_message()
anomalyLikelihoodRegion1.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = AnomalyLikelihoodRegionProto.read(f)
# # Load the deserialized proto
anomalyLikelihoodRegion2 = AnomalyLikelihoodRegion.read(proto2)
self.assertEqual(anomalyLikelihoodRegion1, anomalyLikelihoodRegion2)
window = parameters['historicWindowSize']['defaultValue']
for _ in xrange(0, window + 1):
inputs['rawAnomalyScore'] = numpy.array([random.random()])
inputs['metricValue'] = numpy.array([random.random()])
anomalyLikelihoodRegion1.compute(inputs, outputs)
score1 = outputs['anomalyLikelihood'][0]
anomalyLikelihoodRegion2.compute(inputs, outputs)
score2 = outputs['anomalyLikelihood'][0]
self.assertEqual(score1, score2)
def testLikelihoodValues(self):
""" test to see if the region keeps track of state correctly and produces
the same likelihoods as the AnomalyLikelihood module """
anomalyLikelihoodRegion = AnomalyLikelihoodRegion()
anomalyLikelihood = AnomalyLikelihood()
inputs = AnomalyLikelihoodRegion.getSpec()['inputs']
outputs = AnomalyLikelihoodRegion.getSpec()['outputs']
with open(_INPUT_DATA_FILE) as f:
reader = csv.reader(f)
reader.next()
for record in reader:
consumption = float(record[1])
anomalyScore = float(record[2])
likelihood1 = anomalyLikelihood.anomalyProbability(
consumption, anomalyScore)
inputs['rawAnomalyScore'] = numpy.array([anomalyScore])
inputs['metricValue'] = numpy.array([consumption])
anomalyLikelihoodRegion.compute(inputs, outputs)
likelihood2 = outputs['anomalyLikelihood'][0]
self.assertEqual(likelihood1, likelihood2)