def testAnomalyProbabilityResultsDuringProbationaryPeriod(self):
originalUpdateAnomalyLikelihoods = an.updateAnomalyLikelihoods
def updateAnomalyLikelihoodsWrap(anomalyScores, params, verbosity=0):
likelihoods, avgRecordList, params = originalUpdateAnomalyLikelihoods(
anomalyScores=anomalyScores,
params=params,
verbosity=verbosity)
self.assertEqual(len(likelihoods), 1)
return [0.1], avgRecordList, params
updateAnomalyLikelihoodsPatch = mock.patch(
"htm.algorithms.anomaly_likelihood.updateAnomalyLikelihoods",
side_effect=updateAnomalyLikelihoodsWrap,
autospec=True)
with updateAnomalyLikelihoodsPatch:
l = an.AnomalyLikelihood(learningPeriod=2,
estimationSamples=2,
historicWindowSize=3)
# 0.5 result is expected during burn-in
self.assertEqual(l.anomalyProbability(10, 0.1, timestamp=1), 0.5)
self.assertEqual(l.anomalyProbability(10, 0.1, timestamp=2), 0.5)
self.assertEqual(l.anomalyProbability(10, 0.1, timestamp=3), 0.5)
self.assertEqual(l.anomalyProbability(10, 0.1, timestamp=4), 0.5)
self.assertEqual(l.anomalyProbability(10, 0.1, timestamp=5), 0.9)
self.assertEqual(l.anomalyProbability(10, 0.1, timestamp=6), 0.9)
def testReestimationPeriodArg(self):
estimateAnomalyLikelihoodsWrap = mock.Mock(
wraps=an.estimateAnomalyLikelihoods, autospec=True)
estimateAnomalyLikelihoodsPatch = mock.patch(
"htm.algorithms.anomaly_likelihood.estimateAnomalyLikelihoods",
side_effect=estimateAnomalyLikelihoodsWrap,
autospec=True)
with estimateAnomalyLikelihoodsPatch:
l = an.AnomalyLikelihood(learningPeriod=2,
estimationSamples=2,
historicWindowSize=3,
reestimationPeriod=2)
# burn-in
l.anomalyProbability(10, 0.1, timestamp=1)
l.anomalyProbability(10, 0.1, timestamp=2)
l.anomalyProbability(10, 0.1, timestamp=3)
l.anomalyProbability(10, 0.1, timestamp=4)
self.assertEqual(estimateAnomalyLikelihoodsWrap.call_count, 0)
l.anomalyProbability(10, 0.1, timestamp=5)
self.assertEqual(estimateAnomalyLikelihoodsWrap.call_count, 1)
l.anomalyProbability(10, 0.1, timestamp=6)
self.assertEqual(estimateAnomalyLikelihoodsWrap.call_count, 1)
l.anomalyProbability(10, 0.1, timestamp=7)
self.assertEqual(estimateAnomalyLikelihoodsWrap.call_count, 2)
l.anomalyProbability(10, 0.1, timestamp=8)
self.assertEqual(estimateAnomalyLikelihoodsWrap.call_count, 2)
def testSerialization(self):
"""serialization using pickle"""
l = an.AnomalyLikelihood(learningPeriod=2, estimationSamples=2)
l.anomalyProbability("hi", 0.1, timestamp=1) # burn in
l.anomalyProbability("hi", 0.1, timestamp=2)
l.anomalyProbability("hello", 0.3, timestamp=3)
stored = pickle.dumps(l)
restored = pickle.loads(stored)
self.assertEqual(l, restored)
def testdWindowSizeImpactOnEstimateAnomalyLikelihoodsArgs(self):
# Verify that AnomalyLikelihood's historicWindowSize plays nice with args
# passed to estimateAnomalyLikelihoods"""
originalEstimateAnomalyLikelihoods = an.estimateAnomalyLikelihoods
estimationArgs = []
def estimateAnomalyLikelihoodsWrap(anomalyScores,
averagingWindow=10,
skipRecords=0,
verbosity=0):
estimationArgs.append((tuple(anomalyScores), skipRecords))
return originalEstimateAnomalyLikelihoods(
anomalyScores,
averagingWindow=averagingWindow,
skipRecords=skipRecords,
verbosity=verbosity)
estimateAnomalyLikelihoodsPatch = mock.patch(
"htm.algorithms.anomaly_likelihood.estimateAnomalyLikelihoods",
side_effect=estimateAnomalyLikelihoodsWrap,
autospec=True)
with estimateAnomalyLikelihoodsPatch as estimateAnomalyLikelihoodsMock:
l = an.AnomalyLikelihood(learningPeriod=2,
estimationSamples=2,
historicWindowSize=3)
l.anomalyProbability(10, 0.1, timestamp=1)
self.assertEqual(estimateAnomalyLikelihoodsMock.call_count, 0)
l.anomalyProbability(20, 0.2, timestamp=2)
self.assertEqual(estimateAnomalyLikelihoodsMock.call_count, 0)
l.anomalyProbability(30, 0.3, timestamp=3)
self.assertEqual(estimateAnomalyLikelihoodsMock.call_count, 0)
l.anomalyProbability(40, 0.4, timestamp=4)
self.assertEqual(estimateAnomalyLikelihoodsMock.call_count, 0)
# Estimation should kick in after learningPeriod + estimationSamples
# samples have been ingested
l.anomalyProbability(50, 0.5, timestamp=5)
self.assertEqual(estimateAnomalyLikelihoodsMock.call_count, 1)
# NOTE: we cannot use mock's assert_called_with, because the sliding
# window container changes in-place after estimateAnomalyLikelihoods is
# called
scores, numSkip = estimationArgs.pop()
self.assertEqual(scores,
((2, 20, 0.2), (3, 30, 0.3), (4, 40, 0.4)))
self.assertEqual(numSkip, 1)
def testEquals(self):
l = an.AnomalyLikelihood(learningPeriod=2, estimationSamples=2)
l2 = an.AnomalyLikelihood(learningPeriod=2, estimationSamples=2)
self.assertEqual(l, l2)
# Use 5 iterations to force the distribution to be created (4 probationary
# samples + 1)
l2.anomalyProbability(5, 0.1, timestamp=1) # burn in
l2.anomalyProbability(5, 0.1, timestamp=2)
l2.anomalyProbability(5, 0.1, timestamp=3)
l2.anomalyProbability(5, 0.1, timestamp=4)
self.assertIsNone(l2._distribution)
l2.anomalyProbability(1, 0.3, timestamp=5)
self.assertIsNotNone(l2._distribution)
self.assertNotEqual(l, l2)
l.anomalyProbability(5, 0.1, timestamp=1) # burn in
l.anomalyProbability(5, 0.1, timestamp=2)
l.anomalyProbability(5, 0.1, timestamp=3)
l.anomalyProbability(5, 0.1, timestamp=4)
self.assertIsNone(l._distribution)
l.anomalyProbability(1, 0.3, timestamp=5)
self.assertIsNotNone(l._distribution)
self.assertEqual(l, l2, "equal? \n%s\n vs. \n%s" % (l, l2))
def testHistoricWindowSize(self):
l = an.AnomalyLikelihood(learningPeriod=2,
estimationSamples=2,
historicWindowSize=3)
l.anomalyProbability(5, 0.1, timestamp=1) # burn in
self.assertEqual(len(l._historicalScores), 1)
l.anomalyProbability(5, 0.1, timestamp=2)
self.assertEqual(len(l._historicalScores), 2)
l.anomalyProbability(5, 0.1, timestamp=3)
self.assertEqual(len(l._historicalScores), 3)
l.anomalyProbability(5, 0.1, timestamp=4)
self.assertEqual(len(l._historicalScores), 3)