def testCalcScoreByThresholdReturnsExpectedScores(self):
fnWeight = 5.0
o = Sweeper()
o.fnWeight = fnWeight
fakeInput = [
AnomalyPoint(0, 0.5, -1000, 'probationary'), # Should never contribute to score (probationary)
AnomalyPoint(1, 0.5, -1000, 'probationary'), # Should never contribute to score (probationary)
AnomalyPoint(2, 0.0, -3, None), # Should never contribute to score (anomaly == 0.0)
AnomalyPoint(4, 0.2, 20, 'windowA'), # Should be used instead of next row when threshold <= 0.2
AnomalyPoint(5, 0.3, 10, 'windowA'), # Should be used for winowA _until_ threshold <= 0.2
AnomalyPoint(6, 0.5, 5, 'windowB'), # Only score for windowB, but won't be used until threshold <= 0.5
AnomalyPoint(7, 0.5, -3, None),
]
expectedScoresByThreshold = [
ThresholdScore(1.1, -2 * fnWeight, 0, 2, 0, 3, 5), # two windows, both false negatives at this threshold
ThresholdScore(0.5, 5 - 3 - fnWeight, 1, 1, 1, 2, 5), # Both 'anomalyScore == 0.5' score, windowA is still FN
ThresholdScore(0.3, 5 - 3 + 10, 2, 1, 1, 1, 5), # Both windows now have a TP
ThresholdScore(0.2, 5 - 3 + 20, 3, 1, 1, 0, 5), # windowA gets a new max value due to row 4 becoming active
ThresholdScore(0.0, 5 - 3 + 20 - 3, 3, 0, 2, 0, 5),
]
actual = o.calcScoreByThreshold(fakeInput)
assert actual == expectedScoresByThreshold
def testPrepareScoreParts(self):
fakeInput = [
AnomalyPoint(0, 0.5, 0, 'probationary'),
AnomalyPoint(1, 0.5, 0, 'probationary'),
AnomalyPoint(2, 0.0, 0, None),
AnomalyPoint(4, 0.2, 0, 'windowA'),
AnomalyPoint(5, 0.2, 0, 'windowA'),
AnomalyPoint(6, 0.5, 0, 'windowB'),
AnomalyPoint(7, 0.5, 0, None),
]
fakeFNWeight = 33.0
o = Sweeper()
o.fnWeight = fakeFNWeight
# Expect one entry for all false positives and one entry per unique window name,
# initialized to a starting score of `-self.fnWeight`
expectedOutput = {
"fp": 0,
"windowA": -fakeFNWeight,
"windowB": -fakeFNWeight
}
actualScoreParts = o._prepareScoreByThresholdParts(fakeInput)
assert actualScoreParts == expectedOutput
def testPrepareScoreParts(self):
fakeInput = [
AnomalyPoint(0, 0.5, 0, 'probationary'),
AnomalyPoint(1, 0.5, 0, 'probationary'),
AnomalyPoint(2, 0.0, 0, None),
AnomalyPoint(4, 0.2, 0, 'windowA'),
AnomalyPoint(5, 0.2, 0, 'windowA'),
AnomalyPoint(6, 0.5, 0, 'windowB'),
AnomalyPoint(7, 0.5, 0, None),
]
fakeFNWeight = 33.0
o = Sweeper()
o.fnWeight = fakeFNWeight
# Expect one entry for all false positives and one entry per unique window name,
# initialized to a starting score of `-self.fnWeight`
expectedOutput = {
"fp": 0,
"windowA": -fakeFNWeight,
"windowB": -fakeFNWeight
}
actualScoreParts = o._prepareScoreByThresholdParts(fakeInput)
assert actualScoreParts == expectedOutput
def testCalcScoreByThresholdReturnsExpectedScores(self):
fnWeight = 5.0
o = Sweeper()
o.fnWeight = fnWeight
fakeInput = [
AnomalyPoint(0, 0.5, -1000, 'probationary'
), # Should never contribute to score (probationary)
AnomalyPoint(1, 0.5, -1000, 'probationary'
), # Should never contribute to score (probationary)
AnomalyPoint(
2, 0.0, -3,
None), # Should never contribute to score (anomaly == 0.0)
AnomalyPoint(
4, 0.2, 20, 'windowA'
), # Should be used instead of next row when threshold <= 0.2
AnomalyPoint(
5, 0.3, 10, 'windowA'
), # Should be used for winowA _until_ threshold <= 0.2
AnomalyPoint(
6, 0.5, 5, 'windowB'
), # Only score for windowB, but won't be used until threshold <= 0.5
AnomalyPoint(7, 0.5, -3, None),
]
expectedScoresByThreshold = [
ThresholdScore(
1.1, -2 * fnWeight, 0, 2, 0, 3,
5), # two windows, both false negatives at this threshold
ThresholdScore(
0.5, 5 - 3 - fnWeight, 1, 1, 1, 2,
5), # Both 'anomalyScore == 0.5' score, windowA is still FN
ThresholdScore(0.3, 5 - 3 + 10, 2, 1, 1, 1,
5), # Both windows now have a TP
ThresholdScore(
0.2, 5 - 3 + 20, 3, 1, 1, 0, 5
), # windowA gets a new max value due to row 4 becoming active
ThresholdScore(0.0, 5 - 3 + 20 - 3, 3, 0, 2, 0, 5),
]
actual = o.calcScoreByThreshold(fakeInput)
assert actual == expectedScoresByThreshold
