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Avoid creating SparseVectors for LOCO #377

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merged 19 commits into from
Aug 21, 2019
Merged

Avoid creating SparseVectors for LOCO #377

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66a5e99
WIP
gerashegalov Aug 3, 2019
77b97ee
code check fixes
gerashegalov Aug 5, 2019
35b8b5c
try sparse vector clone for test fix
gerashegalov Aug 5, 2019
cb2dc05
Merge branch 'master' into gera/perf-regression
michaelweilsalesforce Aug 7, 2019
a494129
WIP
gerashegalov Aug 3, 2019
1c383e5
code check fixes
gerashegalov Aug 5, 2019
a1d7d81
try sparse vector clone for test fix
gerashegalov Aug 5, 2019
f95f4bf
better names
gerashegalov Aug 8, 2019
a8ff84f
Skip Zeros when computing LOCOs for Text and Date fields
mweilsalesforce Aug 8, 2019
709594f
Merge branch 'gera/perf-regression' of github.com:gerashegalov/Transm…
mweilsalesforce Aug 8, 2019
96b2941
Fix Scalastyle
mweilsalesforce Aug 8, 2019
ee76053
minor fixes
gerashegalov Aug 8, 2019
2248d49
zipped
gerashegalov Aug 8, 2019
c3f743d
redundant toArray
gerashegalov Aug 8, 2019
dc39cb3
Updating comments + adding extra checks
mweilsalesforce Aug 9, 2019
25a6ac5
Merge branch 'master' into gera/perf-regression
leahmcguire Aug 12, 2019
3b7b63a
Adding TODO
mweilsalesforce Aug 12, 2019
b271b74
Merge branch 'master' into gera/perf-regression
tovbinm Aug 20, 2019
087b1d2
Merge branch 'master' into gera/perf-regression
leahmcguire Aug 21, 2019
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code check fixes
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@gerashegalov
gerashegalov committed Aug 8, 2019
commit 1c383e5390074d7d3069423f76aef9ea79b692b0
30 changes: 17 additions & 13 deletions core/src/main/scala/com/salesforce/op/stages/impl/insights/RecordInsightsLOCO.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -128,7 +128,7 @@ class RecordInsightsLOCO[T <: Model[T]]
.map(_.index)
.distinct.sorted

private def computeDiffs
private def computeDiff
(
i: Int,
oldInd: Int,
Expand Down Expand Up @@ -168,13 +168,12 @@ class RecordInsightsLOCO[T <: Model[T]]
k: Int,
indexToExamine: Int
): Seq[LOCOValue] = {
val zdif = Array.fill(baseScore.length)(0.0)
val minMaxHeap = new MinMaxHeap(k)
val aggregationMap = mutable.Map.empty[String, (Array[Int], Array[Double])]

agggregateDiffs(0, Left(featureSparse), indexToExamine, zdif, minMaxHeap, aggregationMap,
agggregateDiffs(0, Left(featureSparse), indexToExamine, minMaxHeap, aggregationMap,
baseScore)
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So the sparse features you just put in a value of 0? Cant we just skip adding them to the heap?

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@gerashegalov gerashegalov Aug 6, 2019 •
edited
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I had the same idea but in one of the iteration I ran into test failures and deferred it to later. I'll recheck now that I have everything green. @michaelweilsalesforce any thoughts?

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What kind of failures have you encountered?

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it may be that we were doing an unnecessary calculation and that just happened to be captured in the test...

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@michaelweilsalesforce you can reproduce it by commenting out the line 171-172.

Aggregate all the derived hashing tf features of rawFeature - text. 0.08025355373244505 was not less than 1.0E-10 expected aggregated LOCO value (0.006978569889777832) should be the same as actual (0.08723212362222289)

Aggregate x_HourOfDay and y_HourOfDay of rawFeature - dateFeature. 0.016493734169231777 was not less than 1.0E-10 expected aggregated LOCO value (0.016493734169231777) should be the same as actual (0.032987468338463555)

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@sanmitra

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@leahmcguire @gerashegalov The reason for tracking zero values is whenever we want to average LOCOs of a same raw text feature we are also including the zero values.
E.g if text feature TextA has on a row 6 non zero values loco1, ..., loco6 and 4 0s, we are dividing by 10 :
(loco1 + loco2 + ... + loco6)/10

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LEt me write a fix that will not go over the zeros

agggregateDiffs(featureSparse.size, Right(zeroValIndices), indexToExamine, zdif, minMaxHeap,
agggregateDiffs(featureSparse.size, Right(zeroValIndices), indexToExamine, minMaxHeap,
aggregationMap, baseScore)

// Adding LOCO results from aggregation map into heaps
Expand All @@ -192,19 +191,11 @@ class RecordInsightsLOCO[T <: Model[T]]
offset: Int,
featureVec: Either[SparseVector, Array[Int]],
indexToExamine: Int,
zdif: Array[Double],
minMaxHeap: MinMaxHeap,
aggregationMap: mutable.Map[String, (Array[Int], Array[Double])],
baseScore: Array[Double]
): Unit = {
(
featureVec match {
case Left(sparse) => (0 until sparse.size, sparse.indices).zipped
.map { case ( i, oldInd) => (i, oldInd, computeDiffs(i, oldInd, sparse, baseScore)) }
case Right(zeroeIndices) => (0 until zeroeIndices.length, zeroeIndices).zipped
.map { case (i, oldInd) => (i + offset, oldInd, zdif) }
}
).foreach { case (i, oldInd, diffToExamine) =>
computeDiffs(featureVec, offset, baseScore).foreach { case (i, oldInd, diffToExamine) =>
val history = histories(oldInd)
history match {
// If indicator value and descriptor value of a derived text feature are empty, then it is likely
Expand All @@ -227,6 +218,19 @@ class RecordInsightsLOCO[T <: Model[T]]
}
}

private def computeDiffs(
featureVec: Either[SparseVector, Array[Int]],
offset: Int, baseScore: Array[Double]
) = {
val zdif = Array.fill(baseScore.length)(0.0)
featureVec match {
case Left(sparse) => (0 until sparse.size, sparse.indices).zipped
.map { case (i, oldInd) => (i, oldInd, computeDiff(i, oldInd, sparse, baseScore)) }
case Right(zeroeIndices) => (0 until zeroeIndices.length, zeroeIndices).zipped
.map { case (i, oldInd) => (i + offset, oldInd, zdif) }
}
}

override def transformFn: OPVector => TextMap = features => {
val baseResult = modelApply(labelDummy, features)
val baseScore = baseResult.score
Expand Down