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bootstrapFunctions.R
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bootstrapFunctions.R
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# Setup
getOpList = function(opFilename) {
load(opFilename)
ops = list(op1=op1$latency_ms, op2=op2$latency_ms, op3=op3$latency_ms, op4=op4$latency_ms, op5=op5$latency_ms, op6=op6$latency_ms, op7=op7$latency_ms, op8=op8$latency_ms, op9=op9$latency_ms)
return(ops)
}
# Prediction procedures
predictWithTruncatedOpHist = function(queryType, quantileToTruncate=0.9, quantileOfTruncatedDistr=0.9, ops, numSamples) {
# Truncate op hist
op1Hist = truncateOpHistToLeftOfQuantile(ops$op1, quantile=quantileToTruncate)
op2Hist = truncateOpHistToLeftOfQuantile(ops$op2, quantile=quantileToTruncate)
op3Hist = truncateOpHistToLeftOfQuantile(ops$op3, quantile=quantileToTruncate)
op4Hist = truncateOpHistToLeftOfQuantile(ops$op4, quantile=quantileToTruncate)
op5Hist = truncateOpHistToLeftOfQuantile(ops$op5, quantile=quantileToTruncate)
op6Hist = truncateOpHistToLeftOfQuantile(ops$op6, quantile=quantileToTruncate)
op7Hist = truncateOpHistToLeftOfQuantile(ops$op7, quantile=quantileToTruncate)
op8Hist = truncateOpHistToLeftOfQuantile(ops$op8, quantile=quantileToTruncate)
op9Hist = truncateOpHistToLeftOfQuantile(ops$op9, quantile=quantileToTruncate)
# Use truncated hist for query pred
opHists=list(op1Hist=op1Hist, op2Hist=op2Hist, op3Hist=op3Hist, op4Hist=op4Hist, op5Hist=op5Hist, op6Hist=op6Hist, op7Hist=op7Hist, op8Hist=op8Hist, op9Hist=op9Hist)
samples = querySampler(opHists, queryType, numSamples)
return(quantile(samples, quantileOfTruncatedDistr))
}
truncateOpHistToLeftOfQuantile = function(op, quantile) {
#print(length(op))
h=hist(op, breaks=1000, plot=FALSE)
q=quantile(op, quantile)
# Find closest break to given quantile
breakDists = h$breaks - q
bin=which.min(abs(breakDists))
# Truncate all latency values to the left of the bin
h$density[1:bin-1] = 0 # used by samplers
h$counts[1:bin-1] = 0 # used by R's plotting
return(h)
}
predictWithFullOpHist = function(queryType, quantileToPredict, ops, numSamples) {
# Obtain op hist
op1Hist = hist(ops$op1, breaks=1000, plot=FALSE)
op2Hist = hist(ops$op2, breaks=1000, plot=FALSE)
op3Hist = hist(ops$op3, breaks=1000, plot=FALSE)
op4Hist = hist(ops$op4, breaks=1000, plot=FALSE)
op5Hist = hist(ops$op5, breaks=1000, plot=FALSE)
op6Hist = hist(ops$op6, breaks=1000, plot=FALSE)
op7Hist = hist(ops$op7, breaks=1000, plot=FALSE)
op8Hist = hist(ops$op8, breaks=1000, plot=FALSE)
op9Hist = hist(ops$op9, breaks=1000, plot=FALSE)
opHists=list(op1Hist=op1Hist, op2Hist=op2Hist, op3Hist=op3Hist, op4Hist=op4Hist, op5Hist=op5Hist, op6Hist=op6Hist, op7Hist=op7Hist, op8Hist=op8Hist, op9Hist=op9Hist)
samples = querySampler(opHists, queryType, numSamples)
return(quantile(samples, quantileToPredict))
}
predictWithExtremeOpHist = function(queryType, quantileToPredict, ops, numSamples) {
source("empiricalEstimMethods-functions.R")
op1Hist = createExtremeHistogramForRawData(op1$latency_ms)
op2Hist = createExtremeHistogramForRawData(op2$latency_ms)
op3Hist = createExtremeHistogramForRawData(op3$latency_ms)
op4Hist = createExtremeHistogramForRawData(op4$latency_ms)
op5Hist = createExtremeHistogramForRawData(op5$latency_ms)
op6Hist = createExtremeHistogramForRawData(op6$latency_ms)
op7Hist = createExtremeHistogramForRawData(op7$latency_ms)
op8Hist = createExtremeHistogramForRawData(op8$latency_ms)
op9Hist = createExtremeHistogramForRawData(op9$latency_ms)
opHists=list(op1Hist=op1Hist, op2Hist=op2Hist, op3Hist=op3Hist, op4Hist=op4Hist, op5Hist=op5Hist, op6Hist=op6Hist, op7Hist=op7Hist, op8Hist=op8Hist, op9Hist=op9Hist)
samples = querySampler(opHists, queryType, numSamples)
return(quantile(samples, quantileToPredict))
}
predictByConvolvingFullOpHist = function(queryType, quantileToPredict, ops, numAlignmentPoints) {
source("convolutionForOpCombination-functions.R")
queryDensity = getQueryDensityViaConvolution(ops, queryType, numAlignmentPoints)
#return(getMultipleQuantilesFromDensity(queryDensity, quantileToPredict))
return(getQuantileFromDensity(queryDensity, quantileToPredict))
}
predictWithGMMOpFits = function(queryType, quantileToPredict, ops, numSamples) {
#source("/Users/radlab/Desktop/ksauer/Desktop/scads/experiments/client/performance/logparsing/src/main/R/querySamplers.R")
#source("/Users/radlab/Desktop/ksauer/Desktop/scads/experiments/client/performance/logparsing/src/main/R/emFitAndSample.R")
source("querySamplers.R")
source("emFitAndSample.R")
k=c(3,8,5,3,5,4,4,5,2) # from CV
opParamFile="~/Desktop/opGMMParams.RData"
if (queryType == "needsApproval") {
op1GMMParams = fitEM(observationsVector=ops$op1, k=k[1])
op3GMMParams = fitEM(observationsVector=ops$op3, k=k[3])
op4GMMParams = fitEM(observationsVector=ops$op4, k=k[4])
op6GMMParams = fitEM(observationsVector=ops$op6, k=k[6])
op9GMMParams = fitEM(observationsVector=ops$op9, k=k[9])
save(op1GMMParams, op3GMMParams, op4GMMParams, op6GMMParams, op9GMMParams, file=opParamFile)
samples = needsApprovalGMMSampler(opParamFile, numSamples)
} else if (queryType == "userByHometown") {
op2GMMParams = fitEM(observationsVector=ops$op2, k=k[2])
op3GMMParams = fitEM(observationsVector=ops$op3, k=k[3])
op6GMMParams = fitEM(observationsVector=ops$op6, k=k[6])
save(op2GMMParams, op3GMMParams, op6GMMParams, file=opParamFile)
samples = userByHometownGMMSampler(opParamFile, numSamples)
} else if (queryType == "myFollowing") {
op1GMMParams = fitEM(observationsVector=ops$op1, k=k[1])
op4GMMParams = fitEM(observationsVector=ops$op4, k=k[4])
op5GMMParams = fitEM(observationsVector=ops$op5, k=k[5])
op6GMMParams = fitEM(observationsVector=ops$op6, k=k[6])
save(op1GMMParams, op4GMMParams, op5GMMParams, op6GMMParams, file=opParamFile)
samples = myFollowingGMMSampler(opParamFile, numSamples)
} else if (queryType == "myThoughts") {
op1GMMParams = fitEM(observationsVector=ops$op1, k=k[1])
op4GMMParams = fitEM(observationsVector=ops$op4, k=k[4])
op6GMMParams = fitEM(observationsVector=ops$op6, k=k[6])
op9GMMParams = fitEM(observationsVector=ops$op9, k=k[9])
save(op1GMMParams, op4GMMParams, op6GMMParams, op9GMMParams, file=opParamFile)
samples = myThoughtsGMMSampler(opParamFile, numSamples)
} else {
print("Unsupported query type.")
}
return(quantile(samples, quantileToPredict))
}
querySampler = function(opHists, queryType, numSamples) {
#source("~/Desktop/ksauer/Desktop/scads/experiments/client/performance/logparsing/src/main/R/querySamplers.R")
source("querySamplers.R")
if (queryType == "needsApproval") {
samples=needsApprovalSampler(opHists$op1Hist, opHists$op3Hist, opHists$op4Hist, opHists$op6Hist, opHists$op9Hist, numSamples)
} else if (queryType == "userByHometown") {
samples=userByHometownSampler(opHists$op2Hist, opHists$op3Hist, opHists$op6Hist, numSamples)
} else if (queryType == "myFollowing") {
samples=myFollowingSampler(opHists$op1Hist, opHists$op4Hist, opHists$op5Hist, opHists$op6Hist, numSamples)
} else if (queryType == "myThoughts") {
samples=myThoughtsSampler(opHists$op1Hist, opHists$op4Hist, opHists$op6Hist, opHists$op9Hist, numSamples)
} else {
print("Unsupported query type.")
}
return(samples)
}
resampleDataset = function(dataset) {
return(sample(dataset, size=length(dataset), replace=TRUE, prob=rep.int(1/length(dataset), length(dataset))))
}
resampleOps = function(ops) {
op1 = resampleDataset(ops$op1)
op2 = resampleDataset(ops$op2)
op3 = resampleDataset(ops$op3)
op4 = resampleDataset(ops$op4)
op5 = resampleDataset(ops$op5)
op6 = resampleDataset(ops$op6)
op7 = resampleDataset(ops$op7)
op8 = resampleDataset(ops$op8)
op9 = resampleDataset(ops$op9)
resampledOps = list(op1=op1, op2=op2, op3=op3, op4=op4, op5=op5, op6=op6, op7=op7, op8=op8, op9=op9)
return(resampledOps)
}
resampleQueries = function(queriesList) {
nA = resampleDataset(queriesList$nA)
uBH = resampleDataset(queriesList$uBH)
mF = resampleDataset(queriesList$mF)
mT = resampleDataset(queriesList$mT)
resampledQueriesList = list(nA=nA, uBH=uBH, mF=mF, mT=mT)
return(resampledQueriesList)
}
getRangeForQuantileValues = function(values) {
#return(range(values))
return(quantile(values, probs=c(0.025,0.975)))
}
getSummaryOfQuantileValues = function(values, quantiles) {
return(quantile(values, probs=quantiles))
}
bootstrapQueryLatencyDistr = function(estimationMethod, numResamples, queryType, opListFilename, latencyQuantilesToEstimate=c(0.5,0.9,0.99), noiseQuantiles=c(0.025, 0.5, 0.975), numSamples, desiredOutput, quantileToTruncate=0.9, quantileOfTruncatedDistr=0.9, numAlignmentPoints) {
load(opListFilename) # ops
quantileEstimates = matrix(nrow=numResamples, ncol=length(latencyQuantilesToEstimate))
colnames(quantileEstimates) = latencyQuantilesToEstimate
for (i in 1:numResamples) {
print(paste("resample", i))
resampledOps = resampleOps(ops)
if (estimationMethod == "empiricalFull") {
quantileEstimates[i,] = predictWithFullOpHist(queryType, latencyQuantilesToEstimate, resampledOps, numSamples)
} else if (estimationMethod == "empiricalTruncated") {
quantileEstimates[i,] = predictWithTruncatedOpHist(queryType, quantileToTruncate=quantileToTruncate, quantileOfTruncatedDistr=quantileOfTruncatedDistr, resampledOps, numSamples)
} else if (estimationMethod == "gmm") {
quantileEstimates[i,] = predictWithGMMOpFits(queryType, latencyQuantilesToEstimate, resampledOps, numSamples)
} else if (estimationMethod == "empiricalExtreme") {
quantileEstimates[i,] = predictWithExtremeOpHist(queryType, latencyQuantilesToEstimate, resampledOps, numSamples)
} else if (estimationMethod == "empiricalConvolution") {
quantileEstimates[i,] = predictByConvolvingFullOpHist(queryType, latencyQuantilesToEstimate, resampledOps, numAlignmentPoints)
} else {
print("Unsupported estimation method.")
}
}
if (desiredOutput == "quantiles") {
return(getNoiseOfQuantileEstimates(quantileEstimates, noiseQuantiles))
} else if (desiredOutput == "values") {
return(quantileEstimates)
}
}
bootstrapActualQueryLatencyDistr = function(numResamples, queryType, queryListFilename, latencyQuantilesToEstimate=c(0.5,0.9,0.99), noiseQuantiles=c(0.025, 0.5, 0.975), desiredOutput) {
load(queryListFilename) # queriesList
quantileEstimates=matrix(nrow=numResamples, ncol=length(latencyQuantilesToEstimate))
colnames(quantileEstimates) = latencyQuantilesToEstimate
for (i in 1:numResamples) {
print(paste("resample", i))
resampledQueriesList = resampleQueries(queriesList)
if (queryType == "needsApproval") {
queries = resampledQueriesList$nA
} else if (queryType == "userByHometown") {
queries = resampledQueriesList$uBH
} else if (queryType == "myFollowing") {
queries = resampledQueriesList$mF
} else if (queryType == "myThoughts") {
queries = resampledQueriesList$mT
} else {
print("Unsupported query type.")
}
quantileEstimates[i,] = quantile(queries, latencyQuantilesToEstimate)
}
if (desiredOutput == "quantiles") {
return(getNoiseOfQuantileEstimates(quantileEstimates, noiseQuantiles))
} else if (desiredOutput == "values") {
return(quantileEstimates)
}
}
getNoiseOfQuantileEstimates = function(quantileEstimates, noiseQuantiles) {
# quantileEstimates: rows = resamples, cols = quantiles of predictions corresponding to that resample
bootstrapNoise = matrix(nrow=ncol(quantileEstimates), ncol=length(noiseQuantiles))
rownames(bootstrapNoise) = colnames(quantileEstimates)
colnames(bootstrapNoise) = noiseQuantiles
for (j in 1:ncol(quantileEstimates)) {
bootstrapNoise[j,] = quantile(quantileEstimates[,j], noiseQuantiles)
}
return(bootstrapNoise)
}
getPredictionsMatrix = function(queryType, numResamples, numSamples, quantile) {
predictionsMatrix = matrix(nrow=numResamples, ncol=4)
colnames(predictionsMatrix) = c("actual", "empFull", "empTruncated", "gmm")
print("actual...")
predictionsMatrix[,1] = bootstrapActualQueryLatencyDistr(numResamples, queryType, "~/Desktop/queriesList.RData", desiredOutput="values", latencyQuantilesToEstimate=quantile)
print("full empirical...")
predictionsMatrix[,2] = bootstrapQueryLatencyDistr("empiricalFull", numResamples, queryType, "~/Desktop/opsList.RData", numSamples=numSamples, desiredOutput="values", latencyQuantilesToEstimate=quantile)
print("truncated empirical...")
predictionsMatrix[,3] = bootstrapQueryLatencyDistr("empiricalTruncated", numResamples, queryType, "~/Desktop/opsList.RData", numSamples=numSamples, desiredOutput="values", latencyQuantilesToEstimate=quantile, quantileToTruncate=0.9, quantileOfTruncatedDistr=0.5)
print("gmm...")
predictionsMatrix[,4] = bootstrapQueryLatencyDistr("gmm", numResamples, queryType, "~/Desktop/opsList.RData", numSamples=numSamples, desiredOutput="values", latencyQuantilesToEstimate=quantile)
return(predictionsMatrix)
}