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bootstrapQueryLatency.R
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rm(list=ls())
# Get the bootstrapped results
# supporting functions
resampleOp = function(op) {
return(sample(op$latency_ms, size=nrow(op), replace=TRUE, prob=rep.int(1/nrow(op), nrow(op))))
}
getRangeForQuantileValues = function(values) {
#return(range(values))
return(quantile(values, probs=c(0.025,0.975)))
}
# START
#queryType="needsApproval"
queryType="userByHometown"
#queryType="myFollowing"
#queryType="myThoughts"
ylim=80
whichPred = "empirical"
#whichPred="gmm"
numResampledSets=10
path=paste("~/Desktop/bootstrap-", whichPred, sep="")
dir.create(path)
# Bootstrap the actual
load("~/Desktop/queryObs.RData")
ls()
if (queryType == "needsApproval") {
queries = nAqueries
} else if (queryType == "userByHometown") {
queries = uBHqueries
} else if (queryType == "myFollowing") {
queries = mFqueries
} else if (queryType == "myThoughts") {
queries = mTqueries
}
numSamples = nrow(queries)
actualQuantiles = matrix(nrow=numResampledSets, ncol=3)
for (i in 1:numResampledSets) {
samples = sample(queries$latency_ms, size=numSamples, replace=TRUE, prob=rep.int(1/numSamples, numSamples))
actualQuantiles[i,] = quantile(samples, c(0.5,0.9,0.99))
}
#hist(quantiles, breaks=25)
#abline(v=quantile(quantiles,0.025), lw=2, col="blue")
#abline(v=quantile(quantiles,0.975), lw=2, col="blue")
#summary(quantiles)
# Bootstrap the predictions too
if (whichPred == "empirical") {
load("~/Desktop/ops.RData")
ls()
source("/Users/radlab/Desktop/ksauer/Desktop/scads/experiments/client/performance/logparsing/src/main/R/querySamplers.R")
numSamples=1000
#numResampledSets=100
sampleQuantiles = matrix(nrow=numResampledSets, ncol=3)
for (i in 1:numResampledSets) {
print(i)
op1ResampleHist = hist(resampleOp(op1), breaks=1000, plot=FALSE)
op2ResampleHist = hist(resampleOp(op2), breaks=1000, plot=FALSE)
op3ResampleHist = hist(resampleOp(op3), breaks=1000, plot=FALSE)
op4ResampleHist = hist(resampleOp(op4), breaks=1000, plot=FALSE)
op5ResampleHist = hist(resampleOp(op5), breaks=1000, plot=FALSE)
op6ResampleHist = hist(resampleOp(op6), breaks=1000, plot=FALSE)
op7ResampleHist = hist(resampleOp(op7), breaks=1000, plot=FALSE)
op8ResampleHist = hist(resampleOp(op8), breaks=1000, plot=FALSE)
op9ResampleHist = hist(resampleOp(op9), breaks=1000, plot=FALSE)
if (queryType == "needsApproval") {
samples = needsApprovalSampler(op1ResampleHist, op3ResampleHist, op4ResampleHist, op6ResampleHist, op9ResampleHist, numSamples)
} else if (queryType == "userByHometown") {
samples = userByHometownSampler(op2ResampleHist, op3ResampleHist, op6ResampleHist, numSamples)
} else if (queryType == "myFollowing") {
samples = myFollowingSampler(op1ResampleHist, op4ResampleHist, op5ResampleHist, op6ResampleHist, numSamples)
} else if (queryType == "myThoughts") {
samples = myThoughtsSampler(op1ResampleHist, op4ResampleHist, op6ResampleHist, op9ResampleHist, numSamples)
}
sampleQuantiles[i,] = quantile(samples, c(0.5,0.9,0.99))
}
} else if (whichPred == "gmm") {
load("~/Desktop/ops.RData")
ls()
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")
# setup
numSamples=1000
sampleQuantiles = matrix(nrow=numResampledSets, ncol=3)
k=c(3,8,5,3,5,4,4,5,2) # from CV
for (i in 1:numResampledSets) {
print(i)
# resample the ops
op1Resample = resampleOp(op1)
op2Resample = resampleOp(op2)
op3Resample = resampleOp(op3)
op4Resample = resampleOp(op4)
op5Resample = resampleOp(op5)
op6Resample = resampleOp(op6)
op7Resample = resampleOp(op7)
op8Resample = resampleOp(op8)
op9Resample = resampleOp(op9)
# fit gmm to resampled data
opParamFile = "~/Desktop/opGMMParams.RData"
if (queryType == "needsApproval") {
# fit gmms to resampled data
op1GMMParams = fitEM(observationsVector=op1Resample, k=k[1])
op3GMMParams = fitEM(observationsVector=op3Resample, k=k[3])
op4GMMParams = fitEM(observationsVector=op4Resample, k=k[4])
op6GMMParams = fitEM(observationsVector=op6Resample, k=k[6])
op9GMMParams = fitEM(observationsVector=op9Resample, k=k[9])
save(op1GMMParams, op3GMMParams, op4GMMParams, op6GMMParams, op9GMMParams, file=opParamFile)
samples = needsApprovalGMMSampler(opParamFile, numSamples)
} else if (queryType == "userByHometown") {
op2GMMParams = fitEM(observationsVector=op2Resample, k=k[2])
op3GMMParams = fitEM(observationsVector=op3Resample, k=k[3])
op6GMMParams = fitEM(observationsVector=op6Resample, k=k[6])
save(op2GMMParams, op3GMMParams, op6GMMParams, file=opParamFile)
samples = userByHometownGMMSampler(opParamFile, numSamples)
} else if (queryType == "myFollowing") {
op1GMMParams = fitEM(observationsVector=op1Resample, k=k[1])
op4GMMParams = fitEM(observationsVector=op4Resample, k=k[4])
op5GMMParams = fitEM(observationsVector=op5Resample, k=k[5])
op6GMMParams = fitEM(observationsVector=op6Resample, k=k[6])
save(op1GMMParams, op4GMMParams, op5GMMParams, op6GMMParams, file=opParamFile)
samples = myFollowingGMMSampler(opParamFile, numSamples)
} else if (queryType == "myThoughts") {
op1GMMParams = fitEM(observationsVector=op1Resample, k=k[1])
op4GMMParams = fitEM(observationsVector=op4Resample, k=k[4])
op6GMMParams = fitEM(observationsVector=op6Resample, k=k[6])
op9GMMParams = fitEM(observationsVector=op9Resample, k=k[9])
save(op1GMMParams, op4GMMParams, op6GMMParams, op9GMMParams, file=opParamFile)
samples = myThoughtsGMMSampler(opParamFile, numSamples)
}
sampleQuantiles[i,] = quantile(samples, c(0.5,0.9,0.99))
}
} else {
print("Unsupported prediction type.")
}
# Get results
source("/Users/radlab/Desktop/ksauer/Desktop/scads/experiments/client/performance/logparsing/src/main/R/graphical-compare-actual-predicted-query-distr.R")
queriesRange = c(getRangeForQuantileValues(actualQuantiles[,1]), getRangeForQuantileValues(actualQuantiles[,2]), getRangeForQuantileValues(actualQuantiles[,3]))
samplesRange = c(getRangeForQuantileValues(sampleQuantiles[,1]), getRangeForQuantileValues(sampleQuantiles[,2]), getRangeForQuantileValues(sampleQuantiles[,3]))
queriesMedians = c(median(actualQuantiles[,1]), median(actualQuantiles[,2]), median(actualQuantiles[,3]))
samplesMedians = c(median(sampleQuantiles[,1]), median(sampleQuantiles[,2]), median(sampleQuantiles[,3]))
#plotActualAndPredictedQueryQuantileBarplotWithErrorBars(path, queryType, queries, samples, queriesRange, samplesRange, c(0,ylim))
plotActualAndPredictedQueryQuantileBarplotWithErrorBars(path, queryType, queries, samples, queriesRange, samplesRange, queriesMedians, samplesMedians, c(0,ylim))