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# ************************************************************************
# Main functions for descriptive statistics within classyfire
#
# Functions:
# getAcc: Get the test and train accuracies within the ensemble
# getAvgAcc: Get the average test and train overall accuracy of the ensemble
# getOptParam: Get the optimal hyperparameters
# getConfMatr: Get the overall confusion matrix of the ensemble
# getPerm5Num: Get the descriptive statistics (five number summary) of the permutation run
#
# ***********************************************************************
# Get the test and train accuracies within the ensemble
getAcc <- function(ensObj) {
.argsCheck(ensObj, "cfBuild")
testAcc <- round(ensObj$testAcc, digits=2)
trainAcc <- round(ensObj$trainAcc, digits=2)
return (list(Test = testAcc, Train = trainAcc))
}
# Get the average test and train overall accuracy of the ensemble
getAvgAcc <- function(ensObj) {
.argsCheck(ensObj, "cfBuild")
avgTest <- round(mean(ensObj$testAcc), digits=2)
avgTrain <- round(mean(ensObj$trainAcc), digits=2)
return (list(Test = avgTest, Train = avgTrain))
}
# Get the optimal hyperparameters
getOptParam <- function(ensObj) {
.argsCheck(ensObj, "cfBuild")
gamma <- ensObj$optGamma
cost <- ensObj$optCost
optHyper <- cbind(gamma, cost)
colnames(optHyper) <- c("Opt Gamma", "Opt Cost")
return (optHyper)
}
# Get the overall confusion matrix of the ensemble
getConfMatr <- function(ensObj) {
.argsCheck(ensObj, "cfBuild")
totalConf <- Reduce("+", ensObj$confMatr)
propTable <- round(prop.table(totalConf, 1)*100)
return(propTable)
}
# Get the descriptive statistics (five number summary) of the permutation run
getPerm5Num <- function(permObj) {
.argsCheck(permObj, "cfPermute")
minVal <- min(permObj$avgAcc)
maxVal <- max(permObj$avgAcc)
medianVal <- median(permObj$avgAcc)
quantiles <- quantile(permObj$avgAcc)
return (list(minimum = minVal,
lowerQ = quantiles[2],
median = medianVal,
upperQ = quantiles[4],
maximum = maxVal))
}
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