## Prediction function for the elastic net logistic regression classifier
##
## Prediction function for the elastic net logistic regression classifier
## for a variety of thresholds and calculate
## the associated loss
##
## Let \code{z} represent class denoted by the last level of \code{truthLabels}.
## Then the probability returned by \code{predict(glmnetFit, newData, type = 'response')}
## is the probability that the observations belong to class z. If this probabilty
## exceeds \code{tau}, we will classify the observation as belonging to \code{z}.
##
## @author Landon Sego
##
## @param glmnetFit The glmnet fitted object (returned from \code{glmnet}
## that inherits from the \code{lognet} and \code{glmnet} classes.
##
## @param newData A matrix or dataframe of new instances that match the
## predictors in \code{glmnetFit}
##
## @param truthDataLabels A factor vector containing the corresponding truth
## labels in \code{newData}.
##
## @param lossMat A loss matrix of class \code{lossMat}, returned by
## \code{lossMatrix}
##
## @param tauVec A numeric sequence of threshold values for the binary
## classification.
##
## @param lossWeight A numeric vector indicating the relative weight to ascribe
## to each row of \code{newData}
##
## @param lambdaVec A numeric vector of lambda values for which the loss will
## be calculated.
##
## @return A data frame containing \code{weightedSumLoss} (the sum of the
## product of the weights and the loss) and \code{sumWeights}
## (the sum of the weights) for each value of \code{tau} and \code{lambda}. The
## loss (for \code{newData}) is given by \code{weightedSumLoss} divided by
## \code{sumWeights}.
predLoss_glmnetLRC <- function(glmnetFit, newData, truthLabels, lossMat,
tauVec = seq(0.1, 0.9, by = 0.1),
lossWeight = rep(1, NROW(newData)),
lambdaVec = NULL) {
# Force the lossWeight to resolve to avoid any issues with lazy evaluation
force(lossWeight)
# Check inputs
Smisc::stopifnotMsg(
# Ensure the object is of the correct class
inherits(glmnetFit, "lognet"), "'glmnetFit' must inherit from the 'lognet' class",
inherits(glmnetFit, "glmnet"), "'glmnetFit' must inherit from the 'glmnet' class",
# Check truthLabels
if (is.factor(truthLabels)) {
nlevels(truthLabels) == 2
} else FALSE,
"'truthLabels' must be a factor with 2 levels",
# Verify lengths match
length(lossWeight) == NROW(newData),
"the length of 'lossWeight' must be the same as the number of rows in 'newData'")
# Ensure factors were constructed the same way for
# the training data and the newdata. This is important
# to ensure our labels don't get messed up
if (!all(glmnetFit$classnames == levels(truthLabels))) {
stop("Factor levels in 'glmnetFit' object do not match the levels of 'truthLabels'")
}
# For each lambda, make probabality predictions that the instance is an
# element of the class with the largest factor level
preds <- predict(glmnetFit, newData, s = lambdaVec, type = "response")
# Let Z be the last level of the response (Z = levels(truthLabels)[2])
# The preds matrix returned by predict(glmnet, ...) is P(x elem Z)
# The rule will be: If P(x elem Z) > tau --> x is assigned to Z
# Create a logical matrix of whether x is assigned to Z for a given tau
# Calculate the loss over the lambdas for a given tau
calcLossOverLambda <- function(x, tau = 0.5) {
# x is the probabilty returned by predict(glmnetFit, ...), i.e.,
# it is P(x elem Z)
# Dichotomize the prediction for tau
predLabels <- factor(x > tau, levels = c(FALSE, TRUE),
labels = glmnetFit$classnames)
# Calculate the loss
cl <- calcLoss(truthLabels, predLabels, lossMat, lossWeight = lossWeight)
return(cl)
} # calcClossOverLambda
# Calculate the loss over the taus
calcLossOverTau <- function(x) {
# x is a value of tau
out <- Smisc::list2df(apply(preds, 2, calcLossOverLambda, tau = x))
out$tau <- x
# if lambdaVec was supplied, then preditions were made using lambdaVec
if (!is.null(lambdaVec)) {
out$lambda <- lambdaVec
}
# otherwise predictions would have been made using the lambdas in the
# fitted glmnet object
else {
out$lambda <- glmnetFit$lambda
}
return(out)
} # calcClossOverTau
# Calculate the weighted sum of the loss and the sum of the weights
# (aggregated over the observations provided in 'newData') for
# each combination of tau and lambda
return(Smisc::list2df(lapply(tauVec, calcLossOverTau)))
} # predLoss_glmnetLRC
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