Nothing
PerformanceUplift <- function(data, treat, outcome, prediction, nb.group = 10,
equal.intervals = TRUE, rank.precision = 2){
# Computes the performance of an uplift estimator.
#
# Args:
# data: a data frame containing the treatment, the outcome and the predictors.
# treat: name of a binary (numeric) vector representing the treatment
# assignment (coded as 0/1).
# outcome: name of a binary response (numeric) vector (coded as 0/1).
# prediction: a predicted uplift to sorts the observations from highest
# to lowest uplift.
# ... and default parameters.
#
# Returns:
# Descriptive statistics for the performance of an uplift estimator.
# Error handling
if (nb.group < 2) {
stop("The number of groups must be greater or equal to 2")
}
if (rank.precision <= 0) {
stop("The rank.precision must be 1 or 2")
}
if (rank.precision > 2) {
stop("The rank.precision must be 1 or 2")
}
if (length(unique(data[[prediction]])) < nb.group) {
equal.intervals <- FALSE
warning("The number of unique predictions will be used as the number of groups")
}
# First, we need to rank and sort the observations
data$rank <- 0
data$rank <- round(rank(-data[[prediction]], ties.method = "max") / nrow(data), rank.precision)
if (equal.intervals == TRUE){
dataResults <- data.frame(matrix(rep(0), nb.group, 8))
colnames(dataResults) <- c("cum_per", "T_Y1", "T_n", "C_Y1",
"C_n", "incremental_Y1", "inc_uplift", "uplift")
data$group <- 0
for(i in 1:nb.group) {
data$group[data$rank > (i-1)/nb.group & data$rank <= i/nb.group] <- i
}
# Incremental observed uplift
for(i in 1:nb.group){
subset <- data[data$group <= i, ]
dataResults[i,1] <- i/nb.group
dataResults[i,2] <- sum(subset[[treat]] == 1 & subset[[outcome]] == 1)
dataResults[i,3] <- sum(subset[[treat]] == 1)
dataResults[i,4] <- sum(subset[[treat]] == 0 & subset[[outcome]] == 1)
dataResults[i,5] <- sum(subset[[treat]] == 0)
dataResults[i,6] <- dataResults[i, 2] - dataResults[i, 4]*dataResults[i, 3]/dataResults[i, 5]
}
dataResults[,7] <- dataResults[,6]/dataResults[nb.group,3]*100
# Observed uplift in each group
for (i in 1:nb.group){
subset <- data[data$group == i, ]
dataResults[i,8] <- sum(subset[[treat]] == 1 & subset[[outcome]] == 1) / sum(subset[[treat]] == 1) -
sum(subset[[treat]] == 0 & subset[[outcome]] == 1) / sum(subset[[treat]] == 0)
}
dataResults[,8] <- dataResults[,8]*100
}
if (equal.intervals == FALSE) {
no_of_groups <- sort(unique(data$rank))
no_of_groups <- no_of_groups[no_of_groups>0]
nb.group <- length(no_of_groups)
dataResults <- data.frame(matrix(rep(0), nb.group, 8))
colnames(dataResults) <- c("cum_per", "T_Y1", "T_n", "C_Y1",
"C_n", "incremental_Y1", "inc_uplift", "uplift")
# Incremental observed uplift
for (i in 1:nb.group){
subset <- data[data$rank <= no_of_groups[i], ]
dataResults[i,1] <- no_of_groups[i]
dataResults[i,2] <- sum(subset[[treat]] == 1 & subset[[outcome]] == 1)
dataResults[i,3] <- sum(subset[[treat]] == 1)
dataResults[i,4] <- sum(subset[[treat]] == 0 & subset[[outcome]] == 1)
dataResults[i,5] <- sum(subset[[treat]] == 0)
dataResults[i,6] <- dataResults[i, 2] - dataResults[i, 4]*dataResults[i, 3]/dataResults[i, 5]
}
dataResults[,7] <- dataResults[,6]/dataResults[nb.group,3]*100
# Observed uplift in each group
for (i in 1:nb.group){
if (i == 1) {
subset <- data[data$rank <= no_of_groups[i], ]
}
else {
subset <- data[no_of_groups[i-1] < data$rank & data$rank <= no_of_groups[i], ]
}
dataResults[i,8] <- sum(subset[[treat]] == 1 & subset[[outcome]] == 1) / sum(subset[[treat]] == 1) -
sum(subset[[treat]] == 0 & subset[[outcome]] == 1) / sum(subset[[treat]] == 0)
}
dataResults[,8] <- dataResults[,8]*100
}
class(dataResults) <- "PerformanceUplift"
return(dataResults)
}
# END FUN
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