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Copy pathfun_create_infotable.R
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217 lines (217 loc) · 9.24 KB
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create_infotables <- function (data = NULL, valid = NULL, y = NULL, bins = 10, trt = NULL,
ncore = NULL, parallel = TRUE)
{
combine <- function(x, ...) {
lapply(seq_along(x), function(i) c(x[[i]], lapply(list(...),
function(y) y[[i]])))
}
if (is.null(data)) {
stop("Error: No dataset provided")
}
crossval <- TRUE
if (is.null(valid)) {
crossval <- FALSE
}
c <- CheckInputs(data, valid, trt, y)
data <- c[[1]]
if (crossval == TRUE) {
valid <- c[[2]]
}
variables <- names(data)[!(names(data) %in% c(trt, y))]
d_netlift <- 0
if (is.null(trt) == FALSE) {
d_netlift <- 1
}
i <- NULL
if (length(variables) == 0) {
stop("ERROR: no variables left after screening")
}
if (parallel == TRUE) {
if (is.null(ncore)) {
ncore <- detectCores() - 1
}
if (ncore < 1)
ncore <- 1
registerDoParallel(ncore)
loopResult <- foreach(i = 1:length(variables), .combine = "combine",
.multicombine = TRUE, .init = list(list(), list())) %dopar%
{
data$var <- data[[variables[i]]]
cuts <- NULL
if (crossval == TRUE) {
valid$var <- valid[[variables[i]]]
if (is.character(data[[variables[i]]]) != is.character(valid[[variables[i]]])) {
stop(paste0("ERROR: variable type mismatch for ",
variables[i], " across validation and training (not a character in both dataframes)"))
}
if (is.factor(data[[variables[i]]]) != is.factor(valid[[variables[i]]])) {
stop(paste0("ERROR: variable type mismatchfor ",
variables[i], " across validation and training dataframes (not a factor in both dataframes)"))
}
}
if (is.character(data[[variables[i]]]) == FALSE &
is.factor(data[[variables[i]]]) == FALSE) {
q <- quantile(data[[variables[i]]], probs = c(1:(bins -
1)/bins), na.rm = TRUE, type = 3)
cuts <- unique(q)
}
summary_train <- Aggregate(data, variables[i],
y, cuts, trt)
if (crossval == TRUE) {
summary_valid <- Aggregate(valid, variables[i],
y, cuts, trt)
}
if (d_netlift == 0) {
woe_train <- WOE(summary_train, variables[i])
if (crossval == TRUE) {
woe_valid <- WOE(summary_valid, variables[i])
}
if (crossval == TRUE) {
woe_train <- penalty(woe_train, woe_valid,
0)
}
woe_train[, c("IV_weight")] <- NULL
strength <- data.frame(variables[i], woe_train[nrow(woe_train),
"IV"])
names(strength) <- c("Variable", "IV")
if (crossval == TRUE) {
strength$PENALTY <- woe_train[nrow(woe_train),
"PENALTY"]
strength$AdjIV <- strength$IV - strength$PENALTY
}
}
else {
nwoe_train <- NWOE(summary_train, variables[i])
if (crossval == TRUE) {
nwoe_valid <- NWOE(summary_valid, variables[i])
}
if (crossval == TRUE) {
nwoe_train <- penalty(nwoe_train, nwoe_valid,
1)
}
nwoe_train[, c("NIV_weight")] <- NULL
strength <- data.frame(variables[i], nwoe_train[nrow(nwoe_train),
"NIV"])
names(strength) <- c("Variable", "NIV")
if (crossval == TRUE) {
strength$PENALTY <- nwoe_train[nrow(nwoe_train),
"PENALTY"]
strength$AdjNIV <- strength$NIV - strength$PENALTY
}
}
if (d_netlift == 1) {
nwoe_train$key <- NULL
list(nwoe_train, strength)
}
else {
woe_train$key <- NULL
list(woe_train, strength)
}
}
tables <- loopResult[[1]]
stats <- data.frame(rbindlist(loopResult[[2]]))
stopImplicitCluster()
rm(loopResult)
gc()
}
else {
statslist <- list(length = length(variables))
tables <- list(length = length(variables))
for (i in 1:length(variables)) {
data$var <- data[[variables[i]]]
cuts <- NULL
if (crossval == TRUE) {
valid$var <- valid[[variables[i]]]
if (is.character(data[[variables[i]]]) != is.character(valid[[variables[i]]])) {
stop(paste0("ERROR: variable type mismatch for ",
variables[i], " across validation and training (not a character in both dataframes)"))
}
if (is.factor(data[[variables[i]]]) != is.factor(valid[[variables[i]]])) {
stop(paste0("ERROR: variable type mismatchfor ",
variables[i], " across validation and training dataframes (not a factor in both dataframes)"))
}
}
if (is.character(data[[variables[i]]]) == FALSE &
is.factor(data[[variables[i]]]) == FALSE) {
q <- quantile(data[[variables[i]]], probs = c(1:(bins -
1)/bins), na.rm = TRUE, type = 3)
cuts <- unique(q)
}
summary_train <- Aggregate(data, variables[i], y,
cuts, trt)
if (crossval == TRUE) {
summary_valid <- Aggregate(valid, variables[i],
y, cuts, trt)
}
if (d_netlift == 0) {
woe_train <- WOE(summary_train, variables[i])
if (crossval == TRUE) {
woe_valid <- WOE(summary_valid, variables[i])
}
if (crossval == TRUE) {
woe_train <- penalty(woe_train, woe_valid,
0)
}
woe_train[, c("IV_weight")] <- NULL
strength <- data.frame(variables[i], woe_train[nrow(woe_train),
"IV"])
names(strength) <- c("Variable", "IV")
if (crossval == TRUE) {
strength$PENALTY <- woe_train[nrow(woe_train),
"PENALTY"]
strength$AdjIV <- strength$IV - strength$PENALTY
}
}
else {
nwoe_train <- NWOE(summary_train, variables[i])
if (crossval == TRUE) {
nwoe_valid <- NWOE(summary_valid, variables[i])
}
if (crossval == TRUE) {
nwoe_train <- penalty(nwoe_train, nwoe_valid,
1)
}
nwoe_train[, c("NIV_weight")] <- NULL
strength <- data.frame(variables[i], nwoe_train[nrow(nwoe_train),
"NIV"])
names(strength) <- c("Variable", "NIV")
if (crossval == TRUE) {
strength$PENALTY <- nwoe_train[nrow(nwoe_train),
"PENALTY"]
strength$AdjNIV <- strength$NIV - strength$PENALTY
}
}
statslist[[i]] <- strength
if (d_netlift == 1) {
nwoe_train$key <- NULL
tables[[i]] <- nwoe_train
}
else {
woe_train$key <- NULL
tables[[i]] <- woe_train
}
}
stats <- data.frame(rbindlist(statslist))
}
if (crossval == TRUE) {
if (d_netlift == 0) {
stats <- stats[order(-stats$AdjIV), ]
}
else {
stats <- stats[order(-stats$AdjNIV), ]
}
}
else {
if (d_netlift == 0) {
stats <- stats[order(-stats$IV), ]
}
else {
stats <- stats[order(-stats$NIV), ]
}
}
stats$Variable <- as.character(stats$Variable)
names(tables) <- variables
object <- list(Tables = tables, Summary = stats)
class(object) <- "Information"
return(object)
}