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custom_function.R
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custom_function.R
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wlogloss <- function(y, p, w = rep(1, length(y)) / length(y)) {
# return weighted log loss
# y: actual y
# p: prediction prob
# w: case weight
eps <- 1e-15
p <- pmax(pmin(p, 1 - eps), eps)
w <- w / sum(w)
out <- -sum(w * (y * log(p) + (1 - y) * log(1 - p)))
return(out)
}
order_level <- function(x, label) {
tab <- table(x, label)
cp <- tab / apply(tab, 1, sum)
ll <- row.names(tab[order(cp[, 2]), ])
return(ll)
}
to_factor <- function(x, label = ytrain) {
out <- factor(x, levels = order_level(x, label))
return(out)
}
find_continent <- function(x) {
if (x %in% c("Panama", "Guatemala", "Honduras", "Dominican-Republic", "El-Salvador", "Columbia", "Nicaragua", "Trinadad&Tobago", "Puerto-Rico", "Haiti", "Peru", "Ecuador", "Jamaica", "Cuba")) {
out <- "South.America"
} else if (x %in% c("Japan", "Iran", "Laos", "India", "Outlying-U S (Guam USVI etc)", "Vietnam", "Taiwan", "China", "Cambodia", "Thailand", "South Korea", "Philippines", "Hong Kong")) {
out <- "Asia"
} else if (x %in% c("Portugal", "Italy", "Yugoslavia", "Greece", "Poland", "Germany", "England", "Hungary", "Scotland", "France", "Ireland", "Holand-Netherlands")) {
out <- "Europe"
} else if (x %in% c("United-States", "Canada", "Mexico")) {
out <- "North.America"
} else {
out <- NA
}
return(out)
}
plot_y <- function(x1, x2, point_size = 0.2, x1lab = NULL, x2lab = NULL, label = ytrain) {
dd <- data.frame(x1, x2, label)
pp <-
ggplot(data = dd, aes(x = x1, y = x2, label = label, color = as.factor(label))) +
geom_point(size = point_size, position = "jitter") +
theme_bw() +
theme(
legend.position = "none",
axis.line = element_line(colour = "black"),
# panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
# panel.border = element_blank()
panel.background = element_blank()
) +
labs(y = x2lab, x = x1lab)
return(pp)
}
plot_prob <- function(x1, x2, point_size = 0.2, x1lab = NULL, x2lab = NULL, prob) {
dd <- data.frame(x1, x2, prob)
pp <-
ggplot(data = dd, aes(x = x1, y = x2, color = prob)) +
geom_point(size = point_size, position = "jitter") +
theme_bw() +
theme(
legend.position = "none",
axis.line = element_line(colour = "black"),
# panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
# panel.border = element_blank()
panel.background = element_blank()
) +
labs(y = x2lab, x = x1lab)
return(pp)
}
mylogit <- function(x) {
eps <- 10^(-100)
log(x + eps) / log(1 - x - eps)
}
caret_wlogloss <- function(data, pos = "cor", neg = "no_cor", cut = 0.5) {
ff <- data$Resample
obs <- 1 * (data$obs == pos)
prob <- data[[pos]]
pred <- 1 * (prob > cut)
if (is.null(data$weights)) {
ww <- rep(1, length(pred))
} else {
ww <- data$weights
}
return(wlogloss(y = obs, p = prob, w = ww))
}
caret_wf1 <- function(data, cut = 0.5) {
ff <- data$Resample
obs <- 1 * (data$obs == "above50k")
prob <- data$above50k
pred <- 1 * (prob > cut)
if (is.null(data$weights)) {
ww <- rep(1, length(pred))
} else {
ww <- data$weights
}
ww <- ww / sum(ww)
TP <- sum(1 * ww * (pred == 1 & obs == 1))
FP <- sum(1 * ww * (pred == 1 & obs == 0))
TN <- sum(1 * ww * (pred == 0 & obs == 0))
FN <- sum(1 * ww * (pred == 0 & obs == 1))
precision <- TP / (TP + FP)
recall <- TP / (TP + FN)
f1 <- 2 * precision * recall / (precision + recall)
return(list(precision = precision, recall = recall, f1 = f1))
}
ssbar <- function(varn, cc) {
out <- list()
tf <- wtd.table(cl, varn, weights = sweight) / gw
wc <- which(apply(tf, 2, sum) < cc)
Other <- tf[, wc]
Other <- apply(Other, 1, sum)
if (length(wc) > 0) {
tf <- tf[, -wc]
out$table <- cbind(tf, Other)
} else {
out$table <- tf
}
tf <- as.data.frame(tf)
of <- data.frame(Var1 = 1:K, Var2 = rep("Other", K), Freq = Other)
tf <- rbind(tf, of)
colnames(tf) <- c("Cluster", "Category", "Conditional.Frequency")
pp <- ggplot(tf, aes(Cluster, Conditional.Frequency, fill = Category)) +
geom_bar(position = "dodge", stat = "identity")
out$plot <- pp
return(out)
}
## row.names(vimp) should be variable names
## vimp should be p by 1 (matrix, array, or data.frame)
require(ggplot2)
plot_vimp <- function(vimp, cutoff = 1) {
tryCatch(
{
if (dim(vimp)[1] < dim(vimp)[2]) vimp <- t(vimp)
},
error = function(e) {
stop("vimp should be a p by 1 matrix, array, or data.frame.")
}
)
## Get the relative importance
mval <- max(vimp[, 1])
vimp[, 2] <- (vimp[, 1] / mval) * 100
cid <- vimp[, 2] > cutoff
rnimp <- vimp[, 2][cid]
imp.var <- row.names(vimp)[cid]
imp.dat <- data.frame(imp.var, rnimp)
## barplot of relative importance
gg <- ggplot(data = imp.dat, aes(x = reorder(imp.var, rnimp), y = rnimp, fill = TRUE)) +
ylab("Relative importance") +
xlab("Feature") +
geom_bar(stat = "identity", size = 3, width = 0.5) +
coord_flip() +
scale_fill_discrete(guide = FALSE) +
theme_bw() +
theme(
axis.line = element_line(colour = "black"),
text = element_text(size = 20),
axis.text = element_text(size = 15),
## panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
## panel.border = element_blank()
panel.background = element_blank()
)
return(gg)
}
## Define the predictive function
yhat <- function(X.model, newdata) {
pp <- as.numeric(predict(X.model, newdata)$predictions[, 2])
eps <- .Machine$double.eps
out <- log((pp + eps) / (1 - pp + eps))
return(out)
}