我正在尝试创建一个自定义GBM模型，用于调整二进制分类问题的分类阈值。在插入符号网站here上提供了一个很好的示例，但是当我尝试应用类似于GBM的内容时，我收到以下错误：
Error in { : task 1 failed - "argument 1 is not a vector"
不幸的是，我不知道错误在哪里，错误也没有用。
以下是一个示例，其中包含我用于定义自定义GBM的代码
library(caret)
library(gbm)
library(pROC)
#### DEFINE A CUSTOM GBM MODEL FOR PROBABILITY THRESHOLD TUNING ####
## Get the model code for the original gbm method from caret
customGBM <- getModelInfo("gbm", regex = FALSE)[[1]]
customGBM$type <- c("Classification")
## Add the threshold (i.e. class cutoff) as another tuning parameter
customGBM$parameters <- data.frame(parameter = c("n.trees", "interaction.depth", "shrinkage",
"n.minobsinnode", "threshold"),
class = rep("numeric", 5),
label = c("# Boosting Iterations", "Max Tree Depth", "Shrinkage",
"Min. Terminal Node Size", "Probability Cutoff"))
## Customise the tuning grid:
## Some paramters are fixed. Will give a tuning grid of 2,500 values if len = 100
customGBM$grid <- function(x, y, len = NULL, search = "grid") {
if (search == "grid") {
grid <- expand.grid(n.trees = seq(50, 250, 50),
interaction.depth = 2, ### fix interaction depth at 2
shrinkage = 0.0001, ### fix learning rate at 0.0001
n.minobsinnode = seq(2, 10, 2),
threshold = seq(.01, .99, length = len))
} else {
grid <- expand.grid(n.trees = floor(runif(len, min = 1, max = 5000)),
interaction.depth = sample(1:10, replace = TRUE, size = len),
shrinkage = runif(len, min = .001, max = .6),
n.minobsinnode = sample(5:25, replace = TRUE, size = len),
threshold = runif(1, 0, size = len))
grid <- grid[!duplicated(grid),] ### remove any duplicated rows in the training grid
}
grid
}
## Here we fit a single gbm model and loop over the threshold values to get predictions from the
## same gbm model.
customGBM$loop = function(grid) {
library(plyr)
loop <- ddply(grid, c("n.trees", "shrinkage", "interaction.depth", "n.minobsinnode"),
function(x) c(threshold = max(x$threshold)))
submodels <- vector(mode = "list", length = nrow(loop))
for (i in seq(along = loop$threshold)) {
index <- which(grid$n.trees == loop$n.trees[i] &
grid$interaction.depth == loop$interaction.depth[i] &
grid$shrinkage == loop$shrinkage[i] &
grid$n.minobsinnode == loop$n.minobsinnode[i])
cuts <- grid[index, "threshold"]
submodels[[i]] <- data.frame(threshold = cuts[cuts != loop$threshold[i]])
}
list(loop = loop, submodels = submodels)
}
## Fit the model independent of the threshold parameter
customGBM$fit = function(x, y, wts, param, lev, last, classProbs, ...) {
theDots <- list(...)
if (any(names(theDots) == "distribution")) {
modDist <- theDots$distribution
theDots$distribution <- NULL
} else {
if (is.numeric(y)) {
stop("This works only for 2-class classification problems")
} else modDist <- if (length(lev) == 2) "bernoulli" else
stop("This works only for 2-class classification problems")
}
# if (length(levels(y)) != 2)
# stop("This works only for 2-class problems")
## check to see if weights were passed in (and availible)
if (!is.null(wts)) theDots$w <- wts
if (is.factor(y) && length(lev) == 2) y <- ifelse(y == lev[1], 1, 0)
modArgs <- list(x = x,
y = y,
interaction.depth = param$interaction.depth,
n.trees = param$n.trees,
shrinkage = param$shrinkage,
n.minobsinnode = param$n.minobsinnode,
distribution = modDist)
do.call("gbm.fit", modArgs)
}
## Now get a probability prediction and use different thresholds to
## get the predicted class
customGBM$predict = function(modelFit, newdata, submodels = NULL) {
out <- predict(modelFit, newdata, n.trees = modelFit$tuneValue$n.trees,
type = "response")#[, modelFit$obsLevels[1]]
out[is.nan(out)] <- NA
class1Prob <- ifelse(out >= modelFit$tuneValue$threshold,
modelFit$obsLevels[1],
modelFit$obsLevels[2])
## Raise the threshold for class #1 and a higher level of
## evidence is needed to call it class 1 so it should
## decrease sensitivity and increase specificity
out <- ifelse(class1Prob >= modelFit$tuneValue$threshold,
modelFit$obsLevels[1],
modelFit$obsLevels[2])
if (!is.null(submodels)) {
tmp2 <- out
out <- vector(mode = "list", length = length(submodels$threshold))
out[[1]] <- tmp2
for (i in seq(along = submodels$threshold)) {
out[[i + 1]] <- ifelse(class1Prob >= submodels$threshold[[i]],
modelFit$obsLevels[1],
modelFit$obsLevels[2])
}
}
out
}
## The probabilities are always the same but we have to create
## mulitple versions of the probs to evaluate the data across
## thresholds
customGBM$prob = function(modelFit, newdata, submodels = NULL) {
out <- predict(modelFit, newdata, type = "response",
n.trees = modelFit$tuneValue$n.trees)
out[is.nan(out)] <- NA
out <- cbind(out, 1 - out)
colnames(out) <- modelFit$obsLevels
if (!is.null(submodels)) {
tmp <- predict(modelFit, newdata, type = "response", n.trees = submodels$n.trees)
tmp <- as.list(as.data.frame(tmp))
lapply(tmp, function(x, lvl) {
x <- cbind(x, 1 - x)
colnames(x) <- lvl
x}, lvl = modelFit$obsLevels)
out <- c(list(out), tmp)
}
out
}
fourStats <- function (data, lev = levels(data$obs), model = NULL) {
## This code will get use the area under the ROC curve and the
## sensitivity and specificity values using the current candidate
## value of the probability threshold.
out <- c(twoClassSummary(data, lev = levels(data$obs), model = NULL))
## The best possible model has sensitivity of 1 and specificity of 1.
## How far are we from that value?
coords <- matrix(c(1, 1, out["Spec"], out["Sens"]),
ncol = 2,
byrow = TRUE)
colnames(coords) <- c("Spec", "Sens")
rownames(coords) <- c("Best", "Current")
c(out, Dist = dist(coords)[1])
}
然后是一些显示如何使用自定义模型的代码
set.seed(949)
trainingSet <- twoClassSim(500, -9)
mod1 <- train(Class ~ ., data = trainingSet,
method = customGBM, metric = "Dist",
maximize = FALSE, tuneLength = 10,
trControl = trainControl(method = "cv", number = 5,
classProbs = TRUE,
summaryFunction = fourStats))
模型似乎正在运行，但是从上面的错误结束。如果有人可以帮我定制GBM模型来调整GBM参数，以及那些很棒的类的概率阈值。