R Caret的rfe [{错误:任务1失败-"rfe期望184个重要值，但只有2个"] [英] R Caret's rfe [Error in { : task 1 failed - "rfe is expecting 184 importance values but only has 2"]

问题描述

我正在使用Caret的rfe进行回归应用.我的数据(在data.table中)具有176个预测变量(包括49个因子预测变量).运行该函数时，出现此错误:

I am using Caret's rfe for a regression application. My data (in data.table) has 176 predictors (including 49 factor predictors). When I run the function, I get this error:

Error in { :  task 1 failed - "rfe is expecting 176 importance values but only has 2"

然后，我使用model.matrix( ~ . - 1, data = as.data.frame(train_model_sell_single_bid))将因子预测变量转换为虚拟变量.但是，我遇到了类似的错误:

Then, I used model.matrix( ~ . - 1, data = as.data.frame(train_model_sell_single_bid)) to convert the factor predictors to dummy variables. However, I got similar error:

Error in { :  task 1 failed - "rfe is expecting 184 importance values but only has 2"

我在Windows 7(64位)上使用R版本3.1.1，在插入符号版本6.0-41中使用.我还安装了Revolution R Enterprise 7.3版(64位). 但是在具有R版本3.0.1和Caret版本6.0-24的Amazon EC2(c3.8xlarge)Linux实例上也重现了相同的错误.

I'm using R version 3.1.1 on Windows 7 (64-bit), Caret version 6.0-41. I also have Revolution R Enterprise version 7.3 (64-bit) installed. But the same error was reproduced on Amazon EC2 (c3.8xlarge) Linux instance with R version 3.0.1 and Caret version 6.0-24.

使用的数据集(重现我的错误):

Datasets used (to reproduce my error):

https://www.dropbox.com/s/utuk9bpxl2996dy/train_model_sell_single_bid.RData?dl = 0 https://www.dropbox.com/s/s9xcgfit3iqjffp/train_model_bid_outcomes_sell_s ?dl = 0

我的代码:

library(caret)
library(data.table)
library(bit64)
library(doMC)

load("train_model_sell_single_bid.RData")
load("train_model_bid_outcomes_sell_single.RData")

subsets <- seq(from = 4, to = 184, by= 4)

registerDoMC(cores = 32)

set.seed(1015498)
ctrl <- rfeControl(functions = lmFuncs,
                   method = "repeatedcv",
                   repeats = 1,
                   #saveDetails = TRUE,
                   verbose = FALSE)

x <- as.data.frame(train_model_sell_single_bid[,!"security_id", with=FALSE])
y <- train_model_bid_outcomes_sell_single[,bid100]

lmProfile_single_bid100 <- rfe(x, y,
                               sizes = subsets,
                               preProc = c("center", "scale"),
                               rfeControl = ctrl)

推荐答案

似乎您可能具有高度相关的预测变量.
在选择功能之前，您应该运行:

It seems that you might have highly correlated predictors.
Prior to feature selection you should run:

crrltn = findCorrelation(correlations, cutoff = .90)
if (length(crrltn) != 0)
  x <- x[,-crrltn]

如果在此之后问题仍然存在，则可能与自动生成的折叠内的预测变量的高度相关性有关，您可以尝试使用以下方法控制生成的折叠:

If after this the problem persists, it might be related to high correlation of the predictors within folds automatically generated, you can try to control the generated folds with:

set.seed(12213)
index <- createFolds(y, k = 10, returnTrain = T)

，然后将它们作为rfeControl函数的参数:

and then give these as arguments to the rfeControl function:

lmctrl <- rfeControl(functions = lmFuncs, 
                     method = "repeatedcv", 
                     index = index,
                     verbose = TRUE)

set.seed(111333)
lrprofile <- rfe( z , x,
                  sizes = sizes,
                  rfeControl = lmctrl)

如果您仍然遇到相同的问题，请检查各折中的预测变量之间是否存在高度相关性:

If you keep having the same problem, check if there are highly correlated between predictors within each fold:

for(i in 1:length(index)){
  crrltn = cor(x[index[[i]],])     
  findCorrelation(crrltn, cutoff = .90, names = T, verbose = T)
}

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