尝试使用Caret运行XGBoost时出错

时间:2017-05-03 19:40:57

标签: r r-caret xgboost

当我尝试使用Caret

运行XGBoost时出现此错误

train.default中的错误(x = x,y = y,trControl = xgb_trcontrol,tuneGrid = xgb_grid,:   至少有一个类级别不是有效的R变量名称;这会在生成类概率时导致错误,因为变量名称将转换为X0,X1。请使用可用作有效R变量名称的因子级别(请参阅?make.names以获取帮助)。

这是数据的结构

str(datafinal)
'data.frame':   747467 obs. of  11 variables:
 $ CustSegment : Factor w/ 5 levels "Corporate","Legal",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ SubSegment1 : Factor w/ 23 levels "Academic","Association.foundation.Trust",..: 21 21 22 22 22 22 22 22 22 22 ...
 $ BrandCode   : Factor w/ 2 levels "PPC","QF": 1 1 1 1 1 1 1 1 1 1 ...
 $ Qty         : int  1 1 1 0 -1 -1 1 -1 -1 1 ...
 $ Price       : num  0 285 395 415 180 ...
 $ Discount    : num  0 0 20 20 0 0 0 0 0 20 ...
 $ ProductGroup: Factor w/ 228 levels "AAL","AAT","AAU",..: 20 20 11 11 11 11 11 11 11 20 ...
 $ Year        : int  2016 2016 2014 2014 2016 2016 2016 2017 2016 2014 ...
 $ Month       : int  8 8 4 11 4 4 6 1 12 5 ...
 $ Top1Percent : Factor w/ 2 levels "No","Yes": 1 1 2 2 2 2 2 2 2 2 ...
 $ estore      : int  0 0 0 0 0 0 0 0 0 0 ...

这是代码

# import data
library(caret)
library(readr)
datafinal <- read_csv("~/Document_Repository/estore_Cleaned_Data.csv")

# convert characters to factors and ensure syntactically valid names out of character vectors 

datafinal$CustSegment <- make.names(datafinal$CustSegment)
datafinal$SubSegment1 <- make.names(datafinal$SubSegment1)
datafinal$BrandCode <- make.names(datafinal$BrandCode)
datafinal$ProductGroup <- make.names(datafinal$ProductGroup)
datafinal$Top1Percent <- make.names(datafinal$Top1Percent)

datafinal$CustSegment <- as.factor(datafinal$CustSegment)
datafinal$SubSegment1 <- as.factor(datafinal$SubSegment1)
datafinal$BrandCode <- as.factor(datafinal$BrandCode)
datafinal$ProductGroup <- as.factor(datafinal$ProductGroup)
datafinal$Top1Percent <- as.factor(datafinal$Top1Percent)


colnames(datafinal) <- make.names(colnames(datafinal))

# run xgboost analysis
library(xgboost)

set.seed(123)
train= sample(c(TRUE,TRUE,TRUE,FALSE), nrow(datafinal),rep=TRUE)
test = (!train)
datafinal.train <- datafinal[train,]
datafinal.test <- datafinal[test,]

# set up cross validated hyper parameter search

xgb_grid <- expand.grid(
    nrounds = 100,
    eta = c(0.3,0.1,0.01),
    max_depth = c(1,2,4,8),
    gamma = c(0,1)
)

xgb_trcontrol <- trainControl(
    method = "cv",
    number = 5,
    verboseIter = T,
    returnData = F,
    returnResamp = "all",
    classProbs = T,
    summaryFunction = twoClassSummary,
    allowParallel = T)

# train the model   
library(plyr)
library(dplyr)

x = as.matrix(datafinal.train %>% 
                        select (-estore))

y = as.factor(datafinal.train$estore)                       

xgb_train <- train(
    x = x,
    y = y,
    trControl = xgb_trcontrol,
    tuneGrid = xgb_grid,
    method = "xgbTree"
)

Error in train.default(x = x, y = y, trControl = xgb_trcontrol, tuneGrid = xgb_grid,  : 
  At least one of the class levels is not a valid R variable name; This will cause errors when class probabilities are generated because the variables names will be converted to  X0, X1 . Please use factor levels that can be used as valid R variable names  (see ?make.names for help).

我知道ProductGroup有228个级别,因此我尝试使用以下代码对该列进行一次编码,并在结果列上使用make.names()。但没有运气和同样的错误。

simpleMod <- dummyVars(~ProductGroup, data = datafinal, levelsOnly = T)
ProductGroup <- as.data.frame(predict(simpleMod, datafinal))

datafinal <- cbind(datafinal,ProductGroup)
datafinal$ProductGroup <- NULL

我甚至尝试过对所有列进行热编码,但仍然没有香槟。

这是新的数据结构

str(datafinal, list.len = 270)
'data.frame':   747467 obs. of  264 variables:
 $ BrandCode                   : num  1 1 1 1 1 1 1 1 1 1 ...
 $ Qty                         : int  1 1 1 0 -1 -1 1 -1 -1 1 ...
 $ Price                       : num  0 285 395 415 180 ...
 $ Discount                    : num  0 0 20 20 0 0 0 0 0 20 ...
 $ Year                        : int  2016 2016 2014 2014 2016 2016 2016 2017 2016 2014 ...
 $ Month                       : int  8 8 4 11 4 4 6 1 12 5 ...
 $ Top1Percent                 : num  0 0 1 1 1 1 1 1 1 1 ...
 $ estore                      : int  0 0 0 0 0 0 0 0 0 0 ...
 $ AAL                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AAT                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AAU                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AET                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AFI                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AFT                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ AIT                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ ALG                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ ANN                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ ARE                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ ASB                         : num  0 0 1 1 1 1 1 1 1 0 ...
 $ BCL                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ BKR                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ BSL                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ BTB                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ BTT                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ BVC                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ BVT                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ CAB                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ CAR                         : num  1 1 0 0 0 0 0 0 0 1 ...
 $ CAS                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ CAT                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ CBL                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ CCT                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ CD_                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ CON                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ CRB                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ CRT                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DBP                         : num  0 0 0 0 0 0 0 0 0 0 ...
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 $ RPV                         : num  0 0 0 0 0 0 0 0 0 0 ...
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 $ WCA                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ WHW                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ WML                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ WNA                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ WNR                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ WSP                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ WUS                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ X706                        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ X990                        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XBS                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XCA                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XCE                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XCG                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XCP                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XCR                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XDT                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XFL                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XIR                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XLI                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XSE                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XTR                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XTT                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ XTX                         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Academic                    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Association.foundation.Trust: num  0 0 0 0 0 0 0 0 0 0 ...
 $ Church                      : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Federal..Government         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Local.Government            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Miscellaneous               : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Non.Profit.Organization     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Other                       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Reserved                    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Revenue..1B                 : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Revenue..500M..749          : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Revenue..750M.999M          : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Revenue.less.than..500M     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ State.Government            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Unknown                     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ X0.20.Attorneys             : num  0 0 0 0 0 0 0 0 0 0 ...
 $ X0.3.employee               : num  0 0 0 0 0 0 0 0 0 0 ...
 $ X16.30.employees            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ X21.79.Attorneys            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ X31..employees              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ X4.7.employees              : num  1 1 0 0 0 0 0 0 0 0 ...
 $ X8.15.employees             : num  0 0 1 1 1 1 1 1 1 1 ...
 $ X80..Attorneys              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Corporate                   : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Legal                       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Other CustSegment           : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Professional                : num  1 1 1 1 1 1 1 1 1 1 ...
 $ PublicSector                : num  0 0 0 0 0 0 0 0 0 0 ...

0 个答案:

没有答案