当因子在测试集中具有新级别时,避免失败

时间:2017-05-14 11:13:49

标签: r glm

我有一个数据集,我将按以下方式分为训练和测试子集:

train_ind <- sample(seq_len(nrow(dataset)), size=(2/3)*nrow(dataset))
train <- dataset[train_ind]
test <- dataset[-train_ind]

然后,我用它训练一个glm:

glm.res <- glm(response ~ ., data=dataset, subset=train_ind, family = binomial(link=logit)) 

最后,我用它来预测我的测试集:

preds <- predict(glm.res, test, type="response")

根据样本,此操作失败并显示错误:

  

model.frame.default中的错误(条款,newdata,na.action = na.action,   xlev = object $ xlevels):         因素有新的水平

请注意,该值显示在完整数据集中,但显然不在训练集上。我想做的是让预测函数忽略这些新因素。即使它已经对因子进行了二值化,我也不明白为什么它可以假设新值(因此,线性模型中的变量)只是0,这将产生正确的行为。

有办法做到这一点吗?

1 个答案:

答案 0 :(得分:1)

我从以下数据生成过程开始(二元响应变量,一个数值自变量和3个分类独立变量):

set.seed(1)
n <- 500
y <- factor(rbinom(n, size=1, p=0.7))
x1 <- rnorm(n)
x2 <- cut(runif(n), breaks=seq(0,1,0.2))
x3 <- cut(runif(n), breaks=seq(0,1,0.25))
x4 <- cut(runif(n), breaks=seq(0,1,0.1))
df <- data.frame(y, x1, x2, x3, x4)

在这里,我构建训练和测试集,以便在测试集中具有一些分类协变量(x2x3),其中的类别比训练集中的更多:

idx <- which(df$x2!="(0.6,0.8]" & df$x3!="(0,0.25]")
train_ind <- sample(idx, size=(2/3)*length(idx))
train <- df[train_ind,]
train$x2 <- droplevels(train$x2)
train$x3 <- droplevels(train$x3)
test <- df[-train_ind,]

table(train$x2)
(0,0.2] (0.2,0.4] (0.4,0.6]   (0.8,1] 
     55        40        53        49 

table(test$x2)
(0,0.2] (0.2,0.4] (0.4,0.6] (0.6,0.8]   (0.8,1] 
     58        48        45        90        62 

table(train$x3)
(0.25,0.5] (0.5,0.75]   (0.75,1] 
        66         61         70 

table(test$x3)
(0,0.25] (0.25,0.5] (0.5,0.75]   (0.75,1] 
     131         63         47         62 

当然,predict会产生@ Setzer22上面描述的消息错误:

glm.res <- glm(y ~ ., data=train, family = binomial(link=logit)) 
preds <- predict(glm.res, test, type="response")
  

model.frame.default中的错误(条款,newdata,na.action = na.action,   xlev = object $ xlevels):因子x2具有新的水平(0.6,0.8)

这是删除协变量中具有新级别的train行的一种(非优雅)方式:

dropcats <- function(k) {
   xtst <- test[,k]
   xtrn <- train[,k]
   cmp.tst.trn <- (unique(xtst) %in% unique(xtrn))
   if (is.factor(xtst) & any(!cmp.tst.trn)) {
      cat.tst <- unique(xtst)
      apply(test[,k]==matrix(rep(cat.tst[cmp.tst.trn],each=nrow(test)),
                      nrow=nrow(test)),1,any)
   } else {
      rep(TRUE,nrow(test))
   }
}   
filt <- apply(sapply(2:ncol(df),dropcats),1,all)
subset.test <- test[filt,]

在测试集subset.test的子集x2中,x3没有新的类别:

table(subset.test[,"x2"])
  (0,0.2] (0.2,0.4] (0.4,0.6] (0.6,0.8]   (0.8,1] 
       26        25        20         0        28

table(subset.test[,"x3"])
  (0,0.25] (0.25,0.5] (0.5,0.75]   (0.75,1] 
         0         29         29         41 

现在predict效果很好:

preds <- predict(glm.res, subset(test,filt), type="response")
head(preds)

       30        39        41        49        55        56 
0.7732564 0.8361226 0.7576259 0.5589563 0.8965357 0.8058025

希望这可以帮到你。