Avoid failing when a factor has new levels in test set

Question

I have a dataset, which I am splitting into train and test subsets in the following way:

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

Then, I use it to train a glm:

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

And finally, I use it to predict on my test set:

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

Depending on the sample, this fails with error:

Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : factor has new levels

Note that the value appears on the full dataset, but apparently not on the training set. What I want to do is make the predict function ignore these new factors. Even if it has performed binarization for the factors, I don't see why it can assume that new values (thus, not variables in the linear model) are simply 0, that would yield the correct behaviour.

Is there a way to do this?

Answer 1

I start with the following data generating process (a binary response variable, one numerical independent variable and 3 categorical independent variables):

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)

Here I build the training and testing set in a way to have some categorical covariates (x2 and x3) in the testing set with more categories than in the training set:

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 

Of course, predict yields the message error that is described above by @Setzer22:

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

Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : factor x2 has new levels (0.6,0.8]

Here is a (not elegant) way to delete rows of test which have new levels in the covariates:

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,]

In the subset subset.test of the testing set x2 and x3 have no new categories:

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 

Now predict works nicely:

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

Hope this can help you.