e1071 SVM: Error trying to predict - r

Question

I keep receiving this error and I cannot figure out why.
Error in scale.default(newdata[, object$scaled, drop = FALSE], center
= object$x.scale$"scaled:center", : length of 'center' must equal the number of columns of 'x'
I'm using the default iris dataset, and here is all of my code. It's an attempt at implementing a multiclass SVM using the pairwise method.
# pass in the dataframe & the number of classes
multiclass.svm <- function(data) {
class.vec = data[,length(data)]
levels = levels(class.vec)
pair1 <- data[which(class.vec == levels[1]),]
pair1 <- droplevels(pair1)
pair2 <- data[which(class.vec == levels[length(levels)]),]
pair2 <- droplevels(pair2)
pairs = list(rbind(pair1, pair2))
# print(pairs)
for(i in 2:length(levels)){
L1 <- data[which(class.vec == levels[i-1]),]
L1 <- droplevels(L1)
L2 <- data[which(class.vec == levels[i]),]
L2 <- droplevels(L2)
pair <- list(rbind(L1, L2))
pairs <- c(pairs, pair)
}
# now we construct our (n choose 2) binary models
models = list()
for(pair in pairs){
classifier = pair[,length(pair)]
p.svm = svm(formula=classifier~., data=pair)
models = c(models, list(p.svm))
}
for(model in models){
test = iris[1,]
print(predict(model, test))
}
return(models)
}
Testing/usage:
> h = multiclass.svm(iris)
Show Traceback
Rerun with Debug
Error in scale.default(newdata[, object$scaled, drop = FALSE], center = object$x.scale$"scaled:center", :
length of 'center' must equal the number of columns of 'x'
>
Any help would be very much appreciated... I've found a few other questions on this very topic to no avail. Thank you.

Answer 1

Okay, so the answer is unfortunately quite tricky (depending on your dataset.) The problem is that in the iris dataset, there are THREE levels of classification. Since I'm breaking the classes into pairs, each of my models only have TWO levels of classification.
When using predict the model you've trained on AND the value you're testing must both have the same levels. So, the tricky part (at least in this case) is deleting the unnecessary levels from each pair.
I recommend using the library plyr for its revalue function. To remove specific levels (instead of all unused a la the drop levels function) you can use revalue and rename each unwanted level to an existing one (essentially destroying it.)
Credit to this polish blogger for steering me in the right direction:
http://ppiotrow.blogspot.com/2013/04/solved-r-svm-test-data-does-not-match.html
The quick and easy way, though, to solve my specific problem was simply removing all of the droplevels calls haha. Since the SVM won't find any points to draw upon the unused level, there is no actual problem with leaving the extraneous level in.
Hope this helps someone out there.
Mike

Answer 2

I have experienced the same issue. I fixed the error by converting all of the predictors in the test set to their correct class, i.e., as.factor, as.numeric.
For example, if a numeric predictor variable in the training set is in memory as a character variable in your test set you will get this error. I hope this helps.