error with mnLogloss for multinomial classifier using caret/gbm - r

Question

I am trying to perform a multinomial classifier. It seems to work and I am able to generate a plot with minimized logLoss vs boosting iterations, however I am having trouble extracting the error value. This is the error when I run the mnLogLoss function.
Error in mnLogLoss(predicted, lev = predicted$label) :
'data' should have columns consistent with 'lev'
data has been partitioned into.
-training
-testing
-in both, the column "label" contains the ground truth
library(MLmetrics)
fitControl <- trainControl(method = "repeatedcv", number=10, repeats=3, verboseIter = FALSE,
savePredictions = TRUE, classProbs = TRUE, summaryFunction= mnLogLoss)
gbmGrid1 <- expand.grid(.interaction.depth = (1:3), .n.trees = (1:10)*20, .shrinkage = 0.01, .n.minobsinnode = 3)
system.time(
gbmFit1 <- train(label~., data = training, method = "gbm", trControl=fitControl,
verbose = 1, metric = "logLoss", tuneGrid = gbmGrid1)
)
gbmPredictions <- predict(gbmFit1, testing)
predicted <- cbind(gbmPredictions, testing)
mnLogLoss(predicted, lev = levels(predicted$label))

Answer 1

For mnLogLoss, it says in the vignette:
data: a data frame with columns ‘obs’ and ‘pred’ for the observed
and predicted outcomes. For metrics that rely on class
probabilities, such as ‘twoClassSummary’, columns should also
include predicted probabilities for each class. See the
‘classProbs’ argument to ‘trainControl’.
So it's not asking for the training data. The data parameter here is just an input, so i use some simulated data:
library(caret)
df = data.frame(label=factor(sample(c("a","b"),100,replace=TRUE)),
matrix(runif(500),ncol=50))
training = df[1:50,]
testing = df[1:50,]
fitControl <- trainControl(method = "repeatedcv", number=10, repeats=3, verboseIter = FALSE,
savePredictions = TRUE, classProbs = TRUE, summaryFunction= mnLogLoss)
gbmGrid1 <- expand.grid(.interaction.depth = (1:3), .n.trees = (1:10)*20, .shrinkage = 0.01, .n.minobsinnode = 3)
gbmFit1 <- train(label~., data = training, method = "gbm", trControl=fitControl,verbose = 1, metric = "logLoss", tuneGrid = gbmGrid1)
)
And we put together obs, pred and the last two columns are probabilities of each class:
predicted <- data.frame(obs=testing$label,
pred=predict(gbmFit1, testing),
predict(gbmFit1, testing,type="prob"))
head(predicted)
obs pred a b
1 b a 0.5506054 0.4493946
2 b a 0.5107631 0.4892369
3 a b 0.4859799 0.5140201
4 b a 0.5090264 0.4909736
5 b b 0.4545746 0.5454254
6 a a 0.6211514 0.3788486
mnLogLoss(predicted, lev = levels(predicted$obs))
logLoss
0.6377392