{caret}xgbTree model not running when weights included, runs fine without them - r

Question

I have a dataset off which I have no problem building an xgbTree model without weights, but once I include weights -- even if the weights are just all 1 -- the model doesn't converge. I get the
Something is wrong; all the RMSE metric values are missing: error and when I print the warnings, I get In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, ... :There were missing values in resampled performance measures. as the last message.
This is a drive link to the RData file containing the info -- it was too big to print, and smaller samples didn't always reproduce the error.
It contains 3 objects: input_x, input_y, and wts -- the last one is just a vector of 1s, but it should eventually it should be able to accept numbers on the interval (0,1), ideally. The code I used is shown below. Note the comment next to the weight argument that produces the error.
nrounds<-1000
tune_grid <- expand.grid(
nrounds = seq(from = 200, to = nrounds, by = 50),
eta = c(0.025, 0.05, 0.1, 0.3),
max_depth = c(2, 3, 4, 5),
gamma = 0,
colsample_bytree = 1,
min_child_weight = 1,
subsample = 1
)
tune_control <- caret::trainControl(
method = "cv",
number = 3,
verboseIter = FALSE,
allowParallel = TRUE
)
xgb_tune <- caret::train(
x = input_x,
y = input_y,
weights = wts, # If I remove this line, the code works fine. When included, even if just 1s, it throws an error.
trControl = tune_control,
tuneGrid = tune_grid,
method = "xgbTree",
verbose = TRUE
)

Answer 1

EDIT 13.10.2021. thanks to #waterpolo
The correct way to specify weights is via the weights argument to caret::train
xgb_tune <- caret::train(
x = input_x,
y = input_y,
weights = wts,
trControl = tune_control,
tuneGrid = tune_grid,
method = "xgbTree",
verbose = TRUE
)
see a more verbose answer here: Non-tree model error when using xgbTree method with Caret and weights to target variable when applying the varImp function
Old incorrect answer below:
According to the function source weights argument is called wts.
Line:
if (!is.null(wts))
xgboost::setinfo(x, 'weight', wts)
Running
xgb_tune <- caret::train(
x = input_x,
y = input_y,
wts = wts,
trControl = tune_control,
tuneGrid = tune_grid,
method = "xgbTree",
verbose = TRUE
)
should produce the desired result.

Answer 2

Just wanted to add #missuse response from another post (Non-tree model error when using xgbTree method with Caret and weights to target variable when applying the varImp function). The correct argument is weights .
Code:
xgb_tune <- caret::train(x = input_x,
y = input_y,
weights = wts,
trControl = tune_control,
tuneGrid = tune_grid,
method = "xgbTree",
verbose = TRUE
)
The other thing that I found was that I needed to use weights > 1 or I would receive the same error message as you. For example, if I used inverse weighting I would receive the same message as you. Hope this helps.
Thanks #missuse for the lovely response in the other thread!