[R] Fix CRAN test notes. (#8428)

- Limit the number of used CPU cores in examples.
- Add a note for the constraint.
- Bring back the cleanup script.
This commit is contained in:
Jiaming Yuan
2022-11-09 02:03:30 +08:00
committed by GitHub
parent 8e76f5f595
commit 0b36f8fba1
22 changed files with 81 additions and 49 deletions

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@@ -15,9 +15,11 @@ selected per iteration.}
}
\value{
Results are stored in the \code{coefs} element of the closure.
The \code{\link{xgb.gblinear.history}} convenience function provides an easy way to access it.
The \code{\link{xgb.gblinear.history}} convenience function provides an easy
way to access it.
With \code{xgb.train}, it is either a dense of a sparse matrix.
While with \code{xgb.cv}, it is a list (an element per each fold) of such matrices.
While with \code{xgb.cv}, it is a list (an element per each fold) of such
matrices.
}
\description{
Callback closure for collecting the model coefficients history of a gblinear booster
@@ -38,7 +40,7 @@ Callback function expects the following values to be set in its calling frame:
# without considering the 2nd order interactions:
x <- model.matrix(Species ~ .^2, iris)[,-1]
colnames(x)
dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"))
dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"), nthread = 2)
param <- list(booster = "gblinear", objective = "reg:logistic", eval_metric = "auc",
lambda = 0.0003, alpha = 0.0003, nthread = 2)
# For 'shotgun', which is a default linear updater, using high eta values may result in
@@ -63,14 +65,14 @@ matplot(xgb.gblinear.history(bst), type = 'l')
# For xgb.cv:
bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 100, eta = 0.8,
callbacks = list(cb.gblinear.history()))
callbacks = list(cb.gblinear.history()))
# coefficients in the CV fold #3
matplot(xgb.gblinear.history(bst)[[3]], type = 'l')
#### Multiclass classification:
#
dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1)
dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1, nthread = 2)
param <- list(booster = "gblinear", objective = "multi:softprob", num_class = 3,
lambda = 0.0003, alpha = 0.0003, nthread = 2)
# For the default linear updater 'shotgun' it sometimes is helpful