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To submit to CRAN we cannot use more than 2 threads in our examples/vignettes

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hetong
2015-03-03 00:21:24 -08:00
parent 87ec48c1d3
commit 41b080e35f
36 changed files with 61 additions and 59 deletions
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2
R-package/man/predict-xgb.Booster-method.Rd
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@@ -37,7 +37,7 @@ data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
pred <- predict(bst, test$data)
}

2
R-package/man/xgb.cv.Rd
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@@ -78,7 +78,7 @@ This function only accepts an \code{xgb.DMatrix} object as the input.
\examples{
data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
history <- xgb.cv(data = dtrain, nround=3, nthread = 2, nfold = 5, metrics=list("rmse","auc"),
"max.depth"=3, "eta"=1, "objective"="binary:logistic")
print(history)
}

2
R-package/man/xgb.dump.Rd
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@@ -35,7 +35,7 @@ data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
# save the model in file 'xgb.model.dump'
xgb.dump(bst, 'xgb.model.dump', with.stats = TRUE)

2
R-package/man/xgb.importance.Rd
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@@ -59,7 +59,7 @@ data(agaricus.train, package='xgboost')
train <- agaricus.train
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
# train$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.importance(train$data@Dimnames[[2]], model = bst)

2
R-package/man/xgb.load.Rd
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@@ -18,7 +18,7 @@ data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
xgb.save(bst, 'xgb.model')
bst <- xgb.load('xgb.model')
pred <- predict(bst, test$data)

2
R-package/man/xgb.model.dt.tree.Rd
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@@ -51,7 +51,7 @@ data(agaricus.train, package='xgboost')
train <- agaricus.train
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.model.dt.tree(agaricus.train$data@Dimnames[[2]], model = bst)

2
R-package/man/xgb.plot.importance.Rd
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@@ -31,7 +31,7 @@ data(agaricus.train, package='xgboost')
train <- agaricus.train
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#train$data@Dimnames[[2]] represents the column names of the sparse matrix.
importance_matrix <- xgb.importance(train$data@Dimnames[[2]], model = bst)

2
R-package/man/xgb.plot.tree.Rd
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@@ -50,7 +50,7 @@ data(agaricus.train, package='xgboost')
train <- agaricus.train
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.plot.tree(agaricus.train$data@Dimnames[[2]], model = bst)

2
R-package/man/xgb.save.Rd
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@@ -20,7 +20,7 @@ data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
xgb.save(bst, 'xgb.model')
bst <- xgb.load('xgb.model')
pred <- predict(bst, test$data)

2
R-package/man/xgb.save.raw.Rd
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@@ -19,7 +19,7 @@ data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
raw <- xgb.save.raw(bst)
bst <- xgb.load(raw)
pred <- predict(bst, test$data)

2
R-package/man/xgb.train.Rd
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@@ -121,6 +121,6 @@ evalerror <- function(preds, dtrain) {
err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
return(list(metric = "error", value = err))
}
bst <- xgb.train(param, dtrain, nround = 2, watchlist, logregobj, evalerror)
bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist, logregobj, evalerror)
}

2
R-package/man/xgboost.Rd
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@@ -59,7 +59,7 @@ data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
pred <- predict(bst, test$data)
}