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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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4
Makefile
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@ -94,8 +94,8 @@ Rpack:
cp xgboost/src/Makevars xgboost/src/Makevars.win
# R CMD build --no-build-vignettes xgboost
R CMD build xgboost
rm -rf xgboost
R CMD check --as-cran xgboost*.tar.gz
#rm -rf xgboost
#R CMD check --as-cran xgboost*.tar.gz
clean:
$(RM) -rf $(OBJ) $(BIN) $(MPIBIN) $(MPIOBJ) $(SLIB) *.o */*.o */*/*.o *~ */*~ */*/*~

2
R-package/NAMESPACE
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@ -24,6 +24,7 @@ importFrom(Ckmeans.1d.dp,Ckmeans.1d.dp)
importFrom(DiagrammeR,mermaid)
importFrom(Matrix,cBind)
importFrom(Matrix,colSums)
importFrom(Matrix,sparseVector)
importFrom(data.table,":=")
importFrom(data.table,as.data.table)
importFrom(data.table,copy)
@ -51,4 +52,3 @@ importFrom(stringr,str_match)
importFrom(stringr,str_replace)
importFrom(stringr,str_split)
importFrom(stringr,str_trim)
import(vcd)

2
R-package/R/predict.xgb.Booster.R
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@ -26,7 +26,7 @@ setClass("xgb.Booster",
#' 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)
#' @export
#'

2
R-package/R/xgb.cv.R
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@ -66,7 +66,7 @@
#' @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)
#' @export

2
R-package/R/xgb.dump.R
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@ -29,7 +29,7 @@
#' 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/R/xgb.importance.R
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@ -57,7 +57,7 @@
#' 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/R/xgb.load.R
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@ -10,7 +10,7 @@
#' 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/R/xgb.model.dt.tree.R
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@ -49,7 +49,7 @@
#' 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/R/xgb.plot.importance.R
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@ -33,7 +33,7 @@
#' 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/R/xgb.plot.tree.R
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@ -48,7 +48,7 @@
#' 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/R/xgb.save.R
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@ -11,7 +11,7 @@
#' 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/R/xgb.save.raw.R
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@ -11,7 +11,7 @@
#' 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/R/xgb.train.R
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@ -108,7 +108,7 @@
#' 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)
#' @export
#'
xgb.train <- function(params=list(), data, nrounds, watchlist = list(),

2
R-package/R/xgboost.R
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@ -45,7 +45,7 @@
#' 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)
#'
#' @export

19
R-package/demo/basic_walkthrough.R
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@ -16,27 +16,28 @@ class(train$data)
# use sparse matrix when your feature is sparse(e.g. when you using one-hot encoding vector)
print("training xgboost with sparseMatrix")
bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2,
objective = "binary:logistic")
nthread = 2, objective = "binary:logistic")
# alternatively, you can put in dense matrix, i.e. basic R-matrix
print("training xgboost with Matrix")
bst <- xgboost(data = as.matrix(train$data), label = train$label, max.depth = 2, eta = 1, nround = 2,
objective = "binary:logistic")
nthread = 2, objective = "binary:logistic")
# you can also put in xgb.DMatrix object, stores label, data and other meta datas needed for advanced features
print("training xgboost with xgb.DMatrix")
dtrain <- xgb.DMatrix(data = train$data, label = train$label)
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic")
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2, nthread = 2,
objective = "binary:logistic")
# Verbose = 0,1,2
print ('train xgboost with verbose 0, no message')
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2,
objective = "binary:logistic", verbose = 0)
nthread = 2, objective = "binary:logistic", verbose = 0)
print ('train xgboost with verbose 1, print evaluation metric')
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2,
objective = "binary:logistic", verbose = 1)
nthread = 2, objective = "binary:logistic", verbose = 1)
print ('train xgboost with verbose 2, also print information about tree')
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2,
objective = "binary:logistic", verbose = 2)
nthread = 2, objective = "binary:logistic", verbose = 2)
# you can also specify data as file path to a LibSVM format input
# since we do not have this file with us, the following line is just for illustration
@ -77,19 +78,19 @@ watchlist <- list(train=dtrain, test=dtest)
# watchlist allows us to monitor the evaluation result on all data in the list
print ('train xgboost using xgb.train with watchlist')
bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nround=2, watchlist=watchlist,
objective = "binary:logistic")
nthread = 2, objective = "binary:logistic")
# we can change evaluation metrics, or use multiple evaluation metrics
print ('train xgboost using xgb.train with watchlist, watch logloss and error')
bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nround=2, watchlist=watchlist,
eval.metric = "error", eval.metric = "logloss",
objective = "binary:logistic")
nthread = 2, objective = "binary:logistic")
# xgb.DMatrix can also be saved using xgb.DMatrix.save
xgb.DMatrix.save(dtrain, "dtrain.buffer")
# to load it in, simply call xgb.DMatrix
dtrain2 <- xgb.DMatrix("dtrain.buffer")
bst <- xgb.train(data=dtrain2, max.depth=2, eta=1, nround=2, watchlist=watchlist,
objective = "binary:logistic")
nthread = 2, objective = "binary:logistic")
# information can be extracted from xgb.DMatrix using getinfo
label = getinfo(dtest, "label")
pred <- predict(bst, dtest)

2
R-package/demo/boost_from_prediction.R
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@ -11,7 +11,7 @@ watchlist <- list(eval = dtest, train = dtrain)
#
print('start running example to start from a initial prediction')
# train xgboost for 1 round
param <- list(max.depth=2,eta=1,silent=1,objective='binary:logistic')
param <- list(max.depth=2,eta=1,nthread = 2, silent=1,objective='binary:logistic')
bst <- xgb.train( param, dtrain, 1, watchlist )
# Note: we need the margin value instead of transformed prediction in set_base_margin
# do predict with output_margin=TRUE, will always give you margin values before logistic transformation

2
R-package/demo/create_sparse_matrix.R
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@ -64,7 +64,7 @@ output_vector = df[,Y:=0][Improved == "Marked",Y:=1][,Y]
# Following is the same process as other demo
cat("Learning...\n")
bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
eta = 1, nround = 10,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 10,objective = "binary:logistic")
xgb.dump(bst, 'xgb.model.dump', with.stats = T)
# sparse_matrix@Dimnames[[2]] represents the column names of the sparse matrix.

2
R-package/demo/cross_validation.R
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@ -6,7 +6,7 @@ dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
nround <- 2
param <- list(max.depth=2,eta=1,silent=1,objective='binary:logistic')
param <- list(max.depth=2,eta=1,silent=1,nthread = 2, objective='binary:logistic')
cat('running cross validation\n')
# do cross validation, this will print result out as

2
R-package/demo/custom_objective.R
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@ -8,7 +8,7 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
# note: for customized objective function, we leave objective as default
# note: what we are getting is margin value in prediction
# you must know what you are doing
param <- list(max.depth=2,eta=1,silent=1)
param <- list(max.depth=2,eta=1,nthread = 2, silent=1)
watchlist <- list(eval = dtest, train = dtrain)
num_round <- 2

2
R-package/demo/generalized_linear_model.R
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@ -15,7 +15,7 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
# lambda is the L2 regularizer
# you can also set lambda_bias which is L2 regularizer on the bias term
param <- list(objective = "binary:logistic", booster = "gblinear",
alpha = 0.0001, lambda = 1)
nthread = 2, alpha = 0.0001, lambda = 1)
# normally, you do not need to set eta (step_size)
# XGBoost uses a parallel coordinate descent algorithm (shotgun),

2
R-package/demo/predict_first_ntree.R
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@ -10,7 +10,7 @@ watchlist <- list(eval = dtest, train = dtrain)
nround = 2
# training the model for two rounds
bst = xgb.train(param, dtrain, nround, watchlist)
bst = xgb.train(param, dtrain, nround, nthread = 2, watchlist)
cat('start testing prediction from first n trees\n')
labels <- getinfo(dtest,'label')

2
R-package/demo/predict_leaf_indices.R
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@ -10,7 +10,7 @@ watchlist <- list(eval = dtest, train = dtrain)
nround = 5
# training the model for two rounds
bst = xgb.train(param, dtrain, nround, watchlist)
bst = xgb.train(param, dtrain, nround, nthread = 2, watchlist)
cat('start testing prediction from first n trees\n')
### predict using first 2 tree

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)
}

2
R-package/vignettes/discoverYourData.Rmd
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@ -153,7 +153,7 @@ The code below is very usual. For more information, you can look at the document
```{r}
bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 4,
eta = 1, nround = 10,objective = "binary:logistic")
eta = 1, nthread = 2, nround = 10,objective = "binary:logistic")
```

21
R-package/vignettes/xgboostPresentation.Rmd
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@ -141,10 +141,11 @@ We will train decision tree model using the following parameters:
* `objective = "binary:logistic"`: we will train a binary classification model ;
* `max.deph = 2`: the trees won't be deep, because our case is very simple ;
* `nthread = 2`: the number of cpu threads we are going to use;
* `nround = 2`: there will be two passes on the data, the second one will enhance the model by further reducing the difference between ground truth and prediction.
```{r trainingSparse, message=F, warning=F}
bstSparse <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic")
bstSparse <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
```
> More complex the relationship between your features and your `label` is, more passes you need.
@ -156,7 +157,7 @@ bstSparse <- xgboost(data = train$data, label = train$label, max.depth = 2, eta
Alternatively, you can put your dataset in a *dense* matrix, i.e. a basic **R** matrix.
```{r trainingDense, message=F, warning=F}
bstDense <- xgboost(data = as.matrix(train$data), label = train$label, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic")
bstDense <- xgboost(data = as.matrix(train$data), label = train$label, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
```
#### xgb.DMatrix
@ -165,7 +166,7 @@ bstDense <- xgboost(data = as.matrix(train$data), label = train$label, max.depth
```{r trainingDmatrix, message=F, warning=F}
dtrain <- xgb.DMatrix(data = train$data, label = train$label)
bstDMatrix <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic")
bstDMatrix <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
```
#### Verbose option
@ -176,17 +177,17 @@ One of the simplest way to see the training progress is to set the `verbose` opt
```{r trainingVerbose0, message=T, warning=F}
# verbose = 0, no message
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic", verbose = 0)
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic", verbose = 0)
```
```{r trainingVerbose1, message=T, warning=F}
# verbose = 1, print evaluation metric
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic", verbose = 1)
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic", verbose = 1)
```
```{r trainingVerbose2, message=T, warning=F}
# verbose = 2, also print information about tree
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic", verbose = 2)
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic", verbose = 2)
```
Basic prediction using Xgboost
@ -279,7 +280,7 @@ For the purpose of this example, we use `watchlist` parameter. It is a list of `
```{r watchlist, message=F, warning=F}
watchlist <- list(train=dtrain, test=dtest)
bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nround=2, watchlist=watchlist, objective = "binary:logistic")
bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nthread = 2, nround=2, watchlist=watchlist, objective = "binary:logistic")
```
**Xgboost** has computed at each round the same average error metric than seen above (we set `nround` to 2, that is why we have two lines). Obviously, the `train-error` number is related to the training dataset (the one the algorithm learns from) and the `test-error` number to the test dataset.
@ -291,7 +292,7 @@ If with your own dataset you have not such results, you should think about how y
For a better understanding of the learning progression, you may want to have some specific metric or even use multiple evaluation metrics.
```{r watchlist2, message=F, warning=F}
bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nround=2, watchlist=watchlist, eval.metric = "error", eval.metric = "logloss", objective = "binary:logistic")
bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nthread = 2, nround=2, watchlist=watchlist, eval.metric = "error", eval.metric = "logloss", objective = "binary:logistic")
```
> `eval.metric` allows us to monitor two new metrics for each round, `logloss` and `error`.
@ -302,7 +303,7 @@ Linear boosting
Until know, all the learnings we have performed were based on boosting trees. **Xgboost** implements a second algorithm, based on linear boosting. The only difference with previous command is `booster = "gblinear"` parameter (and removing `eta` parameter).
```{r linearBoosting, message=F, warning=F}
bst <- xgb.train(data=dtrain, booster = "gblinear", max.depth=2, nround=2, watchlist=watchlist, eval.metric = "error", eval.metric = "logloss", objective = "binary:logistic")
bst <- xgb.train(data=dtrain, booster = "gblinear", max.depth=2, nthread = 2, nround=2, watchlist=watchlist, eval.metric = "error", eval.metric = "logloss", objective = "binary:logistic")
```
In this specific case, *linear boosting* gets sligtly better performance metrics than decision trees based algorithm.
@ -320,7 +321,7 @@ Like saving models, `xgb.DMatrix` object (which groups both dataset and outcome)
xgb.DMatrix.save(dtrain, "dtrain.buffer")
# to load it in, simply call xgb.DMatrix
dtrain2 <- xgb.DMatrix("dtrain.buffer")
bst <- xgb.train(data=dtrain2, max.depth=2, eta=1, nround=2, watchlist=watchlist, objective = "binary:logistic")
bst <- xgb.train(data=dtrain2, max.depth=2, eta=1, nthread = 2, nround=2, watchlist=watchlist, objective = "binary:logistic")
```
```{r DMatrixDel, include=FALSE}