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

R-package/demo/basic_walkthrough.R

@@ -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)

R-package/demo/boost_from_prediction.R

@@ -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

R-package/demo/create_sparse_matrix.R

@@ -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.

R-package/demo/cross_validation.R

@@ -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

R-package/demo/custom_objective.R

@@ -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
 

R-package/demo/generalized_linear_model.R

@@ -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), 

R-package/demo/predict_first_ntree.R

@@ -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')
 

R-package/demo/predict_leaf_indices.R

@@ -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