[R] Rename watchlist -> evals (#10032)

R-package/demo/basic_walkthrough.R

@@ -74,17 +74,17 @@ print(paste("sum(abs(pred3-pred))=", sum(abs(pred3 - pred))))
 # to use advanced features, we need to put data in xgb.DMatrix
 dtrain <- xgb.DMatrix(data = train$data, label = train$label)
 dtest <- xgb.DMatrix(data = test$data, label = test$label)
-#---------------Using watchlist----------------
-# watchlist is a list of xgb.DMatrix, each of them is tagged with name
-watchlist <- list(train = dtrain, test = dtest)
-# to train with watchlist, use xgb.train, which contains more advanced features
-# 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, nrounds = 2, watchlist = watchlist,
+#---------------Using an evaluation set----------------
+# 'evals' is a list of xgb.DMatrix, each of them is tagged with name
+evals <- list(train = dtrain, test = dtest)
+# to train with an evaluation set, use xgb.train, which contains more advanced features
+# 'evals' argument allows us to monitor the evaluation result on all data in the list
+print("Train xgboost using xgb.train with evaluation data")
+bst <- xgb.train(data = dtrain, max_depth = 2, eta = 1, nrounds = 2, evals = evals,
                  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, nrounds = 2, watchlist = watchlist,
+print("train xgboost using xgb.train with evaluation data, watch logloss and error")
+bst <- xgb.train(data = dtrain, max_depth = 2, eta = 1, nrounds = 2, evals = evals,
                  eval_metric = "error", eval_metric = "logloss",
                  nthread = 2, objective = "binary:logistic")
 
@@ -92,7 +92,7 @@ bst <- xgb.train(data = dtrain, max_depth = 2, eta = 1, nrounds = 2, watchlist =
 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, nrounds = 2, watchlist = watchlist,
+bst <- xgb.train(data = dtrain2, max_depth = 2, eta = 1, nrounds = 2, evals = evals,
                  nthread = 2, objective = "binary:logistic")
 # information can be extracted from xgb.DMatrix using getinfo
 label <- getinfo(dtest, "label")

R-package/demo/boost_from_prediction.R

@@ -5,14 +5,14 @@ data(agaricus.test, package = 'xgboost')
 dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 
-watchlist <- list(eval = dtest, train = dtrain)
+evals <- list(eval = dtest, train = dtrain)
 ###
 # advanced: start from a initial base prediction
 #
 print('start running example to start from a initial prediction')
 # train xgboost for 1 round
 param <- list(max_depth = 2, eta = 1, nthread = 2, objective = 'binary:logistic')
-bst <- xgb.train(param, dtrain, 1, watchlist)
+bst <- xgb.train(param, dtrain, 1, evals)
 # 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
 ptrain <- predict(bst, dtrain, outputmargin = TRUE)
@@ -23,4 +23,4 @@ setinfo(dtrain, "base_margin", ptrain)
 setinfo(dtest, "base_margin", ptest)
 
 print('this is result of boost from initial prediction')
-bst <- xgb.train(params = param, data = dtrain, nrounds = 1, watchlist = watchlist)
+bst <- xgb.train(params = param, data = dtrain, nrounds = 1, evals = evals)

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
-watchlist <- list(eval = dtest, train = dtrain)
+evals <- list(eval = dtest, train = dtrain)
 num_round <- 2
 
 # user define objective function, given prediction, return gradient and second order gradient
@@ -38,7 +38,7 @@ param <- list(max_depth = 2, eta = 1, nthread  =  2, verbosity = 0,
 print('start training with user customized objective')
 # training with customized objective, we can also do step by step training
 # simply look at xgboost.py's implementation of train
-bst <- xgb.train(param, dtrain, num_round, watchlist)
+bst <- xgb.train(param, dtrain, num_round, evals)
 
 #
 # there can be cases where you want additional information
@@ -62,4 +62,4 @@ param <- list(max_depth = 2, eta = 1, nthread  =  2, verbosity = 0,
 print('start training with user customized objective, with additional attributes in DMatrix')
 # training with customized objective, we can also do step by step training
 # simply look at xgboost.py's implementation of train
-bst <- xgb.train(param, dtrain, num_round, watchlist)
+bst <- xgb.train(param, dtrain, num_round, evals)

R-package/demo/early_stopping.R

@@ -8,7 +8,7 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 # note: what we are getting is margin value in prediction
 # you must know what you are doing
 param <- list(max_depth = 2, eta = 1, nthread = 2, verbosity = 0)
-watchlist <- list(eval = dtest)
+evals <- list(eval = dtest)
 num_round <- 20
 # user define objective function, given prediction, return gradient and second order gradient
 # this is log likelihood loss
@@ -32,7 +32,7 @@ evalerror <- function(preds, dtrain) {
 }
 print('start training with early Stopping setting')
 
-bst <- xgb.train(param, dtrain, num_round, watchlist,
+bst <- xgb.train(param, dtrain, num_round, evals,
                  objective = logregobj, eval_metric = evalerror, maximize = FALSE,
                  early_stopping_round = 3)
 bst <- xgb.cv(param, dtrain, num_round, nfold = 5,

R-package/demo/generalized_linear_model.R

@@ -25,9 +25,9 @@ param <- list(objective = "binary:logistic", booster = "gblinear",
 ##
 # the rest of settings are the same
 ##
-watchlist <- list(eval = dtest, train = dtrain)
+evals <- list(eval = dtest, train = dtrain)
 num_round <- 2
-bst <- xgb.train(param, dtrain, num_round, watchlist)
+bst <- xgb.train(param, dtrain, num_round, evals)
 ypred <- predict(bst, dtest)
 labels <- getinfo(dtest, 'label')
 cat('error of preds=', mean(as.numeric(ypred > 0.5) != labels), '\n')

R-package/demo/gpu_accelerated.R

@@ -23,7 +23,7 @@ y <- rbinom(N, 1, plogis(m))
 tr <- sample.int(N, N * 0.75)
 dtrain <- xgb.DMatrix(X[tr, ], label = y[tr])
 dtest <- xgb.DMatrix(X[-tr, ], label = y[-tr])
-wl <- list(train = dtrain, test = dtest)
+evals <- list(train = dtrain, test = dtest)
 
 # An example of running 'gpu_hist' algorithm
 # which is
@@ -35,11 +35,11 @@ wl <- list(train = dtrain, test = dtest)
 param <- list(objective = 'reg:logistic', eval_metric = 'auc', subsample = 0.5, nthread = 4,
               max_bin = 64, tree_method = 'gpu_hist')
 pt <- proc.time()
-bst_gpu <- xgb.train(param, dtrain, watchlist = wl, nrounds = 50)
+bst_gpu <- xgb.train(param, dtrain, evals = evals, nrounds = 50)
 proc.time() - pt
 
 # Compare to the 'hist' algorithm:
 param$tree_method <- 'hist'
 pt <- proc.time()
-bst_hist <- xgb.train(param, dtrain, watchlist = wl, nrounds = 50)
+bst_hist <- xgb.train(param, dtrain, evals = evals, nrounds = 50)
 proc.time() - pt

R-package/demo/predict_first_ntree.R

@@ -6,11 +6,11 @@ dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 
 param <- list(max_depth = 2, eta = 1, objective = 'binary:logistic')
-watchlist <- list(eval = dtest, train = dtrain)
+evals <- list(eval = dtest, train = dtrain)
 nrounds <- 2
 
 # training the model for two rounds
-bst <- xgb.train(param, dtrain, nrounds, nthread = 2, watchlist)
+bst <- xgb.train(param, dtrain, nrounds, nthread = 2, evals = evals)
 cat('start testing prediction from first n trees\n')
 labels <- getinfo(dtest, 'label')
 

R-package/demo/predict_leaf_indices.R

@@ -43,7 +43,6 @@ colnames(new.features.test) <- colnames(new.features.train)
 # learning with new features
 new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
 new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label)
-watchlist <- list(train = new.dtrain)
 bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
 
 # Model accuracy with new features

R-package/demo/tweedie_regression.R

@@ -39,7 +39,7 @@ bst <- xgb.train(
   data = d_train,
   params = params,
   maximize = FALSE,
-  watchlist = list(train = d_train),
+  evals = list(train = d_train),
   nrounds = 20)
 
 var_imp <- xgb.importance(attr(x, 'Dimnames')[[2]], model = bst)