[R] resolve assignment_linter warnings (#8599)

demo/kaggle-higgs/higgs-train.R

@@ -23,9 +23,9 @@ param <- list("objective" = "binary:logitraw",
               "eval_metric" = "ams@0.15",
               "nthread" = 16)
 watchlist <- list("train" = xgmat)
-nrounds = 120
+nrounds <- 120
 print ("loading data end, start to boost trees")
-bst = xgb.train(param, xgmat, nrounds, watchlist );
+bst <- xgb.train(param, xgmat, nrounds, watchlist );
 # save out model
 xgb.save(bst, "higgs.model")
 print ('finish training')

demo/kaggle-higgs/speedtest.R

@@ -6,7 +6,7 @@ require(methods)
 testsize <- 550000
 
 dtrain <- read.csv("data/training.csv", header=TRUE, nrows=350001)
-dtrain$Label = as.numeric(dtrain$Label=='s')
+dtrain$Label <- as.numeric(dtrain$Label=='s')
 # gbm.time = system.time({
 #   gbm.model <- gbm(Label ~ ., data = dtrain[, -c(1,32)], n.trees = 120, 
 #                    interaction.depth = 6, shrinkage = 0.1, bag.fraction = 1,
@@ -24,11 +24,11 @@ sumwpos <- sum(weight * (label==1.0))
 sumwneg <- sum(weight * (label==0.0))
 print(paste("weight statistics: wpos=", sumwpos, "wneg=", sumwneg, "ratio=", sumwneg / sumwpos))
 
-xgboost.time = list()
-threads = c(1,2,4,8,16)
+xgboost.time <- list()
+threads <- c(1,2,4,8,16)
 for (i in 1:length(threads)){
-  thread = threads[i]
-  xgboost.time[[i]] = system.time({
+  thread <- threads[i]
+  xgboost.time[[i]] <- system.time({
     xgmat <- xgb.DMatrix(data, label = label, weight = weight, missing = -999.0)
     param <- list("objective" = "binary:logitraw",
                   "scale_pos_weight" = sumwneg / sumwpos,
@@ -38,9 +38,9 @@ for (i in 1:length(threads)){
                   "eval_metric" = "ams@0.15",
                   "nthread" = thread)
     watchlist <- list("train" = xgmat)
-    nrounds = 120
+    nrounds <- 120
     print ("loading data end, start to boost trees")
-    bst = xgb.train(param, xgmat, nrounds, watchlist );
+    bst <- xgb.train(param, xgmat, nrounds, watchlist );
     # save out model
     xgb.save(bst, "higgs.model")
     print ('finish training')