Lint fix on consistent assignment

R-package/R/predict.xgb.Booster.R

@@ -48,7 +48,7 @@ setMethod("predict", signature = "xgb.Booster",
       stop("predict: ntreelimit must be equal to or greater than 1")
     }
   }
-  option = 0
+  option <- 0
   if (outputmargin) {
     option <- option + 1
   }

R-package/R/utils.R

@@ -261,7 +261,7 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
   ret <- list()
   for (k in 1:nfold) {
     dtest <- slice(dall, folds[[k]])
-    didx = c()
+    didx <- c()
     for (i in 1:nfold) {
       if (i != k) {
         didx <- append(didx, folds[[i]])

R-package/R/xgb.cv.R

@@ -124,15 +124,15 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         stop("xgb.cv: cannot assign two different objectives")
     if (!is.null(params$objective))
         if (class(params$objective) == 'function') {
-            obj = params$objective
+            obj <- params$objective
-            params[['objective']] = NULL
+            params[['objective']] <- NULL
         }
     # if (!is.null(params$eval_metric) && !is.null(feval))
     #  stop("xgb.cv: cannot assign two different evaluation metrics")
     if (!is.null(params$eval_metric))
         if (class(params$eval_metric)=='function') {
-            feval = params$eval_metric
+            feval <- params$eval_metric
-            params[['eval_metric']] = NULL
+            params[['eval_metric']] <- NULL
         }
     
     # Early Stopping
@@ -144,9 +144,9 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         if (is.null(maximize))
         {
             if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
-                maximize = FALSE
+                maximize <- FALSE
             } else {
-                maximize = TRUE
+                maximize <- TRUE
             }
         }
         
@@ -167,16 +167,16 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
     mat_pred <- FALSE
     if (!is.null(obj_type) && obj_type == 'multi:softprob')
     {
-        num_class = params[['num_class']]
+        num_class <- params[['num_class']]
         if (is.null(num_class))
             stop('must set num_class to use softmax')
         predictValues <- matrix(0,xgb.numrow(dtrain),num_class)
-        mat_pred = TRUE
+        mat_pred <- TRUE
     }
     else
         predictValues <- rep(0,xgb.numrow(dtrain))
     history <- c()
-    print.every.n = max(as.integer(print.every.n), 1L)
+    print.every.n <- max(as.integer(print.every.n), 1L)
     for (i in 1:nrounds) {
         msg <- list()
         for (k in 1:nfold) {

R-package/R/xgb.importance.R

@@ -125,7 +125,7 @@ treeDump <- function(feature_names, text, keepDetail){
 }
 
 linearDump <- function(feature_names, text){
-  which(text == "weight:") %>% {a=.+1;text[a:length(text)]} %>% as.numeric %>% data.table(Feature = feature_names, Weight = .)
+  which(text == "weight:") %>% {a <- .+1; text[a:length(text)]} %>% as.numeric %>% data.table(Feature = feature_names, Weight = .)
 }
 
 # Avoid error messages during CRAN check.

R-package/R/xgb.model.dt.tree.R

@@ -81,7 +81,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model
   }
 
   if(!is.null(model)){
-    text = xgb.dump(model = model, with.stats = T)
+    text <- xgb.dump(model = model, with.stats = T)
   } else if(!is.null(filename_dump)){
     text <- readLines(filename_dump) %>% str_trim(side = "both")
   }

R-package/R/xgb.train.R

@@ -140,27 +140,27 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
     warning('watchlist is provided but verbose=0, no evaluation information will be printed')
   }
   
-  dot.params = list(...)
+  dot.params <- list(...)
-  nms.params = names(params)
+  nms.params <- names(params)
-  nms.dot.params = names(dot.params)
+  nms.dot.params <- names(dot.params)
   if (length(intersect(nms.params,nms.dot.params))>0)
     stop("Duplicated term in parameters. Please check your list of params.")
-  params = append(params, dot.params)
+  params <- append(params, dot.params)
   
   # customized objective and evaluation metric interface
   if (!is.null(params$objective) && !is.null(obj))
     stop("xgb.train: cannot assign two different objectives")
   if (!is.null(params$objective))
     if (class(params$objective)=='function') {
-      obj = params$objective
+      obj <- params$objective
-      params$objective = NULL
+      params$objective <- NULL
     }
   if (!is.null(params$eval_metric) && !is.null(feval))
     stop("xgb.train: cannot assign two different evaluation metrics")
   if (!is.null(params$eval_metric))
     if (class(params$eval_metric)=='function') {
-      feval = params$eval_metric
+      feval <- params$eval_metric
-      params$eval_metric = NULL
+      params$eval_metric <- NULL
     }
     
   # Early stopping
@@ -174,19 +174,19 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
     if (is.null(maximize))
     {
       if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
-        maximize = FALSE
+        maximize <- FALSE
       } else {
-        maximize = TRUE
+        maximize <- TRUE
       }
     }
     
     if (maximize) {
-      bestScore = 0
+      bestScore <- 0
     } else {
-      bestScore = Inf
+      bestScore <- Inf
     }
-    bestInd = 0
+    bestInd <- 0
-    earlyStopflag = FALSE
+    earlyStopflag <- FALSE
     
     if (length(watchlist)>1)
       warning('Only the first data set in watchlist is used for early stopping process.')
@@ -195,7 +195,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
   
   handle <- xgb.Booster(params, append(watchlist, dtrain))
   bst <- xgb.handleToBooster(handle)
-  print.every.n=max( as.integer(print.every.n), 1L)
+  print.every.n <- max( as.integer(print.every.n), 1L)
   for (i in 1:nrounds) {
     succ <- xgb.iter.update(bst$handle, dtrain, i - 1, obj)
     if (length(watchlist) != 0) {
@@ -204,14 +204,14 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
 	    cat(paste(msg, "\n", sep=""))
       if (!is.null(early.stop.round))
       {
-        score = strsplit(msg,':|\\s+')[[1]][3]
+        score <- strsplit(msg,':|\\s+')[[1]][3]
-        score = as.numeric(score)
+        score <- as.numeric(score)
         if ((maximize && score>bestScore) || (!maximize && score<bestScore)) {
-          bestScore = score
+          bestScore <- score
-          bestInd = i
+          bestInd <- i
         } else {
           if (i-bestInd>=early.stop.round) {
-            earlyStopflag = TRUE
+            earlyStopflag <- TRUE
             cat('Stopping. Best iteration:',bestInd)
             break
           }
@@ -226,8 +226,8 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
   }
   bst <- xgb.Booster.check(bst)
   if (!is.null(early.stop.round)) {
-    bst$bestScore = bestScore
+    bst$bestScore <- bestScore
-    bst$bestInd = bestInd
+    bst$bestInd <- bestInd
   }
   return(bst)
 } 

R-package/tests/testthat/test_basic.R

@@ -4,30 +4,30 @@ context("basic functions")
 
 data(agaricus.train, package='xgboost')
 data(agaricus.test, package='xgboost')
-train = agaricus.train
+train <- agaricus.train
-test = agaricus.test
+test <- agaricus.test
 
 test_that("train and predict", {
-  bst = xgboost(data = train$data, label = train$label, max.depth = 2,
+  bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
                 eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
-  pred = predict(bst, test$data)
+  pred <- predict(bst, test$data)
 })
 
 
 test_that("early stopping", {
-  res = xgb.cv(data = train$data, label = train$label, max.depth = 2, nfold = 5,
+  res <- xgb.cv(data = train$data, label = train$label, max.depth = 2, nfold = 5,
                eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
                early.stop.round = 3, maximize = FALSE)
   expect_true(nrow(res)<20)
-  bst = xgboost(data = train$data, label = train$label, max.depth = 2,
+  bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
                 eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
                 early.stop.round = 3, maximize = FALSE)
-  pred = predict(bst, test$data)
+  pred <- predict(bst, test$data)
 })
 
 test_that("save_period", {
-  bst = xgboost(data = train$data, label = train$label, max.depth = 2,
+  bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
                 eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
                 save_period = 10, save_name = "xgb.model")
-  pred = predict(bst, test$data)
+  pred <- predict(bst, test$data)
 })

R-package/tests/testthat/test_helpers.R

@@ -11,8 +11,8 @@ df <- data.table(Arthritis, keep.rownames = F)
 df[,AgeDiscret:= as.factor(round(Age/10,0))]
 df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))]
 df[,ID:=NULL]
-sparse_matrix = sparse.model.matrix(Improved~.-1, data = df)
+sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
-output_vector = df[,Y:=0][Improved == "Marked",Y:=1][,Y]
+output_vector <- df[,Y:=0][Improved == "Marked",Y:=1][,Y]
 bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
                eta = 1, nthread = 2, nround = 10,objective = "binary:logistic")