Merge pull request #561 from terrytangyuan/test

Added test for code quality check

R-package/R/getinfo.xgb.DMatrix.R

@@ -54,4 +54,3 @@ setMethod("getinfo", signature = "xgb.DMatrix",
               }
               return(ret)
           })
-

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

@@ -68,4 +68,3 @@ setMethod("predict", signature = "xgb.Booster",
   }
   return(ret)
 })
-

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

@@ -13,7 +13,6 @@ setMethod("predict", signature = "xgb.Booster.handle",
 
   bst <- xgb.handleToBooster(object)
 
-  ret = predict(bst, ...)
+  ret <- predict(bst, ...)
   return(ret)
 })
-

R-package/R/utils.R

@@ -142,8 +142,7 @@ xgb.iter.boost <- function(booster, dtrain, gpair) {
   if (class(dtrain) != "xgb.DMatrix") {
     stop("xgb.iter.update: second argument must be type xgb.DMatrix")
   }
-  .Call("XGBoosterBoostOneIter_R", booster, dtrain, gpair$grad, gpair$hess, 
-        PACKAGE = "xgboost")
+  .Call("XGBoosterBoostOneIter_R", booster, dtrain, gpair$grad, gpair$hess, PACKAGE = "xgboost")
   return(TRUE)
 }
 
@@ -253,10 +252,10 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
       kstep <- length(randidx) %/% nfold
       folds <- list()
       for (i in 1:(nfold-1)) {
-        folds[[i]] = randidx[1:kstep]
-        randidx = setdiff(randidx, folds[[i]])
+        folds[[i]] <- randidx[1:kstep]
+        randidx <- setdiff(randidx, folds[[i]])
       }
-      folds[[nfold]] = randidx
+      folds[[nfold]] <- randidx
     }
   }
   ret <- list()
@@ -270,7 +269,7 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
     }
     dtrain <- slice(dall, didx)
     bst <- xgb.Booster(param, list(dtrain, dtest))
-    watchlist = list(train=dtrain, test=dtest)
+    watchlist <- list(train=dtrain, test=dtest)
     ret[[k]] <- list(dtrain=dtrain, booster=bst, watchlist=watchlist, index=folds[[k]])
   }
   return (ret)

R-package/R/xgb.cv.R

@@ -108,9 +108,9 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         stop("nfold must be bigger than 1")
     }
     dtrain <- xgb.get.DMatrix(data, label, missing)
-    dot.params = list(...)
-    nms.params = names(params)
-    nms.dot.params = names(dot.params)
+    dot.params <- list(...)
+    nms.params <- names(params)
+    nms.dot.params <- names(dot.params)
     if (length(intersect(nms.params,nms.dot.params)) > 0)
         stop("Duplicated defined term in parameters. Please check your list of params.")
     params <- append(params, dot.params)
@@ -151,20 +151,20 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         }
         
         if (maximize) {
-            bestScore = 0
+            bestScore <- 0
         } else {
-            bestScore = Inf
+            bestScore <- Inf
         }
-        bestInd = 0
-        earlyStopflag = FALSE
+        bestInd <- 0
+        earlyStopflag <- FALSE
         
         if (length(metrics)>1)
             warning('Only the first metric is used for early stopping process.')
     }
   
     xgb_folds <- xgb.cv.mknfold(dtrain, nfold, params, stratified, folds)
-    obj_type = params[['objective']]
-    mat_pred = FALSE
+    obj_type <- params[['objective']]
+    mat_pred <- FALSE
     if (!is.null(obj_type) && obj_type == 'multi:softprob')
     {
         num_class = params[['num_class']]
@@ -192,15 +192,15 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         
         # early_Stopping
         if (!is.null(early.stop.round)){
-            score = strsplit(ret,'\\s+')[[1]][1+length(metrics)+2]
-            score = strsplit(score,'\\+|:')[[1]][[2]]
-            score = as.numeric(score)
+            score <- strsplit(ret,'\\s+')[[1]][1+length(metrics)+2]
+            score <- strsplit(score,'\\+|:')[[1]][[2]]
+            score <- as.numeric(score)
             if ((maximize && score > bestScore) || (!maximize && score < bestScore)) {
-                bestScore = score
-                bestInd = i
+                bestScore <- score
+                bestInd <- i
             } else {
                 if (i-bestInd >= early.stop.round) {
-                    earlyStopflag = TRUE
+                    earlyStopflag <- TRUE
                     cat('Stopping. Best iteration:',bestInd)
                     break
                 }
@@ -211,17 +211,17 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
     
     if (prediction) {
         for (k in 1:nfold) {
-            fd = xgb_folds[[k]]
+            fd <- xgb_folds[[k]]
             if (!is.null(early.stop.round) && earlyStopflag) {
-              res = xgb.iter.eval(fd$booster, fd$watchlist, bestInd - 1, feval, prediction)
+              res <- xgb.iter.eval(fd$booster, fd$watchlist, bestInd - 1, feval, prediction)
             } else {
-              res = xgb.iter.eval(fd$booster, fd$watchlist, nrounds - 1, feval, prediction)
+              res <- xgb.iter.eval(fd$booster, fd$watchlist, nrounds - 1, feval, prediction)
             }
             if (mat_pred) {
-                pred_mat = matrix(res[[2]],num_class,length(fd$index))
-                predictValues[fd$index,] = t(pred_mat)
+                pred_mat <- matrix(res[[2]],num_class,length(fd$index))
+                predictValues[fd$index,] <- t(pred_mat)
             } else {
-                predictValues[fd$index] = res[[2]]
+                predictValues[fd$index] <- res[[2]]
             }
         }
     }