enable returning prediction in cv

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

@@ -39,7 +39,7 @@ setMethod("getinfo", signature = "xgb.DMatrix",
               if (name != "nrow"){
                   ret <- .Call("XGDMatrixGetInfo_R", object, name, PACKAGE = "xgboost")
               } else {
-                  ret <- .Call("XGDMatrixNumRow_R", object, PACKAGE = "xgboost")
+                  ret <- xgb.numrow(object)
               }
               return(ret)
           })

R-package/R/utils.R

@@ -131,7 +131,7 @@ xgb.iter.update <- function(booster, dtrain, iter, obj = NULL) {
 }
 
 # iteratively evaluate one iteration
-xgb.iter.eval <- function(booster, watchlist, iter, feval = NULL) {
+xgb.iter.eval <- function(booster, watchlist, iter, feval = NULL, prediction = FALSE) {
   if (class(booster) != "xgb.Booster") {
     stop("xgb.eval: first argument must be type xgb.Booster")
   }
@@ -169,18 +169,27 @@ xgb.iter.eval <- function(booster, watchlist, iter, feval = NULL) {
   } else {
     msg <- ""
   }
+  if (prediction){
+    preds <- predict(booster,watchlist[[2]])
+    return(list(msg,preds))
+  }
   return(msg)
 }
 #------------------------------------------
 # helper functions for cross validation
 #
 xgb.cv.mknfold <- function(dall, nfold, param) {
-  randidx <- sample(1 : xgb.numrow(dall))
+  if (nfold <= 1) {
-  kstep <- length(randidx) / nfold
+    stop("nfold must be bigger than 1")
-  idset <- list()
-  for (i in 1:nfold) {
-    idset[[i]] <- randidx[ ((i-1) * kstep + 1) : min(i * kstep, length(randidx)) ]
   }
+  randidx <- sample(1 : xgb.numrow(dall))
+  kstep <- length(randidx) %/% nfold
+  idset <- list()
+  for (i in 1:(nfold-1)) {
+    idset[[i]] = randidx[1:kstep]
+    randidx = setdiff(randidx,idset[[i]])
+  }
+  idset[[nfold]] = randidx
   ret <- list()
   for (k in 1:nfold) {
     dtest <- slice(dall, idset[[k]])
@@ -193,7 +202,7 @@ xgb.cv.mknfold <- function(dall, nfold, param) {
     dtrain <- slice(dall, didx)
     bst <- xgb.Booster(param, list(dtrain, dtest))
     watchlist = list(train=dtrain, test=dtest)
-    ret[[k]] <- list(dtrain=dtrain, booster=bst, watchlist=watchlist)
+    ret[[k]] <- list(dtrain=dtrain, booster=bst, watchlist=watchlist, index=idset[[k]])
   }
   return (ret)
 }

R-package/R/xgb.cv.R

@@ -31,6 +31,9 @@
 #' @param nrounds the max number of iterations
 #' @param nfold number of folds used
 #' @param label option field, when data is Matrix
+#' @param missing Missing is only used when input is dense matrix, pick a float
+#     value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
+#' @param prediction A logical value indicating whether to return the prediction vector.
 #' @param showsd \code{boolean}, whether show standard deviation of cross validation
 #' @param metrics, list of evaluation metrics to be used in corss validation,
 #'   when it is not specified, the evaluation metric is chosen according to objective function.
@@ -71,7 +74,8 @@
 #' @export
 #'
 xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NULL, 
-                   showsd = TRUE, metrics=list(), obj = NULL, feval = NULL, verbose = T,...) {
+                   prediction = FALSE, showsd = TRUE, metrics=list(), 
+                   obj = NULL, feval = NULL, verbose = T,...) {
   if (typeof(params) != "list") {
     stop("xgb.cv: first argument params must be list")
   }
@@ -90,13 +94,20 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   }
 
   folds <- xgb.cv.mknfold(dtrain, nfold, params)
+  predictValues <- rep(0,xgb.numrow(dtrain))
   history <- c()
   for (i in 1:nrounds) {
     msg <- list()
     for (k in 1:nfold) {
       fd <- folds[[k]]
       succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)
-      msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
+      if (!prediction){
+        msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
+      } else {
+        res <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval, prediction)
+        predictValues[fd$index] <- res[[2]]
+        msg[[k]] <- res[[1]] %>% str_split("\t") %>% .[[1]]
+      }
     }
     ret <- xgb.cv.aggcv(msg, showsd)
     history <- c(history, ret)
@@ -115,5 +126,9 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   split <- str_split(string = history, pattern = "\t")
   
   for(line in split) dt <- line[2:length(line)] %>% str_extract_all(pattern = "\\d*\\.+\\d*") %>% unlist %>% as.list %>% {vec <- .; rbindlist(list(dt, vec), use.names = F, fill = F)}
-  dt
+  
+  if (prediction) {
+    return(list(dt,predictValues))
+  }
+  return(dt)
 }