Merge pull request #106 from tqchen/master

pull master into unity

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

@@ -25,9 +25,13 @@ setClass("xgb.Booster")
 #' @export
 #' 
 setMethod("predict", signature = "xgb.Booster", 
-          definition = function(object, newdata, outputmargin = FALSE, ntreelimit = NULL) {
+          definition = function(object, newdata, missing = NULL, outputmargin = FALSE, ntreelimit = NULL) {
   if (class(newdata) != "xgb.DMatrix") {
-    newdata <- xgb.DMatrix(newdata)
+    if (is.null(missing)) {
+      newdata <- xgb.DMatrix(newdata)
+    } else {
+      newdata <- xgb.DMatrix(newdata, missing = missing)
+    }
   }
   if (is.null(ntreelimit)) {
     ntreelimit <- 0

R-package/R/utils.R

@@ -68,13 +68,17 @@ xgb.Booster <- function(params = list(), cachelist = list(), modelfile = NULL) {
 ## ----the following are low level iteratively function, not needed if
 ## you do not want to use them ---------------------------------------
 # get dmatrix from data, label
-xgb.get.DMatrix <- function(data, label = NULL) {
+xgb.get.DMatrix <- function(data, label = NULL, missing = NULL) {
   inClass <- class(data)
   if (inClass == "dgCMatrix" || inClass == "matrix") {
     if (is.null(label)) {
       stop("xgboost: need label when data is a matrix")
     }
-    dtrain <- xgb.DMatrix(data, label = label)
+    if (is.null(missing)){
+      dtrain <- xgb.DMatrix(data, label = label)
+    } else {
+      dtrain <- xgb.DMatrix(data, label = label, missing = missing)
+    }
   } else {
     if (!is.null(label)) {
       warning("xgboost: label will be ignored.")

R-package/R/xgb.cv.R

@@ -53,7 +53,7 @@
 #'                   "max.depth"=3, "eta"=1, "objective"="binary:logistic")
 #' @export
 #'
-xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL,
+xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NULL, 
                    showsd = TRUE, metrics=list(), obj = NULL, feval = NULL, ...) {
   if (typeof(params) != "list") {
     stop("xgb.cv: first argument params must be list")
@@ -61,7 +61,11 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL,
   if (nfold <= 1) {
     stop("nfold must be bigger than 1")
   }
-  dtrain <- xgb.get.DMatrix(data, label)
+  if (is.null(missing)) {
+    dtrain <- xgb.get.DMatrix(data, label)
+  } else {
+    dtrain <- xgb.get.DMatrix(data, label, missing)
+  }
   params <- append(params, list(...))
   params <- append(params, list(silent=1))
   for (mc in metrics) {

R-package/R/xgboost.R

@@ -43,9 +43,14 @@
 #' 
 #' @export
 #' 
-xgboost <- function(data = NULL, label = NULL, params = list(), nrounds, 
+xgboost <- function(data = NULL, label = NULL, missing = NULL, params = list(), nrounds, 
                     verbose = 1, ...) {
-  dtrain <- xgb.get.DMatrix(data, label)  
+  if (is.null(missing)) {
+    dtrain <- xgb.get.DMatrix(data, label)
+  } else {
+    dtrain <- xgb.get.DMatrix(data, label, missing)
+  }
+    
   params <- append(params, list(...))
   
   if (verbose > 0) {

R-package/demo/custom_objective.R

@@ -37,3 +37,26 @@ 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, logregobj, evalerror)
+
+#
+# there can be cases where you want additional information 
+# being considered besides the property of DMatrix you can get by getinfo
+# you can set additional information as attributes if DMatrix
+
+# set label attribute of dtrain to be label, we use label as an example, it can be anything 
+attr(dtrain, 'label') <- getinfo(dtrain, 'label')
+# this is new customized objective, where you can access things you set
+# same thing applies to customized evaluation function
+logregobjattr <- function(preds, dtrain) {
+  # now you can access the attribute in customized function
+  labels <- attr(dtrain, 'label')
+  preds <- 1/(1 + exp(-preds))
+  grad <- preds - labels
+  hess <- preds * (1 - preds)
+  return(list(grad = grad, hess = hess))
+}
+
+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, logregobjattr, evalerror)