add saveload to raw

R-package/NAMESPACE

@@ -13,6 +13,7 @@ export(xgb.model.dt.tree)
 export(xgb.plot.importance)
 export(xgb.plot.tree)
 export(xgb.save)
+export(xgb.save.raw)
 export(xgb.train)
 export(xgboost)
 exportMethods(predict)

R-package/R/utils.R

@@ -57,10 +57,13 @@ xgb.Booster <- function(params = list(), cachelist = list(), modelfile = NULL) {
     }
   }
   if (!is.null(modelfile)) {
-    if (typeof(modelfile) != "character") {
-      stop("xgb.Booster: modelfile must be character")
+    if (typeof(modelfile) == "character") {
+      .Call("XGBoosterLoadModel_R", handle, modelfile, PACKAGE = "xgboost")
+    } else if (typeof(modelfile) == "raw") {
+      .Call("XGBoosterLoadModelFromRaw_R", handle, modelfile, PACKAGE = "xgboost")      
+    } else {
+      stop("xgb.Booster: modelfile must be character or raw vector")
     }
-    .Call("XGBoosterLoadModel_R", handle, modelfile, PACKAGE = "xgboost")
   }
   return(structure(handle, class = "xgb.Booster"))
 }

R-package/R/xgb.save.raw.R

@@ -0,0 +1,27 @@
+#' Save xgboost model to R's raw vector,
+#' user can call xgb.load to load the model back from raw vector
+#' 
+#' Save xgboost model from xgboost or xgb.train
+#' 
+#' @param model the model object.
+#' 
+#' @examples
+#' data(agaricus.train, package='xgboost')
+#' data(agaricus.test, package='xgboost')
+#' train <- agaricus.train
+#' test <- agaricus.test
+#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+#'                eta = 1, nround = 2,objective = "binary:logistic")
+#' raw <- xgb.save(bst)
+#' bst <- xgb.load(raw)
+#' pred <- predict(bst, test$data)
+#' @export
+#' 
+xgb.save.raw <- function(model) {
+  if (class(model) == "xgb.Booster") {
+    raw <- .Call("XGBoosterModelToRaw_R", model, PACKAGE = "xgboost")
+    return(raw)
+  }
+  stop("xgb.raw: the input must be xgb.Booster. Use xgb.DMatrix.save to save
+       xgb.DMatrix object.")
+}

R-package/demo/basic_walkthrough.R

@@ -58,6 +58,14 @@ pred2 <- predict(bst2, test$data)
 # pred2 should be identical to pred
 print(paste("sum(abs(pred2-pred))=", sum(abs(pred2-pred))))
 
+# save model to R's raw vector
+raw = xgb.save.raw(bst)
+# load binary model to R
+bst3 <- xgb.load(raw)
+pred3 <- predict(bst2, test$data)
+# pred2 should be identical to pred
+print(paste("sum(abs(pred3-pred))=", sum(abs(pred2-pred))))
+
 #----------------Advanced features --------------
 # to use advanced features, we need to put data in xgb.DMatrix
 dtrain <- xgb.DMatrix(data = train$data, label=train$label)

R-package/src/xgboost_R.cpp

@@ -274,6 +274,23 @@ extern "C" {
     XGBoosterSaveModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname)));
     _WrapperEnd();
   }
+  void XGBoosterLoadModelFromRaw_R(SEXP handle, SEXP raw) {    
+    _WrapperBegin();
+    XGBoosterLoadModelFromBuffer(R_ExternalPtrAddr(handle),
+                                 RAW(raw),
+                                 length(raw));
+    _WrapperEnd();
+  }
+  SEXP XGBoosterModelToRaw_R(SEXP handle) {
+    bst_ulong olen;
+    _WrapperBegin();
+    const char *raw = XGBoosterGetModelRaw(R_ExternalPtrAddr(handle), &olen);
+    _WrapperEnd();
+    SEXP ret = PROTECT(allocVector(RAWSXP, olen));
+    memcpy(RAW(ret), raw, olen);
+    UNPROTECT(1);    
+    return ret;
+  }
   SEXP XGBoosterDumpModel_R(SEXP handle, SEXP fmap, SEXP with_stats) {
     _WrapperBegin();
     bst_ulong olen;

R-package/src/xgboost_R.h

@@ -127,6 +127,17 @@ extern "C" {
    * \param fname file name
    */    
   void XGBoosterSaveModel_R(SEXP handle, SEXP fname);
+  /*!
+   * \brief load model from raw array
+   * \param handle handle
+   */    
+  void XGBoosterLoadModelFromRaw_R(SEXP handle, SEXP raw);
+  /*!
+   * \brief save model into R's raw array
+   * \param handle handle
+   * \return raw array
+   */
+  SEXP XGBoosterModelToRaw_R(SEXP handle);
   /*!
    * \brief dump model into a string
    * \param handle handle