[R-package] Stop using temporary model files (#551)

* Not using temporary files anymore RDS

* Attempt to avoid using a temporary file

R-package/R/lgb.Booster.R

@@ -366,8 +366,8 @@ Booster <- R6Class(
       
       # Return model string
       return(lgb.call.return.str("LGBM_BoosterSaveModelToString_R",
-                          private$handle,
-                          as.integer(num_iteration)))
+                                 private$handle,
+                                 as.integer(num_iteration)))
       
     },
     
@@ -416,17 +416,8 @@ Booster <- R6Class(
     # Save model to temporary file for in-memory saving
     save = function() {
       
-      # Create temporary file
-      temp <- tempfile()
-      
-      # Save model to file
-      lgb.save(self, temp)
-      
       # Overwrite model in object
-      self$raw <- readChar(temp, file.info(temp)$size)
-      
-      # Remove temporary file
-      file.remove(temp)
+      self$raw <- self$save_model_to_string(NULL)
       
     }
     
@@ -680,7 +671,7 @@ predict.lgb.Booster <- function(object, data,
 #' @param filename path of model file
 #' @param model_str a str containing the model
 #'
-#' @return booster
+#' @return lgb.Booster
 #' 
 #' @examples
 #' \dontrun{
@@ -701,8 +692,9 @@ predict.lgb.Booster <- function(object, data,
 #'                    learning_rate = 1,
 #'                    early_stopping_rounds = 10)
 #' lgb.save(model, "model.txt")
-#' load_booster <- lgb.load("model.txt")
-#' load_booster_from_str <- lgb.load(model$raw)
+#' load_booster <- lgb.load(filename = "model.txt")
+#' model_string <- model$save_model_to_string(NULL) # saves best iteration
+#' load_booster_from_str <- lgb.load(model_str = model_string)
 #' }
 #' 
 #' @rdname lgb.load
@@ -739,7 +731,7 @@ lgb.load <- function(filename = NULL, model_str = NULL){
 #' @param filename saved filename
 #' @param num_iteration number of iteration want to predict with, NULL or <= 0 means use best iteration
 #'
-#' @return booster
+#' @return lgb.Booster
 #' 
 #' @examples
 #' \dontrun{

R-package/R/readRDS.lgb.Booster.R

@@ -5,7 +5,7 @@
 #' @param file a connection or the name of the file where the R object is saved to or read from.
 #' @param refhook a hook function for handling reference objects.
 #' 
-#' @return an R object.
+#' @return lgb.Booster.
 #' 
 #' @examples
 #' \dontrun{
@@ -38,11 +38,8 @@ readRDS.lgb.Booster <- function(file = "", refhook = NULL) {
   # Check if object has the model stored
   if (!is.na(object$raw)) {
     
-    # Create temporary file for the model loading
-    temp <- tempfile()
-    write(object$raw, temp)
-    object2 <- lgb.load(temp)
-    file.remove(temp)
+    # Create temporary model for the model loading
+    object2 <- lgb.load(model_str = object$raw)
     
     # Restore best iteration and recorded evaluations
     object2$best_iter <- object$best_iter

R-package/man/lgb.load.Rd

@@ -12,7 +12,7 @@ lgb.load(filename = NULL, model_str = NULL)
 \item{model_str}{a str containing the model}
 }
 \value{
-booster
+lgb.Booster
 }
 \description{
 Load LightGBM model from saved model file or string
@@ -38,8 +38,9 @@ model <- lgb.train(params,
                    learning_rate = 1,
                    early_stopping_rounds = 10)
 lgb.save(model, "model.txt")
-load_booster <- lgb.load("model.txt")
-load_booster_from_str <- lgb.load(model$raw)
+load_booster <- lgb.load(filename = "model.txt")
+model_string <- model$save_model_to_string(NULL) # saves best iteration
+load_booster_from_str <- lgb.load(model_str = model_string)
 }
 
 }

R-package/man/lgb.save.Rd

@@ -14,7 +14,7 @@ lgb.save(booster, filename, num_iteration = NULL)
 \item{num_iteration}{number of iteration want to predict with, NULL or <= 0 means use best iteration}
 }
 \value{
-booster
+lgb.Booster
 }
 \description{
 Save LightGBM model

R-package/man/readRDS.lgb.Booster.Rd

@@ -12,7 +12,7 @@ readRDS.lgb.Booster(file = "", refhook = NULL)
 \item{refhook}{a hook function for handling reference objects.}
 }
 \value{
-an R object.
+lgb.Booster.
 }
 \description{
 Attemps to load a model using RDS.