[R-package] separate the verbose and record

R-package/R/callback.R

@@ -92,7 +92,8 @@ cb.print.evaluation <- function(period = 1){
       if ( (i - 1) %% period == 0
          | i == env$begin_iteration
          | i == env$end_iteration ) {
-        cat(merge.eval.string(env), "\n")
+        msg <- merge.eval.string(env)
+        if (nchar(msg) > 0) { cat(merge.eval.string(env), "\n") }
       }
     }
   }

R-package/R/lgb.Booster.R

@@ -6,7 +6,6 @@ Booster <- R6Class(
     record_evals = list(),
     finalize     = function() {
       if (!lgb.is.null.handle(private$handle)) {
-        cat("freeing booster handle\n")
         lgb.call("LGBM_BoosterFree_R", ret = NULL, private$handle)
         private$handle <- NULL
       }
@@ -50,7 +49,7 @@ Booster <- R6Class(
       }
       class(handle)     <- "lgb.Booster.handle"
       private$handle    <- handle
-      private$num_class <- as.integer(1)
+      private$num_class <- 1L
       private$num_class <-
         lgb.call("LGBM_BoosterGetNumClasses_R", ret = private$num_class, private$handle)
     },
@@ -107,6 +106,10 @@ Booster <- R6Class(
       } else {
         if (!is.function(fobj)) { stop("lgb.Booster.update: fobj should be a function") }
         gpair <- fobj(private$inner_predict(1), private$train_set)
+        if(is.null(gpair$grad) | is.null(gpair$hess)){
+          stop("lgb.Booster.update: custom objective should 
+            return a list with attributes (hess, grad)")
+        }
         ret   <- lgb.call(
             "LGBM_BoosterUpdateOneIterCustom_R", ret = NULL,
             private$handle,
@@ -128,7 +131,7 @@ Booster <- R6Class(
       self
     },
     current_iter = function() {
-      cur_iter <- as.integer(0)
+      cur_iter <- 0L
       lgb.call("LGBM_BoosterGetCurrentIteration_R", ret = cur_iter, private$handle)
     },
     eval = function(data, name, feval = NULL) {
@@ -214,7 +217,7 @@ Booster <- R6Class(
         stop("data_idx should not be greater than num_dataset")
       }
       if (is.null(private$predict_buffer[[data_name]])) {
-        npred <- as.integer(0)
+        npred <- 0L
         npred <- lgb.call("LGBM_BoosterGetNumPredict_R",
                 ret = npred,
                 private$handle,
@@ -275,7 +278,12 @@ Booster <- R6Class(
         }
         data <- private$train_set
         if (data_idx > 1) { data <- private$valid_sets[[data_idx - 1]] }
-        res           <- feval(private$inner_predict(data_idx), data)
+        res <- feval(private$inner_predict(data_idx), data)
+        if(is.null(res$name) | is.null(res$value) | 
+           is.null(res$higher_better)) {
+          stop("lgb.Booster.eval: custom eval function should return a 
+            list with attribute (name, value, higher_better)");
+        }
         res$data_name <- data_name
         ret           <- append(ret, list(res))
       }

R-package/R/lgb.Dataset.R

@@ -4,7 +4,6 @@ Dataset <- R6Class(
   public = list(
     finalize = function() {
       if (!lgb.is.null.handle(private$handle)) {
-        cat("free dataset handle\n")
         lgb.call("LGBM_DatasetFree_R", ret = NULL, private$handle)
         private$handle <- NULL
       }
@@ -200,8 +199,8 @@ Dataset <- R6Class(
     },
     dim = function() {
       if (!lgb.is.null.handle(private$handle)) {
-        num_row <- as.integer(0)
-        num_col <- as.integer(0)
+        num_row <- 0L
+        num_col <- 0L
 
         c(
           lgb.call("LGBM_DatasetGetNumData_R",    ret = num_row, private$handle),
@@ -252,7 +251,7 @@ Dataset <- R6Class(
         )
       }
       if (is.null(private$info[[name]]) && !lgb.is.null.handle(private$handle)) {
-        info_len <- as.integer(0)
+        info_len <- 0L
         info_len <- lgb.call("LGBM_DatasetGetFieldSize_R",
                              ret = info_len,
                              private$handle,

R-package/R/lgb.Predictor.R

@@ -31,7 +31,7 @@ Predictor <- R6Class(
       predleaf = FALSE, header = FALSE, reshape = FALSE) {
 
       if (is.null(num_iteration)) { num_iteration <- -1 }
-      num_row <- 0
+      num_row <- 0L
       if (is.character(data)) {
         tmp_filename <- tempfile(pattern = "lightgbm_")
         on.exit(unlink(tmp_filename), add = TRUE)
@@ -46,7 +46,7 @@ Predictor <- R6Class(
         preds   <- as.vector(t(preds))
       } else {
         num_row <- nrow(data)
-        npred   <- as.integer(0)
+        npred   <- 0L
         npred   <- lgb.call("LGBM_BoosterCalcNumPredict_R", ret = npred,
           private$handle,
           as.integer(num_row),

R-package/R/lgb.cv.R

@@ -28,9 +28,9 @@ CVBooster <- R6Class(
 #' @param weight vector of response values. If not NULL, will set to dataset
 #' @param obj objective function, can be character or custom objective function
 #' @param eval evaluation function, can be (list of) character or custom eval function
-#' @param verbose verbosity for output
-#'        if verbose > 0 , also will record iteration message to booster$record_evals
-#' @param eval_freq evalutaion output frequence
+#' @param verbose verbosity for output, if <= 0, also will disable the print of evalutaion during training
+#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals} 
+#' @param eval_freq evalutaion output frequence, only effect when verbose > 0
 #' @param showsd \code{boolean}, whether to show standard deviation of cross validation
 #' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified
 #'        by the values of outcome labels.
@@ -51,7 +51,7 @@ CVBooster <- R6Class(
 #' @param callbacks list of callback functions
 #'        List of callback functions that are applied at each iteration.
 #' @param ... other parameters, see parameters.md for more informations
-#' @return a trained booster model \code{lgb.Booster}.
+#' @return a trained model \code{lgb.CVBooster}.
 #' @examples
 #' \dontrun{
 #'   library(lightgbm)
@@ -63,16 +63,23 @@ CVBooster <- R6Class(
 #' }
 #' @rdname lgb.train
 #' @export
-lgb.cv <- function(params=list(), data, nrounds = 10, nfold = 3,
-                  label = NULL, weight = NULL,
-                  obj = NULL, eval = NULL,
-                  verbose = 1, eval_freq = 1L, showsd = TRUE,
-                  stratified = TRUE, folds = NULL,
-                  init_model = NULL,
-                  colnames= NULL,
-                  categorical_feature = NULL,
-                  early_stopping_rounds = NULL,
-                  callbacks = list(), ...) {
+lgb.cv <- function(params=list(), data, nrounds = 10, 
+                   nfold                 = 3,
+                   label                 = NULL,
+                   weight                = NULL,
+                   obj                   = NULL,
+                   eval                  = NULL,
+                   verbose               = 1,
+                   record                = TRUE,
+                   eval_freq             = 1L,
+                   showsd                = TRUE,
+                   stratified            = TRUE,
+                   folds                 = NULL,
+                   init_model            = NULL,
+                   colnames              = NULL,
+                   categorical_feature   = NULL,
+                   early_stopping_rounds = NULL,
+                   callbacks             = list(), ...) {
   addiction_params <- list(...)
   params           <- append(params, addiction_params)
   params$verbose   <- verbose
@@ -112,7 +119,7 @@ lgb.cv <- function(params=list(), data, nrounds = 10, nfold = 3,
   data$construct()
 
   if (!is.null(folds)) {
-    if (!is.list(folds) || length(folds) < 2)
+    if (!is.list(folds) | length(folds) < 2)
       stop(sQuote("folds"), " must be a list with 2 or more elements that are vectors of indices for each CV-fold")
     nfold <- length(folds)
   } else {
@@ -120,11 +127,11 @@ lgb.cv <- function(params=list(), data, nrounds = 10, nfold = 3,
     folds <- generate.cv.folds(nfold, nrow(data), stratified, getinfo(data, 'label'), params)
   }
 
-  if (eval_freq > 0) {
+  if (verbose > 0 & eval_freq > 0) {
     callbacks <- add.cb(callbacks, cb.print.evaluation(eval_freq))
   }
 
-  if (verbose > 0) { callbacks <- add.cb(callbacks, cb.record.evaluation()) }
+  if (record) { callbacks <- add.cb(callbacks, cb.record.evaluation()) }
 
   if (!is.null(early_stopping_rounds)) {
     if (early_stopping_rounds > 0) {

R-package/R/lgb.train.R

@@ -6,9 +6,9 @@
 #' @param valids a list of \code{lgb.Dataset} objects, used for validation
 #' @param obj objective function, can be character or custom objective function
 #' @param eval evaluation function, can be (a list of) character or custom eval function
-#' @param verbose verbosity for output
-#'        if \code{verbose > 0}, also will record iteration message to \code{booster$record_evals}
-#' @param eval_freq evalutaion output frequency
+#' @param verbose verbosity for output, if <= 0, also will disable the print of evalutaion during training
+#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals} 
+#' @param eval_freq evalutaion output frequency, only effect when verbose > 0
 #' @param init_model path of model file of \code{lgb.Booster} object, will continue training from this model
 #' @param colnames feature names, if not null, will use this to overwrite the names in dataset
 #' @param categorical_feature list of str or int
@@ -44,6 +44,7 @@ lgb.train <- function(params = list(), data, nrounds = 10,
                       obj                   = NULL,
                       eval                  = NULL,
                       verbose               = 1,
+                      record                = TRUE,
                       eval_freq             = 1L,
                       init_model            = NULL,
                       colnames              = NULL,
@@ -111,11 +112,11 @@ lgb.train <- function(params = list(), data, nrounds = 10,
     }
   }
   # process callbacks
-  if (eval_freq > 0) {
+  if (verbose > 0 & eval_freq > 0) {
     callbacks <- add.cb(callbacks, cb.print.evaluation(eval_freq))
   }
 
-  if (verbose > 0 && length(valids) > 0) {
+  if (record & length(valids) > 0) {
     callbacks <- add.cb(callbacks, cb.record.evaluation())
   }