check num_boost_round in python/R package.

R-package/R/lgb.cv.R

@@ -107,6 +107,10 @@ lgb.cv <- function(params = list(),
   params <- lgb.check.eval(params, eval)
   fobj <- NULL
   feval <- NULL
+
+  if (nrounds <= 0) {
+    stop("nrounds should be greater than zero")
+  }
 
   # Check for objective (function or not)
   if (is.function(params$objective)) {

R-package/R/lgb.train.R

@@ -83,6 +83,10 @@ lgb.train <- function(params = list(),
   fobj <- NULL
   feval <- NULL
 
+  if (nrounds <= 0) {
+    stop("nrounds should be greater than zero")
+  }
+
   # Check for objective (function or not)
   if (is.function(params$objective)) {
     fobj <- params$objective

R-package/R/lightgbm.R

@@ -18,7 +18,9 @@ lightgbm <- function(data,
 
   # Set data to a temporary variable
   dtrain <- data
-
+  if (nrounds <= 0) {
+    stop("nrounds should be greater than zero")
+  }
   # Check whether data is lgb.Dataset, if not then create lgb.Dataset manually
   if (!lgb.is.Dataset(dtrain)) {
     dtrain <- lgb.Dataset(data, label = label, weight = weight)

python-package/lightgbm/engine.py

@@ -104,6 +104,8 @@ def train(params, train_set, num_boost_round=100,
             warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
             break
 
+    if num_boost_round <= 0:
+        raise ValueError("num_boost_round should be greater than zero.")
     if isinstance(init_model, string_type):
         predictor = _InnerPredictor(model_file=init_model)
     elif isinstance(init_model, Booster):
@@ -394,6 +396,8 @@ def cv(params, train_set, num_boost_round=100,
             early_stopping_rounds = params.pop(alias)
             break
 
+    if num_boost_round <= 0:
+        raise ValueError("num_boost_round should be greater than zero.")
     if isinstance(init_model, string_type):
         predictor = _InnerPredictor(model_file=init_model)
     elif isinstance(init_model, Booster):