added new early_stopping param alias (#2431)

R-package/R/lgb.cv.R

@@ -212,7 +212,7 @@ lgb.cv <- function(params = list(),
   }
 
   # Check for early stopping passed as parameter when adding early stopping callback
-  early_stop <- c("early_stopping_round", "early_stopping_rounds", "early_stopping")
+  early_stop <- c("early_stopping_round", "early_stopping_rounds", "early_stopping", "n_iter_no_change")
   if (any(names(params) %in% early_stop)) {
     if (params[[which(names(params) %in% early_stop)[1]]] > 0) {
       callbacks <- add.cb(callbacks, cb.early.stop(params[[which(names(params) %in% early_stop)[1]]], verbose = verbose))

R-package/R/lgb.train.R

@@ -195,7 +195,7 @@ lgb.train <- function(params = list(),
   }
 
   # Check for early stopping passed as parameter when adding early stopping callback
-  early_stop <- c("early_stopping_round", "early_stopping_rounds", "early_stopping")
+  early_stop <- c("early_stopping_round", "early_stopping_rounds", "early_stopping", "n_iter_no_change")
   if (any(names(params) %in% early_stop)) {
     if (params[[which(names(params) %in% early_stop)[1]]] > 0) {
       callbacks <- add.cb(callbacks, cb.early.stop(params[[which(names(params) %in% early_stop)[1]]], verbose = verbose))

docs/Parameters.rst

@@ -276,7 +276,7 @@ Learning Control Parameters
 
    -  random seed for ``feature_fraction``
 
--  ``early_stopping_round`` :raw-html:`<a id="early_stopping_round" title="Permalink to this parameter" href="#early_stopping_round">&#x1F517;&#xFE0E;</a>`, default = ``0``, type = int, aliases: ``early_stopping_rounds``, ``early_stopping``
+-  ``early_stopping_round`` :raw-html:`<a id="early_stopping_round" title="Permalink to this parameter" href="#early_stopping_round">&#x1F517;&#xFE0E;</a>`, default = ``0``, type = int, aliases: ``early_stopping_rounds``, ``early_stopping``, ``n_iter_no_change``
 
    -  will stop training if one metric of one validation data doesn't improve in last ``early_stopping_round`` rounds
 

include/LightGBM/config.h

@@ -288,7 +288,7 @@ struct Config {
   // desc = random seed for ``feature_fraction``
   int feature_fraction_seed = 2;
 
-  // alias = early_stopping_rounds, early_stopping
+  // alias = early_stopping_rounds, early_stopping, n_iter_no_change
   // desc = will stop training if one metric of one validation data doesn't improve in last ``early_stopping_round`` rounds
   // desc = ``<= 0`` means disable
   int early_stopping_round = 0;

python-package/lightgbm/engine.py

@@ -147,7 +147,7 @@ def train(params, train_set, num_boost_round=100,
             num_boost_round = params.pop(alias)
             warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
             break
-    for alias in ["early_stopping_round", "early_stopping_rounds", "early_stopping"]:
+    for alias in ["early_stopping_round", "early_stopping_rounds", "early_stopping", "n_iter_no_change"]:
         if alias in params:
             early_stopping_rounds = params.pop(alias)
             warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
@@ -502,7 +502,7 @@ def cv(params, train_set, num_boost_round=100,
             warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
             num_boost_round = params.pop(alias)
             break
-    for alias in ["early_stopping_round", "early_stopping_rounds", "early_stopping"]:
+    for alias in ["early_stopping_round", "early_stopping_rounds", "early_stopping", "n_iter_no_change"]:
         if alias in params:
             warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
             early_stopping_rounds = params.pop(alias)

src/io/config_auto.cpp

@@ -72,6 +72,7 @@ std::unordered_map<std::string, std::string> Config::alias_table({
   {"colsample_bynode", "feature_fraction_bynode"},
   {"early_stopping_rounds", "early_stopping_round"},
   {"early_stopping", "early_stopping_round"},
+  {"n_iter_no_change", "early_stopping_round"},
   {"max_tree_output", "max_delta_step"},
   {"max_leaf_output", "max_delta_step"},
   {"reg_alpha", "lambda_l1"},