[R-package] Fix best_iter and best_score (#2159)

* Callback for NA handling

* lgb.Booster default score => NA

* lgb.cv default best score => NA

* Fix back callback

* lgb.train with booster check at the end

manual tests done: 
* With early stopping + with validation set
* With early stopping + without validation set
* Without early stopping + with validation set
* Without early stopping + without validation set

And with multiple metrics / validation sets.

* lgb.cv with booster check at the end

manual tests done: 
* With early stopping + with validation set
* With early stopping + without validation set
* Without early stopping + with validation set
* Without early stopping + without validation set

And with multiple metrics / validation sets.

R-package/R/callback.R

@@ -10,7 +10,7 @@ CB_ENV <- R6::R6Class(
     eval_list = list(),
     eval_err_list = list(),
     best_iter = -1,
-    best_score = -1,
+    best_score = NA,
     met_early_stop = FALSE
   )
 )
@@ -360,6 +360,7 @@ cb.early.stop <- function(stopping_rounds, verbose = TRUE) {
           }
           
         }
+      
       if (!isTRUE(env$met_early_stop) && cur_iter == env$end_iteration) {
         # Check if model is not null
         if (!is.null(env$model)) {

R-package/R/lgb.Booster.R

@@ -5,7 +5,7 @@ Booster <- R6::R6Class(
   public = list(
 
     best_iter = -1,
-    best_score = -1,
+    best_score = NA,
     record_evals = list(),
 
     # Finalize will free up the handles

R-package/R/lgb.cv.R

@@ -4,7 +4,7 @@ CVBooster <- R6::R6Class(
   cloneable = FALSE,
   public = list(
     best_iter = -1,
-    best_score = -1,
+    best_score = NA,
     record_evals = list(),
     boosters = list(),
     initialize = function(x) {
@@ -305,6 +305,17 @@ lgb.cv <- function(params = list(),
     if (env$met_early_stop) break
     
   }
+  
+  if (record && is.na(env$best_score)) {
+    if (env$eval_list[[1]]$higher_better[1] == TRUE) {
+      cv_booster$best_iter <- unname(which.max(unlist(cv_booster$record_evals[[2]][[1]][[1]])))
+      cv_booster$best_score <- cv_booster$record_evals[[2]][[1]][[1]][[cv_booster$best_iter]]
+    } else {
+      cv_booster$best_iter <- unname(which.min(unlist(cv_booster$record_evals[[2]][[1]][[1]])))
+      cv_booster$best_score <- cv_booster$record_evals[[2]][[1]][[1]][[cv_booster$best_iter]]
+    }
+  }
+  
   if (reset_data) {
     lapply(cv_booster$boosters, function(fd) {
       # Store temporarily model data elsewhere
@@ -318,6 +329,7 @@ lgb.cv <- function(params = list(),
       fd$booster$record_evals <- booster_old$record_evals
     })
   }
+  
   # Return booster
   return(cv_booster)
   

R-package/R/lgb.train.R

@@ -268,6 +268,17 @@ lgb.train <- function(params = list(),
     
   }
   
+  # When early stopping is not activated, we compute the best iteration / score ourselves by selecting the first metric and the first dataset
+  if (record && length(valids) > 0 && is.na(env$best_score)) {
+    if (env$eval_list[[1]]$higher_better[1] == TRUE) {
+      booster$best_iter <- unname(which.max(unlist(booster$record_evals[[2]][[1]][[1]])))
+      booster$best_score <- booster$record_evals[[2]][[1]][[1]][[booster$best_iter]]
+    } else {
+      booster$best_iter <- unname(which.min(unlist(booster$record_evals[[2]][[1]][[1]])))
+      booster$best_score <- booster$record_evals[[2]][[1]][[1]][[booster$best_iter]]
+    }
+  }
+  
   # Check for booster model conversion to predictor model
   if (reset_data) {