[R-package] fix best_score using custom evaluation (fixes #3112) (#3117)

R-package/R/lgb.train.R

@@ -298,7 +298,15 @@ 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(non_train_valid_names) > 0L && is.na(env$best_score)) {
-    first_metric <- booster$.__enclos_env__$private$eval_names[1L]
+
+    # when using a custom eval function, the metric name is returned from the
+    # function, so figure it out from record_evals
+    if (!is.null(feval)) {
+      first_metric <- names(booster$record_evals[[first_valid_name]])[1L]
+    } else {
+      first_metric <- booster$.__enclos_env__$private$eval_names[1L]
+    }
+
     .find_best <- which.min
     if (isTRUE(env$eval_list[[1L]]$higher_better[1L])) {
       .find_best <- which.max

R-package/tests/testthat/test_custom_objective.R

@@ -6,6 +6,8 @@ dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)
 dtest <- lgb.Dataset(agaricus.test$data, label = agaricus.test$label)
 watchlist <- list(eval = dtest, train = dtrain)
 
+TOLERANCE <- 1e-6
+
 logregobj <- function(preds, dtrain) {
   labels <- getinfo(dtrain, "label")
   preds <- 1.0 / (1.0 + exp(-preds))
@@ -41,3 +43,27 @@ test_that("custom objective works", {
   bst <- lgb.train(param, dtrain, num_round, watchlist, eval = evalerror)
   expect_false(is.null(bst$record_evals))
 })
+
+test_that("using a custom objective, custom eval, and no other metrics works", {
+  set.seed(708L)
+  bst <- lgb.train(
+    params = list(
+      num_leaves = 8L
+      , learning_rate = 1.0
+    )
+    , data = dtrain
+    , nrounds = 4L
+    , valids = watchlist
+    , obj = logregobj
+    , eval = evalerror
+  )
+  expect_false(is.null(bst$record_evals))
+  expect_equal(bst$best_iter, 4L)
+  expect_true(abs(bst$best_score - 0.000621) < TOLERANCE)
+
+  eval_results <- bst$eval_valid(feval = evalerror)[[1L]]
+  expect_true(eval_results[["data_name"]] == "eval")
+  expect_true(abs(eval_results[["value"]] - 0.0006207325) < TOLERANCE)
+  expect_true(eval_results[["name"]] == "error")
+  expect_false(eval_results[["higher_better"]])
+})