[R-package] respect aliases for objective and metric and lgb.train() and lgb.cv() (#4913)

* [R-package] respect aliases for objective and metric

* move eval code closer to eval processing

* remove unnecessary diff

* Update R-package/tests/testthat/test_basic.R

R-package/R/lgb.cv.R

@@ -105,12 +105,6 @@ lgb.cv <- function(params = list()
     data <- lgb.Dataset(data = data, label = label)
   }
 
-  # Setup temporary variables
-  params <- lgb.check.obj(params = params, obj = obj)
-  params <- lgb.check.eval(params = params, eval = eval)
-  fobj <- NULL
-  eval_functions <- list(NULL)
-
   # set some parameters, resolving the way they were passed in with other parameters
   # in `params`.
   # this ensures that the model stored with Booster$save() correctly represents
@@ -125,6 +119,16 @@ lgb.cv <- function(params = list()
     , params = params
     , alternative_kwarg_value = nrounds
   )
+  params <- lgb.check.wrapper_param(
+    main_param_name = "metric"
+    , params = params
+    , alternative_kwarg_value = NULL
+  )
+  params <- lgb.check.wrapper_param(
+    main_param_name = "objective"
+    , params = params
+    , alternative_kwarg_value = NULL
+  )
   params <- lgb.check.wrapper_param(
     main_param_name = "early_stopping_round"
     , params = params
@@ -132,7 +136,9 @@ lgb.cv <- function(params = list()
   )
   early_stopping_rounds <- params[["early_stopping_round"]]
 
-  # Check for objective (function or not)
+  # extract any function objects passed for objective or metric
+  params <- lgb.check.obj(params = params, obj = obj)
+  fobj <- NULL
   if (is.function(params$objective)) {
     fobj <- params$objective
     params$objective <- "NONE"
@@ -142,6 +148,8 @@ lgb.cv <- function(params = list()
   # (for backwards compatibility). If it is a list of functions, store
   # all of them. This makes it possible to pass any mix of strings like "auc"
   # and custom functions to eval
+  params <- lgb.check.eval(params = params, eval = eval)
+  eval_functions <- list(NULL)
   if (is.function(eval)) {
     eval_functions <- list(eval)
   }

R-package/R/lgb.train.R

@@ -73,12 +73,6 @@ lgb.train <- function(params = list(),
     }
   }
 
-  # Setup temporary variables
-  params <- lgb.check.obj(params = params, obj = obj)
-  params <- lgb.check.eval(params = params, eval = eval)
-  fobj <- NULL
-  eval_functions <- list(NULL)
-
   # set some parameters, resolving the way they were passed in with other parameters
   # in `params`.
   # this ensures that the model stored with Booster$save() correctly represents
@@ -93,6 +87,16 @@ lgb.train <- function(params = list(),
     , params = params
     , alternative_kwarg_value = nrounds
   )
+  params <- lgb.check.wrapper_param(
+    main_param_name = "metric"
+    , params = params
+    , alternative_kwarg_value = NULL
+  )
+  params <- lgb.check.wrapper_param(
+    main_param_name = "objective"
+    , params = params
+    , alternative_kwarg_value = NULL
+  )
   params <- lgb.check.wrapper_param(
     main_param_name = "early_stopping_round"
     , params = params
@@ -100,7 +104,9 @@ lgb.train <- function(params = list(),
   )
   early_stopping_rounds <- params[["early_stopping_round"]]
 
-  # Check for objective (function or not)
+  # extract any function objects passed for objective or metric
+  params <- lgb.check.obj(params = params, obj = obj)
+  fobj <- NULL
   if (is.function(params$objective)) {
     fobj <- params$objective
     params$objective <- "NONE"
@@ -110,6 +116,8 @@ lgb.train <- function(params = list(),
   # (for backwards compatibility). If it is a list of functions, store
   # all of them. This makes it possible to pass any mix of strings like "auc"
   # and custom functions to eval
+  params <- lgb.check.eval(params = params, eval = eval)
+  eval_functions <- list(NULL)
   if (is.function(eval)) {
     eval_functions <- list(eval)
   }

R-package/tests/testthat/test_basic.R

@@ -547,6 +547,52 @@ test_that("lgb.cv() respects showsd argument", {
   expect_identical(evals_no_showsd[["eval_err"]], list())
 })
 
+test_that("lgb.cv() respects parameter aliases for objective", {
+  nrounds <- 3L
+  nfold <- 4L
+  dtrain <- lgb.Dataset(
+    data = train$data
+    , label = train$label
+  )
+  cv_bst <- lgb.cv(
+    data = dtrain
+    , params = list(
+      num_leaves = 5L
+      , application = "binary"
+      , num_iterations = nrounds
+    )
+    , nfold = nfold
+  )
+  expect_equal(cv_bst$best_iter, nrounds)
+  expect_named(cv_bst$record_evals[["valid"]], "binary_logloss")
+  expect_length(cv_bst$record_evals[["valid"]][["binary_logloss"]][["eval"]], nrounds)
+  expect_length(cv_bst$boosters, nfold)
+})
+
+test_that("lgb.cv() respects parameter aliases for metric", {
+  nrounds <- 3L
+  nfold <- 4L
+  dtrain <- lgb.Dataset(
+    data = train$data
+    , label = train$label
+  )
+  cv_bst <- lgb.cv(
+    data = dtrain
+    , params = list(
+      num_leaves = 5L
+      , objective = "binary"
+      , num_iterations = nrounds
+      , metric_types = c("auc", "binary_logloss")
+    )
+    , nfold = nfold
+  )
+  expect_equal(cv_bst$best_iter, nrounds)
+  expect_named(cv_bst$record_evals[["valid"]], c("auc", "binary_logloss"))
+  expect_length(cv_bst$record_evals[["valid"]][["binary_logloss"]][["eval"]], nrounds)
+  expect_length(cv_bst$record_evals[["valid"]][["auc"]][["eval"]], nrounds)
+  expect_length(cv_bst$boosters, nfold)
+})
+
 test_that("lgb.cv() respects eval_train_metric argument", {
   dtrain <- lgb.Dataset(train$data, label = train$label)
   params <- list(
@@ -616,6 +662,53 @@ test_that("lgb.train() works as expected with multiple eval metrics", {
   )
 })
 
+test_that("lgb.train() respects parameter aliases for objective", {
+  nrounds <- 3L
+  dtrain <- lgb.Dataset(
+    data = train$data
+    , label = train$label
+  )
+  bst <- lgb.train(
+    data = dtrain
+    , params = list(
+      num_leaves = 5L
+      , application = "binary"
+      , num_iterations = nrounds
+    )
+    , valids = list(
+      "the_training_data" = dtrain
+    )
+  )
+  expect_named(bst$record_evals[["the_training_data"]], "binary_logloss")
+  expect_length(bst$record_evals[["the_training_data"]][["binary_logloss"]][["eval"]], nrounds)
+  expect_equal(bst$params[["objective"]], "binary")
+})
+
+test_that("lgb.train() respects parameter aliases for metric", {
+  nrounds <- 3L
+  dtrain <- lgb.Dataset(
+    data = train$data
+    , label = train$label
+  )
+  bst <- lgb.train(
+    data = dtrain
+    , params = list(
+      num_leaves = 5L
+      , objective = "binary"
+      , num_iterations = nrounds
+      , metric_types = c("auc", "binary_logloss")
+    )
+    , valids = list(
+      "train" = dtrain
+    )
+  )
+  record_results <- bst$record_evals[["train"]]
+  expect_equal(sort(names(record_results)), c("auc", "binary_logloss"))
+  expect_length(record_results[["auc"]][["eval"]], nrounds)
+  expect_length(record_results[["binary_logloss"]][["eval"]], nrounds)
+  expect_equal(bst$params[["metric"]], list("auc", "binary_logloss"))
+})
+
 test_that("lgb.train() rejects negative or 0 value passed to nrounds", {
   dtrain <- lgb.Dataset(train$data, label = train$label)
   params <- list(