[R-package] update parameter 'verbosity' based on keyword arg 'verbose' (#4899)

* [R-package] update parameter 'verbosity' based on keyword arg 'verbose'

* add tests

R-package/R/lgb.cv.R

@@ -102,7 +102,6 @@ lgb.cv <- function(params = list()
   }
 
   # Setup temporary variables
-  params$verbose <- verbose
   params <- lgb.check.obj(params = params, obj = obj)
   params <- lgb.check.eval(params = params, eval = eval)
   fobj <- NULL
@@ -112,6 +111,11 @@ lgb.cv <- function(params = list()
   # in `params`.
   # this ensures that the model stored with Booster$save() correctly represents
   # what was passed in
+  params <- lgb.check.wrapper_param(
+    main_param_name = "verbosity"
+    , params = params
+    , alternative_kwarg_value = verbose
+  )
   params <- lgb.check.wrapper_param(
     main_param_name = "num_iterations"
     , params = params

R-package/R/lgb.train.R

@@ -74,7 +74,6 @@ lgb.train <- function(params = list(),
   }
 
   # Setup temporary variables
-  params$verbose <- verbose
   params <- lgb.check.obj(params = params, obj = obj)
   params <- lgb.check.eval(params = params, eval = eval)
   fobj <- NULL
@@ -84,6 +83,11 @@ lgb.train <- function(params = list(),
   # in `params`.
   # this ensures that the model stored with Booster$save() correctly represents
   # what was passed in
+  params <- lgb.check.wrapper_param(
+    main_param_name = "verbosity"
+    , params = params
+    , alternative_kwarg_value = verbose
+  )
   params <- lgb.check.wrapper_param(
     main_param_name = "num_iterations"
     , params = params

R-package/tests/testthat/test_basic.R

@@ -1559,6 +1559,60 @@ test_that("lgb.train() works with integer, double, and numeric data", {
   }
 })
 
+test_that("lgb.train() updates params based on keyword arguments", {
+  dtrain <- lgb.Dataset(
+    data = matrix(rnorm(400L), ncol =  4L)
+    , label = rnorm(100L)
+  )
+
+  # defaults from keyword arguments should be used if not specified in params
+  invisible(
+    capture.output({
+      bst <- lgb.train(
+        data = dtrain
+        , obj = "regression"
+        , params = list()
+      )
+    })
+  )
+  expect_equal(bst$params[["verbosity"]], 1L)
+  expect_equal(bst$params[["num_iterations"]], 100L)
+
+  # main param names should be preferred to keyword arguments
+  invisible(
+    capture.output({
+      bst <- lgb.train(
+        data = dtrain
+        , obj = "regression"
+        , params = list(
+          "verbosity" = 5L
+          , "num_iterations" = 2L
+        )
+      )
+    })
+  )
+  expect_equal(bst$params[["verbosity"]], 5L)
+  expect_equal(bst$params[["num_iterations"]], 2L)
+
+  # aliases should be preferred to keyword arguments, and converted to main parameter name
+  invisible(
+    capture.output({
+      bst <- lgb.train(
+        data = dtrain
+        , obj = "regression"
+        , params = list(
+          "verbose" = 5L
+          , "num_boost_round" = 2L
+        )
+      )
+    })
+  )
+  expect_equal(bst$params[["verbosity"]], 5L)
+  expect_false("verbose" %in% bst$params)
+  expect_equal(bst$params[["num_iterations"]], 2L)
+  expect_false("num_boost_round" %in% bst$params)
+})
+
 test_that("when early stopping is not activated, best_iter and best_score come from valids and not training data", {
   set.seed(708L)
   trainDF <- data.frame(
@@ -1978,6 +2032,74 @@ test_that("early stopping works with lgb.cv()", {
   )
 })
 
+test_that("lgb.cv() updates params based on keyword arguments", {
+  dtrain <- lgb.Dataset(
+    data = matrix(rnorm(400L), ncol =  4L)
+    , label = rnorm(100L)
+  )
+
+  # defaults from keyword arguments should be used if not specified in params
+  invisible(
+    capture.output({
+      cv_bst <- lgb.cv(
+        data = dtrain
+        , obj = "regression"
+        , params = list()
+        , nfold = 2L
+      )
+    })
+  )
+
+  for (bst in cv_bst$boosters) {
+    bst_params <- bst[["booster"]]$params
+    expect_equal(bst_params[["verbosity"]], 1L)
+    expect_equal(bst_params[["num_iterations"]], 100L)
+  }
+
+  # main param names should be preferred to keyword arguments
+  invisible(
+    capture.output({
+      cv_bst <- lgb.cv(
+        data = dtrain
+        , obj = "regression"
+        , params = list(
+          "verbosity" = 5L
+          , "num_iterations" = 2L
+        )
+        , nfold = 2L
+      )
+    })
+  )
+  for (bst in cv_bst$boosters) {
+    bst_params <- bst[["booster"]]$params
+    expect_equal(bst_params[["verbosity"]], 5L)
+    expect_equal(bst_params[["num_iterations"]], 2L)
+  }
+
+  # aliases should be preferred to keyword arguments, and converted to main parameter name
+  invisible(
+    capture.output({
+      cv_bst <- lgb.cv(
+        data = dtrain
+        , obj = "regression"
+        , params = list(
+          "verbose" = 5L
+          , "num_boost_round" = 2L
+        )
+        , nfold = 2L
+      )
+    })
+  )
+  for (bst in cv_bst$boosters) {
+    bst_params <- bst[["booster"]]$params
+    expect_equal(bst_params[["verbosity"]], 5L)
+    expect_false("verbose" %in% bst_params)
+    expect_equal(bst_params[["num_iterations"]], 2L)
+    expect_false("num_boost_round" %in% bst_params)
+  }
+
+})
+
 context("linear learner")
 
 test_that("lgb.train() fit on linearly-relatead data improves when using linear learners", {