[R-package] construct dataset earlier in lgb.train and lgb.cv (fixes #3583)

R-package/R/lgb.cv.R

@@ -164,6 +164,10 @@ lgb.cv <- function(params = list()
   }
   end_iteration <- begin_iteration + params[["num_iterations"]] - 1L
 
+  # Construct datasets, if needed
+  data$update_params(params = params)
+  data$construct()
+
   # Check interaction constraints
   cnames <- NULL
   if (!is.null(colnames)) {
@@ -194,9 +198,6 @@ lgb.cv <- function(params = list()
     data$set_categorical_feature(categorical_feature)
   }
 
-  # Construct datasets, if needed
-  data$construct()
-
   # Check for folds
   if (!is.null(folds)) {
 

R-package/R/lgb.train.R

@@ -142,6 +142,10 @@ lgb.train <- function(params = list(),
   }
   end_iteration <- begin_iteration + params[["num_iterations"]] - 1L
 
+  # Construct datasets, if needed
+  data$update_params(params = params)
+  data$construct()
+
   # Check interaction constraints
   cnames <- NULL
   if (!is.null(colnames)) {
@@ -167,8 +171,6 @@ lgb.train <- function(params = list(),
     data$set_categorical_feature(categorical_feature)
   }
 
-  # Construct datasets, if needed
-  data$construct()
   valid_contain_train <- FALSE
   train_data_name <- "train"
   reduced_valid_sets <- list()

R-package/tests/testthat/test_dataset.R

@@ -205,3 +205,63 @@ test_that("Dataset$update_params() works correctly for recognized Dataset parame
     expect_identical(new_params[[param_name]], updated_params[[param_name]])
   }
 })
+
+test_that("lgb.Dataset: should be able to run lgb.train() immediately after using lgb.Dataset() on a file", {
+  dtest <- lgb.Dataset(
+    data = test_data
+    , label = test_label
+  )
+  tmp_file <- tempfile(pattern = "lgb.Dataset_")
+  lgb.Dataset.save(
+    dataset = dtest
+    , fname = tmp_file
+  )
+
+  # read from a local file
+  dtest_read_in <- lgb.Dataset(data = tmp_file)
+
+  param <- list(
+    objective = "binary"
+    , metric = "binary_logloss"
+    , num_leaves = 5L
+    , learning_rate = 1.0
+  )
+
+  # should be able to train right away
+  bst <- lgb.train(
+    params = param
+    , data = dtest_read_in
+  )
+
+  expect_true(lgb.is.Booster(x = bst))
+})
+
+test_that("lgb.Dataset: should be able to run lgb.cv() immediately after using lgb.Dataset() on a file", {
+  dtest <- lgb.Dataset(
+    data = test_data
+    , label = test_label
+  )
+  tmp_file <- tempfile(pattern = "lgb.Dataset_")
+  lgb.Dataset.save(
+    dataset = dtest
+    , fname = tmp_file
+  )
+
+  # read from a local file
+  dtest_read_in <- lgb.Dataset(data = tmp_file)
+
+  param <- list(
+    objective = "binary"
+    , metric = "binary_logloss"
+    , num_leaves = 5L
+    , learning_rate = 1.0
+  )
+
+  # should be able to train right away
+  bst <- lgb.cv(
+    params = param
+    , data = dtest_read_in
+  )
+
+  expect_is(bst, "lgb.CVBooster")
+})