[R-package] added tests on LGBM_BoosterResetTrainingData_R (microsoft…

…#3020)

R-package/tests/testthat/test_lgb.Booster.R

@@ -311,3 +311,66 @@ test_that("Booster$rollback_one_iter() should work as expected", {
     logloss <- bst$eval_train()[[1L]][["value"]]
     expect_equal(logloss, 0.027915146)
 })
+
+test_that("Booster$update() passing a train_set works as expected", {
+    set.seed(708L)
+    data(agaricus.train, package = "lightgbm")
+    nrounds <- 2L
+
+    # train with 2 rounds and then update
+    bst <- lightgbm(
+        data = as.matrix(agaricus.train$data)
+        , label = agaricus.train$label
+        , num_leaves = 4L
+        , learning_rate = 1.0
+        , nrounds = nrounds
+        , objective = "binary"
+    )
+    expect_true(lgb.is.Booster(bst))
+    expect_equal(bst$current_iter(), nrounds)
+    bst$update(
+        train_set = Dataset$new(
+            data = agaricus.train$data
+            , label = agaricus.train$label
+        )
+    )
+    expect_true(lgb.is.Booster(bst))
+    expect_equal(bst$current_iter(), nrounds + 1L)
+
+    # train with 3 rounds directlry
+    bst2 <- lightgbm(
+        data = as.matrix(agaricus.train$data)
+        , label = agaricus.train$label
+        , num_leaves = 4L
+        , learning_rate = 1.0
+        , nrounds = nrounds +  1L
+        , objective = "binary"
+    )
+    expect_true(lgb.is.Booster(bst2))
+    expect_equal(bst2$current_iter(), nrounds +  1L)
+
+    # model with 2 rounds + 1 update should be identical to 3 rounds
+    expect_equal(bst2$eval_train()[[1L]][["value"]], 0.04806585)
+    expect_equal(bst$eval_train()[[1L]][["value"]], bst2$eval_train()[[1L]][["value"]])
+})
+
+test_that("Booster$update() throws an informative error if you provide a non-Dataset to update()", {
+    set.seed(708L)
+    data(agaricus.train, package = "lightgbm")
+    nrounds <- 2L
+
+    # train with 2 rounds and then update
+    bst <- lightgbm(
+        data = as.matrix(agaricus.train$data)
+        , label = agaricus.train$label
+        , num_leaves = 4L
+        , learning_rate = 1.0
+        , nrounds = nrounds
+        , objective = "binary"
+    )
+    expect_error({
+        bst$update(
+            train_set = data.frame(x = rnorm(10L))
+        )
+    }, regexp = "lgb.Booster.update: Only can use lgb.Dataset", fixed = TRUE)
+})