context("lgb.get.eval.result")

test_that("lgb.get.eval.result() should throw an informative error if booster is not an lgb.Booster", {
    bad_inputs <- list(
        matrix(1.0:10.0, 2L, 5L)
        , TRUE
        , c("a", "b")
        , NA
        , 10L
        , lgb.Dataset(
            data = matrix(1.0:10.0, 2L, 5L)
            , params = list()
        )
    )
    for (bad_input in bad_inputs) {
        expect_error({
            lgb.get.eval.result(
                booster = bad_input
                , data_name = "test"
                , eval_name = "l2"
            )
        }, regexp = "Can only use", fixed = TRUE)
    }
})

test_that("lgb.get.eval.result() should throw an informative error for incorrect data_name", {
    data(agaricus.train, package = "lightgbm")
    data(agaricus.test, package = "lightgbm")
    dtrain <- lgb.Dataset(
        agaricus.train$data
        , label = agaricus.train$label
    )
    model <- lgb.train(
        params = list(
            objective = "regression"
            , metric = "l2"
        )
        , data = dtrain
        , nrounds = 5L
        , valids = list(
            "test" = lgb.Dataset.create.valid(
                dtrain
                , agaricus.test$data
                , label = agaricus.test$label
            )
        )
        , min_data = 1L
        , learning_rate = 1.0
    )
    expect_error({
        eval_results <- lgb.get.eval.result(
            booster = model
            , data_name = "testing"
            , eval_name = "l2"
        )
    }, regexp = "Only the following datasets exist in record evals: [test]", fixed = TRUE)
})

test_that("lgb.get.eval.result() should throw an informative error for incorrect eval_name", {
    data(agaricus.train, package = "lightgbm")
    data(agaricus.test, package = "lightgbm")
    dtrain <- lgb.Dataset(
        agaricus.train$data
        , label = agaricus.train$label
    )
    model <- lgb.train(
        params = list(
            objective = "regression"
            , metric = "l2"
        )
        , data = dtrain
        , nrounds = 5L
        , valids = list(
            "test" = lgb.Dataset.create.valid(
                dtrain
                , agaricus.test$data
                , label = agaricus.test$label
            )
        )
        , min_data = 1L
        , learning_rate = 1.0
    )
    expect_error({
        eval_results <- lgb.get.eval.result(
            booster = model
            , data_name = "test"
            , eval_name = "l1"
        )
    }, regexp = "Only the following eval_names exist for dataset 'test': [l2]", fixed = TRUE)
})