wehub-resource-sync
86 lines
3.0 KiB
R
86 lines
3.0 KiB
R
data(agaricus.train, package = "lightgbm")
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data(agaricus.test, package = "lightgbm")
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dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)
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dtest <- lgb.Dataset(agaricus.test$data, label = agaricus.test$label)
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watchlist <- list(eval = dtest, train = dtrain)
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logregobj <- function(preds, dtrain) {
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labels <- get_field(dtrain, "label")
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preds <- 1.0 / (1.0 + exp(-preds))
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grad <- preds - labels
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hess <- preds * (1.0 - preds)
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return(list(grad = grad, hess = hess))
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}
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# User-defined evaluation function returns a pair (metric_name, result, higher_better)
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# NOTE: when you do customized loss function, the default prediction value is margin
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# This may make built-in evaluation metric calculate wrong results
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# Keep this in mind when you use the customization, and maybe you need write customized evaluation function
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evalerror <- function(preds, dtrain) {
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labels <- get_field(dtrain, "label")
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preds <- 1.0 / (1.0 + exp(-preds))
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err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels)
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return(list(
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name = "error"
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, value = err
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, higher_better = FALSE
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))
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}
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param <- list(
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num_leaves = 8L
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, learning_rate = 1.0
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, objective = logregobj
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, metric = "auc"
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, verbose = .LGB_VERBOSITY
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, num_threads = .LGB_MAX_THREADS
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)
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num_round <- 10L
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test_that("custom objective works", {
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bst <- lgb.train(param, dtrain, num_round, watchlist, eval = evalerror)
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expect_false(is.null(bst$record_evals))
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})
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test_that("using a custom objective, custom eval, and no other metrics works", {
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set.seed(708L)
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bst <- lgb.train(
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params = list(
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num_leaves = 8L
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, learning_rate = 1.0
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, verbose = .LGB_VERBOSITY
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, num_threads = .LGB_MAX_THREADS
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)
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, data = dtrain
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, nrounds = 4L
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, valids = watchlist
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, obj = logregobj
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, eval = evalerror
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)
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expect_false(is.null(bst$record_evals))
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expect_equal(bst$best_iter, 4L)
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expect_true(abs(bst$best_score - 0.000621) < .LGB_NUMERIC_TOLERANCE)
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eval_results <- bst$eval_valid(feval = evalerror)[[1L]]
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expect_true(eval_results[["data_name"]] == "eval")
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expect_true(abs(eval_results[["value"]] - 0.0006207325) < .LGB_NUMERIC_TOLERANCE)
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expect_true(eval_results[["name"]] == "error")
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expect_false(eval_results[["higher_better"]])
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})
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test_that("using a custom objective that returns wrong shape grad or hess raises an informative error", {
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bad_grad <- function(preds, dtrain) {
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return(list(grad = numeric(0L), hess = rep(1.0, length(preds))))
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}
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bad_hess <- function(preds, dtrain) {
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return(list(grad = rep(1.0, length(preds)), hess = numeric(0L)))
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}
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params <- list(num_leaves = 3L, verbose = .LGB_VERBOSITY)
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expect_error({
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lgb.train(params = params, data = dtrain, obj = bad_grad)
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}, sprintf("Expected custom objective function to return grad with length %d, got 0.", nrow(dtrain)))
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expect_error({
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lgb.train(params = params, data = dtrain, obj = bad_hess)
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}, sprintf("Expected custom objective function to return hess with length %d, got 0.", nrow(dtrain)))
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})
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