144 lines
4.1 KiB
R
144 lines
4.1 KiB
R
context('Test models with custom objective')
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set.seed(1994)
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data(agaricus.train, package = 'xgboost')
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data(agaricus.test, package = 'xgboost')
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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dtest <- xgb.DMatrix(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 <- getinfo(dtrain, "label")
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preds <- 1 / (1 + exp(-preds))
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grad <- preds - labels
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hess <- preds * (1 - preds)
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return(list(grad = grad, hess = hess))
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}
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evalerror <- function(preds, dtrain) {
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labels <- getinfo(dtrain, "label")
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err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels)
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return(list(metric = "error", value = err))
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}
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param <- list(max_depth = 2, eta = 1, nthread = 2,
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objective = logregobj, eval_metric = evalerror)
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num_round <- 2
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test_that("custom objective works", {
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bst <- xgb.train(param, dtrain, num_round, watchlist)
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expect_equal(class(bst), "xgb.Booster")
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expect_false(is.null(bst$evaluation_log))
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expect_false(is.null(bst$evaluation_log$eval_error))
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expect_lt(bst$evaluation_log[num_round, eval_error], 0.03)
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})
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test_that("custom objective in CV works", {
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cv <- xgb.cv(param, dtrain, num_round, nfold = 10, verbose = FALSE)
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expect_false(is.null(cv$evaluation_log))
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expect_equal(dim(cv$evaluation_log), c(2, 5))
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expect_lt(cv$evaluation_log[num_round, test_error_mean], 0.03)
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})
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test_that("custom objective with early stop works", {
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bst <- xgb.train(param, dtrain, 10, watchlist)
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expect_equal(class(bst), "xgb.Booster")
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train_log <- bst$evaluation_log$train_error
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expect_true(all(diff(train_log) <= 0))
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})
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test_that("custom objective using DMatrix attr works", {
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attr(dtrain, 'label') <- getinfo(dtrain, 'label')
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logregobjattr <- function(preds, dtrain) {
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labels <- attr(dtrain, 'label')
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preds <- 1 / (1 + exp(-preds))
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grad <- preds - labels
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hess <- preds * (1 - preds)
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return(list(grad = grad, hess = hess))
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}
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param$objective <- logregobjattr
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bst <- xgb.train(param, dtrain, num_round, watchlist)
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expect_equal(class(bst), "xgb.Booster")
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})
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test_that("custom objective with multi-class shape", {
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data <- as.matrix(iris[, -5])
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label <- as.numeric(iris$Species) - 1
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dtrain <- xgb.DMatrix(data = data, label = label)
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n_classes <- 3
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fake_softprob <- function(preds, dtrain) {
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expect_true(all(matrix(preds) == 0.5))
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## use numeric vector here to test compatibility with XGBoost < 2.1
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grad <- rnorm(length(as.matrix(preds)))
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expect_equal(dim(data)[1] * n_classes, dim(as.matrix(preds))[1] * n_classes)
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hess <- rnorm(length(as.matrix(preds)))
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return(list(grad = grad, hess = hess))
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}
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fake_merror <- function(preds, dtrain) {
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expect_equal(dim(data)[1] * n_classes, dim(as.matrix(preds))[1])
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}
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param$objective <- fake_softprob
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param$eval_metric <- fake_merror
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bst <- xgb.train(param, dtrain, 1, num_class = n_classes)
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})
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softmax <- function(values) {
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values <- as.numeric(values)
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exps <- exp(values)
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den <- sum(exps)
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return(exps / den)
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}
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softprob <- function(predt, dtrain) {
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y <- getinfo(dtrain, "label")
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n_samples <- dim(predt)[1]
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n_classes <- dim(predt)[2]
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grad <- matrix(nrow = n_samples, ncol = n_classes)
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hess <- matrix(nrow = n_samples, ncol = n_classes)
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for (i in seq_len(n_samples)) {
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t <- y[i]
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p <- softmax(predt[i, ])
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for (c in seq_len(n_classes)) {
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g <- if (c - 1 == t) {
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p[c] - 1.0
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} else {
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p[c]
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}
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h <- max((2.0 * p[c] * (1.0 - p[c])), 1e-6)
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grad[i, c] <- g
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hess[i, c] <- h
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}
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}
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return(list(grad = grad, hess = hess))
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}
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test_that("custom objective with multi-class works", {
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data <- as.matrix(iris[, -5])
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label <- as.numeric(iris$Species) - 1
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dtrain <- xgb.DMatrix(data = data, label = label)
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param$num_class <- 3
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param$objective <- softprob
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param$eval_metric <- "merror"
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param$base_score <- 0.5
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custom_bst <- xgb.train(param, dtrain, 2)
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custom_predt <- predict(custom_bst, dtrain)
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param$objective <- "multi:softmax"
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builtin_bst <- xgb.train(param, dtrain, 2)
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builtin_predt <- predict(builtin_bst, dtrain)
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expect_equal(custom_predt, builtin_predt)
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})
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