481 lines
16 KiB
R
481 lines
16 KiB
R
# More specific testing of callbacks
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context("callbacks")
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data(agaricus.train, package = 'xgboost')
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data(agaricus.test, package = 'xgboost')
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train <- agaricus.train
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test <- agaricus.test
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n_threads <- 2
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# add some label noise for early stopping tests
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add.noise <- function(label, frac) {
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inoise <- sample(length(label), length(label) * frac)
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label[inoise] <- !label[inoise]
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label
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}
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set.seed(11)
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ltrain <- add.noise(train$label, 0.2)
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ltest <- add.noise(test$label, 0.2)
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dtrain <- xgb.DMatrix(train$data, label = ltrain, nthread = n_threads)
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dtest <- xgb.DMatrix(test$data, label = ltest, nthread = n_threads)
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evals <- list(train = dtrain, test = dtest)
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err <- function(label, pr) sum((pr > 0.5) != label) / length(label)
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param <- list(objective = "binary:logistic", eval_metric = "error",
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max_depth = 2, nthread = n_threads)
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test_that("xgb.cb.print.evaluation works as expected for xgb.train", {
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logs1 <- capture.output({
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model <- xgb.train(
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data = dtrain,
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params = list(
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objective = "binary:logistic",
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eval_metric = "auc",
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max_depth = 2,
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nthread = n_threads
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),
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nrounds = 10,
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evals = list(train = dtrain, test = dtest),
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callbacks = list(xgb.cb.print.evaluation(period = 1))
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)
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})
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expect_equal(length(logs1), 10)
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expect_true(all(grepl("^\\[\\d{1,2}\\]\ttrain-auc:0\\.\\d+\ttest-auc:0\\.\\d+\\s*$", logs1)))
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lapply(seq(1, 10), function(x) expect_true(grepl(paste0("^\\[", x), logs1[x])))
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logs2 <- capture.output({
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model <- xgb.train(
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data = dtrain,
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params = list(
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objective = "binary:logistic",
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eval_metric = "auc",
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max_depth = 2,
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nthread = n_threads
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),
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nrounds = 10,
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evals = list(train = dtrain, test = dtest),
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callbacks = list(xgb.cb.print.evaluation(period = 2))
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)
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})
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expect_equal(length(logs2), 6)
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expect_true(all(grepl("^\\[\\d{1,2}\\]\ttrain-auc:0\\.\\d+\ttest-auc:0\\.\\d+\\s*$", logs2)))
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seq_matches <- c(seq(1, 10, 2), 10)
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lapply(seq_along(seq_matches), function(x) expect_true(grepl(paste0("^\\[", seq_matches[x]), logs2[x])))
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})
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test_that("xgb.cb.print.evaluation works as expected for xgb.cv", {
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logs1 <- capture.output({
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model <- xgb.cv(
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data = dtrain,
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params = list(
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objective = "binary:logistic",
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eval_metric = "auc",
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max_depth = 2,
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nthread = n_threads
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),
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nrounds = 10,
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nfold = 3,
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callbacks = list(xgb.cb.print.evaluation(period = 1, showsd = TRUE))
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)
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})
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expect_equal(length(logs1), 10)
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expect_true(all(grepl("^\\[\\d{1,2}\\]\ttrain-auc:0\\.\\d+±0\\.\\d+\ttest-auc:0\\.\\d+±0\\.\\d+\\s*$", logs1)))
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lapply(seq(1, 10), function(x) expect_true(grepl(paste0("^\\[", x), logs1[x])))
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logs2 <- capture.output({
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model <- xgb.cv(
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data = dtrain,
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params = list(
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objective = "binary:logistic",
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eval_metric = "auc",
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max_depth = 2,
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nthread = n_threads
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),
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nrounds = 10,
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nfold = 3,
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callbacks = list(xgb.cb.print.evaluation(period = 2, showsd = TRUE))
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)
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})
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expect_equal(length(logs2), 6)
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expect_true(all(grepl("^\\[\\d{1,2}\\]\ttrain-auc:0\\.\\d+±0\\.\\d+\ttest-auc:0\\.\\d+±0\\.\\d+\\s*$", logs2)))
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seq_matches <- c(seq(1, 10, 2), 10)
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lapply(seq_along(seq_matches), function(x) expect_true(grepl(paste0("^\\[", seq_matches[x]), logs2[x])))
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})
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test_that("xgb.cb.evaluation.log works as expected for xgb.train", {
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model <- xgb.train(
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data = dtrain,
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params = list(
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objective = "binary:logistic",
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eval_metric = "auc",
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max_depth = 2,
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nthread = n_threads
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),
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nrounds = 10,
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verbose = FALSE,
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evals = list(train = dtrain, test = dtest),
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callbacks = list(xgb.cb.evaluation.log())
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)
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logs <- attributes(model)$evaluation_log
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expect_equal(nrow(logs), 10)
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expect_equal(colnames(logs), c("iter", "train_auc", "test_auc"))
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})
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test_that("xgb.cb.evaluation.log works as expected for xgb.cv", {
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model <- xgb.cv(
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data = dtrain,
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params = list(
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objective = "binary:logistic",
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eval_metric = "auc",
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max_depth = 2,
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nthread = n_threads
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),
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nrounds = 10,
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verbose = FALSE,
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nfold = 3,
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callbacks = list(xgb.cb.evaluation.log())
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)
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logs <- model$evaluation_log
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expect_equal(nrow(logs), 10)
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expect_equal(
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colnames(logs),
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c("iter", "train_auc_mean", "train_auc_std", "test_auc_mean", "test_auc_std")
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)
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})
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param <- list(objective = "binary:logistic", eval_metric = "error",
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max_depth = 4, nthread = n_threads)
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test_that("can store evaluation_log without printing", {
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expect_silent(
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bst <- xgb.train(param, dtrain, nrounds = 10, evals = evals, eta = 1, verbose = 0)
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)
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expect_false(is.null(attributes(bst)$evaluation_log))
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expect_false(is.null(attributes(bst)$evaluation_log$train_error))
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expect_lt(attributes(bst)$evaluation_log[, min(train_error)], 0.2)
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})
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test_that("xgb.cb.reset.parameters works as expected", {
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# fixed eta
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set.seed(111)
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bst0 <- xgb.train(param, dtrain, nrounds = 2, evals = evals, eta = 0.9, verbose = 0)
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expect_false(is.null(attributes(bst0)$evaluation_log))
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expect_false(is.null(attributes(bst0)$evaluation_log$train_error))
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# same eta but re-set as a vector parameter in the callback
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set.seed(111)
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my_par <- list(eta = c(0.9, 0.9))
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bst1 <- xgb.train(param, dtrain, nrounds = 2, evals = evals, verbose = 0,
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callbacks = list(xgb.cb.reset.parameters(my_par)))
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expect_false(is.null(attributes(bst1)$evaluation_log$train_error))
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expect_equal(attributes(bst0)$evaluation_log$train_error,
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attributes(bst1)$evaluation_log$train_error)
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# same eta but re-set via a function in the callback
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set.seed(111)
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my_par <- list(eta = function(itr, itr_end) 0.9)
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bst2 <- xgb.train(param, dtrain, nrounds = 2, evals = evals, verbose = 0,
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callbacks = list(xgb.cb.reset.parameters(my_par)))
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expect_false(is.null(attributes(bst2)$evaluation_log$train_error))
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expect_equal(attributes(bst0)$evaluation_log$train_error,
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attributes(bst2)$evaluation_log$train_error)
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# different eta re-set as a vector parameter in the callback
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set.seed(111)
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my_par <- list(eta = c(0.6, 0.5))
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bst3 <- xgb.train(param, dtrain, nrounds = 2, evals = evals, verbose = 0,
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callbacks = list(xgb.cb.reset.parameters(my_par)))
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expect_false(is.null(attributes(bst3)$evaluation_log$train_error))
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expect_false(all(attributes(bst0)$evaluation_log$train_error == attributes(bst3)$evaluation_log$train_error))
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# resetting multiple parameters at the same time runs with no error
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my_par <- list(eta = c(1., 0.5), gamma = c(1, 2), max_depth = c(4, 8))
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expect_error(
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bst4 <- xgb.train(param, dtrain, nrounds = 2, evals = evals, verbose = 0,
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callbacks = list(xgb.cb.reset.parameters(my_par)))
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, NA) # NA = no error
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# CV works as well
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expect_error(
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bst4 <- xgb.cv(param, dtrain, nfold = 2, nrounds = 2, verbose = 0,
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callbacks = list(xgb.cb.reset.parameters(my_par)))
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, NA) # NA = no error
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# expect no learning with 0 learning rate
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my_par <- list(eta = c(0., 0.))
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bstX <- xgb.train(param, dtrain, nrounds = 2, evals = evals, verbose = 0,
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callbacks = list(xgb.cb.reset.parameters(my_par)))
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expect_false(is.null(attributes(bstX)$evaluation_log$train_error))
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er <- unique(attributes(bstX)$evaluation_log$train_error)
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expect_length(er, 1)
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expect_gt(er, 0.4)
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})
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test_that("xgb.cb.save.model works as expected", {
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files <- c('xgboost_01.json', 'xgboost_02.json', 'xgboost.json')
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files <- unname(sapply(files, function(f) file.path(tempdir(), f)))
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for (f in files) if (file.exists(f)) file.remove(f)
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bst <- xgb.train(param, dtrain, nrounds = 2, evals = evals, eta = 1, verbose = 0,
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save_period = 1, save_name = file.path(tempdir(), "xgboost_%02d.json"))
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expect_true(file.exists(files[1]))
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expect_true(file.exists(files[2]))
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b1 <- xgb.load(files[1])
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xgb.parameters(b1) <- list(nthread = 2)
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expect_equal(xgb.get.num.boosted.rounds(b1), 1)
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b2 <- xgb.load(files[2])
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xgb.parameters(b2) <- list(nthread = 2)
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expect_equal(xgb.get.num.boosted.rounds(b2), 2)
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xgb.config(b2) <- xgb.config(bst)
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expect_equal(xgb.config(bst), xgb.config(b2))
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expect_equal(xgb.save.raw(bst), xgb.save.raw(b2))
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# save_period = 0 saves the last iteration's model
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bst <- xgb.train(param, dtrain, nrounds = 2, evals = evals, eta = 1, verbose = 0,
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save_period = 0, save_name = file.path(tempdir(), 'xgboost.json'))
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expect_true(file.exists(files[3]))
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b2 <- xgb.load(files[3])
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xgb.config(b2) <- xgb.config(bst)
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expect_equal(xgb.save.raw(bst), xgb.save.raw(b2))
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for (f in files) if (file.exists(f)) file.remove(f)
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})
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test_that("early stopping xgb.train works", {
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set.seed(11)
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expect_output(
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bst <- xgb.train(param, dtrain, nrounds = 20, evals = evals, eta = 0.3,
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early_stopping_rounds = 3, maximize = FALSE)
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, "Stopping. Best iteration")
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expect_false(is.null(xgb.attr(bst, "best_iteration")))
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expect_lt(xgb.attr(bst, "best_iteration"), 19)
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pred <- predict(bst, dtest)
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expect_equal(length(pred), 1611)
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err_pred <- err(ltest, pred)
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err_log <- attributes(bst)$evaluation_log[xgb.attr(bst, "best_iteration") + 1, test_error]
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expect_equal(err_log, err_pred, tolerance = 5e-6)
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set.seed(11)
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expect_silent(
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bst0 <- xgb.train(param, dtrain, nrounds = 20, evals = evals, eta = 0.3,
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early_stopping_rounds = 3, maximize = FALSE, verbose = 0)
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)
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expect_equal(attributes(bst)$evaluation_log, attributes(bst0)$evaluation_log)
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fname <- file.path(tempdir(), "model.bin")
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xgb.save(bst, fname)
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loaded <- xgb.load(fname)
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expect_false(is.null(xgb.attr(loaded, "best_iteration")))
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expect_equal(xgb.attr(loaded, "best_iteration"), xgb.attr(bst, "best_iteration"))
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})
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test_that("early stopping using a specific metric works", {
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set.seed(11)
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expect_output(
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bst <- xgb.train(param[-2], dtrain, nrounds = 20, evals = evals, eta = 0.6,
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eval_metric = "logloss", eval_metric = "auc",
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callbacks = list(xgb.cb.early.stop(stopping_rounds = 3, maximize = FALSE,
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metric_name = 'test_logloss')))
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, "Stopping. Best iteration")
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expect_false(is.null(xgb.attr(bst, "best_iteration")))
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expect_lt(xgb.attr(bst, "best_iteration"), 19)
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pred <- predict(bst, dtest, iterationrange = c(1, xgb.attr(bst, "best_iteration") + 1))
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expect_equal(length(pred), 1611)
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logloss_pred <- sum(-ltest * log(pred) - (1 - ltest) * log(1 - pred)) / length(ltest)
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logloss_log <- attributes(bst)$evaluation_log[xgb.attr(bst, "best_iteration") + 1, test_logloss]
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expect_equal(logloss_log, logloss_pred, tolerance = 1e-5)
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})
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test_that("early stopping works with titanic", {
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if (!requireNamespace("titanic")) {
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testthat::skip("Optional testing dependency 'titanic' not found.")
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}
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# This test was inspired by https://github.com/dmlc/xgboost/issues/5935
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# It catches possible issues on noLD R
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titanic <- titanic::titanic_train
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titanic$Pclass <- as.factor(titanic$Pclass)
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dtx <- model.matrix(~ 0 + ., data = titanic[, c("Pclass", "Sex")])
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dty <- titanic$Survived
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xgboost::xgb.train(
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data = xgb.DMatrix(dtx, label = dty),
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objective = "binary:logistic",
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eval_metric = "auc",
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nrounds = 100,
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early_stopping_rounds = 3,
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nthread = n_threads,
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evals = list(train = xgb.DMatrix(dtx, label = dty))
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)
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expect_true(TRUE) # should not crash
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})
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test_that("early stopping xgb.cv works", {
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set.seed(11)
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expect_output(
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cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.3, nrounds = 20,
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early_stopping_rounds = 3, maximize = FALSE)
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, "Stopping. Best iteration")
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expect_false(is.null(cv$early_stop$best_iteration))
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expect_lt(cv$early_stop$best_iteration, 19)
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# the best error is min error:
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expect_true(cv$evaluation_log[, test_error_mean[cv$early_stop$best_iteration] == min(test_error_mean)])
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})
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test_that("prediction in xgb.cv works", {
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set.seed(11)
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nrounds <- 4
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cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE, verbose = 0)
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expect_false(is.null(cv$evaluation_log))
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expect_false(is.null(cv$cv_predict$pred))
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expect_length(cv$cv_predict$pred, nrow(train$data))
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err_pred <- mean(sapply(cv$folds, function(f) mean(err(ltrain[f], cv$cv_predict$pred[f]))))
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err_log <- cv$evaluation_log[nrounds, test_error_mean]
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expect_equal(err_pred, err_log, tolerance = 1e-6)
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# save CV models
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set.seed(11)
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cvx <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE, verbose = 0,
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callbacks = list(xgb.cb.cv.predict(save_models = TRUE)))
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expect_equal(cv$evaluation_log, cvx$evaluation_log)
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expect_length(cvx$cv_predict$models, 5)
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expect_true(all(sapply(cvx$cv_predict$models, class) == 'xgb.Booster'))
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})
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test_that("prediction in xgb.cv works for gblinear too", {
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set.seed(11)
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p <- list(booster = 'gblinear', objective = "reg:logistic", nthread = n_threads)
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cv <- xgb.cv(p, dtrain, nfold = 5, eta = 0.5, nrounds = 2, prediction = TRUE, verbose = 0)
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expect_false(is.null(cv$evaluation_log))
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expect_false(is.null(cv$cv_predict$pred))
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expect_length(cv$cv_predict$pred, nrow(train$data))
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})
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test_that("prediction in early-stopping xgb.cv works", {
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set.seed(11)
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expect_output(
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cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.1, nrounds = 20,
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early_stopping_rounds = 5, maximize = FALSE, stratified = FALSE,
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prediction = TRUE, base_score = 0.5)
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, "Stopping. Best iteration")
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expect_false(is.null(cv$early_stop$best_iteration))
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expect_lt(cv$early_stop$best_iteration, 19)
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expect_false(is.null(cv$evaluation_log))
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expect_false(is.null(cv$cv_predict$pred))
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expect_length(cv$cv_predict$pred, nrow(train$data))
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err_pred <- mean(sapply(cv$folds, function(f) mean(err(ltrain[f], cv$cv_predict$pred[f]))))
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err_log <- cv$evaluation_log[cv$early_stop$best_iteration, test_error_mean]
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expect_equal(err_pred, err_log, tolerance = 1e-6)
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err_log_last <- cv$evaluation_log[cv$niter, test_error_mean]
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expect_gt(abs(err_pred - err_log_last), 1e-4)
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})
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test_that("prediction in xgb.cv for softprob works", {
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lb <- as.numeric(iris$Species) - 1
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set.seed(11)
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expect_warning(
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cv <- xgb.cv(data = as.matrix(iris[, -5]), label = lb, nfold = 4,
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eta = 0.5, nrounds = 5, max_depth = 3, nthread = n_threads,
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subsample = 0.8, gamma = 2, verbose = 0,
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prediction = TRUE, objective = "multi:softprob", num_class = 3)
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, NA)
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expect_false(is.null(cv$cv_predict$pred))
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expect_equal(dim(cv$cv_predict$pred), c(nrow(iris), 3))
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expect_lt(diff(range(rowSums(cv$cv_predict$pred))), 1e-6)
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})
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test_that("prediction in xgb.cv works for multi-quantile", {
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data(mtcars)
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y <- mtcars$mpg
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x <- as.matrix(mtcars[, -1])
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dm <- xgb.DMatrix(x, label = y, nthread = 1)
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cv <- xgb.cv(
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data = dm,
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params = list(
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objective = "reg:quantileerror",
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quantile_alpha = c(0.1, 0.2, 0.5, 0.8, 0.9),
|
|
nthread = 1
|
|
),
|
|
nrounds = 5,
|
|
nfold = 3,
|
|
prediction = TRUE,
|
|
verbose = 0
|
|
)
|
|
expect_equal(dim(cv$cv_predict$pred), c(nrow(x), 5))
|
|
})
|
|
|
|
test_that("prediction in xgb.cv works for multi-output", {
|
|
data(mtcars)
|
|
y <- mtcars$mpg
|
|
x <- as.matrix(mtcars[, -1])
|
|
dm <- xgb.DMatrix(x, label = cbind(y, -y), nthread = 1)
|
|
cv <- xgb.cv(
|
|
data = dm,
|
|
params = list(
|
|
tree_method = "hist",
|
|
multi_strategy = "multi_output_tree",
|
|
objective = "reg:squarederror",
|
|
nthread = n_threads
|
|
),
|
|
nrounds = 5,
|
|
nfold = 3,
|
|
prediction = TRUE,
|
|
verbose = 0
|
|
)
|
|
expect_equal(dim(cv$cv_predict$pred), c(nrow(x), 2))
|
|
})
|
|
|
|
test_that("prediction in xgb.cv works for multi-quantile", {
|
|
data(mtcars)
|
|
y <- mtcars$mpg
|
|
x <- as.matrix(mtcars[, -1])
|
|
dm <- xgb.DMatrix(x, label = y, nthread = 1)
|
|
cv <- xgb.cv(
|
|
data = dm,
|
|
params = list(
|
|
objective = "reg:quantileerror",
|
|
quantile_alpha = c(0.1, 0.2, 0.5, 0.8, 0.9),
|
|
nthread = 1
|
|
),
|
|
nrounds = 5,
|
|
nfold = 3,
|
|
prediction = TRUE,
|
|
verbose = 0
|
|
)
|
|
expect_equal(dim(cv$cv_predict$pred), c(nrow(x), 5))
|
|
})
|
|
|
|
test_that("prediction in xgb.cv works for multi-output", {
|
|
data(mtcars)
|
|
y <- mtcars$mpg
|
|
x <- as.matrix(mtcars[, -1])
|
|
dm <- xgb.DMatrix(x, label = cbind(y, -y), nthread = 1)
|
|
cv <- xgb.cv(
|
|
data = dm,
|
|
params = list(
|
|
tree_method = "hist",
|
|
multi_strategy = "multi_output_tree",
|
|
objective = "reg:squarederror",
|
|
nthread = n_threads
|
|
),
|
|
nrounds = 5,
|
|
nfold = 3,
|
|
prediction = TRUE,
|
|
verbose = 0
|
|
)
|
|
expect_equal(dim(cv$cv_predict$pred), c(nrow(x), 2))
|
|
})
|