bdfa8c0e09
* [R] fix #1465 * [R] add sanity check to fix #1434 * [R] some clean-ups for custom obj&eval; require maximize only for early stopping
305 lines
11 KiB
R
305 lines
11 KiB
R
# More specific testing of callbacks
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require(xgboost)
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require(data.table)
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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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# 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$data, label = ltrain)
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dtest <- xgb.DMatrix(test$data, label = ltest)
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watchlist = 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", max_depth = 2, nthread = 2)
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test_that("cb.print.evaluation works as expected", {
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bst_evaluation <- c('train-auc'=0.9, 'test-auc'=0.8)
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bst_evaluation_err <- NULL
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begin_iteration <- 1
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end_iteration <- 7
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f0 <- cb.print.evaluation(period=0)
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f1 <- cb.print.evaluation(period=1)
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f5 <- cb.print.evaluation(period=5)
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expect_false(is.null(attr(f1, 'call')))
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expect_equal(attr(f1, 'name'), 'cb.print.evaluation')
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iteration <- 1
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expect_silent(f0())
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expect_output(f1(), "\\[1\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
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expect_output(f5(), "\\[1\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
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expect_null(f1())
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iteration <- 2
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expect_output(f1(), "\\[2\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
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expect_silent(f5())
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iteration <- 7
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expect_output(f1(), "\\[7\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
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expect_output(f5(), "\\[7\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
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bst_evaluation_err <- c('train-auc'=0.1, 'test-auc'=0.2)
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expect_output(f1(), "\\[7\\]\ttrain-auc:0.900000\\+0.100000\ttest-auc:0.800000\\+0.200000")
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})
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test_that("cb.evaluation.log works as expected", {
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bst_evaluation <- c('train-auc'=0.9, 'test-auc'=0.8)
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bst_evaluation_err <- NULL
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evaluation_log <- list()
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f <- cb.evaluation.log()
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expect_false(is.null(attr(f, 'call')))
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expect_equal(attr(f, 'name'), 'cb.evaluation.log')
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iteration <- 1
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expect_silent(f())
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expect_equal(evaluation_log,
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list(c(iter=1, bst_evaluation)))
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iteration <- 2
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expect_silent(f())
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expect_equal(evaluation_log,
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list(c(iter=1, bst_evaluation), c(iter=2, bst_evaluation)))
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expect_silent(f(finalize = TRUE))
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expect_equal(evaluation_log,
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data.table(iter=1:2, train_auc=c(0.9,0.9), test_auc=c(0.8,0.8)))
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bst_evaluation_err <- c('train-auc'=0.1, 'test-auc'=0.2)
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evaluation_log <- list()
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f <- cb.evaluation.log()
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iteration <- 1
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expect_silent(f())
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expect_equal(evaluation_log,
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list(c(iter=1, c(bst_evaluation, bst_evaluation_err))))
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iteration <- 2
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expect_silent(f())
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expect_equal(evaluation_log,
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list(c(iter=1, c(bst_evaluation, bst_evaluation_err)),
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c(iter=2, c(bst_evaluation, bst_evaluation_err))))
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expect_silent(f(finalize = TRUE))
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expect_equal(evaluation_log,
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data.table(iter=1:2,
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train_auc_mean=c(0.9,0.9), train_auc_std=c(0.1,0.1),
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test_auc_mean=c(0.8,0.8), test_auc_std=c(0.2,0.2)))
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})
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param <- list(objective = "binary:logistic", max_depth = 4, nthread = 2)
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test_that("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, watchlist, eta = 0.9)
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expect_false(is.null(bst0$evaluation_log))
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expect_false(is.null(bst0$evaluation_log$train_error))
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# same eta but re-set as a vector parameter in the callback
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set.seed(111)
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my_par <- list(eta = c(0.9, 0.9))
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bst1 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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callbacks = list(cb.reset.parameters(my_par)))
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expect_false(is.null(bst1$evaluation_log$train_error))
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expect_equal(bst0$evaluation_log$train_error,
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bst1$evaluation_log$train_error)
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# same eta but re-set via a function in the callback
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set.seed(111)
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my_par <- list(eta = function(itr, itr_end) 0.9)
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bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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callbacks = list(cb.reset.parameters(my_par)))
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expect_false(is.null(bst2$evaluation_log$train_error))
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expect_equal(bst0$evaluation_log$train_error,
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bst2$evaluation_log$train_error)
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# different eta re-set as a vector parameter in the callback
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set.seed(111)
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my_par <- list(eta = c(0.6, 0.5))
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bst3 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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callbacks = list(cb.reset.parameters(my_par)))
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expect_false(is.null(bst3$evaluation_log$train_error))
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expect_false(all(bst0$evaluation_log$train_error == bst3$evaluation_log$train_error))
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# resetting multiple parameters at the same time runs with no error
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my_par <- list(eta = c(1., 0.5), gamma = c(1, 2), max_depth = c(4, 8))
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expect_error(
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bst4 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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callbacks = list(cb.reset.parameters(my_par)))
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, NA) # NA = no error
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# expect no learning with 0 learning rate
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my_par <- list(eta = c(0., 0.))
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bstX <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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callbacks = list(cb.reset.parameters(my_par)))
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expect_false(is.null(bstX$evaluation_log$train_error))
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er <- unique(bstX$evaluation_log$train_error)
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expect_length(er, 1)
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expect_gt(er, 0.4)
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})
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test_that("cb.save.model works as expected", {
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files <- c('xgboost_01.model', 'xgboost_02.model', 'xgboost.model')
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for (f in files) if (file.exists(f)) file.remove(f)
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bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1,
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save_period = 1, save_name = "xgboost_%02d.model")
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expect_true(file.exists('xgboost_01.model'))
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expect_true(file.exists('xgboost_02.model'))
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b1 <- xgb.load('xgboost_01.model')
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expect_equal(xgb.ntree(b1), 1)
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b2 <- xgb.load('xgboost_02.model')
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expect_equal(xgb.ntree(b2), 2)
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expect_equal(bst$raw, b2$raw)
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# save_period = 0 saves the last iteration's model
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bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1, save_period = 0)
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expect_true(file.exists('xgboost.model'))
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b2 <- xgb.load('xgboost.model')
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expect_equal(bst$raw, b2$raw)
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for (f in files) if (file.exists(f)) file.remove(f)
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})
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test_that("can store evaluation_log without printing", {
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expect_silent(
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bst <- xgb.train(param, dtrain, nrounds = 10, watchlist, eta = 1,
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verbose = 0, callbacks = list(cb.evaluation.log()))
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)
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expect_false(is.null(bst$evaluation_log))
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expect_false(is.null(bst$evaluation_log$train_error))
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expect_lt(bst$evaluation_log[, min(train_error)], 0.2)
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})
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test_that("early stopping xgb.train works", {
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set.seed(11)
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expect_output(
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bst <- xgb.train(param, dtrain, nrounds = 20, watchlist, eta = 0.3,
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early_stopping_rounds = 3, maximize = FALSE)
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, "Stopping. Best iteration")
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expect_false(is.null(bst$best_iteration))
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expect_lt(bst$best_iteration, 19)
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expect_equal(bst$best_iteration, bst$best_ntreelimit)
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pred <- predict(bst, dtest)
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expect_equal(length(pred), 1611)
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err_pred <- err(ltest, pred)
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err_log <- bst$evaluation_log[bst$best_iteration, test_error]
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expect_equal(err_log, err_pred, tolerance = 5e-6)
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})
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test_that("early stopping using a specific metric works", {
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set.seed(11)
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expect_output(
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bst <- xgb.train(param, dtrain, nrounds = 20, watchlist, eta = 0.6,
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eval_metric="logloss", eval_metric="auc",
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callbacks = list(cb.early.stop(stopping_rounds = 3, maximize = FALSE,
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metric_name = 'test_logloss')))
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, "Stopping. Best iteration")
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expect_false(is.null(bst$best_iteration))
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expect_lt(bst$best_iteration, 19)
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expect_equal(bst$best_iteration, bst$best_ntreelimit)
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pred <- predict(bst, dtest, ntreelimit = bst$best_ntreelimit)
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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 <- bst$evaluation_log[bst$best_iteration, test_logloss]
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expect_equal(logloss_log, logloss_pred, tolerance = 5e-6)
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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$best_iteration))
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expect_lt(cv$best_iteration, 19)
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expect_equal(cv$best_iteration, cv$best_ntreelimit)
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# the best error is min error:
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expect_true(cv$evaluation_log[, test_error_mean[cv$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)
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expect_false(is.null(cv$evaluation_log))
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expect_false(is.null(cv$pred))
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expect_length(cv$pred, nrow(train$data))
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err_pred <- mean( sapply(cv$folds, function(f) mean(err(ltrain[f], cv$pred[f]))) )
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err_log <- cv$evaluation_log[nrounds, test_error_mean]
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expect_equal(err_pred, err_log, tolerance = 1e-6)
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# save CV models
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set.seed(11)
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cvx <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE,
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callbacks = list(cb.cv.predict(save_models = TRUE)))
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expect_equal(cv$evaluation_log, cvx$evaluation_log)
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expect_length(cvx$models, 5)
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expect_true(all(sapply(cvx$models, class) == 'xgb.Booster'))
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})
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test_that("prediction in xgb.cv works for gblinear too", {
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set.seed(11)
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p <- list(booster = 'gblinear', objective = "reg:logistic", nthread = 2)
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cv <- xgb.cv(p, dtrain, nfold = 5, eta = 0.5, nrounds = 2, prediction = TRUE)
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expect_false(is.null(cv$evaluation_log))
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expect_false(is.null(cv$pred))
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expect_length(cv$pred, nrow(train$data))
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})
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test_that("prediction in early-stopping xgb.cv works", {
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set.seed(1)
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expect_output(
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cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.1, nrounds = 20,
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early_stopping_rounds = 5, maximize = FALSE, prediction = TRUE)
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, "Stopping. Best iteration")
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expect_false(is.null(cv$best_iteration))
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expect_lt(cv$best_iteration, 19)
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expect_false(is.null(cv$evaluation_log))
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expect_false(is.null(cv$pred))
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expect_length(cv$pred, nrow(train$data))
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err_pred <- mean( sapply(cv$folds, function(f) mean(err(ltrain[f], cv$pred[f]))) )
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err_log <- cv$evaluation_log[cv$best_iteration, test_error_mean]
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expect_equal(err_pred, err_log, tolerance = 1e-6)
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err_log_last <- cv$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 = 2,
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subsample = 0.8, gamma = 2,
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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$pred))
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expect_equal(dim(cv$pred), c(nrow(iris), 3))
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expect_lt(diff(range(rowSums(cv$pred))), 1e-6)
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})
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