Nothing
# More specific testing of callbacks
require(xgboost)
require(data.table)
require(titanic)
context("callbacks")
data(agaricus.train, package = 'xgboost')
data(agaricus.test, package = 'xgboost')
train <- agaricus.train
test <- agaricus.test
# add some label noise for early stopping tests
add.noise <- function(label, frac) {
inoise <- sample(length(label), length(label) * frac)
label[inoise] <- !label[inoise]
label
}
set.seed(11)
ltrain <- add.noise(train$label, 0.2)
ltest <- add.noise(test$label, 0.2)
dtrain <- xgb.DMatrix(train$data, label = ltrain)
dtest <- xgb.DMatrix(test$data, label = ltest)
watchlist <- list(train = dtrain, test = dtest)
err <- function(label, pr) sum((pr > 0.5) != label) / length(label)
param <- list(objective = "binary:logistic", eval_metric = "error",
max_depth = 2, nthread = 2)
test_that("cb.print.evaluation works as expected", {
bst_evaluation <- c('train-auc' = 0.9, 'test-auc' = 0.8)
bst_evaluation_err <- NULL
begin_iteration <- 1
end_iteration <- 7
f0 <- cb.print.evaluation(period = 0)
f1 <- cb.print.evaluation(period = 1)
f5 <- cb.print.evaluation(period = 5)
expect_false(is.null(attr(f1, 'call')))
expect_equal(attr(f1, 'name'), 'cb.print.evaluation')
iteration <- 1
expect_silent(f0())
expect_output(f1(), "\\[1\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
expect_output(f5(), "\\[1\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
expect_null(f1())
iteration <- 2
expect_output(f1(), "\\[2\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
expect_silent(f5())
iteration <- 7
expect_output(f1(), "\\[7\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
expect_output(f5(), "\\[7\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
bst_evaluation_err <- c('train-auc' = 0.1, 'test-auc' = 0.2)
expect_output(f1(), "\\[7\\]\ttrain-auc:0.900000\\+0.100000\ttest-auc:0.800000\\+0.200000")
})
test_that("cb.evaluation.log works as expected", {
bst_evaluation <- c('train-auc' = 0.9, 'test-auc' = 0.8)
bst_evaluation_err <- NULL
evaluation_log <- list()
f <- cb.evaluation.log()
expect_false(is.null(attr(f, 'call')))
expect_equal(attr(f, 'name'), 'cb.evaluation.log')
iteration <- 1
expect_silent(f())
expect_equal(evaluation_log,
list(c(iter = 1, bst_evaluation)))
iteration <- 2
expect_silent(f())
expect_equal(evaluation_log,
list(c(iter = 1, bst_evaluation), c(iter = 2, bst_evaluation)))
expect_silent(f(finalize = TRUE))
expect_equal(evaluation_log,
data.table(iter = 1:2, train_auc = c(0.9, 0.9), test_auc = c(0.8, 0.8)))
bst_evaluation_err <- c('train-auc' = 0.1, 'test-auc' = 0.2)
evaluation_log <- list()
f <- cb.evaluation.log()
iteration <- 1
expect_silent(f())
expect_equal(evaluation_log,
list(c(iter = 1, c(bst_evaluation, bst_evaluation_err))))
iteration <- 2
expect_silent(f())
expect_equal(evaluation_log,
list(c(iter = 1, c(bst_evaluation, bst_evaluation_err)),
c(iter = 2, c(bst_evaluation, bst_evaluation_err))))
expect_silent(f(finalize = TRUE))
expect_equal(evaluation_log,
data.table(iter = 1:2,
train_auc_mean = c(0.9, 0.9), train_auc_std = c(0.1, 0.1),
test_auc_mean = c(0.8, 0.8), test_auc_std = c(0.2, 0.2)))
})
param <- list(objective = "binary:logistic", eval_metric = "error",
max_depth = 4, nthread = 2)
test_that("can store evaluation_log without printing", {
expect_silent(
bst <- xgb.train(param, dtrain, nrounds = 10, watchlist, eta = 1, verbose = 0)
)
expect_false(is.null(bst$evaluation_log))
expect_false(is.null(bst$evaluation_log$train_error))
expect_lt(bst$evaluation_log[, min(train_error)], 0.2)
})
test_that("cb.reset.parameters works as expected", {
# fixed eta
set.seed(111)
bst0 <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 0.9, verbose = 0)
expect_false(is.null(bst0$evaluation_log))
expect_false(is.null(bst0$evaluation_log$train_error))
# same eta but re-set as a vector parameter in the callback
set.seed(111)
my_par <- list(eta = c(0.9, 0.9))
bst1 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
callbacks = list(cb.reset.parameters(my_par)))
expect_false(is.null(bst1$evaluation_log$train_error))
expect_equal(bst0$evaluation_log$train_error,
bst1$evaluation_log$train_error)
# same eta but re-set via a function in the callback
set.seed(111)
my_par <- list(eta = function(itr, itr_end) 0.9)
bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
callbacks = list(cb.reset.parameters(my_par)))
expect_false(is.null(bst2$evaluation_log$train_error))
expect_equal(bst0$evaluation_log$train_error,
bst2$evaluation_log$train_error)
# different eta re-set as a vector parameter in the callback
set.seed(111)
my_par <- list(eta = c(0.6, 0.5))
bst3 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
callbacks = list(cb.reset.parameters(my_par)))
expect_false(is.null(bst3$evaluation_log$train_error))
expect_false(all(bst0$evaluation_log$train_error == bst3$evaluation_log$train_error))
# resetting multiple parameters at the same time runs with no error
my_par <- list(eta = c(1., 0.5), gamma = c(1, 2), max_depth = c(4, 8))
expect_error(
bst4 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
callbacks = list(cb.reset.parameters(my_par)))
, NA) # NA = no error
# CV works as well
expect_error(
bst4 <- xgb.cv(param, dtrain, nfold = 2, nrounds = 2, verbose = 0,
callbacks = list(cb.reset.parameters(my_par)))
, NA) # NA = no error
# expect no learning with 0 learning rate
my_par <- list(eta = c(0., 0.))
bstX <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
callbacks = list(cb.reset.parameters(my_par)))
expect_false(is.null(bstX$evaluation_log$train_error))
er <- unique(bstX$evaluation_log$train_error)
expect_length(er, 1)
expect_gt(er, 0.4)
})
test_that("cb.save.model works as expected", {
files <- c('xgboost_01.json', 'xgboost_02.json', 'xgboost.json')
for (f in files) if (file.exists(f)) file.remove(f)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1, verbose = 0,
save_period = 1, save_name = "xgboost_%02d.json")
expect_true(file.exists('xgboost_01.json'))
expect_true(file.exists('xgboost_02.json'))
b1 <- xgb.load('xgboost_01.json')
expect_equal(xgb.ntree(b1), 1)
b2 <- xgb.load('xgboost_02.json')
expect_equal(xgb.ntree(b2), 2)
xgb.config(b2) <- xgb.config(bst)
expect_equal(xgb.config(bst), xgb.config(b2))
expect_equal(bst$raw, b2$raw)
# save_period = 0 saves the last iteration's model
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1, verbose = 0,
save_period = 0, save_name = 'xgboost.json')
expect_true(file.exists('xgboost.json'))
b2 <- xgb.load('xgboost.json')
xgb.config(b2) <- xgb.config(bst)
expect_equal(bst$raw, b2$raw)
for (f in files) if (file.exists(f)) file.remove(f)
})
test_that("early stopping xgb.train works", {
set.seed(11)
expect_output(
bst <- xgb.train(param, dtrain, nrounds = 20, watchlist, eta = 0.3,
early_stopping_rounds = 3, maximize = FALSE)
, "Stopping. Best iteration")
expect_false(is.null(bst$best_iteration))
expect_lt(bst$best_iteration, 19)
expect_equal(bst$best_iteration, bst$best_ntreelimit)
pred <- predict(bst, dtest)
expect_equal(length(pred), 1611)
err_pred <- err(ltest, pred)
err_log <- bst$evaluation_log[bst$best_iteration, test_error]
expect_equal(err_log, err_pred, tolerance = 5e-6)
set.seed(11)
expect_silent(
bst0 <- xgb.train(param, dtrain, nrounds = 20, watchlist, eta = 0.3,
early_stopping_rounds = 3, maximize = FALSE, verbose = 0)
)
expect_equal(bst$evaluation_log, bst0$evaluation_log)
xgb.save(bst, "model.bin")
loaded <- xgb.load("model.bin")
expect_false(is.null(loaded$best_iteration))
expect_equal(loaded$best_iteration, bst$best_ntreelimit)
expect_equal(loaded$best_ntreelimit, bst$best_ntreelimit)
file.remove("model.bin")
})
test_that("early stopping using a specific metric works", {
set.seed(11)
expect_output(
bst <- xgb.train(param[-2], dtrain, nrounds = 20, watchlist, eta = 0.6,
eval_metric = "logloss", eval_metric = "auc",
callbacks = list(cb.early.stop(stopping_rounds = 3, maximize = FALSE,
metric_name = 'test_logloss')))
, "Stopping. Best iteration")
expect_false(is.null(bst$best_iteration))
expect_lt(bst$best_iteration, 19)
expect_equal(bst$best_iteration, bst$best_ntreelimit)
pred <- predict(bst, dtest, ntreelimit = bst$best_ntreelimit)
expect_equal(length(pred), 1611)
logloss_pred <- sum(-ltest * log(pred) - (1 - ltest) * log(1 - pred)) / length(ltest)
logloss_log <- bst$evaluation_log[bst$best_iteration, test_logloss]
expect_equal(logloss_log, logloss_pred, tolerance = 1e-5)
})
test_that("early stopping works with titanic", {
# This test was inspired by https://github.com/dmlc/xgboost/issues/5935
# It catches possible issues on noLD R
titanic <- titanic::titanic_train
titanic$Pclass <- as.factor(titanic$Pclass)
dtx <- model.matrix(~ 0 + ., data = titanic[, c("Pclass", "Sex")])
dty <- titanic$Survived
xgboost::xgboost(
data = dtx,
label = dty,
objective = "binary:logistic",
eval_metric = "auc",
nrounds = 100,
early_stopping_rounds = 3
)
expect_true(TRUE) # should not crash
})
test_that("early stopping xgb.cv works", {
set.seed(11)
expect_output(
cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.3, nrounds = 20,
early_stopping_rounds = 3, maximize = FALSE)
, "Stopping. Best iteration")
expect_false(is.null(cv$best_iteration))
expect_lt(cv$best_iteration, 19)
expect_equal(cv$best_iteration, cv$best_ntreelimit)
# the best error is min error:
expect_true(cv$evaluation_log[, test_error_mean[cv$best_iteration] == min(test_error_mean)])
})
test_that("prediction in xgb.cv works", {
set.seed(11)
nrounds <- 4
cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE, verbose = 0)
expect_false(is.null(cv$evaluation_log))
expect_false(is.null(cv$pred))
expect_length(cv$pred, nrow(train$data))
err_pred <- mean(sapply(cv$folds, function(f) mean(err(ltrain[f], cv$pred[f]))))
err_log <- cv$evaluation_log[nrounds, test_error_mean]
expect_equal(err_pred, err_log, tolerance = 1e-6)
# save CV models
set.seed(11)
cvx <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE, verbose = 0,
callbacks = list(cb.cv.predict(save_models = TRUE)))
expect_equal(cv$evaluation_log, cvx$evaluation_log)
expect_length(cvx$models, 5)
expect_true(all(sapply(cvx$models, class) == 'xgb.Booster'))
})
test_that("prediction in xgb.cv works for gblinear too", {
set.seed(11)
p <- list(booster = 'gblinear', objective = "reg:logistic", nthread = 2)
cv <- xgb.cv(p, dtrain, nfold = 5, eta = 0.5, nrounds = 2, prediction = TRUE, verbose = 0)
expect_false(is.null(cv$evaluation_log))
expect_false(is.null(cv$pred))
expect_length(cv$pred, nrow(train$data))
})
test_that("prediction in early-stopping xgb.cv works", {
set.seed(11)
expect_output(
cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.1, nrounds = 20,
early_stopping_rounds = 5, maximize = FALSE, stratified = FALSE,
prediction = TRUE)
, "Stopping. Best iteration")
expect_false(is.null(cv$best_iteration))
expect_lt(cv$best_iteration, 19)
expect_false(is.null(cv$evaluation_log))
expect_false(is.null(cv$pred))
expect_length(cv$pred, nrow(train$data))
err_pred <- mean(sapply(cv$folds, function(f) mean(err(ltrain[f], cv$pred[f]))))
err_log <- cv$evaluation_log[cv$best_iteration, test_error_mean]
expect_equal(err_pred, err_log, tolerance = 1e-6)
err_log_last <- cv$evaluation_log[cv$niter, test_error_mean]
expect_gt(abs(err_pred - err_log_last), 1e-4)
})
test_that("prediction in xgb.cv for softprob works", {
lb <- as.numeric(iris$Species) - 1
set.seed(11)
expect_warning(
cv <- xgb.cv(data = as.matrix(iris[, -5]), label = lb, nfold = 4,
eta = 0.5, nrounds = 5, max_depth = 3, nthread = 2,
subsample = 0.8, gamma = 2, verbose = 0,
prediction = TRUE, objective = "multi:softprob", num_class = 3)
, NA)
expect_false(is.null(cv$pred))
expect_equal(dim(cv$pred), c(nrow(iris), 3))
expect_lt(diff(range(rowSums(cv$pred))), 1e-6)
})
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