Nothing
require(xgboost)
library(Matrix)
context("basic functions")
data(agaricus.train, package = 'xgboost')
data(agaricus.test, package = 'xgboost')
train <- agaricus.train
test <- agaricus.test
set.seed(1994)
# disable some tests for Win32
windows_flag <- .Platform$OS.type == "windows" &&
.Machine$sizeof.pointer != 8
solaris_flag <- (Sys.info()['sysname'] == "SunOS")
test_that("train and predict binary classification", {
nrounds <- 2
expect_output(
bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
eta = 1, nthread = 2, nrounds = nrounds, objective = "binary:logistic",
eval_metric = "error")
, "train-error")
expect_equal(class(bst), "xgb.Booster")
expect_equal(bst$niter, nrounds)
expect_false(is.null(bst$evaluation_log))
expect_equal(nrow(bst$evaluation_log), nrounds)
expect_lt(bst$evaluation_log[, min(train_error)], 0.03)
pred <- predict(bst, test$data)
expect_length(pred, 1611)
pred1 <- predict(bst, train$data, ntreelimit = 1)
expect_length(pred1, 6513)
err_pred1 <- sum((pred1 > 0.5) != train$label) / length(train$label)
err_log <- bst$evaluation_log[1, train_error]
expect_lt(abs(err_pred1 - err_log), 10e-6)
pred2 <- predict(bst, train$data, iterationrange = c(1, 2))
expect_length(pred1, 6513)
expect_equal(pred1, pred2)
})
test_that("parameter validation works", {
p <- list(foo = "bar")
nrounds <- 1
set.seed(1994)
d <- cbind(
x1 = rnorm(10),
x2 = rnorm(10),
x3 = rnorm(10))
y <- d[, "x1"] + d[, "x2"]^2 +
ifelse(d[, "x3"] > .5, d[, "x3"]^2, 2^d[, "x3"]) +
rnorm(10)
dtrain <- xgb.DMatrix(data = d, info = list(label = y))
correct <- function() {
params <- list(max_depth = 2, booster = "dart",
rate_drop = 0.5, one_drop = TRUE,
objective = "reg:squarederror")
xgb.train(params = params, data = dtrain, nrounds = nrounds)
}
expect_silent(correct())
incorrect <- function() {
params <- list(max_depth = 2, booster = "dart",
rate_drop = 0.5, one_drop = TRUE,
objective = "reg:squarederror",
foo = "bar", bar = "foo")
output <- capture.output(
xgb.train(params = params, data = dtrain, nrounds = nrounds))
print(output)
}
expect_output(incorrect(), '\\\\"bar\\\\", \\\\"foo\\\\"')
})
test_that("dart prediction works", {
nrounds <- 32
set.seed(1994)
d <- cbind(
x1 = rnorm(100),
x2 = rnorm(100),
x3 = rnorm(100))
y <- d[, "x1"] + d[, "x2"]^2 +
ifelse(d[, "x3"] > .5, d[, "x3"]^2, 2^d[, "x3"]) +
rnorm(100)
set.seed(1994)
booster_by_xgboost <- xgboost(data = d, label = y, max_depth = 2, booster = "dart",
rate_drop = 0.5, one_drop = TRUE,
eta = 1, nthread = 2, nrounds = nrounds, objective = "reg:squarederror")
pred_by_xgboost_0 <- predict(booster_by_xgboost, newdata = d, ntreelimit = 0)
pred_by_xgboost_1 <- predict(booster_by_xgboost, newdata = d, ntreelimit = nrounds)
expect_true(all(matrix(pred_by_xgboost_0, byrow = TRUE) == matrix(pred_by_xgboost_1, byrow = TRUE)))
pred_by_xgboost_2 <- predict(booster_by_xgboost, newdata = d, training = TRUE)
expect_false(all(matrix(pred_by_xgboost_0, byrow = TRUE) == matrix(pred_by_xgboost_2, byrow = TRUE)))
set.seed(1994)
dtrain <- xgb.DMatrix(data = d, info = list(label = y))
booster_by_train <- xgb.train(params = list(
booster = "dart",
max_depth = 2,
eta = 1,
rate_drop = 0.5,
one_drop = TRUE,
nthread = 1,
tree_method = "exact",
objective = "reg:squarederror"
),
data = dtrain,
nrounds = nrounds
)
pred_by_train_0 <- predict(booster_by_train, newdata = dtrain, ntreelimit = 0)
pred_by_train_1 <- predict(booster_by_train, newdata = dtrain, ntreelimit = nrounds)
pred_by_train_2 <- predict(booster_by_train, newdata = dtrain, training = TRUE)
expect_true(all(matrix(pred_by_train_0, byrow = TRUE) == matrix(pred_by_xgboost_0, byrow = TRUE)))
expect_true(all(matrix(pred_by_train_1, byrow = TRUE) == matrix(pred_by_xgboost_1, byrow = TRUE)))
expect_true(all(matrix(pred_by_train_2, byrow = TRUE) == matrix(pred_by_xgboost_2, byrow = TRUE)))
})
test_that("train and predict softprob", {
lb <- as.numeric(iris$Species) - 1
set.seed(11)
expect_output(
bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
max_depth = 3, eta = 0.5, nthread = 2, nrounds = 5,
objective = "multi:softprob", num_class = 3, eval_metric = "merror")
, "train-merror")
expect_false(is.null(bst$evaluation_log))
expect_lt(bst$evaluation_log[, min(train_merror)], 0.025)
expect_equal(bst$niter * 3, xgb.ntree(bst))
pred <- predict(bst, as.matrix(iris[, -5]))
expect_length(pred, nrow(iris) * 3)
# row sums add up to total probability of 1:
expect_equal(rowSums(matrix(pred, ncol = 3, byrow = TRUE)), rep(1, nrow(iris)), tolerance = 1e-7)
# manually calculate error at the last iteration:
mpred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE)
expect_equal(as.numeric(t(mpred)), pred)
pred_labels <- max.col(mpred) - 1
err <- sum(pred_labels != lb) / length(lb)
expect_equal(bst$evaluation_log[5, train_merror], err, tolerance = 5e-6)
# manually calculate error at the 1st iteration:
mpred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, ntreelimit = 1)
pred_labels <- max.col(mpred) - 1
err <- sum(pred_labels != lb) / length(lb)
expect_equal(bst$evaluation_log[1, train_merror], err, tolerance = 5e-6)
mpred1 <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, iterationrange = c(1, 2))
expect_equal(mpred, mpred1)
d <- cbind(
x1 = rnorm(100),
x2 = rnorm(100),
x3 = rnorm(100)
)
y <- sample.int(10, 100, replace = TRUE) - 1
dtrain <- xgb.DMatrix(data = d, info = list(label = y))
booster <- xgb.train(
params = list(tree_method = "hist"), data = dtrain, nrounds = 4, num_class = 10,
objective = "multi:softprob"
)
predt <- predict(booster, as.matrix(d), reshape = TRUE, strict_shape = FALSE)
expect_equal(ncol(predt), 10)
expect_equal(rowSums(predt), rep(1, 100), tolerance = 1e-7)
})
test_that("train and predict softmax", {
lb <- as.numeric(iris$Species) - 1
set.seed(11)
expect_output(
bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
max_depth = 3, eta = 0.5, nthread = 2, nrounds = 5,
objective = "multi:softmax", num_class = 3, eval_metric = "merror")
, "train-merror")
expect_false(is.null(bst$evaluation_log))
expect_lt(bst$evaluation_log[, min(train_merror)], 0.025)
expect_equal(bst$niter * 3, xgb.ntree(bst))
pred <- predict(bst, as.matrix(iris[, -5]))
expect_length(pred, nrow(iris))
err <- sum(pred != lb) / length(lb)
expect_equal(bst$evaluation_log[5, train_merror], err, tolerance = 5e-6)
})
test_that("train and predict RF", {
set.seed(11)
lb <- train$label
# single iteration
bst <- xgboost(data = train$data, label = lb, max_depth = 5,
nthread = 2, nrounds = 1, objective = "binary:logistic", eval_metric = "error",
num_parallel_tree = 20, subsample = 0.6, colsample_bytree = 0.1)
expect_equal(bst$niter, 1)
expect_equal(xgb.ntree(bst), 20)
pred <- predict(bst, train$data)
pred_err <- sum((pred > 0.5) != lb) / length(lb)
expect_lt(abs(bst$evaluation_log[1, train_error] - pred_err), 10e-6)
#expect_lt(pred_err, 0.03)
pred <- predict(bst, train$data, ntreelimit = 20)
pred_err_20 <- sum((pred > 0.5) != lb) / length(lb)
expect_equal(pred_err_20, pred_err)
pred1 <- predict(bst, train$data, iterationrange = c(1, 2))
expect_equal(pred, pred1)
})
test_that("train and predict RF with softprob", {
lb <- as.numeric(iris$Species) - 1
nrounds <- 15
set.seed(11)
bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
max_depth = 3, eta = 0.9, nthread = 2, nrounds = nrounds,
objective = "multi:softprob", eval_metric = "merror",
num_class = 3, verbose = 0,
num_parallel_tree = 4, subsample = 0.5, colsample_bytree = 0.5)
expect_equal(bst$niter, 15)
expect_equal(xgb.ntree(bst), 15 * 3 * 4)
# predict for all iterations:
pred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE)
expect_equal(dim(pred), c(nrow(iris), 3))
pred_labels <- max.col(pred) - 1
err <- sum(pred_labels != lb) / length(lb)
expect_equal(bst$evaluation_log[nrounds, train_merror], err, tolerance = 5e-6)
# predict for 7 iterations and adjust for 4 parallel trees per iteration
pred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, ntreelimit = 7 * 4)
err <- sum((max.col(pred) - 1) != lb) / length(lb)
expect_equal(bst$evaluation_log[7, train_merror], err, tolerance = 5e-6)
})
test_that("use of multiple eval metrics works", {
expect_output(
bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic",
eval_metric = 'error', eval_metric = 'auc', eval_metric = "logloss")
, "train-error.*train-auc.*train-logloss")
expect_false(is.null(bst$evaluation_log))
expect_equal(dim(bst$evaluation_log), c(2, 4))
expect_equal(colnames(bst$evaluation_log), c("iter", "train_error", "train_auc", "train_logloss"))
})
test_that("training continuation works", {
dtrain <- xgb.DMatrix(train$data, label = train$label)
watchlist <- list(train = dtrain)
param <- list(objective = "binary:logistic", max_depth = 2, eta = 1, nthread = 2)
# for the reference, use 4 iterations at once:
set.seed(11)
bst <- xgb.train(param, dtrain, nrounds = 4, watchlist, verbose = 0)
# first two iterations:
set.seed(11)
bst1 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0)
# continue for two more:
bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = bst1)
if (!windows_flag && !solaris_flag)
expect_equal(bst$raw, bst2$raw)
expect_false(is.null(bst2$evaluation_log))
expect_equal(dim(bst2$evaluation_log), c(4, 2))
expect_equal(bst2$evaluation_log, bst$evaluation_log)
# test continuing from raw model data
bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = bst1$raw)
if (!windows_flag && !solaris_flag)
expect_equal(bst$raw, bst2$raw)
expect_equal(dim(bst2$evaluation_log), c(2, 2))
# test continuing from a model in file
xgb.save(bst1, "xgboost.json")
bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = "xgboost.json")
if (!windows_flag && !solaris_flag)
expect_equal(bst$raw, bst2$raw)
expect_equal(dim(bst2$evaluation_log), c(2, 2))
file.remove("xgboost.json")
})
test_that("model serialization works", {
out_path <- "model_serialization"
dtrain <- xgb.DMatrix(train$data, label = train$label)
watchlist <- list(train = dtrain)
param <- list(objective = "binary:logistic")
booster <- xgb.train(param, dtrain, nrounds = 4, watchlist)
raw <- xgb.serialize(booster)
saveRDS(raw, out_path)
raw <- readRDS(out_path)
loaded <- xgb.unserialize(raw)
raw_from_loaded <- xgb.serialize(loaded)
expect_equal(raw, raw_from_loaded)
file.remove(out_path)
})
test_that("xgb.cv works", {
set.seed(11)
expect_output(
cv <- xgb.cv(data = train$data, label = train$label, max_depth = 2, nfold = 5,
eta = 1., nthread = 2, nrounds = 2, objective = "binary:logistic",
eval_metric = "error", verbose = TRUE)
, "train-error:")
expect_is(cv, 'xgb.cv.synchronous')
expect_false(is.null(cv$evaluation_log))
expect_lt(cv$evaluation_log[, min(test_error_mean)], 0.03)
expect_lt(cv$evaluation_log[, min(test_error_std)], 0.008)
expect_equal(cv$niter, 2)
expect_false(is.null(cv$folds) && is.list(cv$folds))
expect_length(cv$folds, 5)
expect_false(is.null(cv$params) && is.list(cv$params))
expect_false(is.null(cv$callbacks))
expect_false(is.null(cv$call))
})
test_that("xgb.cv works with stratified folds", {
dtrain <- xgb.DMatrix(train$data, label = train$label)
set.seed(314159)
cv <- xgb.cv(data = dtrain, max_depth = 2, nfold = 5,
eta = 1., nthread = 2, nrounds = 2, objective = "binary:logistic",
verbose = TRUE, stratified = FALSE)
set.seed(314159)
cv2 <- xgb.cv(data = dtrain, max_depth = 2, nfold = 5,
eta = 1., nthread = 2, nrounds = 2, objective = "binary:logistic",
verbose = TRUE, stratified = TRUE)
# Stratified folds should result in a different evaluation logs
expect_true(all(cv$evaluation_log[, test_logloss_mean] != cv2$evaluation_log[, test_logloss_mean]))
})
test_that("train and predict with non-strict classes", {
# standard dense matrix input
train_dense <- as.matrix(train$data)
bst <- xgboost(data = train_dense, label = train$label, max_depth = 2,
eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
pr0 <- predict(bst, train_dense)
# dense matrix-like input of non-matrix class
class(train_dense) <- 'shmatrix'
expect_true(is.matrix(train_dense))
expect_error(
bst <- xgboost(data = train_dense, label = train$label, max_depth = 2,
eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
, regexp = NA)
expect_error(pr <- predict(bst, train_dense), regexp = NA)
expect_equal(pr0, pr)
# dense matrix-like input of non-matrix class with some inheritance
class(train_dense) <- c('pphmatrix', 'shmatrix')
expect_true(is.matrix(train_dense))
expect_error(
bst <- xgboost(data = train_dense, label = train$label, max_depth = 2,
eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
, regexp = NA)
expect_error(pr <- predict(bst, train_dense), regexp = NA)
expect_equal(pr0, pr)
# when someone inherits from xgb.Booster, it should still be possible to use it as xgb.Booster
class(bst) <- c('super.Booster', 'xgb.Booster')
expect_error(pr <- predict(bst, train_dense), regexp = NA)
expect_equal(pr0, pr)
})
test_that("max_delta_step works", {
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
watchlist <- list(train = dtrain)
param <- list(objective = "binary:logistic", eval_metric = "logloss", max_depth = 2, nthread = 2, eta = 0.5)
nrounds <- 5
# model with no restriction on max_delta_step
bst1 <- xgb.train(param, dtrain, nrounds, watchlist, verbose = 1)
# model with restricted max_delta_step
bst2 <- xgb.train(param, dtrain, nrounds, watchlist, verbose = 1, max_delta_step = 1)
# the no-restriction model is expected to have consistently lower loss during the initial iterations
expect_true(all(bst1$evaluation_log$train_logloss < bst2$evaluation_log$train_logloss))
expect_lt(mean(bst1$evaluation_log$train_logloss) / mean(bst2$evaluation_log$train_logloss), 0.8)
})
test_that("colsample_bytree works", {
# Randomly generate data matrix by sampling from uniform distribution [-1, 1]
set.seed(1)
train_x <- matrix(runif(1000, min = -1, max = 1), ncol = 100)
train_y <- as.numeric(rowSums(train_x) > 0)
test_x <- matrix(runif(1000, min = -1, max = 1), ncol = 100)
test_y <- as.numeric(rowSums(test_x) > 0)
colnames(train_x) <- paste0("Feature_", sprintf("%03d", 1:100))
colnames(test_x) <- paste0("Feature_", sprintf("%03d", 1:100))
dtrain <- xgb.DMatrix(train_x, label = train_y)
dtest <- xgb.DMatrix(test_x, label = test_y)
watchlist <- list(train = dtrain, eval = dtest)
## Use colsample_bytree = 0.01, so that roughly one out of 100 features is chosen for
## each tree
param <- list(max_depth = 2, eta = 0, nthread = 2,
colsample_bytree = 0.01, objective = "binary:logistic",
eval_metric = "auc")
set.seed(2)
bst <- xgb.train(param, dtrain, nrounds = 100, watchlist, verbose = 0)
xgb.importance(model = bst)
# If colsample_bytree works properly, a variety of features should be used
# in the 100 trees
expect_gte(nrow(xgb.importance(model = bst)), 30)
})
test_that("Configuration works", {
bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic",
eval_metric = 'error', eval_metric = 'auc', eval_metric = "logloss")
config <- xgb.config(bst)
xgb.config(bst) <- config
reloaded_config <- xgb.config(bst)
expect_equal(config, reloaded_config);
})
test_that("strict_shape works", {
n_rounds <- 2
test_strict_shape <- function(bst, X, n_groups) {
predt <- predict(bst, X, strict_shape = TRUE)
margin <- predict(bst, X, outputmargin = TRUE, strict_shape = TRUE)
contri <- predict(bst, X, predcontrib = TRUE, strict_shape = TRUE)
interact <- predict(bst, X, predinteraction = TRUE, strict_shape = TRUE)
leaf <- predict(bst, X, predleaf = TRUE, strict_shape = TRUE)
n_rows <- nrow(X)
n_cols <- ncol(X)
expect_equal(dim(predt), c(n_groups, n_rows))
expect_equal(dim(margin), c(n_groups, n_rows))
expect_equal(dim(contri), c(n_cols + 1, n_groups, n_rows))
expect_equal(dim(interact), c(n_cols + 1, n_cols + 1, n_groups, n_rows))
expect_equal(dim(leaf), c(1, n_groups, n_rounds, n_rows))
if (n_groups != 1) {
for (g in seq_len(n_groups)) {
expect_lt(max(abs(colSums(contri[, g, ]) - margin[g, ])), 1e-5)
}
}
}
test_iris <- function() {
y <- as.numeric(iris$Species) - 1
X <- as.matrix(iris[, -5])
bst <- xgboost(data = X, label = y,
max_depth = 2, nrounds = n_rounds,
objective = "multi:softprob", num_class = 3, eval_metric = "merror")
test_strict_shape(bst, X, 3)
}
test_agaricus <- function() {
data(agaricus.train, package = 'xgboost')
X <- agaricus.train$data
y <- agaricus.train$label
bst <- xgboost(data = X, label = y, max_depth = 2,
nrounds = n_rounds, objective = "binary:logistic",
eval_metric = 'error', eval_metric = 'auc', eval_metric = "logloss")
test_strict_shape(bst, X, 1)
}
test_iris()
test_agaricus()
})
test_that("'predict' accepts CSR data", {
X <- agaricus.train$data
y <- agaricus.train$label
x_csc <- as(X[1L, , drop = FALSE], "CsparseMatrix")
x_csr <- as(x_csc, "RsparseMatrix")
x_spv <- as(x_csc, "sparseVector")
bst <- xgboost(data = X, label = y, objective = "binary:logistic",
nrounds = 5L, verbose = FALSE)
p_csc <- predict(bst, x_csc)
p_csr <- predict(bst, x_csr)
p_spv <- predict(bst, x_spv)
expect_equal(p_csc, p_csr)
expect_equal(p_csc, p_spv)
})
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