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Fix R dart prediction. (#5204)

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* Fix R dart prediction and add test.
This commit is contained in:
Jiaming Yuan
2020-01-16 12:11:04 +08:00
committed by GitHub
parent 808f61081b
commit 5199b86126
5 changed files with 80 additions and 35 deletions
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62
R-package/tests/testthat/test_basic.R
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@@ -27,7 +27,7 @@ test_that("train and predict binary classification", {
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)
@@ -35,6 +35,54 @@ test_that("train and predict binary classification", {
expect_lt(abs(err_pred1 - err_log), 10e-6)
})
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",
verbosity = 3,
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$Species) - 1
set.seed(11)
@@ -74,7 +122,7 @@ test_that("train and predict softmax", {
expect_false(is.null(bst$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)
@@ -90,12 +138,12 @@ test_that("train and predict RF", {
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)
@@ -211,7 +259,7 @@ test_that("train and predict with non-strict classes", {
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))
@@ -221,7 +269,7 @@ test_that("train and predict with non-strict classes", {
, 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))
@@ -231,7 +279,7 @@ test_that("train and predict with non-strict classes", {
, regexp = NA)
expect_error(pr <- predict(bst, train_dense), regexp = NA)
expect_equal(pr0, pr)
# when someone inhertis 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)