Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[R] Use new predict function. (#6819)

Browse Source 
* Call new C prediction API.
* Add `strict_shape`.
* Add `iterationrange`.
* Update document.
This commit is contained in:
Jiaming Yuan
2021-06-11 13:03:29 +08:00
committed by GitHub
parent 25514e104a
commit b56614e9b8
18 changed files with 293 additions and 160 deletions
Download Patch File Download Diff File Expand all files Collapse all files

67
R-package/tests/testthat/test_basic.R
View File

@@ -34,6 +34,10 @@ test_that("train and predict binary classification", {
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", {
@@ -143,6 +147,9 @@ test_that("train and predict softprob", {
pred_labels <- max.col(mpred) - 1
err <- sum(pred_labels != lb) / length(lb)
expect_equal(bst$evaluation_log[1, train_merror], err, tolerance = 5e-6)
mpred1 <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, iterationrange = c(1, 2))
expect_equal(mpred, mpred1)
})
test_that("train and predict softmax", {
@@ -182,10 +189,8 @@ test_that("train and predict RF", {
pred_err_20 <- sum((pred > 0.5) != lb) / length(lb)
expect_equal(pred_err_20, pred_err)
#pred <- predict(bst, train$data, ntreelimit = 1)
#pred_err_1 <- sum((pred > 0.5) != lb)/length(lb)
#expect_lt(pred_err, pred_err_1)
#expect_lt(pred_err, 0.08)
pred1 <- predict(bst, train$data, iterationrange = c(1, 2))
expect_equal(pred, pred1)
})
test_that("train and predict RF with softprob", {
@@ -385,3 +390,57 @@ test_that("Configuration works", {
reloaded_config <- xgb.config(bst)
expect_equal(config, reloaded_config);
})
test_that("strict_shape works", {
n_rounds <- 2
test_strict_shape <- function(bst, X, n_groups) {
predt <- predict(bst, X, strict_shape = TRUE)
margin <- predict(bst, X, outputmargin = TRUE, strict_shape = TRUE)
contri <- predict(bst, X, predcontrib = TRUE, strict_shape = TRUE)
interact <- predict(bst, X, predinteraction = TRUE, strict_shape = TRUE)
leaf <- predict(bst, X, predleaf = TRUE, strict_shape = TRUE)
n_rows <- nrow(X)
n_cols <- ncol(X)
expect_equal(dim(predt), c(n_groups, n_rows))
expect_equal(dim(margin), c(n_groups, n_rows))
expect_equal(dim(contri), c(n_cols + 1, n_groups, n_rows))
expect_equal(dim(interact), c(n_cols + 1, n_cols + 1, n_groups, n_rows))
expect_equal(dim(leaf), c(1, n_groups, n_rounds, n_rows))
if (n_groups != 1) {
for (g in seq_len(n_groups)) {
expect_lt(max(abs(colSums(contri[, g, ]) - margin[g, ])), 1e-5)
}
}
}
test_iris <- function() {
y <- as.numeric(iris$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()
})