Nothing
require(xgboost)
require(Matrix)
context('Learning to rank')
n_threads <- 2
test_that('Test ranking with unweighted data', {
X <- sparseMatrix(i = c(2, 3, 7, 9, 12, 15, 17, 18),
j = c(1, 1, 2, 2, 3, 3, 4, 4),
x = rep(1.0, 8), dims = c(20, 4))
y <- c(0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0)
group <- c(5, 5, 5, 5)
dtrain <- xgb.DMatrix(X, label = y, group = group, nthread = n_threads)
params <- list(eta = 1, tree_method = 'exact', objective = 'rank:pairwise', max_depth = 1,
eval_metric = 'auc', eval_metric = 'aucpr', nthread = n_threads)
bst <- xgb.train(params, dtrain, nrounds = 10, watchlist = list(train = dtrain))
# Check if the metric is monotone increasing
expect_true(all(diff(bst$evaluation_log$train_auc) >= 0))
expect_true(all(diff(bst$evaluation_log$train_aucpr) >= 0))
})
test_that('Test ranking with weighted data', {
X <- sparseMatrix(i = c(2, 3, 7, 9, 12, 15, 17, 18),
j = c(1, 1, 2, 2, 3, 3, 4, 4),
x = rep(1.0, 8), dims = c(20, 4))
y <- c(0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0)
group <- c(5, 5, 5, 5)
weight <- c(1.0, 2.0, 3.0, 4.0)
dtrain <- xgb.DMatrix(
X, label = y, group = group, weight = weight, nthread = n_threads
)
params <- list(
eta = 1, tree_method = "exact", objective = "rank:pairwise", max_depth = 1,
eval_metric = "auc", eval_metric = "aucpr", nthread = n_threads
)
bst <- xgb.train(params, dtrain, nrounds = 10, watchlist = list(train = dtrain))
# Check if the metric is monotone increasing
expect_true(all(diff(bst$evaluation_log$train_auc) >= 0))
expect_true(all(diff(bst$evaluation_log$train_aucpr) >= 0))
for (i in 1:10) {
pred <- predict(bst, newdata = dtrain, ntreelimit = i)
# is_sorted[i]: is i-th group correctly sorted by the ranking predictor?
is_sorted <- lapply(seq(1, 20, by = 5),
function (k) {
ind <- order(-pred[k:(k + 4)])
z <- y[ind + (k - 1)]
all(diff(z) <= 0) # Check if z is monotone decreasing
})
# Since we give weights 1, 2, 3, 4 to the four query groups,
# the ranking predictor will first try to correctly sort the last query group
# before correctly sorting other groups.
expect_true(all(diff(as.numeric(is_sorted)) >= 0))
}
})
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