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[R] Enable weighted learning to rank (#5945)

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* [R] enable weighted learning to rank

* Add R unit test for ranking

* Fix lint
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Philip Hyunsu Cho 2020-07-26 21:10:36 -07:00 committed by GitHub
parent ace7fd328b
commit 6347fa1c2e
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2 changed files with 51 additions and 2 deletions
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2
R-package/R/xgb.DMatrix.R
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@ -257,8 +257,6 @@ setinfo.xgb.DMatrix <- function(object, name, info, ...) {
return(TRUE) return(TRUE)
} }
if (name == "weight") { if (name == "weight") {
if (length(info) != nrow(object))
stop("The length of weights must equal to the number of rows in the input data")
.Call(XGDMatrixSetInfo_R, object, name, as.numeric(info)) .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
return(TRUE) return(TRUE)
} }

51
R-package/tests/testthat/test_ranking.R Normal file
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@ -0,0 +1,51 @@
require(xgboost)
require(Matrix)
context('Learning to rank')
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)
params <- list(eta = 1, tree_method = 'exact', objective = 'rank:pairwise', max_depth = 1,
eval_metric = 'auc', eval_metric = 'aucpr')
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)
params <- list(eta = 1, tree_method = 'exact', objective = 'rank:pairwise', max_depth = 1,
eval_metric = 'auc', eval_metric = 'aucpr')
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))
}
})