74db9757b3
* [R] MSVC compatibility * [GPU] allow seed in BernoulliRng up to size_t and scale to uint32_t * R package build with cmake and CUDA * R package CUDA build fixes and cleanups * always export the R package native initialization routine on windows * update the install instructions doc * fix lint * use static_cast directly to set BernoulliRng seed * [R] demo for GPU accelerated algorithm * tidy up the R package cmake stuff * R pack cmake: installs main dependency packages if needed * [R] version bump in DESCRIPTION * update NEWS * added short missing/sparse values explanations to FAQ
46 lines
1.5 KiB
R
46 lines
1.5 KiB
R
# An example of using GPU-accelerated tree building algorithms
|
|
#
|
|
# NOTE: it can only run if you have a CUDA-enable GPU and the package was
|
|
# specially compiled with GPU support.
|
|
#
|
|
# For the current functionality, see
|
|
# https://xgboost.readthedocs.io/en/latest/gpu/index.html
|
|
#
|
|
|
|
library('xgboost')
|
|
|
|
# Simulate N x p random matrix with some binomial response dependent on pp columns
|
|
set.seed(111)
|
|
N <- 1000000
|
|
p <- 50
|
|
pp <- 25
|
|
X <- matrix(runif(N * p), ncol = p)
|
|
betas <- 2 * runif(pp) - 1
|
|
sel <- sort(sample(p, pp))
|
|
m <- X[, sel] %*% betas - 1 + rnorm(N)
|
|
y <- rbinom(N, 1, plogis(m))
|
|
|
|
tr <- sample.int(N, N * 0.75)
|
|
dtrain <- xgb.DMatrix(X[tr,], label = y[tr])
|
|
dtest <- xgb.DMatrix(X[-tr,], label = y[-tr])
|
|
wl <- list(train = dtrain, test = dtest)
|
|
|
|
# An example of running 'gpu_hist' algorithm
|
|
# which is
|
|
# - similar to the 'hist'
|
|
# - the fastest option for moderately large datasets
|
|
# - current limitations: max_depth < 16, does not implement guided loss
|
|
# You can use tree_method = 'gpu_exact' for another GPU accelerated algorithm,
|
|
# which is slower, more memory-hungry, but does not use binning.
|
|
param <- list(objective = 'reg:logistic', eval_metric = 'auc', subsample = 0.5, nthread = 4,
|
|
max_bin = 64, tree_method = 'gpu_hist')
|
|
pt <- proc.time()
|
|
bst_gpu <- xgb.train(param, dtrain, watchlist = wl, nrounds = 50)
|
|
proc.time() - pt
|
|
|
|
# Compare to the 'hist' algorithm:
|
|
param$tree_method <- 'hist'
|
|
pt <- proc.time()
|
|
bst_hist <- xgb.train(param, dtrain, watchlist = wl, nrounds = 50)
|
|
proc.time() - pt
|