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| # 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_hist' 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 |