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# Script to generate reference models. The reference models are used to test backward compatibility
# of saved model files from XGBoost version 0.90 and 1.0.x.
library(xgboost)
library(Matrix)
set.seed(0)
metadata <- list(
kRounds = 2,
kRows = 1000,
kCols = 4,
kForests = 2,
kMaxDepth = 2,
kClasses = 3
)
X <- Matrix(data = rnorm(metadata$kRows * metadata$kCols), nrow = metadata$kRows,
ncol = metadata$kCols, sparse = TRUE)
w <- runif(metadata$kRows)
version <- packageVersion('xgboost')
target_dir <- 'models'
save_booster <- function(booster, model_name) {
booster_bin <- function(model_name) {
return(file.path(target_dir, paste('xgboost-', version, '.', model_name, '.bin', sep = '')))
}
booster_json <- function(model_name) {
return(file.path(target_dir, paste('xgboost-', version, '.', model_name, '.json', sep = '')))
}
booster_rds <- function(model_name) {
return(file.path(target_dir, paste('xgboost-', version, '.', model_name, '.rds', sep = '')))
}
xgb.save(booster, booster_bin(model_name))
saveRDS(booster, booster_rds(model_name))
if (version >= '1.0.0') {
xgb.save(booster, booster_json(model_name))
}
}
generate_regression_model <- function() {
print('Regression')
y <- rnorm(metadata$kRows)
data <- xgb.DMatrix(X, label = y, nthread = 1)
params <- list(tree_method = 'hist', num_parallel_tree = metadata$kForests,
max_depth = metadata$kMaxDepth)
booster <- xgb.train(params, data, nrounds = metadata$kRounds)
save_booster(booster, 'reg')
}
generate_logistic_model <- function() {
print('Binary classification with logistic loss')
y <- sample(0:1, size = metadata$kRows, replace = TRUE)
stopifnot(max(y) == 1, min(y) == 0)
objective <- c('binary:logistic', 'binary:logitraw')
name <- c('logit', 'logitraw')
for (i in seq_len(length(objective))) {
data <- xgb.DMatrix(X, label = y, weight = w, nthread = 1)
params <- list(tree_method = 'hist', num_parallel_tree = metadata$kForests,
max_depth = metadata$kMaxDepth, objective = objective[i])
booster <- xgb.train(params, data, nrounds = metadata$kRounds)
save_booster(booster, name[i])
}
}
generate_classification_model <- function() {
print('Multi-class classification')
y <- sample(0:(metadata$kClasses - 1), size = metadata$kRows, replace = TRUE)
stopifnot(max(y) == metadata$kClasses - 1, min(y) == 0)
data <- xgb.DMatrix(X, label = y, weight = w, nthread = 1)
params <- list(num_class = metadata$kClasses, tree_method = 'hist',
num_parallel_tree = metadata$kForests, max_depth = metadata$kMaxDepth,
objective = 'multi:softmax')
booster <- xgb.train(params, data, nrounds = metadata$kRounds)
save_booster(booster, 'cls')
}
generate_ranking_model <- function() {
print('Learning to rank')
y <- sample(0:4, size = metadata$kRows, replace = TRUE)
stopifnot(max(y) == 4, min(y) == 0)
kGroups <- 20
w <- runif(kGroups)
g <- rep(50, times = kGroups)
data <- xgb.DMatrix(X, label = y, group = g, nthread = 1)
# setinfo(data, 'weight', w)
# ^^^ does not work in version <= 1.1.0; see https://github.com/dmlc/xgboost/issues/5942
# So call low-level function XGDMatrixSetInfo_R directly. Since this function is not an exported
# symbol, use the triple-colon operator.
.Call(xgboost:::XGDMatrixSetInfo_R, data, 'weight', as.numeric(w))
params <- list(objective = 'rank:ndcg', num_parallel_tree = metadata$kForests,
tree_method = 'hist', max_depth = metadata$kMaxDepth)
booster <- xgb.train(params, data, nrounds = metadata$kRounds)
save_booster(booster, 'ltr')
}
dir.create(target_dir)
invisible(generate_regression_model())
invisible(generate_logistic_model())
invisible(generate_classification_model())
invisible(generate_ranking_model())
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