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我一直在尝试使用 mlr3 为 xgboost 做一些超参数调整。我想比较三种不同的模型：

  1. xgboost 仅调整了 alpha 超参数
  2. xgboost 调整了 alpha 和 lambda 超参数
  3. xgboost 调整了 alpha、lambda 和 maxdepth 超参数。

在阅读了 mlr3 的书之后，我认为使用 AutoTuner 进行嵌套重采样和基准测试是最好的方法。这是我尝试过的：

task_mpcr <- TaskRegr$new(id = "mpcr", backend = data.numeric, target = "n_reads")

measure <- msr("poisson_loss")

xgb_learn <- lrn("regr.xgboost")

set.seed(103)
fivefold.cv = rsmp("cv", folds = 5)

param.list <- list(  alpha = p_dbl(lower = 0.001, upper = 100, logscale = TRUE),
                 lambda = p_dbl(lower = 0.001, upper = 100, logscale = TRUE),
                 max_depth = p_int(lower = 2, upper = 10)
)


model.list <- list()
for(model.i in 1:length(param.list)){

  param.list.subset <- param.list[1:model.i]
  search_space <- do.call(ps, param.list.subset)

  model.list[[model.i]] <- AutoTuner$new(
    learner = xgb_learn,
    resampling = fivefold.cv,
    measure = measure,
    search_space = search_space,
    terminator = trm("none"),
    tuner = tnr("grid_search", resolution = 10),
    store_tuning_instance = TRUE
  )
}
grid <- benchmark_grid(
task = task_mpcr,
learner = model.list,
resampling = rsmp("cv", folds =3)
)

bmr <- benchmark(grid, store_models = TRUE)

请注意，我添加了泊松损失作为我正在使用的计数数据的度量。出于某种原因，在运行基准函数后，我所有模型的泊松损失每次折叠几乎相同，这让我认为没有进行任何调整。

我也找不到一种方法来访问用于在每次训练/测试迭代中获得最低损失的超参数。我是否完全滥用了基准功能？此外，这是我关于 SO 的第一个问题，因此任何格式建议都将不胜感激！

4

1 回答 1

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要查看调整是否有效，您只需将未调整的学习器添加到基准测试中即可。否则，结论可能是调整 alpha 对于您的示例就足够了。

我修改了代码，使其与示例任务一起运行。

library(mlr3verse)

task <- tsk("mtcars")

measure <- msr("regr.rmse")

xgb_learn <- lrn("regr.xgboost")

param.list <- list(
  alpha = p_dbl(lower = 0.001, upper = 100, logscale = TRUE),
  lambda = p_dbl(lower = 0.001, upper = 100, logscale = TRUE)
)

model.list <- list()
for(model.i in 1:length(param.list)){
  
  param.list.subset <- param.list[1:model.i]
  search_space <- do.call(ps, param.list.subset)
  
  at <- AutoTuner$new(
    learner = xgb_learn,
    resampling = rsmp("cv", folds = 5),
    measure = measure,
    search_space = search_space,
    terminator = trm("none"),
    tuner = tnr("grid_search", resolution = 5),
    store_tuning_instance = TRUE
  )
  at$id = paste0(at$id, model.i)
  
  model.list[[model.i]] <- at
}

model.list <- c(model.list, list(xgb_learn)) # add baseline learner

grid <- benchmark_grid(
  task = task,
  learner = model.list,
  resampling = rsmp("cv", folds =3)
)

bmr <- benchmark(grid, store_models = TRUE)

autoplot(bmr)

bmr_data = bmr$data$as_data_table() # convert benchmark result to a handy data.table
bmr_data$learner[[1]]$learner$param_set$values # the final learner used by AutoTune is nested in $learner

# best found value during grid search
bmr_data$learner[[1]]$archive$best()

# transformed value (the one that is used for the learner)
bmr_data$learner[[1]]$archive$best()$x_domain

在最后几行中，您将看到如何访问基准测试的各个运行。在我的示例中，我们有 9 次运行导致 3 个学习者和 3 个外部重采样折叠。

于 2021-03-24T09:04:44.080 回答