如何在R包XGBoost中为xgb.cv指定训练和测试索引

Question

我最近发现了folds参数in xgb.cv,它允许指定验证集的索引.xgb.cv.mknfold然后在xgb.cv其中调用辅助函数,然后将每个折叠的剩余索引作为相应折叠的训练集的索引.

问题:我可以通过xgboost接口中的任何接口指定训练和验证索引吗？

我的主要动机是执行时间序列交叉验证,我不希望将"非验证"索引自动指定为训练数据.举例说明我想做的事情:

# assume i have 100 strips of time-series data, where each strip is X_i
# validate only on 10 points after training
fold1:  train on X_1-X_10, validate on X_11-X_20
fold2:  train on X_1-X_20, validate on X_21-X_30
fold3:  train on X_1-X_30, validate on X_31-X_40
...

目前,使用该folds参数将迫使我使用剩余的示例作为验证集,这极大地增加了误差估计的方差,因为剩余数据大大超过训练数据并且可能具有与训练数据非常不同的分布,尤其是对于训练数据.较早的折叠.这就是我的意思:

fold1:  train on X_1-X_10, validate on X_11-X100 # huge error
...

如果它们方便(即不要求我撬开源代码)并且不会使原始xgboost实现中的效率无效,我对其他软件包的解决方案持开放态度.

Answer 1

我认为问题的底部是错误的方式，可能应该说：

强迫我使用剩余的例子作为训练集

似乎提到的辅助函数xgb.cv.mknfold已经不存在了。请注意我的xgboost版本是0.71.2.

然而，这似乎可以通过对 进行小的修改来相当直接地实现xgb.cv，例如：

xgb.cv_new <- function(params = list(), data, nrounds, nfold, label = NULL, 
          missing = NA, prediction = FALSE, showsd = TRUE, metrics = list(), 
          obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, folds_train = NULL, 
          verbose = TRUE, print_every_n = 1L, early_stopping_rounds = NULL, 
          maximize = NULL, callbacks = list(), ...) {
  check.deprecation(...)
  params <- check.booster.params(params, ...)
  for (m in metrics) params <- c(params, list(eval_metric = m))
  check.custom.obj()
  check.custom.eval()
  if ((inherits(data, "xgb.DMatrix") && is.null(getinfo(data, 
                                                        "label"))) || (!inherits(data, "xgb.DMatrix") && is.null(label))) 
    stop("Labels must be provided for CV either through xgb.DMatrix, or through 'label=' when 'data' is matrix")
  if (!is.null(folds)) {
    if (!is.list(folds) || length(folds) < 2) 
      stop("'folds' must be a list with 2 or more elements that are vectors of indices for each CV-fold")
    nfold <- length(folds)
  }
  else {
    if (nfold <= 1) 
      stop("'nfold' must be > 1")
    folds <- generate.cv.folds(nfold, nrow(data), stratified, 
                               label, params)
  }
  params <- c(params, list(silent = 1))
  print_every_n <- max(as.integer(print_every_n), 1L)
  if (!has.callbacks(callbacks, "cb.print.evaluation") && verbose) {
    callbacks <- add.cb(callbacks, cb.print.evaluation(print_every_n, 
                                                       showsd = showsd))
  }
  evaluation_log <- list()
  if (!has.callbacks(callbacks, "cb.evaluation.log")) {
    callbacks <- add.cb(callbacks, cb.evaluation.log())
  }
  stop_condition <- FALSE
  if (!is.null(early_stopping_rounds) && !has.callbacks(callbacks, 
                                                        "cb.early.stop")) {
    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds, 
                                                 maximize = maximize, verbose = verbose))
  }
  if (prediction && !has.callbacks(callbacks, "cb.cv.predict")) {
    callbacks <- add.cb(callbacks, cb.cv.predict(save_models = FALSE))
  }
  cb <- categorize.callbacks(callbacks)
  dall <- xgb.get.DMatrix(data, label, missing)
  bst_folds <- lapply(seq_along(folds), function(k) {
    dtest <- slice(dall, folds[[k]])
    if (is.null(folds_train))
      dtrain <- slice(dall, unlist(folds[-k]))
    else
      dtrain <- slice(dall, folds_train[[k]])
    handle <- xgb.Booster.handle(params, list(dtrain, dtest))
    list(dtrain = dtrain, bst = handle, watchlist = list(train = dtrain, 
                                                         test = dtest), index = folds[[k]])
  })
  rm(dall)
  basket <- list()
  num_class <- max(as.numeric(NVL(params[["num_class"]], 1)), 
                   1)
  num_parallel_tree <- max(as.numeric(NVL(params[["num_parallel_tree"]], 
                                          1)), 1)
  begin_iteration <- 1
  end_iteration <- nrounds
  for (iteration in begin_iteration:end_iteration) {
    for (f in cb$pre_iter) f()
    msg <- lapply(bst_folds, function(fd) {
      xgb.iter.update(fd$bst, fd$dtrain, iteration - 1, 
                      obj)
      xgb.iter.eval(fd$bst, fd$watchlist, iteration - 1, 
                    feval)
    })
    msg <- simplify2array(msg)
    bst_evaluation <- rowMeans(msg)
    bst_evaluation_err <- sqrt(rowMeans(msg^2) - bst_evaluation^2)
    for (f in cb$post_iter) f()
    if (stop_condition) 
      break
  }
  for (f in cb$finalize) f(finalize = TRUE)
  ret <- list(call = match.call(), params = params, callbacks = callbacks, 
              evaluation_log = evaluation_log, niter = end_iteration, 
              nfeatures = ncol(data), folds = folds)
  ret <- c(ret, basket)
  class(ret) <- "xgb.cv.synchronous"
  invisible(ret)
}

我刚刚添加了一个可选参数folds_train = NULL，并稍后在函数中以这种方式使用它（见上文）：

if (is.null(folds_train))
  dtrain <- slice(dall, unlist(folds[-k]))
else
  dtrain <- slice(dall, folds_train[[k]])

然后您可以使用该功能的新版本，例如如下所示：

# save original version
orig <- xgboost::xgb.cv

# devtools::install_github("miraisolutions/godmode")
godmode:::assignAnywhere("xgb.cv", xgb.cv_new)

# now you can use (call) xgb.cv with the additional argument

# once you are done, or may want to switch back to the original version
# (if you restart R you will also be back to the original version):
godmode:::assignAnywhere("xgb.cv", orig)

所以现在您应该能够使用额外的参数调用该函数，为训练数据提供额外的索引。

请注意，我还没有时间对此进行测试。