更新到3.18后,估计h2o中的xgboost时出错

我遇到了一个已知的问题,即无法保存xgboost模型并在稍后加载它以获得预测,而它可能在h2o 3.18中发生了更改(问题出现在3.16中)。我从h2o的网站(可下载的zip)更新了包,现在没有问题的模型出现以下错误:

Error in .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = urlSuffix,  : 
  Unexpected CURL error: Failed to connect to localhost port 54321: Connection refused

这仅适用于xgboost(二进制分类),因为我使用的其他模型工作正常。当然,h2o已经初始化,之前的模型估计没有问题。有人知道问题出在哪里吗?

编辑:以下是一个产生错误的可复制示例(基于Erin的答案):

library(h2o)
library(caret)
h2o.init()

# Import a sample binary outcome train set into H2O
train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")

# Identify predictors and response
y <- "response"
x <- setdiff(names(train), y)

# Assigning fold column
set.seed(1)
cv_folds <- createFolds(as.data.frame(train)$response,
                        k = 5,
                        list = FALSE,
                        returnTrain = FALSE)

# version 1
train <- train %>%
    as.data.frame() %>% 
    mutate(fold_assignment = cv_folds) %>%
    as.h2o()

# version 2
train <- h2o.cbind(train, as.h2o(cv_folds))
names(train)[dim(train)[2]] <- c("fold_assignment")


# For binary classification, response should be a factor
train[,y] <- as.factor(train[,y])

xgb <- h2o.xgboost(x = x,
                   y = y, 
                   seed = 1,
                   training_frame = train,
                   fold_column = "fold_assignment",
                   keep_cross_validation_predictions = TRUE,
                   eta = 0.01,
                   max_depth = 3,
                   sample_rate = 0.8,
                   col_sample_rate = 0.6,
                   ntrees = 500,
                   reg_lambda = 0,
                   reg_alpha = 1000,
                   distribution = 'bernoulli') 

创建列车数据的两个版本。帧会导致相同的错误。