h2oensemble 值 [[3L]](cond) 错误：参数 "training_frame" 必须是有效的 H2O H2OFrame 或 id

Question

在尝试从 Rstudio 中运行在 http://learn.h2o.ai/content/tutorials/ensembles-stacking/index.html 上找到的 H2OEnsemble 上的示例时，我遇到了以下错误：

Error in value[3L] : argument "training_frame" must be a valid H2O H2OFrame or id

定义整体后

fit <- h2o.ensemble(x = x, y = y, 
                    training_frame = train, 
                     family = family, 
                     learner = learner, 
                     metalearner = metalearner,
                     cvControl = list(V = 5, shuffle = TRUE))

我安装了 h2o 和 h2oEnsemble 的最新版本，但问题仍然存在。我在这里 `h2o.cbind` accepts only of H2OFrame objects - R 了解到 h2o 中的命名约定随着时间的推移发生了变化，但我认为通过安装最新版本的两者，这应该不再是问题。

有什么建议吗？

library(readr)
library(h2oEnsemble)  # Requires version >=0.0.4 of h2oEnsemble
library(cvAUC)  # Used to calculate test set AUC (requires version >=1.0.1 of cvAUC)
localH2O <-  h2o.init(nthreads = -1)  # Start an H2O cluster with nthreads = num cores on your machine





# Import a sample binary outcome train/test set into R
train <- h2o.importFile("http://www.stat.berkeley.edu/~ledell/data/higgs_10k.csv")
test <- h2o.importFile("http://www.stat.berkeley.edu/~ledell/data/higgs_test_5k.csv")
y <- "C1"
x <- setdiff(names(train), y)
family <- "binomial"

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


# Specify the base learner library & the metalearner
learner <- c("h2o.glm.wrapper", "h2o.randomForest.wrapper", 
               "h2o.gbm.wrapper", "h2o.deeplearning.wrapper")
metalearner <- "h2o.deeplearning.wrapper"


# Train the ensemble using 5-fold CV to generate level-one data
# More CV folds will take longer to train, but should increase performance
fit <- h2o.ensemble(x = x, y = y, 
                    training_frame = train, 
                    family = family, 
                    learner = learner, 
                    metalearner = metalearner,
                    cvControl = list(V = 5, shuffle = TRUE))

Answer 1

这个错误是最近由对 h2o R 代码的 class 名称的批量 find/replace 更改引入的。更改也无意中应用于集成代码文件夹（我们目前在其中进行手动而不是自动测试——很快就会自动进行以防止此类事情发生）。我已经修复了这个错误。

要修复，请从 GitHub 重新安装 h2oEnsemble 包：

library(devtools)
install_github("h2oai/h2o-3/h2o-r/ensemble/h2oEnsemble-package")

感谢您的报告！为了更快地响应，post 错误和问题在这里：https://groups.google.com/forum/#!forum/h2ostream

h2oensemble 值 [[3L]](cond) 错误：参数 "training_frame" 必须是有效的 H2O H2OFrame 或 id

h2oensemble Error in value[[3L]](cond) : argument "training_frame" must be a valid H2O H2OFrame or id

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ensemble-learning

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