R 运行h2o.0时出错

在R中运行h2o.ensemble时出错。这是错误输出

[1] "Cross-validating and training base learner 1: h2o.glm.wrapper"
  |======================================================================| 100%
[1] "Cross-validating and training base learner 2: h2o.randomForest.1"
  |==============                                                        |  19%

Got exception 'class java.lang.AssertionError', with msg 'null'
java.lang.AssertionError
    at hex.tree.DHistogram.scoreMSE(DHistogram.java:323)
    at hex.tree.DTree$DecidedNode$FindSplits.compute2(DTree.java:441)
    at hex.tree.DTree$DecidedNode.bestCol(DTree.java:421)
    at hex.tree.DTree$DecidedNode.<init>(DTree.java:449)
    at hex.tree.SharedTree.makeDecided(SharedTree.java:489)
    at hex.tree.SharedTree$ScoreBuildOneTree.onCompletion(SharedTree.java:436)
    at jsr166y.CountedCompleter.__tryComplete(CountedCompleter.java:425)
    at jsr166y.CountedCompleter.tryComplete(CountedCompleter.java:383)
    at water.MRTask.compute2(MRTask.java:683)
    at water.H2O$H2OCountedCompleter.compute(H2O.java:1069)
    at jsr166y.CountedCompleter.exec(CountedCompleter.java:468)
    at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)
    at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974)
    at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477)
    at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)


Error: 'null'

[1]“交叉验证和培训基地学员1:h2o.glm.wrapper”
|======================================================================| 100%
[1] “交叉验证和培训基地学员2:h2o.1”
|==============                                                        |  19%
获取异常“class java.lang.AssertionError”，消息为“null”
java.lang.AssertionError
在hex.tree.DHistogram.scoreMSE处（DHistogram.java:323）
在hex.tree.DTree$decisedNode$FindSplits.compute2（DTree.java:441）
位于hex.tree.DTree$decisedNode.bestCol（DTree.java:421）
在hex.tree.DTree$decisedNode处。（DTree.java:449）
在hex.tree.SharedTree.MakedDeterming（SharedTree.java:489）
在hex.tree.SharedTree$ScoreBuildOneTree.onCompletion（SharedTree.java:436）
在jsr166y.CountedCompleter.\uu tryComplete（CountedCompleter.java:425）
在jsr166y.CountedCompleter.tryComplete（CountedCompleter.java:383）
at water.MRTask.compute2（MRTask.java:683）
at water.H2O$H2OCountedCompleter.compute（H2O.java:1069）
在jsr166y.CountedCompleter.exec（CountedCompleter.java:468）
位于jsr166y.ForkJoinTask.doExec（ForkJoinTask.java:263）
位于jsr16y.ForkJoinPool$WorkQueue.runTask（ForkJoinPool.java:974）
位于jsr166y.ForkJoinPool.runWorker（ForkJoinPool.java:1477）
在jsr166y.ForkJoinWorkerThread.run（ForkJoinWorkerThread.java:104）
错误：“null”

这是我正在使用的代码。我使用这个脚本解决回归问题。“销售”栏用于预测产量。其余的专栏都是训练用的

response <- "Sales"
predictors <- setdiff(names(train), response)

h2o.glm.1 <- function(..., alpha = 0.0) h2o.glm.wrapper(..., alpha = alpha)
h2o.glm.2 <- function(..., alpha = 0.5) h2o.glm.wrapper(..., alpha = alpha)
h2o.glm.3 <- function(..., alpha = 1.0) h2o.glm.wrapper(..., alpha = alpha)
h2o.randomForest.1 <- function(..., ntrees = 200, nbins = 50, seed = 1) h2o.randomForest.wrapper(..., ntrees = ntrees, nbins = nbins, seed = seed)
h2o.randomForest.2 <- function(..., ntrees = 200, sample_rate = 0.75, seed = 1) h2o.randomForest.wrapper(..., ntrees = ntrees, sample_rate = sample_rate, seed = seed)
h2o.gbm.1 <- function(..., ntrees = 100, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, seed = seed)
h2o.gbm.6 <- function(..., ntrees = 100, col_sample_rate = 0.6, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, col_sample_rate = col_sample_rate, seed = seed)
h2o.gbm.8 <- function(..., ntrees = 100, max_depth = 3, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, max_depth = max_depth, seed = seed)
h2o.deeplearning.1 <- function(..., hidden = c(500,500), activation = "Rectifier", epochs = 50, seed = 1)  h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed)
h2o.deeplearning.6 <- function(..., hidden = c(50,50), activation = "Rectifier", epochs = 50, seed = 1)  h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed)
h2o.deeplearning.7 <- function(..., hidden = c(100,100), activation = "Rectifier", epochs = 50, seed = 1)  h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed)

print("learning starts ")
#### Customized base learner library
learner <- c("h2o.glm.wrapper",
             "h2o.randomForest.1", "h2o.randomForest.2",
             "h2o.gbm.1", "h2o.gbm.6", "h2o.gbm.8",
             "h2o.deeplearning.1", "h2o.deeplearning.6", "h2o.deeplearning.7")

metalearner <- "h2o.glm.wrapper"
#
#Train with new library:
fit <- h2o.ensemble(
  x =  predictors, 
  y= response,
  training_frame=train,
  family = "gaussian", 
  learner = learner, 
  metalearner = metalearner,
  cvControl = list(V = 5))

响应如

在h2o初始化中将断言设置为FALSE可能会起到作用

h2o.init(nthreads=-1, assertion = FALSE)

在应用更改之前，确保正确关闭/重新启动h2o

h2o.shutdown()
h2o.init(nthreads=-1, assertion = FALSE)

更新后的方法是

h2o.init(nthreads=-1,enable_assertions = FALSE)