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Я получаю сообщение об ошибке при запуске h2o.ensemble в R. Это ошибка выходаОшибка в управлении h2o.ensemble
[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'
Это мой код, который я использую. Я использую этот скрипт для проблемы регрессии. Столбец «Продажи» предназначен для прогнозирования выходных данных. Остальные столбцы предназначены для обучения.
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))
Все столбцы данных поезда - цифры. Я использую R 3.2.2.