我只是测试了h2o，因为我听说过很棒的事情 . 到目前为止，我一直在使用以下代码：
library(h2o)
h2o.removeAll() # Clean up. Just in case H2O was already running
h2o.init(nthreads = -1, max_mem_size="22G") # Start an H2O cluster with all threads available
train <- read.csv("TRAIN")
test <- read.csv("TEST")
target <- as.factor(train$target)
feature_names <- names(train)[1:(ncol(train)-1)]
train_h2o <- as.h2o(train)
test_h2o <- as.h2o(test)
prob <- test[, "id", drop = FALSE]
model_glm <- h2o.glm(x = feature_names, y = "target", training_frame = train_h2o)
h2o.performance(model_glm)
pred_glm <- predict(model_glm, newdata = test_h2o)
相关部分真的是最后一行，我得到以下错误：
DistributedException from localhost/127.0.0.1:54321, caused by java.lang.ArrayIndexOutOfBoundsException
DistributedException from localhost/127.0.0.1:54321, caused by java.lang.ArrayIndexOutOfBoundsException
at water.MRTask.getResult(MRTask.java:478)
at water.MRTask.getResult(MRTask.java:486)
at water.MRTask.doAll(MRTask.java:390)
at water.MRTask.doAll(MRTask.java:396)
at hex.glm.GLMModel.predictScoreImpl(GLMModel.java:1198)
at hex.Model.score(Model.java:1030)
at water.api.ModelMetricsHandler$1.compute2(ModelMetricsHandler.java:345)
at water.H2O$H2OCountedCompleter.compute(H2O.java:1241)
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)
Caused by: java.lang.ArrayIndexOutOfBoundsException
有没有人遇到过这个？有什么简单的解决方案可能会让我失踪吗？提前致谢 .
1 回答
如注释中所述，Train和Test数据集中的列名称需要完全匹配，否则您将收到错误消息 . 很高兴您能够找到问题所在 .