嗨，我开始使用包 h2o ensemble（这里：https ://github.com/h2oai/h2o-3/tree/master/h2o-r/ensemble进行一些数据分析并尝试了演示代码。
该代码之前运行良好：
## ## setting up h2o
library(h2oEnsemble)
nodes <- 2 ## number of processes
localH2O <- h2o.init(nthreads=nodes)
## ## simulated data set
dat <- matrix(rnorm(6e3), ncol=3, dimnames=list(NULL, c("W", "X", "Y")))
dat <- as.data.frame(dat)
Z <- as.factor(rbinom(nrow(dat), size=1, prob=plogis(.2+.1*dat$W-.2*dat$X)))
dat <- cbind(dat, Z=Z)
## W,X,Y: Input
## Z: output
dat.app <- dat[1:1e3, ]
dat.val <- dat[1e3+(1:1e3), ]
## ## h2o procedure
dat.h2o.app <- as.h2o(localH2O, dat.app) ## learning
dat.h2o.val <- as.h2o(localH2O, dat.val) ## validation
library.h2o <- c("h2o.deeplearning.Tanh",
"h2o.randomForest.1000x100")
h2o.randomForest.1000x100 <- function(...,ntrees=1000,nbins=100) {
h2oEnsemble::h2o.randomForest.wrapper(..., ntrees=ntrees, nbins=nbins,seed=1)
}
h2o.deeplearning.Tanh <- function(...,hidden=c(200, 200,200),activation="Tanh" ) {
h2oEnsemble::h2o.deeplearning.wrapper(..., hidden=hidden, activation=activation,seed=1)
}
h2o.model <- h2o.ensemble(y="Z", x=c("W", "X", "Y"),
training_frame=dat.h2o.app,
family="binomial",
learner=library.h2o,
cvControl=list(V=10, shuffle=TRUE),
metalearner="h2o.glm.wrapper") # getting the 400 bad request
h2o.pred.val <- predict(h2o.model, newdat=dat.h2o.val)$pred
table((h2o.pred.val>0.5)+0, dat.val$Z)
它突然向我抛出了一个 400 错误请求（RTMP_5 已经存在）
R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree"
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> ## ## setting up h2o
> library(h2oEnsemble)
> nodes <- 2 ## number of processes
> localH2O <- h2o.init(nthreads=nodes)
Successfully connected to http://127.0.0.1:54321/
R is connected to the H2O cluster:
H2O cluster uptime: 9 days 19 hours
H2O cluster version: 3.6.0.8
H2O cluster name: H2O_started_from_R_root_afl027
H2O cluster total nodes: 1
H2O cluster total memory: 6.98 GB
H2O cluster total cores: 6
H2O cluster allowed cores: 2
H2O cluster healthy: TRUE
>
> ## ## simulated data set
> dat <- matrix(rnorm(6e3), ncol=3, dimnames=list(NULL, c("W", "X", "Y")))
> dat <- as.data.frame(dat)
> Z <- as.factor(rbinom(nrow(dat), size=1, prob=plogis(.2+.1*dat$W-.2*dat$X)))
> dat <- cbind(dat, Z=Z)
> ## W,X,Y: input
> ## Z: output
> dat.app <- dat[1:1e3, ]
> dat.val <- dat[1e3+(1:1e3), ]
>
> ## ## h2o procedure
> dat.h2o.app <- as.h2o(dat.app) ## apprentissage
|
| | 0%
|
|======================================================================| 100%
> dat.h2o.val <- as.h2o(dat.val) ## validation
|
| | 0%
|
|======================================================================| 100%
>
> library.h2o <- c("h2o.deeplearning.Tanh",
+ "h2o.randomForest.1000x100")
>
> h2o.model <- h2o.ensemble(y="Z", x=c("W", "X", "Y"),
+ training_frame=dat.h2o.app,
+ family="binomial",
+ learner=library.h2o,
+ cvControl=list(V=10, shuffle=TRUE),
+ metalearner="h2o.glm.wrapper")
ERROR: Unexpected HTTP Status code: 400 Bad Request (url = http://127.0.0.1:54321/99/Rapids)
java.lang.IllegalArgumentException
[1] "water.rapids.ASTTmpAssign.apply(ASTAssign.java:254)"
[2] "water.rapids.ASTTmpAssign.apply(ASTAssign.java:248)"
[3] "water.rapids.ASTExec.exec(ASTExec.java:46)"
[4] "water.rapids.Session.exec(Session.java:56)"
[5] "water.rapids.Exec.exec(Exec.java:63)"
[6] "water.api.RapidsHandler.exec(RapidsHandler.java:23)"
[7] "sun.reflect.GeneratedMethodAccessor6.invoke(Unknown Source)"
[8] "sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)"
[9] "java.lang.reflect.Method.invoke(Method.java:622)"
[10] "water.api.Handler.handle(Handler.java:64)"
[11] "water.api.RequestServer.handle(RequestServer.java:644)"
[12] "water.api.RequestServer.serve(RequestServer.java:585)"
[13] "water.JettyHTTPD$H2oDefaultServlet.doGeneric(JettyHTTPD.java:617)"
[14] "water.JettyHTTPD$H2oDefaultServlet.doPost(JettyHTTPD.java:565)"
[15] "javax.servlet.http.HttpServlet.service(HttpServlet.java:755)"
[16] "javax.servlet.http.HttpServlet.service(HttpServlet.java:848)"
[17] "org.eclipse.jetty.servlet.ServletHolder.handle(ServletHolder.java:684)"
Error in .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = page, :
Temp ID RTMP_5 already exists
Calls : h2o.ensemble ... .eval.driver -> .h2o.__remoteSend -> .h2o.doSafeREST
Execution halted
我有点迷路了，不明白为什么它现在不起作用，训练集应该是好的格式。有人遇到这个问题吗？如果是，你是如何克服这个错误的？