Question

我是h2o的新手，并试图在网格搜索中使用xgboost。我在edgenode上运行我的东西，包含40个内核和26 GB内存，以及R和h2o中版本3.20.0.2的h2o包。只是cpu作为后端。

我已经运行gbm和randomforest没有问题（一些gbm需要大约2个小时才能完成网格搜索，它们都运行良好）。但是，当我试图运行xgboost时，我总是会收到错误。

如果我运行一个没有网格搜索的简单示例，它将运行。然而，当我使用网格搜索运行xgboost时，我总是得到错误＆＃34; .h2o.doSafeREST中的错误（h2oRestApiVersion = h2oRestApiVersion，urlSuffix = urlSuffix，：意外的CURL错误：Recv失败：重置连接＆＃34; 。

我在线搜索并试图找出发生了什么。我发现了两个由LeDell给出的例子，一个是有效但不是另一个。

我在R中遇到错误＆＃34; .h2o.doSafeREST错误（h2oRestApiVersion = h2oRestApiVersion，urlSuffix = urlSuffix，：意外的CURL错误：Recv失败：连接已重置＆＃34;代码如下 https://gist.github.com/ledell/71e0b8861d4fa35b59dde2af282815a5

library(h2o)
h2o.init()


# Load the HIGGS dataset
train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
test <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")
y <- "response"
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])

# Some XGboost/GBM hyperparameters
hyper_params <- list(ntrees = seq(10, 1000, 1),
                     learn_rate = seq(0.0001, 0.2, 0.0001),
                     max_depth = seq(1, 20, 1),
                     sample_rate = seq(0.5, 1.0, 0.0001),
                     col_sample_rate = seq(0.2, 1.0, 0.0001))
search_criteria <- list(strategy = "RandomDiscrete",
                        max_models = 10, 
                        seed = 1)

# Train the grid
xgb_grid <- h2o.grid(algorithm = "xgboost",
                     x = x, y = y,
                     training_frame = train,
                     nfolds = 5,
                     seed = 1,
                     hyper_params = hyper_params,
                     search_criteria = search_criteria)


# Sort the grid by CV AUC
grid <- h2o.getGrid(grid_id = xgb_grid@grid_id, sort_by = "AUC", decreasing = TRUE)
grid_top_model <- grid@summary_table[1, "model_ids"]

另外，我的edgenode也出错了 libgomp：线程创建失败：资源暂时不可用＃ [thread 140207508600576也有错误]

Java运行时环境检测到致命错误： SIGSEGV（0xb）at pc = xxxxxxxxxxx [thread 140207503337216也有错误] [thread 140207504389888也有错误]，pid = 40095，tid = 0x00007f849aaea700

JRE版本：Java（TM）SE运行时环境（8.0_162-b12）（版本1.8.0_162-b12） Java VM：Java HotSpot（TM）64位服务器VM（25.162-b12混合模式linux-amd64压缩oops）

有问题的框架：

C [libc.so.6 + 0x358e5]退出+ 0x35

但是当我在下面运行代码时我没有遇到任何问题（这也是LeDell在另一篇文章中给出的一个例子）

train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")

y <- "response"
x <- setdiff(names(train), y)

train[,y] <- as.factor(train[,y])

hyperparameters_xgboost <- list(ntrees = seq(10, 20, 10),
                     learn_rate = seq(0.1, 0.2, 0.1),
                    sample_rate = seq(0.9, 1.0, 0.1),
                     col_sample_rate = seq(0.5, 0.6, 0.1))

xgb <- h2o.grid("xgboost",
                x = x,
                y = y, 
                seed = 1,
                training_frame = train,
                max_depth = 3,
                hyper_params = hyperparameters_xgboost)

因此，我不知道出了什么问题？最初我认为xgboost不起作用，然后我成功运行xgboost（没有网格）。然后我想它必须是网格搜索部分，然后我确实用后一个例子获得了成功的运行。我没有想法，想知道是否有人可能对我的错误有一些见解？

Answer 1

我无法在H2O 3.20.0.2上重现此错误：

> library(h2o)
> h2o.init()
 Connection successful!

R is connected to the H2O cluster: 
    H2O cluster uptime:         18 hours 58 minutes 
    H2O cluster timezone:       America/Los_Angeles 
    H2O data parsing timezone:  UTC 
    H2O cluster version:        3.20.0.2 
    H2O cluster version age:    6 days  
    H2O cluster name:           H2O_started_from_R_me_ves048 
    H2O cluster total nodes:    1 
    H2O cluster total memory:   3.28 GB 
    H2O cluster total cores:    8 
    H2O cluster allowed cores:  8 
    H2O cluster healthy:        TRUE 
    H2O Connection ip:          localhost 
    H2O Connection port:        54321 
    H2O Connection proxy:       NA 
    H2O Internal Security:      FALSE 
    H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4 
    R Version:                  R version 3.5.0 (2018-04-23) 

> # Load the HIGGS dataset
> train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
  |=================================================================================================| 100%
> test <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")
  |=================================================================================================| 100%
> y <- "response"
> 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])
> # Some XGboost/GBM hyperparameters
> hyper_params <- list(ntrees = seq(10, 1000, 1),
+                      learn_rate = seq(0.0001, 0.2, 0.0001),
+                      max_depth = seq(1, 20, 1),
+                      sample_rate = seq(0.5, 1.0, 0.0001),
+                      col_sample_rate = seq(0.2, 1.0, 0.0001))
> search_criteria <- list(strategy = "RandomDiscrete",
+                         max_models = 10, 
+                         seed = 1)
> # Train the grid
> xgb_grid <- h2o.grid(algorithm = "xgboost",
+                      x = x, y = y,
+                      training_frame = train,
+                      nfolds = 5,
+                      seed = 1,
+                      hyper_params = hyper_params,
+                      search_criteria = search_criteria)
  |=================================================================================================| 100%
>

h2o中网格搜索xgboost的错误（版本3.20.0.2）

1 个答案: