在R

Question

我正在R上运行h2o网格搜索。该模型是使用gamma分布的glm。 我已经使用以下设置定义了网格。 hyper_parameters = list（alpha = c（0，.5），missing_values_handling = c（“ Skip”，“ MeanImputation”））

                                 h2o.grid(algorithm = "glm",                            # Setting algorithm type
                                 grid_id = "grid.s",                                    # Id so retrieving information on iterations will be easier later
                                 x = predictors,                                        # Setting predictive features
                                 y = response,                                          # Setting target variable
                                 training_frame = data,                                 # Setting training set
                                 validation_frame = validate,                           # Setting validation frame
                                 hyper_params = hyper_parameters,                       # Setting apha values for iterations
                                 remove_collinear_columns = T,                          # Parameter to remove collinear columns
                                 lambda_search = T,                                     # Setting parameter to find optimal lambda value
                                 seed = 1234,                                           # Setting to ensure replicateable results
                                 keep_cross_validation_predictions = F,                 # Setting to save cross validation predictions
                                 compute_p_values = F,                                  # Calculating p-values of the coefficients
                                 family = 'gamma',                                      # Distribution type used
                                 standardize = T,                                       # Standardizing continuous variables
                                 nfolds = 2,                                            # Number of cross-validations
                                 fold_assignment = "Modulo",                            # Specifying fold assignment type to use for cross validations
                                 link = "log") 

运行上面的脚本时，出现以下错误： hyper_names [[index2]]错误：下标超出范围

请帮助我找出错误所在

Answer 1

正如注释中所讨论的，很难说出没有样本error和data的原因code可能是什么。 out-of-bounds error可能是因为code试图访问input中不存在的值。因此，可能是inputs到h2o.grid()中的任何一个。我将检查train和validation数据集中的列和行。问题中的hyperparameters与family="binomial"一起很好地运行。

下面的code与glm()可以很好地运行。我做了几个假设，例如：（1）基于创建的family=binomial使用了family=gamma而不是sample data，（2）响应y是binary， （3）train和test split ratio，（4）responses的数量限制为三个predictors或independent variables（{{1} }，x1，x2），（5）一个x3 y`）。

导入库

binary response variable (

创建示例数据

library(h2o)
library(h2oEnsemble)

初始化x1 <- abs(100*rnorm(100)) x2 <- 10+abs(100*rnorm(100)) x3 <- 100+abs(100*rnorm(100)) #y <- ronorm(100) y <- floor(runif(100,0,1.5)) df <- data.frame(x1, x2, x3,y) df$y <- ifelse(df$y==1, 'yes', 'no') df$y <- as.factor(df$y) head(df)

h2o

以必需的h2o.init() 格式准备data

h2o

设置df <- as.h2o(df) y <- "y" x <- setdiff( names(df), y ) df<- df[ df$y %in% c("no", "yes"), ] h2o.setLevels(df$y, c("no","yes") ) # Split data into train and validate sets data <- h2o.splitFrame( df, ratios = c(.6, 0.15) ) names(data) <- c('train', 'valid', 'test') data$train

parameters

适合grid_id <- 'glm_grid' hyper_parameters <- list( alpha = c(0, .5, 1), lambda = c(1, 0.5, 0.1, 0.01), missing_values_handling = c("Skip", "MeanImputation"), tweedie_variance_power = c(0, 1, 1.1,1.8,1.9,2,2.1,2.5,2.6,3, 5, 7), #tweedie_variance_power = c(0, 1, 1.1,1.8,1.9,2,2.1,2.5,2.6,3, 5, 7), seed = 1234 )

h2o.grid()

输出