Running h2o Grid search on R - r

Question

I am running h2o grid search on R. The model is a glm using a gamma distribution.
I have defined the grid using the following settings.
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")
When i run the above script, i get the following error:
Error in hyper_names[[index2]] : subscript out of bounds
Please can you help me find where the error is

Answer 1

As disucssed in the comments it is difficult to tell what the cause for the error could be without sample data and code. The out-of-bounds error could be because the code is trying to access a value that does not exist in the input. So possibly, it could be either of the inputs to the h2o.grid(). I would check columns and rows in the train and validation data sets. The hyperparameters from the question run fine with family="binomial".
The code below runs fine with glm(). I have made several assumptions such as: (1) family=binomial instead of family=gamma was used based on sample data created, (2) response y is binary, (3) train and test split ratio, (4) number of responses are limited to three predictors or independent variables (x1, x2, x3), (5) one binary response variable (y`).
Import libraries
library(h2o)
library(h2oEnsemble)
Create sample data
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)
Initialize h2o
h2o.init()
Prepare data in required h2o format
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$train
Set parameters
grid_id <- 'glm_grid'
hyper_parameters <- list( alpha = c(0, .5, 1),
lambda = c(1, 0.5, 0.1, 0.01),
missing_values_handling = c("Skip", "MeanImputation"),
tweedie_variance_power = c(0, 1, 1.1,1.8,1.9,2,2.1,2.5,2.6,3, 5, 7),
#tweedie_variance_power = c(0, 1, 1.1,1.8,1.9,2,2.1,2.5,2.6,3, 5, 7),
seed = 1234
)
Fit h2o.grid()
h2o.grid(
algorithm = "glm",
#grid_id = grid_id,
hyper_params = hyper_parameters,
training_frame = data$train,
validation_frame = data$valid,
x = x,
y = y,
lambda_search = TRUE,
remove_collinear_columns = T,
keep_cross_validation_predictions = F,
compute_p_values = F,
standardize = T,
nfolds = 2,
fold_assignment = "Modulo",
family = "binomial"
)
Output