Error modeltime.h2o issue with modeltime_fit_resamples()

[Shafi2016]

Hello @mdancho84, Here is reproducible codes for the error. I am using modeltime.h2o with modeltime resamples

library(Quandl)

Tidymodeling

library(modeltime.ensemble)
library(modeltime)
library(tidymodels)

Base Models

library(glmnet)
library(xgboost)

Core Packages

library(tidyverse)
library(lubridate)
library(timetk)

library(modeltime.h2o)
library(tidymodels)
library(h2o)
h2o.init()
h2o.removeAll()
df1 <- Quandl(code = "FRED/PINCOME",
type = "raw",
collapse = "monthly",
order = "asc",
end_date="2017-12-31")
df2 <- Quandl(code = "FRED/GDP",
type = "raw",
collapse = "monthly",
order = "asc",
end_date="2017-12-31")

per <- df1 %>% rename(PI = Value)%>% select(-Date)
gdp <- df2 %>% rename(GDP = Value)

data <- cbind(gdp,per)
data1 <- tk_augment_differences(
.data = data,
.value = GDP:PI,
.lags = 1,
.differences = 1,
.log = TRUE,
.names = "auto") %>%
select(-GDP,-PI) %>%

rename(GDP = GDP_lag1_diff1,PI = PI_lag1_diff1) %>%
drop_na()

horizon <- 6
lag_period <- 6
rolling_periods <- c(10:12)
data_pre_full <- data1 %>%

Add future window----

#bind_rows(

future_frame(.data = .,.date_var = Date, .length_out = horizon)

#) %>%

add lags----

tk_augment_lags(
.value = GDP : PI ,
.lags = lag_period)

%>%

add lag rolling averages

tk_augment_slidify(
.value = PI_lag6,
.period = rolling_periods,
.f = mean,
.align = "center",
.partial = TRUE)

2.0 STEP 2 - SEPARATE INTO MODELING & FORECAST DATA ----

data_prepared_tbl <- data_pre_full %>%

filter(!is.na(GDP)) %>%
dplyr::select(-PI) %>%
drop_na()

splits <- time_series_split(data_prepared_tbl, assess = 8, cumulative = TRUE)

recipe_spec <- recipe(GDP~ ., data = training(splits)) # %>%

train_tbl <- rsample::training(splits) %>% bake(prep(recipe_spec), .)
test_tbl <- rsample::testing(splits) %>% bake(prep(recipe_spec), .)

MODEL SPEC ----

model_spec <- automl_reg(mode = 'regression') %>%
parsnip::set_engine(
engine = 'h2o',
max_runtime_secs = 99999999999999999,
max_runtime_secs_per_model = 3600,
project_name = 'project_01',
nfolds = 0,
max_models = 2,
#exclude_algos = c("DeepLearning"),
include_algos = c("GLM"),
seed = 786
)

model_fitted <- model_spec %>%

fit(GDP ~ ., data = training(splits))

leaderboard <- automl_leaderboard(model_fitted)
leaderboard

model2 <- leaderboard$model_id[[1]]
model_fit_2 <- automl_update_model(model_fitted, model2)

MODELTIME ----

calibration_tbl <- modeltime_table(
model_fit_2)

resample_spec <- rolling_origin(
data_prepared_tbl,
initial = 100,
assess = 6,
cumulative = TRUE,
skip = 0,
lag = 0,
overlap = 0
)

resamples_fitted <- calibration_tbl %>%
modeltime_fit_resamples(
resamples = resample_spec ,
control = control_resamples(verbose = TRUE))

resamples_fitted %>%
modeltime_resample_accuracy(
metric_set = metric_set(rmse, rsq))