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))