Error in refit_tbl

[ichsan2895]

I have followed this tutorial, but I got error in this syntax

https://www.business-science.io/code-tools/2021/03/15/introducing-modeltime-h2o.html

refit_tbl %>%
modeltime_forecast(
new_data = future_prepared_tbl,
actual_data = data_prepared_tbl,
keep_data = TRUE
)

Error: Problem with filter() input ..1. x object '.key' not found i Input ..1 is `.model_desc == "ACTUAL" | .key == "prediction"

My software version
Windows 10 Education 64 bit
R = 3.6.3 (64 bit)
H2O = 3.32.0.1
modeltime = 0.5.1
modeltime.h2o = 0.1.1

[mdancho84]

I just ran the tutorial. I've increased the number of models slightly to improve results. It seems to run OK for me.

library(tidymodels)
library(modeltime.h2o)
library(tidyverse)
library(timetk)

data_tbl <- walmart_sales_weekly %>%
  select(id, Date, Weekly_Sales)

splits <- time_series_split(data_tbl, assess = "3 month", cumulative = TRUE)

recipe_spec <- recipe(Weekly_Sales ~ ., data = training(splits)) %>%
  step_timeseries_signature(Date) 

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

h2o.init(
  nthreads = -1,
  ip       = 'localhost',
  port     = 54321
)
#>  Connection successful!
#> 
#> R is connected to the H2O cluster: 
#>     H2O cluster uptime:         3 minutes 24 seconds 
#>     H2O cluster timezone:       America/New_York 
#>     H2O data parsing timezone:  UTC 
#>     H2O cluster version:        3.32.0.1 
#>     H2O cluster version age:    6 months and 18 days !!! 
#>     H2O cluster name:           H2O_started_from_R_mdancho_rvk435 
#>     H2O cluster total nodes:    1 
#>     H2O cluster total memory:   7.96 GB 
#>     H2O cluster total cores:    12 
#>     H2O cluster allowed cores:  12 
#>     H2O cluster healthy:        TRUE 
#>     H2O Connection ip:          localhost 
#>     H2O Connection port:        54321 
#>     H2O Connection proxy:       NA 
#>     H2O Internal Security:      FALSE 
#>     H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4 
#>     R Version:                  R version 4.0.2 (2020-06-22)
#> Warning in h2o.clusterInfo(): 
#> Your H2O cluster version is too old (6 months and 18 days)!
#> Please download and install the latest version from http://h2o.ai/download/

# Optional - Turn off progress indicators during training runs
h2o.no_progress()


model_spec <- automl_reg(mode = 'regression') %>%
  set_engine(
    engine                     = 'h2o',
    max_runtime_secs           = 15, 
    max_runtime_secs_per_model = 15,
    max_models                 = 10,
    nfolds                     = 5,
    exclude_algos              = c("DeepLearning"),
    verbosity                  = NULL,
    seed                       = 786
  ) 

model_fitted <- model_spec %>%
  fit(Weekly_Sales ~ ., data = train_tbl)


modeltime_tbl <- modeltime_table(
  model_fitted
) 

modeltime_tbl
#> # Modeltime Table
#> # A tibble: 1 x 3
#>   .model_id .model   .model_desc                 
#>       <int> <list>   <chr>                       
#> 1         1 <fit[+]> H2O AUTOML - STACKEDENSEMBLE

modeltime_tbl %>%
  modeltime_calibrate(test_tbl) %>%
  modeltime_forecast(
    new_data    = test_tbl,
    actual_data = data_tbl,
    keep_data   = TRUE
  ) %>%
  group_by(id) %>%
  plot_modeltime_forecast(
    .facet_ncol = 2, 
    .interactive = FALSE
  )

data_prepared_tbl <- bind_rows(train_tbl, test_tbl)

future_tbl <- data_prepared_tbl %>%
  group_by(id) %>%
  future_frame(.length_out = "1 year") %>%
  ungroup()
#> .date_var is missing. Using: Date

future_prepared_tbl <- bake(prep(recipe_spec), future_tbl)

refit_tbl <- modeltime_tbl %>%
  modeltime_refit(data_prepared_tbl)

refit_tbl %>%
  modeltime_forecast(
    new_data    = future_prepared_tbl,
    actual_data = data_prepared_tbl,
    keep_data   = TRUE
  ) %>%
  group_by(id) %>%
  plot_modeltime_forecast(
    .facet_ncol  = 2,
    .interactive = FALSE
  )
#> Converting to H2OFrame...
#> Warning: Expecting the following names to be in the data frame: .conf_hi, .conf_lo. 
#> Proceeding with '.conf_interval_show = FALSE' to visualize the forecast without confidence intervals.
#> Alternatively, try using `modeltime_calibrate()` before forecasting to add confidence intervals.

^{Created on 2021-04-27 by the reprex package (v1.0.0)}

Session Info

> devtools::session_info()
─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.2 (2020-06-22)
 os       OS X  11.2.3                
 system   x86_64, darwin17.0          
 ui       RStudio                     
 language (EN)                        
 collate  en_US.UTF-8                 
 ctype    en_US.UTF-8                 
 tz       America/New_York            
 date     2021-04-27                  

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[1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library

[ichsan2895]

Hello, recently, I know where the problem is...

In this example, we must select just 3 columns
data_tbl <- My_DF %>% select(id, Date, Value)

If we doesn't select 3 columns (so, all columns in My_DF become data_tbl )
It will be error like I said before

Error: Problem with filter() input ..1. x object '.key' not found i Input ..1 is `.model_desc == "ACTUAL" | .key == "prediction"

Please try this one :
data_tbl <- walmart_sales_weekly %>% select(id, Date, Weekly_Sales) will be error if we remove %>% select(id, Date, Weekly_Sales)

I don't know, its bug or feature?

[mdancho84]

Will need to look into this.