Running 'testthat.R' [174s/189s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > 
  > # Machine Learning
  > library(tidymodels)
  -- Attaching packages -------------------------------------- tidymodels 0.2.0 --
  v broom        0.8.0     v recipes      0.2.0
  v dials        0.1.1     v rsample      0.1.1
  v dplyr        1.0.9     v tibble       3.1.7
  v ggplot2      3.3.6     v tidyr        1.2.0
  v infer        1.0.0     v tune         0.2.0
  v modeldata    0.1.1     v workflows    0.2.6
  v parsnip      0.2.1     v workflowsets 0.2.1
  v purrr        0.3.4     v yardstick    0.0.9
  -- Conflicts ----------------------------------------- tidymodels_conflicts() --
  x purrr::discard() masks scales::discard()
  x dplyr::filter()  masks stats::filter()
  x purrr::is_null() masks testthat::is_null()
  x dplyr::lag()     masks stats::lag()
  x tidyr::matches() masks rsample::matches(), dplyr::matches(), testthat::matches()
  x recipes::step()  masks stats::step()
  * Dig deeper into tidy modeling with R at https://www.tmwr.org
  > library(modeltime)
  > library(modeltime.ensemble)
  Loading required package: modeltime.resample
  > library(modeltime.resample)
  > 
  > # Model dependencies
  > library(xgboost)
  
  Attaching package: 'xgboost'
  
  The following object is masked from 'package:dplyr':
  
      slice
  
  > library(earth)
  Loading required package: Formula
  Loading required package: plotmo
  Loading required package: plotrix
  
  Attaching package: 'plotrix'
  
  The following object is masked from 'package:scales':
  
      rescale
  
  Loading required package: TeachingDemos
  > library(glmnet)
  Loading required package: Matrix
  
  Attaching package: 'Matrix'
  
  The following objects are masked from 'package:tidyr':
  
      expand, pack, unpack
  
  Loaded glmnet 4.1-4
  > 
  > # Core Packages
  > library(tidyverse)
  -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
  v readr   2.1.2     v forcats 0.5.1
  v stringr 1.4.0     
  -- Conflicts ------------------------------------------ tidyverse_conflicts() --
  x readr::col_factor()    masks scales::col_factor()
  x purrr::discard()       masks scales::discard()
  x readr::edition_get()   masks testthat::edition_get()
  x Matrix::expand()       masks tidyr::expand()
  x dplyr::filter()        masks stats::filter()
  x stringr::fixed()       masks recipes::fixed()
  x purrr::is_null()       masks testthat::is_null()
  x dplyr::lag()           masks stats::lag()
  x readr::local_edition() masks testthat::local_edition()
  x tidyr::matches()       masks rsample::matches(), dplyr::matches(), testthat::matches()
  x Matrix::pack()         masks tidyr::pack()
  x xgboost::slice()       masks dplyr::slice()
  x readr::spec()          masks yardstick::spec()
  x Matrix::unpack()       masks tidyr::unpack()
  > library(timetk)
  > library(lubridate)
  
  Attaching package: 'lubridate'
  
  The following objects are masked from 'package:base':
  
      date, intersect, setdiff, union
  
  > 
  > test_check("modeltime.ensemble")
  Rows: 282
  Columns: 32
  $ value             <dbl> 6370, 6430, 6520, 6580, 6620, 6690, 6000, 5450, 6480~
  $ date_index.num    <dbl> -1.722511, -1.710022, -1.698742, -1.686253, -1.67416~
  $ date_year         <dbl> -1.655438, -1.655438, -1.655438, -1.655438, -1.65543~
  $ date_half         <dbl> -0.9772078, -0.9772078, -0.9772078, -0.9772078, -0.9~
  $ date_quarter      <dbl> -1.3205024, -1.3205024, -1.3205024, -0.4275008, -0.4~
  $ date_month        <dbl> -1.5722388, -1.2830207, -0.9938026, -0.7045845, -0.4~
  $ date_wday         <dbl> -1.008456499, 0.490935765, 0.490935765, -1.508253920~
  $ date_qday         <dbl> -1.22677390, 0.01953144, 1.14522658, -1.22677390, -0~
  $ date_yday         <dbl> -1.5656189, -1.2710608, -1.0050084, -0.7104503, -0.4~
  $ date_week         <dbl> -1.5443776, -1.2769447, -1.0095118, -0.7420789, -0.4~
  $ date_week2        <dbl> 0.9911707, 0.9911707, 0.9911707, 0.9911707, -1.00533~
  $ date_week3        <dbl> -0.07314123, 1.21597292, -1.36225538, -0.07314123, -~
  $ date_week4        <dbl> -0.3589423, -0.3589423, -0.3589423, -0.3589423, 0.59~
  $ date_month.lbl_01 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0~
  $ date_month.lbl_02 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0~
  $ date_month.lbl_03 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0~
  $ date_month.lbl_04 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0~
  $ date_month.lbl_05 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0~
  $ date_month.lbl_06 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1~
  $ date_month.lbl_07 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
  $ date_month.lbl_08 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
  $ date_month.lbl_09 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0~
  $ date_month.lbl_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0~
  $ date_month.lbl_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0~
  $ date_month.lbl_12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0~
  $ date_wday.lbl_1   <dbl> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
  $ date_wday.lbl_2   <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0~
  $ date_wday.lbl_3   <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0~
  $ date_wday.lbl_4   <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0~
  $ date_wday.lbl_5   <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0~
  $ date_wday.lbl_6   <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0~
  $ date_wday.lbl_7   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1~
  -- Modeltime Ensemble -------------------------------------------
  Ensemble of 3 Models (WEIGHTED)
  
  # Modeltime Table
  # A tibble: 3 x 4
    .model_id .model     .model_desc             .loadings
        <int> <list>     <chr>                       <dbl>
  1         1 <workflow> ARIMA(0,1,1)(0,1,1)[12]     0.5  
  2         2 <workflow> PROPHET                     0.333
  3         3 <workflow> GLMNET                      0.167
  
  
  
  
  
  
  
  
  [ FAIL 2 | WARN 5 | SKIP 5 | PASS 86 ]
  
  == Skipped tests ===============================================================
  * On CRAN (5)
  
  == Failed tests ================================================================
  -- Error (test-ensemble_weighted.R:36:5): ensemble_weighted() ------------------
  Error in `dplyr::filter(., .model_desc == "ACTUAL" | .key == "prediction")`: Problem while computing `..1 = .model_desc == "ACTUAL" | .key ==
  "prediction"`.
  Caused by error:
  ! object '.key' not found
  Backtrace:
       x
    1. +-modeltime_table(ensemble_fit_wt) %>% ... at test-ensemble_weighted.R:36:4
    2. +-modeltime::modeltime_forecast(., testing(m750_splits))
    3. +-modeltime:::modeltime_forecast.mdl_time_tbl(., testing(m750_splits))
    4. | \-ret %>% ...
    5. +-dplyr::filter(., .model_desc == "ACTUAL" | .key == "prediction")
    6. +-dplyr:::filter.data.frame(...)
    7. | \-dplyr:::filter_rows(.data, ..., caller_env = caller_env())
    8. |   \-dplyr:::filter_eval(dots, mask = mask, error_call = error_call)
    9. |     +-base::withCallingHandlers(...)
   10. |     \-mask$eval_all_filter(dots, env_filter)
   11. \-base::.handleSimpleError(...)
   12.   \-dplyr h(simpleError(msg, call))
   13.     \-rlang::abort(bullets, call = error_call, parent = skip_internal_condition(e))
  -- Failure (test-panel-data.R:134:5): ensemble_weighted(): Forecast Jumbled ----
  accuracy_tbl$mae < 400 is not TRUE
  
  `actual`:   FALSE
  `expected`: TRUE 
  
  [ FAIL 2 | WARN 5 | SKIP 5 | PASS 86 ]
  Error: Test failures
  Execution halted