`modeltime_calibrate()` Unable to calibrate a classfiication model (R 4.3.1) - .pred_class column returned

[sebsfox]

I'm new to {modeltime} and I think it is a great package - thank you for putting so much time into it.
I'm trying to do perform a classification task with time series data.

When I get to modeltime_calibrate() I get an error, which looks to be because it is looking for the .pred column, but the predict() function returns a .pred_class column. It might be related to #228.

Reproducible example below:

library(tsibbledata) # Diverse Datasets for 'tsibble'
library(tidymodels)
library(modeltime)
library(dplyr)
library(timetk)

# create high demand variable
df <- vic_elec |> #vic_elec is in tsibbledata package
  mutate(
    high_demand = case_when(
      Demand > quantile(Demand, probs = 0.95) ~ "Yes",
      .default = "No"
    )
  )

splits <- initial_time_split(
  df,
  prop = 0.8
)

recipe_spec <- recipe(
  high_demand ~ Time, # consider Temperature and Holiday
  data = training(splits)
)

model_fit_glm <- logistic_reg() |>
  set_engine(
    "glm",
    family = stats::binomial(link = "logit")
  )

wflw_glm <- workflow() |>
  add_recipe(recipe_spec) |>
  add_model(model_fit_glm) |>
  fit(training(splits))

models_tbl <- modeltime::modeltime_table(
  wflw_glm
)

calibration_tbl <- models_tbl |> 
  modeltime::modeltime_calibrate(
    new_data = testing(splits),
    quiet = FALSE
  )
#> Error: ℹ In argument: `.pred = ifelse(is.na(.pred), high_demand, .pred)`.
#> Caused by error:
#> ! object '.pred' not found
#> 
#> ── Model Calibration Failure Report ────────────────────────
#> # A tibble: 1 × 4
#>   .model_id .model     .model_desc .nested.col
#>       <int> <list>     <chr>       <lgl>      
#> 1         1 <workflow> GLM         NA
#> All models failed Modeltime Calibration:
#> - Model 1: Failed Calibration.
#> 
#> Potential Solution: Use `modeltime_calibrate(quiet = FALSE)` AND Check the Error/Warning Messages for clues as to why your model(s) failed calibration.
#> ── End Model Calibration Failure Report ────────────────────
#> Error in `validate_modeltime_calibration()`:
#> ! All models failed Modeltime Calibration.


predict(
  wflw_glm,
  new_data = testing(splits)
)
#> # A tibble: 10,522 × 1
#>    .pred_class
#>    <fct>      
#>  1 No         
#>  2 No         
#>  3 No         
#>  4 No         
#>  5 No         
#>  6 No         
#>  7 No         
#>  8 No         
#>  9 No         
#> 10 No         
#> # ℹ 10,512 more rows

^{Created on 2024-08-29 with reprex v2.1.1}

Session info

─ Session info ────────────────────────────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14 ucrt)
 os       Windows 10 x64 (build 19045)
 system   x86_64, mingw32
 ui       RStudio
 language (EN)
 collate  English_United Kingdom.utf8
 ctype    English_United Kingdom.utf8
 tz       Europe/London
 date     2024-08-29
 rstudio  2024.04.2+764 Chocolate Cosmos (desktop)
 pandoc   3.1.11 @ C:/Users/Sebastian.Fox/AppData/Local/Programs/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ────────────────────────────────────────────────────────────────────────────────────────
 ! package      * version    date (UTC) lib source
   backports      1.5.0      2024-05-23 [1] CRAN (R 4.4.0)
   broom        * 1.0.6      2024-05-17 [1] CRAN (R 4.4.1)
   callr          3.7.6      2024-03-25 [1] CRAN (R 4.4.1)
   class          7.3-22     2023-05-03 [1] CRAN (R 4.4.1)
   cli            3.6.3      2024-06-21 [1] CRAN (R 4.4.1)
   clipr          0.8.0      2022-02-22 [1] CRAN (R 4.4.1)
   clock          0.7.0      2023-05-15 [1] CRAN (R 4.4.1)
   codetools      0.2-20     2024-03-31 [1] CRAN (R 4.4.1)
   colorspace     2.1-0      2023-01-23 [1] CRAN (R 4.4.1)
   crayon         1.5.3      2024-06-20 [1] CRAN (R 4.4.1)
   data.table     1.15.4     2024-03-30 [1] CRAN (R 4.4.1)
   dials        * 1.2.1      2024-02-22 [1] CRAN (R 4.4.1)
   DiceDesign     1.10       2023-12-07 [1] CRAN (R 4.4.1)
   digest         0.6.36     2024-06-23 [1] CRAN (R 4.4.1)
   dplyr        * 1.1.4      2023-11-17 [1] CRAN (R 4.4.1)
   evaluate       0.24.0     2024-06-10 [1] CRAN (R 4.4.1)
   fansi          1.0.6      2023-12-08 [1] CRAN (R 4.4.1)
   farver         2.1.2      2024-05-13 [1] CRAN (R 4.4.1)
   fastmap        1.2.0      2024-05-15 [1] CRAN (R 4.4.1)
   forcats        1.0.0      2023-01-29 [1] CRAN (R 4.4.1)
   foreach        1.5.2      2022-02-02 [1] CRAN (R 4.4.1)
   fs             1.6.4      2024-04-25 [1] CRAN (R 4.4.1)
   furrr          0.3.1      2022-08-15 [1] CRAN (R 4.4.1)
   future         1.33.2     2024-03-26 [1] CRAN (R 4.4.1)
   future.apply   1.11.2     2024-03-28 [1] CRAN (R 4.4.1)
   generics       0.1.3      2022-07-05 [1] CRAN (R 4.4.1)
   ggplot2      * 3.5.1      2024-04-23 [1] CRAN (R 4.4.1)
   globals        0.16.3     2024-03-08 [1] CRAN (R 4.4.0)
   glue           1.7.0      2024-01-09 [1] CRAN (R 4.4.1)
   gower          1.0.1      2022-12-22 [1] CRAN (R 4.4.0)
   GPfit          1.0-8      2019-02-08 [1] CRAN (R 4.4.1)
   gtable         0.3.5      2024-04-22 [1] CRAN (R 4.4.1)
   hardhat        1.4.0      2024-06-02 [1] CRAN (R 4.4.1)
   htmltools      0.5.8.1    2024-04-04 [1] CRAN (R 4.4.1)
   infer        * 1.0.7      2024-03-25 [1] CRAN (R 4.4.1)
   ipred          0.9-14     2023-03-09 [1] CRAN (R 4.4.1)
   iterators      1.0.14     2022-02-05 [1] CRAN (R 4.4.1)
   knitr          1.48       2024-07-07 [1] CRAN (R 4.4.1)
   labeling       0.4.3      2023-08-29 [1] CRAN (R 4.4.0)
   lattice        0.22-6     2024-03-20 [1] CRAN (R 4.4.1)
   lava           1.8.0      2024-03-05 [1] CRAN (R 4.4.1)
   lhs            1.2.0      2024-06-30 [1] CRAN (R 4.4.1)
   lifecycle      1.0.4      2023-11-07 [1] CRAN (R 4.4.1)
   listenv        0.9.1      2024-01-29 [1] CRAN (R 4.4.1)
   lubridate      1.9.3      2023-09-27 [1] CRAN (R 4.4.1)
   magrittr       2.0.3      2022-03-30 [1] CRAN (R 4.4.1)
   MASS           7.3-60.2   2024-04-26 [1] CRAN (R 4.4.1)
   Matrix         1.7-0      2024-04-26 [1] CRAN (R 4.4.1)
   modeldata    * 1.4.0      2024-06-19 [1] CRAN (R 4.4.1)
   modeltime      1.3.0      2024-07-29 [1] CRAN (R 4.4.1)
   munsell        0.5.1      2024-04-01 [1] CRAN (R 4.4.1)
   nnet           7.3-19     2023-05-03 [1] CRAN (R 4.4.1)
   parallelly     1.37.1     2024-02-29 [1] CRAN (R 4.4.0)
   parsnip      * 1.2.1      2024-03-22 [1] CRAN (R 4.4.1)
   pillar         1.9.0      2023-03-22 [1] CRAN (R 4.4.1)
   pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.4.1)
   pkgload        1.4.0      2024-06-28 [1] CRAN (R 4.4.1)
   processx       3.8.4      2024-03-16 [1] CRAN (R 4.4.1)
   prodlim        2024.06.25 2024-06-24 [1] CRAN (R 4.4.1)
   ps             1.7.6      2024-01-18 [1] CRAN (R 4.4.1)
   purrr        * 1.0.2      2023-08-10 [1] CRAN (R 4.4.1)
   R6             2.5.1      2021-08-19 [1] CRAN (R 4.4.1)
   ranger         0.16.0     2023-11-12 [1] CRAN (R 4.4.1)
   rappdirs       0.3.3      2021-01-31 [1] CRAN (R 4.4.1)
   Rcpp           1.0.12     2024-01-09 [1] CRAN (R 4.4.1)
 D RcppParallel   5.1.9      2024-08-19 [1] CRAN (R 4.4.1)
   recipes      * 1.1.0      2024-07-04 [1] CRAN (R 4.4.1)
   reprex         2.1.1      2024-07-06 [1] CRAN (R 4.4.1)
   rlang          1.1.4      2024-06-04 [1] CRAN (R 4.4.1)
   rmarkdown      2.27       2024-05-17 [1] CRAN (R 4.4.1)
   rpart          4.1.23     2023-12-05 [1] CRAN (R 4.4.1)
   rsample      * 1.2.1      2024-03-25 [1] CRAN (R 4.4.1)
   rstudioapi     0.16.0     2024-03-24 [1] CRAN (R 4.4.1)
   scales       * 1.3.0      2023-11-28 [1] CRAN (R 4.4.1)
   sessioninfo    1.2.2      2021-12-06 [1] CRAN (R 4.4.1)
   StanHeaders    2.32.10    2024-07-15 [1] CRAN (R 4.4.1)
   stringi        1.8.4      2024-05-06 [1] CRAN (R 4.4.0)
   stringr        1.5.1      2023-11-14 [1] CRAN (R 4.4.1)
   survival       3.6-4      2024-04-24 [1] CRAN (R 4.4.1)
   tibble       * 3.2.1      2023-03-20 [1] CRAN (R 4.4.1)
   tidymodels   * 1.2.0      2024-03-25 [1] CRAN (R 4.4.1)
   tidyr        * 1.3.1      2024-01-24 [1] CRAN (R 4.4.1)
   tidyselect     1.2.1      2024-03-11 [1] CRAN (R 4.4.1)
   timechange     0.3.0      2024-01-18 [1] CRAN (R 4.4.1)
   timeDate       4032.109   2023-12-14 [1] CRAN (R 4.4.1)
   timetk       * 2.9.0      2023-10-31 [1] CRAN (R 4.4.1)
   tsibbledata  * 0.4.1      2022-09-01 [1] CRAN (R 4.4.1)
   tune         * 1.2.1      2024-04-18 [1] CRAN (R 4.4.1)
   tzdb           0.4.0      2023-05-12 [1] CRAN (R 4.4.1)
   utf8           1.2.4      2023-10-22 [1] CRAN (R 4.4.1)
   vctrs          0.6.5      2023-12-01 [1] CRAN (R 4.4.1)
   withr          3.0.0      2024-01-16 [1] CRAN (R 4.4.1)
   workflows    * 1.1.4      2024-02-19 [1] CRAN (R 4.4.1)
   workflowsets * 1.1.0      2024-03-21 [1] CRAN (R 4.4.1)
   xfun           0.45       2024-06-16 [1] CRAN (R 4.4.1)
   xts            0.14.0     2024-06-05 [1] CRAN (R 4.4.1)
   yaml           2.3.9      2024-07-05 [1] CRAN (R 4.4.1)
   yardstick    * 1.3.1      2024-03-21 [1] CRAN (R 4.4.1)
   zoo            1.8-12     2023-04-13 [1] CRAN (R 4.4.1)

 [1] C:/Program Files/R/R-4.4.1/library

 D ── DLL MD5 mismatch, broken installation.

───────────────────────────────────────────────────────────────────────────────────────────────────

[sebsfox]

perhaps this line needs updating to:

nms_final <- stringr::str_replace(nms_final, ".pred_res|.pred_class", ".pred")

[mdancho84]

The problem:

• Modeltime is designed for regression (numeric values) for forecasting
• Using a logistic_reg() model is a classification task

Solution:

• For now I'd make it a regression problem. For example instead of Yes or No, convert to 1 or 0. Then have model prediction be a numeric value that you can compare to the true values.

[sebsfox]

Thanks for responding and putting it on your backlog. I've gone onto another project but I may come back to this at some point