This code was working until yesterday, when I uninstalled the recipeselectors and colino packages. I've looked everywhere and no one has reported a similar error.
Link to download the database used:
Reproducible example
*** Load the following R packages ----
library(remotes)
library(pak)
remotes::install_github("business-science/modeltime.ensemble")
remotes::install_github("tidymodels/recipes")
library(recipes)
library(tune)
library(keras)
library(ForecastTB)
library(ggplot2)
library(zoo)
library(forecast)
library(lmtest)
library(urca)
library(stats)
library(nnfor)
library(forecastHybrid)
library(pastecs)
library(forecastML)
library(Rcpp)
library(modeltime.ensemble)
library(tidymodels)
library(modeltime)
library(lubridate)
library(tidyverse)
library(timetk)
library(tidyquant)
library(yardstick)
library(reshape)
library(plotly)
library(xgboost)
library(rsample)
library(targets)
library(tidymodels)
library(modeltime)
library(timetk)
library(tidyverse)
library(tidyquant)
library(LiblineaR)
library(parsnip)
library(ranger)
library(kknn)
library(readxl)
library(lifecycle)
library(skimr)
library(remotes)
remotes::install_github("tidymodels/bonsai")
library(bonsai)
library(lightgbm)
remotes::install_github("curso-r/treesnip")
library(treesnip)
library(rio)
library(pak)
library(devtools)
devtools::install_github("stevenpawley/recipeselectors")
devtools::install_github("stevenpawley/colino")
library(colino)
library(recipeselectors)
library(FSelectorRcpp)
library(care)
library(parsnip)
library(Boruta)
library(praznik)
library(parallel)
library(foreach)
library(doParallel)
library(RcppParallel)
Preparing data for preprocessing with recipe
data_tbl <- datasets %>%
select(id, Date, attendences, average_temperature, min, max, sunday, monday, tuesday, wednesday, thursday, friday, saturday, Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec) %>%
set_names(c("id", "date", "value","tempe_verage", "tempemin", "tempemax", "sunday", "monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))
data_tbl
Full = Training + Forecast Datasets
full_data_tbl <- datasets %>%
select(id, Date, attendences, average_temperature, min, max, sunday, monday, tuesday, wednesday, thursday, friday, saturday, Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec) %>%
set_names(c("id", "date", "value","tempe_verage", "tempemin", "tempemax", "sunday", "monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")) %>%
Apply Group-wise Time Series Manipulations
group_by(id) %>%
future_frame(
.date_var = date,
.length_out = "45 days",
.bind_data = TRUE
) %>%
ungroup() %>%
Consolidate IDs
mutate(id = fct_drop(id))
Training Data
data_prepared_tbl <- full_data_tbl %>%
filter(!is.na(value))
Forecast Data
future_tbl <- full_data_tbl %>%
filter(is.na(value))
data_prepared_tbl %>% glimpse()
** Summary Diagnostics. Let us check the regularity of all time series with timetk::tk_summary_diagnostics()
** Check for summary of timeseries data for training set
data_prepared_tbl %>%
group_by(id) %>%
tk_summary_diagnostics()
Data Splitting ----
Now we set aside the future data (we would only need that later when we make forecast)
And focus on training data
* 4.1 Panel Data Splitting ----
Split the dataset into analyis/assessment set
`emergency_tscv <- data_prepared_tbl %>%
time_series_cv(
date_var = date,
assess = "45 days",
skip = "30 days",
cumulative = TRUE,
slice_limit = 5
)
emergency_tscv`
Feature Selection and preprocessing ----
Information gain feature selection ----
recipe_spec <- recipe(value ~ .,
data = training(emergency_tscv$splits[[1]])) %>%
step_timeseries_signature(date) %>%
step_rm(matches("(.iso$)|(.xts$)|(.lbl$)|(hour)|(minute)|(second)|(am.pm)|(date_year$)")) %>%
step_normalize (date_index.num,tempe_verage,tempemin,tempemax, -all_outcomes())%>%
step_select_infgain(all_predictors(), scores = TRUE, top_p = 22, outcome = "value") %>%
step_mutate(data = factor(value, ordered = TRUE))%>%
step_dummy(all_nominal(), one_hot = TRUE)
recipe_spec %>% prep() %>% juice() %>% glimpse()
*** Model 4: GLMNET ----
wflw_fit_glmnet <- workflow() %>%
add_model(
linear_reg(mixture = tune(),
penalty = tune()) %>% set_engine("glmnet", num.threads = 20))%>%
add_recipe(recipe_spec %>% step_rm(date)) %>%
tune_grid(grid = 1, recipe_spec, resamples = emergency_tscv, control = control_grid(verbose = TRUE, parallel_over = "resamples", allow_par = TRUE), metrics = metric_set(rmse))
### reported error ###
Error in `map()`:
ℹ In index: 4.
Caused by error in `tibble::tibble()`:
! Tibble columns must have compatible sizes.
• Size 2: Existing data.
• Size 4: Column `call_info`.
ℹ Only values of size one are recycled.
Run `rlang::last_trace()` to see where the error occurred.
Warning message:
The `...` are not used in this function but one or more objects were passed: ''
> rlang::last_trace()
<error/purrr_error_indexed>
Error in `map()`:
ℹ In index: 4.
Caused by error in `tibble::tibble()`:
! Tibble columns must have compatible sizes.
• Size 2: Existing data.
• Size 4: Column `call_info`.
ℹ Only values of size one are recycled.
---
Backtrace:
▆
1. ├─... %>% ...
2. ├─tune::tune_grid(...)
3. └─tune:::tune_grid.workflow(...)
4. └─tune:::tune_grid_workflow(...)
5. └─tune::check_parameters(...)
6. ├─hardhat::extract_parameter_set_dials(wflow)
7. └─workflows:::extract_parameter_set_dials.workflow(wflow)
8. ├─hardhat::extract_parameter_set_dials(recipe)
9. └─recipes:::extract_parameter_set_dials.recipe(recipe)
10. ├─generics::tunable(x)
11. └─recipes:::tunable.recipe(x)
12. └─purrr::map_dfr(x$steps, tunable)
13. └─purrr::map(.x, .f, ...)
14. └─purrr:::map_("list", .x, .f, ..., .progress = .progress)
15. ├─purrr:::with_indexed_errors(...)
16. │ └─base::withCallingHandlers(...)
17. ├─purrr:::call_with_cleanup(...)
18. ├─generics (local) .f(.x[[i]], ...)
19. └─colino:::tunable.step_select_infgain(.x[[i]], ...)
20. └─tibble::tibble(...)
See that if we run the model training without the step of selecting variables for information gain, it works normally generating the grid search. It could be some package conflict that I am not able to identify because the preprocessing with the information gain step was working normally
recipe_spec <- recipe(value ~ .,
data = training(emergency_tscv$splits[[1]])) %>%
step_timeseries_signature(date) %>%
step_rm(matches("(.iso$)|(.xts$)|(.lbl$)|(hour)|(minute)|(second)|(am.pm)|(date_year$)")) %>%
step_mutate(data = factor(value, ordered = TRUE))%>%
step_dummy(all_nominal(), one_hot = TRUE)%>%
step_normalize (date_index.num,tempe_verage,tempemin,tempemax, -all_outcomes())
recipe_spec %>% prep() %>% juice() %>% glimpse()
wflw_fit_glmnet <- workflow() %>%
add_model(
linear_reg(mixture = tune(),
penalty = tune()) %>% set_engine("glmnet", num.threads = 20))%>%
add_recipe(recipe_spec %>% step_rm(date)) %>%
tune_grid(grid = 1, recipe_spec, resamples = emergency_tscv, control = control_grid(verbose = TRUE, parallel_over = "resamples", allow_par = TRUE), metrics = metric_set(rmse))
i Slice1: preprocessor 1/1
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i Slice5: preprocessor 1/1, model 1/1 (predictions)
glmnet_tune_All_45 <- wflw_fit_glmnet
wflw_fit_glmnet %>% show_best(metric = "rmse")
result
A tibble: 1 × 8
penalty mixture .metric .estimator mean n std_err .config
<dbl> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.000110 0.762 rmse standard 4.75 5 0.339 Preprocessor1_Model1
> session_info()
─ Session info ─────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.3.0 (2023-04-21 ucrt)
os Windows 11 x64 (build 22621)
system x86_64, mingw32
ui RStudio
language (EN)
collate English_United States.utf8
ctype English_United States.utf8
tz America/Sao_Paulo
date 2023-06-18
rstudio 2023.06.0+421 Mountain Hydrangea (desktop)
pandoc NA
─ Packages ─────────────────────────────────────────────────────────────────────────────────────────────────
! package * version date (UTC) lib source
backports 1.4.1 2021-12-13 [1] CRAN (R 4.3.0)
base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.3.0)
bonsai * 0.2.1.9000 2023-06-15 [1] Github (tidymodels/bonsai@aab79d5)
boot 1.3-28.1 2022-11-22 [2] CRAN (R 4.3.0)
broom * 1.0.5 2023-06-09 [1] CRAN (R 4.3.0)
cachem 1.0.8 2023-05-01 [1] CRAN (R 4.3.0)
callr 3.7.3 2022-11-02 [1] CRAN (R 4.3.0)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.3.0)
circlize 0.4.15 2022-05-10 [1] CRAN (R 4.3.0)
class 7.3-21 2023-01-23 [2] CRAN (R 4.3.0)
cli 3.6.1 2023-03-23 [1] CRAN (R 4.3.0)
cluster 2.1.4 2022-08-22 [2] CRAN (R 4.3.0)
codetools 0.2-19 2023-02-01 [2] CRAN (R 4.3.0)
colino * 0.0.1 2023-06-18 [1] Github (stevenpawley/colino@6738db1)
colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.3.0)
crayon 1.5.2 2022-09-29 [1] CRAN (R 4.3.0)
curl 5.0.1 2023-06-07 [1] CRAN (R 4.3.0)
data.table 1.14.8 2023-02-17 [1] CRAN (R 4.3.0)
desc 1.4.2 2022-09-08 [1] CRAN (R 4.3.0)
devtools * 2.4.5 2022-10-11 [1] CRAN (R 4.3.0)
dials * 1.2.0 2023-04-03 [1] CRAN (R 4.3.0)
DiceDesign 1.9 2021-02-13 [1] CRAN (R 4.3.0)
digest 0.6.31 2022-12-11 [1] CRAN (R 4.3.0)
doParallel 1.0.17 2022-02-07 [1] CRAN (R 4.3.0)
dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.3.0)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.3.0)
fansi 1.0.4 2023-01-22 [1] CRAN (R 4.3.0)
fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.0)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.3.0)
foreach 1.5.2 2022-02-02 [1] CRAN (R 4.3.0)
forecast * 8.21 2023-02-27 [1] CRAN (R 4.3.0)
forecastHybrid * 5.0.19 2020-08-28 [1] CRAN (R 4.3.0)
forecastML * 0.9.0 2020-05-07 [1] CRAN (R 4.3.0)
ForecastTB * 1.0.1 2020-03-14 [1] CRAN (R 4.3.0)
foreign 0.8-84 2022-12-06 [2] CRAN (R 4.3.0)
fracdiff 1.5-2 2022-10-31 [1] CRAN (R 4.3.0)
fs 1.6.2 2023-04-25 [1] CRAN (R 4.3.0)
FSelectorRcpp 0.3.11 2023-04-28 [1] CRAN (R 4.3.0)
furrr 0.3.1 2022-08-15 [1] CRAN (R 4.3.0)
future 1.32.0 2023-03-07 [1] CRAN (R 4.3.0)
future.apply 1.11.0 2023-05-21 [1] CRAN (R 4.3.0)
generics * 0.1.3 2022-07-05 [1] CRAN (R 4.3.0)
ggplot2 * 3.4.2 2023-04-03 [1] CRAN (R 4.3.0)
ggtext 0.1.2 2022-09-16 [1] CRAN (R 4.3.0)
glmnet * 4.1-7 2023-03-23 [1] CRAN (R 4.3.0)
GlobalOptions 0.1.2 2020-06-10 [1] CRAN (R 4.3.0)
globals 0.16.2 2022-11-21 [1] CRAN (R 4.3.0)
glue 1.6.2 2022-02-24 [1] CRAN (R 4.3.0)
gower 1.0.1 2022-12-22 [1] CRAN (R 4.3.0)
GPfit 1.0-8 2019-02-08 [1] CRAN (R 4.3.0)
greybox 1.0.8 2023-04-02 [1] CRAN (R 4.3.0)
gridExtra 2.3 2017-09-09 [1] CRAN (R 4.3.0)
gridtext 0.1.5 2022-09-16 [1] CRAN (R 4.3.0)
gtable 0.3.3 2023-03-21 [1] CRAN (R 4.3.0)
hardhat 1.3.0 2023-03-30 [1] CRAN (R 4.3.0)
haven 2.5.2 2023-02-28 [1] CRAN (R 4.3.0)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.3.0)
htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.3.0)
htmlwidgets 1.6.2 2023-03-17 [1] CRAN (R 4.3.0)
httpuv 1.6.11 2023-05-11 [1] CRAN (R 4.3.0)
httr 1.4.6 2023-05-08 [1] CRAN (R 4.3.0)
imputeTestbench 3.0.3 2019-07-05 [1] CRAN (R 4.3.0)
imputeTS 3.3 2022-09-09 [1] CRAN (R 4.3.0)
infer * 1.0.4 2022-12-02 [1] CRAN (R 4.3.0)
ipred 0.9-14 2023-03-09 [1] CRAN (R 4.3.0)
iterators 1.0.14 2022-02-05 [1] CRAN (R 4.3.0)
jsonlite 1.8.5 2023-06-05 [1] CRAN (R 4.3.0)
keras * 2.11.1 2023-03-20 [1] CRAN (R 4.3.0)
later 1.3.1 2023-05-02 [1] CRAN (R 4.3.0)
lattice 0.21-8 2023-04-05 [2] CRAN (R 4.3.0)
lava 1.7.2.1 2023-02-27 [1] CRAN (R 4.3.0)
lhs 1.1.6 2022-12-17 [1] CRAN (R 4.3.0)
lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.3.0)
lightgbm * 3.3.5 2023-01-16 [1] CRAN (R 4.3.0)
listenv 0.9.0 2022-12-16 [1] CRAN (R 4.3.0)
lmtest * 0.9-40 2022-03-21 [1] CRAN (R 4.3.0)
lubridate * 1.9.2 2023-02-10 [1] CRAN (R 4.3.0)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.0)
MAPA 2.0.5 2022-07-10 [1] CRAN (R 4.3.0)
MASS 7.3-58.4 2023-03-07 [2] CRAN (R 4.3.0)
Matrix * 1.5-4 2023-04-04 [2] CRAN (R 4.3.0)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.3.0)
mime 0.12 2021-09-28 [1] CRAN (R 4.3.0)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.3.0)
modeldata * 1.1.0 2023-01-25 [1] CRAN (R 4.3.0)
modeltime * 1.2.6 2023-03-31 [1] CRAN (R 4.3.0)
modeltime.ensemble * 1.0.3 2023-04-18 [1] CRAN (R 4.3.0)
modeltime.resample * 0.2.3 2023-04-12 [1] CRAN (R 4.3.0)
munsell 0.5.0 2018-06-12 [1] CRAN (R 4.3.0)
nlme 3.1-162 2023-01-31 [2] CRAN (R 4.3.0)
nloptr 2.0.3 2022-05-26 [1] CRAN (R 4.3.0)
nnet 7.3-18 2022-09-28 [2] CRAN (R 4.3.0)
nnfor * 0.9.8 2022-07-09 [1] CRAN (R 4.3.0)
openxlsx 4.2.5.2 2023-02-06 [1] CRAN (R 4.3.0)
pak * 0.5.1 2023-04-27 [1] CRAN (R 4.3.0)
parallelly 1.36.0 2023-05-26 [1] CRAN (R 4.3.0)
parsnip * 1.1.0 2023-04-12 [1] CRAN (R 4.3.0)
pastecs * 1.3.21 2018-03-15 [1] CRAN (R 4.3.0)
PerformanceAnalytics * 2.0.4 2020-02-06 [1] CRAN (R 4.3.0)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.0)
pkgbuild 1.4.1 2023-06-14 [1] CRAN (R 4.3.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.0)
pkgload 1.3.2 2022-11-16 [1] CRAN (R 4.3.0)
plyr 1.8.8 2022-11-11 [1] CRAN (R 4.3.0)
png 0.1-8 2022-11-29 [1] CRAN (R 4.3.0)
pracma 2.4.2 2022-09-22 [1] CRAN (R 4.3.0)
prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.3.0)
processx 3.8.1 2023-04-18 [1] CRAN (R 4.3.0)
prodlim 2023.03.31 2023-04-02 [1] CRAN (R 4.3.0)
profvis 0.3.8 2023-05-02 [1] CRAN (R 4.3.0)
promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.3.0)
ps 1.7.5 2023-04-18 [1] CRAN (R 4.3.0)
PSF 0.5 2022-05-01 [1] CRAN (R 4.3.0)
purrr * 1.0.1 2023-01-10 [1] CRAN (R 4.3.0)
quadprog 1.5-8 2019-11-20 [1] CRAN (R 4.3.0)
Quandl 2.11.0 2021-08-11 [1] CRAN (R 4.3.0)
quantmod * 0.4.23 2023-06-15 [1] CRAN (R 4.3.0)
R6 * 2.5.1 2021-08-19 [1] CRAN (R 4.3.0)
RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.3.0)
Rcpp * 1.0.10 2023-01-22 [1] CRAN (R 4.3.0)
D RcppParallel 5.1.7 2023-02-27 [1] CRAN (R 4.3.0)
readr * 2.1.4 2023-02-10 [1] CRAN (R 4.3.0)
readxl * 1.4.2 2023-02-09 [1] CRAN (R 4.3.0)
recipes * 1.0.6.9000 2023-06-18 [1] Github (tidymodels/recipes@0d528b2)
remotes 2.4.2 2021-11-30 [1] CRAN (R 4.3.0)
reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.3.0)
reticulate 1.30 2023-06-09 [1] CRAN (R 4.3.0)
rio * 0.5.29 2021-11-22 [1] CRAN (R 4.3.0)
rlang 1.1.1 2023-04-28 [1] CRAN (R 4.3.0)
rpart 4.1.19 2022-10-21 [2] CRAN (R 4.3.0)
rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.3.0)
rsample * 1.1.1 2022-12-07 [1] CRAN (R 4.3.0)
rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.3.0)
scales * 1.2.1 2022-08-20 [1] CRAN (R 4.3.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.0)
shape 1.4.6 2021-05-19 [1] CRAN (R 4.3.0)
shiny 1.7.4 2022-12-15 [1] CRAN (R 4.3.0)
smooth 3.2.1 2023-06-01 [1] CRAN (R 4.3.0)
StanHeaders 2.26.27 2023-06-14 [1] CRAN (R 4.3.0)
statmod 1.5.0 2023-01-06 [1] CRAN (R 4.3.0)
stinepack 1.4 2018-07-30 [1] CRAN (R 4.3.0)
stringi 1.7.12 2023-01-11 [1] CRAN (R 4.3.0)
stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.3.0)
survival 3.5-5 2023-03-12 [2] CRAN (R 4.3.0)
tensorflow 2.11.0 2022-12-19 [1] CRAN (R 4.3.0)
texreg 1.38.6 2022-04-06 [1] CRAN (R 4.3.0)
tfruns 1.5.1 2022-09-05 [1] CRAN (R 4.3.0)
thief * 0.3 2018-01-24 [1] CRAN (R 4.3.0)
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.3.0)
tidymodels * 1.1.0 2023-05-01 [1] CRAN (R 4.3.1)
tidyquant * 1.0.7 2023-03-31 [1] CRAN (R 4.3.0)
tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.3.0)
tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.3.0)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.3.0)
timechange 0.2.0 2023-01-11 [1] CRAN (R 4.3.0)
timeDate 4022.108 2023-01-07 [1] CRAN (R 4.3.0)
timetk * 2.8.3 2023-03-30 [1] CRAN (R 4.3.0)
tree 1.0-43 2023-02-05 [1] CRAN (R 4.3.0)
tseries 0.10-54 2023-05-02 [1] CRAN (R 4.3.0)
tsutils 0.9.3 2022-07-05 [1] CRAN (R 4.3.0)
TTR * 0.24.3 2021-12-12 [1] CRAN (R 4.3.0)
tune * 1.1.1 2023-04-11 [1] CRAN (R 4.3.0)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.3.0)
urca * 1.3-3 2022-08-29 [1] CRAN (R 4.3.0)
urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.3.0)
uroot 2.1-2 2020-09-04 [1] CRAN (R 4.3.0)
usethis * 2.2.0 2023-06-06 [1] CRAN (R 4.3.0)
utf8 1.2.3 2023-01-31 [1] CRAN (R 4.3.0)
vctrs 0.6.3 2023-06-14 [1] CRAN (R 4.3.0)
whisker 0.4.1 2022-12-05 [1] CRAN (R 4.3.0)
withr 2.5.0 2022-03-03 [1] CRAN (R 4.3.0)
workflows * 1.1.3 2023-02-22 [1] CRAN (R 4.3.0)
workflowsets * 1.0.1 2023-04-06 [1] CRAN (R 4.3.0)
xml2 1.3.4 2023-04-27 [1] CRAN (R 4.3.0)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.3.0)
xts * 0.13.1 2023-04-16 [1] CRAN (R 4.3.0)
yardstick * 1.2.0 2023-04-21 [1] CRAN (R 4.3.0)
zeallot 0.1.0 2018-01-28 [1] CRAN (R 4.3.0)
zip 2.3.0 2023-04-17 [1] CRAN (R 4.3.0)
zoo * 1.8-12 2023-04-13 [1] CRAN (R 4.3.0)
[1] C:/Users/Bruno Matos Porto/AppData/Local/R/win-library/4.3
[2] C:/Program Files/R/R-4.3.0/library
D ── DLL MD5 mismatch, broken installation.
────────────────
See that if we run the model training without the step of selecting variables for information gain, it works normally generating the grid search. It could be some package conflict that I am not able to identify because the preprocessing with the information gain step was working normally
recipe_spec <- recipe(value ~ .,
data = training(emergency_tscv$splits[[1]])) %>%
step_timeseries_signature(date) %>%
step_rm(matches("(.iso$)|(.xts$)|(.lbl$)|(hour)|(minute)|(second)|(am.pm)|(date_year$)")) %>%
step_mutate(data = factor(value, ordered = TRUE))%>%
step_dummy(all_nominal(), one_hot = TRUE)%>%
step_normalize (date_index.num,tempe_verage,tempemin,tempemax, -all_outcomes())
recipe_spec %>% prep() %>% juice() %>% glimpse()
wflw_fit_glmnet <- workflow() %>%
add_model(
linear_reg(mixture = tune(),
penalty = tune()) %>% set_engine("glmnet", num.threads = 20))%>%
add_recipe(recipe_spec %>% step_rm(date)) %>%
tune_grid(grid = 1, recipe_spec, resamples = emergency_tscv, control = control_grid(verbose = TRUE, parallel_over = "resamples", allow_par = TRUE), metrics = metric_set(rmse))
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glmnet_tune_All_45 <- wflw_fit_glmnet
wflw_fit_glmnet %>% show_best(metric = "rmse")
result
A tibble: 1 × 8
penalty mixture .metric .estimator mean n std_err .config
<dbl> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.000110 0.762 rmse standard 4.75 5 0.339 Preprocessor1_Model1
