As part of the Applied ML course, I have run into the following error when using parallel processing on a Windows machine.
library(tidymodels)
#> -- Attaching packages ---------------------------------------- tidymodels 0.0.3 --
#> v broom 0.5.4 v purrr 0.3.3
#> v dials 0.0.4 v recipes 0.1.9
#> v dplyr 0.8.3 v rsample 0.0.5
#> v ggplot2 3.2.1 v tibble 2.1.3
#> v infer 0.5.1 v yardstick 0.0.5
#> v parsnip 0.0.5
#> -- Conflicts ------------------------------------------- tidymodels_conflicts() --
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x ggplot2::margin() masks dials::margin()
#> x recipes::step() masks stats::step()
#> x recipes::yj_trans() masks scales::yj_trans()
library(tune)
library(tidyverse)
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following object is masked from 'package:base':
#>
#> date
data(Chicago)
Chicago
#> # A tibble: 5,698 x 50
#> ridership Austin Quincy_Wells Belmont Archer_35th Oak_Park Western Clark_Lake
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 15.7 1.46 8.37 4.60 2.01 1.42 3.32 15.6
#> 2 15.8 1.50 8.35 4.72 2.09 1.43 3.34 15.7
#> 3 15.9 1.52 8.36 4.68 2.11 1.49 3.36 15.6
#> 4 15.9 1.49 7.85 4.77 2.17 1.44 3.36 15.7
#> 5 15.4 1.50 7.62 4.72 2.06 1.42 3.27 15.6
#> 6 2.42 0.693 0.911 2.27 0.624 0.426 1.11 2.41
#> 7 1.47 0.408 0.414 1.63 0.378 0.225 0.567 1.37
#> 8 15.5 0.987 4.81 3.52 1.34 0.879 1.94 9.02
#> 9 15.9 1.55 8.23 4.71 2.22 1.46 3.46 16.0
#> 10 15.9 1.59 8.25 4.77 2.23 1.48 3.51 15.8
#> # ... with 5,688 more rows, and 42 more variables: Clinton <dbl>,
#> # Merchandise_Mart <dbl>, Irving_Park <dbl>, Washington_Wells <dbl>,
#> # Harlem <dbl>, Monroe <dbl>, Polk <dbl>, Ashland <dbl>, Kedzie <dbl>,
#> # Addison <dbl>, Jefferson_Park <dbl>, Montrose <dbl>, California <dbl>,
#> # temp_min <dbl>, temp <dbl>, temp_max <dbl>, temp_change <dbl>, dew <dbl>,
#> # humidity <dbl>, pressure <dbl>, pressure_change <dbl>, wind <dbl>,
#> # wind_max <dbl>, gust <dbl>, gust_max <dbl>, percip <dbl>, percip_max <dbl>,
#> # weather_rain <dbl>, weather_snow <dbl>, weather_cloud <dbl>,
#> # weather_storm <dbl>, Blackhawks_Away <dbl>, Blackhawks_Home <dbl>,
#> # Bulls_Away <dbl>, Bulls_Home <dbl>, Bears_Away <dbl>, Bears_Home <dbl>,
#> # WhiteSox_Away <dbl>, WhiteSox_Home <dbl>, Cubs_Away <dbl>, Cubs_Home <dbl>,
#> # date <date>
stations
#> [1] "Austin" "Quincy_Wells" "Belmont" "Archer_35th"
#> [5] "Oak_Park" "Western" "Clark_Lake" "Clinton"
#> [9] "Merchandise_Mart" "Irving_Park" "Washington_Wells" "Harlem"
#> [13] "Monroe" "Polk" "Ashland" "Kedzie"
#> [17] "Addison" "Jefferson_Park" "Montrose" "California"
holidays <- timeDate::listHolidays() %>%
str_subset("(^US|Easter)")
Chicago_copy <- mutate(Chicago, year = year(date),
day = wday(date, label = TRUE, abbr = FALSE),
year = year(date))
chi_rec <- recipe(ridership ~ ., data = Chicago_copy) %>%
step_holiday(date, holidays = holidays) %>%
step_date(date) %>%
step_rm(date) %>%
step_dummy(all_nominal()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_predictors())
chi_folds <- rolling_origin(Chicago_copy,
initial = 364 * 15,
assess = 7 * 4,
skip = 7 * 4,
cumulative = FALSE)
glmn_grid <- expand_grid(
penalty = 10 ^ seq(-3, -1, length.out = 20),
mixture = (0:5) / 5)
glmn_mod <- linear_reg(penalty = tune(), mixture = tune()) %>%
set_engine("glmnet")
ctrl <- control_grid(save_pred = TRUE)
library(doParallel)
#> Loading required package: foreach
#>
#> Attaching package: 'foreach'
#> The following objects are masked from 'package:purrr':
#>
#> accumulate, when
#> Loading required package: iterators
#> Loading required package: parallel
cl <- makeCluster(4)
registerDoParallel(cl)
glmn_tune <- tune_grid(chi_rec,
model = glmn_mod,
resamples = chi_folds,
grid = glmn_grid,
control = ctrl)
#> Warning: All models failed in tune_grid(). See the `.notes` column.
stopCluster(cl)
glmn_tune$.notes[[1]]
#> # A tibble: 1 x 1
#> .notes
#> <chr>
#> 1 "recipe: Error: could not find function \"all_nominal\""
As part of the Applied ML course, I have run into the following error when using parallel processing on a Windows machine.