Chicago_grid.Rout

R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)

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> library(tidymodels)
Registered S3 method overwritten by 'xts':
  method     from
  as.zoo.xts zoo 
── Attaching packages ────────────────────────────────────── tidymodels 0.0.3 ──
✔ broom     0.5.2          ✔ purrr     0.3.3     
✔ dials     0.0.3.9001     ✔ recipes   0.1.7.9001
✔ discrim   0.0.1          ✔ rsample   0.0.5     
✔ dplyr     0.8.3          ✔ tibble    2.1.3     
✔ ggplot2   3.2.1          ✔ tune      0.0.0.9003
✔ infer     0.5.0          ✔ yardstick 0.0.4     
✔ parsnip   0.0.4          
── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
✖ purrr::discard()  masks scales::discard()
✖ dplyr::filter()   masks stats::filter()
✖ dplyr::lag()      masks stats::lag()
✖ ggplot2::margin() masks dials::margin()
✖ dials::offset()   masks stats::offset()
✖ recipes::step()   masks stats::step()
> library(tune)
> 
> library(doMC)
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
> registerDoMC(cores = 8)
> 
> # ------------------------------------------------------------------------------
> 
> set.seed(7898)
> data_folds <- rolling_origin(Chicago, initial = 364 * 15, assess = 7 * 4, skip = 7 * 4, cumulative = FALSE)
> 
> # ------------------------------------------------------------------------------
> 
> library(stringr)

Attaching package: ‘stringr’

The following object is masked from ‘package:recipes’:

    fixed

> us_hol <- 
+   timeDate::listHolidays() %>% 
+   str_subset("(^US)|(Easter)")
> 
> chi_rec <-
+   recipe(ridership ~ ., data = Chicago) %>%
+   step_holiday(date, holidays = us_hol) %>%
+   step_date(date) %>%
+   step_rm(date) %>%
+   step_dummy(all_nominal()) %>%
+   step_zv(all_predictors())
> 
> mars_rec <- 
+   chi_rec %>% 
+   step_normalize(one_of(!!stations)) %>% 
+   step_pca(one_of(!!stations), num_comp = tune("pca comps"))
> 
> mars_mod <-  
+   mars(num_terms = tune("mars terms"), prod_degree = tune(), prune_method = "none") %>% 
+   set_engine("earth") %>% 
+   set_mode("regression")
> 
> chi_wflow <-
+   workflow() %>%
+   add_recipe(mars_rec) %>%
+   add_model(mars_mod)
> 
> chi_grid <- 
+   expand.grid(
+     `pca comps`  = 0:20,
+     prod_degree = 1:2,
+     `mars terms` = 2:100
+   )
> 
> all_res <-
+   tune_grid(
+     chi_wflow,
+     resamples = data_folds,
+     grid = chi_grid,
+     control = control_grid(verbose = TRUE)
+   )
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> 
> print(all_res$.notes[[1]])
# A tibble: 0 x 1
# … with 1 variable: .notes <chr>
> 
> complete_mars_grid <-
+   all_res %>% 
+   collect_metrics() %>% 
+   dplyr::filter(.metric == "rmse")
> 
> save(complete_mars_grid, file = "complete_mars_grid.RData")
> 
> q("no")
> proc.time()
     user    system   elapsed 
24994.400   347.425  3192.680