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I've put together a data preprocessing recipe for the recent coffee dataset featured on TidyTuesday. My intention is to generate a workflow, and then from there tune a hyperparameter. I'm specifically interesting in manually declaring predictors and outcomes through the various update_role() functions, rather than using a formula, since I have some great plans for this style of variable selection (it's a really great idea!).
The example below produces a recipe that works just fine with prep and bake(coffee_test). It even works if I deselect the outcome column, eg. coffee_recipe %>% bake(select(coffee_test, -cupper_points)). However, when I run the workflow through tune_grid I get the errors as shown. It looks like tune_grid can't find the variables that don't have the "predictor" role, even though bake does just fine.
Now, if I instead do things the normal way with a formula and step_rm the variables I don't care about, then things mostly work --- I get a few warnings for rows with missing country_of_origin values, which I find strange since I should be imputing those. It's entirely possible I've misunderstood the purpose of roles and how to use them.
library(tidyverse)
library(tidymodels)
#> ── Attaching packages ───────────────────────────────────────────────────── tidymodels 0.1.1 ──
#> ✓ broom 0.7.0 ✓ recipes 0.1.13
#> ✓ dials 0.0.8 ✓ rsample 0.0.7
#> ✓ infer 0.5.3 ✓ tune 0.1.1
#> ✓ modeldata 0.0.2 ✓ workflows 0.1.2
#> ✓ parsnip 0.1.2 ✓ yardstick 0.0.7
#> ── Conflicts ──────────────────────────────────────────────────────── tidymodels_conflicts() ──
#> x scales::discard() masks purrr::discard()
#> x dplyr::filter() masks stats::filter()
#> x recipes::fixed() masks stringr::fixed()
#> x dplyr::lag() masks stats::lag()
#> x yardstick::spec() masks readr::spec()
#> x recipes::step() masks stats::step()
set.seed(12345)
coffee <- tidytuesdayR::tt_load(2020, week = 28)$coffee_ratings
#> --- Compiling #TidyTuesday Information for 2020-07-07 ----
#> --- There is 1 file available ---
#> --- Starting Download ---
#>
#> Downloading file 1 of 1: `coffee_ratings.csv`
#> --- Download complete ---
colnames(coffee)
#> [1] "total_cup_points" "species" "owner"
#> [4] "country_of_origin" "farm_name" "lot_number"
#> [7] "mill" "ico_number" "company"
#> [10] "altitude" "region" "producer"
#> [13] "number_of_bags" "bag_weight" "in_country_partner"
#> [16] "harvest_year" "grading_date" "owner_1"
#> [19] "variety" "processing_method" "aroma"
#> [22] "flavor" "aftertaste" "acidity"
#> [25] "body" "balance" "uniformity"
#> [28] "clean_cup" "sweetness" "cupper_points"
#> [31] "moisture" "category_one_defects" "quakers"
#> [34] "color" "category_two_defects" "expiration"
#> [37] "certification_body" "certification_address" "certification_contact"
#> [40] "unit_of_measurement" "altitude_low_meters" "altitude_high_meters"
#> [43] "altitude_mean_meters"
coffee_split <- initial_split(coffee, prop = 0.8)
coffee_train <- training(coffee_split)
coffee_test <- testing(coffee_split)
coffee_recipe <- recipe(coffee_train) %>%
update_role(cupper_points, new_role = "outcome") %>%
update_role(
variety, processing_method, country_of_origin,
aroma, flavor, aftertaste, acidity, sweetness, altitude_mean_meters,
new_role = "predictor"
) %>%
step_string2factor(all_nominal(), -all_outcomes()) %>%
step_knnimpute(
country_of_origin, altitude_mean_meters,
impute_with = imp_vars(
in_country_partner, company, region, farm_name, certification_body
)
) %>%
step_unknown(variety, processing_method, new_level = "Unknown") %>%
step_other(country_of_origin, threshold = 0.01) %>%
step_other(processing_method, threshold = 0.10) %>%
step_other(variety, threshold = 0.10)
coffee_recipe
#> Data Recipe
#>
#> Inputs:
#>
#> role #variables
#> outcome 1
#> predictor 9
#>
#> 33 variables with undeclared roles
#>
#> Operations:
#>
#> Factor variables from all_nominal(), -all_outcomes()
#> K-nearest neighbor imputation for country_of_origin, altitude_mean_meters
#> Unknown factor level assignment for variety, processing_method
#> Collapsing factor levels for country_of_origin
#> Collapsing factor levels for processing_method
#> Collapsing factor levels for variety
# This works just fine
coffee_recipe %>%
prep(coffee_train) %>%
bake(select(coffee_test, -cupper_points)) %>%
head()
#> # A tibble: 6 x 42
#> total_cup_points species owner country_of_orig… farm_name lot_number mill
#> <dbl> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 90.6 Arabica meta… Ethiopia metad plc <NA> meta…
#> 2 87.9 Arabica cqi … other <NA> <NA> <NA>
#> 3 87.9 Arabica grou… United States (… <NA> <NA> <NA>
#> 4 87.3 Arabica ethi… Ethiopia <NA> <NA> <NA>
#> 5 87.2 Arabica cqi … other <NA> <NA> <NA>
#> 6 86.9 Arabica ethi… Ethiopia <NA> <NA> <NA>
#> # … with 35 more variables: ico_number <fct>, company <fct>, altitude <fct>,
#> # region <fct>, producer <fct>, number_of_bags <dbl>, bag_weight <fct>,
#> # in_country_partner <fct>, harvest_year <fct>, grading_date <fct>,
#> # owner_1 <fct>, variety <fct>, processing_method <fct>, aroma <dbl>,
#> # flavor <dbl>, aftertaste <dbl>, acidity <dbl>, body <dbl>, balance <dbl>,
#> # uniformity <dbl>, clean_cup <dbl>, sweetness <dbl>, moisture <dbl>,
#> # category_one_defects <dbl>, quakers <dbl>, color <fct>,
#> # category_two_defects <dbl>, expiration <fct>, certification_body <fct>,
#> # certification_address <fct>, certification_contact <fct>,
#> # unit_of_measurement <fct>, altitude_low_meters <dbl>,
#> # altitude_high_meters <dbl>, altitude_mean_meters <dbl>
# Now let's try putting it into a workflow and running tune_grid
coffee_model <- rand_forest(trees = 500, mtry = tune()) %>%
set_engine("ranger") %>%
set_mode("regression")
coffee_model
#> Random Forest Model Specification (regression)
#>
#> Main Arguments:
#> mtry = tune()
#> trees = 500
#>
#> Computational engine: ranger
coffee_workflow <- workflow() %>%
add_recipe(coffee_recipe) %>%
add_model(coffee_model)
coffee_workflow
#> ══ Workflow ═══════════════════════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: rand_forest()
#>
#> ── Preprocessor ───────────────────────────────────────────────────────────────────────────────
#> 6 Recipe Steps
#>
#> ● step_string2factor()
#> ● step_knnimpute()
#> ● step_unknown()
#> ● step_other()
#> ● step_other()
#> ● step_other()
#>
#> ── Model ──────────────────────────────────────────────────────────────────────────────────────
#> Random Forest Model Specification (regression)
#>
#> Main Arguments:
#> mtry = tune()
#> trees = 500
#>
#> Computational engine: ranger
coffee_grid <- expand_grid(mtry = c(2, 5))
coffee_folds <- vfold_cv(coffee_train, v = 5)
coffee_workflow %>%
tune_grid(
resamples = coffee_folds,
grid = coffee_grid
)
#> x Fold1: model 1/2 (predictions): Error: Can't subset columns that don't exist.
#> x...
#> x Fold1: model 2/2 (predictions): Error: Can't subset columns that don't exist.
#> x...
#> x Fold2: model 1/2 (predictions): Error: Can't subset columns that don't exist.
#> x...
#> x Fold2: model 2/2 (predictions): Error: Can't subset columns that don't exist.
#> x...
#> x Fold3: model 1/2 (predictions): Error: Can't subset columns that don't exist.
#> x...
#> x Fold3: model 2/2 (predictions): Error: Can't subset columns that don't exist.
#> x...
#> x Fold4: model 1/2 (predictions): Error: Can't subset columns that don't exist.
#> x...
#> x Fold4: model 2/2 (predictions): Error: Can't subset columns that don't exist.
#> x...
#> x Fold5: model 1/2 (predictions): Error: Can't subset columns that don't exist.
#> x...
#> x Fold5: model 2/2 (predictions): Error: Can't subset columns that don't exist.
#> x...
#> Warning: All models failed in tune_grid(). See the `.notes` column.
#> Warning: This tuning result has notes. Example notes on model fitting include:
#> model 1/2 (predictions): Error: Can't subset columns that don't exist.
#> x Columns `species`, `owner`, `farm_name`, `lot_number`, `mill`, etc. don't exist.
#> model 1/2 (predictions): Error: Can't subset columns that don't exist.
#> x Columns `species`, `owner`, `farm_name`, `lot_number`, `mill`, etc. don't exist.
#> model 2/2 (predictions): Error: Can't subset columns that don't exist.
#> x Columns `species`, `owner`, `farm_name`, `lot_number`, `mill`, etc. don't exist.
#> # Tuning results
#> # 5-fold cross-validation
#> # A tibble: 5 x 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [857/215]> Fold1 <NULL> <tibble [2 × 1]>
#> 2 <split [857/215]> Fold2 <NULL> <tibble [2 × 1]>
#> 3 <split [858/214]> Fold3 <NULL> <tibble [2 × 1]>
#> 4 <split [858/214]> Fold4 <NULL> <tibble [2 × 1]>
#> 5 <split [858/214]> Fold5 <NULL> <tibble [2 × 1]>
Created on 2020-07-21 by the reprex package (v0.3.0)
Session infodevtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.0.0 (2020-04-24)
#> os Ubuntu 20.04 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language en_AU:en
#> collate en_AU.UTF-8
#> ctype en_AU.UTF-8
#> tz Australia/Melbourne
#> date 2020-07-21
#>
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728
The error here occurs because on step_string2factor() during tuning, the recipe starts trying to handle variables that don't have any roles, like species and owner.
Try setting the role for all of your nominal variables before picking out the outcomes and predictors.
coffee_recipe <- recipe(coffee_train) %>%
update_role(all_nominal(), new_role = "id") %>% ## ADD THIS
update_role(cupper_points, new_role = "outcome") %>%
update_role(
variety, processing_method, country_of_origin,
aroma, flavor, aftertaste, acidity, sweetness, altitude_mean_meters,
new_role = "predictor"
) %>%
step_string2factor(all_nominal(), -all_outcomes()) %>%
step_knnimpute(
country_of_origin, altitude_mean_meters,
impute_with = imp_vars(
in_country_partner, company, region, farm_name, certification_body
)
) %>%
step_unknown(variety, processing_method, new_level = "Unknown") %>%
step_other(country_of_origin, threshold = 0.01) %>%
step_other(processing_method, threshold = 0.10) %>%
step_other(variety, threshold = 0.10)
After I do this, this mostly runs fine, with only some failures to impute altitude. It might be tough to impute both of those things at the same time.
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