I would like to follow up with the closed discussion Error while using `predict` on a tidymodels workflow since the proposed solution does fix the problem on the predict() call as intended but at the same time creates a new problem on a fit_resamples() call that didn't exist before the fix. I have 2 independent datasets, one that I use for training and the other one that I use for testing. I consider 2 versions of the recipes: my original one and the one proposed in the aforementioned closed discussion using skip = TRUE in the recipe step involving the outcome. As said above, introducing skip = TRUE in the recipe does fix the problem in the predict() call but also creates a new problem in the fit_resamples() call I have before. Below is a reproducible example that works whatever the seed you choose:
library(tibble)
library(dplyr)
library(rsample)
library(recipes)
library(themis)
library(parsnip)
library(workflows)
library(yardstick)
library(tune)
# We have 2 independent datasets: one that we will use for training and one that we
# will use for testing:
n1 <- 565
dataset1 <- tibble(
culture = sample(rep(c(FALSE, TRUE), c(531, 34))),
sex = sample(rep(factor(c("female", "male")), c(256, 309))),
age = runif(n1, 0, 89),
cough = sample(rep(c(FALSE, TRUE), c(300, 265))),
diarrhea = sample(rep(c(FALSE, TRUE), c(412, 153))),
vomiting = sample(rep(c(FALSE, TRUE), c(313, 252))),
abdominal_pain = sample(rep(c(FALSE, TRUE), c(397, 168))),
constipation = sample(rep(c(FALSE, TRUE), c(508, 57))),
headache = sample(rep(c(FALSE, TRUE), c(395, 170))),
pulse = as.integer(sample(60:214, n1, TRUE)),
temperature = sample((364:420) / 10, n1, TRUE)
)
n2 <- 601
dataset2 <- tibble(
culture = sample(rep(c(FALSE, TRUE), c(427, 174))),
sex = sample(rep(factor(c("female", "male")), c(200, 401))),
age = runif(n2, 2, 57),
cough = sample(rep(c(FALSE, TRUE), c(345, 256))),
diarrhea = sample(rep(c(FALSE, TRUE), c(456, 145))),
vomiting = sample(rep(c(FALSE, TRUE), c(433, 168))),
abdominal_pain = sample(rep(c(FALSE, TRUE), c(408, 193))),
constipation = sample(rep(c(FALSE, TRUE), c(524, 77))),
headache = sample(rep(c(FALSE, TRUE), c(82, 519))),
pulse = as.integer(sample(60:160, n2, TRUE)),
temperature = sample((3556:4100) / 100, n2, TRUE)
)
splits <- make_splits(list(analysis = 1:n1, assessment = (n1 + 1):(n1 + n2)),
bind_rows(dataset1, dataset2))
training_data <- training(splits)
testing_data <- testing(splits)
# Cross-validation folds:
cv_folds <- vfold_cv(training_data, repeats = 50)
# We consider 2 different recipes (differences are for the lines ending with ###):
recipe1 <- recipe(culture ~ ., training_data) |>
step_bin2factor(all_logical()) |> ### option 1 for recipe 1
step_dummy(all_factor_predictors()) |>
step_smotenc(culture)
recipe2 <- recipe(culture ~ ., training_data) |>
step_bin2factor(all_logical_predictors()) |> ### option 2 for recipe 2
step_bin2factor(culture, skip = TRUE) |> ### option 2 for recipe 2
step_dummy(all_factor_predictors()) |>
step_smotenc(culture)
# The model and the workflows corresponding to the 2 recipes:
lr_classification <- logistic_reg("classification") |>
set_engine("glm")
workflow1 <- workflow(recipe1) |>
add_model(lr_classification)
workflow2 <- workflow(recipe2) |>
add_model(lr_classification)
# The metric of interest:
the_metric <- metric_set(roc_auc)
# Fitting by resampling on the training set: works for workflow1 but not for workflow2:
workflow1 |>
fit_resamples(cv_folds, metrics = the_metric,
control = control_resamples(save_pred = TRUE)) |>
collect_metrics()
workflow2 |>
fit_resamples(cv_folds, metrics = the_metric,
control = control_resamples(save_pred = TRUE)) |>
collect_metrics()
# Fitting on the training set and assessing on the testing set: this time it works for
# workflow2 but not for workflow1:
fitted_model1 <- fit(workflow1, training_data)
fitted_model2 <- fit(workflow2, training_data)
predict(fitted_model1, testing_data)
predict(fitted_model2, testing_data)