<title>reprex_reprex.R</title>
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<h1>
<i class="fa fa-circle-o-notch fa-spin"></i><a href="./">reprex_reprex.R</a>
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## Registered S3 method overwritten by 'tune':
## method from
## required_pkgs.model_spec parsnip
## ── Attaching packages ────────────────────────────────────── tidymodels 0.1.3 ──
## ✓ broom 0.7.9 ✓ recipes 0.1.16
## ✓ dials 0.0.9 ✓ rsample 0.1.0
## ✓ dplyr 1.0.7 ✓ tibble 3.1.3
## ✓ ggplot2 3.3.5 ✓ tidyr 1.1.3
## ✓ infer 1.0.0 ✓ tune 0.1.6
## ✓ modeldata 0.1.1 ✓ workflows 0.2.3
## ✓ parsnip 0.1.7 ✓ workflowsets 0.1.0
## ✓ purrr 0.3.4 ✓ yardstick 0.0.8
## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## x purrr::discard() masks scales::discard()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x recipes::step() masks stats::step()
## • Use tidymodels_prefer() to resolve common conflicts.
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ readr 2.0.1 ✓ forcats 0.5.1
## ✓ stringr 1.4.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x readr::col_factor() masks scales::col_factor()
## x purrr::discard() masks scales::discard()
## x dplyr::filter() masks stats::filter()
## x stringr::fixed() masks recipes::fixed()
## x dplyr::lag() masks stats::lag()
## x readr::spec() masks yardstick::spec()
library(palmerpenguins)
set.seed(99)
penguins_wo_missing_numeric <- penguins %>%
filter(across(where(is.numeric), ~!is.na(.x)))
penguin_split <- initial_split(penguins_wo_missing_numeric, prop = 0.8)
penguin_folds <- vfold_cv(training(penguin_split), v = 5)
usemodels::use_glmnet(
species ~ .,
data = training(penguin_split),
verbose = FALSE,
tune = TRUE,
colors = TRUE
)
## glmnet_recipe <-
## recipe(formula = species ~ ., data = training(penguin_split)) %>%
## step_novel(all_nominal(), -all_outcomes()) %>%
## step_dummy(all_nominal(), -all_outcomes()) %>%
## step_zv(all_predictors()) %>%
## step_normalize(all_predictors(), -all_nominal())
##
## glmnet_spec <-
## multinom_reg(penalty = tune(), mixture = tune()) %>%
## set_mode("classification") %>%
## set_engine("glmnet")
##
## glmnet_workflow <-
## workflow() %>%
## add_recipe(glmnet_recipe) %>%
## add_model(glmnet_spec)
##
## glmnet_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20), mixture = c(0.05,
## 0.2, 0.4, 0.6, 0.8, 1))
##
## glmnet_tune <-
## tune_grid(glmnet_workflow, resamples = stop("add your rsample object"), grid = glmnet_grid)
glmnet_recipe <-
recipe(formula = species ~ ., data = training(penguin_split)) %>%
step_novel(all_nominal(), -all_outcomes()) %>%
step_dummy(all_nominal(), -all_outcomes()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_predictors(), -all_nominal())
glmnet_spec <-
multinom_reg(penalty = tune(), mixture = tune()) %>%
set_mode("classification") %>%
set_engine("glmnet")
glmnet_workflow <-
workflow() %>%
add_recipe(glmnet_recipe) %>%
add_model(glmnet_spec)
glmnet_grid <- tidyr::crossing(
penalty = 10^seq(-6, -1, length.out = 3),
mixture = c(0.05, 0.6)
)
tune_grid(glmnet_workflow, resamples = penguin_folds, grid = glmnet_grid)
## ! Fold1: preprocessor 1/1: There are new levels in a factor: NA
## x Fold1: preprocessor 1/1, model 1/2: Error in lognet(xd, is.sparse, ix, jx, y, w...
## x Fold1: preprocessor 1/1, model 2/2: Error in lognet(xd, is.sparse, ix, jx, y, w...
## ! Fold2: preprocessor 1/1: There are new levels in a factor: NA
## x Fold2: preprocessor 1/1, model 1/2: Error in lognet(xd, is.sparse, ix, jx, y, w...
## x Fold2: preprocessor 1/1, model 2/2: Error in lognet(xd, is.sparse, ix, jx, y, w...
## ! Fold3: preprocessor 1/1: There are new levels in a factor: NA
## x Fold3: preprocessor 1/1, model 1/2: Error in lognet(xd, is.sparse, ix, jx, y, w...
## x Fold3: preprocessor 1/1, model 2/2: Error in lognet(xd, is.sparse, ix, jx, y, w...
## ! Fold4: preprocessor 1/1: There are new levels in a factor: NA
## x Fold4: preprocessor 1/1, model 1/2: Error in lognet(xd, is.sparse, ix, jx, y, w...
## x Fold4: preprocessor 1/1, model 2/2: Error in lognet(xd, is.sparse, ix, jx, y, w...
## ! Fold5: preprocessor 1/1: There are new levels in a factor: NA
## x Fold5: preprocessor 1/1, model 1/2: Error in lognet(xd, is.sparse, ix, jx, y, w...
## x Fold5: preprocessor 1/1, model 2/2: Error in lognet(xd, is.sparse, ix, jx, y, w...
## Warning: All models failed. See the `.notes` column.
## Warning: This tuning result has notes. Example notes on model fitting include:
## preprocessor 1/1, model 1/2: Error in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : NA/NaN/Inf in foreign function call (arg 5)
## preprocessor 1/1, model 2/2: Error in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : NA/NaN/Inf in foreign function call (arg 5)
## preprocessor 1/1, model 1/2: Error in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : NA/NaN/Inf in foreign function call (arg 5)
## # Tuning results
## # 5-fold cross-validation
## # A tibble: 5 × 4
## splits id .metrics .notes
## <list> <chr> <list> <list>
## 1 <split [218/55]> Fold1 <NULL> <tibble [3 × 1]>
## 2 <split [218/55]> Fold2 <NULL> <tibble [3 × 1]>
## 3 <split [218/55]> Fold3 <NULL> <tibble [3 × 1]>
## 4 <split [219/54]> Fold4 <NULL> <tibble [3 × 1]>
## 5 <split [219/54]> Fold5 <NULL> <tibble [3 × 1]>
# with step_unknown
glmnet_recipe <-
recipe(formula = species ~ ., data = training(penguin_split)) %>%
step_unknown(all_nominal(), -all_outcomes()) %>%
step_novel(all_nominal(), -all_outcomes()) %>%
step_dummy(all_nominal(), -all_outcomes()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_predictors(), -all_nominal())
glmnet_workflow <-
glmnet_workflow %>%
update_recipe(glmnet_recipe)
tune_grid(glmnet_workflow, resamples = penguin_folds, grid = glmnet_grid)
## # Tuning results
## # 5-fold cross-validation
## # A tibble: 5 × 4
## splits id .metrics .notes
## <list> <chr> <list> <list>
## 1 <split [218/55]> Fold1 <tibble [12 × 6]> <tibble [0 × 1]>
## 2 <split [218/55]> Fold2 <tibble [12 × 6]> <tibble [0 × 1]>
## 3 <split [218/55]> Fold3 <tibble [12 × 6]> <tibble [0 × 1]>
## 4 <split [219/54]> Fold4 <tibble [12 × 6]> <tibble [0 × 1]>
## 5 <split [219/54]> Fold5 <tibble [12 × 6]> <tibble [0 × 1]>
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