在R中，当在配方中包含step_pca时，工作流中的错误适合

Question

在tidymodels中，我想创建一个基于配方和模型规范的工作流。当我不包含step_pca((时，它会起作用；但是当我包含step_pca((作为设置时，我会出错。请看repex吹。

(如果我不使用workflow((，它可以在fins中工作；但后来我失去了包括更新角色在内的功能(

x1 <- c(1, 6, 4, 2, 3, 4, 5, 7, 8, 2)
x2 <- c(1, 3, 4, 2, 3, 4, 5, 7, 8, 2)
id <- c(1:10)
y <- c(1, 4, 2, 5, 6, 2, 3, 6, 2, 4)
df1_train <- tibble(x1, x2, id, y)
# NA works with workflow
step_PCA_PREPROCESSING = NA
# Does not work with workflow
step_PCA_PREPROCESSING = 0.9
# My recipe
df1_train_recipe <- df1_train %>%
recipes::recipe(y ~ .) %>%
recipes::update_role(id, new_role = "id variable") %>%
recipes::step_center(recipes::all_predictors()) %>%
recipes::step_scale(recipes::all_predictors()) %>%
# Optional step_pca
{
if (!is.na(step_PCA_PREPROCESSING)) {
if (step_PCA_PREPROCESSING >= 1) {
recipes::step_pca(., recipes::all_predictors(), num_comp = step_PCA_PREPROCESSING)
} else if (step_PCA_PREPROCESSING < 1) {
recipes::step_pca(., recipes::all_predictors(), threshold = step_PCA_PREPROCESSING)
} else {
.
}
} else {
.
}
} %>%
recipes::prep()
# Model specifications
model_spec <- parsnip::linear_reg() %>% 
parsnip::set_engine("glmnet") 
# Create workflow (to know variable roles from recipes)
df1_workflow <- workflows::workflow() %>%
workflows::add_recipe(df1_train_recipe) %>%
workflows::add_model(model_spec) 
# Fit model
mod <-  parsnip::fit(df1_workflow, data = df1_train)

提前感谢

Answer 1

我认为最好的方法是使用step_pca()的能力将num_comp设置为零，这意味着没有PCA分解。这对于您的用例来说非常方便，因为threshold将覆盖num_comp。

注意：使用此参数将覆盖并重置给num_comp的任何值。

library(tidymodels)
x1 <- c(1, 6, 4, 2, 3, 4, 5, 7, 8, 2)
x2 <- c(1, 3, 4, 2, 3, 4, 5, 7, 8, 2)
id <- c(1:10)
y <- c(1, 4, 2, 5, 6, 2, 3, 6, 2, 4)
df1_train <- tibble(x1, x2, id, y)
turn_off_pca <- 0
turn_on_pca  <- 1
rec1 <- recipe(y ~ ., data = df1_train) %>%
update_role(id, new_role = "id variable") %>%
step_center(all_predictors()) %>%
step_scale(all_predictors()) %>%
step_pca(all_predictors(), threshold = 0.9, num_comp = turn_off_pca)

rec2 <- recipe(y ~ ., data = df1_train) %>%
update_role(id, new_role = "id variable") %>%
step_center(all_predictors()) %>%
step_scale(all_predictors()) %>%
step_pca(all_predictors(), threshold = 0.9, num_comp = turn_on_pca)
lm_spec <- linear_reg() %>% set_engine("lm")
workflow() %>%
add_model(lm_spec) %>%
add_recipe(rec1) %>%
fit(df1_train)
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 3 Recipe Steps
#> 
#> ● step_center()
#> ● step_scale()
#> ● step_pca()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#> (Intercept)           x1           x2  
#>      3.5000       0.4607      -0.3459
workflow() %>%
add_model(lm_spec) %>%
add_recipe(rec2) %>%
fit(df1_train)
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 3 Recipe Steps
#> 
#> ● step_center()
#> ● step_scale()
#> ● step_pca()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#> (Intercept)          PC1  
#>     3.50000      0.08116

^{reprex包于2020-12-06创建(v0.3.09001(}