tidymodel框架下SVM变量重要性图生成失败及API替代咨询

问题背景 #

由于SVM本身不包含变量重要性信息，采用变量置换法结合tidymodels框架生成SVM的变量重要性图，原代码如下：

library(vip)
library(MASS)
library(tidymodels)

data(Boston, package = "MASS")
df <- Boston

#Split the data into train and test set
set.seed(7)
splits <- initial_split(df)
train <- training(splits)
test <- testing(splits)


#Preprocess with recipe
rec <- recipe(medv~.,data=train) %>%
  step_normalize(all_predictors()) 

svm_spec <- svm_rbf(margin = 0.0937, cost = 26.7, rbf_sigma = 0.0208) %>%
  set_engine("kernlab") %>%
  set_mode("regression")


#Putting into workflow
svr_fit <- workflow() %>%
  add_recipe(rec) %>%
  add_model(svm_spec) %>% 
  fit(data = train)

svr_fit %>%
  pull_workflow_fit() %>%
  vip(method = "permute", nsim = 5,
      target = "medv", metric = "rmse",
      pred_wrapper = kernlab::predict, train = train)

遇到的问题 #

运行代码后出现两个问题：

1. 报错信息：

Error in metric_fun():
! estimate should be a numeric vector, not a numeric matrix.

2. pull_workflow_fit()函数已被弃用

解决方案 #

1. 替代弃用的pull_workflow_fit() #

tidymodels更新后，官方推荐使用extract_fit_parsnip()替代pull_workflow_fit()，用于从工作流中提取拟合完成的模型对象。

2. 解决预测结果格式不匹配问题 #

kernlab::predict()针对SVM回归模型返回的是矩阵格式，但vip的置换法要求预测结果为数值向量。因此需要自定义一个预测包装函数，将矩阵转换为向量：

kernlab_pred_wrapper <- function(object, newdata) {
  as.vector(kernlab::predict(object, newdata))
}

修正后的完整代码 #

library(vip)
library(MASS)
library(tidymodels)

data(Boston, package = "MASS")
df <- Boston

# 划分训练集和测试集
set.seed(7)
splits <- initial_split(df)
train <- training(splits)
test <- testing(splits)

# 数据预处理配方
rec <- recipe(medv~.,data=train) %>%
  step_normalize(all_predictors()) 

# SVM回归模型定义
svm_spec <- svm_rbf(margin = 0.0937, cost = 26.7, rbf_sigma = 0.0208) %>%
  set_engine("kernlab") %>%
  set_mode("regression")

# 构建工作流并拟合
svr_fit <- workflow() %>%
  add_recipe(rec) %>%
  add_model(svm_spec) %>% 
  fit(data = train)

# 自定义预测包装函数：将矩阵转换为向量
kernlab_pred_wrapper <- function(object, newdata) {
  as.vector(kernlab::predict(object, newdata))
}

# 提取模型并计算变量重要性
svr_fit %>%
  extract_fit_parsnip() %>%  # 替代弃用的pull_workflow_fit()
  vip(method = "permute", nsim = 5,
      target = "medv", metric = "rmse",
      pred_wrapper = kernlab_pred_wrapper, 
      train = train)

内容的提问来源于stack exchange，提问作者UseR10085