线性 SVM 和提取权重

Question

我正在使用 iris 数据集在 R 中练习 SVM，我想从我的模型中获取特征 weights/coefficients，但我想我可能误解了一些东西，因为我的输出给了我 32 个支持向量。假设我要分析四个变量，我会得到四个。我知道在使用 svm() 函数时有一种方法可以做到这一点，但我正在尝试使用插入符号中的 train() 函数来生成我的 SVM。

library(caret)

# Define fitControl
fitControl <- trainControl(## 5-fold CV
              method = "cv",
              number = 5,
              classProbs = TRUE,
              summaryFunction = twoClassSummary )

# Define Tune
grid<-expand.grid(C=c(2^-5,2^-3,2^-1))

########## 
df<-iris head(df)
df<-df[df$Species!='setosa',]
df$Species<-as.character(df$Species)
df$Species<-as.factor(df$Species)

# set random seed and run the model
set.seed(321)
svmFit1 <- train(x = df[-5],
                 y=df$Species,
                 method = "svmLinear", 
                 trControl = fitControl,
                 preProc = c("center","scale"),
                 metric="ROC",
                 tuneGrid=grid )
svmFit1

我认为这只是 svmFit1$finalModel@coef但是当我认为我应该得到 4 个时我得到了 32 个向量。这是为什么？

Answer 1

所以coef不是支持向量的权重W。这是 docs 中 ksvm class 的相关部分：

coef The corresponding coefficients times the training labels.

要获得所需内容，您需要执行以下操作：

coefs <- svmFit1$finalModel@coef[[1]]
mat <- svmFit1$finalModel@xmatrix[[1]]

coefs %*% mat

请参阅下面的可重现示例。

library(caret)
#> Loading required package: lattice
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 3.5.2

# Define fitControl
fitControl <- trainControl(
  method = "cv",
  number = 5,
  classProbs = TRUE,
  summaryFunction = twoClassSummary
)

# Define Tune
grid <- expand.grid(C = c(2^-5, 2^-3, 2^-1))

########## 
df <- iris 

df<-df[df$Species != 'setosa', ]
df$Species <- as.character(df$Species)
df$Species <- as.factor(df$Species)

# set random seed and run the model
set.seed(321)
svmFit1 <- train(x = df[-5],
                 y=df$Species,
                 method = "svmLinear", 
                 trControl = fitControl,
                 preProc = c("center","scale"),
                 metric="ROC",
                 tuneGrid=grid )

coefs <- svmFit1$finalModel@coef[[1]]
mat <- svmFit1$finalModel@xmatrix[[1]]

coefs %*% mat
#>      Sepal.Length Sepal.Width Petal.Length Petal.Width
#> [1,]   -0.1338791  -0.2726322    0.9497457    1.027411

^{由 reprex package (v0.2.1.9000)}

创建于 2019-06-11

来源

Answer 2

随着越来越多的人开始从 Caret 转向 Tidymodels，我想我会在 2020 年 8 月为 Tidymodels 提供上述解决方案的一个版本，因为到目前为止我没有看到很多关于这个的讨论，而且它并不是那么简单做。

此处概述了主要步骤，但请查看末尾的链接以详细了解为什么这样做。

1.获取最终模型

set.seed(2020)

# Assuming kernlab linear SVM

# Grid Search Parameters
tune_rs <- tune_grid(
  model_wf,
  train_folds,
  grid = param_grid,
  metrics = classification_measure,
  control = control_grid(save_pred = TRUE)
)

# Finalise workflow with the parameters for best accuracy
best_accuracy <- select_best(tune_rs, "accuracy")

svm_wf_final <- finalize_workflow(
  model_wf,
  best_accuracy
)

# Fit on your final model on all available data at the end of experiment
final_model <- fit(svm_wf_final, data)
# fit takes a model spec and executes the model fit routine (Parsnip)
  # model_spec, formula and data to fit upon

2。提取 KSVM 对象，提取所需信息，计算变量重要性

ksvm_obj <- pull_workflow_fit(final_model)$fit
# Pull_workflow_fit returns the parsnip model fit object
# $fit returns the object produced by the fitting fn (which is what we need! and is dependent on the engine)

coefs <- ksvm_obj@coef[[1]]
# first bit of info we need are the coefficients from the linear fit

mat <- ksvm_obj@xmatrix[[1]]
# xmatrix that we need to matrix multiply against

var_impt <- coefs %*% mat
# var importance

参考：

使用插入符号提取支持向量的权重：
变量重要性（post 的最后一部分）：http://www.rebeccabarter.com/blog/2020-03-25_machine_learning/#finalize-the-workflow

线性 SVM 和提取权重

Linear SVM and extracting the weights

r

svm

r-caret