r - 使插入符号的遗传特征选择更快

Question

我正在尝试通过插入符号遗传算法使用特征选择来优化 xgboost 树

results <- gafs(iris[,1:4], iris[,5],
               iters = 2,
               method = "xgbTree",
               metric = "Accuracy",
               gafsControl = gafsControl(functions=caretGA, method="cv", repeats=2, verbose = TRUE),
               trConrol = trainControl(method = "cv", classProbs = TRUE, verboseIter = TRUE)
               )

但是这非常慢，即使我只是使用 iters = 2 而不是 iters = 200 也更合适。我可以做些什么来加快速度？

Answer 1

这是一个使用 doParallel 包并行化 gafs() 函数并修改其他一些参数以使其更快的示例。在可能的情况下，我包括运行时间。

原始代码是使用交叉验证(method = "cv")而不是重复交叉验证(method = "repeatedcv")，所以我相信repeats = 2 参数被忽略。我没有在并行示例中包含该选项。

首先，使用没有任何修改或并行化的原始代码:

> library(caret)
> data(iris)

> set.seed(1)
> st.01 <- system.time(results.01 <- gafs(iris[,1:4], iris[,5],
                                          iters  = 2, 
                                          method = "xgbTree", 
                                          metric = "Accuracy",
                                          gafsControl = gafsControl(functions = caretGA, 
                                                                    method  = "cv", 
                                                                    repeats = 2, 
                                                                    verbose = TRUE),
                                          trConrol = trainControl(method = "cv", 
                                                                  classProbs  = TRUE, 
                                                                  verboseIter = TRUE)))

Fold01 1 0.9596575 (1)
Fold01 2 0.9596575->0.9667641 (1->1, 100.0%) *
Fold02 1 0.9598146 (1)
Fold02 2 0.9598146->0.9641482 (1->1, 100.0%) *
Fold03 1 0.9502661 (1)

我在一夜之间(8 到 10 小时)运行了上面的代码，但由于完成时间太长而停止运行。粗略估计运行时间至少为 24 小时。

其次，包括减少 popSize 参数(从 50 到 20)、allowParallel 和 genParallel 选项到 gafsControl() 并最终在 gafsControl() 和 trControl() 中减少了折叠的数量(从 10 到 5) >:

> library(doParallel)
> cl <- makePSOCKcluster(detectCores() - 1)
> registerDoParallel(cl)

> set.seed(1)
> st.09 <- system.time(results.09 <- gafs(iris[,1:4], iris[,5],
                                          iters   = 2, 
                                          popSize = 20, 
                                          method  = "xgbTree", 
                                          metric  = "Accuracy",
                                          gafsControl = gafsControl(functions = caretGA, 
                                                                    method    = "cv", 
                                                                    number    = 5, 
                                                                    verbose   = TRUE, 
                                                                    allowParallel = TRUE, 
                                                                    genParallel   = TRUE),
                                          trConrol = trainControl(method      = "cv", 
                                                                  number      = 5, 
                                                                  classProbs  = TRUE, 
                                                                  verboseIter = TRUE)))

 final GA
 1 0.9508099 (4)
 2 0.9508099->0.9561501 (4->1, 25.0%) *
 final model
> st.09
   user   system  elapsed
   3.536    0.173 4152.988

我的系统有 4 个内核，但按照规定它只使用了 3 个，我验证它正在运行 3 个 R 进程。

gafsControl() 文档对allowParallel 和genParallel 的描述如下:

• allowParallel:如果并行后端已加载且可用，函数应该使用它吗？

• genParallel:如果并行后端已加载且可用，应该 'gafs' 使用它并行化内部的适应度计算 重采样中的一代？

插入符号文档表明 allowParallel 选项将比 genParallel 选项提供更大的运行时间改进: https://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html

与原始代码相比，我预计并行化代码的结果至少会略有不同。以下是并行化代码的结果:

> results.09

Genetic Algorithm Feature Selection

150 samples
4 predictors
3 classes: 'setosa', 'versicolor', 'virginica'

Maximum generations: 2
Population per generation: 20
Crossover probability: 0.8
Mutation probability: 0.1
Elitism: 0

Internal performance values: Accuracy, Kappa
Subset selection driven to maximize internal Accuracy

External performance values: Accuracy, Kappa
Best iteration chose by maximizing external Accuracy
External resampling method: Cross-Validated (5 fold)

During resampling:
  * the top 4 selected variables (out of a possible 4):
    Petal.Width (80%), Petal.Length (40%), Sepal.Length (20%), Sepal.Width (20%)
  * on average, 1.6 variables were selected (min = 1, max = 4)

In the final search using the entire training set:
   * 4 features selected at iteration 1 including:
     Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
   * external performance at this iteration is

   Accuracy       Kappa
     0.9467      0.9200