假设我将在R中进行caret
培训,但我想将此培训分为两个运行阶段。
library(mlbench)
data(Sonar)
library(caret)
set.seed(998)
inTraining <- createDataPartition(Sonar$Class, p = .75, list = FALSE)
training <- Sonar[ inTraining,]
testing <- Sonar[-inTraining,]
# First run session
nn.partial <- train(Class ~ ., data = training,
method = "nnet",
max.turns.of.iteration=5) # Non-existent parameter. But represents my goal
让我们假设,nn
完整对象我只有一个具有训练信息的局部对象,直到第五回合为止(即nn.partial
)。因此,将来我可以运行以下代码来完成培训工作:
library(mlbench)
data(Sonar)
library(caret)
set.seed(998)
inTraining <- createDataPartition(Sonar$Class, p = .75, list = FALSE)
training <- Sonar[ inTraining,]
testing <- Sonar[-inTraining,]
nn <- train(Class ~ ., data = training,
method = "nnet",
previous.training=nn.partial) # Non-existent parameter. But represents my goal
我知道max.turns.of.iteration
和previous.training
都不存在于train
函数中。我只是尽力用代码表示如果已经在train
函数中实现的理想世界。但是,由于参数不存在,是否有办法通过某种方式欺骗功能来实现此目标(即,对插入符号进行一次以上的训练)?
我尝试使用trainControl
函数没有成功。
t.control <- trainControl(repeats=5)
nn <- train(Class ~ ., data = training,
method = "nnet",
trControl = t.control)
这样做,迭代匝数仍然比5高得多,正如我在示例中希望得到的那样。
答案 0 :(得分:5)
我几乎可以肯定,在插入符号当前的基础结构中实现这非常复杂。但是,我将向您展示如何使用mlr3开箱即用地实现这种功能。
示例所需的软件包
library(mlr3)
library(mlr3tuning)
library(paradox)
获取示例任务并定义要调整的学习者:
task_sonar <- tsk('sonar')
learner <- lrn('classif.rpart', predict_type = 'prob')
定义要调整的超级参数:
ps <- ParamSet$new(list(
ParamDbl$new("cp", lower = 0.001, upper = 0.1),
ParamInt$new("minsplit", lower = 1, upper = 10)
))
定义调谐器和重采样策略
tuner <- tnr("random_search")
cv3 <- rsmp("cv", folds = 3)
定义调整实例
instance <- TuningInstance$new(
task = task_sonar,
learner = learner,
resampling = cv3,
measures = msr("classif.auc"),
param_set = ps,
terminator = term("evals", n_evals = 100) #one can combine multiple terminators such as clock time, number of evaluations, early stopping (stagnation), performance reached - ?Terminator
)
曲调:
tuner$tune(instance)
现在请稍等片刻,按Stop停止Rstudio中的任务
instance$archive()
nr batch_nr resample_result task_id learner_id resampling_id iters params tune_x warnings errors classif.auc
1: 1 1 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7105586
2: 2 2 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7372720
3: 3 3 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7335368
4: 4 4 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7335368
5: 5 5 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7276246
6: 6 6 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7111217
7: 7 7 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.6915560
8: 8 8 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7452875
9: 9 9 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7372720
10: 10 10 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7172328
就我而言,它完成了10次随机搜索迭代。 现在,您可以例如致电
save.image()
关闭RStudio并重新打开同一项目
或对要保留的对象使用saveRDS
/ readRDS
saveRDS(instance, "i.rds")
instance <- readRDS("i.rds")
在加载所需的软件包后,使用
恢复培训tuner$tune(instance)
几秒钟后再次停止:
就我而言,它又完成了12次迭代:
instance$archive()
nr batch_nr resample_result task_id learner_id resampling_id iters params tune_x warnings errors classif.auc
1: 1 1 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7105586
2: 2 2 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7372720
3: 3 3 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7335368
4: 4 4 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7335368
5: 5 5 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7276246
6: 6 6 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7111217
7: 7 7 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.6915560
8: 8 8 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7452875
9: 9 9 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7372720
10: 10 10 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7172328
11: 11 11 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7325289
12: 12 12 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7105586
13: 13 13 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7215133
14: 14 14 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.6915560
15: 15 15 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.6915560
16: 16 16 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7335368
17: 17 17 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7276246
18: 18 18 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7111217
19: 19 19 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7172328
20: 20 20 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7276246
21: 21 21 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7105586
22: 22 22 <ResampleResult> sonar classif.rpart cv 3 <list> <list> 0 0 0.7276246
不按停止键再次运行
tuner$tune(instance)
它将完成100次评估
限制:上面的示例将调整(超参数的评估)划分为多个会话。但是,它不会将一个训练实例分成多个会话-很少有软件包在R中支持这种事情-keras / tensorflow是我所知道的唯一一个。
但是,不管一种算法的一个训练实例的长度如何,这种算法的调整(超参数评估)都需要花费更多的时间,因此能够像上面那样暂停/恢复调整更为有利。例子。
如果您觉得这很有趣,这里有一些学习mlr3的资源
https://mlr3book.mlr-org.com/
https://mlr3gallery.mlr-org.com/
还看一看mlr3pipelines-https://mlr3pipelines.mlr-org.com/articles/introduction.html