问题描述
使用非常有用的mlr3 book中的示例,我试图简单地返回堆叠模型输出的平均得分。有人可以解释一下如何使用mlr3吗?我曾尝试同时使用 LearnerClassifAvg $ new(id =“ classif.avg”)和 po(“ classifavg”),但不确定我是否正确应用了这些代码,谢谢
示例:
library("magrittr")
library("mlr3learners") # for classif.glmnet
task = mlr_tasks$get("iris")
train.idx = sample(seq_len(task$nrow),120)
test.idx = setdiff(seq_len(task$nrow),train.idx)
rprt = lrn("classif.rpart",predict_type = "prob")
glmn = lrn("classif.glmnet",predict_type = "prob")
# Create Learner CV Operators
lrn_0 = PipeOpLearnerCV$new(rprt,id = "rpart_cv_1")
lrn_0$param_set$values$maxdepth = 5L
lrn_1 = PipeOpPCA$new(id = "pca1") %>>% PipeOpLearnerCV$new(rprt,id = "rpart_cv_2")
lrn_1$param_set$values$rpart_cv_2.maxdepth = 1L
lrn_2 = PipeOpPCA$new(id = "pca2") %>>% PipeOpLearnerCV$new(glmn)
# Union them with a PipeOpNULL to keep original features
level_0 = gunion(list(lrn_0,lrn_1,lrn_2,PipeOpnop$new(id = "nop1")))
# Cbind the output 3 times,train 2 learners but also keep level
# 0 predictions
level_1 = level_0 %>>%
PipeOpFeatureUnion$new(4) %>>%
PipeOpcopy$new(3) %>>%
gunion(list(
PipeOpLearnerCV$new(rprt,id = "rpart_cv_l1"),PipeOpLearnerCV$new(glmn,id = "glmnt_cv_l1"),PipeOpnop$new(id = "nop_l1")
))
level_1$plot(html = FALSE)
level_2 <- level_1 %>>%
PipeOpFeatureUnion$new(3,id = "u2") %>>%
LearnerClassifAvg$new( id = "classif.avg")
level_2$plot(html = FALSE)
lrn = GraphLearner$new(level_2)
lrn$
train(task,train.idx)$
predict(task,test.idx)$
score()
## returns: Error: Trying to predict response,but incoming data has no factors
解决方法
如果我们不将特征传递给 library(modelsummary)
dat <- mtcars
dat$weights <- dat$mpg
datasummary_balance(~vs,data = dat)
(classif.avg
),我们仍然会遇到同样的错误:
PipeOpNOP
Error: Trying to predict response,but incoming data has no factors
library("magrittr")
library("mlr3learners") # for classif.glmnet
library("mlr3verse") #for LearnerClassifAvg
library("mlr3pipelines") # for pipelines
task = mlr_tasks$get("iris")
train.idx = sample(seq_len(task$nrow),120)
test.idx = setdiff(seq_len(task$nrow),train.idx)
rprt = lrn("classif.rpart",predict_type = "prob")
glmn = lrn("classif.glmnet",predict_type = "prob")
# Create Learner CV Operators
lrn_0 = PipeOpLearnerCV$new(rprt,id = "rpart_cv_1")
lrn_0$param_set$values$maxdepth = 5L
lrn_1 = PipeOpPCA$new(id = "pca1") %>>% PipeOpLearnerCV$new(rprt,id = "rpart_cv_2")
lrn_1$param_set$values$rpart_cv_2.maxdepth = 1L
lrn_2 = PipeOpPCA$new(id = "pca2") %>>% PipeOpLearnerCV$new(glmn)
# Union them with a PipeOpNULL to keep original features
level_0 = gunion(list(lrn_0,lrn_1,lrn_2,PipeOpNOP$new(id = "NOP1")))
# Cbind the output 3 times,train 2 learners but also keep level
# 0 predictions
level_1 = level_0 %>>%
PipeOpFeatureUnion$new(4) %>>%
PipeOpCopy$new(2) %>>%
gunion(list(
PipeOpLearnerCV$new(rprt,id = "rpart_cv_l1"),PipeOpLearnerCV$new(glmn,id = "glmnt_cv_l1")
# PipeOpNOP$new(id = "NOP_l1") #leave out features here
))
level_2 <- level_1 %>>%
PipeOpFeatureUnion$new(2,id = "u2") %>>%
LearnerClassifAvg$new( id = "classif.avg")
level_2$plot(html = FALSE)
由 reprex package (v1.0.0) 于 2021 年 3 月 27 日创建
可以通过设置学习器的正确预测类型来减轻此错误:
lrn = GraphLearner$new(level_2)
lrn$
train(task,train.idx)$
predict(task,test.idx)$
score()
#> INFO [20:42:55.490] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3)
#> INFO [20:42:55.557] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3)
#> INFO [20:42:55.591] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3)
#> INFO [20:42:55.810] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3)
#> INFO [20:42:55.849] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3)
#> INFO [20:42:55.901] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3)
#> INFO [20:42:56.188] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 3/3)
#> INFO [20:42:56.299] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 1/3)
#> INFO [20:42:56.374] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 2/3)
#> INFO [20:42:56.634] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3)
#> INFO [20:42:56.699] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3)
#> INFO [20:42:56.765] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3)
#> INFO [20:42:57.065] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 2/3)
#> INFO [20:42:57.177] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 1/3)
#> INFO [20:42:57.308] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 3/3)
#> Error: Trying to predict response,but incoming data has no factors
在此处检查错误消息:https://github.com/cran/mlr3pipelines/blob/master/R/LearnerAvg.R
lrn_avg <- LearnerClassifAvg$new( id = "classif.avg")
lrn_avg$predict_type ="prob"
用更简单的集成演示了解决方案
if (all(fcts) != (self$predict_type == "response")) {
stopf("Trying to predict %s,but incoming data has %sfactors",self$predict_type,if (all(fcts)) "only " else "no "
library("magrittr")
library("mlr3learners") # for classif.glmnet
#> Lade nötiges Paket: mlr3
library("mlr3verse") #for LearnerClassifAvg
library("mlr3pipelines") # for pipelines
# Define task
task = mlr_tasks$get("iris")
train.idx = sample(seq_len(task$nrow),predict_type = "prob")
# Define level 0
level_0 =
gunion(list(
PipeOpLearnerCV$new(rprt,id = "glmnt_cv_l1")
# PipeOpNOP$new(id = "NOP_l1")
))
# Create "averager" learner (and set predict type to "prob")
lrn_avg <- LearnerClassifAvg$new( id = "classif.avg")
lrn_avg$predict_type ="prob"
# Combine level 0 and "averager" learner
level_1 <- level_0 %>>%
PipeOpFeatureUnion$new(2,id = "u1") %>>%
lrn_avg
# Show ensemble
level_1$plot(html = FALSE)
由 reprex package (v1.0.0) 于 2021 年 3 月 28 日创建