MLR3 整体的平均分数
MLR3 average scores from an ensemble
使用非常有用的示例 mlr3 book，我试图简单地 return 堆叠模型输出的平均分数。有人可以解释如何使用 mlr3 执行此操作吗？我试过同时使用 LearnerClassifAvg$new( id = "classif.avg") 和 po("classifavg")，但没有确定我已经正确应用了这些，谢谢
示例：
library("magrittr")
library("mlr3learners") # for classif.glmnet
task = mlr_tasks$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
如果我们不将特征传递给 classif.avg (PipeOpNOP)，我们仍然会遇到同样的错误：
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$plot(html = FALSE)
lrn = GraphLearner$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
由 reprex package (v1.0.0)
于 2021-03-27 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_avg <- LearnerClassifAvg$new( id = "classif.avg")
lrn_avg$predict_type ="prob"
在此处检查错误消息：https://github.com/cran/mlr3pipelines/blob/master/R/LearnerAvg.R
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 "
使用更简单的集成演示的解决方案
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), 120)
test.idx = setdiff(seq_len(task$nrow), train.idx)
rprt = lrn("classif.rpart", predict_type = "prob")
glmn = lrn("classif.glmnet", predict_type = "prob")
# Define level 0
level_0 =
gunion(list(
PipeOpLearnerCV$new(rprt, id = "rpart_cv_l1"),
PipeOpLearnerCV$new(glmn, id = "glmnt_cv_l1")
# PipeOpNOP$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)
# Turn into learner
lrn = GraphLearner$new(level_1)
# Make predictions
set.seed(123)
lrn$
train(task, train.idx)$
predict(task, test.idx)$
score()
#> INFO [14:32:46.626] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3)
#> INFO [14:32:46.692] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3)
#> INFO [14:32:46.724] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3)
#> INFO [14:32:47.060] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 2/3)
#> INFO [14:32:47.136] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 1/3)
#> INFO [14:32:47.209] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 3/3)
#> classif.ce
#> 0.1
由 reprex package (v1.0.0)
于 2021-03-28 创建
使用非常有用的示例 mlr3 book，我试图简单地 return 堆叠模型输出的平均分数。有人可以解释如何使用 mlr3 执行此操作吗？我试过同时使用 LearnerClassifAvg$new( id = "classif.avg") 和 po("classifavg")，但没有确定我已经正确应用了这些，谢谢
示例：
library("magrittr")
library("mlr3learners") # for classif.glmnet
task = mlr_tasks$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
如果我们不将特征传递给 classif.avg (PipeOpNOP)，我们仍然会遇到同样的错误：
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$plot(html = FALSE)
lrn = GraphLearner$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
由 reprex package (v1.0.0)
于 2021-03-27 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_avg <- LearnerClassifAvg$new( id = "classif.avg")
lrn_avg$predict_type ="prob"
在此处检查错误消息：https://github.com/cran/mlr3pipelines/blob/master/R/LearnerAvg.R
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 "
使用更简单的集成演示的解决方案
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), 120)
test.idx = setdiff(seq_len(task$nrow), train.idx)
rprt = lrn("classif.rpart", predict_type = "prob")
glmn = lrn("classif.glmnet", predict_type = "prob")
# Define level 0
level_0 =
gunion(list(
PipeOpLearnerCV$new(rprt, id = "rpart_cv_l1"),
PipeOpLearnerCV$new(glmn, id = "glmnt_cv_l1")
# PipeOpNOP$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)
# Turn into learner
lrn = GraphLearner$new(level_1)
# Make predictions
set.seed(123)
lrn$
train(task, train.idx)$
predict(task, test.idx)$
score()
#> INFO [14:32:46.626] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3)
#> INFO [14:32:46.692] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3)
#> INFO [14:32:46.724] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3)
#> INFO [14:32:47.060] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 2/3)
#> INFO [14:32:47.136] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 1/3)
#> INFO [14:32:47.209] [mlr3] Applying learner 'classif.glmnet' on task 'iris' (iter 3/3)
#> classif.ce
#> 0.1
由 reprex package (v1.0.0)
于 2021-03-28 创建