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首页 > 解决方案 > 使用 mlr3 结合弹性网络和逻辑回归的两级堆叠学习器（集成模型）

r - 使用 mlr3 结合弹性网络和逻辑回归的两级堆叠学习器（集成模型）

问题描述

我试图解决医学中的一个常见问题：预测模型与其他来源的结合，例如，专家意见 [有时在医学中非常强调]，在这篇文章中称为superdoc预测器。

这可以通过将模型与逻辑回归（输入专家意见）堆叠起来来解决，如本文第 26 页所述：

Afshar P, Mohammadi A, Plataniotis KN, Oikonomou A, Benali H. 从手工制作到基于深度学习的癌症放射组学：挑战和机遇。IEEE 信号处理杂志 2019；36：132-60。在这里可用

我在这里尝试过，没有考虑过拟合（我没有应用低级学习者的非折叠预测）：

示例数据

# library
library(tidyverse)
library(caret)
library(glmnet)
library(mlbench)

# get example data
data(PimaIndiansDiabetes, package="mlbench")
data <- PimaIndiansDiabetes

# add the super doctors opinion to the data
set.seed(2323)
data %>% 
  rowwise() %>% 
  mutate(superdoc=case_when(diabetes=="pos" ~ as.numeric(sample(0:2,1)), TRUE~ 0)) -> data

# separate the data in a training set and test set
train.data <- data[1:550,]
test.data <- data[551:768,]

不考虑折叠预测的堆叠模型：

# elastic net regression (without the superdoc's opinion)
set.seed(2323)
model <- train(
  diabetes ~., data = train.data %>% select(-superdoc), method = "glmnet",
  trControl = trainControl("repeatedcv",
                           number = 10,
                           repeats=10,
                           classProbs = TRUE,
                           savePredictions = TRUE,
                           summaryFunction = twoClassSummary),
  tuneLength = 10,
  metric="ROC" #ROC metric is in twoClassSummary
)


# extract the coefficients for the best alpha and lambda  
coef(model$finalModel, model$finalModel$lambdaOpt) -> coeffs
tidy(coeffs) %>% tibble() -> coeffs

coef.interc = coeffs %>% filter(row=="(Intercept)") %>% pull(value)
coef.pregnant = coeffs %>% filter(row=="pregnant") %>% pull(value)
coef.glucose = coeffs %>% filter(row=="glucose") %>% pull(value)
coef.pressure = coeffs %>% filter(row=="pressure") %>% pull(value)
coef.mass = coeffs %>% filter(row=="mass") %>% pull(value)
coef.pedigree = coeffs %>% filter(row=="pedigree") %>% pull(value)
coef.age = coeffs %>% filter(row=="age") %>% pull(value)


# combine the model with the superdoc's opinion in a logistic regression model
finalmodel = glm(diabetes ~ superdoc + I(coef.interc + coef.pregnant*pregnant + coef.glucose*glucose + coef.pressure*pressure + coef.mass*mass + coef.pedigree*pedigree + coef.age*age),family=binomial, data=train.data)


# make predictions on the test data
predict(finalmodel,test.data, type="response") -> predictions


# check the AUC of the model in the test data
roc(test.data$diabetes,predictions, ci=TRUE) 
#> Setting levels: control = neg, case = pos
#> Setting direction: controls < cases
#> 
#> Call:
#> roc.default(response = test.data$diabetes, predictor = predictions,     ci = TRUE)
#> 
#> Data: predictions in 145 controls (test.data$diabetes neg) < 73 cases (test.data$diabetes pos).
#> Area under the curve: 0.9345
#> 95% CI: 0.8969-0.9721 (DeLong)

现在我想mlr3根据这篇非常有用的帖子来考虑使用包系列的非折叠预测：调整堆叠学习器

#library
library(mlr3)
library(mlr3learners)
library(mlr3pipelines)
library(mlr3filters)
library(mlr3tuning)
library(paradox)
library(glmnet)

# creat elastic net regression
glmnet_lrn =  lrn("classif.cv_glmnet", predict_type = "prob")

# create the learner out-of-bag predictions
glmnet_cv1 = po("learner_cv", glmnet_lrn, id = "glmnet") #I could not find a setting to filter the predictors (ie, not send the superdoc predictor here)

# summarize steps 
level0 = gunion(list(
  glmnet_cv1,
  po("nop", id = "only_superdoc_predictor")))  %>>% #I could not find a setting to send only the superdoc predictor to "union1"
  po("featureunion", id = "union1")


# final logistic regression
log_reg_lrn = lrn("classif.log_reg", predict_type = "prob")

# combine ensemble model
ensemble = level0 %>>% log_reg_lrn
ensemble$plot(html = FALSE)

由reprex 包于 2021-03-15 创建(v1.0.0)

我的问题（我对mlr3包系列比较陌生）

  1. 包装系列是否mlr3非常适合我尝试构建的集成模型？
  2. 如果是，我最终确定集成模型并预测test.data

标签： rmachine-learningensemble-learningmlr3

解决方案

我认为mlr3/mlr3pipelines非常适合您的任务。看来您缺少的主要是PipeOpSelect/po("select")，它可以让您根据名称或其他属性提取特征并使用Selector对象。您的代码应该看起来像

library("mlr3")
library("mlr3pipelines")
library("mlr3learners")

# creat elastic net regression
glmnet_lrn = lrn("classif.cv_glmnet", predict_type = "prob")

# create the learner out-of-bag predictions
glmnet_cv1 = po("learner_cv", glmnet_lrn, id = "glmnet")

# PipeOp that drops 'superdoc', i.e. selects all except 'superdoc'
# (ID given to avoid ID clash with other selector)
drop_superdoc = po("select", id = "drop.superdoc",
  selector = selector_invert(selector_name("superdoc")))

# PipeOp that selects 'superdoc' (and drops all other columns)
select_superdoc = po("select", id = "select.superdoc",
  selector = selector_name("superdoc"))

# superdoc along one path, the fitted model along the other
stacking_layer = gunion(list(
  select_superdoc,
  drop_superdoc %>>% glmnet_cv1
)) %>>% po("featureunion", id = "union1")

# final logistic regression
log_reg_lrn = lrn("classif.log_reg", predict_type = "prob")

# combine ensemble model
ensemble = stacking_layer %>>% log_reg_lrn

这是它的样子：

ensemble$plot(html = FALSE)

堆叠图。

为了训练和评估模型，我们需要创建Task对象：

train.task <- TaskClassif$new("train.data", train.data, target = "diabetes")
test.task <- TaskClassif$new("test.data", test.data, target = "diabetes")

现在可以训练模型，然后可以将其用于预测，并且可以评估预测的质量。如果我们把ensemble变成 a效果最好Learner：

elearner = as_learner(ensemble)
# Train the Learner:
elearner$train(train.task)
# (The training may give a warning because the glm gets the colinear features:
# The positive and the negative probabilities)

获取测试集上的预测：

prediction = elearner$predict(test.task)
print(prediction)
#> <PredictionClassif> for 218 observations:
#>     row_ids truth response  prob.neg   prob.pos
#>           1   neg      neg 0.9417067 0.05829330
#>           2   neg      neg 0.9546343 0.04536566
#>           3   neg      neg 0.9152019 0.08479810
#> ---                                            
#>         216   neg      neg 0.9147406 0.08525943
#>         217   pos      neg 0.9078216 0.09217836
#>         218   neg      neg 0.9578515 0.04214854

预测是在 上进行的Task，因此它可以直接用于根据实际情况衡量性能，例如使用"classif.auc" Measure：

msr("classif.auc")$score(prediction)
#> [1] 0.9308455

这里有两个注意事项：

  1. 您已手动将数据拆分为训练集和测试集。mlr3使您可以基于单个对象自动进行重新采样。Task然后，这可以超越简单的训练测试拆分。使用datafrom the question，并进行 10 倍交叉验证将如下所示： 
    all.task <- TaskClassif$new("all.data", data, target = "diabetes")
rr = resample(all.task, elearner, rsmp("cv"))  # will take some time
rr$aggregate(msr("classif.auc"))
#> classif.auc 
#>   0.9366438
  2. 我已经展示了如何使用 s 来构造图形，因为它是完全通用的：您可以选择在 s中以及直接在po("select") PipeOps 中都有一些特征，方法是玩弄这些值。如果您真正想要做的只是从单个操作中“转移”一个功能，您还可以使用to a来选择您想要的列。以下创建了一个完全相同的（线性）图，但不太灵活： glmnet_lrn Learnerlog_reg_lrnselectoraffect_columnsSelector
    glmnet_cv1_nosuperdoc = po("learner_cv", glmnet_lrn, id = "glmnet",
  affect_columns = selector_invert(selector_name("superdoc")))
ensemble2 = glmnet_cv1_nosuperdoc %>>% log_reg_lrn
e2learner = as_learner(ensemble2)
# etc.

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