mlr3

Question

在这里使用 lmr3verse 包。假设我对用于训练 Learner:

的训练集应用了以下预处理

preprocess <- po("scale", param_vals = list(center = TRUE, scale = TRUE)) %>>%
              po("encode",param_vals = list(method = "one-hot"))

我想用命令 predict(Learner, newdata = pred, predict_type="prob") 预测数据框中包含的新观测值的标签（使用原始变量）pred。这行不通，因为 Learner 是使用居中、缩放和单热编码变量训练的。

如何将训练集上使用的相同预处理应用于新数据（仅特征，而非响应）以进行预测？

Answer 1

我不是 100% 确定，但您似乎可以将新数据提供给新任务并将其提供给 predict。 This page shows an example of combining mlr_pipeops and learner objects.

library(dplyr)
library(mlr3verse)
df_iris <- iris
df_iris$Petal.Width = df_iris$Petal.Width %>% cut( breaks = c(0,0.5,1,1.5,2,Inf))

task = TaskClassif$new(id = "my_iris", 
                       backend = df_iris, 
                       target = "Species")

train_set = sample(task$nrow, 0.8 * task$nrow)
test_set = setdiff(seq_len(task$nrow), train_set)

task_train = TaskClassif$new(id = "my_iris", 
                       backend = df_iris[train_set,], # use train_set
                       target = "Species")

graph = po("scale", param_vals = list(center = TRUE, scale = TRUE)) %>>%
  po("encode", param_vals = list(method = "one-hot")) %>>%
  mlr_pipeops$get("learner",
                  learner = mlr_learners$get("classif.rpart"))

graph$train(task_train)
graph$pipeops$encode$state$outtasklayout # inspect model input types

graph$pipeops$classif.rpart$predict_type = "prob" 

task_test = TaskClassif$new(id = "my_iris_test",
                       backend = df_iris[test_set,], # use test_set
                       target = "Species")
pred = graph$predict(task_test)
pred$classif.rpart.output$prob

# when you don't have a target variable, just make up one
df_test2 <- df_iris[test_set,]
df_test2$Species = sample(df_iris$Species, length(test_set)) # made-up target

task_test2 = TaskClassif$new(id = "my_iris_test", 
                            backend = df_test2, # use test_set
                            target = "Species")

pred2= graph$predict(task_test2)
pred2$classif.rpart.output$prob

Answer 2

正如@missuse 所建议的，通过使用 graph <- preprocess %>>% Learner 然后 graph_learner <- GraphLearner$new(graph) 命令，我可以预测 --- predict(TunedLearner, newdata = pred, predict_type="prob") --- 使用原始 data.frame。

mlr3 - 对新数据进行预处理

mlr3 - Apply pre-processing to new data

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