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我试图理解图和图学习器之间的区别。我可以用图表来 $train 和 $predict 。但我需要“包装器”才能使用行选择和分数（见下面的代码）。

有没有什么可以用不同时是学习者的图来完成？（在带有gr但不带有的代码中glrn？



gr = po(lrn("classif.kknn", predict_type = "prob"),
        param_vals = list(k = 10, distance=2, kernel='rectangular' )) %>>%
  po("threshold", param_vals = list(thresholds = 0.6))


glrn = GraphLearner$new(gr)  # build Graph Learner from graph

glrn$train(task, row_ids=1:300)    # n.b.: We need to construct a graph learner in order to use row_ids etc.


predictions=glrn$predict(task,row_ids = 327:346) # would not work with gr

predictions$score(msr("classif.acc"))
predictions$print()
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1 回答 1

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AGraphLearner总是包装 a Graph，它接受一个Task作为输入并产生一个Prediction作为输出。然而， AGraph可以代表任何类型的计算，甚至可以接受多个输入/产生多个输出。在构建对Graph单个任务进行训练的 a时，您通常会将它们用作中间构建块，给出单个预测，然后将其包装为.GraphLearner

在某些情况下，如果您进行某种预处理（例如插补或 PCA），这也可能会有所帮助，这些预处理也应该应用于某种看不见的数据（即应用与 PCA 相同的旋转），即使您的整个过程不是经典机器学习产生预测模型：

data <- tsk("pima")
trainingset <- sample(seq(0, 1, length.out = data$nrow) < 2/3)
data.t <- data$clone(deep = TRUE)$filter(which(trainingset))
data.p <- data$clone(deep = TRUE)$filter(which(!trainingset))

# Operation:
# 1. impute missing values with mean of non-missings in same column
# 2. rotate to principal component axes
imputepca <- po("imputemean") %>>% po("pca")

# Need to take element 1 of result here: 'Graph' could have multiple
# outputs and therefore returns a list. In our case we only have one
# result that we care about.
rotated.t <- imputepca$train(data.t)[[1]]

rotated.t$head(2)
#>    diabetes       PC1       PC2        PC3      PC4       PC5       PC6       PC7        PC8
#> 1:      pos -4.744963  27.76824 -5.2432401 9.817512 -9.042784 0.4979002 0.4574355 -0.1058608
#> 2:      neg  6.341357 -37.18033 -0.1210501 3.731123 -1.451952 3.6890699 2.3901156  0.0755521

# this data is imputed using the column means of the training data, and then
# rotated by the same rotation as the training data.
rotated.p <- imputepca$predict(data.p)[[1]]

rotated.p$head(2)
#>    diabetes        PC1       PC2        PC3       PC4        PC5       PC6       PC7        PC8
#> 1:      pos -11.535952  9.358736 25.1073705  4.761627 -23.313410 -9.743428  3.412071 -1.6403521
#> 2:      neg   1.189971 -7.098455 -0.2785817 -3.280845  -0.281516 -2.277787 -6.746323  0.3434535

然而，由于mlr3pipelines主要是为 s 构建的mlr3，它是关于让Learners 可以被训练和重新采样等，你通常最终会将你Graph的 s 包装在GraphLearners 中。

于 2021-04-09T19:57:08.400 回答