图和图学习器之间的区别

Question

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

有没有可以用同时不是学习者的图来做的事情？（在代码中 gr 而不是 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()

Answer 1

A GraphLearner 总是包装一个 Graph，它将单个 Task 作为输入并产生单个 Prediction 作为输出。但是，Graph 可以表示任何类型的计算，甚至可以接受多个输入/产生多个输出。在构建 对单个任务进行 训练的 Graph 时，您通常会使用这些作为中间构建块，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）也应该应用于某种看不见的数据（即应用与 PCA 相同的旋转），即使您的过程总的来说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

但是，由于 mlr3pipelines 主要是为 mlr3 构建的，也就是说 Learner 可以进行训练和重新采样等，因此您通常最终会包装您的 Graphs 在 GraphLearners.

图和图学习器之间的区别

Difference between graph and graph learner

r

mlr3