3

我想使用 mlr3 包创建的模型对以前未知的新数据进行预测。我使用 AutoTuner 功能训练模型。

我阅读了 mlr3 书的“3.4.1.4 预测”一章，但该解决方案对于我想使用全新数据的示例没有用。

library("mlr3")
library("paradox")
library("mlr3learners")
library("mlr3tuning")
library("data.table")

set.seed(1)

x1 = 1:100
x2 = 2 * x1
y = x1^2 - x2 + rnorm(100)

data = data.table(
   x1 = x1,
   x2 = x2,
   y = y
)

newdata = data.table(x1 = 101:150, x2 = 2 * 101:150)

task = TaskRegr$new("task", backend = data, target = "y")

lrn_xgb = mlr_learners$get("regr.xgboost")

ps = ParamSet$new(
   params = list(
      ParamInt$new(id = "max_depth", lower = 4, upper = 10)
   ))

at = AutoTuner$new(learner = lrn_xgb, 
                   resampling = rsmp("cv", folds = 2),
                   measures = msr("regr.rmse"), 
                   tune_ps = ps,
                   terminator = term("evals", n_evals = 1),
                   tuner = tnr("random_search"))

resampling_outer = rsmp("cv", folds = 2)

rr = resample(task = task, learner = at, resampling = resampling_outer)

at$train(task)

at$predict_newdata(task, newdata)

会话信息：

R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 8.1 x64 (build 9600)

Matrix products: default

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods  
[7] base     

other attached packages:
[1] mlr3learners_0.1.3 mlr3tuning_0.1.0   data.table_1.12.2 
[4] paradox_0.1.0      mlr3_0.1.3

loaded via a namespace (and not attached):
 [1] lgr_0.3.3        lattice_0.20-38  mlr3misc_0.1.4  
 [4] digest_0.6.21    crayon_1.3.4     grid_3.6.1      
 [7] R6_2.4.0         backports_1.1.4  magrittr_1.5    
[10] stringi_1.4.3    uuid_0.1-2       Matrix_1.2-17   
[13] checkmate_1.9.4  xgboost_0.90.0.2 tools_3.6.1     
[16] compiler_3.6.1   Metrics_0.1.4
4

1 回答 1

4

您需要训练选定的学习者（正如您在评论中指出的那样），然后使用predict_newdata()：

at$train(task)
at$predict_newdata(task, newdata)
于 2019-10-03T17:38:37.510 回答