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我正在使用mlr3proba包进行机器学习生存分析。
我的数据集包含因子、数字和整数特征。
我使用 'scale' 和 'encode' pipeops 对 deephit 和 deepsurv 神经网络方法的数据集进行预处理，代码如下：

task.mlr <- TaskSurv$new(id = "id", backend = dataset, time = time, event = status)

inner.rsmp <- rsmp("cv", folds = 5)

measure <- msr("surv.cindex")

tuner <- tnr("random_search")

terminator <- trm("evals", n_evals = 30)

deephit.learner <- lrn("surv.deephit", optimizer = "adam", epochs = 50)

nn.search_space <- ps(dropout = p_dbl(lower = 0, upper = 1),alpha = p_dbl(lower = 0, upper = 1))

deephit.learner <- po("encode") %>>% po("scale") %>>% po("learner", deephit.learner)

deephit.instance <- TuningInstanceSingleCrit$new(
   task = task.mlr,
   learner = deephit.learner,
   search_space = nn.search_space,
   resampling = inner.rsmp,
   measure = measure,
   terminator = terminator
)

tuner$optimize(deephit.instance)

但是当我运行最后一行时，它显示以下错误：

Error in self$assert(xs):
   Assertion on 'xs' failed: Parameter 'dropout' not available. Did you mean 'encode.method'/'encode.affect_columns' / 'scale.center'?.

我真的很感谢你的帮助。

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1 回答 1

3

您好感谢您使用 mlr3proba！这是因为参数名称在包装在管道中时会发生变化，您可以在下面的示例中看到这一点。有几个选项可以解决这个问题，您可以更改参数 ids 以匹配在 PipeOps 中包装后的新名称（下面的选项 1），或者您可以先为学习者指定调整范围，然后将其包装在 PipeOp 中（选项 2下面），或者您可以使用 AutoTuner 并将其包装在 PipeOps 中。我在本教程中使用最后一个选项。

library(mlr3proba)
library(mlr3)
library(paradox)
library(mlr3tuning)
library(mlr3extralearners)
library(mlr3pipelines)

task.mlr <- tsk("rats")
inner.rsmp <- rsmp("holdout")
measure <- msr("surv.cindex")
tuner <- tnr("random_search")
terminator <- trm("evals", n_evals = 2)

###########
# Option 1
###########
deephit.learner <- lrn("surv.deephit", optimizer = "adam", epochs = 50)
deephit.learner <- po("encode") %>>% po("scale") %>>% po("learner", deephit.learner)

deephit.learner$param_set$ids()
#>  [1] "encode.method"               "encode.affect_columns"      
#>  [3] "scale.center"                "scale.scale"                
#>  [5] "scale.robust"                "scale.affect_columns"       
#>  [7] "surv.deephit.frac"           "surv.deephit.cuts"          
#>  [9] "surv.deephit.cutpoints"      "surv.deephit.scheme"        
#> [11] "surv.deephit.cut_min"        "surv.deephit.num_nodes"     
#> [13] "surv.deephit.batch_norm"     "surv.deephit.dropout"       
#> [15] "surv.deephit.activation"     "surv.deephit.custom_net"    
#> [17] "surv.deephit.device"         "surv.deephit.mod_alpha"     
#> [19] "surv.deephit.sigma"          "surv.deephit.shrink"        
#> [21] "surv.deephit.optimizer"      "surv.deephit.rho"           
#> [23] "surv.deephit.eps"            "surv.deephit.lr"            
#> [25] "surv.deephit.weight_decay"   "surv.deephit.learning_rate" 
#> [27] "surv.deephit.lr_decay"       "surv.deephit.betas"         
#> [29] "surv.deephit.amsgrad"        "surv.deephit.lambd"         
#> [31] "surv.deephit.alpha"          "surv.deephit.t0"            
#> [33] "surv.deephit.momentum"       "surv.deephit.centered"      
#> [35] "surv.deephit.etas"           "surv.deephit.step_sizes"    
#> [37] "surv.deephit.dampening"      "surv.deephit.nesterov"      
#> [39] "surv.deephit.batch_size"     "surv.deephit.epochs"        
#> [41] "surv.deephit.verbose"        "surv.deephit.num_workers"   
#> [43] "surv.deephit.shuffle"        "surv.deephit.best_weights"  
#> [45] "surv.deephit.early_stopping" "surv.deephit.min_delta"     
#> [47] "surv.deephit.patience"       "surv.deephit.interpolate"   
#> [49] "surv.deephit.inter_scheme"   "surv.deephit.sub"

nn.search_space <- ps(surv.deephit.dropout = p_dbl(lower = 0, upper = 1),
                      surv.deephit.alpha = p_dbl(lower = 0, upper = 1))

deephit.instance <- TuningInstanceSingleCrit$new(
  task = task.mlr,
  learner = deephit.learner,
  search_space = nn.search_space,
  resampling = inner.rsmp,
  measure = measure,
  terminator = terminator
)

tuner$optimize(deephit.instance)
#> INFO  [08:15:29.770] [bbotk] Starting to optimize 2 parameter(s) with '<OptimizerRandomSearch>' and '<TerminatorEvals> [n_evals=2]' 
#> INFO  [08:15:29.841] [bbotk] Evaluating 1 configuration(s) 
#> INFO  [08:15:30.115] [mlr3]  Running benchmark with 1 resampling iterations 
#> INFO  [08:15:30.314] [mlr3]  Applying learner 'encode.scale.surv.deephit' on task 'rats' (iter 1/1) 
#> INFO  [08:15:39.997] [mlr3]  Finished benchmark 
#> INFO  [08:15:40.296] [bbotk] Result of batch 1: 
#> INFO  [08:15:40.302] [bbotk]  surv.deephit.dropout surv.deephit.alpha surv.harrell_c 
#> INFO  [08:15:40.302] [bbotk]            0.06494213          0.7109244      0.7516212 
#> INFO  [08:15:40.302] [bbotk]                                 uhash 
#> INFO  [08:15:40.302] [bbotk]  27794d84-ba46-4900-8835-de24fcda8c7f 
#> INFO  [08:15:40.307] [bbotk] Evaluating 1 configuration(s) 
#> INFO  [08:15:40.395] [mlr3]  Running benchmark with 1 resampling iterations 
#> INFO  [08:15:40.406] [mlr3]  Applying learner 'encode.scale.surv.deephit' on task 'rats' (iter 1/1) 
#> INFO  [08:15:41.807] [mlr3]  Finished benchmark 
#> INFO  [08:15:41.903] [bbotk] Result of batch 2: 
#> INFO  [08:15:41.905] [bbotk]  surv.deephit.dropout surv.deephit.alpha surv.harrell_c 
#> INFO  [08:15:41.905] [bbotk]            0.05524693          0.2895437      0.7749676 
#> INFO  [08:15:41.905] [bbotk]                                 uhash 
#> INFO  [08:15:41.905] [bbotk]  013795a3-766c-48f9-a3fe-2aae5d4cad48 
#> INFO  [08:15:41.918] [bbotk] Finished optimizing after 2 evaluation(s) 
#> INFO  [08:15:41.919] [bbotk] Result: 
#> INFO  [08:15:41.920] [bbotk]  surv.deephit.dropout surv.deephit.alpha learner_param_vals  x_domain 
#> INFO  [08:15:41.920] [bbotk]            0.05524693          0.2895437          <list[6]> <list[2]> 
#> INFO  [08:15:41.920] [bbotk]  surv.harrell_c 
#> INFO  [08:15:41.920] [bbotk]       0.7749676
#>    surv.deephit.dropout surv.deephit.alpha learner_param_vals  x_domain
#> 1:           0.05524693          0.2895437          <list[6]> <list[2]>
#>    surv.harrell_c
#> 1:      0.7749676

###########
# Option 2
###########
deephit.learner <- lrn("surv.deephit", optimizer = "adam", epochs = 50)
deephit.learner$param_set$values = list(
  dropout = to_tune(0, 1),
  alpha = to_tune(0, 1)
)

deephit.learner <- po("encode") %>>% 
  po("scale") %>>% 
  po("learner", deephit.learner)

deephit.learner = GraphLearner$new(deephit.learner)

tuned.deephit = tune_nested(
  method = "random_search",
  task = task.mlr,
  learner = deephit.learner, 
  inner_resampling = rsmp("holdout"),
  outer_resampling = rsmp("holdout"),
  measure = msr("surv.cindex"), 
  term_evals = 2
)
#> INFO  [08:15:43.167] [mlr3]  Applying learner 'encode.scale.surv.deephit.tuned' on task 'rats' (iter 1/1) 
#> INFO  [08:15:43.477] [bbotk] Starting to optimize 2 parameter(s) with '<OptimizerRandomSearch>' and '<TerminatorRunTime> [secs=2]' 
#> INFO  [08:15:43.495] [bbotk] Evaluating 1 configuration(s) 
#> INFO  [08:15:43.565] [mlr3]  Running benchmark with 1 resampling iterations 
#> INFO  [08:15:43.575] [mlr3]  Applying learner 'encode.scale.surv.deephit' on task 'rats' (iter 1/1) 
#> INFO  [08:15:44.969] [mlr3]  Finished benchmark 
#> INFO  [08:15:45.058] [bbotk] Result of batch 1: 
#> INFO  [08:15:45.064] [bbotk]  surv.deephit.dropout surv.deephit.alpha surv.harrell_c 
#> INFO  [08:15:45.064] [bbotk]             0.3492627          0.2304623      0.6745362 
#> INFO  [08:15:45.064] [bbotk]                                 uhash 
#> INFO  [08:15:45.064] [bbotk]  4ce96658-4d4a-4835-9d9f-a93398471aed 
#> INFO  [08:15:45.069] [bbotk] Evaluating 1 configuration(s) 
#> INFO  [08:15:45.127] [mlr3]  Running benchmark with 1 resampling iterations 
#> INFO  [08:15:45.136] [mlr3]  Applying learner 'encode.scale.surv.deephit' on task 'rats' (iter 1/1) 
#> INFO  [08:15:46.064] [mlr3]  Finished benchmark 
#> INFO  [08:15:46.171] [bbotk] Result of batch 2: 
#> INFO  [08:15:46.176] [bbotk]  surv.deephit.dropout surv.deephit.alpha surv.harrell_c 
#> INFO  [08:15:46.176] [bbotk]             0.1118406          0.7810053      0.6020236 
#> INFO  [08:15:46.176] [bbotk]                                 uhash 
#> INFO  [08:15:46.176] [bbotk]  6a065d27-a7e0-4e72-8e1e-6151408510cf 
#> INFO  [08:15:46.186] [bbotk] Finished optimizing after 2 evaluation(s) 
#> INFO  [08:15:46.187] [bbotk] Result: 
#> INFO  [08:15:46.191] [bbotk]  surv.deephit.dropout surv.deephit.alpha learner_param_vals  x_domain 
#> INFO  [08:15:46.191] [bbotk]             0.3492627          0.2304623          <list[4]> <list[2]> 
#> INFO  [08:15:46.191] [bbotk]  surv.harrell_c 
#> INFO  [08:15:46.191] [bbotk]       0.6745362

由reprex 包于 2021-04-26 创建(v0.3.0)

于 2021-04-26T07:15:07.323 回答