I am using mlr3proba package for machine learning survival analysis.
My dataset contains factor, numeric and integer features.
I used 'scale' and 'encode' pipeops to preprocess my dataset for deephit and deepsurv neural network methods as following codes:
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$optimize(deephit.instance)
But when I run the last line, it shows the error below:
Error in self$assert(xs):
Assertion on 'xs' failed: Parameter 'dropout' not available. Did you mean 'encode.method'/'encode.affect_columns' / 'scale.center'?.
I really appreciate your help.
Hi thanks for using mlr3proba! The reason for this is because the parameter names change when wrapped in the pipeline, you can see this in the example below. There are a few options to solve this, you could change the parameter ids to match the new names after wrapping in PipeOps (Option 1 below), or you could specify the tuning ranges for the learner first then wrap it in the PipeOp (Option 2 below), or you could use an AutoTuner and wrap this in the PipeOps. I use the final option in this tutorial.
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
Created on 2021-04-26 by the reprex package (v0.3.0)