requireNamespace("kknn")Loading required namespace: kknnIntroduction
Tuner for real-valued search spaces are not able to tune on integer hyperparameters. However, it is possible to round the real values proposed by a Tuner to integers before passing them to the learner in the evaluation. We show how to apply a parameter transformation to a ParamSet and use this set in the tuning process.
We load the mlr3verse package which pulls in the most important packages for this example.
library(mlr3verse)Loading required package: mlr3We initialize the random number generator with a fixed seed for reproducibility, and decrease the verbosity of the logger to keep the output clearly represented.
set.seed(7832)
lgr::get_logger("mlr3")$set_threshold("warn")
lgr::get_logger("bbotk")$set_threshold("warn")Task and Learner
In this example, we use the k-Nearest-Neighbor classification learner. We want to tune the integer-valued hyperparameter k which defines the numbers of neighbors.
learner = lrn("classif.kknn")
print(learner$param_set$params$k)NULLTuning
We choose generalized simulated annealing as tuning strategy. The param_classes field of TunerBatchGenSA states that the tuner only supports real-valued (ParamDbl) hyperparameter tuning.
print(tnr("gensa"))<TunerBatchGenSA>: Generalized Simulated Annealing
* Parameters: smooth=FALSE
* Parameter classes: ParamDbl
* Properties: single-crit
* Packages: mlr3tuning, bbotk, GenSATo get integer-valued hyperparameter values for k, we construct a search space with a transformation function. The as.integer() function converts any real valued number to an integer by removing the decimal places.
search_space = ps(
k = p_dbl(lower = 3, upper = 7.99, trafo = as.integer)
)We start the tuning and compare the results of the search space to the results in the space of the learners hyperparameter set.
instance = tune(
tuner = tnr("gensa"),
task = tsk("iris"),
learner = learner,
resampling = rsmp("holdout"),
measure = msr("classif.ce"),
term_evals = 20,
search_space = search_space)Warning in optim(theta.old, fun, gradient, control = control, method = method, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directlyThe optimal k is still a real number in the search space.
instance$result_x_search_space k
<num>
1: 3.82686However, in the learners hyperparameters space, k is an integer value.
instance$result_x_domain$k
[1] 3The archive shows us that for all real-valued k proposed by GenSA, an integer-valued k in the learner hyperparameter space (x_domain_k) was created.
as.data.table(instance$archive)[, .(k, classif.ce, x_domain_k)] k classif.ce x_domain_k
<num> <num> <int>
1: 3.826860 0.06 3
2: 5.996323 0.06 5
3: 5.941332 0.06 5
4: 3.826860 0.06 3
5: 3.826860 0.06 3
6: 3.826860 0.06 3
7: 4.209546 0.06 4
8: 3.444174 0.06 3
9: 4.018203 0.06 4
10: 3.635517 0.06 3
11: 3.922532 0.06 3
12: 3.731189 0.06 3
13: 3.874696 0.06 3
14: 3.779024 0.06 3
15: 3.850778 0.06 3
16: 3.802942 0.06 3
17: 3.838819 0.06 3
18: 3.814901 0.06 3
19: 3.832840 0.06 3
20: 3.820881 0.06 3
k classif.ce x_domain_kInternally, TunerBatchGenSA was given the parameter types of the search space and therefore suggested real numbers for k. Before the performance of the different k values was evaluated, the transformation function of the search_space parameter set was called and k was transformed to an integer value.
Note that the tuner is not aware of the transformation. This has two problematic consequences: First, the tuner might propose different real valued configurations that after rounding end up to be already evaluated configurations and we end up with re-evaluating the same hyperparameter configuration. This is only problematic, if we only optimze integer parameters. Second, the rounding introduces discontinuities which can be problematic for some tuners.
We successfully tuned a integer-valued hyperparameter with TunerBatchGenSA which is only suitable for an real-valued search space. This technique is not limited to tuning problems. Optimizer in bbotk can be also used in the same way to produce points with integer parameters.
Session Information
sessioninfo::session_info(info = "packages")═ Session info ═══════════════════════════════════════════════════════════════════════════════════════════════════════
─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
! package * version date (UTC) lib source
backports 1.5.0 2024-05-23 [1] CRAN (R 4.4.1)
bbotk 1.1.1 2024-10-15 [1] CRAN (R 4.4.1)
checkmate 2.3.2 2024-07-29 [1] CRAN (R 4.4.1)
P class 7.3-22 2023-05-03 [?] CRAN (R 4.4.0)
cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.1)
clue 0.3-65 2023-09-23 [1] CRAN (R 4.4.1)
P cluster 2.1.6 2023-12-01 [?] CRAN (R 4.4.0)
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colorspace 2.1-1 2024-07-26 [1] CRAN (R 4.4.1)
crayon 1.5.3 2024-06-20 [1] CRAN (R 4.4.1)
data.table * 1.16.2 2024-10-10 [1] CRAN (R 4.4.1)
DEoptimR 1.1-3 2023-10-07 [1] CRAN (R 4.4.1)
digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1)
diptest 0.77-1 2024-04-10 [1] CRAN (R 4.4.1)
dplyr 1.1.4 2023-11-17 [1] CRAN (R 4.4.1)
evaluate 1.0.1 2024-10-10 [1] CRAN (R 4.4.1)
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fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.1)
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future.apply 1.11.2 2024-03-28 [1] CRAN (R 4.4.1)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.1)
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igraph 2.0.3 2024-03-13 [1] CRAN (R 4.4.1)
jsonlite 1.8.9 2024-09-20 [1] CRAN (R 4.4.1)
kernlab 0.9-33 2024-08-13 [1] CRAN (R 4.4.1)
kknn 1.3.1 2016-03-26 [1] CRAN (R 4.4.1)
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[1] /home/marc/repositories/mlr3website/mlr-org/renv/library/linux-ubuntu-noble/R-4.4/x86_64-pc-linux-gnu
[2] /home/marc/.cache/R/renv/sandbox/linux-ubuntu-noble/R-4.4/x86_64-pc-linux-gnu/9a444a72
P ── Loaded and on-disk path mismatch.
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