Feature Selection with Shadow Variable Search
Source:R/FSelectorShadowVariableSearch.R
mlr_fselectors_shadow_variable_search.RdFeature selection using the Shadow Variable Search Algorithm. Shadow variable search creates for each feature a permutated copy and stops when one of them is selected.
Source
Thomas J, Hepp T, Mayr A, Bischl B (2017). “Probing for Sparse and Fast Variable Selection with Model-Based Boosting.” Computational and Mathematical Methods in Medicine, 2017, 1--8. doi:10.1155/2017/1421409 .
Wu Y, Boos DD, Stefanski LA (2007). “Controlling Variable Selection by the Addition of Pseudovariables.” Journal of the American Statistical Association, 102(477), 235--243. doi:10.1198/016214506000000843 .
Details
The feature selection terminates itself when the first shadow variable is selected. It is not necessary to set a termination criterion.
Dictionary
This FSelector can be instantiated with the associated sugar function fs():
fs("shadow_variable_search")Super class
mlr3fselect::FSelector -> FSelectorShadowVariableSearch
Methods
Method optimization_path()
Returns the optimization path.
Arguments
inst(FSelectInstanceSingleCrit)
Instance optimized with FSelectorShadowVariableSearch.
Examples
# Feature Selection
# \donttest{
# retrieve task and load learner
task = tsk("penguins")
learner = lrn("classif.rpart")
# run feature selection on the Palmer Penguins data set
instance = fselect(
method = fs("shadow_variable_search"),
task = task,
learner = learner,
resampling = rsmp("holdout"),
measure = msr("classif.ce"),
)
# best performing feature subset
instance$result
#> bill_depth bill_length body_mass flipper_length island sex year
#> 1: FALSE TRUE FALSE TRUE TRUE FALSE FALSE
#> features classif.ce
#> 1: bill_length,flipper_length,island 0.06086957
# all evaluated feature subsets
as.data.table(instance$archive)
#> bill_depth bill_length body_mass flipper_length island sex year
#> 1: TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> 2: FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> 3: FALSE FALSE TRUE FALSE FALSE FALSE FALSE
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#> 76: TRUE TRUE TRUE TRUE TRUE TRUE FALSE
#> 77: TRUE TRUE TRUE TRUE TRUE TRUE FALSE
#> bill_depth bill_length body_mass flipper_length island sex year
#> classif.ce runtime_learners timestamp batch_nr
#> 1: 0.21739130 0.017 2023-01-26 18:34:05 1
#> 2: 0.20000000 0.038 2023-01-26 18:34:05 1
#> 3: 0.25217391 0.018 2023-01-26 18:34:05 1
#> 4: 0.18260870 0.018 2023-01-26 18:34:05 1
#> 5: 0.31304348 0.017 2023-01-26 18:34:05 1
#> 6: 0.56521739 0.017 2023-01-26 18:34:05 1
#> 7: 0.56521739 0.017 2023-01-26 18:34:05 1
#> 8: 0.54782609 0.015 2023-01-26 18:34:05 1
#> 9: 0.54782609 0.014 2023-01-26 18:34:05 1
#> 10: 0.60869565 0.014 2023-01-26 18:34:05 1
#> 11: 0.56521739 0.032 2023-01-26 18:34:05 1
#> 12: 0.56521739 0.018 2023-01-26 18:34:05 1
#> 13: 0.56521739 0.015 2023-01-26 18:34:05 1
#> 14: 0.56521739 0.014 2023-01-26 18:34:05 1
#> 15: 0.18260870 0.017 2023-01-26 18:34:06 2
#> 16: 0.06956522 0.017 2023-01-26 18:34:06 2
#> 17: 0.19130435 0.016 2023-01-26 18:34:06 2
#> 18: 0.13043478 0.017 2023-01-26 18:34:06 2
#> 19: 0.18260870 0.017 2023-01-26 18:34:06 2
#> 20: 0.17391304 0.017 2023-01-26 18:34:06 2
#> 21: 0.15652174 0.016 2023-01-26 18:34:06 2
#> 22: 0.18260870 0.022 2023-01-26 18:34:06 2
#> 23: 0.15652174 0.018 2023-01-26 18:34:06 2
#> 24: 0.18260870 0.017 2023-01-26 18:34:06 2
#> 25: 0.18260870 0.017 2023-01-26 18:34:06 2
#> 26: 0.18260870 0.017 2023-01-26 18:34:06 2
#> 27: 0.14782609 0.017 2023-01-26 18:34:06 2
#> 28: 0.06956522 0.018 2023-01-26 18:34:06 3
#> 29: 0.06956522 0.017 2023-01-26 18:34:06 3
#> 30: 0.06086957 0.018 2023-01-26 18:34:06 3
#> 31: 0.06956522 0.018 2023-01-26 18:34:06 3
#> 32: 0.06956522 0.055 2023-01-26 18:34:06 3
#> 33: 0.06956522 0.020 2023-01-26 18:34:06 3
#> 34: 0.06956522 0.017 2023-01-26 18:34:06 3
#> 35: 0.06956522 0.017 2023-01-26 18:34:06 3
#> 36: 0.06956522 0.017 2023-01-26 18:34:06 3
#> 37: 0.06956522 0.017 2023-01-26 18:34:06 3
#> 38: 0.06956522 0.017 2023-01-26 18:34:06 3
#> 39: 0.06956522 0.017 2023-01-26 18:34:06 3
#> 40: 0.06086957 0.018 2023-01-26 18:34:07 4
#> 41: 0.06086957 0.018 2023-01-26 18:34:07 4
#> 42: 0.06086957 0.017 2023-01-26 18:34:07 4
#> 43: 0.06086957 0.017 2023-01-26 18:34:07 4
#> 44: 0.06086957 0.022 2023-01-26 18:34:07 4
#> 45: 0.06086957 0.019 2023-01-26 18:34:07 4
#> 46: 0.06086957 0.018 2023-01-26 18:34:07 4
#> 47: 0.06086957 0.017 2023-01-26 18:34:07 4
#> 48: 0.06086957 0.017 2023-01-26 18:34:07 4
#> 49: 0.06086957 0.018 2023-01-26 18:34:07 4
#> 50: 0.06086957 0.017 2023-01-26 18:34:07 4
#> 51: 0.06086957 0.018 2023-01-26 18:34:07 5
#> 52: 0.06086957 0.018 2023-01-26 18:34:07 5
#> 53: 0.06086957 0.017 2023-01-26 18:34:07 5
#> 54: 0.06086957 0.017 2023-01-26 18:34:07 5
#> 55: 0.06086957 0.036 2023-01-26 18:34:07 5
#> 56: 0.06086957 0.022 2023-01-26 18:34:07 5
#> 57: 0.06086957 0.019 2023-01-26 18:34:07 5
#> 58: 0.06086957 0.018 2023-01-26 18:34:07 5
#> 59: 0.06086957 0.017 2023-01-26 18:34:07 5
#> 60: 0.06086957 0.019 2023-01-26 18:34:07 5
#> 61: 0.06086957 0.018 2023-01-26 18:34:08 6
#> 62: 0.06086957 0.018 2023-01-26 18:34:08 6
#> 63: 0.06086957 0.019 2023-01-26 18:34:08 6
#> 64: 0.06086957 0.018 2023-01-26 18:34:08 6
#> 65: 0.06086957 0.017 2023-01-26 18:34:08 6
#> 66: 0.06086957 0.017 2023-01-26 18:34:08 6
#> 67: 0.06086957 0.021 2023-01-26 18:34:08 6
#> 68: 0.06086957 0.023 2023-01-26 18:34:08 6
#> 69: 0.06086957 0.019 2023-01-26 18:34:08 6
#> 70: 0.06086957 0.034 2023-01-26 18:34:08 7
#> 71: 0.06086957 0.023 2023-01-26 18:34:08 7
#> 72: 0.06086957 0.019 2023-01-26 18:34:08 7
#> 73: 0.06086957 0.019 2023-01-26 18:34:08 7
#> 74: 0.06086957 0.021 2023-01-26 18:34:08 7
#> 75: 0.06086957 0.020 2023-01-26 18:34:08 7
#> 76: 0.06086957 0.019 2023-01-26 18:34:08 7
#> 77: 0.06086957 0.018 2023-01-26 18:34:08 7
#> classif.ce runtime_learners timestamp batch_nr
#> permuted__bill_depth permuted__bill_length permuted__body_mass
#> 1: FALSE FALSE FALSE
#> 2: FALSE FALSE FALSE
#> 3: FALSE FALSE FALSE
#> 4: FALSE FALSE FALSE
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#> 75: FALSE FALSE FALSE
#> 76: FALSE FALSE FALSE
#> 77: FALSE FALSE FALSE
#> permuted__bill_depth permuted__bill_length permuted__body_mass
#> permuted__flipper_length permuted__island permuted__sex permuted__year
#> 1: FALSE FALSE FALSE FALSE
#> 2: FALSE FALSE FALSE FALSE
#> 3: FALSE FALSE FALSE FALSE
#> 4: FALSE FALSE FALSE FALSE
#> 5: FALSE FALSE FALSE FALSE
#> 6: FALSE FALSE FALSE FALSE
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#> 77: FALSE FALSE FALSE TRUE
#> permuted__flipper_length permuted__island permuted__sex permuted__year
#> warnings errors resample_result
#> 1: 0 0 <ResampleResult[21]>
#> 2: 0 0 <ResampleResult[21]>
#> 3: 0 0 <ResampleResult[21]>
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#> 75: 0 0 <ResampleResult[21]>
#> 76: 0 0 <ResampleResult[21]>
#> 77: 0 0 <ResampleResult[21]>
#> warnings errors resample_result
# subset the task and fit the final model
task$select(instance$result_feature_set)
learner$train(task)
# }