Package: aslib 0.1.2

Lars Kotthoff

aslib: Interface to the Algorithm Selection Benchmark Library

Provides an interface to the algorithm selection benchmark library at <http://www.aslib.net> and the 'LLAMA' package (<https://cran.r-project.org/package=llama>) for building algorithm selection models; see Bischl et al. (2016) <doi:10.1016/j.artint.2016.04.003>.

Authors:Bernd Bischl <[email protected]>, Lars Kotthoff <[email protected]>, Pascal Kerschke <[email protected]> [ctb], Damir Pulatov <[email protected]> [ctb]

aslib_0.1.2.tar.gz
aslib_0.1.2.tar.gz(r-4.5-noble)aslib_0.1.2.tar.gz(r-4.4-noble)
aslib_0.1.2.tgz(r-4.4-emscripten)aslib_0.1.2.tgz(r-4.3-emscripten)
aslib.pdf |aslib.html✨
aslib/json (API)
NEWS

# Install 'aslib' in R:
install.packages('aslib', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/coseal/aslib-r/issues

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT

openjdk

1.51 score 1 stars 32 scripts 290 downloads 32 exports 59 dependencies

Last updated 2 years agofrom:294679046c. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 08 2024
R-4.5-linuxNOTEDec 08 2024

Exports:checkDuplicatedInstancesconvertAlgoPerfToWideFormatconvertToLlamaconvertToLlamaCVFoldscreateCVSplitsfindDominatedAlgosfixFeckingPresolvegetAlgorithmNamesgetCosealASScenariogetCostsAndPresolvedStatusgetDefaultFeatureStepNamesgetFeatureNamesgetFeatureStepNamesgetInstanceNamesgetNumberOfCVFoldsgetNumberOfCVRepsgetProvidedFeaturesgetSummedFeatureCostsimputeAlgoPerfparseASScenarioplotAlgoCorMatrixplotAlgoPerfBoxplotsplotAlgoPerfCDFsplotAlgoPerfDensitiesplotAlgoPerfScatterMatrixrunLlamaModelssummarizeAlgoPerfsummarizeAlgoRunstatussummarizeFeatureStepssummarizeFeatureValuessummarizeLlamaExpswriteASScenario

Dependencies:backportsbase64urlbatchtoolsBBmiscbrewcheckmateclicolorspacecorrplotcrayondata.tabledigestfansifarverfastmatchfsggplot2gluegtablehmsisobandlabelinglatticelifecyclellamamagrittrMASSMatrixmgcvmlrmunsellnlmeparallelMapParamHelperspillarpkgconfigplyrprettyunitsprogressR6rappdirsRColorBrewerRcppreshape2rJavarlangRWekaRWekajarsscalesstringistringrsurvivaltibbleutf8vctrsviridisLitewithrXMLyaml

Readme and manuals

Help Manual

Help pageTopics
S3 class for ASScenarioDesc.ASScenarioDesc
Checks the feature data set for duplicated instances.checkDuplicatedInstances
Converts 'algo.runs' object of a scenario to wide format.convertAlgoPerfToWideFormat
Convert an ASScenario scenario object to a llama data object.convertToLlama
Convert an ASScenario scenario object to a llama data object with cross-validation folds.convertToLlamaCVFolds
Create cross-validation splits for a scenario.createCVSplits
Creates a table that shows the dominance of one algorithm over another one.findDominatedAlgos
Bakes presolving stuff into a LLAMA data frame.fixFeckingPresolve
Returns algorithm names of scenario.getAlgorithmNames
Retrieves a scenario from the Coseal Github repository and parses into an S3 object.getCosealASScenario
Return whether an instance was presolved and which step did it.getCostsAndPresolvedStatus
Returns the default feature step names of scenario.getDefaultFeatureStepNames
Returns feature names of scenario.getFeatureNames
Returns feature step names of scenario.getFeatureStepNames
Returns instance names of scenario.getInstanceNames
Returns number of CV folds.getNumberOfCVFolds
Returns number of CV repetitions.getNumberOfCVReps
Return features that are useable for a given set of feature steps.getProvidedFeatures
Returns feature costs of scenario, summed over all instances.getSummedFeatureCosts
Imputes algorithm performance for runs which have NA performance values.imputeAlgoPerf
Parses the data files of an algorithm selection scenario into an S3 object.ASScenario parseASScenario
Plots the correlation matrix of the algorithms.plotAlgoCorMatrix
EDA plots for performance values of algorithms across all instances.plotAlgoPerf plotAlgoPerfBoxplots plotAlgoPerfCDFs plotAlgoPerfDensities plotAlgoPerfScatterMatrix
Creates a registry which can be used for running several Llama models on a cluster.runLlamaModels
Creates summary data.frame for algorithm performance values across all instances.summarizeAlgoPerf
Creates summary data.frame for algorithm runstatus across all instances.summarizeAlgoRunstatus
Creates a data.frame that summarizes the feature steps.summarizeFeatureSteps
Creates summary data.frame for feature values across all instances.summarizeFeatureValues
Creates summary data.table for runLlamaModel experiments.summarizeLlamaExps
Writes an algorithm selection scenario to a directory.writeASScenario