Package: llama 0.10.1

llama: Leveraging Learning to Automatically Manage Algorithms

Provides functionality to train and evaluate algorithm selection models for portfolios.

Authors:Lars Kotthoff [aut,cre], Bernd Bischl [aut], Barry Hurley [ctb], Talal Rahwan [ctb], Damir Pulatov [ctb]

llama_0.10.1.tar.gz
llama_0.10.1.zip(r-4.7-any)llama_0.10.1.zip(r-4.6-any)llama_0.10.1.zip(r-4.5-any)
llama_0.10.1.tgz(r-4.6-any)llama_0.10.1.tgz(r-4.5-any)
llama_0.10.1.tar.gz(r-4.7-any)llama_0.10.1.tar.gz(r-4.6-any)
llama_0.10.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
llama/json (API)

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

Bug tracker:https://bitbucket.org/lkotthoff/llama

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT
Datasets:
  • satsolvers - Example data for Leveraging Learning to Automatically Manage Algorithms

On CRAN:

Conda:

openjdk

2.89 score 4 stars 1 packages 64 scripts 377 downloads 28 exports 33 dependencies

Last updated from:f95206aaeb. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK162
source / vignettesOK230
linux-releaseOK151
macos-releaseOK165
macos-oldrelOK147
windows-develOK128
windows-releaseOK109
windows-oldrelOK89
wasm-releaseOK147

Exports:bsFoldsclassifyclassifyPairsclustercontributionscvFoldsimputeCensoredinputmakeRLearner.classif.constantmisclassificationPenaltiesnormalizeparscoresperfScatterPlotpredictLearner.classif.constantpredTableprint.llama.dataprint.llama.modelregressionregressionPairssingleBestsingleBestByCountsingleBestByParsingleBestBySuccessessuccessestrainLearner.classif.constanttrainTesttuneModelvbs

Dependencies:backportsBBmisccheckmateclicpp11data.tablefarverfastmatchggplot2gluegtableisobandlabelinglatticelifecycleMatrixmlrparallelMapParamHelpersplyrR6RColorBrewerRcpprJavarlangS7scalesstringisurvivalvctrsviridisLitewithrXML

Readme and manuals

Help Manual

Help pageTopics
Leveraging Learning to Automatically Manage Algorithmsllama-package llama
Analysis functionscontributions
Bootstrapping foldsbsFolds
Classification modelclassify
Classification model for pairs of algorithmsclassifyPairs
Cluster modelcluster
Cross-validation foldscvFolds
HelpersmakeRLearner.classif.constant predictLearner.classif.constant print.llama.data print.llama.model trainLearner.classif.constant
Impute censored valuesimputeCensored
Read datainput
Convenience functionspredTable singleBest singleBestByCount singleBestByPar singleBestBySuccesses vbs
Misclassification penaltymisclassificationPenalties
Normalize featuresnormalize
Penalized average runtime scoreparscores
Plot convenience functions to visualise selectorsperfScatterPlot
Regression modelregression
Regression model for pairs of algorithmsregressionPairs
Example data for Leveraging Learning to Automatically Manage Algorithmssatsolvers
Successsuccesses
Train / test splittrainTest
Tune the hyperparameters of the machine learning algorithm underlying a modeltuneModel