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)llama_0.10.1.zip(r-4.6)llama_0.10.1.zip(r-4.5)
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 65 scripts 245 downloads 28 exports 33 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64OK176
source / vignettesOK243
linux-release-x86_64OK179
macos-release-arm64OK206
macos-oldrel-arm64OK218
windows-develOK130
windows-releaseOK133
windows-oldrelOK114
wasm-releaseOK153

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