Package: ASML 1.1.0
ASML: Algorithm Portfolio Selection with Machine Learning
A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) <doi:10.1287/ijoc.2022.0090>).
Authors:
ASML_1.1.0.tar.gz
ASML_1.1.0.zip(r-4.7-any)ASML_1.1.0.zip(r-4.6-any)ASML_1.1.0.zip(r-4.5-any)
ASML_1.1.0.tgz(r-4.6-any)ASML_1.1.0.tgz(r-4.5-any)
ASML_1.1.0.tar.gz(r-4.7-any)ASML_1.1.0.tar.gz(r-4.6-any)
ASML_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ASML/json (API)
| # Install 'ASML' in R: |
| install.packages('ASML', repos = c('https://brais-gonzalez.r-universe.dev', 'https://cloud.r-project.org')) |
- branching - Branching point selection in Polynomial Optimization
- branchingsmall - Branching point selection in Polynomial Optimization
- SpMVformat - Automatic selection of the most suitable storage format for sparse matrices on GPUs
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:65b0b4cb04. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 143 | ||
| source / vignettes | OK | 178 | ||
| linux-release | OK | 135 | ||
| macos-release | OK | 161 | ||
| macos-oldrel | OK | 176 | ||
| windows-devel | OK | 79 | ||
| windows-release | OK | 105 | ||
| windows-oldrel | OK | 79 | ||
| wasm-release | OK | 102 |
Exports:ASexplainerASpredictAStrainboxplotsfigure_comparisonKPI_summary_tableKPI_tablemlpartition_and_normalizeranking
Dependencies:caretclasscliclockcodetoolscolorspacecpp11DALEXdata.tablediagramdigestdoFuturedplyre1071farverforeachfuturefuture.applygenericsggplot2globalsgluegowergridExtragtablehardhatiBreakDowningredientsipredisobanditeratorskernelshapKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixModelMetricsnlmennetnumDerivparallellypillarpkgconfigplyrPolychromepROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartS7scalesscatterplot3dshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Create DALEX explainers for multiple ASML-trained models | ASexplainer |
| Predicting the KPI value for the algorithms | ASpredict.as_train |
| Training models for posterior selection of algorithms | AStrain.as_data |
| Boxplots | boxplots.as_data |
| Branching point selection in Polynomial Optimization | branching |
| Branching point selection in Polynomial Optimization | branchingsmall |
| Figure Comparison | figure_comparison.as_data |
| KPI summary table | KPI_summary_table.as_data |
| KPI table | KPI_table.as_data |
| Machine learning process | ml |
| Partition and Normalize | partition_and_normalize |
| Plot | plot.as_data |
| Ranking Plot | ranking.as_data |
| Automatic selection of the most suitable storage format for sparse matrices on GPUs | SpMVformat |