tidymodels / desirability2

Desirability Functions for Multiparameter Optimization

tidymodels

Overview

tidymodels is a “meta-package” for modeling and statistical analysis that shares the underlying design philosophy, grammar, and data structures of the tidyverse.

It includes a core set of packages that are loaded on startup:

• broom takes the messy output of built-in functions in R, such as lm, nls, or t.test, and turns them into tidy data frames.

• dials has tools to create and manage values of tuning parameters.

• dplyr contains a grammar for data manipulation.

• ggplot2 implements a grammar of graphics.

• infer is a modern approach to statistical inference.

• parsnip is a tidy, unified interface to creating models.

• purrr is a functional programming toolkit.

• recipes is a general data preprocessor with a modern interface. It can create model matrices that incorporate feature engineering, imputation, and other help tools.

• rsample has infrastructure for resampling data so that models can be assessed and empirically validated.

• tibble has a modern re-imagining of the data frame.

• tune contains the functions to optimize model hyper-parameters.

• workflows has methods to combine pre-processing steps and models into a single object.

• yardstick contains tools for evaluating models (e.g. accuracy, RMSE, etc.).

A list of all tidymodels functions across different CRAN packages can be found at https://www.tidymodels.org/find/.

You can install the released version of tidymodels from CRAN with:

install.packages("tidymodels")

Install the development version from GitHub with:

# install.packages("pak")
pak::pak("tidymodels/tidymodels")

When loading the package, the versions and conflicts are listed:

library(tidymodels)
#> ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
#> ✔ broom        1.0.5      ✔ recipes      1.0.10
#> ✔ dials        1.2.1      ✔ rsample      1.2.0 
#> ✔ dplyr        1.1.4      ✔ tibble       3.2.1 
#> ✔ ggplot2      3.5.0      ✔ tidyr        1.3.1 
#> ✔ infer        1.0.6      ✔ tune         1.2.0 
#> ✔ modeldata    1.3.0      ✔ workflows    1.1.4 
#> ✔ parsnip      1.2.1      ✔ workflowsets 1.1.0 
#> ✔ purrr        1.0.2      ✔ yardstick    1.3.1
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter()  masks stats::filter()
#> ✖ dplyr::lag()     masks stats::lag()
#> ✖ recipes::step()  masks stats::step()
#> • Learn how to get started at https://www.tidymodels.org/start/

Contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

• For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.

• Most issues will likely belong on the GitHub repo of an individual package. If you think you have encountered a bug with the tidymodels metapackage itself, please submit an issue.

• Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.

• Check out further details on contributing guidelines for tidymodels packages and how to get help.

desirability2's Issues

Use of `use_data` argument

I don't find it intuitive what use_data did without looking at the source. Maybe add an example, or note that use_data is only used if low, high, or target is missing.

No guard for scale argument

There is no checking for the scale argument so you could end up with weird things happening.

library(desirability2)

d_max(5, 0, 10, scale = 1)
#> [1] 0.5

d_max(5, 0, 10, scale = 1:2)
#> Warning in out[middle] <- ((x[middle] - low)/(high - low))^scale: number of
#> items to replace is not a multiple of replacement length
#> [1] 0.5

d_max(5, 0, 10, scale = -2)
#> [1] 4

d_max(5, 0, 10, scale = NA)
#> [1] NA

^{Created on 2021-10-16 by the reprex package (v2.0.1)}

Inconsistent return for check functions

Some check functions return invisible(NULL) and some return invisible(TRUE). Ideally these should all return the same.

Upkeep for desirability2

2023

Necessary:

• Update copyright holder in DESCRIPTION: person(given = "Posit Software, PBC", role = c("cph", "fnd"))
• Double check license file uses '[package] authors' as copyright holder. Run use_mit_license()
• Update email addresses *@rstudio.com -> *@posit.co
• Update logo (https://github.com/rstudio/hex-stickers); run use_tidy_logo()
• usethis::use_tidy_coc()
• usethis::use_tidy_github_actions()

Optional:

• Review 2022 checklist to see if you completed the pkgdown updates
• Prefer pak::pak("org/pkg") over devtools::install_github("org/pkg") in README
• Consider running use_tidy_dependencies() and/or replace compat files with use_standalone()
• use_standalone("r-lib/rlang", "types-check") instead of home grown argument checkers
• Add alt-text to pictures, plots, etc; see https://posit.co/blog/knitr-fig-alt/ for examples

More descriptive documentation of what scale, scale_low, and scale_high does

Right now it says the following in the documentation

desirability2/R/in-line.R

Lines 16 to 17 in b8547b2

	#' @param scale,scale_low,scale_high A single numeric value to rescale the
	#' desirability function.

{desirability} wrote

Values less than one make the criteria more difficult to satisfy while values greater than one make it easier.

In addition, is it not documented what the difference between scale, scale_low, and scale_high are.

I would like to see documented how exactly the scale affects the desirability, which as far as I can tell is just a power

`scale` transformation is limited to powers

I haven't read too much about desirability functions. But is there a reason why the scaling is limited to powers and not any order-preserving transformation that maps the region [0, 1] to [0, 1]?