tidymodels/discrim

Wrappers for discriminant analysis and naive Bayes models for use with the parsnip package

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.

discrim's Issues

Release discrim 1.0.1

Prepare for release:

• git pull
• Check current CRAN check results
• Polish NEWS
• usethis::use_github_links()
• urlchecker::url_check()
• devtools::build_readme()
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• revdepcheck::cloud_check()
• Update cran-comments.md
• git push

Submit to CRAN:

• usethis::use_version('patch')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• usethis::use_dev_version(push = TRUE)

Upkeep for discrim

Pre-history

• usethis::use_readme_rmd()
• usethis::use_roxygen_md()
• usethis::use_github_links()
• usethis::use_pkgdown_github_pages()
• usethis::use_tidy_github_labels()
• usethis::use_tidy_style()
• usethis::use_tidy_description()
• urlchecker::url_check()

2020

• usethis::use_package_doc()
  Consider letting usethis manage your @importFrom directives here.
  usethis::use_import_from() is handy for this.
• usethis::use_testthat(3) and upgrade to 3e, testthat 3e vignette
• Align the names of R/ files and test/ files for workflow happiness.
  usethis::rename_files() can be helpful.

2021

• usethis::use_tidy_dependencies()
• usethis::use_tidy_github_actions() and update artisanal actions to use setup-r-dependencies
• Remove check environments section from cran-comments.md
• Bump required R version in DESCRIPTION to 3.4
• Use lifecycle instead of artisanal deprecation messages, as described in Communicate lifecycle changes in your functions
• Make sure RStudio appears in Authors@R of DESCRIPTION like so, if appropriate:
  person("RStudio", role = c("cph", "fnd"))

2022

• usethis::use_tidy_coc()
• Handle and close any still-open master --> main issues
• Update README badges, instructions in r-lib/usethis#1594
• Update errors to rlang 1.0.0. Helpful guides:
  https://rlang.r-lib.org/reference/topic-error-call.html
  https://rlang.r-lib.org/reference/topic-error-chaining.html
  https://rlang.r-lib.org/reference/topic-condition-formatting.html
• Update pkgdown site using instructions at https://tidytemplate.tidyverse.org
• Ensure pkgdown development is mode: auto in pkgdown config
• Re-publish released site; see How to update a released site

Strimmer's shrinkage discriminant analysis

Feature

The R package "sda" [1] implements shrinkage discriminant analysis which is designed for high-dimensional data [2]. A major advantage of this technique is that by default it optimizes the shrinkage parameter on the training data (using James-Stein shrinkage estimator) and therefore performs well without parameter tuning. In neuroimaging analysis it is common to run thousands of machine learning models over successive "searchlight" windows covering the brain and therefore parameter tuning for every model is not feasible. I have also found sda to perform very well for noisy and highly correlated data. It's also very fast because it uses computational tricks to speed up covariance estimation. So I think "sda" would be a nice addition to the "discrim" package.

I created a bare bones implementation of sda for parsnip here:

https://github.com/bbuchsbaum/shrinkagediscrim

[1] https://cran.r-project.org/web/packages/sda/index.html
[2] Ahdesmaki, M., Zuber, V., & Strimmer, K. (2013). Shrinkage discriminant analysis and CAT score variable selection.

Release discrim 0.0.2

Prepare for release:

• devtools::build_readme()
• Check current CRAN check results
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• revdepcheck::revdep_check(num_workers = 4)
• Update cran-comments.md
• Polish NEWS
• Review pkgdown reference index for, e.g., missing topics

Submit to CRAN:

• usethis::use_version('patch')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• usethis::use_dev_version()

move to testthat edition 3

Use of set_dependency() should use `mode` argument

Original bug found in tidymodels/censored#269.

It isn't that much work for us to specify it and it stops us from being bitten later down the line.

Release discrim 0.1.1

Prepare for release:

• devtools::build_readme()
• Check current CRAN check results
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• revdepcheck::revdep_check(num_workers = 4)
• Update cran-comments.md
• Polish NEWS

Submit to CRAN:

• usethis::use_version('patch')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• usethis::use_dev_version()

Move `master` branch to `main`

The master branch of this repository will soon be renamed to main, as part of a coordinated change across several GitHub organizations (including, but not limited to: tidyverse, r-lib, tidymodels, and sol-eng). We anticipate this will happen by the end of September 2021.

That will be preceded by a release of the usethis package, which will gain some functionality around detecting and adapting to a renamed default branch. There will also be a blog post at the time of this master --> main change.

The purpose of this issue is to:

• Help us firm up the list of targetted repositories
• Make sure all maintainers are aware of what's coming
• Give us an issue to close when the job is done
• Give us a place to put advice for collaborators re: how to adapt

message id: euphoric_snowdog

Release discrim 0.1.0

Prepare for release:

• devtools::build_readme()
• Check current CRAN check results
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• revdepcheck::revdep_check(num_workers = 4)
• Update cran-comments.md
• Polish NEWS
• Review pkgdown reference index for, e.g., missing topics
• Draft blog post

Submit to CRAN:

• usethis::use_version('minor')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• usethis::use_dev_version()
• Finish blog post
• Tweet
• Add link to blog post in pkgdown news menu

L1 regularization with sparseLDA

Originally posted here.

Release discrim 0.2.0

Prepare for release:

• git pull
• Check current CRAN check results
• Polish NEWS
• devtools::build_readme()
• urlchecker::url_check()
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• revdepcheck::revdep_check(num_workers = 4)
• Update cran-comments.md
• Review pkgdown reference index for, e.g., missing topics
• git push
• Draft blog post

Submit to CRAN:

• usethis::use_version('minor')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• git push
• usethis::use_github_release()
• usethis::use_dev_version()
• git push
• Finish blog post
• Tweet
• Add link to blog post in pkgdown news menu

Upkeep for discrim

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

Interface to naivebayes::multinomial_naive_bayes

Interface to Multinomial Naive Bayes function

Parsnip already offers interfaces to several packages that perform Naive Bayes. However, none of the functions supported seem to accept data in the format of counts of features (particularly useful when you have a document term matrix).

The naivebayes package already supported has a function multinomial_naive_bayes which is designed for that case. But at the moment parsnip only offers an interface to the generic naive_bayes function which doesn't include multinomial naive Bayes as an option.

Could you add support for this function too, please? (See discussion on SO.)

Naive Bayes models don't allow for user-specified priors (as opposed to the function in the underlying klaR package)

?dsicrim::naive_Bayes shows that in the tidymodels framework, you can only pass two parameters to the function: smoothness and Laplace. However, the underlying function in klaR::NaiveBayes does allow for another user-specified parameter, namely:

prior
the prior probabilities of class membership. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels.

I either don't understand how to set this parameter in the tidymodels framework or it is not possible to set this parameter, in which case it would be important to allow setting this parameter.

Many of the tidymodels/discrim documentation websites aren't accessible anymore

E.g. https://discrim.tidymodels.org/reference/details_naive_Bayes_naivebayes.html throws a 404 error (so do many others).

Update documentation of discrim_regularized() to show it can perform QDA

Googling parsnip QDA and discrim QDA doesn't give very informative results. I found the connection with tidymodels QDA and this post.

The only reference in the package itself is

discrim/R/discrim_regularized.R

Line 11 in 7088529

#' The model is from Friedman (1989) and can create LDA models, QDA models,

someone who hasn't learned what RDA is yet might overlook this.

Release discrim 0.1.3

Prepare for release:

• Check current CRAN check results
• Polish NEWS
• devtools::build_readme()
• urlchecker::url_check()
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• revdepcheck::revdep_check(num_workers = 4)
• Update cran-comments.md

Submit to CRAN:

• usethis::use_version('patch')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• usethis::use_dev_version()

Add hex

It's not in the readme or on the pkgdown site yet!

Release discrim 0.1.2

Prepare for release:

• Check current CRAN check results
• Polish NEWS
• devtools::build_readme()
• urlchecker::url_check()
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• revdepcheck::revdep_check(num_workers = 4)
• Update cran-comments.md

Submit to CRAN:

• usethis::use_version('patch')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• usethis::use_dev_version()

Re-licensing discrim 🌟

We are systematically re-licensing tidymodels packages to use the MIT license, to make our package licenses as clear and permissive as possible. To do so, we need the approval of all copyright holders, which I have found by reviewing contributions from all non-RStudio contributors.

@EmilHvitfeldt, that is just you! 😄 Would you permit us to re-license discrim with the MIT license? If so, please comment "I agree" below.

• @EmilHvitfeldt

Also, would you like to give blanket permission for re-licensing other tidymodels packages to MIT? If so, please also indicate that here. (I won't have to include you in other requests this way.)

Error fitting resamples in parallel

Trying to fit_resamples in parallel fails for discrim_linear.

library(doFuture)
#> Loading required package: globals
#> Loading required package: future
#> Loading required package: foreach
#> Loading required package: iterators
#> Loading required package: parallel
library(tune)
library(rsample)
library(discrim)
#> Loading required package: parsnip

registerDoFuture()
plan(multisession, workers = 2)

set.seed(123)

discrim_mod <- discrim_linear() %>% 
  set_engine("MASS")

folds <- vfold_cv(iris, v = 2)

fit_resamples(discrim_mod, Species ~ ., folds)
#> x id, out_id, in_id, data: internal: Error in rlang::env_get(mod_env, items): argument "default" is...
#> x id, out_id, in_id, data: internal: Error in rlang::env_get(mod_env, items): argument "default" is...
#> Warning: All models failed in [fit_resamples()]. See the `.notes` column.
#> Warning: This tuning result has notes. Example notes on model fitting include:
#> internal: Error in rlang::env_get(mod_env, items): argument "default" is missing, with no default
#> internal: Error in rlang::env_get(mod_env, items): argument "default" is missing, with no default
#> # Resampling results
#> # 2-fold cross-validation 
#> # A tibble: 2 x 4
#>   splits          id    .metrics .notes          
#>   <list>          <chr> <list>   <list>          
#> 1 <split [75/75]> Fold1 <NULL>   <tibble [1 x 1]>
#> 2 <split [75/75]> Fold2 <NULL>   <tibble [1 x 1]>

move model definitions to parsnip

We are standardizing to have our parsnip-adjacent package keep their model definitions in parsnip so that other packages can add engines.

This package should take a version dependency of parsnip >= 0.1.7.9000.

Release discrim 1.0.0

Prepare for release:

• git pull
• Check current CRAN check results
• Check if any deprecation processes should be advanced, as described in Gradual deprecation
• Polish NEWS
• devtools::build_readme()
• urlchecker::url_check()
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• revdepcheck::cloud_check()
• Update cran-comments.md
• git push
• Draft blog post
• Slack link to draft blog in #open-source-comms

Submit to CRAN:

• usethis::use_version('major')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• git push
• usethis::use_github_release()
• usethis::use_dev_version()
• git push
• Finish blog post
• Tweet
• Add link to blog post in pkgdown news menu

Adding fastNaiveBayes as engine to naive_bayes()

Would you be open for a PR adding https://github.com/mskogholt/fastNaiveBayes as an engine to naive_bayes()?

Why is `naive_Bayes()` not lowercase

As the title says. Why is the name of this function not fully lowercase like most other parsnip model specifications? Is it because Bayes is a proper noun?

discrim_regularized tune error

library(tidymodels)
#> ── Attaching packages ────────────────────────────────────── tidymodels 0.1.2 ──
#> ✓ broom     0.7.3      ✓ recipes   0.1.15
#> ✓ dials     0.0.9      ✓ rsample   0.0.8 
#> ✓ dplyr     1.0.2      ✓ tibble    3.0.4 
#> ✓ ggplot2   3.3.2      ✓ tidyr     1.1.2 
#> ✓ infer     0.5.3      ✓ tune      0.1.2 
#> ✓ modeldata 0.1.0      ✓ workflows 0.2.1 
#> ✓ parsnip   0.1.4      ✓ yardstick 0.0.7 
#> ✓ purrr     0.3.4
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter()  masks stats::filter()
#> x dplyr::lag()     masks stats::lag()
#> x recipes::step()  masks stats::step()
library(discrim)
#> 
#> Attaching package: 'discrim'
#> The following object is masked from 'package:dials':
#> 
#>     smoothness

set.seed(777)
df_split <- initial_split(iris,prob=0.80,strata=Species)
df_train <- training(df_split)

recipe_rda <- df_train %>%
  recipe(Species ~ .) %>%
  step_zv(all_predictors()) %>%
  step_corr(all_predictors())

spec_rda <- discrim_regularized(frac_common_cov = tune(), frac_identity = tune()) %>% 
  set_mode("classification") %>% 
  set_engine("klaR") 

wf_rda <- workflow() %>%
  add_recipe(recipe_rda) %>%
  add_model(spec_rda)

grid_rda <- grid_regular(frac_common_cov(),frac_identity(),levels=5)
#> Error: Element `id` should have unique values. Duplicates exist for item(s): 'threshold'

^{Created on 2021-01-04 by the reprex package (v0.3.0)}

Session info

devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value                       
#>  version  R version 4.0.0 (2020-04-24)
#>  os       Ubuntu 20.04.1 LTS          
#>  system   x86_64, linux-gnu           
#>  ui       X11                         
#>  language en_GB:en                    
#>  collate  en_GB.UTF-8                 
#>  ctype    en_GB.UTF-8                 
#>  tz       Europe/Stockholm            
#>  date     2021-01-04                  
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version    date       lib source        
#>  assertthat    0.2.1      2019-03-21 [1] CRAN (R 4.0.0)
#>  backports     1.2.1      2020-12-09 [1] CRAN (R 4.0.0)
#>  broom       * 0.7.3      2020-12-16 [1] CRAN (R 4.0.0)
#>  callr         3.5.1      2020-10-13 [1] CRAN (R 4.0.0)
#>  class         7.3-16     2020-03-25 [2] CRAN (R 4.0.0)
#>  cli           2.2.0      2020-11-20 [1] CRAN (R 4.0.0)
#>  codetools     0.2-16     2018-12-24 [2] CRAN (R 4.0.0)
#>  colorspace    2.0-0      2020-11-11 [1] CRAN (R 4.0.0)
#>  crayon        1.3.4      2017-09-16 [1] CRAN (R 4.0.0)
#>  desc          1.2.0      2018-05-01 [1] CRAN (R 4.0.0)
#>  devtools      2.3.2      2020-09-18 [1] CRAN (R 4.0.0)
#>  dials       * 0.0.9      2020-09-16 [1] CRAN (R 4.0.0)
#>  DiceDesign    1.8-1      2019-07-31 [1] CRAN (R 4.0.0)
#>  digest        0.6.27     2020-10-24 [1] CRAN (R 4.0.0)
#>  discrim     * 0.1.1      2020-10-28 [1] CRAN (R 4.0.0)
#>  dplyr       * 1.0.2      2020-08-18 [1] CRAN (R 4.0.0)
#>  ellipsis      0.3.1      2020-05-15 [1] CRAN (R 4.0.0)
#>  evaluate      0.14       2019-05-28 [1] CRAN (R 4.0.0)
#>  fansi         0.4.1      2020-01-08 [1] CRAN (R 4.0.0)
#>  foreach       1.5.1      2020-10-15 [1] CRAN (R 4.0.0)
#>  fs            1.5.0      2020-07-31 [1] CRAN (R 4.0.0)
#>  furrr         0.2.1      2020-10-21 [1] CRAN (R 4.0.0)
#>  future        1.21.0     2020-12-10 [1] CRAN (R 4.0.0)
#>  generics      0.1.0      2020-10-31 [1] CRAN (R 4.0.0)
#>  ggplot2     * 3.3.2      2020-06-19 [1] CRAN (R 4.0.0)
#>  globals       0.14.0     2020-11-22 [1] CRAN (R 4.0.0)
#>  glue          1.4.2      2020-08-27 [1] CRAN (R 4.0.0)
#>  gower         0.2.2      2020-06-23 [1] CRAN (R 4.0.0)
#>  GPfit         1.0-8      2019-02-08 [1] CRAN (R 4.0.0)
#>  gtable        0.3.0      2019-03-25 [1] CRAN (R 4.0.0)
#>  highr         0.8        2019-03-20 [1] CRAN (R 4.0.0)
#>  htmltools     0.5.0      2020-06-16 [1] CRAN (R 4.0.0)
#>  infer       * 0.5.3      2020-07-14 [1] CRAN (R 4.0.0)
#>  ipred         0.9-9      2019-04-28 [1] CRAN (R 4.0.0)
#>  iterators     1.0.13     2020-10-15 [1] CRAN (R 4.0.0)
#>  knitr         1.30       2020-09-22 [1] CRAN (R 4.0.0)
#>  lattice       0.20-41    2020-04-02 [2] CRAN (R 4.0.0)
#>  lava          1.6.8.1    2020-11-04 [1] CRAN (R 4.0.0)
#>  lhs           1.1.1      2020-10-05 [1] CRAN (R 4.0.0)
#>  lifecycle     0.2.0      2020-03-06 [1] CRAN (R 4.0.0)
#>  listenv       0.8.0      2019-12-05 [1] CRAN (R 4.0.0)
#>  lubridate     1.7.9.2    2020-11-13 [1] CRAN (R 4.0.0)
#>  magrittr      2.0.1      2020-11-17 [1] CRAN (R 4.0.0)
#>  MASS          7.3-53     2020-09-09 [1] CRAN (R 4.0.0)
#>  Matrix        1.2-18     2019-11-27 [2] CRAN (R 4.0.0)
#>  memoise       1.1.0      2017-04-21 [1] CRAN (R 4.0.0)
#>  modeldata   * 0.1.0      2020-10-22 [1] CRAN (R 4.0.0)
#>  munsell       0.5.0      2018-06-12 [1] CRAN (R 4.0.0)
#>  nnet          7.3-13     2020-02-25 [2] CRAN (R 4.0.0)
#>  parallelly    1.22.0     2020-12-13 [1] CRAN (R 4.0.0)
#>  parsnip     * 0.1.4      2020-10-27 [1] CRAN (R 4.0.0)
#>  pillar        1.4.7      2020-11-20 [1] CRAN (R 4.0.0)
#>  pkgbuild      1.2.0      2020-12-15 [1] CRAN (R 4.0.0)
#>  pkgconfig     2.0.3      2019-09-22 [1] CRAN (R 4.0.0)
#>  pkgload       1.1.0      2020-05-29 [1] CRAN (R 4.0.0)
#>  plyr          1.8.6      2020-03-03 [1] CRAN (R 4.0.0)
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