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Concept for creating an R model as a package, with execution separate from training
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This package exists to explore the concept of creating a machine learning model as an R package, similar to the established concept of an analysis as an R package. The idea here is that, using vignettes, we can train the model by installing the package. The functions in the package then allow the user to score new data with the trained model. To demonstrate this I've created an extremely simple sentiment analysis model based on review data from the UCI Machine Learning Repository.

You can install this package with devtools::install_github("mdneuzerling/ModelAsAPackage", build_vignettes = TRUE). The package can be loaded and attached with library(ModelAsAPackage) and the training vignette opened with vignette("model-training", package = "ModelAsAPackage").

Want to see the (not-so-great) model score some text? Try giving an argument to the sentiment function. I'm happy to report that sentiment("love") == "good".

I thought this might work because of a few things:

  • Vignettes are created before the source code is bundled, so in theory we can train a model before the package has finished compiling.
  • R uses lazy evaluation, so if a package function refers to an object that doesn't yet exist (because it hasn't been created by the vignette) that's okay.
  • I like using the same functions for model training as I do for model scoring, like the map_to_dtm function in this package.
  • I wanted to take full advantage of roxygen2 for documenting package functions, and testthat for unit tests. I especially like the ease with which you can test within RStudio.

However, I have my doubts:

  • In order for tests to work, I have to run devtools::build_vignettes before running devtools::install. There's something here with namespaces whereby the data objects suddenly become "unexported" after tests are attempted. I don't know why!
  • There are some relative paths in the code. I'm assuming that the working directory is <package_root>/vignettes when this vignette is knitted, so I can move up one level to obtain the root directory of the package. This should be okay if we're following the standard package structure, but I've been hurt too many times by relative file paths to feel comfortable about this.
  • I'm not sure how this would operate with the plumber package. I don't know how we can integrate the expected plumber.R file in the package, except for sticking it in the inst directory and then finding it with system.file.
  • This all seems like a lot of complexity for not too much benefit. Maybe doing this again would be easier now that I have a template.

No matter what, I think these sorts of projects have to be shared, even if I don't think that this is a major success!

A quick shout out for the excellent book on R packages by Hadley Wickham. It's well worth keeping bookmarked.

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