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Category: R

Kernel SHAP in R and Python

Published 2022-10-06 by Kevin Feasel

Michael Mayer and Christian Lorentzen team up:

SHAP is one of the most used model interpretation technique in Machine Learning. It decomposes predictions into additive contributions of the features in a fair way. For tree-based methods, the fast TreeSHAP algorithm exists. For general models, one has to resort to computationally expensive Monte-Carlo sampling or the faster Kernel SHAP algorithm. Kernel SHAP uses a regression trick to get the SHAP values of an observation with a comparably small number of calls to the predict function of the model. Still, it is much slower than TreeSHAP.

Read on to see how to do this in both R and Python. With libraries the way they are, the code is very similar and the results are basically the same.

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Oracle Machine Learning for R in Oracle Autonomous Database

Published 2022-09-30 by Kevin Feasel

Brendan Tierney notes an update to Oracle Autonomous Database:

Oracle Machine Learning for R (OML4R) is available on Oracle Autonomous Database. Finally. After waiting for way, way too long we can now run R code in the Autonomous Database (in the Cloud). It’s based on using Oracle R Distribution 4.0.5 (which is based on R 4.0.5). This product was previously called Oracle R Enterprise, which I was a fan of many few years ago, so much so I wrote a book about it.

I’m curious how well this has done compared to SQL Server ML Services—my experience is that getting ML Services adoption has been a struggle.

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Testing API Packages in R

Published 2022-09-30 by Kevin Feasel

Jamie Owen does some testing:

This blog post is a follow on to our API as a package series, which looks to expand on the topic of testing {plumber} API applications within the package structure leveraging {testthat}. As a reminder of the situation, so far we have an R package that defines functions that will be used as endpoints in a {plumber} API application. The API routes defined via {plumber} decorators in inst simply map the package functions to URLs.

Jamie covers a lot of testing ground in that post as well, so check it out.

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Using Google Fonts on RMarkdown Pages

Published 2022-09-27 by Kevin Feasel

Thomas Williams has a change of font:

Setting a different font is a minor tweak to an R Markdown file that might help it fit better with a corporate or company look. Google Fonts is an industry-accepted method to reference fonts on web pages and can be used with R Markdown rendered by Shiny. There’s no need to download anything while developing the R Markdown file in RStudio, or viewing it in a web browser from a Shiny server.

I’ve recently used this technique to integrate the “Atkinson Hyperlegible” font, a font with “…greater legibility and readability for low vision readers…” from https://brailleinstitute.org/freefont. Since Shiny themes already specify a font, the steps below show how to override that with the Google Font, demo’d with the spacelab Shiny theme.

Click through to see how you can use this font, as well as others, in Shiny apps.

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Parameter Reloading in RMarkdown

Published 2022-09-23 by Kevin Feasel

Thomas Williams wants to improve the user experience:

Recently I needed to reload a parameter, without reloading the page. The parameter was bound to a data frame, where end-users selected a value and then I looked up other fields in the data frame further down the page (for example, a name was selected, but I wanted the identifier from the same record). It wasn’t exactly intuitive, so here’s how I did it.

Click through for code and explanation.

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Tips on Logging in R Packages

Published 2022-09-23 by Kevin Feasel

Jamie Owen continues a series on building a package around an API:

Part 1 of this series laid out some ideas for how one might structure a {plumber} application as an R package, inspired by solutions such as {golem} and {leprechaun} for {shiny}. In this installment of the series we look at adding some functions to our package that will take care of logging as our application runs. If you haven’t already, we recommend reading the first installment of this series as the example package created for that post will form the basis of the starting point for this one.

Read the whole thing.

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Survival Analysis Model Explanations with survex

Published 2022-09-22 by Kevin Feasel

Mikolaj Spytek promotes an R package:

You can learn about it in this blog, but long story short, survival models (most often) predict a survival function. It tells us what is the probability of an event not happening until a given time t. The output can also be a single value (e.g., risk score) but these scores are always some aggregates of the survival function and this naturally leads to a loss of information included in the prediction.

The complexity of the output of survival models means that standard explanation methods cannot be applied directly.

Because of this, we (I and the team: Mateusz Krzyziński, Hubert Baniecki, and Przemyslaw Biecek) developed an R package — survex, which provides explanations for survival models. We hope this tool allows for more widespread usage of complex machine learning survival analysis models. Until now, simpler statistical models such as Cox Proportional Hazards were preferred due to their interpretability — vital in areas such as medicine, even though they were frequently outperformed by complex machine learning models.

Read on to dive into the topic. H/T R-Bloggers.

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Keyboard Focus on Controls in Shiny

Published 2022-09-20 by Kevin Feasel

Thomas Williams wants to take control:

It is possible – though not the default – set set keyboard focus on load to a control in an R Markdown web page rendered with Shiny.

Setting keyboard focus when an R Markdown page is loaded is beneficial to users, who can start interacting with the page without having to first click the control.

Click through to learn how. This is one of the biggest losses we have in the GUI era: that you have a mouse necessitates using the mouse for everything. But that’s a “get off my lawn” rant for another day.

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Setting a Loading Message for a DataTable in RMarkdown

Published 2022-09-19 by Kevin Feasel

Thomas Williams wants you to wait patiently:

Waits are inevitable, whether getting data from an API or database, or manipulating data in an interactive R Markdown document. Showing a “loading” or “updating” message is a beneficial incremental improvement to users’ experience.

The code at https://github.com/thomasswilliams/r-markdown-snippets/blob/main/loading-message-datatable.Rmd, when run from RStudio, demonstrates using CSS and pseudo-elements to display text in a DataTables (DT package).

Read on to see an example of how to use this.

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