rvw 0.6.0: First release

[This article was first published on Thinking inside the box , and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Note: Crossposted by Ivan, James and myself.

Today Dirk Eddelbuettel, James Balamuta and Ivan Pavlov are happy to announce the first release of a reworked R interface to the Vowpal Wabbit machine learning system.

Started as a GSoC 2018 project, the new rvw package was built to give R users easier access to a variety of efficient machine learning algorithms. Key features that promote this idea and differentiate the new rvw from existing Vowpal Wabbit packages in R are:

  • A reworked interface that simplifies model manipulations (direct usage of CLI arguments is also available)
  • Support of the majority of Vowpal Wabbit learning algorithms and reductions
  • Extended data.frame converter covering different variations of Vowpal Wabbit input formats

Below is a simple example of how to use the renewed rvw’s interface:

library(mlbench)   # for a dataset

# Basic data preparation
data("BreastCancer", package = "mlbench")
data_full <- BreastCancer
ind_train <- sample(1:nrow(data_full), 0.8*nrow(data_full))
data_full <- data_full[,-1]
data_full$Class <- ifelse(data_full$Class == "malignant", 1, -1)
data_train <- data_full[ind_train,]
data_test <- data_full[-ind_train,]

# Simple Vowpal Wabbit model for binary classification
vwmodel <-  vwsetup(dir = "./",
                    model = "mdl.vw",
                   option = "binary")

# Training 
vwtrain(vwmodel = test_vwmodel,
       data = data_train,
       passes = 10,
       targets = "Class")

# And testing
vw_output <- vwtest(vwmodel = test_vwmodel, data = data_test)

More information is available in the Introduction and Examples sections of the wiki.

The rvw links directly to libvw and so initially we offer a Docker container in order to ship the most up to date package with everything needed.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.


To leave a comment for the author, please follow the link and comment on their blog: Thinking inside the box .

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)