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:
data.frameconverter covering different variations of Vowpal Wabbit input formats
Below is a simple example of how to use the renewed rvw’s interface:
library(rvw) 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.