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
data.frameconverter covering different variations of Vowpal Wabbit input formats
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)
libvwand so initially we offer a Docker container in order to ship the most up to date package with everything needed.