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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.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)
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.
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