Skip to content
Switch branches/tags

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

Build Status


Development of rvw package started as R Vowpal Wabbit (Google Summer of Code 2018) project.

Vowpal Wabbit is an online machine learning system that is known for its speed and scalability and is widely used in research and industry.

This package aims to bring its functionality to R.


From Source

First, you have to install Vowpal Wabbit itself here.

Next, once the required library is installed, you can install the rvw package using remotes:

install.packages("remotes")  ## or devtools

or (in case you have the package sources) via a standard R CMD INSTALL ..

This installation from source currently works best on Linux; on macOS you have to locally compile using the R-compatible toolchain (and not the brew-based one as the Vowpal Wabbit documentation suggests).

There is one possible shortcut: you can use the Debian/Ubuntu package as our Docker container does: sudo apt-get install libvw-dev vowpal-wabbit libboost-program-options-dev.

Using Docker

We use Docker for the Travis CI tests, and also provide a container for deployment. Do

docker pull rvowpalwabbit/run                 ## one time 
docker run --rm -ti rvowpalwabbit/run bash    ## launch container

to start the container with rvw installed. See the Boettiger and Eddelbuettel RJournal paper for more on Docker for R, and the Rocker Project used here.

Getting Started




In this example we will try to predict age groups (based on number of abalone shell rings) from physical measurements. We will use Abalone Data Set from UCI Machine Learning Repository.

First we prepare our data:


aburl = ''
abnames = c('sex','length','diameter','height','weight.w','weight.s','weight.v','','rings')
abalone = read.table(aburl, header = F , sep = ',', col.names = abnames)
data_full <- abalone

# Split number of rings into groups with equal (as possible) number of observations
data_full$group <- bin_data(data_full$rings, bins=3, binType = "quantile")
group_lvls <- levels(data_full$group)
levels(data_full$group) <- c(1, 2, 3)

# Prepare indices to split data
ind_train <- sample(1:nrow(data_full), 0.8*nrow(data_full))
# Split data into train and test subsets
df_train <- data_full[ind_train,]
df_test <- data_full[-ind_train,]

Then we set up a Vowpal Wabbit model:

vwmodel <- vwsetup(option = "ect", num_classes = 3)
  • option = "ect" - we will use Error Correcting Tournament algorithm to train multiclass classification model;
  • num_classes = 3 - number of classes in our data;

Now we start training:

vwtrain(vwmodel, data = df_train,
        namespaces = list(NS1 = list("sex", "rings"),
                          NS2 = list("weight.w","weight.s","weight.v","", "diameter", "length", "height")),
        targets = "group"

And we get: average loss = 0.278060

  • namespaces - We will split our features into two namespaces NS1 and NS2;
  • targets = "group" - ground truth labels;

And finally compute predictions using trained model:

predict.vw(vwmodel, data = df_test)

Here we get: average loss = 0.221292

We can add more learning algorithms to our model. For example we want to use boosting algorithm with 100 "weak" learners. Then we will just add this option to our model and train again:

vwmodel <- add_option(vwmodel, option = "boosting", num_learners=100)

vwtrain(vwmodel, data = df_train,
        namespaces = list(NS1 = list("sex", "rings"),
                          NS2 = list("weight.w","weight.s","weight.v","", "diameter", "length", "height")),
        targets = "group"

We get: average loss = 0.229273

And compute predictions:

predict.vw(vwmodel, data = df_test)

Finally we get: average loss = 0.081340

In order to inspect parameters of our model we can simply print it:

	Vowpal Wabbit model
Learning algorithm:   sgd 
Working directory:   /var/folders/yx/6949djdd3yb4qsw7x_95wfjr0000gn/T//RtmpjO3DD1 
Model file:   /var/folders/yx/6949djdd3yb4qsw7x_95wfjr0000gn/T//RtmpjO3DD1/vw_1534253637_mdl.vw 
General parameters: 
	 random_seed :   0 
	 ring_size :  Not defined
	 holdout_off :   FALSE 
	 holdout_period :   10 
	 holdout_after :   0 
	 early_terminate :   3 
	 loss_function :   squared 
	 link :   identity 
	 quantile_tau :   0.5 
Feature parameters: 
	 bit_precision :   18 
	 quadratic :  Not defined
	 cubic :  Not defined
	 interactions :  Not defined
	 permutations :   FALSE 
	 leave_duplicate_interactions :   FALSE 
	 noconstant :   FALSE 
	 feature_limit :  Not defined
	 ngram :  Not defined
	 skips :  Not defined
	 hash :  Not defined
	 affix :  Not defined
	 spelling :  Not defined
Learning algorithms / Reductions: 
	 ect :
		 num_classes :   3 
	 boosting :
		 num_learners :   100 
		 gamma :   0.1 
		 alg :   BBM 
Optimization parameters: 
	 adaptive :   TRUE 
	 normalized :   TRUE 
	 invariant :   TRUE 
	 adax :   FALSE 
	 sparse_l2 :   0 
	 l1_state :   0 
	 l2_state :   1 
	 learning_rate :   0.5 
	 initial_pass_length :  Not defined
	 l1 :   0 
	 l2 :   0 
	 no_bias_regularization :  Not defined
	 feature_mask :  Not defined
	 decay_learning_rate :   1 
	 initial_t :   0 
	 power_t :   0.5 
	 initial_weight :   0 
	 random_weights :  Not defined
	 normal_weights :  Not defined
	 truncated_normal_weights :  Not defined
	 sparse_weights :   FALSE 
	 input_feature_regularizer :  Not defined
Model evaluation. Training: 
	 num_examples :   3341 
	 weighted_example_sum :   3341 
	 weighted_label_sum :   0 
	 avg_loss :   0.2292727 
	 total_feature :   33408 
Model evaluation. Testing: 
	 num_examples :   836 
	 weighted_example_sum :   836 
	 weighted_label_sum :   0 
	 avg_loss :   0.08133971 
	 total_feature :   8360