mlpack/doc/quickstart/r.md

mlpack in R quickstart guide

This page describes how you can quickly get started using mlpack from R and gives a few examples of usage, and pointers to deeper documentation.

This quickstart guide is also available for C++, Python, Julia, the command line, and Go.

Installing mlpack

Installing the mlpack bindings for R is straightforward; you can just use CRAN:

install.packages('mlpack')

Building the R bindings from scratch is a little more in-depth, though. For information on that, follow the instructions in the installation guide.

Simple mlpack quickstart example

As a really simple example of how to use mlpack from R, let's do some simple classification on a subset of the standard machine learning covertype dataset. We'll first split the dataset into a training set and a testing set, then we'll train an mlpack random forest on the training data, and finally we'll print the accuracy of the random forest on the test dataset.

You can copy-paste this code directly into R to run it.

if(!requireNamespace("data.table", quietly = TRUE)) { install.packages("data.table") }
suppressMessages({
    library("mlpack")
    library("data.table")
})

# Load the dataset from an online URL.  Replace with 'covertype.csv.gz' if you
# want to use on the full dataset.
df <- fread("https://www.mlpack.org/datasets/covertype-small.csv.gz")

# Split the labels.
labels <- df[, .(label)]
dataset <- df[, label:=NULL]

# Split the dataset using mlpack.
prepdata <- preprocess_split(input = dataset,
                             input_labels = labels,
                             test_ratio = 0.3,
                             verbose = TRUE)

# Train a random forest.
output <- random_forest(training = prepdata$training,
                        labels = prepdata$training_labels,
                        print_training_accuracy = TRUE,
                        num_trees = 10,
                        minimum_leaf_size = 3,
                        verbose = TRUE)
rf_model <- output$output_model

# Predict the labels of the test points.
output <- random_forest(input_model = rf_model,
                        test = prepdata$test,
                        verbose = TRUE)

# Now print the accuracy.  The third return value ('probabilities'), which we
# ignored here, could also be used to generate an ROC curve.
correct <- sum(output$predictions == prepdata$test_labels)
cat(correct, "out of", length(prepdata$test_labels), "test points correct",
    correct / length(prepdata$test_labels) * 100.0, "%\n")

We can see that we achieve reasonably good accuracy on the test dataset (80%+); if we use the full covertype.csv.gz, the accuracy should increase significantly (but training will take longer).

It's easy to modify the code above to do more complex things, or to use different mlpack learners, or to interface with other machine learning toolkits.

Using mlpack for movie recommendations

In this example, we'll train a collaborative filtering model using mlpack's cf() method. We'll train this on the MovieLens dataset, and then we'll use the model that we train to give recommendations.

You can copy-paste this code directly into R to run it.

if(!requireNamespace("data.table", quietly = TRUE)) { install.packages("data.table") }
suppressMessages({
    library("mlpack")
    library("data.table")
})

# First, load the MovieLens dataset.  This is taken from files.grouplens.org/
# but reposted on mlpack.org as unpacked and slightly preprocessed data.
ratings <- fread("http://www.mlpack.org/datasets/ml-20m/ratings-only.csv.gz")
movies <- fread("http://www.mlpack.org/datasets/ml-20m/movies.csv.gz")

# Hold out 10% of the dataset into a test set so we can evaluate performance.
predata <- preprocess_split(input = ratings,
                            test_ratio = 0.1,
                            verbose = TRUE)

# Train the model.  Change the rank to increase/decrease the complexity of the
# model.
output <- cf(training = predata$training,
             test = predata$test,
             rank = 10,
             verbose = TRUE,
             max_iteration=2,
             algorithm = "RegSVD")
cf_model <- output$output_model

# Now query the 5 top movies for user 1.
output <- cf(input_model = cf_model,
             query = matrix(1),
             recommendations = 10,
             verbose = TRUE)

# Get the names of the movies for user 1.
cat("Recommendations for user 1:\n")
for (i in 1:10) {
  cat("  ", i, ":", as.character(movies[output$output[i], 3]), "\n")
}

Here is some example output, showing that user 1 seems to have good taste in movies:

Recommendations for user 1:
  0: Casablanca (1942)
  1: Pan's Labyrinth (Laberinto del fauno, El) (2006)
  2: Godfather, The (1972)
  3: Answer This! (2010)
  4: Life Is Beautiful (La Vita è bella) (1997)
  5: Adventures of Tintin, The (2011)
  6: Dark Knight, The (2008)
  7: Out for Justice (1991)
  8: Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1964)
  9: Schindler's List (1993)

Next steps with mlpack

After working through this overview to mlpack's R package, we hope you are inspired to use mlpack' in your data science workflow. However, the two examples above have only shown a little bit of the functionality of mlpack. Lots of other functions are available with different functionality. A full list of each of these functions and full documentation can be found on the following page:

• R documentation

Also, mlpack is much more flexible from C++ and allows much greater functionality. So, more complicated tasks are possible if you are willing to write C++ (or perhaps Rcpp). To get started learning about mlpack in C++, a good starting point is the C++ quickstart guide.