mlpack command-line quickstart guide


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

This quickstart guide is also available for Python.

Installing mlpack

Installing the mlpack is straightforward and can be done with your system's package manager.

For instance, for Ubuntu or Debian the command is simply

sudo apt-get install mlpack-bin

On Fedora or Red Hat:

sudo dnf install mlpack

If you use a different distribution, mlpack may be packaged under a different name. And if it is not packaged, you can use a Docker image from Dockerhub:

docker run -it mlpack/mlpack /bin/bash

This Docker image has mlpack already built and installed.

If you prefer to build mlpack from scratch, see Building mlpack From Source.

Simple mlpack quickstart example

As a really simple example of how to use mlpack from the command-line, 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 your shell to run it.

# Get the dataset and unpack it.
gunzip covertype-small.labels.csv.gz
# Split the dataset; 70% into a training set and 30% into a test set.
# Each of these options has a shorthand single-character option but here we type
# it all out for clarity.
mlpack_preprocess_split \
--input_file \
--input_labels_file covertype-small.labels.csv \
--training_file covertype-small.train.csv \
--training_labels_file covertype-small.train.labels.csv \
--test_file covertype-small.test.csv \
--test_labels_file covertype-small.test.labels.csv \
--test_ratio 0.3 \
# Train a random forest.
mlpack_random_forest \
--training_file covertype-small.train.csv \
--labels_file covertype-small.train.labels.csv \
--num_trees 10 \
--minimum_leaf_size 3 \
--print_training_accuracy \
--output_model_file rf-model.bin \
# Now predict the labels of the test points and print the accuracy.
# Also, save the test set predictions to the file 'predictions.csv'.
mlpack_random_forest \
--input_model_file rf-model.bin \
--test_file covertype-small.test.csv \
--test_labels_file covertype-small.test.labels.csv \
--predictions_file predictions.csv \

We can see by looking at the output that we achieve reasonably good accuracy on the test dataset (80%+). The file predictions.csv could also be used by other tools; for instance, we can easily calculate the number of points that were predicted incorrectly:

$ diff -U 0 predictions.csv covertype-small.test.labels.csv | grep '^@@' | wc -l

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.

What else does mlpack implement?

The example above has only shown a little bit of the functionality of mlpack. Lots of other commands are available with different functionality. A full list of commands and full documentation for each can be found on the following page:

For more information on what mlpack does, see Next, let's go through another example for providing movie recommendations with mlpack.

Using mlpack for movie recommendations

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

You can copy-paste this code directly into the command line to run it.

gunzip ratings-only.csv.gz
gunzip movies.csv.gz
# Hold out 10% of the dataset into a test set so we can evaluate performance.
mlpack_preprocess_split \
--input_file ratings-only.csv \
--training_file ratings-train.csv \
--test_file ratings-test.csv \
--test_ratio 0.1 \
# Train the model. Change the rank to increase/decrease the complexity of the
# model.
mlpack_cf \
--training_file ratings-train.csv \
--test_file ratings-test.csv \
--rank 10 \
--algorithm RegSVD \
--output_model_file cf-model.bin \
# Now query the 5 top movies for user 1.
echo "1" > query.csv;
mlpack_cf \
--input_model_file cf-model.bin \
--query_file query.csv \
--recommendations 10 \
--output_file recommendations.csv \
# Get the names of the movies for user 1.
echo "Recommendations for user 1:"
for i in `seq 1 10`; do
item=`cat recommendations.csv | awk -F',' '{ print $'$i' }'`;
head -n $(($item + 2)) movies.csv | tail -1 | \
sed 's/^[^,]*,[^,]*,//' | \
sed 's/\(.*\),.*$/\1/' | sed 's/"//g';

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

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

Next steps with mlpack

Now that you have done some simple work with mlpack, you have seen how it can easily plug into a data science production workflow for the command line. A great thing to do next would be to look at more documentation for the mlpack command-line programs:

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++. To get started learning about mlpack in C++, the following resources might be helpful: