mlpack  3.0.0
mlpack in Python quickstart guide


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

This quickstart guide is also available for the command-line.

Installing mlpack

(This section will be simplified when mlpack is available in PyPI or conda.)

Installing the mlpack bindings for Python is straightforward. First we have to install the dependencies (the code below is for Ubuntu), then we can build and install mlpack. You can copy-paste the commands into your shell.

sudo apt-get install libboost-all-dev g++ cmake libarmadillo-dev python-pip wget
sudo pip install cython setuptools distutils numpy pandas
tar -xvzpf mlpack-3.0.0.tar.gz
mkdir -p mlpack-3.0.0/build/ && cd mlpack-3.0.0/build/
cmake ../ && make -j4 && sudo make install

You can also use the mlpack Docker image on Dockerhub, which has all the Python bindings pre-installed:

docker run -it mlpack/mlpack /bin/bash

Simple mlpack quickstart example

As a really simple example of how to use mlpack from Python, 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 Python to run it.

import mlpack
import pandas as pd
import numpy as np
# Load the dataset from an online URL. Replace with 'covertype.csv.gz' if you
# want to use on the full dataset.
df = pd.read_csv('')
# Split the labels.
labels = df['label']
dataset = df.drop('label', 1)
# Split the dataset using mlpack. The output comes back as a dictionary,
# which we'll unpack for clarity of code.
output = mlpack.preprocess_split(input=dataset,
training_set = output['training']
training_labels = output['training_labels']
test_set = output['test']
test_labels = output['test_labels']
# Train a random forest.
output = mlpack.random_forest(training=training_set,
random_forest = output['output_model']
# Predict the labels of the test points.
output = mlpack.random_forest(input_model=random_forest,
# Now print the accuracy. The 'probabilities' output could also be used
# to generate an ROC curve.
correct = np.sum(output['predictions'] == test_labels)
print(str(correct) + ' correct out of ' + str(len(test_labels)) + ' (' +
str(100 * float(correct) / float(len(test_labels))) + '%).')

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.

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. Below is a list of all the mlpack functionality offered through Python, split into some categories.

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 cf() method. 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 Python to run it.

import mlpack
import pandas as pd
import numpy as np
# First, load the MovieLens dataset. This is taken from
# but reposted on as unpacked and slightly preprocessed data.
ratings = pd.read_csv('')
movies = pd.read_csv('')
# Hold out 10% of the dataset into a test set so we can evaluate performance.
output = mlpack.preprocess_split(input=ratings, test_ratio=0.1, verbose=True)
ratings_train = output['training']
ratings_test = output['test']
# Train the model. Change the rank to increase/decrease the complexity of the
# model.
output =,
cf_model = output['output_model']
# Now query the 5 top movies for user 1.
output =,
# Get the names of the movies for user 1.
print("Recommendations for user 1:")
for i in range(10):
print(" " + str(i) + ": " + str(movies.loc[movies['movieId'] ==
output['output'][0, i]].iloc[0]['title']))

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 รจ 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

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

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