mlpack in Go quickstart guide


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

This quickstart guide is also available for Python, the command-line, Julia and R.

Installing mlpack

Installing the mlpack bindings for Go is somewhat time-consuming as the library must be built; you can run the following code:

go get -u -d
cd ${GOPATH}/src/
make install

Building the Go bindings from scratch is a little more in-depth, though. For information on that, follow the instructions on the Building mlpack From Source page, and be sure to specify -DBUILD_GO_BINDINGS=ON to CMake;

Simple mlpack quickstart example

As a really simple example of how to use mlpack from Go, 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 main.go to run it.

package main
import (
func main() {
// Download dataset.
// Extract/Unzip the dataset.
mlpack.UnZip("data.csv.gz", "data.csv")
dataset, _ := mlpack.Load("data.csv")
mlpack.UnZip("labels.csv.gz", "labels.csv")
labels, _ := mlpack.Load("labels.csv")
// Split the dataset using mlpack.
params := mlpack.PreprocessSplitOptions()
params.InputLabels = labels
params.TestRatio = 0.3
params.Verbose = true
test, test_labels, train, train_labels :=
mlpack.PreprocessSplit(dataset, params)
// Train a random forest.
rf_params := mlpack.RandomForestOptions()
rf_params.NumTrees = 10
rf_params.MinimumLeafSize = 3
rf_params.PrintTrainingAccuracy = true
rf_params.Training = train
rf_params.Labels = train_labels
rf_params.Verbose = true
rf_model, _, _ := mlpack.RandomForest(rf_params)
// Predict the labels of the test points.
rf_params_2 := mlpack.RandomForestOptions()
rf_params_2.Test = test
rf_params_2.InputModel = &rf_model
rf_params_2.Verbose = true
_, predictions, _ := mlpack.RandomForest(rf_params_2)
// Now print the accuracy.
rows, _ := predictions.Dims()
var sum int = 0
for i := 0; i < rows; i++ {
if (predictions.At(i, 0) == test_labels.At(i, 0)) {
sum = sum + 1
fmt.Print(sum, " correct out of ", rows, " (",
(float64(sum) / float64(rows)) * 100, "%).\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.

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 each of these commands and full documentation can be found on the following page:

You can also use the GoDoc to explore the mlpack module and its functions; every function comes with comprehensive documentation.

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 main.go to run it.

package main
import (
func main() {
// Download dataset.
// Extract dataset.
mlpack.UnZip("ratings-only.csv.gz", "ratings-only.csv")
ratings, _ := mlpack.Load("ratings-only.csv")
mlpack.UnZip("movies.csv.gz", "movies.csv")
table, _ := csv.NewTable(csv.FromFile("movies.csv"), csv.LoadHeaders())
movies, _ := table.ReadColumn("title")
// Split the dataset using mlpack.
params := mlpack.PreprocessSplitOptions()
params.TestRatio = 0.1
params.Verbose = true
ratings_test, _, ratings_train, _ := mlpack.PreprocessSplit(ratings, params)
// Train the model. Change the rank to increase/decrease the complexity of the
// model.
cf_params := mlpack.CfOptions()
cf_params.Training = ratings_train
cf_params.Test = ratings_test
cf_params.Rank = 10
cf_params.Verbose = true
cf_params.Algorithm = "RegSVD"
_, cf_model := mlpack.Cf(cf_params)
// Now query the 5 top movies for user 1.
cf_params_2 := mlpack.CfOptions()
cf_params_2.InputModel = &cf_model
cf_params_2.Recommendations = 10
cf_params_2.Query = mat.NewDense(1, 1, []float64{1})
cf_params_2.Verbose = true
cf_params_2.MaxIterations = 10
output, _ := mlpack.Cf(cf_params_2)
// Get the names of the movies for user 1.
fmt.Println("Recommendations for user 1")
for i := 0; i < 10; i++ {
fmt.Println(i, ":", movies[int(output.At(0 , i))])

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 Go. A great thing to do next would be to look at more documentation for the Go 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++. To get started learning about mlpack in C++, the following resources might be helpful: