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 C++, Python, Julia, the command line, 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 mlpack.org/v1/mlpack
cd ${GOPATH}/src/mlpack.org/v1/mlpack
make install

Building the Go bindings from scratch is a little more in-depth, though. For information on that, follow the instructions in the main README.

🔗 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 (
  "mlpack.org/v1/mlpack"
  "fmt"
)
func main() {

  // Download dataset.
  mlpack.DownloadFile("https://www.mlpack.org/datasets/covertype-small.data.csv.gz",
                      "data.csv.gz")
  mlpack.DownloadFile("https://www.mlpack.org/datasets/covertype-small.labels.csv.gz",
                      "labels.csv.gz")

  // 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.

🔗 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 main.go to run it.

package main

import (
  "github.com/frictionlessdata/tableschema-go/csv"
  "mlpack.org/v1/mlpack"
  "gonum.org/v1/gonum/mat"
  "fmt"
)
func main() {

  // Download dataset.
  mlpack.DownloadFile("https://www.mlpack.org/datasets/ml-20m/ratings-only.csv.gz",
                      "ratings-only.csv.gz")
  mlpack.DownloadFile("https://www.mlpack.org/datasets/ml-20m/movies.csv.gz",
                      "movies.csv.gz")

  // 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 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

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. But the two examples above have only shown a little bit of the functionality of mlpack. Lots of other methods are available with different functionality. A full list of each of these methods and full documentation can be found on the following page:

mlpack Go binding documentation

You can also use GoDoc to explore the mlpack module and its functions; every function comes with comprehensive 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++. To get started learning about mlpack in C++, the C++ quickstart is a good resource to visit next.