Sample C++ ML App for Windows

by German Lancioni

This tutorial will help you create a sample machine learning app using mlpack/C++. Although this app does not cover all the mlpack capabilities, it will walkthrough several APIs to understand how everything connects. This Windows sample app is created using Visual Studio, but you can easily adapt it to a different platform by following the provided source code.

Note: before starting, make sure you have built mlpack for Windows following this Windows guide.

๐Ÿ”— Creating the Visual Studio project

๐Ÿ”— Project configuration

There are different ways in which you can configure your project to link with dependencies. This configuration is for x64 Debug Mode. If you need Release Mode, please change the paths accordingly (assuming you have built mlpack and dependencies in Release Mode).

Note: recent versions of Visual Studio set โ€œConformance Modeโ€ enabled by default. This causes some issues with the Armadillo library. If you encounter this issue, disable โ€œConformance Modeโ€ under C/C++ > Language.

Note: you may need to change the paths of the include directories or libraries above, given how you installed the dependencies.

Note: mlpack requires that the /std:c++17 option be set for the Visual Studio compiler. This is done by default in the provided example, but for your own projects, make sure that these options are set, otherwise compilation will fail.

๐Ÿ”— The Appโ€™s Goal

This app aims to exercise an end-to-end machine learning workflow. We will cover:

๐Ÿ”— Headers and namespaces

For this app, we will need to include the mlpack header (i.e. add into stdafx.h):

// Define these to print extra informational output and warnings.

#include "mlpack.hpp"

Also, we will use the following namespaces:

using namespace arma;
using namespace mlpack;
using namespace mlpack::tree;

๐Ÿ”— Loading the dataset

The first step is about loading the dataset. Different dataset file formats are supported, but here we load a CSV dataset, and we assume the labels donโ€™t require normalization.

Note: make sure you update the path to your dataset file. For this sample, you can simply copy mlpack/tests/data/german.csv and paste into a new data folder in your project directory.

mat dataset;
bool loaded = mlpack::data::Load("data/german.csv", dataset);
if (!loaded)
  return -1;

Then we need to extract the labels from the last dimension of the dataset and remove the labels from the training set:

Row<size_t> labels;
labels = conv_to<Row<size_t>>::from(dataset.row(dataset.n_rows - 1));
dataset.shed_row(dataset.n_rows - 1);

We now have our dataset ready for training.

๐Ÿ”— Training

This app will use a Random Forest classifier. At first we define the classifier parameters and then we create the classifier to train it.

const size_t numClasses = 2;
const size_t minimumLeafSize = 5;
const size_t numTrees = 10;

RandomForest<GiniGain, RandomDimensionSelect> rf;

rf = RandomForest<GiniGain, RandomDimensionSelect>(dataset, labels,
    numClasses, numTrees, minimumLeafSize);

Now that the training is completed, we quickly compute the training accuracy:

Row<size_t> predictions;
rf.Classify(dataset, predictions);
const size_t correct = arma::accu(predictions == labels);
cout << "\nTraining Accuracy: " << (double(correct) / double(labels.n_elem));

๐Ÿ”— Cross-validating

Instead of training the Random Forest directly, we could also use K-fold cross-validation for training, which will give us a measure of performance on a held-out test set. This can give us a better estimate of how the model will perform when given new data. We also define which metric to use in order to assess the quality of the trained model.

const size_t k = 10;
KFoldCV<RandomForest<GiniGain, RandomDimensionSelect>, Accuracy> cv(k, 
    dataset, labels, numClasses);
double cvAcc = cv.Evaluate(numTrees, minimumLeafSize);
cout << "\nKFoldCV Accuracy: " << cvAcc;

To compute other relevant metrics, such as Precision, Recall and F1:

double cvPrecision = Precision<Binary>::Evaluate(rf, dataset, labels);
cout << "\nPrecision: " << cvPrecision;

double cvRecall = Recall<Binary>::Evaluate(rf, dataset, labels);
cout << "\nRecall: " << cvRecall;

double cvF1 = F1<Binary>::Evaluate(rf, dataset, labels);
cout << "\nF1: " << cvF1;

๐Ÿ”— Saving the model

Now that our model is trained and validated, we save it to a file so we can use it later. Here we save the model that was trained using the entire dataset. Alternatively, we could extract the model from the cross-validation stage by using cv.Model().

mlpack::data::Save("mymodel.xml", "model", rf, false);

We can also save the model in bin format ("mymodel.bin") which would result in a smaller file.

๐Ÿ”— Loading the model

In a real-life application, you may want to load a previously trained model to classify new samples. We load the model from a file using:

mlpack::data::Load("mymodel.xml", "model", rf);

๐Ÿ”— Classifying a new sample

Finally, the ultimate goal is to classify a new sample using the previously trained model. Since the Random Forest classifier provides both predictions and probabilities, we obtain both.

// Create a test sample containing only one point.  Because Armadillo is
// column-major, this matrix has one column (one point) and the number of rows
// is equal to the dimensionality of the point (23).
mat sample("2; 12; 2; 13; 1; 2; 2; 1; 3; 24; 3; 1; 1; 1; 1; 1; 0; 1; 0; 1;"
    " 0; 0; 0");
mat probabilities;
rf.Classify(sample, predictions, probabilities);
u64 result =;
cout << "\nClassification result: " << result << " , Probabilities: " << << "/" <<;

๐Ÿ”— Final thoughts

Building real-life applications and services using machine learning can be challenging. Hopefully, this tutorial provides a good starting point that covers the basic workflow you may need to follow while developing it. You can take a look at the entire source code in the provided sample project located here: doc/examples/sample-ml-app.