dlib C++ Library - multiclass_classification_ex.cpp

    // The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
    /*
        This is an example illustrating the use of the multiclass classification tools
        from the dlib C++ Library.  Specifically, this example will make points from
        three classes and show you how to train a multiclass classifier to recognize
        these three classes.

        The classes are as follows:
            - class 1: points very close to the origin
            - class 2: points on the circle of radius 10 around the origin
            - class 3: points that are on a circle of radius 4 but not around the origin at all
    */

    #include <dlib/svm_threaded.h>

    #include <iostream>
    #include <vector>

    #include <dlib/rand.h>

    using namespace std;
    using namespace dlib;

    // Our data will be 2-dimensional data. So declare an appropriate type to contain these points.
    typedef matrix<double,2,1> sample_type;

    // ----------------------------------------------------------------------------------------

    void generate_data (
        std::vector<sample_type>& samples,
        std::vector<double>& labels
    );
    /*!
        ensures
            - make some 3 class data as described above.
            - Create 60 points from class 1
            - Create 70 points from class 2
            - Create 80 points from class 3
    !*/

    // ----------------------------------------------------------------------------------------

    int main()
    {
        try
        {
            std::vector<sample_type> samples;
            std::vector<double> labels;

            // First, get our labeled set of training data
            generate_data(samples, labels);

            cout << "samples.size(): "<< samples.size() << endl;

            // The main object in this example program is the one_vs_one_trainer.  It is essentially
            // a container class for regular binary classifier trainer objects.  In particular, it
            // uses the any_trainer object to store any kind of trainer object that implements a
            // .train(samples,labels) function which returns some kind of learned decision function.
            // It uses these binary classifiers to construct a voting multiclass classifier.  If
            // there are N classes then it trains N*(N-1)/2 binary classifiers, one for each pair of
            // labels, which then vote on the label of a sample.
            //
            // In this example program we will work with a one_vs_one_trainer object which stores any
            // kind of trainer that uses our sample_type samples.
            typedef one_vs_one_trainer<any_trainer<sample_type> > ovo_trainer;

            // Finally, make the one_vs_one_trainer.
            ovo_trainer trainer;

            // Next, we will make two different binary classification trainer objects.  One
            // which uses kernel ridge regression and RBF kernels and another which uses a
            // support vector machine and polynomial kernels.  The particular details don't matter.
            // The point of this part of the example is that you can use any kind of trainer object
            // with the one_vs_one_trainer.
            typedef polynomial_kernel<sample_type> poly_kernel;
            typedef radial_basis_kernel<sample_type> rbf_kernel;

            // make the binary trainers and set some parameters
            krr_trainer<rbf_kernel> rbf_trainer;
            svm_nu_trainer<poly_kernel> poly_trainer;
            poly_trainer.set_kernel(poly_kernel(0.1, 1, 2));
            rbf_trainer.set_kernel(rbf_kernel(0.1));

            // Now tell the one_vs_one_trainer that, by default, it should use the rbf_trainer
            // to solve the individual binary classification subproblems.
            trainer.set_trainer(rbf_trainer);
            // We can also get more specific.  Here we tell the one_vs_one_trainer to use the
            // poly_trainer to solve the class 1 vs class 2 subproblem.  All the others will
            // still be solved with the rbf_trainer.
            trainer.set_trainer(poly_trainer, 1, 2);

            // Now let's do 5-fold cross-validation using the one_vs_one_trainer we just setup.
            // As an aside, always shuffle the order of the samples before doing cross validation.
            // For a discussion of why this is a good idea see the svm_ex.cpp example.
            randomize_samples(samples, labels);
            cout << "cross validation: \n" << cross_validate_multiclass_trainer(trainer, samples, labels, 5) << endl;
            // The output is shown below.  It is the confusion matrix which describes the results.  Each row
            // corresponds to a class of data and each column to a prediction.  Reading from top to bottom,
            // the rows correspond to the class labels if the labels have been listed in sorted order.  So the
            // top row corresponds to class 1, the middle row to class 2, and the bottom row to class 3.  The
            // columns are organized similarly, with the left most column showing how many samples were predicted
            // as members of class 1.
            //
            // So in the results below we can see that, for the class 1 samples, 60 of them were correctly predicted
            // to be members of class 1 and 0 were incorrectly classified.  Similarly, the other two classes of data
            // are perfectly classified.
            /*
                cross validation:
                60  0  0
                0 70  0
                0  0 80
            */

            // Next, if you wanted to obtain the decision rule learned by a one_vs_one_trainer you
            // would store it into a one_vs_one_decision_function.
            one_vs_one_decision_function<ovo_trainer> df = trainer.train(samples, labels);

            cout << "predicted label: "<< df(samples[0])  << ", true label: "<< labels[0] << endl;
            cout << "predicted label: "<< df(samples[90]) << ", true label: "<< labels[90] << endl;
            // The output is:
            /*
                predicted label: 2, true label: 2
                predicted label: 1, true label: 1
            */

            // If you want to save a one_vs_one_decision_function to disk, you can do
            // so.  However, you must declare what kind of decision functions it contains.
            one_vs_one_decision_function<ovo_trainer,
            decision_function<poly_kernel>,  // This is the output of the poly_trainer
            decision_function<rbf_kernel>    // This is the output of the rbf_trainer
            > df2, df3;

            // Put df into df2 and then save df2 to disk.  Note that we could have also said
            // df2 = trainer.train(samples, labels);  But doing it this way avoids retraining.
            df2 = df;
            serialize("df.dat") << df2;

            // load the function back in from disk and store it in df3.
            deserialize("df.dat") >> df3;

            // Test df3 to see that this worked.
            cout << endl;
            cout << "predicted label: "<< df3(samples[0])  << ", true label: "<< labels[0] << endl;
            cout << "predicted label: "<< df3(samples[90]) << ", true label: "<< labels[90] << endl;
            // Test df3 on the samples and labels and print the confusion matrix.
            cout << "test deserialized function: \n" << test_multiclass_decision_function(df3, samples, labels) << endl;

            // Finally, if you want to get the binary classifiers from inside a multiclass decision
            // function you can do it by calling get_binary_decision_functions() like so:
            one_vs_one_decision_function<ovo_trainer>::binary_function_table functs;
            functs = df.get_binary_decision_functions();
            cout << "number of binary decision functions in df: " << functs.size() << endl;
            // The functs object is a std::map which maps pairs of labels to binary decision
            // functions.  So we can access the individual decision functions like so:
            decision_function<poly_kernel> df_1_2 = any_cast<decision_function<poly_kernel> >(functs[make_unordered_pair(1,2)]);
            decision_function<rbf_kernel>  df_1_3 = any_cast<decision_function<rbf_kernel>  >(functs[make_unordered_pair(1,3)]);
            // df_1_2 contains the binary decision function that votes for class 1 vs. 2.
            // Similarly, df_1_3 contains the classifier that votes for 1 vs. 3.

            // Note that the multiclass decision function doesn't know what kind of binary
            // decision functions it contains.  So we have to use any_cast to explicitly cast
            // them back into the concrete type.  If you make a mistake and try to any_cast a
            // binary decision function into the wrong type of function any_cast will throw a
            // bad_any_cast exception.
        }
        catch (std::exception& e)
        {
            cout << "exception thrown!" << endl;
            cout << e.what() << endl;
        }
    }

    // ----------------------------------------------------------------------------------------

    void generate_data (
        std::vector<sample_type>& samples,
        std::vector<double>& labels
    )
    {
        const long num = 50;

        sample_type m;

        dlib::rand rnd;

        // make some samples near the origin
        double radius = 0.5;
        for (long i = 0; i < num+10; ++i)
        {
            double sign = 1;
            if (rnd.get_random_double() < 0.5)
                sign = -1;
            m(0) = 2*radius*rnd.get_random_double()-radius;
            m(1) = sign*sqrt(radius*radius - m(0)*m(0));

            // add this sample to our set of training samples
            samples.push_back(m);
            labels.push_back(1);
        }

        // make some samples in a circle around the origin but far away
        radius = 10.0;
        for (long i = 0; i < num+20; ++i)
        {
            double sign = 1;
            if (rnd.get_random_double() < 0.5)
                sign = -1;
            m(0) = 2*radius*rnd.get_random_double()-radius;
            m(1) = sign*sqrt(radius*radius - m(0)*m(0));

            // add this sample to our set of training samples
            samples.push_back(m);
            labels.push_back(2);
        }

        // make some samples in a circle around the point (25,25)
        radius = 4.0;
        for (long i = 0; i < num+30; ++i)
        {
            double sign = 1;
            if (rnd.get_random_double() < 0.5)
                sign = -1;
            m(0) = 2*radius*rnd.get_random_double()-radius;
            m(1) = sign*sqrt(radius*radius - m(0)*m(0));

            // translate this point away from the origin
            m(0) += 25;
            m(1) += 25;

            // add this sample to our set of training samples
            samples.push_back(m);
            labels.push_back(3);
        }
    }

    // ----------------------------------------------------------------------------------------