example_decisiontree.cpp

Programa para el testeo de las funciones.Para compilar o código example_decisiontree.cpp:

g++ -static -o example_decisiontree example_decisiontree.cpp -lpdsmlmm -lpdsramm -lpdsspmm

Para executar o programa:

./example_decisiontree

Retornando por consola:

Pds::DecisionTree::Counter: 43

Training metrics:
╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.5             ║
║          Samples: 2000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 980     22      ║
║ Pred.[1]/Act.[*]: 20      978     ║
╠═══════════════════════════════════╣
║         Accuracy: 97.9  %         ║
║        Precision: 98    %         ║
║           Recall: 97.8  %         ║
╠═══════════════════════════════════╣
║           FScore: 97.9  %         ║
╚═══════════════════════════════════╝

Testing metrics:
╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.5             ║
║          Samples: 2000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 970     35      ║
║ Pred.[1]/Act.[*]: 30      965     ║
╠═══════════════════════════════════╣
║         Accuracy: 96.75 %         ║
║        Precision: 96.98 %         ║
║           Recall: 96.5  %         ║
╠═══════════════════════════════════╣
║           FScore: 96.74 %         ║
╚═══════════════════════════════════╝

example_decisiontree_data.png

example_decisiontree_training.png

example_decisiontree_testing.png

Código example_decisiontree.cpp:

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
    
int main(void)
{
    Pds::Octave::Colormap="jet";
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    
    Pds::ClassificationMetrics Metrics;
    
    Pds::IterationConf Conf;
    Conf.Show=false; Conf.SetMinError(1e-07);    Conf.SetAlpha(0.1);
    
    Pds::Vector Yp;
    
    // =========================================================================
    // Load training data
    Pds::Matrix X(Pds::Ra::TextFormat,"../test/dataset/data_x_yinyang.txt");
    Pds::Vector Y(Pds::Ra::TextFormat,"../test/dataset/data_y_yinyang.txt");
    //Pds::Matrix X(Pds::Ra::TextFormat,"../test/dataset/data_hard14_x.txt");
    //Pds::Vector Y(Pds::Ra::TextFormat,"../test/dataset/data_hard14_y.txt");
    
    Pds::Octave::Plot::PointsX2DY(X,Y,"testando.m","example_decisiontree_data.png");
    
    // =========================================================================
    // Creando un arbol
    Pds::DecisionTree::Counter=0;
    Pds::DecisionTree Arbol("k2means",Conf,X,Y,0.99,2);
    std::cout<<"Pds::DecisionTree::Counter: "<<Pds::DecisionTree::Counter<<std::endl;
 
    // Exportando arbol en archivo en formato dot.
    Arbol.ExportDotFile("arbol.dot");
    
    // Predict training data
    Yp=Arbol.Predict(X);
    
    // Metrics of training
    Metrics = Pds::ClassificationMetrics::Calculate(0.5,Yp,Y);
    Metrics.Print("\nTraining metrics:\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_decisiontree_training.png");
    
    // =========================================================================
    // Load testing data
    Pds::Matrix Xtt(Pds::Ra::TextFormat,"../test/dataset/data_x_yinyang_test.txt");
    Pds::Vector Ytt(Pds::Ra::TextFormat,"../test/dataset/data_y_yinyang_test.txt");
    //Pds::Matrix Xtt(Pds::Ra::TextFormat,"../test/dataset/data_hard14_x.txt");
    //Pds::Vector Ytt(Pds::Ra::TextFormat,"../test/dataset/data_hard14_y.txt");
    
    // Predict testing data
    Yp=Arbol.Predict(Xtt);
    
    // Metrics of testing
    Metrics = Pds::ClassificationMetrics::Calculate(0.5,Yp,Ytt);
    Metrics.Print("\nTesting metrics:\n");
    
    Pds::Octave::Plot::PointsX2DY(Xtt,Yp,"testando.m","example_decisiontree_testing.png");
 
    return 0;
}