example_decisiontree_simple.cpp

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

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

Para executar o programa:

./example_decisiontree_simple

Retornando por consola:

Pds::DecisionTree::Counter: 2

╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.5             ║
║          Samples: 2000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 994     1       ║
║ Pred.[1]/Act.[*]: 6       999     ║
╠═══════════════════════════════════╣
║         Accuracy: 99.65 %         ║
║        Precision: 99.4  %         ║
║           Recall: 99.9  %         ║
╠═══════════════════════════════════╣
║           FScore: 99.65 %         ║
╚═══════════════════════════════════╝

╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.5             ║
║          Samples: 2000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 991     4       ║
║ Pred.[1]/Act.[*]: 9       996     ║
╠═══════════════════════════════════╣
║         Accuracy: 99.35 %         ║
║        Precision: 99.1  %         ║
║           Recall: 99.6  %         ║
╠═══════════════════════════════════╣
║           FScore: 99.35 %         ║
╚═══════════════════════════════════╝

example_decisiontree_simple_data.png

example_decisiontree_simple_training.png

example_decisiontree_simple_testing.png

example_decisiontree_simple_arbol.png

Código example_decisiontree_simple.cpp:

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
    
int main(void)
{
    Pds::ClassificationMetrics Metrics;
    Pds::Vector Yp;
    
    Pds::IterationConf Conf;
    Conf.Show=false; Conf.SetMinError(1e-07);    Conf.SetAlpha(0.1);
    
    // =========================================================================
    // Load training data
    unsigned int L=1000;
    Pds::Matrix X;
    Pds::Vector Y;
    Pds::DataSet::Classification::LoadDataBand(L,X,Y);
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DY(X,Y,"testando.m","example_decisiontree_simple_data.png");
    
    // =========================================================================
    // Creando un arbol
    Pds::DecisionTree::Counter=0;
    Pds::DecisionTree Arbol("k2means",Conf,X,Y,0.8,4);
    std::cout<<"Pds::DecisionTree::Counter: "<<Pds::DecisionTree::Counter<<std::endl;
    
    // Exportando arbol en archivo en formato dot.
    Arbol.ExportDotFile("arbol.dot");
    Pds::Ra::System("dot -Tpng -o example_decisiontree_simple_arbol.png arbol.dot");
        
    // Predict training data
    Yp=Arbol.Predict(X);
    
    // Metrics of training
    Metrics = Pds::ClassificationMetrics::Calculate(0.5,Yp,Y);
    Metrics.Print("\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_decisiontree_simple_training.png");
    
    // =========================================================================
    // Load testing data
    Pds::DataSet::Classification::LoadDataBand(L,X,Y); // Load new data
    
    // Predict testing data
    Yp=Arbol.Predict(X);
    
    // Metrics of testing
    Metrics = Pds::ClassificationMetrics::Calculate(0.5,Yp,Y);
    Metrics.Print("\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_decisiontree_simple_testing.png");
 
    return 0;
}