KNNCrossValidationTutorial.cpp

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//===========================================================================
/*!
 * 
 *
 * \brief       Nearest Neighbor Tutorial Sample Code
 * 
 * 
 * 
 *
 * \author      C. Igel
 * \date        2011
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/Shark/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
//===========================================================================
 
#include <shark/Data/Csv.h>
#include <shark/Models/NearestNeighborModel.h>
#include <shark/Algorithms/NearestNeighbors/SimpleNearestNeighbors.h>
#include <shark/Models/Kernels/LinearKernel.h>
#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h>
#include <shark/Data/CVDatasetTools.h>
#include <iostream>
 
using namespace shark;
using namespace std;
 
int main(int argc, char **argv) {
    if(argc < 2) {
        cerr << "usage: " << argv[0] << " (filename)" << endl;
        exit(EXIT_FAILURE);
    }
    // read data
    ClassificationDataset data;
    try {
        importCSV(data, argv[1], LAST_COLUMN, ' ');
    } 
    catch (...) {
        cerr << "unable to read data from file " <<  argv[1] << endl;
        exit(EXIT_FAILURE);
    }
 
    cout << "number of data points: " << data.numberOfElements()
         << " number of classes: " << numberOfClasses(data)
         << " input dimension: " << inputDimension(data) << endl;
 
    // split data into training and test set
    ClassificationDataset dataTest = splitAtElement(data, .5 * data.numberOfElements());
    cout << "training data points: " << data.numberOfElements() << endl;
    cout << "test data points: " << dataTest.numberOfElements() << endl;
 
    //create 10 CV-Folds
    const unsigned int NFolds= 10;
    CVFolds<ClassificationDataset> folds = createCVSameSizeBalanced(data, NFolds);
 
    //we have 5 different values of k to test
    unsigned int k[]={1,3,5,7,9};
    unsigned int numParameters = 5;
 
    ZeroOneLoss<unsigned int> loss;//loss for evaluation
    LinearKernel<> metric;//linear distance measure
 
    //find best #-neighbors using CV
    unsigned int best_k = 0;
    double best_error = 2;//maximum 0-1loss is 1
    //for every parameter....
    for(std::size_t p = 0; p != numParameters; ++p){
        double error = 0;
        //calculate CV-error
        for(std::size_t i = 0; i != NFolds; ++i){
            SimpleNearestNeighbors<RealVector, unsigned int> algorithm(folds.training(i), &metric);
            NearestNeighborModel<RealVector, unsigned int> KNN(&algorithm, k[p]);
            error += loss(folds.validation(i).labels(),KNN(folds.validation(i).inputs()));
        }
        error /=NFolds;
        //print cv-error for current parameter
        std::cout<<k[p]<<" "<<error<<std::endl;
        //if the error is better, we keep it.
        if(error < best_error){
            best_k = k[p];
            best_error = error;
        }
    }
    //evaluate the best paramter found on test set using the full training set
    SimpleNearestNeighbors<RealVector, unsigned int> algorithm(data, &metric);
    NearestNeighborModel<RealVector, unsigned int> KNN(&algorithm, best_k);
    std::cout<<"NearestNeighbors: " << loss(dataTest.labels(),KNN(dataTest.inputs()))<<'\n';
    std::cout<<"K: "<<best_k<<std::endl; 
}