regressionTutorial.cpp

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#include <shark/Data/Csv.h>
#include <shark/Algorithms/GradientDescent/CG.h>
#include <shark/ObjectiveFunctions/ErrorFunction.h>
#include <shark/ObjectiveFunctions/Loss/SquaredLoss.h>
#include <shark/Models/LinearModel.h>
 
#include <iostream>
 
using namespace shark;
using namespace std;
 
 
//loads a pair of files
RegressionDataset loadData(const std::string& dataFile,const std::string& labelFile){
    //we first load two separate data files for the training inputs and the labels of the data point
    Data<RealVector> inputs;
    Data<RealVector> labels;
    try {
        importCSV(inputs, dataFile, ' ');
        importCSV(labels, labelFile, ' ');
    } catch (...) {
        cerr << "Unable to open file " <<  dataFile << " and/or " << labelFile << ". Check paths!" << endl;
        exit(EXIT_FAILURE);
    }
    //now we create a complete dataset which represents pairs of inputs and labels
    RegressionDataset data(inputs, labels);
    return data;
}
 
int main(){
    //load some data set and split a test set from the dataset. The first 80% of data points are training data.
    RegressionDataset data = loadData("data/regressionInputs.csv","data/regressionLabels.csv");
    RegressionDataset test = splitAtElement(data,static_cast<std::size_t>(0.8*data.numberOfElements()));
    
    //a linear model with as many in and outputs as the data has
    LinearModel<> model(inputDimension(data), labelDimension(data));
    
    //the squared loss can be used to calculate the mean squared error of the data and the model
    //the ErrorFunction brings model, loss and data together and so automates evaluation
    SquaredLoss<> loss;
    ErrorFunction<> errorFunction(data, &model,&loss);
 
    CG<> optimizer;
    errorFunction.init();
    optimizer.init(errorFunction);
    for(int i = 0; i != 100; ++i)
    {
        optimizer.step(errorFunction);
    }
    //copy solution parameters into model
    model.setParameterVector(optimizer.solution().point);
    
    //save training error
    double trainingError = optimizer.solution().value;
 
    //evaluate test error
    Data<RealVector> predictions = model(test.inputs());
    double testError = loss.eval(test.labels(),predictions);
    
    //print the results
    cout << "RESULTS: " << endl;
    cout << "======== \n" << endl;
    cout << "training error " << trainingError << endl;
    cout << "test error: " << testError << endl;
}