/// <summary>
/// Writes the results to .csv file
/// </summary>
/// <param name="testingSet">Testing set with given special coverages</param>
public void WriteResults(TestingSet testingSet)
{
StringBuilder csv = new StringBuilder(); //String builder for csv file with results
csv.AppendLine("id;specialCoverage"); //Header line
foreach (Article article in testingSet.articles.Values)
{
csv.AppendLine(string.Format("{0};{1}", article.id[0], article.specialCoverage[0]));
}
File.WriteAllText(@path, csv.ToString());
}
/// <summary>
/// Constructor that creates object with given training set and testing set
/// </summary>
/// <param name="trainingSet">Training set loaded from a file</param>
/// <param name="testingSet">Testing set loaded from a file</param>
public SVMClassifier(TrainingSet trainingSet, TestingSet testingSet)
{
this.trainingSet = trainingSet;
vocabulary = new HashSet <string>();
x = new List <string>();
y = new List <double>();
foreach (Article article in trainingSet.articles.Values) //load data from the training set
{
string features = ArticleFeatures(article);
//add features and special coverages to lists
AddFeaturesToVocabulary(features);
x.Add(features);
y.Add(article.specialCoverage[0]);
}
foreach (Article article in testingSet.articles.Values) //load articles with given specialCoverage from the testing set
{
if (article.specialCoverage != null)
{
string features = ArticleFeatures(article);
//add features and special coverages to lists
AddFeaturesToVocabulary(features);
x.Add(features);
y.Add(article.specialCoverage[0]);
}
}
//create new problem
ProblemBuilder problemBuilder = new ProblemBuilder();
var problem = problemBuilder.CreateProblem(x, y.ToArray(), vocabulary.ToList());
//create new model using linear kernel
const int C = 1; //C parameter for C_SVC
model = new C_SVC(problem, KernelHelper.LinearKernel(), C);
}
static void Main(string[] args)
{
trainingSet = new TrainingSet();
testingSet = new TestingSet();
System.Console.WriteLine("Data loaded");
System.Console.WriteLine("Training set: " + trainingSet.articles.Count);
System.Console.WriteLine("Testing set: " + testingSet.articles.Count);
sureClassifiers = new SureClassifiers(trainingSet, testingSet);
//1. Looking for previous articles
foreach (Article article in testingSet.articles.Values.OrderBy(key => key.id[0]))
{
if (article.specialCoverage == null) //if article doesn't have specialCoverage
{
int specialCoverage = sureClassifiers.PreviousArticle(article);
if (specialCoverage > 0) //if specialCoverage was given by the method
{
article.specialCoverage = new int[1];
article.specialCoverage[0] = specialCoverage;
}
}
}
//2. Looking for next articles
foreach (Article article in testingSet.articles.Values.OrderByDescending(key => key.id[0]))
{
if (article.specialCoverage == null) //if article doesn't have specialCoverage
{
int specialCoverage = sureClassifiers.NextArticle(article);
if (specialCoverage > 0) //if specialCoverage was given by the method
{
article.specialCoverage = new int[1];
article.specialCoverage[0] = specialCoverage;
}
}
}
//3. Looking for related articles
foreach (Article article in testingSet.articles.Values.OrderBy(key => key.id[0]))
{
if (article.specialCoverage == null) //if article doesn't have specialCoverage
{
int specialCoverage = sureClassifiers.RelatedArticles(article);
if (specialCoverage > 0) //if specialCoverage was given by the method
{
article.specialCoverage = new int[1];
article.specialCoverage[0] = specialCoverage;
}
}
}
// 4. Getting special coverage from a SVM classifier
svmClassifier = new SVMClassifier(trainingSet, testingSet);
foreach (Article article in testingSet.articles.Values)
{
if (article.specialCoverage == null) //if article doesn't have specialCoverage
{
article.specialCoverage = new int[1];
article.specialCoverage[0] = svmClassifier.Classify(article);
}
}
System.Console.WriteLine("Special coverages given");
//Write the results to .csv
ResultsWriter resultsWriter = new ResultsWriter();
resultsWriter.WriteResults(testingSet);
System.Console.WriteLine("Results written");
System.Console.ReadKey();
}
