private static double Evaluate(List <int[]> instances, Id3Bagger bagger)
{
// Accuracy helpers
double totalExamples = instances.Count;
double correctAnswers = 0;
foreach (int[] instance in instances)
{
if (bagger.GetClass(instance) == instance[ClassIndex])
{
correctAnswers++;
}
}
return(correctAnswers / totalExamples);
}
static void Main(string[] args)
{
// Training
ArffHeader header = null;
List <object[]> instances = new List <object[]>();
using (ArffReader arffReader = new ArffReader(TrainingArffFile))
{
header = arffReader.ReadHeader();
object[] instance;
while ((instance = arffReader.ReadInstance()) != null)
{
instances.Add(instance);
}
}
List <int[]> trainingData = new List <int[]>(instances.Select(objectArray => objectArray.Select(o => o == null ? -1 : (int)o).ToArray()));
// Test
instances = new List <object[]>();
using (ArffReader arffReader = new ArffReader(TestArffFile))
{
header = arffReader.ReadHeader();
object[] instance;
while ((instance = arffReader.ReadInstance()) != null)
{
instances.Add(instance);
}
}
List <int[]> testData = new List <int[]>(instances.Select(objectArray => objectArray.Select(o => o == null ? -1 : (int)o).ToArray()));
Console.WriteLine("Number of Samples, training accuracy, test accuracy");
// Do this excercise multiple times as sampling is random.
Parallel.For(0, 100, (k) =>
{
// Dictionaries to store results for different accuracies. KEEP IN SYNC :)
ConcurrentDictionary <int, double> sampleTrainingAccuraciesMap = new ConcurrentDictionary <int, double>(new Dictionary <int, double>
{
{ 1, 0 },
{ 3, 0 },
{ 5, 0 },
{ 10, 0 },
{ 20, 0 },
{ 25, 0 },
{ 50, 0 },
{ 75, 0 },
{ 100, 0 }
});
ConcurrentDictionary <int, double> sampleTestAccuraciesMap = new ConcurrentDictionary <int, double>(new Dictionary <int, double>
{
{ 1, 0 },
{ 3, 0 },
{ 5, 0 },
{ 10, 0 },
{ 20, 0 },
{ 25, 0 },
{ 50, 0 },
{ 75, 0 },
{ 100, 0 }
});
// Calculate different sample accuracies in parallel.
Parallel.ForEach(sampleTrainingAccuraciesMap.Keys, numOfSamples =>
{
Id3Bagger bagger = new Id3Bagger(numOfSamples);
bagger.Train(trainingData, ClassIndex, Confidence);
// Evaluate training and test to look out for overfitting.
sampleTrainingAccuraciesMap[numOfSamples] = Evaluate(trainingData, bagger);
sampleTestAccuraciesMap[numOfSamples] = Evaluate(testData, bagger);
});
lock (_lockConsole)
{
foreach (int numOfSamples in sampleTestAccuraciesMap.Keys.OrderBy(n => n))
{
Console.WriteLine($"{numOfSamples},{sampleTrainingAccuraciesMap[numOfSamples]},{sampleTestAccuraciesMap[numOfSamples]}");
}
}
});
Console.WriteLine("Press ENTER to exit...");
Console.ReadLine();
}