static void Main(string[] args)
{
string errorMessage = "";
if (!File.Exists(DataSetPath))
{
errorMessage += $"Failed to find file ${DataSetPath} - please update variable ${nameof(DataSetPath)} or create that file.\n";
}
if (!File.Exists(TestSetPath))
{
errorMessage += $"Failed to find file ${TestSetPath} - please update variable ${nameof(TestSetPath)} or create that file.\n";
}
if (errorMessage != "")
{
Console.ForegroundColor = ConsoleColor.Red;
Console.WriteLine("Not all files available - not running!");
Console.WriteLine(errorMessage);
Console.ResetColor();
Console.WriteLine("Press any key to continue...");
Console.ReadKey();
return;
}
Random rnd = new Random();
Console.WriteLine("Reading training data...");
ParserResults trainingData = ParserUtils.ParseData(DataSetPath, convertContinuousValues: true);
Console.WriteLine("Validating training set");
DataSetCleaner.ValidateDataSet(trainingData.Attributes, trainingData.Values);
Console.WriteLine("Getting test set...");
ParserResults testData = ParserUtils.ParseData(TestSetPath, trainingData.Attributes, convertContinuousValues: true);
Console.WriteLine("Validating test set");
DataSetCleaner.ValidateDataSet(testData.Attributes, testData.Values);
const string svmTrainingPath = "svmtraining";
const string svmOutputPath = "output";
List <int> kernelsToRunIn = new List <int>()
{
0, 1, 2, 3
};
string originalTrainingFile = "originalTraining.txt";
LibSvmConverter.ConvertToLibSvm(trainingData.Values, "originalTraining.txt");
// Run all kernels in parallel
kernelsToRunIn.Select((kernel) =>
{
// Run all iterations in parallel
Enumerable.Range(0, TotalSamplesForBiasAndVariance).Select((i) =>
{
Console.WriteLine("Doing loop {0} for kernel {1}", i, kernel);
var postFix = string.Format("K{0}-I{1}.txt", kernel, i);
string trainingPath = svmTrainingPath + postFix;
string trainingModelPath = trainingPath + ".model";
string outputPath = svmOutputPath + postFix;
if (!File.Exists(trainingModelPath))
{
List <List <DataSetValue> > differentTrainingData = Bagging.ProduceDifferentDataSets(trainingData.Values, 1, rnd);
LibSvmConverter.ConvertToLibSvm(differentTrainingData.Single(), trainingPath);
RunTrainingExe(trainingPath, kernel);
}
if (!File.Exists(outputPath))
{
RunEvaluateExe(originalTrainingFile, trainingModelPath, outputPath);
}
return(0);
}).ToList();
return(0);
}).ToList();
// Evaluate bias and variance
foreach (var kernel in kernelsToRunIn)
{
List <List <bool> > allPredictions = new List <List <bool> >();
for (int i = 0; i < TotalSamplesForBiasAndVariance; i++)
{
Console.WriteLine("Evaluating loop {0} for kernel {1}", i, kernel);
var postFix = string.Format("K{0}-I{1}.txt", kernel, i);
string trainingPath = svmTrainingPath + postFix;
string outputPath = svmOutputPath + postFix;
if (!File.Exists(trainingPath) || !File.Exists(outputPath))
{
continue;
}
List <bool> predictions = GetPredictionsFromOutputPath(outputPath);
allPredictions.Add(predictions);
}
if (allPredictions.Count == 0)
{
Console.WriteLine("Not enough information to evaluate kernel {0}", kernel);
continue;
}
double bias;
double variance = BiasAndVarianceCalculator.CalculateBiasAndVariance(trainingData, allPredictions, out bias);
Console.WriteLine("Bias:{0:0.00000} Variance:{1:0.00000}", bias, variance);
}
Console.WriteLine("Press any key to quit...");
Console.ReadKey();
}
static void Main(string[] args)
{
string errorMessage = "";
if (!File.Exists(DataSetPath))
{
errorMessage += $"Failed to find file ${DataSetPath} - please update variable ${nameof(DataSetPath)} or create that file.\n";
}
if (!File.Exists(TestSetPath))
{
errorMessage += $"Failed to find file ${TestSetPath} - please update variable ${nameof(TestSetPath)} or create that file.\n";
}
if (errorMessage != "")
{
Console.ForegroundColor = ConsoleColor.Red;
Console.WriteLine("Not all files available - not running!");
Console.WriteLine(errorMessage);
Console.ResetColor();
Console.WriteLine("Press any key to continue...");
Console.ReadKey();
return;
}
var startTime = DateTime.Now;
Console.WriteLine(startTime);
Random rnd = new Random();
Console.WriteLine("Reading training data...");
ParserResults trainingData = ParserUtils.ParseData(DataSetPath, convertContinuousValues: true);
Console.WriteLine("Validating training set");
DataSetCleaner.ValidateDataSet(trainingData.Attributes, trainingData.Values);
Console.WriteLine("Getting test set...");
ParserResults testData = ParserUtils.ParseData(TestSetPath, trainingData.Attributes, convertContinuousValues: true);
Console.WriteLine("Validating test set");
DataSetCleaner.ValidateDataSet(testData.Attributes, testData.Values);
List <List <DataSetValue> > differentDataSets = Bagging.ProduceDifferentDataSets(trainingData.Values, TotalSamplesForBiasAndVariance, rnd);
List <IPredictor> naiveBayesPredictors = new List <IPredictor>();
foreach (var differentTrainingDataSet in differentDataSets)
{
var naiveBayesPredictor = TrainNaiveBayes(differentTrainingDataSet);
naiveBayesPredictors.Add(naiveBayesPredictor);
}
double bias;
double variance = BiasAndVarianceCalculator.CalculateBiasAndVariance(trainingData, naiveBayesPredictors, out bias);
Console.WriteLine($"Bias:{bias} Variance:{variance}");
var originalNaiveBayesPredictor = TrainNaiveBayes(trainingData.Values);
EvaluateNaiveBayesPredictor(testData, originalNaiveBayesPredictor);
var endTime = DateTime.Now;
Console.WriteLine(endTime);
var totalMinutes = (endTime - startTime).TotalMinutes;
Console.WriteLine("Took {0} minutes.", totalMinutes);
Console.WriteLine("Press any key to quit...");
Console.ReadKey();
}