// Commandline Argument: 0,3,4,5,6 5 16 C:\Users\dilet\Desktop\TwitterDB\RWR_EGO_RESULT.txt C:\Users\dilet\Desktop\TwitterDB\EgoNetwork_Analysis.txt C:\Users\dilet\Desktop\TwitterDB\EgoNetwork_Classification.txt
static void Main(string[] args)
{
// Select Columns for decision attributes
int[] columnNumberList = Array.ConvertAll<string, int>(args[0].Split(','), new Converter<string, int>(int.Parse));
SortedList columnList = new SortedList(); // <Column Nmber, Column Name>
for (int i = 0; i < columnNumberList.Length; i++)
{
int columnNumber = columnNumberList[i];
columnList.Add(columnNumber, candidateColumns[columnNumber]);
}
int nFold = int.Parse(args[1]);
int classLabelCount = int.Parse(args[2]);
string rwrResultFilePath = args[3];
string egoNetworkAnalysisFilePath = args[4];
string classificationResultFilePath = args[5];
if (File.Exists(classificationResultFilePath))
File.Delete(classificationResultFilePath);
// DataPreprocess: Split K-Fold DataSets
DataPreprocess dataPreprocess = new DataPreprocess(nFold);
dataPreprocess.dataSetConfiguration(columnList, rwrResultFilePath, egoNetworkAnalysisFilePath);
// K-Fold Cross Validation
double correctPredictRatio, sumOfCorrectPredictRatio = 0.0;
double learningError, sumOfLearningError = 0.0;
double sumOfMAP = 0.0;
for (int k = 0; k < nFold; k++)
{
// Train & Test DataSet
var dataSets = dataPreprocess.getTrainTestSet(k);
DataSet trainSet, testSet;
trainSet = (DataSet)dataSets.Item1;
testSet = (DataSet)dataSets.Item2;
// Decision Treee Configuration, Learning & Prediction
Classification classification = new Classification(columnList, classLabelCount);
learningError = classification.learnDecisionTreeModel(trainSet);
sumOfLearningError += learningError;
classification.prediction(testSet);
// Correct Recommender Prdicted Label Ratio
correctPredictRatio = testSet.validation();
sumOfCorrectPredictRatio += correctPredictRatio;
// Output Classification Result into File
testSet.logClassificationResult(classificationResultFilePath);
// Get MAP of TrainSet
sumOfMAP += trainSet.MAP();
}
double averageCorrectPredictRatio = sumOfCorrectPredictRatio / nFold;
double averageLearningError = sumOfLearningError / nFold;
double averageMAP = sumOfMAP / nFold;
Console.WriteLine("Average Learning Error: {0:F15}", averageLearningError);
Console.WriteLine("Correct Predict Ratio: {0:F15}", averageCorrectPredictRatio);
Console.WriteLine("Average MAP: {0:F15}", averageMAP);
}
// Commandline Argument: 0,3,4,5,6 5 16 C:\Users\dilet\Desktop\TwitterDB\RWR_EGO_RESULT.txt C:\Users\dilet\Desktop\TwitterDB\EgoNetwork_Analysis.txt C:\Users\dilet\Desktop\TwitterDB\EgoNetwork_Classification.txt
static void Main(string[] args)
{
// Select Columns for decision attributes
int[] columnNumberList = Array.ConvertAll <string, int>(args[0].Split(','), new Converter <string, int>(int.Parse));
SortedList columnList = new SortedList(); // <Column Nmber, Column Name>
for (int i = 0; i < columnNumberList.Length; i++)
{
int columnNumber = columnNumberList[i];
columnList.Add(columnNumber, candidateColumns[columnNumber]);
}
int nFold = int.Parse(args[1]);
int classLabelCount = int.Parse(args[2]);
string rwrResultFilePath = args[3];
string egoNetworkAnalysisFilePath = args[4];
string classificationResultFilePath = args[5];
if (File.Exists(classificationResultFilePath))
{
File.Delete(classificationResultFilePath);
}
// DataPreprocess: Split K-Fold DataSets
DataPreprocess dataPreprocess = new DataPreprocess(nFold);
dataPreprocess.dataSetConfiguration(columnList, rwrResultFilePath, egoNetworkAnalysisFilePath);
// K-Fold Cross Validation
double correctPredictRatio, sumOfCorrectPredictRatio = 0.0;
double learningError, sumOfLearningError = 0.0;
double sumOfMAP = 0.0;
for (int k = 0; k < nFold; k++)
{
// Train & Test DataSet
var dataSets = dataPreprocess.getTrainTestSet(k);
DataSet trainSet, testSet;
trainSet = (DataSet)dataSets.Item1;
testSet = (DataSet)dataSets.Item2;
// Decision Treee Configuration, Learning & Prediction
Classification classification = new Classification(columnList, classLabelCount);
learningError = classification.learnDecisionTreeModel(trainSet);
sumOfLearningError += learningError;
classification.prediction(testSet);
// Correct Recommender Prdicted Label Ratio
correctPredictRatio = testSet.validation();
sumOfCorrectPredictRatio += correctPredictRatio;
// Output Classification Result into File
testSet.logClassificationResult(classificationResultFilePath);
// Get MAP of TrainSet
sumOfMAP += trainSet.MAP();
}
double averageCorrectPredictRatio = sumOfCorrectPredictRatio / nFold;
double averageLearningError = sumOfLearningError / nFold;
double averageMAP = sumOfMAP / nFold;
Console.WriteLine("Average Learning Error: {0:F15}", averageLearningError);
Console.WriteLine("Correct Predict Ratio: {0:F15}", averageCorrectPredictRatio);
Console.WriteLine("Average MAP: {0:F15}", averageMAP);
}
// Commandline Argument: C:\Users\dilet\Desktop\TwitterDB C:\Users\dilet\Desktop\TwitterDB\EgoNetwork_Analysis.txt 8192 5 16
static void Main(string[] args)
{
string dirPath = args[0];
string[] rwrFileCollection = Directory.GetFiles(dirPath, "RWR_EGO_RESULT*.txt");
foreach (string rwrFilePath in rwrFileCollection)
{
// Experiment Environment Setting
string egoNetworkAnalysisFilePath = args[1];
int combinationCount = int.Parse(args[2]);
int padding = (int)Math.Ceiling(Math.Log(combinationCount, 2.0));
int nFold = int.Parse(args[3]);
int classCount = int.Parse(args[4]);
string classificationFilePath = dirPath + Path.DirectorySeparatorChar + Path.GetFileNameWithoutExtension(rwrFilePath) + "_CLASSIFICATION.txt";
Console.WriteLine(classificationFilePath);
if (File.Exists(classificationFilePath))
File.Delete(classificationFilePath);
using (StreamWriter classificationLogger = new StreamWriter(classificationFilePath))
{
// Experiment Argument Setting
for (int combination = 0; combination < combinationCount; combination++)
{
string s = Convert.ToString(combination, 2);
int[] combinationBitArray = s.PadLeft(padding, '0') // Add 0's from left
.Select(c => int.Parse(c.ToString())) // convert each char to int
.ToArray(); // Convert IEnumerable from select to Array
SortedList columnList = new SortedList();
for (int i = 0; i < combinationBitArray.Length; i++)
{
if (combinationBitArray[i] == 1)
columnList.Add(i, candidateColumns[i]);
}
// DataPreprocess: Split K-Fold DataSets
DataPreprocess dataPreprocess = new DataPreprocess(nFold);
dataPreprocess.dataSetConfiguration(columnList, rwrFilePath, egoNetworkAnalysisFilePath);
// K-Fold Cross Validation
double correctPredictRatio = 0.0, sumOfCorrectPredictRatio = 0.0;
double learningError = 0.0, sumOfLearningError = 0.0;
double sumOfMAP = 0.0;
for (int k = 0; k < nFold; k++)
{
// Train & Test DataSet
var dataSets = dataPreprocess.getTrainTestSet(k);
DataSet trainSet, testSet;
trainSet = (DataSet)dataSets.Item1;
testSet = (DataSet)dataSets.Item2;
// Decision Treee Configuration, Learning & Prediction
Classification classification = new Classification(columnList, classCount);
learningError = classification.learnDecisionTreeModel(trainSet);
sumOfLearningError += learningError;
classification.prediction(testSet);
// Correct Recommender Prdicted Label Ratio
correctPredictRatio = testSet.validation();
sumOfCorrectPredictRatio += correctPredictRatio;
// Get MAP of TrainSet
sumOfMAP += trainSet.MAP();
}
double averageMAP = sumOfMAP / nFold;
double averageCorrectPredictRatio = sumOfCorrectPredictRatio / nFold;
double averageLearning = sumOfLearningError / nFold;
classificationLogger.WriteLine("{0}\t{1:F15}\t{2:F15}\t{3:F15}",
combination, averageMAP, averageCorrectPredictRatio, 1.0 - averageLearning);
}
}
}
}
// Commandline Argument: C:\Users\dilet\Desktop\TwitterDB C:\Users\dilet\Desktop\TwitterDB\EgoNetwork_Analysis.txt 8192 5 16
static void Main(string[] args)
{
string dirPath = args[0];
string[] rwrFileCollection = Directory.GetFiles(dirPath, "RWR_EGO_RESULT*.txt");
foreach (string rwrFilePath in rwrFileCollection)
{
// Experiment Environment Setting
string egoNetworkAnalysisFilePath = args[1];
int combinationCount = int.Parse(args[2]);
int padding = (int)Math.Ceiling(Math.Log(combinationCount, 2.0));
int nFold = int.Parse(args[3]);
int classCount = int.Parse(args[4]);
string classificationFilePath = dirPath + Path.DirectorySeparatorChar + Path.GetFileNameWithoutExtension(rwrFilePath) + "_CLASSIFICATION.txt";
Console.WriteLine(classificationFilePath);
if (File.Exists(classificationFilePath))
{
File.Delete(classificationFilePath);
}
using (StreamWriter classificationLogger = new StreamWriter(classificationFilePath))
{
// Experiment Argument Setting
for (int combination = 0; combination < combinationCount; combination++)
{
string s = Convert.ToString(combination, 2);
int[] combinationBitArray = s.PadLeft(padding, '0') // Add 0's from left
.Select(c => int.Parse(c.ToString())) // convert each char to int
.ToArray(); // Convert IEnumerable from select to Array
SortedList columnList = new SortedList();
for (int i = 0; i < combinationBitArray.Length; i++)
{
if (combinationBitArray[i] == 1)
{
columnList.Add(i, candidateColumns[i]);
}
}
// DataPreprocess: Split K-Fold DataSets
DataPreprocess dataPreprocess = new DataPreprocess(nFold);
dataPreprocess.dataSetConfiguration(columnList, rwrFilePath, egoNetworkAnalysisFilePath);
// K-Fold Cross Validation
double correctPredictRatio = 0.0, sumOfCorrectPredictRatio = 0.0;
double learningError = 0.0, sumOfLearningError = 0.0;
double sumOfMAP = 0.0;
for (int k = 0; k < nFold; k++)
{
// Train & Test DataSet
var dataSets = dataPreprocess.getTrainTestSet(k);
DataSet trainSet, testSet;
trainSet = (DataSet)dataSets.Item1;
testSet = (DataSet)dataSets.Item2;
// Decision Treee Configuration, Learning & Prediction
Classification classification = new Classification(columnList, classCount);
learningError = classification.learnDecisionTreeModel(trainSet);
sumOfLearningError += learningError;
classification.prediction(testSet);
// Correct Recommender Prdicted Label Ratio
correctPredictRatio = testSet.validation();
sumOfCorrectPredictRatio += correctPredictRatio;
// Get MAP of TrainSet
sumOfMAP += trainSet.MAP();
}
double averageMAP = sumOfMAP / nFold;
double averageCorrectPredictRatio = sumOfCorrectPredictRatio / nFold;
double averageLearning = sumOfLearningError / nFold;
classificationLogger.WriteLine("{0}\t{1:F15}\t{2:F15}\t{3:F15}",
combination, averageMAP, averageCorrectPredictRatio, 1.0 - averageLearning);
}
}
}
}