public static double MeanAverageError(Matrix <int> ratingsMatrix, Matrix <float> weightsMatrix, RatingPredictor ratingPredictor)
{
int ratings = 0;
double totalErrors = 0;
double mae = 0;
int predictedRatings = 0;
int totalRatings = 0;
foreach (int user in ratingsMatrix.Rows)
{
List <int> userRatedItems = ratingsMatrix[user].ToList <int>();
foreach (int item in userRatedItems)
{
int originalRating = ratingsMatrix[user, item];
ratingsMatrix.remove(user, item);
totalRatings++;
double predictedRating = Math.Round(ratingPredictor.PredictRating(ratingsMatrix, weightsMatrix, user, item, true));
if (predictedRating > 0)
{
if (predictedRating > 5)
{
predictedRating = 5;
}
else if (predictedRating < 1)
{
predictedRating = 1;
}
ratings++;
predictedRatings++;
totalErrors += Math.Abs(predictedRating - originalRating);
}
ratingsMatrix[user, item] = originalRating;
}
}
mae = totalErrors / ratings;
//double coverage = (double)predictedRatings / totalRatings;
//Console.WriteLine($"Coverage: {coverage}");
return(mae);
}
static void Main(string[] args)
{
//while (true)
//{
//Console.WriteLine("Choose your trust propagation metric");
//Console.WriteLine("1 - Linear Trust Propagation");
//Console.WriteLine("2 - Resource Allocation");
//int metricInput = Convert.ToInt32(Console.ReadLine());
//Console.WriteLine("Choose the propagation distance");
//int propagationDistance = Convert.ToInt32(Console.ReadLine());
//Console.WriteLine("Choose top N for trust propagation");
//int topN = Convert.ToInt32(Console.ReadLine());
//Console.WriteLine("Choose top K ecommendations");
//int topK = Convert.ToInt32(Console.ReadLine());
//EMetric metric = (EMetric)metricInput;
//Console.WriteLine("New Execution!");
//Console.WriteLine($"Metric: {metric.ToString()}");
//Console.WriteLine($"TopN: { topN }");
//Console.WriteLine($"TopK: {topK}");
//Console.WriteLine($"Propagation Distance: {propagationDistance}");
//Matrix<float> originalTrustMatrix = new Matrix<float>(Resources.trust_data_file);
//Console.WriteLine("Building estimated trust matrix...");
//Matrix<float> estimatedTrustMatrix = GetEstimatedTrustMatrix(originalTrustMatrix, metric, propagationDistance, topN);
//Console.WriteLine("Estimated trust matrix built");
//Matrix<int> ratingsMatrix = new Matrix<int>(Resources.rating_data_file);
//Matrix<int> maxRatings = GetMaxRatings(ratingsMatrix);
//maxRatings.WriteToFile("maxRatings");
//RatingPredictor ratingPredictor = new RatingPredictor();
//Dictionary<int, double> usersAverageRatings = GetUsersAverageRatings(ratingsMatrix, ratingPredictor);
//ratingPredictor.setUsersAverageRatings(usersAverageRatings);
//Stopwatch timer = new Stopwatch();
//timer.Start();
//Console.WriteLine("Running top k recommendation experiment...");
//TopKRecommendation evaluator = new TopKRecommendation(topK);
//evaluator.Evaluate(ratingsMatrix, estimatedTrustMatrix, maxRatings, ratingPredictor);
//timer.Stop();
//Console.WriteLine(timer.Elapsed);
//Console.WriteLine();
//Console.WriteLine($"Recall: {evaluator.Recall}");
//Console.WriteLine($"Precision: {evaluator.Precision}");
//Console.WriteLine();
for (int propagationDistance = 3; propagationDistance <= 3; propagationDistance++)
{
for (int metricInput = 2; metricInput <= 2; metricInput++)
{
for (int n = 1; n < topNValues.Length; n++)
{
for (int k = 0; k < topKValues.Length; k++)
{
int topN = topNValues[n];
int topK = topKValues[k];
//topN = 20;
//topK = 20;
//propagationDistance = 2;
//metricInput = 2;
EMetric metric = (EMetric)metricInput;
Console.WriteLine("New Execution!");
Console.WriteLine($"Metric: {metric.ToString()}");
Console.WriteLine($"TopN: { topN }");
Console.WriteLine($"TopK: {topK}");
Console.WriteLine($"Propagation Distance: {propagationDistance}");
Matrix <float> originalTrustMatrix = new Matrix <float>(Resources.trust_data_file);
Console.WriteLine("Building estimated trust matrix...");
Matrix <float> estimatedTrustMatrix = GetEstimatedTrustMatrix(originalTrustMatrix, metric, propagationDistance, topN);
Console.WriteLine("Estimated trust matrix built");
Matrix <int> originalRatingsMatrix = new Matrix <int>(Resources.rating_data_file);
Matrix <int> ratingsMatrix = new Matrix <int>(Resources.rating_data_file);
Matrix <int> maxRatings = GetMaxRatings(ratingsMatrix, estimatedTrustMatrix);
maxRatings.WriteToFile("maxRatings");
RatingPredictor ratingPredictor = new RatingPredictor();
Dictionary <int, double> usersAverageRatings = GetUsersAverageRatings(ratingsMatrix, ratingPredictor);
ratingPredictor.setUsersAverageRatings(usersAverageRatings);
Stopwatch timer = new Stopwatch();
timer.Start();
Console.WriteLine("Running top k recommendation experiment...");
TopKRecommendation evaluator = new TopKRecommendation(topK);
evaluator.Evaluate(ratingsMatrix, estimatedTrustMatrix, maxRatings, ratingPredictor);
timer.Stop();
Console.WriteLine(timer.Elapsed);
Console.WriteLine();
Console.WriteLine($"Recall: {evaluator.Recall}");
Console.WriteLine($"Precision: {evaluator.Precision}");
Console.WriteLine($"Predicted Ratings: { evaluator.RatingsPredicted}");
Console.WriteLine($"Rated items predicted: { evaluator.ItemsPredicted }");
Console.WriteLine($"Coverage: {evaluator.Coverage}");
//Console.WriteLine($"Mean Average Error: {Experiment.MeanAverageError(originalRatingsMatrix, estimatedTrustMatrix, ratingPredictor)}");
Console.WriteLine();
using (StreamWriter writer = new StreamWriter(Resources.results, true))
{
writer.WriteLine("-------------------------------------");
writer.WriteLine($"Metric: {metric.ToString()}");
writer.WriteLine($"TopN: { topN }");
writer.WriteLine($"TopK: {topK}");
writer.WriteLine($"Propagation Distance: {propagationDistance}");
writer.WriteLine();
writer.WriteLine($"Recall: {evaluator.Recall}");
writer.WriteLine($"Precision: {evaluator.Precision}");
writer.WriteLine($"Predicted Ratings: { evaluator.RatingsPredicted}");
writer.WriteLine($"Rated items predicted: { evaluator.ItemsPredicted }");
writer.WriteLine($"Coverage: {evaluator.Coverage}");
writer.WriteLine($"Execution time: {timer.Elapsed}");
writer.WriteLine("----------------------------------------");
writer.WriteLine();
}
}
}
}
}
//}
}
public static double MeanAbsoluteUserError(Matrix <int> ratingsMatrix, Matrix <float> weightsMatrix, RatingPredictor ratingPredictor)
{
double usersMAE = 0;
foreach (int user in ratingsMatrix.Rows)
{
double totalErrors = 0;
int ratings = 0;
List <int> userRatedItems = ratingsMatrix[user].ToList <int>();
foreach (int item in userRatedItems)
{
int originalRating = ratingsMatrix[user, item];
ratingsMatrix.remove(user, item);
double predictedRating = Math.Round(ratingPredictor.PredictRating(ratingsMatrix, weightsMatrix, user, item, true));
if (predictedRating > 0)
{
if (predictedRating > 5)
{
predictedRating = 5;
}
else if (predictedRating < 1)
{
predictedRating = 1;
}
ratings++;
totalErrors += Math.Abs(predictedRating - originalRating);
}
ratingsMatrix[user, item] = originalRating;
}
if (ratings > 0)
{
usersMAE += totalErrors / ratings;
}
}
double maue = usersMAE / ratingsMatrix.Rows.Count();
return(maue);
}
private static Dictionary <int, double> GetUsersAverageRatings(Matrix <int> ratingsMatrix, RatingPredictor ratingPredictor)
{
string averageRatingsFile = Resources.users_average_ratings;
Dictionary <int, double> averageRatings;
if (!File.Exists(averageRatingsFile))
{
averageRatings = ratingPredictor.CalculateUsersAverageRatings(ratingsMatrix);
SaveDictionary(averageRatings, averageRatingsFile);
}
else
{
averageRatings = ReadDictionary(averageRatingsFile);
}
return(averageRatings);
}