public void TestImplicitTrust()
{
var socialReguls = new float[] { 1F };
var numFactors = new uint[] { 5, 10 };
var trustScores = new string[] { "trust_values_LATHIA.dat", "trust_values_HWANGCHEN.dat",
"trust_values_ODONOVAN.dat", "trust_values_PEARSON.dat", "trust_values_SHAMBOURLU.dat" };
// step 1: dataset
var config = new CsvConfiguration();
config.Delimiter = " ";
var trainReader = new CsvReader <ItemRating>(Paths.EpinionTrain80, config, new ItemRatingMap());
var testReader = new CsvReader <ItemRating>(Paths.EpinionTest20, config, new ItemRatingMap());
var dataset = new Dataset <ItemRating>(trainReader, testReader);
foreach (string scoreFile in trustScores)
{
var relations = File.ReadAllLines(Paths.EpinionRelationsImplicit + scoreFile).ToCsvDictionary('\t')
.Select(i => new Relation()
{
UserId = i["UserId"],
ConnectedId = i["ConnectionId"],
ConnectionStrength = float.Parse(i["Strength"])
});
//.Where(r => r.ConnectionStrength > 1F);
string rmseValues = "", maeValues = "";
foreach (uint num in numFactors)
{
// step 2: recommender
var algorithm = new SocialMF();
algorithm.SocialRegularization = 1;
algorithm.NumFactors = num;
var recommender = new MediaLiteRatingPredictor(algorithm, relations);
// step3: evaluation
var context = new EvalutationContext <ItemRating>(recommender, dataset);
var ep = new EvaluationPipeline <ItemRating>(context);
ep.Evaluators.Add(new RMSE());
ep.Evaluators.Add(new MAE());
ep.Run();
rmseValues += context["RMSE"] + "\t";
maeValues += context["MAE"] + "\t";
}
Console.WriteLine(scoreFile + "\t" + rmseValues + "\t" + maeValues);
}
}
public void TestExplicitTrust()
{
//var socialReguls = new float[] { 0.1F, 0.2F, 0.5F, 0.8F, 1F, 1.5F, 2F, 3F, 5F};
//var numFactors = new uint[] {2, 5, 10, 15, 20};
var socialReguls = new float[] { 1 };
var numFactors = new uint[] { 5, 10 };
// step 1: dataset
var config = new CsvConfiguration();
config.Delimiter = " ";
var trainReader = new CsvReader <ItemRating>(Paths.EpinionTrain75, config, new ItemRatingMap());
var testReader = new CsvReader <ItemRating>(Paths.EpinionTest25, config, new ItemRatingMap());
var dataset = new Dataset <ItemRating>(trainReader, testReader);
var relations = File.ReadAllLines(Paths.EpinionRelations).ToCsvDictionary(' ')
.Select(i => new Relation()
{
UserId = i["UserId"], ConnectedId = i["ConnectionId"], DatasetId = 1
});
foreach (uint num in numFactors)
{
string rmseValues = "", maeValues = "";
foreach (float regul in socialReguls)
{
// step 2: recommender
var algorithm = new MatrixFactorization();
algorithm.NumFactors = num;
//algorithm.SocialRegularization = regul;
var recommender = new MediaLiteRatingPredictor(algorithm, relations);
// step3: evaluation
var context = new EvalutationContext <ItemRating>(recommender, dataset);
var ep = new EvaluationPipeline <ItemRating>(context);
ep.Evaluators.Add(new RMSE());
ep.Evaluators.Add(new MAE());
ep.Run();
rmseValues += context["RMSE"] + "\t";
maeValues += context["MAE"] + "\t";
}
Console.WriteLine(num + "\t" + rmseValues + "\t" + maeValues);
}
}
public void TrainAndTest()
{
// step 1: dataset
var dataContext = DataManager.GetDataContext();
var modelDataset = dataContext.Datasets.Where(d => d.Name.ToLower() == "epinion").Single();
var dataset = new RatingDataset(modelDataset, dataContext, new TrainTestSplitter(0.3));
// step 2: recommender
var recommender = new MediaLiteRatingPredictor(new BiasedMatrixFactorization());
// step3: evaluation
var ep = new EvaluationPipeline <Rating>(new EvalutationContext <Rating>(recommender, dataset));
//ep.Evaluators.Add(new RMSE());
ep.Run();
}
public void RunDemo()
{
// step 1: load dataset
var container = new CrowdRecDataContainer();
var reader = new CrowdRecDataReader(_entitesFile, _relationsFile);
reader.LoadData(container);
var dataset = new ItemRatingDataset(container, 0.3f);
// step 2: recommender
var recommender = new MediaLiteRatingPredictor(new BiasedMatrixFactorization());
// step 3: evaluations
var pipline = new EvaluationPipeline<ItemRating>(new EvalutationContext<ItemRating>(recommender, dataset));
pipline.Evaluators.Add(new RMSE());
pipline.Evaluators.Add(new MAE());
pipline.Run();
}
public void TestBooksWithMF()
{
// step 1: dataset
if (!File.Exists(_trainPath))
FileHelper.SplitLines(_booksPath, _trainPath, _testPath, 0.75, true, true);
var trainReader = new CsvReader<ItemRating>(_trainPath, new ItemRatingMap());
var testReader = new CsvReader<ItemRating>(_testPath, new ItemRatingMap());
var dataset = new Dataset<ItemRating>(trainReader, testReader);
// step 2: recommender
var recommender = new MediaLiteRatingPredictor(new BiasedMatrixFactorization());
// step3: evaluation
var ep = new EvaluationPipeline<ItemRating>(new EvalutationContext<ItemRating>(recommender, dataset));
ep.Evaluators.Add(new RMSE());
ep.Run();
}
public void RunDemo()
{
// step 1: load dataset
var container = new CrowdRecDataContainer();
var reader = new CrowdRecDataReader(_entitesFile, _relationsFile);
reader.LoadData(container);
var dataset = new ItemRatingDataset(container, 0.3f);
// step 2: recommender
var recommender = new MediaLiteRatingPredictor(new BiasedMatrixFactorization());
// step 3: evaluations
var pipline = new EvaluationPipeline <ItemRating>(new EvalutationContext <ItemRating>(recommender, dataset));
pipline.Evaluators.Add(new RMSE());
pipline.Evaluators.Add(new MAE());
pipline.Run();
}
public void TestBooksWithMF()
{
// step 1: dataset
if (!File.Exists(_trainPath))
{
FileHelper.SplitLines(_booksPath, _trainPath, _testPath, 0.75, true, true);
}
var trainReader = new CsvReader <ItemRating>(_trainPath, new ItemRatingMap());
var testReader = new CsvReader <ItemRating>(_testPath, new ItemRatingMap());
var dataset = new Dataset <ItemRating>(trainReader, testReader);
// step 2: recommender
var recommender = new MediaLiteRatingPredictor(new BiasedMatrixFactorization());
// step3: evaluation
var ep = new EvaluationPipeline <ItemRating>(new EvalutationContext <ItemRating>(recommender, dataset));
ep.Evaluators.Add(new RMSE());
ep.Run();
}
public void TestAmazonDatasetSingle()
{
// step 1: dataset
var config = new CsvConfiguration()
{
Delimiter = ",",
HasHeaderRecord = true
};
var trainReader = new CsvReader <ItemRating>(Paths.AmazonBooksTrain75, config, new ItemRatingMap());
var testReader = new CsvReader <ItemRating>(Paths.AmazonBooksTest25, config, new ItemRatingMap());
var dataset = new Dataset <ItemRating>(trainReader, testReader);
// step 2: recommender
var recommender = new MediaLiteRatingPredictor(new BiasedMatrixFactorization());
// step3: evaluation
var ep = new EvaluationPipeline <ItemRating>(new EvalutationContext <ItemRating>(recommender, dataset));
ep.Evaluators.Add(new RMSE());
ep.Evaluators.Add(new MAE());
ep.Run();
}
public void TestAmazonDatasetSingle()
{
// step 1: dataset
var config = new CsvConfiguration()
{
Delimiter = ",",
HasHeaderRecord = true
};
var trainReader = new CsvReader<ItemRating>(Paths.AmazonBooksTrain75, config, new ItemRatingMap());
var testReader = new CsvReader<ItemRating>(Paths.AmazonBooksTest25, config, new ItemRatingMap());
var dataset = new Dataset<ItemRating>(trainReader, testReader);
// step 2: recommender
var recommender = new MediaLiteRatingPredictor(new BiasedMatrixFactorization());
// step3: evaluation
var ep = new EvaluationPipeline<ItemRating>(new EvalutationContext<ItemRating>(recommender, dataset));
ep.Evaluators.Add(new RMSE());
ep.Evaluators.Add(new MAE());
ep.Run();
}
