public static void SVDPlusPlusTest()
{
List <Rating> baseRatings = Tools.GetRatings(BaseRatingFile, " ");
List <Rating> testRatings = Tools.GetRatings(TestRatingFile, " ");
SVDPlusPlus model = new SVDPlusPlus(MaxUserId, MaxItemId);
model.TrySGD(baseRatings, testRatings, 100, 0.005, 0.02, 0.98);
}
public void TestDefaultBehaviorIsNoDecay()
{
var mf = new SVDPlusPlus()
{
LearnRate = 1.1f, NumIter = 10, Ratings = TestUtils.CreateRatings()
};
mf.Train();
Assert.AreEqual(1.1f, mf.current_learnrate);
}
public void TestCurrentLearnRate()
{
var mf = new SVDPlusPlus()
{
LearnRate = 1.1f, Ratings = TestUtils.CreateRatings()
};
mf.InitModel();
Assert.AreEqual(1.1f, mf.LearnRate);
Assert.AreEqual(1.1f, mf.current_learnrate);
}
public static void SVDPlusPlusTest()
{
List <Rating> baseRatings = Tools.GetRatings(BaseRatingFile);
List <Rating> testRatings = Tools.GetRatings(TestRatingFile);
Tools.UpdateIndexesToZeroBased(baseRatings);
Tools.UpdateIndexesToZeroBased(testRatings);
SVDPlusPlus model = new SVDPlusPlus(MaxUserId, MaxItemId);
model.TrySGD(baseRatings, testRatings, 100, 0.01, 0.01, 0.96);
}
public static void SVDPlusPlusTest(double testSize = 0.1)
{
List <Rating> ratings = Tools.GetRatings(DefaultRatingFile, "::");
Tools.UpdateIndexesToZeroBased(ratings);
var t = Tools.TrainTestSplit(ratings, testSize);
SVDPlusPlus model = new SVDPlusPlus(MaxUserId, MaxItemId);
model.TrySGD(t.Item1, t.Item2, 100);
}
public void TestDecay()
{
var svdpp = new SVDPlusPlus()
{
LearnRate = 1.0f, Decay = 0.5f,
NumIter = 1, Ratings = TestUtils.CreateRatings()
};
svdpp.Train();
Assert.AreEqual(0.5f, svdpp.current_learnrate);
svdpp.Iterate();
Assert.AreEqual(0.25f, svdpp.current_learnrate);
}
public void TestMatrixInit()
{
var svdpp = new SVDPlusPlus()
{
Ratings = TestUtils.CreateRatings()
};
svdpp.InitModel();
Assert.IsNotNull(svdpp.user_factors);
Assert.IsNotNull(svdpp.item_factors);
Assert.IsNotNull(svdpp.p);
Assert.IsNotNull(svdpp.y);
Assert.IsNotNull(svdpp.user_bias);
Assert.IsNotNull(svdpp.item_bias);
}
public void TestFoldIn()
{
var svdpp = new SVDPlusPlus()
{
Ratings = TestUtils.CreateRatings()
};
svdpp.Train();
var user_ratings = new List <Tuple <int, float> >();
user_ratings.Add(new Tuple <int, float>(0, 4.0f));
var candidate_items = new List <int> {
0, 1
}; // have a known and an unknown item
var results = svdpp.ScoreItems(user_ratings, candidate_items);
Assert.AreEqual(2, results.Count);
}
static void Main(string[] args)
{
String path_training_data = "user_ratedmovies.dat";
String path_user_data = "users.dat";
String path_atr_data = "movie_genres.dat";
String path_pred_data = "predict.dat";
var user_mapping = new Mapping();
var item_mapping = new Mapping();
//var user_mapping = new EntityMapping();
//var item_mapping = new EntityMapping();
//String path_training_data = "1.rec.train";
//String path_test_data = "1.rec.test";
//String path_training_data = "u1.base";
//String path_test_data = "u1.test";
//var training_data = ItemData.Read(path_training_data);
//var test_data = ItemData.Read(path_test_data);
var training_data = RatingData.Read(path_training_data, user_mapping, item_mapping);
var relevant_items = item_mapping.InternalIDs;
var atr_data = AttributeData.Read(path_atr_data, item_mapping);
var pred_data = RatingData.Read(path_pred_data, user_mapping, item_mapping);
//var user_data = ItemData.Read(path_user_data, user_mapping);
Console.WriteLine("I'm here");
//Console.WriteLine(String.Join(";", user_data.AllUsers));
//var test_data = RatingData.Read(path_test_data);
//var recommender = new ItemKNN();
//recommender.Feedback = training_data;
//recommender.Train();
//var recommender = new GSVDPlusPlus();
//var recommender = new MatrixFactorization();
//recommender.Ratings = training_data;
// recommender.MaxRating = 5;
// recommender.MinRating = 1;
//recommender.ItemAttributes = atr_data;
//recommender.NumFactors = 5;
//recommender.NumIter = 10;
//recommender.Train();
var recommender = new SVDPlusPlus();
recommender.Ratings = training_data;
//recommender.AdditionalFeedback = user_data;
Console.WriteLine("I'm here2");
Console.WriteLine(recommender.ToString());
//recommender.ItemAttributes = atr_data;
recommender.NumFactors = 10;
Console.WriteLine("I'm here3");
Console.WriteLine(recommender.ToString());
recommender.Train();
Console.WriteLine("I'm here4");
//var results = recommender.DoIterativeCrossValidation(recommdender, 10, 1, 1, true);
var results = recommender.DoCrossValidation(10, false, true);
////var results = recommender.Evaluate(test_data, training_data);
foreach (var key in results.Keys)
{
Console.WriteLine("{0}={1}", key, results[key]);
}
Console.WriteLine(results);
//recommender.Predict();
recommender.WritePredictions(pred_data, "test45.dat", user_mapping, item_mapping, "{0}|{1}|{2}");
Console.WriteLine(recommender.Predict(1, 1));
Console.ReadLine();
}
