public static void BiasedMatrixFactorizationTest()
{
List <Rating> baseRatings = Tools.GetRatings(BaseRatingFile, " ");
List <Rating> testRatings = Tools.GetRatings(TestRatingFile, " ");
BiasedMatrixFactorization model = new BiasedMatrixFactorization(MaxUserId, MaxItemId);
model.TrySGD(baseRatings, testRatings, 100, 0.005, 0.02, 0.98);
}
public static void BiasedMatrixFactorizationTest(double testSize = 0.1)
{
List <Rating> ratings = Tools.GetRatings(RatingFile, ",");
var max = Tools.GetMaxUserIdAndItemId(ratings);
var maxUserId = max.Item1 + 1;
var maxItemId = max.Item2 + 1;
BiasedMatrixFactorization model = new BiasedMatrixFactorization(maxUserId, maxItemId);
var t = Tools.TrainTestSplit(ratings, testSize);
model.TrySGD(t.Item1, t.Item2, 100, 0.005);
}
public static void BiasedMatrixFactorizationTest(double testSize = 0.1)
{
List <Rating> ratings = Tools.GetRatings(DefaultRatingFile, "::");
Tools.UpdateIndexesToZeroBased(ratings);
var t = Tools.TrainTestSplit(ratings, testSize);
BiasedMatrixFactorization model = new BiasedMatrixFactorization(MaxUserId, MaxItemId);
model.TrySGD(t.Item1, t.Item2, 100, 0.005);
}
public static void BiasedMatrixFactorizationTest()
{
List <Rating> baseRatings = Tools.GetRatings(BaseRatingFile);
List <Rating> testRatings = Tools.GetRatings(TestRatingFile);
Tools.UpdateIndexesToZeroBased(baseRatings);
Tools.UpdateIndexesToZeroBased(testRatings);
BiasedMatrixFactorization model = new BiasedMatrixFactorization(MaxUserId, MaxItemId);
model.TrySGD(baseRatings, testRatings, 100, 0.01, 0.01, 0.95);
}
public static void MatrixFactorization_IJCAI2018_Test2()
{
double testSize = 0.2;
List <Rating> ratings = Tools.GetRatings(@"D:\data\ijcai2018task\ex\train.shop.csv", ",");
var data = Tools.TrainTestSplit(ratings, testSize);
var max = Tools.GetMaxUserIdAndItemId(ratings);
Tools.WriteRatings(data.Item1, @"D:\data\ijcai2018task\ex\train.shop.1.csv");
Tools.WriteRatings(data.Item2, @"D:\data\ijcai2018task\ex\train.shop.2.csv");
BiasedMatrixFactorization model = new BiasedMatrixFactorization(max.Item1 + 1, max.Item2 + 1, 100);
model.TrySGD(data.Item1, data.Item2, 100, 0.01, 0.01, 0.94, 0, 1);
}
public static void BiasedMatrixFactorization(Dictionary <string, string> pairs)
{
string separator = "\t";
if (pairs.Keys.Contains("separator"))
{
separator = pairs["separator"];
}
List <Rating> baseRatings = Tools.GetRatings(pairs["train"], separator);
List <Rating> testRatings = Tools.GetRatings(pairs["test"], separator);
int maxUserId = System.Math.Max(baseRatings.Max(r => r.UserId), testRatings.Max(r => r.UserId));
int maxItemId = System.Math.Max(baseRatings.Max(r => r.ItemId), testRatings.Max(r => r.ItemId));
Tools.UpdateIndexesToZeroBased(baseRatings);
Tools.UpdateIndexesToZeroBased(testRatings);
int f = 10;
if (pairs.Keys.Contains("f"))
{
f = Int32.Parse(pairs["f"]);
}
int epochs = 100;
if (pairs.Keys.Contains("epochs"))
{
epochs = Int32.Parse(pairs["epochs"]);
}
double gamma = 0.01;
if (pairs.Keys.Contains("gamma"))
{
gamma = Double.Parse(pairs["gamma"]);
}
double lambda = 0.01;
if (pairs.Keys.Contains("lambda"))
{
lambda = Double.Parse(pairs["lambda"]);
}
double min_rating = 1.0;
if (pairs.Keys.Contains("min_rating"))
{
min_rating = Double.Parse(pairs["min_rating"]);
}
double max_rating = 5.0;
if (pairs.Keys.Contains("max_rating"))
{
max_rating = Double.Parse(pairs["max_rating"]);
}
BiasedMatrixFactorization model = new BiasedMatrixFactorization(maxUserId, maxItemId, f);
model.TrySGD(baseRatings, testRatings, epochs, gamma, lambda, min_rating, max_rating);
}
