[Test()] public void TestComputeCorrelations2()
{
// load data from disk
var user_mapping = new EntityMapping();
var item_mapping = new EntityMapping();
var ratings = RatingPrediction.Read("../../../../data/ml100k/u1.base", user_mapping, item_mapping);
Assert.AreEqual(-0.02855815f, Pearson.ComputeCorrelation(ratings, EntityType.ITEM, 45, 311, 200f), 0.00001);
}
private void CreateRecommender()
{
BiasedMatrixFactorization recommender = new BiasedMatrixFactorization();
Console.Error.Write("Reading in ratings ... ");
TimeSpan time = Utils.MeasureTime(delegate() {
recommender.Ratings = RatingPrediction.Read(ratings_file, user_mapping, item_mapping);
});
Console.Error.WriteLine("done ({0,0:0.##}).", time.TotalSeconds.ToString(CultureInfo.InvariantCulture));
//Console.Error.Write("Reading in additional ratings ... ");
//string[] rating_files = Directory.GetFiles("../../saved_data/", "user-ratings-*");
//Console.Error.WriteLine("done.");
foreach (var indices_for_item in recommender.Ratings.ByItem)
{
if (indices_for_item.Count > 0)
{
movies_by_frequency.Add(new WeightedItem(recommender.Ratings.Items[indices_for_item[0]], indices_for_item.Count));
}
}
movies_by_frequency.Sort();
movies_by_frequency.Reverse();
for (int i = 0; i < n_movies; i++)
{
top_n_movies.Add(movies_by_frequency[i].item_id);
}
Console.Error.Write("Loading prediction model ... ");
recommender.UpdateUsers = true;
recommender.UpdateItems = false;
recommender.BiasReg = 0.001;
recommender.Regularization = 0.045;
recommender.NumIter = 60;
time = Utils.MeasureTime(delegate() {
recommender.LoadModel(model_file);
});
Console.Error.WriteLine("done ({0,0:0.##}).", time.TotalSeconds.ToString(CultureInfo.InvariantCulture));
rating_predictor = recommender;
current_user_id = user_mapping.ToInternalID(current_user_external_id);
//rating_predictor.AddUser(current_user_id);
// add movies that were not in the training set
//rating_predictor.AddItem( item_mapping.InternalIDs.Count - 1 );
PredictAllRatings();
}
void LoadRatings(string name)
{
// assumption: ratings, training data and model were reloaded
ratings.Clear();
using (var reader = new StreamReader("../../../../saved_data/user-ratings-" + name))
{
IRatings user_ratings = RatingPrediction.Read(reader, user_mapping, item_mapping);
for (int i = 0; i < user_ratings.Count; i++)
{
ratings[user_ratings.Items[i]] = user_ratings[i];
current_user_id = user_ratings.Users[i]; // TODO check whether user ID is the same for all ratings
}
}
}
