private void CreateRecommender()
{
BiasedMatrixFactorization recommender = new BiasedMatrixFactorization();
Console.Error.Write("Reading in ratings ... ");
TimeSpan time = Wrap.MeasureTime(delegate() {
recommender.Ratings = RatingData.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 Tuple <int, float>(recommender.Ratings.Items[indices_for_item[0]], indices_for_item.Count));
}
}
movies_by_frequency = movies_by_frequency.OrderByDescending(x => x.Item2).ToList();
for (int i = 0; i < n_movies; i++)
{
top_n_movies.Add(movies_by_frequency[i].Item1);
}
Console.Error.Write("Loading prediction model ... ");
recommender.UpdateUsers = true;
recommender.UpdateItems = false;
recommender.BiasReg = 0.001f;
recommender.Regularization = 0.045f;
recommender.NumIter = 60;
time = Wrap.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.ToString());
//rating_predictor.AddUser(current_user_id);
// add movies that were not in the training set
//rating_predictor.AddItem( item_mapping.InternalIDs.Count - 1 );
PredictAllRatings();
}
/// <summary>Performs user-wise fold-in evaluation, but instead of folding in perform incremental training with the new data</summary>
/// <remarks>
/// </remarks>
/// <returns>the evaluation results</returns>
/// <param name='recommender'>a rating predictor capable of performing a user fold-in</param>
/// <param name='update_data'>the rating data used to represent the users</param>
/// <param name='eval_data'>the evaluation data</param>
static public RatingPredictionEvaluationResults EvaluateFoldInIncrementalTraining(this IncrementalRatingPredictor recommender, IRatings update_data, IRatings eval_data)
{
double rmse = 0;
double mae = 0;
double cbd = 0;
int rating_count = 0;
foreach (int user_id in update_data.AllUsers)
{
if (eval_data.AllUsers.Contains(user_id))
{
var local_recommender = (IncrementalRatingPredictor)recommender.Clone();
// add ratings and perform incremental training
var user_ratings = new RatingsProxy(update_data, update_data.ByUser[user_id]);
local_recommender.AddRatings(user_ratings);
var items_to_rate = (from index in eval_data.ByUser[user_id] select eval_data.Items[index]).ToArray();
var predicted_ratings = recommender.Recommend(user_id, candidate_items: items_to_rate);
foreach (var pred in predicted_ratings)
{
float prediction = pred.Item2;
float actual_rating = eval_data.Get(user_id, pred.Item1, eval_data.ByUser[user_id]);
float error = prediction - actual_rating;
rmse += error * error;
mae += Math.Abs(error);
cbd += Eval.Ratings.ComputeCBD(actual_rating, prediction, recommender.MinRating, recommender.MaxRating);
rating_count++;
}
// remove ratings again
local_recommender.RemoveRatings(user_ratings);
Console.Error.Write(".");
}
}
mae = mae / rating_count;
rmse = Math.Sqrt(rmse / rating_count);
cbd = cbd / rating_count;
var result = new RatingPredictionEvaluationResults();
result["RMSE"] = (float)rmse;
result["MAE"] = (float)mae;
result["NMAE"] = (float)mae / (recommender.MaxRating - recommender.MinRating);
result["CBD"] = (float)cbd;
return(result);
}
