public static void Main(string[] args)
{
double min_rating = 1;
double max_rating = 5;
// load the data
var user_mapping = new EntityMapping();
var item_mapping = new EntityMapping();
var training_data = MyMediaLite.IO.RatingPrediction.Read(args[0], min_rating, max_rating, user_mapping, item_mapping);
var test_data = MyMediaLite.IO.RatingPrediction.Read(args[1], min_rating, max_rating, user_mapping, item_mapping);
// set up the recommender
var recommender = new BaselineAutoDiff();
recommender.MinRating = min_rating;
recommender.MaxRating = max_rating;
recommender.Ratings = training_data;
recommender.Train();
// measure the accuracy on the test data set
var results = RatingEval.Evaluate(recommender, test_data);
Console.WriteLine("RMSE={0} MAE={1}", results["RMSE"], results["MAE"]);
// make a prediction for a certain user and item
Console.WriteLine(recommender.Predict(user_mapping.ToInternalID(1), item_mapping.ToInternalID(1)));
}
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 MyRecommender()
{
UserMapping = new EntityMapping();
ItemMapping = new EntityMapping();
// load the data
var data = RatingData.Read("../mymedialite/data/ml-100k/u.data", UserMapping, ItemMapping);
// set up the recommender
recommender = (IncrementalRatingPredictor) Model.Load("bmf.model");
recommender.Ratings = data;
}
public static void Main(string[] args)
{
// load the data
var user_mapping = new EntityMapping();
var item_mapping = new EntityMapping();
var training_data = ItemRecommendation.Read(args[0], user_mapping, item_mapping);
var relevant_users = training_data.AllUsers; // users that will be taken into account in the evaluation
var relevant_items = training_data.AllItems; // items that will be taken into account in the evaluation
var test_data = ItemRecommendation.Read(args[1], user_mapping, item_mapping);
// set up the recommender
var recommender = new MostPopular();
recommender.Feedback = training_data;
recommender.Train();
// measure the accuracy on the test data set
var results = ItemPredictionEval.Evaluate(recommender, test_data, training_data, relevant_users, relevant_items);
foreach (var key in results.Keys)
Console.WriteLine("{0}={1}", key, results[key]);
// make a prediction for a certain user and item
Console.WriteLine(recommender.Predict(user_mapping.ToInternalID(1), item_mapping.ToInternalID(1)));
}
public static void Main(string[] args)
{
AppDomain.CurrentDomain.UnhandledException += new UnhandledExceptionEventHandler(Handlers.UnhandledExceptionHandler);
// check number of command line parameters
if (args.Length < 4)
Usage("Not enough arguments.");
// read command line parameters
RecommenderParameters parameters = null;
try { parameters = new RecommenderParameters(args, 4); }
catch (ArgumentException e) { Usage(e.Message); }
// other parameters
string data_dir = parameters.GetRemoveString( "data_dir");
string relevant_items_file = parameters.GetRemoveString( "relevant_items");
string item_attributes_file = parameters.GetRemoveString( "item_attributes");
string user_attributes_file = parameters.GetRemoveString( "user_attributes");
//string save_mapping_file = parameters.GetRemoveString( "save_model");
int random_seed = parameters.GetRemoveInt32( "random_seed", -1);
bool no_eval = parameters.GetRemoveBool( "no_eval", false);
bool compute_fit = parameters.GetRemoveBool( "compute_fit", false);
if (random_seed != -1)
MyMediaLite.Util.Random.InitInstance(random_seed);
// main data files and method
string trainfile = args[0].Equals("-") ? "-" : Path.Combine(data_dir, args[0]);
string testfile = args[1].Equals("-") ? "-" : Path.Combine(data_dir, args[1]);
string load_model_file = args[2];
string method = args[3];
// set correct recommender
switch (method)
{
case "BPR-MF-ItemMapping":
recommender = Recommender.Configure(bprmf_map, parameters, Usage);
break;
case "BPR-MF-ItemMapping-Optimal":
recommender = Recommender.Configure(bprmf_map_bpr, parameters, Usage);
break;
case "BPR-MF-ItemMapping-Complex":
recommender = Recommender.Configure(bprmf_map_com, parameters, Usage);
break;
case "BPR-MF-ItemMapping-kNN":
recommender = Recommender.Configure(bprmf_map_knn, parameters, Usage);
break;
case "BPR-MF-ItemMapping-SVR":
recommender = Recommender.Configure(bprmf_map_svr, parameters, Usage);
break;
case "BPR-MF-UserMapping":
recommender = Recommender.Configure(bprmf_user_map, parameters, Usage);
break;
case "BPR-MF-UserMapping-Optimal":
recommender = Recommender.Configure(bprmf_user_map_bpr, parameters, Usage);
break;
default:
Usage(string.Format("Unknown method: '{0}'", method));
break;
}
if (parameters.CheckForLeftovers())
Usage(-1);
// ID mapping objects
var user_mapping = new EntityMapping();
var item_mapping = new EntityMapping();
// training data
training_data = ItemRecommendation.Read(Path.Combine(data_dir, trainfile), user_mapping, item_mapping);
recommender.Feedback = training_data;
// relevant items
if (! relevant_items_file.Equals(string.Empty) )
relevant_items = new HashSet<int>(item_mapping.ToInternalID(Utils.ReadIntegers(Path.Combine(data_dir, relevant_items_file))));
else
relevant_items = training_data.AllItems;
// user attributes
if (recommender is IUserAttributeAwareRecommender)
{
if (user_attributes_file.Equals(string.Empty))
Usage("Recommender expects user_attributes=FILE.");
else
((IUserAttributeAwareRecommender)recommender).UserAttributes = AttributeData.Read(Path.Combine(data_dir, user_attributes_file), user_mapping);
}
// item attributes
if (recommender is IItemAttributeAwareRecommender)
{
if (item_attributes_file.Equals(string.Empty))
Usage("Recommender expects item_attributes=FILE.");
else
((IItemAttributeAwareRecommender)recommender).ItemAttributes = AttributeData.Read(Path.Combine(data_dir, item_attributes_file), item_mapping);
}
// test data
test_data = ItemRecommendation.Read( Path.Combine(data_dir, testfile), user_mapping, item_mapping );
TimeSpan seconds;
Recommender.LoadModel(recommender, load_model_file);
// set the maximum user and item IDs in the recommender - this is important for the cold start use case
recommender.MaxUserID = user_mapping.InternalIDs.Max();
recommender.MaxItemID = item_mapping.InternalIDs.Max();
DisplayDataStats();
Console.Write(recommender.ToString() + " ");
if (compute_fit)
{
seconds = Utils.MeasureTime( delegate() {
int num_iter = recommender.NumIterMapping;
recommender.NumIterMapping = 0;
recommender.LearnAttributeToFactorMapping();
Console.Error.WriteLine();
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "iteration {0} fit {1}", -1, recommender.ComputeFit()));
recommender.NumIterMapping = 1;
for (int i = 0; i < num_iter; i++, i++)
{
recommender.IterateMapping();
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "iteration {0} fit {1}", i, recommender.ComputeFit()));
}
recommender.NumIterMapping = num_iter; // restore
} );
}
else
{
seconds = Utils.MeasureTime( delegate() {
recommender.LearnAttributeToFactorMapping();
} );
}
Console.Write("mapping_time " + seconds + " ");
if (!no_eval)
seconds = EvaluateRecommender(recommender, test_data, training_data);
Console.WriteLine();
}
/// <summary>Read movie data from a file</summary>
/// <param name="filename">the name of the file to be read from</param>
/// <param name="item_mapping">ID mapping for the movies</param>
public void Read(string filename, EntityMapping item_mapping)
{
using ( var reader = new StreamReader(filename) )
Read(reader, item_mapping);
}
/// <summary>Read movie data from a StreamReader</summary>
/// <param name="reader">a StreamReader to be read from</param>
/// <param name="item_mapping">ID mapping for the movies</param>
public void Read(StreamReader reader, EntityMapping item_mapping)
{
movie_list = new List<Movie>();
IMDB_KEY_To_ID = new Dictionary<string, int>();
var separators = new string[] { "::" };
string line;
while (!reader.EndOfStream)
{
line = reader.ReadLine();
string[] tokens = line.Split(separators, StringSplitOptions.None);
if (tokens.Length != 3)
throw new IOException("Expected exactly three columns: " + line);
int movie_id = item_mapping.ToInternalID(int.Parse(tokens[0]));
string movie_imdb_key = tokens[1];
//string[] movie_genres = tokens[2].Split('|');
// TODO
int movie_year = 1900;
string movie_title = movie_imdb_key;
movie_list.Add(new Movie(movie_id, movie_title, movie_year, movie_imdb_key));
IMDB_KEY_To_ID[movie_imdb_key] = movie_id;
}
}
/// <summary>Read in rating data from an IDataReader, e.g. a database via DbDataReader</summary>
/// <param name="reader">the <see cref="IDataReader"/> to read from</param>
/// <param name="user_mapping">mapping object for user IDs</param>
/// <param name="item_mapping">mapping object for item IDs</param>
/// <returns>the rating data</returns>
public static IRatings Read(IDataReader reader, EntityMapping user_mapping, EntityMapping item_mapping)
{
var ratings = new Ratings();
if (reader.FieldCount < 3)
throw new IOException("Expected at least three columns.");
while (reader.Read())
{
int user_id = user_mapping.ToInternalID(reader.GetInt32(0));
int item_id = item_mapping.ToInternalID(reader.GetInt32(1));
double rating = reader.GetDouble(2);
ratings.Add(user_id, item_id, rating);
}
return ratings;
}
