protected virtual void LoadData()
{
training_file = Path.Combine(data_dir, training_file);
if (test_file != null)
{
test_file = Path.Combine(data_dir, test_file);
}
// user attributes
if (user_attributes_file != null)
{
user_attributes = AttributeData.Read(Path.Combine(data_dir, user_attributes_file), user_mapping);
}
if (recommender is IUserAttributeAwareRecommender)
{
((IUserAttributeAwareRecommender)recommender).UserAttributes = user_attributes;
}
// item attributes
if (item_attributes_file != null)
{
item_attributes = AttributeData.Read(Path.Combine(data_dir, item_attributes_file), item_mapping);
}
if (recommender is IItemAttributeAwareRecommender)
{
((IItemAttributeAwareRecommender)recommender).ItemAttributes = item_attributes;
}
// user relation
if (recommender is IUserRelationAwareRecommender)
{
((IUserRelationAwareRecommender)recommender).UserRelation = RelationData.Read(Path.Combine(data_dir, user_relations_file), user_mapping);
Console.WriteLine("relation over {0} users", ((IUserRelationAwareRecommender)recommender).NumUsers);
}
// item relation
if (recommender is IItemRelationAwareRecommender)
{
((IItemRelationAwareRecommender)recommender).ItemRelation = RelationData.Read(Path.Combine(data_dir, item_relations_file), item_mapping);
Console.WriteLine("relation over {0} items", ((IItemRelationAwareRecommender)recommender).NumItems);
}
}
static void LoadData()
{
TimeSpan loading_time = Utils.MeasureTime(delegate() {
// training data
training_data = ItemRecommendation.Read(Path.Combine(data_dir, training_file), user_mapping, item_mapping);
// relevant users and items
if (relevant_users_file != null)
{
relevant_users = new HashSet <int>(user_mapping.ToInternalID(Utils.ReadIntegers(Path.Combine(data_dir, relevant_users_file))));
}
else
{
relevant_users = training_data.AllUsers;
}
if (relevant_items_file != null)
{
relevant_items = new HashSet <int>(item_mapping.ToInternalID(Utils.ReadIntegers(Path.Combine(data_dir, relevant_items_file))));
}
else
{
relevant_items = training_data.AllItems;
}
if (!(recommender is MyMediaLite.ItemRecommendation.Random))
{
((ItemRecommender)recommender).Feedback = training_data;
}
// user attributes
if (recommender is IUserAttributeAwareRecommender)
{
if (user_attributes_file == null)
{
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 == null)
{
Usage("Recommender expects --item-attributes=FILE.");
}
else
{
((IItemAttributeAwareRecommender)recommender).ItemAttributes = AttributeData.Read(Path.Combine(data_dir, item_attributes_file), item_mapping);
}
}
if (filtered_eval)
{
if (item_attributes_file == null)
{
Usage("--filtered-evaluation expects --item-attributes=FILE.");
}
else
{
item_attributes = AttributeData.Read(Path.Combine(data_dir, item_attributes_file), item_mapping);
}
}
// user relation
if (recommender is IUserRelationAwareRecommender)
{
if (user_relations_file == null)
{
Usage("Recommender expects --user-relation=FILE.");
}
else
{
((IUserRelationAwareRecommender)recommender).UserRelation = RelationData.Read(Path.Combine(data_dir, user_relations_file), user_mapping);
Console.WriteLine("relation over {0} users", ((IUserRelationAwareRecommender)recommender).NumUsers); // TODO move to DisplayDataStats
}
}
// item relation
if (recommender is IItemRelationAwareRecommender)
{
if (user_relations_file == null)
{
Usage("Recommender expects --item-relation=FILE.");
}
else
{
((IItemRelationAwareRecommender)recommender).ItemRelation = RelationData.Read(Path.Combine(data_dir, item_relations_file), item_mapping);
Console.WriteLine("relation over {0} items", ((IItemRelationAwareRecommender)recommender).NumItems); // TODO move to DisplayDataStats
}
}
// test data
if (test_ratio == 0)
{
if (test_file != null)
{
test_data = ItemRecommendation.Read(Path.Combine(data_dir, test_file), user_mapping, item_mapping);
}
}
else
{
var split = new PosOnlyFeedbackSimpleSplit <PosOnlyFeedback <SparseBooleanMatrix> >(training_data, test_ratio);
training_data = split.Train[0];
test_data = split.Test[0];
}
});
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "loading_time {0,0:0.##}", loading_time.TotalSeconds));
}
protected virtual void LoadData()
{
training_file = Path.Combine(data_dir, training_file);
if (test_file != null)
{
test_file = Path.Combine(data_dir, test_file);
}
// user attributes
if (user_attributes_file != null)
{
string[] files = user_attributes_file.Split(' ');
foreach (string file in files)
{
if (file.Length > 0)
{
user_attributes.Add(AttributeData.Read(Path.Combine(data_dir, file), user_mapping));
}
}
}
//Original
for (int i = 0; i < recommenders.Count; i++)
{
if (recommenders[i] is IUserAttributeAwareRecommender)
{
((IUserAttributeAwareRecommender)recommenders[i]).UserAttributes = user_attributes[0];
if (user_attributes.Count > 1)
{
((IUserAttributeAwareRecommender)recommenders[i]).AdditionalUserAttributes = user_attributes.GetRange(1, user_attributes.Count - 1);
}
else
{
((IUserAttributeAwareRecommender)recommenders[i]).AdditionalUserAttributes = new List <IBooleanMatrix>();
}
}
}
// item attributes
item_attributes = new List <IBooleanMatrix>();
if (item_attributes_file != null)
{
string[] files = item_attributes_file.Split(' ');
foreach (string file in files)
{
if (file.Length > 0)
{
item_attributes.Add(AttributeData.Read(Path.Combine(data_dir, file), item_mapping));
}
}
}
//Cada um pega um metadado
for (int i = 0; i < item_attributes.Count; i++)
{
if (recommenders[i] is IItemAttributeAwareRecommender)
{
((IItemAttributeAwareRecommender)recommenders[i]).ItemAttributes = item_attributes[i];
((IItemAttributeAwareRecommender)recommenders[i]).AdditionalItemAttributes = new List <IBooleanMatrix>();
}
}
//Original
/*
* for (int i = 0; i < recommenders.Count; i++)
* {
* if (recommenders[i] is IItemAttributeAwareRecommender)
* {
* ((IItemAttributeAwareRecommender)recommenders[i]).ItemAttributes = item_attributes[0];
* if (item_attributes.Count > 1)
* ((IItemAttributeAwareRecommender)recommenders[i]).AdditionalItemAttributes = item_attributes.GetRange(1, item_attributes.Count - 1);
* else
* ((IItemAttributeAwareRecommender)recommenders[i]).AdditionalItemAttributes = new List<IBooleanMatrix>();
* }
*
* }
*
*/
for (int i = 0; i < recommenders.Count; i++)
{
// user relation
if (recommenders[i] is IUserRelationAwareRecommender)
{
((IUserRelationAwareRecommender)recommenders[i]).UserRelation = RelationData.Read(Path.Combine(data_dir, user_relations_file), user_mapping);
Console.WriteLine("relation over {0} users", ((IUserRelationAwareRecommender)recommenders[i]).NumUsers);
}
// item relation
if (recommenders[i] is IItemRelationAwareRecommender)
{
((IItemRelationAwareRecommender)recommenders[i]).ItemRelation = RelationData.Read(Path.Combine(data_dir, item_relations_file), item_mapping);
Console.WriteLine("relation over {0} items", ((IItemRelationAwareRecommender)recommenders[i]).NumItems);
}
}
}
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");
//Console.Error.WriteLine("data_dir " + data_dir);
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);
string prediction_file = parameters.GetRemoveString("prediction_file");
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 "MF-ItemMapping":
recommender = Recommender.Configure(mf_map, parameters, Usage);
break;
// case "MF-ItemMapping-Optimal":
// recommender = Recommender.Configure(mf_map_opt, parameters, Usage);
// break;
// case "BPR-MF-ItemMapping-kNN":
// recommender = Recommender.Configure(mf_map_knn, parameters, Usage);
// break;
// case "BPR-MF-ItemMapping-SVR":
// recommender = Recommender.Configure(mf_map_svr, parameters, Usage);
// break;
default:
Usage(string.Format("Unknown method: '{0}'", method));
break;
}
if (parameters.CheckForLeftovers())
{
Usage(-1);
}
// TODO move loading into its own method
// ID mapping objects
EntityMapping user_mapping = new EntityMapping();
EntityMapping item_mapping = new EntityMapping();
// training data
training_data = MyMediaLite.IO.RatingPrediction.Read(Path.Combine(data_dir, trainfile), user_mapping, item_mapping);
recommender.Ratings = training_data;
// 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 = MyMediaLite.IO.RatingPrediction.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();
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "ratings range: [{0}, {1}]", recommender.MinRating, recommender.MaxRating));
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);
}
Console.WriteLine();
if (prediction_file != string.Empty)
{
Console.WriteLine();
seconds = Utils.MeasureTime(
delegate() {
Prediction.WritePredictions(recommender, test_data, user_mapping, item_mapping, prediction_file);
}
);
Console.Error.WriteLine("predicting_time " + seconds);
}
}
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();
}
static void LoadData(string data_dir,
string user_attributes_file, string item_attributes_file,
string user_relation_file, string item_relation_file,
bool static_data)
{
if (training_file == null)
{
Usage("Program expects --training-file=FILE.");
}
TimeSpan loading_time = Utils.MeasureTime(delegate() {
// read training data
if (file_format == RatingFileFormat.DEFAULT)
{
training_data = static_data ? RatingPredictionStatic.Read(Path.Combine(data_dir, training_file), user_mapping, item_mapping, rating_type)
: MyMediaLite.IO.RatingPrediction.Read(Path.Combine(data_dir, training_file), user_mapping, item_mapping);
}
else if (file_format == RatingFileFormat.MOVIELENS_1M)
{
training_data = MovieLensRatingData.Read(Path.Combine(data_dir, training_file), user_mapping, item_mapping);
}
else if (file_format == RatingFileFormat.KDDCUP_2011)
{
training_data = MyMediaLite.IO.KDDCup2011.Ratings.Read(Path.Combine(data_dir, training_file));
}
recommender.Ratings = training_data;
// user attributes
if (recommender is IUserAttributeAwareRecommender) // TODO also support the MovieLens format here
{
if (user_attributes_file == 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 == string.Empty)
{
Usage("Recommender expects --item-attributes=FILE.");
}
else
{
((IItemAttributeAwareRecommender)recommender).ItemAttributes = AttributeData.Read(Path.Combine(data_dir, item_attributes_file), item_mapping);
}
}
// user relation
if (recommender is IUserRelationAwareRecommender)
{
if (user_relation_file == string.Empty)
{
Usage("Recommender expects --user-relations=FILE.");
}
else
{
((IUserRelationAwareRecommender)recommender).UserRelation = RelationData.Read(Path.Combine(data_dir, user_relation_file), user_mapping);
Console.WriteLine("relation over {0} users", ((IUserRelationAwareRecommender)recommender).NumUsers);
}
}
// item relation
if (recommender is IItemRelationAwareRecommender)
{
if (user_relation_file == string.Empty)
{
Usage("Recommender expects --item-relations=FILE.");
}
else
{
((IItemRelationAwareRecommender)recommender).ItemRelation = RelationData.Read(Path.Combine(data_dir, item_relation_file), item_mapping);
Console.WriteLine("relation over {0} items", ((IItemRelationAwareRecommender)recommender).NumItems);
}
}
// read test data
if (test_file != null)
{
if (file_format == RatingFileFormat.MOVIELENS_1M)
{
test_data = MovieLensRatingData.Read(Path.Combine(data_dir, test_file), user_mapping, item_mapping);
}
else
{
test_data = RatingPredictionStatic.Read(Path.Combine(data_dir, test_file), user_mapping, item_mapping, rating_type);
}
// TODO add KDD Cup
}
});
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "loading_time {0,0:0.##}", loading_time.TotalSeconds));
}
protected void Run(string[] args)
{
Console.WriteLine("WISER-RecSys começou");
options = new OptionSet()
{
// string-valued options
{ "arquivo=", v => arquivo = v },
{ "measures=", v => measures = v },
{ "recommender-options=", v => recommender_options += " " + v },
{ "help", v => show_help = v != null },
};
eval_measures = ItemRecommendationEvaluationResults.DefaultMeasuresToShow;
IList <string> extra_args = options.Parse(args);
if (show_help)
{
Usage(0);
}
//eval
if (measures != null)
{
eval_measures = measures.Split(' ', ',');
}
//Rodar o de vocs
//
training_file = "training.data";
test_file = "test.data";
training_partial_file = "training.partial.data";
test_partial_file = "test.partial.data";
for (int i = 0; i < arquivos.Length; i++)
{
MyMediaLite.Random.Seed = 1;
item_attributes_file = "movie_" + arquivos[i] + ".dat_saida";
user_mapping.Add(new Mapping());
item_mapping.Add(new Mapping());
//Setup recommender
recommenders.Add("BPRMFAttr".CreateItemRecommender());
recommenders[i].Configure(recommender_options, (string msg) =>
{
Console.Error.WriteLine(msg); Environment.Exit(-1);
});
// item attributes
if (recommenders[i] is IItemAttributeAwareRecommender && item_attributes_file == null)
{
Abort("Recommender expects --item-attributes=FILE.");
}
if (item_attributes_file != null)
{
item_attributes.Add(AttributeData.Read(item_attributes_file, item_mapping[i]));
}
if (recommenders[i] is IItemAttributeAwareRecommender)
{
((IItemAttributeAwareRecommender)recommenders[i]).ItemAttributes = item_attributes[i];
}
IBooleanMatrix lista_vazia = new SparseBooleanMatrix();
if (recommenders[i] is IUserAttributeAwareRecommender)
{
((IUserAttributeAwareRecommender)recommenders[i]).UserAttributes = lista_vazia;
}
// training data
training_data.Add(ItemData.Read(training_file, user_mapping[i], item_mapping[i], false));
test_data.Add(ItemData.Read(test_file, user_mapping[i], item_mapping[i], false));
test_users.Add(test_data[i].AllUsers);
//Probe
training_probe_data.Add(ItemData.Read(training_partial_file, user_mapping[i], item_mapping[i], false));
test_probe_data.Add(ItemData.Read(test_partial_file, user_mapping[i], item_mapping[i], false));
if (recommenders[i] is MyMediaLite.ItemRecommendation.ItemRecommender)
{
((ItemRecommender)recommenders[i]).Feedback = training_probe_data[i];
}
//Trainar
Console.WriteLine("Vamos ao probe training");
var train_time_span = Wrap.MeasureTime(delegate() { recommenders[i].Train(); });
Console.WriteLine("training_time " + train_time_span + " ");
}
Evaluation evaluation = new Evaluation(recommenders, test_probe_data, training_probe_data);
//Probe learn
Console.WriteLine("Probe learn started");
TimeSpan time_span = Wrap.MeasureTime(delegate() { evaluation.EvaluateProbe(test_users, user_mapping, item_mapping); });
Console.WriteLine(" Probe learn time: " + time_span);
for (int i = 0; i < arquivos.Length; i++)
{
MyMediaLite.Random.Seed = 1;
item_attributes_file = "movie_" + arquivos[i] + ".dat_saida";
//Setup recommender
recommenders[i] = "BPRMFAttr".CreateItemRecommender();
recommenders[i].Configure(recommender_options, (string msg) => { Console.Error.WriteLine(msg); Environment.Exit(-1); });
// item attributes
if (recommenders[i] is IItemAttributeAwareRecommender && item_attributes_file == null)
{
Abort("Recommender expects --item-attributes=FILE.");
}
if (recommenders[i] is IItemAttributeAwareRecommender)
{
((IItemAttributeAwareRecommender)recommenders[i]).ItemAttributes = item_attributes[i];
}
IBooleanMatrix lista_vazia = new SparseBooleanMatrix();
if (recommenders[i] is IUserAttributeAwareRecommender)
{
((IUserAttributeAwareRecommender)recommenders[i]).UserAttributes = lista_vazia;
}
if (recommenders[i] is MyMediaLite.ItemRecommendation.ItemRecommender)
{
((ItemRecommender)recommenders[i]).Feedback = training_data[i];
}
//Trainar
Console.WriteLine("Agora ao treino normal");
var train_time_span = Wrap.MeasureTime(delegate() { recommenders[i].Train(); });
Console.WriteLine("training_time " + train_time_span + " ");
}
var results = evaluation.Evaluate(test_data, training_data, test_users, user_mapping, item_mapping);
foreach (EvaluationResults result in results)
{
Console.WriteLine(result.ToString());
}
Console.WriteLine("Press any key to continue...");
Console.ReadKey();
}
