static void Main(string[] args)
{
Assembly assembly = Assembly.GetExecutingAssembly();
Assembly.LoadFile(Path.GetDirectoryName(assembly.Location) + Path.DirectorySeparatorChar + "MyMediaLiteExperimental.dll");
AppDomain.CurrentDomain.UnhandledException += new UnhandledExceptionEventHandler(Handlers.UnhandledExceptionHandler);
Console.CancelKeyPress += new ConsoleCancelEventHandler(AbortHandler);
// check number of command line parameters
if (args.Length < 1)
{
Usage("Not enough arguments.");
}
// read command line parameters
string method = args[0];
RecommenderParameters parameters = null;
try { parameters = new RecommenderParameters(args, 1); }
catch (ArgumentException e) { Usage(e.Message); }
// arguments for iteration search
find_iter = parameters.GetRemoveInt32("find_iter", 0);
max_iter = parameters.GetRemoveInt32("max_iter", 500);
epsilon = parameters.GetRemoveDouble("epsilon", 1);
err_cutoff = parameters.GetRemoveDouble("err_cutoff", 2);
// data arguments
string data_dir = parameters.GetRemoveString("data_dir");
if (data_dir != string.Empty)
{
data_dir = data_dir + "/mml-track2";
}
else
{
data_dir = "mml-track2";
}
sample_data = parameters.GetRemoveBool("sample_data", false);
predict_rated = parameters.GetRemoveBool("predict_rated", false);
predict_score = parameters.GetRemoveBool("predict_score", false);
// other arguments
save_model_file = parameters.GetRemoveString("save_model");
load_model_file = parameters.GetRemoveString("load_model");
int random_seed = parameters.GetRemoveInt32("random_seed", -1);
prediction_file = parameters.GetRemoveString("prediction_file");
if (predict_rated)
{
predict_score = true;
}
Console.Error.WriteLine("predict_score={0}", predict_score);
if (random_seed != -1)
{
MyMediaLite.Util.Random.InitInstance(random_seed);
}
recommender_validate = Recommender.CreateItemRecommender(method);
if (recommender_validate == null)
{
Usage(string.Format("Unknown method: '{0}'", method));
}
Recommender.Configure(recommender_validate, parameters, Usage);
recommender_final = recommender_validate.Clone() as ItemRecommender;
if (parameters.CheckForLeftovers())
{
Usage(-1);
}
// load all the data
LoadData(data_dir);
if (load_model_file != string.Empty)
{
Recommender.LoadModel(recommender_validate, load_model_file + "-validate");
Recommender.LoadModel(recommender_final, load_model_file + "-final");
}
Console.Write(recommender_validate.ToString());
DoTrack2();
}
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();
}
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)
{
Assembly assembly = Assembly.GetExecutingAssembly();
Assembly.LoadFile(Path.GetDirectoryName(assembly.Location) + Path.DirectorySeparatorChar + "MyMediaLiteExperimental.dll");
AppDomain.CurrentDomain.UnhandledException += new UnhandledExceptionEventHandler(Handlers.UnhandledExceptionHandler);
Console.CancelKeyPress += new ConsoleCancelEventHandler(AbortHandler);
// check number of command line parameters
if (args.Length < 1)
Usage("Not enough arguments.");
// read command line parameters
string method = args[0];
RecommenderParameters parameters = null;
try { parameters = new RecommenderParameters(args, 1); }
catch (ArgumentException e) { Usage(e.Message); }
// arguments for iteration search
find_iter = parameters.GetRemoveInt32( "find_iter", 0);
max_iter = parameters.GetRemoveInt32( "max_iter", 500);
epsilon = parameters.GetRemoveDouble( "epsilon", 1);
err_cutoff = parameters.GetRemoveDouble( "err_cutoff", 2);
// data arguments
string data_dir = parameters.GetRemoveString( "data_dir");
if (data_dir != string.Empty)
data_dir = data_dir + "/mml-track2";
else
data_dir = "mml-track2";
sample_data = parameters.GetRemoveBool( "sample_data", false);
predict_rated = parameters.GetRemoveBool( "predict_rated", false);
predict_score = parameters.GetRemoveBool( "predict_score", false);
// other arguments
save_model_file = parameters.GetRemoveString( "save_model");
load_model_file = parameters.GetRemoveString( "load_model");
int random_seed = parameters.GetRemoveInt32( "random_seed", -1);
prediction_file = parameters.GetRemoveString( "prediction_file");
if (predict_rated)
predict_score = true;
Console.Error.WriteLine("predict_score={0}", predict_score);
if (random_seed != -1)
MyMediaLite.Util.Random.InitInstance(random_seed);
recommender_validate = Recommender.CreateItemRecommender(method);
if (recommender_validate == null)
Usage(string.Format("Unknown method: '{0}'", method));
Recommender.Configure(recommender_validate, parameters, Usage);
recommender_final = recommender_validate.Clone() as ItemRecommender;
if (parameters.CheckForLeftovers())
Usage(-1);
// load all the data
LoadData(data_dir);
if (load_model_file != string.Empty)
{
Recommender.LoadModel(recommender_validate, load_model_file + "-validate");
Recommender.LoadModel(recommender_final, load_model_file + "-final");
}
Console.Write(recommender_validate.ToString());
DoTrack2();
}
static void Main(string[] args)
{
Assembly assembly = Assembly.GetExecutingAssembly();
Assembly.LoadFile(Path.GetDirectoryName(assembly.Location) + Path.DirectorySeparatorChar + "MyMediaLiteExperimental.dll");
double min_rating = 0;
double max_rating = 100;
AppDomain.CurrentDomain.UnhandledException += new UnhandledExceptionEventHandler(Handlers.UnhandledExceptionHandler);
Console.CancelKeyPress += new ConsoleCancelEventHandler(AbortHandler);
// check number of command line parameters
if (args.Length < 1)
{
Usage("Not enough arguments.");
}
// read command line parameters
string method = args[0];
RecommenderParameters parameters = null;
try { parameters = new RecommenderParameters(args, 1); }
catch (ArgumentException e) { Usage(e.Message); }
// arguments for iteration search
find_iter = parameters.GetRemoveInt32("find_iter", 0);
max_iter = parameters.GetRemoveInt32("max_iter", 500);
compute_fit = parameters.GetRemoveBool("compute_fit", false);
epsilon = parameters.GetRemoveDouble("epsilon", 0);
rmse_cutoff = parameters.GetRemoveDouble("rmse_cutoff", double.MaxValue);
mae_cutoff = parameters.GetRemoveDouble("mae_cutoff", double.MaxValue);
// data arguments
string data_dir = parameters.GetRemoveString("data_dir");
track2 = parameters.GetRemoveBool("track2", false);
if (data_dir != string.Empty)
{
data_dir = data_dir + (track2 ? "/mml-track2" : "/track1");
}
else
{
data_dir = track2 ? "/mml-track2" : "track1";
}
sample_data = parameters.GetRemoveBool("sample_data", false);
// other arguments
save_model_file = parameters.GetRemoveString("save_model");
load_model_file = parameters.GetRemoveString("load_model");
int random_seed = parameters.GetRemoveInt32("random_seed", -1);
no_eval = parameters.GetRemoveBool("no_eval", false);
prediction_file = parameters.GetRemoveString("prediction_file");
cross_validation = parameters.GetRemoveInt32("cross_validation", 0);
good_rating_prob = parameters.GetRemoveBool("good_rating_prob", false);
if (good_rating_prob)
{
max_rating = 1;
}
if (random_seed != -1)
{
MyMediaLite.Util.Random.InitInstance(random_seed);
}
recommender = Recommender.CreateRatingPredictor(method);
if (recommender == null)
{
Usage(string.Format("Unknown method: '{0}'", method));
}
Recommender.Configure(recommender, parameters, Usage);
if (parameters.CheckForLeftovers())
{
Usage(-1);
}
// load all the data
TimeSpan loading_time = Utils.MeasureTime(delegate() { LoadData(data_dir); });
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "loading_time {0,0:0.##}", loading_time.TotalSeconds));
recommender.Ratings = training_ratings;
recommender.MinRating = min_rating;
recommender.MaxRating = max_rating;
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "ratings range: [{0}, {1}]", recommender.MinRating, recommender.MaxRating));
if (load_model_file != string.Empty)
{
Recommender.LoadModel(recommender, load_model_file);
}
DoTrack1();
Console.Error.WriteLine("memory {0}", Memory.Usage);
}
static void Main(string[] args)
{
AppDomain.CurrentDomain.UnhandledException += new UnhandledExceptionEventHandler(Handlers.UnhandledExceptionHandler);
Console.CancelKeyPress += new ConsoleCancelEventHandler(AbortHandler);
// check number of command line parameters
if (args.Length < 1)
Usage("Not enough arguments.");
// read command line parameters
string method = args[0];
RecommenderParameters parameters = null;
try { parameters = new RecommenderParameters(args, 1); }
catch (ArgumentException e) { Usage(e.Message); }
// arguments for iteration search
find_iter = parameters.GetRemoveInt32( "find_iter", 0);
max_iter = parameters.GetRemoveInt32( "max_iter", 500);
compute_fit = parameters.GetRemoveBool( "compute_fit", false);
epsilon = parameters.GetRemoveDouble( "epsilon", 0);
rmse_cutoff = parameters.GetRemoveDouble( "rmse_cutoff", double.MaxValue);
mae_cutoff = parameters.GetRemoveDouble( "mae_cutoff", double.MaxValue);
// data arguments
string data_dir = parameters.GetRemoveString( "data_dir");
track2 = parameters.GetRemoveBool( "track2", false);
if (data_dir != string.Empty)
data_dir = data_dir + (track2 ? "/mml-track2" : "/track1");
else
data_dir = track2 ? "/mml-track2" : "track1";
sample_data = parameters.GetRemoveBool( "sample_data", false);
// other arguments
save_model_file = parameters.GetRemoveString( "save_model");
load_model_file = parameters.GetRemoveString( "load_model");
int random_seed = parameters.GetRemoveInt32( "random_seed", -1);
no_eval = parameters.GetRemoveBool( "no_eval", false);
prediction_file = parameters.GetRemoveString( "prediction_file");
cross_validation = parameters.GetRemoveUInt32( "cross_validation", 0);
good_rating_prob = parameters.GetRemoveBool( "good_rating_prob", false);
if (random_seed != -1)
MyMediaLite.Util.Random.Seed = random_seed;
recommender = Recommender.CreateRatingPredictor(method);
if (recommender == null)
Usage(string.Format("Unknown method: '{0}'", method));
Recommender.Configure(recommender, parameters, Usage);
if (parameters.CheckForLeftovers())
Usage(-1);
// load all the data
TimeSpan loading_time = Wrap.MeasureTime(delegate() { LoadData(data_dir); });
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "loading_time {0,0:0.##}", loading_time.TotalSeconds));
recommender.Ratings = training_ratings;
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "ratings range: [{0}, {1}]", recommender.MinRating, recommender.MaxRating));
if (load_model_file != string.Empty)
Model.Load(recommender, load_model_file);
DoTrack1();
Console.Error.WriteLine("memory {0}", Memory.Usage);
}