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); // recommender arguments string method = "BiasedMatrixFactorization"; string recommender_options = string.Empty; // help/version bool show_help = false; bool show_version = false; // arguments for iteration search int find_iter = 0; int max_iter = 500; double epsilon = 0; double rmse_cutoff = double.MaxValue; double mae_cutoff = double.MaxValue; // data arguments string data_dir = string.Empty; string user_attributes_file = string.Empty; string item_attributes_file = string.Empty; string user_relations_file = string.Empty; string item_relations_file = string.Empty; // other arguments bool online_eval = false; bool search_hp = false; string save_model_file = string.Empty; string load_model_file = string.Empty; int random_seed = -1; string prediction_file = string.Empty; string prediction_line = "{0}\t{1}\t{2}"; int cross_validation = 0; double split_ratio = 0; var p = new OptionSet() { // string-valued options { "training-file=", v => training_file = v }, { "test-file=", v => test_file = v }, { "recommender=", v => method = v }, { "recommender-options=", v => recommender_options += " " + v }, { "data-dir=", v => data_dir = v }, { "user-attributes=", v => user_attributes_file = v }, { "item-attributes=", v => item_attributes_file = v }, { "user-relations=", v => user_relations_file = v }, { "item-relations=", v => item_relations_file = v }, { "save-model=", v => save_model_file = v }, { "load-model=", v => load_model_file = v }, { "prediction-file=", v => prediction_file = v }, { "prediction-line=", v => prediction_line = v }, // integer-valued options { "find-iter=", (int v) => find_iter = v }, { "max-iter=", (int v) => max_iter = v }, { "random-seed=", (int v) => random_seed = v }, { "cross-validation=", (int v) => cross_validation = v }, // double-valued options { "epsilon=", (double v) => epsilon = v }, { "rmse-cutoff=", (double v) => rmse_cutoff = v }, { "mae-cutoff=", (double v) => mae_cutoff = v }, { "split-ratio=", (double v) => split_ratio = v }, // enum options { "rating-type=", (RatingType v) => rating_type = v }, { "file-format=", (RatingFileFormat v) => file_format = v }, // boolean options { "compute-fit", v => compute_fit = v != null }, { "online-evaluation", v => online_eval = v != null }, { "search-hp", v => search_hp = v != null }, { "help", v => show_help = v != null }, { "version", v => show_version = v != null }, }; IList <string> extra_args = p.Parse(args); // TODO make sure interaction of --find-iter and --cross-validation works properly bool no_eval = test_file == null; if (show_version) { ShowVersion(); } if (show_help) { Usage(0); } if (extra_args.Count > 0) { Usage("Did not understand " + extra_args[0]); } if (training_file == null) { Usage("Parameter --training-file=FILE is missing."); } if (cross_validation != 0 && split_ratio != 0) { Usage("--cross-validation=K and --split-ratio=NUM are mutually exclusive."); } 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, recommender_options, Usage); // ID mapping objects if (file_format == RatingFileFormat.KDDCUP_2011) { user_mapping = new IdentityMapping(); item_mapping = new IdentityMapping(); } // load all the data LoadData(data_dir, user_attributes_file, item_attributes_file, user_relations_file, item_relations_file, !online_eval); Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "ratings range: [{0}, {1}]", recommender.MinRating, recommender.MaxRating)); if (split_ratio > 0) { var split = new RatingsSimpleSplit(training_data, split_ratio); recommender.Ratings = split.Train[0]; training_data = split.Train[0]; test_data = split.Test[0]; } Utils.DisplayDataStats(training_data, test_data, recommender); if (find_iter != 0) { if (!(recommender is IIterativeModel)) { Usage("Only iterative recommenders support find_iter."); } var iterative_recommender = (IIterativeModel)recommender; Console.WriteLine(recommender.ToString() + " "); if (load_model_file == string.Empty) { recommender.Train(); } else { Recommender.LoadModel(iterative_recommender, load_model_file); } if (compute_fit) { Console.Write(string.Format(CultureInfo.InvariantCulture, "fit {0,0:0.#####} ", iterative_recommender.ComputeFit())); } MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(recommender, test_data)); Console.WriteLine(" iteration " + iterative_recommender.NumIter); for (int i = (int)iterative_recommender.NumIter + 1; i <= max_iter; i++) { TimeSpan time = Utils.MeasureTime(delegate() { iterative_recommender.Iterate(); }); training_time_stats.Add(time.TotalSeconds); if (i % find_iter == 0) { if (compute_fit) { double fit = 0; time = Utils.MeasureTime(delegate() { fit = iterative_recommender.ComputeFit(); }); fit_time_stats.Add(time.TotalSeconds); Console.Write(string.Format(CultureInfo.InvariantCulture, "fit {0,0:0.#####} ", fit)); } Dictionary <string, double> results = null; time = Utils.MeasureTime(delegate() { results = MyMediaLite.Eval.Ratings.Evaluate(recommender, test_data); }); eval_time_stats.Add(time.TotalSeconds); MyMediaLite.Eval.Ratings.DisplayResults(results); rmse_eval_stats.Add(results["RMSE"]); Console.WriteLine(" iteration " + i); Recommender.SaveModel(recommender, save_model_file, i); if (prediction_file != string.Empty) { Prediction.WritePredictions(recommender, test_data, user_mapping, item_mapping, prediction_line, prediction_file + "-it-" + i); } if (epsilon > 0.0 && results["RMSE"] - rmse_eval_stats.Min() > epsilon) { Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "{0} >> {1}", results["RMSE"], rmse_eval_stats.Min())); Console.Error.WriteLine("Reached convergence on training/validation data after {0} iterations.", i); break; } if (results["RMSE"] > rmse_cutoff || results["MAE"] > mae_cutoff) { Console.Error.WriteLine("Reached cutoff after {0} iterations.", i); break; } } } // for DisplayStats(); } else { TimeSpan seconds; if (load_model_file == string.Empty) { if (cross_validation > 0) { Console.Write(recommender.ToString()); Console.WriteLine(); var split = new RatingCrossValidationSplit(training_data, cross_validation); var results = MyMediaLite.Eval.Ratings.EvaluateOnSplit(recommender, split); // TODO if (search_hp) MyMediaLite.Eval.Ratings.DisplayResults(results); no_eval = true; recommender.Ratings = training_data; } else { if (search_hp) { // TODO --search-hp-criterion=RMSE double result = NelderMead.FindMinimum("RMSE", recommender); Console.Error.WriteLine("estimated quality (on split) {0}", result.ToString(CultureInfo.InvariantCulture)); // TODO give out hp search time } Console.Write(recommender.ToString()); seconds = Utils.MeasureTime(delegate() { recommender.Train(); }); Console.Write(" training_time " + seconds + " "); } } else { Recommender.LoadModel(recommender, load_model_file); Console.Write(recommender.ToString() + " "); } if (!no_eval) { if (online_eval) // TODO support also for prediction outputs (to allow external evaluation) { seconds = Utils.MeasureTime(delegate() { MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.EvaluateOnline(recommender, test_data)); }); } else { seconds = Utils.MeasureTime(delegate() { MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(recommender, test_data)); }); } Console.Write(" testing_time " + seconds); } if (compute_fit) { Console.Write("fit "); seconds = Utils.MeasureTime(delegate() { MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(recommender, training_data)); }); Console.Write(string.Format(CultureInfo.InvariantCulture, " fit_time {0,0:0.#####} ", seconds)); } if (prediction_file != string.Empty) { seconds = Utils.MeasureTime(delegate() { Console.WriteLine(); Prediction.WritePredictions(recommender, test_data, user_mapping, item_mapping, prediction_line, prediction_file); }); Console.Error.Write("predicting_time " + seconds); } Console.WriteLine(); Console.Error.WriteLine("memory {0}", Memory.Usage); } Recommender.SaveModel(recommender, save_model_file); }
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); }