public static void Main(string[] args) { Assembly assembly = Assembly.GetExecutingAssembly(); Assembly.LoadFile(Path.GetDirectoryName(assembly.Location) + Path.DirectorySeparatorChar + "MyMediaLiteExperimental.dll"); AppDomain.CurrentDomain.UnhandledException += new UnhandledExceptionEventHandler(MyMediaLite.Util.Handlers.UnhandledExceptionHandler); Console.CancelKeyPress += new ConsoleCancelEventHandler(AbortHandler); // recommender arguments string method = "MostPopular"; string recommender_options = string.Empty; // help/version bool show_help = false; bool show_version = false; // variables for iteration search int find_iter = 0; int max_iter = 500; double auc_cutoff = 0; double prec5_cutoff = 0; compute_fit = false; // other parameters string save_model_file = string.Empty; string load_model_file = string.Empty; int random_seed = -1; string prediction_file = string.Empty; test_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 }, { "relevant-users=", v => relevant_users_file = v }, { "relevant-items=", v => relevant_items_file = 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 }, { "predict-items-number=", (int v) => predict_items_number = v }, // double-valued options // { "epsilon=", (double v) => epsilon = v }, { "auc-cutoff=", (double v) => auc_cutoff = v }, { "prec5-cutoff=", (double v) => prec5_cutoff = v }, { "test-ratio=", (double v) => test_ratio = v }, // enum options // * currently none * // boolean options { "compute-fit", v => compute_fit = v != null }, { "online-evaluation", v => online_eval = v != null }, { "filtered-evaluation", v => filtered_eval = v != null }, { "help", v => show_help = v != null }, { "version", v => show_version = v != null }, }; IList <string> extra_args = p.Parse(args); if (show_version) { ShowVersion(); } if (show_help) { Usage(0); } bool no_eval = test_file == null; if (training_file == null) { Usage("Parameter --training-file=FILE is missing."); } if (extra_args.Count > 0) { Usage("Did not understand " + extra_args[0]); } if (online_eval && filtered_eval) { Usage("Combination of --online-eval and --filtered-eval is not (yet) supported."); } if (random_seed != -1) { MyMediaLite.Util.Random.InitInstance(random_seed); } recommender = Recommender.CreateItemRecommender(method); if (recommender == null) { Usage(string.Format("Unknown method: '{0}'", method)); } Recommender.Configure(recommender, recommender_options, Usage); // load all the data LoadData(); Utils.DisplayDataStats(training_data, test_data, recommender); TimeSpan time_span; if (find_iter != 0) { var iterative_recommender = (IIterativeModel)recommender; Console.WriteLine(recommender.ToString() + " "); if (load_model_file == string.Empty) { iterative_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())); } var result = Evaluate(); Items.DisplayResults(result); Console.WriteLine(" iteration " + iterative_recommender.NumIter); for (int i = (int)iterative_recommender.NumIter + 1; i <= max_iter; i++) { TimeSpan t = Utils.MeasureTime(delegate() { iterative_recommender.Iterate(); }); training_time_stats.Add(t.TotalSeconds); if (i % find_iter == 0) { if (compute_fit) { double fit = 0; t = Utils.MeasureTime(delegate() { fit = iterative_recommender.ComputeFit(); }); fit_time_stats.Add(t.TotalSeconds); Console.Write(string.Format(CultureInfo.InvariantCulture, "fit {0,0:0.#####} ", fit)); } t = Utils.MeasureTime(delegate() { result = Evaluate(); }); eval_time_stats.Add(t.TotalSeconds); Items.DisplayResults(result); Console.WriteLine(" iteration " + i); Recommender.SaveModel(recommender, save_model_file, i); Predict(prediction_file, relevant_users_file, i); if (result["AUC"] < auc_cutoff || result["prec@5"] < prec5_cutoff) { Console.Error.WriteLine("Reached cutoff after {0} iterations.", i); Console.Error.WriteLine("DONE"); break; } } } // for DisplayStats(); } else { if (load_model_file == string.Empty) { Console.Write(recommender.ToString() + " "); time_span = Utils.MeasureTime(delegate() { recommender.Train(); }); Console.Write("training_time " + time_span + " "); } else { Recommender.LoadModel(recommender, load_model_file); Console.Write(recommender.ToString() + " "); // TODO is this the right time to load the model? } if (prediction_file != string.Empty) { Predict(prediction_file, relevant_users_file); } else if (!no_eval) { if (online_eval) { time_span = Utils.MeasureTime(delegate() { var result = Items.EvaluateOnline(recommender, test_data, training_data, relevant_users, relevant_items); // TODO support also for prediction outputs (to allow external evaluation) Items.DisplayResults(result); }); } else { time_span = Utils.MeasureTime(delegate() { var result = Evaluate(); Items.DisplayResults(result); }); } Console.Write(" testing_time " + time_span); } Console.WriteLine(); } 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"); 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(); }