public void SetUp() { training_data = new PosOnlyFeedback<SparseBooleanMatrix>(); training_data.Add(1, 1); training_data.Add(1, 2); training_data.Add(2, 2); training_data.Add(2, 3); training_data.Add(3, 1); training_data.Add(3, 2); recommender = new MostPopular() { Feedback = training_data }; recommender.Train(); test_data = new PosOnlyFeedback<SparseBooleanMatrix>(); test_data.Add(2, 3); test_data.Add(2, 4); test_data.Add(4, 4); all_users = Enumerable.Range(1, 4).ToList(); candidate_items = Enumerable.Range(1, 5).ToList(); }
/// <summary>Evaluate an iterative recommender on the folds of a dataset split, display results on STDOUT</summary> /// <param name="recommender">an item recommender</param> /// <param name="split">a positive-only feedback dataset split</param> /// <param name="test_users">a collection of integers with all test users</param> /// <param name="candidate_items">a collection of integers with all candidate items</param> /// <param name="candidate_item_mode">the mode used to determine the candidate items</param> /// <param name="repeated_events">allow repeated events in the evaluation (i.e. items accessed by a user before may be in the recommended list)</param> /// <param name="max_iter">the maximum number of iterations</param> /// <param name="find_iter">the report interval</param> /// <param name="show_fold_results">if set to true to print per-fold results to STDERR</param> public static void DoIterativeCrossValidation( this IRecommender recommender, ISplit<IPosOnlyFeedback> split, IList<int> test_users, IList<int> candidate_items, CandidateItems candidate_item_mode, RepeatedEvents repeated_events, uint max_iter, uint find_iter = 1, bool show_fold_results = false) { if (!(recommender is IIterativeModel)) throw new ArgumentException("recommender must be of type IIterativeModel"); if (!(recommender is ItemRecommender)) throw new ArgumentException("recommender must be of type ItemRecommender"); var split_recommenders = new ItemRecommender[split.NumberOfFolds]; var iterative_recommenders = new IIterativeModel[split.NumberOfFolds]; var fold_results = new ItemRecommendationEvaluationResults[split.NumberOfFolds]; // initial training and evaluation Parallel.For(0, (int) split.NumberOfFolds, i => { try { split_recommenders[i] = (ItemRecommender) recommender.Clone(); // to avoid changes in recommender split_recommenders[i].Feedback = split.Train[i]; split_recommenders[i].Train(); iterative_recommenders[i] = (IIterativeModel) split_recommenders[i]; fold_results[i] = Items.Evaluate(split_recommenders[i], split.Test[i], split.Train[i], test_users, candidate_items, candidate_item_mode, repeated_events); if (show_fold_results) Console.WriteLine("fold {0} {1} iteration {2}", i, fold_results, iterative_recommenders[i].NumIter); } catch (Exception e) { Console.Error.WriteLine("===> ERROR: " + e.Message + e.StackTrace); throw; } }); Console.WriteLine("{0} iteration {1}", new ItemRecommendationEvaluationResults(fold_results), iterative_recommenders[0].NumIter); // iterative training and evaluation for (int it = (int) iterative_recommenders[0].NumIter + 1; it <= max_iter; it++) { Parallel.For(0, (int) split.NumberOfFolds, i => { try { iterative_recommenders[i].Iterate(); if (it % find_iter == 0) { fold_results[i] = Items.Evaluate(split_recommenders[i], split.Test[i], split.Train[i], test_users, candidate_items, candidate_item_mode, repeated_events); if (show_fold_results) Console.WriteLine("fold {0} {1} iteration {2}", i, fold_results, it); } } catch (Exception e) { Console.Error.WriteLine("===> ERROR: " + e.Message + e.StackTrace); throw; } }); Console.WriteLine("{0} iteration {1}", new ItemRecommendationEvaluationResults(fold_results), it); } }
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(); }