static TimeSpan EvaluateRecommender(BPRMF_Mapping recommender, IPosOnlyFeedback test_data, IPosOnlyFeedback train_data) { Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "fit {0}", recommender.ComputeFit())); TimeSpan seconds = Utils.MeasureTime(delegate() { var result = Items.Evaluate( recommender, test_data, train_data, test_data.AllUsers, relevant_items ); DisplayResults(result); }); Console.Write(" testing " + seconds); return(seconds); }
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(); }