static void DoTrack1() { var rating_predictor_validate = recommender as RatingPredictor; var rating_predictor_final = rating_predictor_validate.Clone() as RatingPredictor; rating_predictor_final.Ratings = complete_ratings; Console.WriteLine("Validation split:"); Utils.DisplayDataStats(training_ratings, validation_ratings, rating_predictor_validate); Console.WriteLine("Test split:"); Utils.DisplayDataStats(complete_ratings, test_data, rating_predictor_final); if (find_iter != 0) { if (!(recommender is IIterativeModel)) { Usage("Only iterative recommenders support find_iter."); } IIterativeModel iterative_recommender_validate = (MatrixFactorization)rating_predictor_validate; IIterativeModel iterative_recommender_final = (MatrixFactorization)rating_predictor_final; Console.WriteLine(recommender.ToString() + " "); if (load_model_file == string.Empty) { iterative_recommender_validate.Train(); iterative_recommender_final.Train(); } else { Recommender.LoadModel(rating_predictor_final, "final-" + load_model_file); } if (compute_fit) { Console.Write(string.Format(CultureInfo.InvariantCulture, "fit {0:0.#####} ", iterative_recommender_validate.ComputeFit())); } MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(rating_predictor_validate, validation_ratings)); Console.WriteLine(" " + iterative_recommender_validate.NumIter); for (int i = (int)iterative_recommender_validate.NumIter + 1; i <= max_iter; i++) { TimeSpan time = Utils.MeasureTime(delegate() { iterative_recommender_validate.Iterate(); iterative_recommender_final.Iterate(); // TODO parallelize this }); training_time_stats.Add(time.TotalSeconds); if (i % find_iter == 0) { if (compute_fit) { double fit = 0; time = Utils.MeasureTime(delegate() { fit = iterative_recommender_validate.ComputeFit(); }); fit_time_stats.Add(time.TotalSeconds); Console.Write(string.Format(CultureInfo.InvariantCulture, "fit {0:0.#####} ", fit)); } // evaluate and save stats // TODO parallelize Dictionary <string, double> results = null; time = Utils.MeasureTime(delegate() { results = MyMediaLite.Eval.Ratings.Evaluate(rating_predictor_validate, validation_ratings); MyMediaLite.Eval.Ratings.DisplayResults(results); rmse_eval_stats.Add(results["RMSE"]); Console.WriteLine(" " + i); }); eval_time_stats.Add(time.TotalSeconds); // write out model files and predictions if (save_model_file != string.Empty) { Recommender.SaveModel(rating_predictor_validate, save_model_file + "-validate", i); Recommender.SaveModel(rating_predictor_final, save_model_file, i); } if (prediction_file != string.Empty) { if (track2) { KDDCup.PredictRatingsDouble(rating_predictor_validate, validation_candidates, prediction_file + "-validate-it-" + i); KDDCup.PredictRatingsDouble(rating_predictor_final, test_data, prediction_file + "-it-" + i); } else { KDDCup.PredictRatings(rating_predictor_validate, validation_ratings, prediction_file + "-validate-it-" + i); KDDCup.PredictRatings(rating_predictor_final, test_data, prediction_file + "-it-" + i); } } // check whether we should abort if (epsilon > 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 DisplayIterationStats(); Recommender.SaveModel(recommender, save_model_file); } else { TimeSpan seconds; if (!no_eval) { if (load_model_file == string.Empty) { Console.Write(recommender.ToString()); if (cross_validation > 0) // TODO cross-validation could also be performed on the complete dataset { // TODO support track2 Console.WriteLine(); var split = new RatingCrossValidationSplit(training_ratings, cross_validation); var results = MyMediaLite.Eval.Ratings.EvaluateOnSplit(rating_predictor_validate, split); MyMediaLite.Eval.Ratings.DisplayResults(results); no_eval = true; rating_predictor_validate.Ratings = training_ratings; } else { seconds = Utils.MeasureTime(delegate() { recommender.Train(); }); Console.Write(" training_time " + seconds + " "); Recommender.SaveModel(recommender, save_model_file); } } Console.Write(recommender.ToString() + " "); seconds = Utils.MeasureTime( delegate() { MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(rating_predictor_validate, validation_ratings)); } ); Console.Write(" testing_time " + seconds); } Console.WriteLine(); if (prediction_file != string.Empty) { Console.WriteLine("Prediction for KDD Cup Track 1:"); seconds = Utils.MeasureTime(delegate() { rating_predictor_final.Train(); }); Console.Write(" training_time " + seconds + " "); if (save_model_file != string.Empty) { Recommender.SaveModel(rating_predictor_validate, save_model_file + "-validate"); Recommender.SaveModel(rating_predictor_final, save_model_file); } Console.WriteLine(); seconds = Utils.MeasureTime(delegate() { KDDCup.PredictRatingsDouble(rating_predictor_final, test_data, prediction_file); if (track2) { KDDCup.PredictRatingsDouble(rating_predictor_validate, validation_candidates, prediction_file + "-validate"); } else { KDDCup.PredictRatings(rating_predictor_validate, validation_ratings, prediction_file + "-validate"); } }); Console.Error.WriteLine("predicting_time " + seconds); } } }