Exemplo n.º 1
0
            public void RMSETest()
            {
                /*
                 * 5  3  0  1
                 * 4  0  0  1
                 * 1  1  0  5
                 * 1  0  0  4
                 * 0  1  5  4
                 */
                DataMatrix R = GetSampleRatingMatrix();

                /*
                 * 3  3  0  1
                 * 4  0  0  5
                 * 1  2  0  5
                 * 1  0  0  4
                 * 0  1  5  4
                 */
                DataMatrix R_predicted = GetSampleRatingMatrix();

                R_predicted[0, 0] = 3;  // was 5
                R_predicted[2, 1] = 2;  // was 1
                R_predicted[1, 3] = 5;  // was 1

                double debug1 = RMSE.Evaluate(R, R_predicted);
                double debug2 = Math.Sqrt(21.0 / 13.0);

                Debug.Assert(debug1 == debug2);
            }
Exemplo n.º 2
0
        public string RunNMFbasedOMF(int maxEpoch, double learnRate, double regularization, int factorCount,
                                     List <double> quantizer, int topN = 0)
        {
            if (!ReadyForNumerical)
            {
                GetReadyForNumerical();
            }
            StringBuilder log = new StringBuilder();

            log.AppendLine(Utils.PrintHeading("NMF based OMF"));

            // NMF Prediction
            // Get ratings from scorer, for both train and test
            // R_all contains indexes of all ratings both train and test
            DataMatrix R_all = new DataMatrix(R_unknown.UserCount, R_unknown.ItemCount);

            R_all.MergeNonOverlap(R_unknown);
            R_all.MergeNonOverlap(R_train.IndexesOfNonZeroElements());
            Utils.StartTimer();
            DataMatrix R_predictedByNMF = NMF.PredictRatings(R_train, R_all, maxEpoch,
                                                             learnRate, regularization, factorCount);

            log.AppendLine(Utils.StopTimer());

            // OMF Prediction
            log.AppendLine(Utils.PrintHeading("Ordinal Matrix Factorization with NMF as scorer"));
            Utils.StartTimer();
            Dictionary <Tuple <int, int>, List <double> > OMFDistributionByUserItem;
            DataMatrix R_predicted;

            log.AppendLine(OMF.PredictRatings(R_train.Matrix, R_unknown.Matrix, R_predictedByNMF.Matrix,
                                              quantizer, out R_predicted, out OMFDistributionByUserItem));
            log.AppendLine(Utils.StopTimer());

            // Numerical Evaluation
            log.AppendLine(Utils.PrintValue("RMSE", RMSE.Evaluate(R_test, R_predicted).ToString("0.0000")));
            log.AppendLine(Utils.PrintValue("MAE", MAE.Evaluate(R_test, R_predicted).ToString("0.0000")));

            // TopN Evaluation
            if (topN != 0)
            {
                var topNItemsByUser = ItemRecommendationCore.GetTopNItemsByUser(R_predicted, topN);
                for (int n = 1; n <= topN; n++)
                {
                    log.AppendLine(Utils.PrintValue("NCDG@" + n, NCDG.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000")));
                }
                for (int n = 1; n <= topN; n++)
                {
                    log.AppendLine(Utils.PrintValue("MAP@" + n, MAP.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000")));
                }
            }

            // Save OMFDistribution to file
            if (!File.Exists(GetDataFileName("RatingOMF_")))
            {
                Utils.IO <Dictionary <Tuple <int, int>, List <double> > > .SaveObject(OMFDistributionByUserItem, GetDataFileName("RatingOMF_"));
            }

            return(log.ToString());
        }
Exemplo n.º 3
0
        public string RunNMFbasedORF(double regularization, double learnRate,
                                     int maxEpoch, List <double> quantizer, int topN = 0)
        {
            // Load OMFDistribution from file
            Dictionary <Tuple <int, int>, List <double> > OMFDistributionByUserItem;

            if (File.Exists(GetDataFileName("RatingOMF_")))
            {
                OMFDistributionByUserItem = Utils.IO <Dictionary <Tuple <int, int>, List <double> > > .LoadObject(GetDataFileName("RatingOMF_"));
            }
            else
            {
                return("Abort, Run OMF first.");
            }

            if (!ReadyForNumerical)
            {
                GetReadyForNumerical();
            }
            StringBuilder log = new StringBuilder();

            log.AppendLine(Utils.PrintHeading("NMF based ORF"));

            // Prediction
            Utils.StartTimer();
            DataMatrix R_predicted_expectations;
            DataMatrix R_predicted_mostlikely;
            ORF        orf = new ORF();

            orf.PredictRatings(R_train, R_unknown, StrongSimilarityIndicatorsByItemRating,
                               OMFDistributionByUserItem, regularization, learnRate, maxEpoch,
                               quantizer.Count, out R_predicted_expectations, out R_predicted_mostlikely);
            log.AppendLine(Utils.StopTimer());

            // Numerical Evaluation
            log.AppendLine(Utils.PrintValue("RMSE", RMSE.Evaluate(R_test, R_predicted_expectations).ToString("0.0000")));
            log.AppendLine(Utils.PrintValue("MAE", RMSE.Evaluate(R_test, R_predicted_mostlikely).ToString("0.0000")));

            // Top-N Evaluation
            if (topN != 0)
            {
                var topNItemsByUser_expectations = ItemRecommendationCore.GetTopNItemsByUser(R_predicted_expectations, topN);
                for (int n = 1; n <= topN; n++)
                {
                    log.AppendLine(Utils.PrintValue("NCDG@" + n, NCDG.Evaluate(RelevantItemsByUser, topNItemsByUser_expectations, n).ToString("0.0000")));
                }
                for (int n = 1; n <= topN; n++)
                {
                    log.AppendLine(Utils.PrintValue("MAP@" + n, MAP.Evaluate(RelevantItemsByUser, topNItemsByUser_expectations, n).ToString("0.0000")));
                }
            }

            return(log.ToString());
        }
Exemplo n.º 4
0
            public void RMSETest()
            {
                var actual   = new Matrix(100, 1);
                var expected = new Matrix(100, 1);

                actual.InRandomize(0.25, 0.75);
                expected.InRandomize(0.25, 0.75);

                var metric = new RMSE();
                var e      = metric.Evaluate(actual, expected);
                var val    = Math.Sqrt(new MSE().Evaluate(actual, expected));

                Assert.IsTrue(Math.Abs(e - val) < 0.01, metric.Type().ToString() + " Evaluate.");
            }
Exemplo n.º 5
0
        /// <summary>
        /// Rating based Non-negative Matrix Factorization
        /// </summary>
        public string RunNMF(int maxEpoch, double learnRate, double regularization,
                             int factorCount, int topN = 0)
        {
            if (!ReadyForNumerical)
            {
                GetReadyForNumerical();
            }
            StringBuilder log = new StringBuilder();

            log.AppendLine(Utils.PrintHeading("NMF"));

            // Prediction
            Utils.StartTimer();
            DataMatrix R_predicted = NMF.PredictRatings(R_train, R_unknown, maxEpoch,
                                                        learnRate, regularization, factorCount);

            log.AppendLine(Utils.StopTimer());

            // Numerical Evaluation
            log.AppendLine(Utils.PrintValue("RMSE", RMSE.Evaluate(R_test, R_predicted).ToString("0.0000")));
            log.AppendLine(Utils.PrintValue("MAE", MAE.Evaluate(R_test, R_predicted).ToString("0.0000")));

            // TopN Evaluation
            if (topN != 0)
            {
                var topNItemsByUser = ItemRecommendationCore.GetTopNItemsByUser(R_predicted, topN);
                for (int n = 1; n <= topN; n++)
                {
                    log.AppendLine(Utils.PrintValue("NCDG@" + n, NCDG.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000")));
                }
                for (int n = 1; n <= topN; n++)
                {
                    log.AppendLine(Utils.PrintValue("MAP@" + n, MAP.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000")));
                }
            }

            return(log.ToString());
        }
Exemplo n.º 6
0
        /// <summary>
        /// Predict all unknown values as global mean rating.
        /// </summary>
        public string RunGlobalMean()
        {
            if (!ReadyForNumerical)
            {
                GetReadyForNumerical();
            }
            StringBuilder log = new StringBuilder();

            log.AppendLine(Utils.PrintHeading("Global Mean"));

            // Prediction
            Utils.StartTimer();
            double     globalMean  = R_train.GetGlobalMean();
            DataMatrix R_predicted = R_unknown.Multiply(globalMean);

            log.AppendLine(Utils.StopTimer());

            // Numerical Evaluation
            log.AppendLine(Utils.PrintValue("RMSE", RMSE.Evaluate(R_test, R_predicted).ToString("0.0000")));
            log.AppendLine(Utils.PrintValue("MAE", MAE.Evaluate(R_test, R_predicted).ToString("0.0000")));

            return(log.ToString());
        }
Exemplo n.º 7
0
        public string RunUserKNN(int topN = 0)
        {
            if (!ReadyForNumerical)
            {
                GetReadyForNumerical();
            }
            StringBuilder log = new StringBuilder();

            log.AppendLine(Utils.PrintHeading("UserKNN"));

            // Prediction
            Utils.StartTimer();
            DataMatrix R_predicted = Numerical.UserKNN.PredictRatings(R_train, R_unknown, UserSimilaritiesOfRating, MaxCountOfNeighbors);

            log.AppendLine(Utils.StopTimer());

            // Numerical Evaluation
            log.AppendLine(Utils.PrintValue("RMSE", RMSE.Evaluate(R_test, R_predicted).ToString("0.0000")));
            log.AppendLine(Utils.PrintValue("MAE", MAE.Evaluate(R_test, R_predicted).ToString("0.0000")));

            // TopN Evaluation
            if (topN != 0)
            {
                var topNItemsByUser = ItemRecommendationCore.GetTopNItemsByUser(R_predicted, topN);
                for (int n = 1; n <= topN; n++)
                {
                    Utils.PrintValue("NCDG@" + n, NCDG.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000"));
                }
                for (int n = 1; n <= topN; n++)
                {
                    log.AppendLine(Utils.PrintValue("MAP@" + n, MAP.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000")));
                }
            }

            return(log.ToString());
        }