Train() public method

public Train ( ) : void
return void
 public void TestFoldIn()
 {
     var mf = new BiasedMatrixFactorization() { Ratings = TestUtils.CreateRatings() };
     mf.Train();
     var user_ratings = new List<Tuple<int, float>>();
     user_ratings.Add(new Tuple<int, float>(0, 4.0f));
     var candidate_items = new List<int> { 0, 1 }; // have a known and an unknown item
     var results = mf.ScoreItems(user_ratings, candidate_items);
     Assert.AreEqual(2, results.Count);
 }
        public void TestDecay()
        {
            var mf = new BiasedMatrixFactorization()
            {
                LearnRate = 1.0f, LearnRateDecay = 0.5f,
                NumIter = 1, Ratings = TestUtils.CreateRatings()
            };

            mf.Train();
            Assert.AreEqual(0.5f, mf.current_learnrate);

            mf.Iterate();
            Assert.AreEqual(0.25f, mf.current_learnrate);
        }
Esempio n. 3
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        static void Main(string[] args)
        {
            var t = new Stopwatch();

            //Utils.Converter(@"D:\__testdata\lab10\train.csv");
            //return;

            t.Restart();
            Console.WriteLine("RatingData.Read Begin");
            var trainingData = RatingData.Read(TRAIN_DATA_FILE_NAME);
            Console.WriteLine("RatingData.Read End {0}", t.Elapsed.TotalSeconds);

            //t.Restart();
            //Console.WriteLine("RatingData.Read Begin");
            //var evalData = RatingData.Read(EVAL_DATA_FILE_NAME);
            //Console.WriteLine("RatingData.Read End {0}", t.Elapsed.TotalSeconds);

            //http://www.mymedialite.net/examples/datasets.html
            // num_factors=120
            // bias_reg=0.001
            // regularization=0.055
            // learn_rate=0.07
            // num_iter=100
            // bold_driver=true
            var recommender = new BiasedMatrixFactorization
            {
                Ratings = trainingData,
                NumIter = 10,
                MaxRating = 5,
                MinRating = 1,
            };

            t.Restart();
            Console.WriteLine("Training begin");
            recommender.Train();
            Console.WriteLine("Training end {0}", t.Elapsed.TotalSeconds);

            //t.Restart();
            //Console.WriteLine("Evaluate begin");
            //var resultsb = recommender.Evaluate(evalData);
            //Console.WriteLine("Evaluate end {0}", t.Elapsed.TotalSeconds);

            Console.WriteLine(recommender.DoCrossValidation());

            //Console.WriteLine("RMSE={0} MAE={1}", resultsb["RMSE"], resultsb["MAE"]);

            t.Restart();
            Console.WriteLine("Result Begin");

            var users = recommender.Ratings.AllUsers.ToArray();
            var items = recommender.Ratings.AllItems.ToArray();
            var ratingAvg = recommender.Ratings.Average;

            using(var testIn = new StreamReader(TEST_FILE_NAME))
            using (var resultOut = new StreamWriter(RESULTS_FILE_NAME))
            {

                var count = 0;
                string line;

                while ((line = testIn.ReadLine()) != null)
                {
                    if (count > 0)
                    {
                        var parts = line.Split(new[] {","}, StringSplitOptions.RemoveEmptyEntries);
                        var userId = int.Parse(parts[0]);
                        var itemId = int.Parse(parts[1]);

                        var rating = !users.Contains(userId) || !items.Contains(itemId)
                            ? ratingAvg
                            : recommender.Predict(userId, itemId);

                        resultOut.WriteLine("{0}, {1}, {2}", userId, itemId, rating);
                    }

                    if (count%10000 == 0)
                    {
                        resultOut.Flush();
                        Console.Write(".");
                    }
                    count++;
                }

                resultOut.Close();
                testIn.Close();
            }

            Console.WriteLine();
            Console.WriteLine("Result End {0}", t.Elapsed.TotalSeconds);

            t.Stop();

            Console.WriteLine("End");
            Console.ReadLine();
        }
 public void TestDefaultBehaviorIsNoDecay()
 {
     var mf = new BiasedMatrixFactorization() { LearnRate = 1.1f, NumIter = 10, Ratings = TestUtils.CreateRatings() };
     mf.Train();
     Assert.AreEqual(1.1f, mf.current_learnrate);
 }
		public void TestIncrementalUpdates()
		{
			var mf = new BiasedMatrixFactorization() { Ratings = TestUtils.CreateRatings(), FrequencyRegularization = true };
			mf.Train();

			var new_ratings = new Ratings();
			new_ratings.Add(mf.MaxUserID + 1, mf.MaxItemID + 1, 3f);
			mf.AddRatings(new_ratings);
		}
Esempio n. 6
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        /// <summary>
        /// Predict the rating of the item by users
        /// </summary>
        private static void PredictRating(string[] args)
        {
            Console.WriteLine("Predicting ratings for Users...");
            String outputFile = Path.Combine(args[2], outputFileForRatings);
            testingFile = Path.Combine(args[1], testingFile);
            testingFileModified = Path.Combine(args[2], testingFileModified);
            ModifyTestingFileForRating(testingFile, testingFileModified);

            float minRating = 1;
            float maxRating = 5;

            var userMapping = new Mapping();
            var itemMapping = new Mapping();
            var trainingData = StaticRatingData.Read(trainingCompactFileForRatings, userMapping, itemMapping, RatingType.FLOAT, TestRatingFileFormat.WITH_RATINGS, false);
            var testUsers = trainingData.AllUsers; // users that will be taken into account in the evaluation
            var candidate_items = trainingData.AllItems; // items that will be taken into account in the evaluation
            var testData = StaticRatingData.Read(testingFileModified, userMapping, itemMapping, RatingType.FLOAT, TestRatingFileFormat.WITH_RATINGS, false);

            var recommender = new BiasedMatrixFactorization();
            recommender.MinRating = minRating;
            recommender.MaxRating = maxRating;
            recommender.Ratings = trainingData;

            recommender.NumFactors = 30;
            recommender.NumIter = 100;
            recommender.RegI = 0.04F;
            recommender.RegU = 0.04F;
            //recommender.BiasReg = 0.09F;
            recommender.FrequencyRegularization = true;
            recommender.BoldDriver = true;
            recommender.LearnRate = 0.07F;

            Stopwatch timeKeeper = new Stopwatch();
            timeKeeper.Start();
            recommender.Train();
            timeKeeper.Stop();
            Console.WriteLine("time passed for training rating prediction model: " + timeKeeper.ElapsedMilliseconds);
            // measure the accuracy on the test data set

            timeKeeper = new Stopwatch();
            timeKeeper.Start();
            var results = recommender.Evaluate(testData);
            timeKeeper.Stop();
            Console.WriteLine("time passed for rating prediction: " + timeKeeper.ElapsedMilliseconds);
            Console.WriteLine("RMSE={0}", results["RMSE"]);

            recommender.WritePredictions(testData, outputFile, userMapping, itemMapping, "{0}-{1},{2}", "userID-itemID,rating");
        }