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Added threading support for running multiple tests at the same time. …
…This is using SmartThreadPool.dll.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,134 @@ | ||
| using System; | ||
| using System.Collections.Generic; | ||
| using System.Diagnostics; | ||
| using System.IO; | ||
| using System.Linq; | ||
| using RecommendationSystem.Entities; | ||
| using RecommendationSystem.Knn; | ||
| using RecommendationSystem.Knn.Models; | ||
| using RecommendationSystem.Knn.RatingAggregation; | ||
| using RecommendationSystem.Knn.Similarity; | ||
| using RecommendationSystem.Models; | ||
| using RecommendationSystem.Recommendations; | ||
| using RecommendationSystem.Training; | ||
|
|
||
| namespace RecommendationSystem.QualityTesting | ||
| { | ||
| public class KnnTester<TRecommender> | ||
| where TRecommender : IRecommender<IKnnModel> | ||
| { | ||
| private readonly Stopwatch timer = new Stopwatch(); | ||
| private CryptoRandom rng = new CryptoRandom(); | ||
| private TextWriter fileWriter; | ||
|
|
||
| public int K { get; set; } | ||
| public ISimilarityEstimator Sim { get; set; } | ||
| public IRatingAggregator Ra { get; set; } | ||
| public List<IUser> TestUsers { get; set; } | ||
| public IKnnModel KnnModel { get; set; } | ||
| public ITrainer<IKnnModel, IUser> Trainer { get; set; } | ||
| public List<IArtist> Artists { get; set; } | ||
| public int NumberOfTests { get; set; } | ||
| public string TestName { get; set; } | ||
|
|
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| public KnnTester() | ||
| { | ||
| } | ||
|
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||
| #region KnnTest | ||
| public void KnnTest() | ||
| { | ||
| var recommender = (TRecommender)Activator.CreateInstance(typeof(TRecommender), new object[] { Sim, Ra, K }); | ||
| TestName = string.Format("Knn-K{0}-{1}-{2}-{3}-T{4}", K, Sim, Ra, recommender, NumberOfTests); | ||
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| InitializeResultWriter(String.Format(@"D:\Dataset\results\{0}.txt", TestName)); | ||
| Write(string.Format("Test {0} ({1})", TestName, DateTime.Now)); | ||
| Write("------------------------------------------------------", false); | ||
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| var rs = new KnnRecommendationSystem(Trainer, recommender); | ||
| timer.Restart(); | ||
| var rv = TestRecommendationSystem(rs); | ||
| timer.Stop(); | ||
|
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| Write("------------------------------------------------------", false); | ||
| Write(string.Format("RMSE({0}) = {1} ({2}ms).", TestName, rv, timer.ElapsedMilliseconds)); | ||
|
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| fileWriter.Close(); | ||
| } | ||
| #endregion | ||
|
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| #region TestRecommendationSystem | ||
| private RmseAndVariance TestRecommendationSystem<TModel, TUser>(IRecommendationSystem<TModel, TUser, ITrainer<TModel, TUser>, IRecommender<TModel>> rs) | ||
| where TModel : IModel | ||
| where TUser : IUser | ||
| { | ||
| var onePrecent = NumberOfTests / 10; | ||
| var rmseList = new List<float>(); | ||
| while (rmseList.Count < NumberOfTests) | ||
| { | ||
| var userIndex = rng.Next(TestUsers.Count); | ||
| var user = TestUsers[userIndex]; | ||
| lock (user) | ||
| { | ||
| //if true we'll remove the only rating | ||
| if (user.Ratings.Count < 2) | ||
| continue; | ||
|
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| var ratingIndex = rng.Next(user.Ratings.Count); | ||
| var rating = user.Ratings[ratingIndex]; | ||
|
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| var error = GerPredictionError(rs, rating, user); | ||
|
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| rmseList.Add((float)Math.Sqrt(error * error)); | ||
| } | ||
|
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| if (rmseList.Count % onePrecent != 0) | ||
| continue; | ||
|
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| Write(string.Format("Test {0} at {1} in {2}ms with RMSE {3} ({4})", TestName, rmseList.Count, timer.ElapsedMilliseconds, rmseList.Sum() / rmseList.Count, DateTime.Now)); | ||
| } | ||
|
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| var rv = new RmseAndVariance(rmseList); | ||
| return rv; | ||
| } | ||
| #endregion | ||
|
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| #region GerPredictionError | ||
| private float GerPredictionError<TModel, TUser>(IRecommendationSystem<TModel, TUser, ITrainer<TModel, TUser>, IRecommender<TModel>> rs, IRating rating, IUser user) | ||
| where TModel : IModel | ||
| where TUser : IUser | ||
| { | ||
| var originalRatings = user.Ratings; | ||
| user.Ratings = user.Ratings.Where(r => r != rating).ToList(); | ||
|
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| var error = rating.Value - rs.Recommender.PredictRatingForArtist(user, (TModel)KnnModel, Artists, rating.ArtistIndex); | ||
|
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| user.Ratings = originalRatings; | ||
| return error; | ||
| } | ||
| #endregion | ||
|
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| #region SavingResults | ||
| private void InitializeResultWriter(string filename) | ||
| { | ||
| var dir = Path.GetDirectoryName(filename); | ||
| if (dir != null && !Directory.Exists(dir)) | ||
| Directory.CreateDirectory(dir); | ||
|
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| fileWriter = new StreamWriter(filename); | ||
| } | ||
|
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| private void Write(string text, bool toConsole = true, bool toFile = true) | ||
| { | ||
| if (toConsole) | ||
| Console.WriteLine(text); | ||
|
|
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| if (!toFile) | ||
| return; | ||
|
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| fileWriter.WriteLine(text); | ||
| fileWriter.Flush(); | ||
| } | ||
| #endregion | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,3 @@ | ||
| <?xml version="1.0"?> | ||
| <configuration> | ||
| <startup><supportedRuntime version="v4.0" sku=".NETFramework,Version=v4.0"/></startup></configuration> |
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