public void analyze() { string CopyToExcel = ""; TrustPredictionMethod method = new TrustPredictionMethod(); int nUsers = 200000; int[] nTagsPerUser = new int[] { 10, 100, 1000 }; foreach (int ntags in nTagsPerUser) { var totalTags = loader.simulate(nUsers, ntags); var Users = (from tag in totalTags group tag by tag.UserID into g select new { userid = g.Key, nTags = g.ToList().Count, tags = g.ToList() }).ToList(); var TagNumberGroups = (from u in Users group u by u.nTags into g orderby g.Key select new { num = g.Key, users = g.ToList() }).ToList(); for (int methodNumber = 0; methodNumber < 3; methodNumber++) { string s = ""; switch (methodNumber) { case 0: method = new MLEProcessor(); s += "MLE\t"; break; case 1: method = new BayesianProcessor(); s += "Bayesian\t"; break; case 2: method = new BetaProcessor(); s += "Beta\t"; break; default: break; } // lets see how well prediction is and takes foreach (var g1 in TagNumberGroups) { Console.WriteLine(g1.num); int SumRates = 0; var stopwatch = new Stopwatch(); stopwatch.Start(); foreach (var user in g1.users) { SumRates += method.ProcessTags(user.tags); } stopwatch.Stop(); //double Error = 1 - ((double)SumRates / g1.users.Count) / 100; //s += Error.ToString() + "\t"; long ticks = stopwatch.ElapsedTicks / g1.users.Count; s += ticks.ToString() + "\t"; } CopyToExcel += s + "\n"; } } using (StreamWriter file = new StreamWriter("CopyToExcel_ticks.txt")) file.Write(CopyToExcel); }
public virtual MethodPerformance Execute() { int experimentMethodCode = Options.ExperimentMethodCode; int nSections = DataSimulation.DefaultGroupSimulationOptions.SectionPriorities.Length * DataSimulation.DefaultGroupSimulationOptions.SectionPriorities[0].Length; List <double> SectionRValue = new List <double>(nSections); List <double> SectionSValue = new List <double>(nSections); for (int i = 0; i < nSections; i++) { SectionRValue.Add(0); SectionSValue.Add(0); } // initialize int[] PredictedTag = new int[nSections], MaxvoteTag = new int[nSections], RandomTag = new int[nSections]; for (int i = 0; i < nSections; i++) // start off with 1 { PredictedTag[i] = 1; MaxvoteTag[i] = 1; RandomTag[i] = 1; } var sectionTable = UserTags.GroupBy(u => new { u.Section, u.Day, u.Hour }, (key, group) => new DayHourSection(group.ToList(), key.Day, key.Hour, key.Section)).ToList(); var Days = UserTags.Max(t => t.Day); StopTrainingDay = (int)Math.Floor(Days * Options.pot); // for portfolio DataPoint point; DataSet TrainingDataset = new DataSet(); TrainingDataset.DataPoints = new List <DataPoint>(); BinaryDecisionTree tree = null; foreach (DayHourSection item in sectionTable) { var tags = item.Reports; int CurrentOccupancyIndex = (from o in Occupancies where o.Day == item.Day && o.Hour == item.Hour && o.Section == item.Section select o.index).ToArray()[0]; int CurrentSection = tags[0].Section; int RealTag = Occupancies[CurrentOccupancyIndex].OccupancyTag; // for all tags, the realtag is the same int CurrentDay = Occupancies[CurrentOccupancyIndex].Day; int CurrentHour = Occupancies[CurrentOccupancyIndex].Hour; bool IsTrainingNow = CurrentDay < StopTrainingDay; int ChoosenClassByPortfolioClassification = 0;; // apply discounting first to all sections if (Options.discounted) { //var indexes = (from o in Occupancies where o.Day >= item.Day && o.Hour >= item.Hour && o.Section == item.Section select o.index).ToArray(); //foreach (var i in indexes) //{ // Occupancies[i].MaxVoteOccupancyTag = 1; // Occupancies[i].PredictedOccupancyTag = 1; // Occupancies[i].RandomOccupancyTag = 1; //} PredictedTag[CurrentSection - 1] = 1; MaxvoteTag[CurrentSection - 1] = 1; RandomTag[CurrentSection - 1] = 1; } RandomTag[CurrentSection - 1] = random.Next(1, DataSimulation.DefaultGroupSimulationOptions.TagOccupancies.Length); if (tags.Count > 0) { MaxvoteTag[CurrentSection - 1] = GetMaxVoteTag(tags); stopwatch.Start(); // let's predict the tag switch (experimentMethodCode) { case 10: var tp1 = new MLEProcessor(); PredictedTag[CurrentSection - 1] = tp1.ProcessTags(tags, Users, Options, RealTag, IsTrainingNow); tp1.UpdateTrustFromObservation(tags, Users, PredictedTag[CurrentSection - 1]); break; case 11: var tp2 = new BayesianProcessor(); PredictedTag[CurrentSection - 1] = tp2.ProcessTags(tags, Users, Options, RealTag, IsTrainingNow); tp2.UpdateTrustFromObservation(tags, Users, PredictedTag[CurrentSection - 1]); break; case 20: var tp3 = new GompertzProcessor(); PredictedTag[CurrentSection - 1] = tp3.ProcessTags(tags, Users, Options, RealTag, IsTrainingNow); tp3.UpdateTrustFromObservation(tags, Users); break; case 21: var tp4 = new RobustAveragingProcessor(); PredictedTag[CurrentSection - 1] = tp4.ProcessTags(tags, Users, Options, RealTag, IsTrainingNow); tp4.UpdateTrustFromObservation(tags, Users); break; case 23: var tp5 = new BetaProcessor(); PredictedTag[CurrentSection - 1] = tp5.ProcessTags(tags, Users, Options, RealTag, IsTrainingNow, SectionRValue, SectionSValue); tp5.UpdateTrustFromObservation(tags, Users, PredictedTag[CurrentSection - 1]); break; case 32: // portfolio classification case 31: // portfolio regression int FolioTag = RealTag; if (!IsTrainingNow) { int[] tgs = new int[5]; tgs[0] = PredictedTag[CurrentSection - 1] = new TagProcessors.MLEProcessor().ProcessTags(tags, Users, Options, RealTag, IsTrainingNow); tgs[1] = PredictedTag[CurrentSection - 1] = new TagProcessors.BayesianProcessor().ProcessTags(tags, Users, Options, RealTag, IsTrainingNow); tgs[2] = PredictedTag[CurrentSection - 1] = new TagProcessors.GompertzProcessor().ProcessTags(tags, Users, Options, RealTag, IsTrainingNow); tgs[3] = PredictedTag[CurrentSection - 1] = new TagProcessors.RobustAveragingProcessor().ProcessTags(tags, Users, Options, RealTag, IsTrainingNow); tgs[4] = PredictedTag[CurrentSection - 1] = new TagProcessors.BetaProcessor().ProcessTags(tags, Users, Options, RealTag, IsTrainingNow, SectionRValue, SectionSValue); bool isRegression = experimentMethodCode == 31; int[] algids = new int[] { 10, 11, 20, 21, 23 }; int featureID = -1; point = new DataPoint(); point.Features = new List <FeatureIndex>(); point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "Hour", Value = CurrentHour }); point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "MaxTrust", Value = Convert.ToDouble(Options.maxTrust) }); //point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "Pop", Value = Options.pop }); //point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "Pou", Value = Options.pou }); //point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "Pot", Value = Options.pot }); point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "Weekday", Value = Occupancies[CurrentOccupancyIndex].Weekday }); point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "Section", Value = CurrentSection }); if (isRegression) { point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "MLE", Value = tgs[0] }); point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "Bayesian", Value = tgs[1] }); point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "Gompertz", Value = tgs[2] }); point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "RobustAveraging", Value = tgs[3] }); point.Features.Add(new FeatureIndex() { ID = featureID++, Name = "Beta", Value = tgs[4] }); } if (CurrentDay >= StopTrainingDay && CurrentDay < StopTrainingDay + 7) { //gather training data if (isRegression) { point.Label = RealTag; } else { point.Label = 0; // maxvote for (int i = 0; i < algids.Length; i++) { if (RealTag == tgs[i]) { point.Label = i + 1; } } } TrainingDataset.DataPoints.Add(point); } else { var multiclassAdaboost = new MultiClassClassificationMethod(TrainingDataset); // train classifier if (tree == null) { //make the classifier tree = multiclassAdaboost.Train(250); } // use the classifier FolioTag = MaxvoteTag[CurrentSection - 1]; try { if (isRegression) { FolioTag = multiclassAdaboost.ClassifyDataPoint(point, tree); } else { ChoosenClassByPortfolioClassification = multiclassAdaboost.ClassifyDataPoint(point, tree); if (ChoosenClassByPortfolioClassification != 0) { FolioTag = tgs[ChoosenClassByPortfolioClassification - 1]; } } } catch { } } } PredictedTag[CurrentSection - 1] = FolioTag; if (IsTrainingNow) { var tp6 = new BetaProcessor(); tp6.UpdateTrustFromObservation(tags, Users, PredictedTag[CurrentSection - 1]); } else { if (experimentMethodCode == 32) { switch (ChoosenClassByPortfolioClassification) { case 1: new MLEProcessor().UpdateTrustFromObservation(tags, Users, PredictedTag[CurrentSection - 1]); break; case 2: new BayesianProcessor().UpdateTrustFromObservation(tags, Users, PredictedTag[CurrentSection - 1]); break; case 3: new GompertzProcessor().UpdateTrustFromObservation(tags, Users); break; case 4: new RobustAveragingProcessor().UpdateTrustFromObservation(tags, Users); break; case 5: new BetaProcessor().UpdateTrustFromObservation(tags, Users, PredictedTag[CurrentSection - 1]); break; default: new BetaProcessor().UpdateTrustFromObservation(tags, Users, PredictedTag[CurrentSection - 1]); break; } } if (experimentMethodCode == 31) { new BetaProcessor().UpdateTrustFromObservation(tags, Users, PredictedTag[CurrentSection - 1]); } } break; default: throw new Exception("experimentMethodCode is not defined"); } stopwatch.Stop(); //db.ExecuteNonQuery } if (PredictedTag[CurrentSection - 1] == 0) { throw new Exception(); } Occupancies[CurrentOccupancyIndex].MaxVoteOccupancyTag = MaxvoteTag[CurrentSection - 1]; Occupancies[CurrentOccupancyIndex].PredictedOccupancyTag = PredictedTag[CurrentSection - 1]; Occupancies[CurrentOccupancyIndex].RandomOccupancyTag = RandomTag[CurrentSection - 1]; Occupancies[CurrentOccupancyIndex].IsTrainingDay = CurrentDay < StopTrainingDay; Occupancies[CurrentOccupancyIndex].CountInEvaluation = true; } return(new MethodPerformance(Occupancies, Users, StopTrainingDay, stopwatch.ElapsedTicks / sectionTable.Count)); }