public List <PredictionResult> DoStacking(SAMDataPoint.FeelingModel feelingsmodel, int nFold, bool useIAPSratings = false, Normalize normalizeFormat = Normalize.OneMinusOne) { List <PredictionResult> classifiers = new List <PredictionResult>(); //For each classifier run a crossvalidation and find the best params foreach (StdClassifier classifier in standardClassifiers) { List <PredictionResult> results = classifier.OldCrossValidate(feelingsmodel, 1, useIAPSratings, normalizeFormat); classifiers.Add(results.OrderBy(x => x.GetAverageFScore()).First()); } List <List <double> > featureList = new List <List <double> >(); //Create a List of list of answers from each machine for (int i = 0; i < samData.dataPoints.Count; i++) { List <double> featuresToDataPoint = new List <double>(); foreach (PredictionResult classifier in classifiers) { featuresToDataPoint.Add(classifier.guesses[i]); } featureList.Add(featuresToDataPoint); } //Split into nfold problems List <Tuple <SVMProblem, SVMProblem> > problems = featureList.GetCrossValidationSets <double>(samData, feelingsmodel, nFold, useIAPSratings); //Get correct results int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray(); List <PredictionResult> finalResults = new List <PredictionResult>(); //Run for each parameter setting int cnt = 1; foreach (SVMParameter SVMpara in Parameters) { if (UpdateCallback != null) { UpdateCallback(cnt++, Parameters.Count); } List <double> guesses = new List <double>(); //model and predict each nfold foreach (Tuple <SVMProblem, SVMProblem> tupleProblem in problems) { guesses.AddRange(tupleProblem.Item2.Predict(tupleProblem.Item1.Train(SVMpara))); } int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel); //Calculate scoring results double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels); List <double> pres = CalculatePrecision(confus, numberOfLabels); List <double> recall = CalculateRecall(confus, numberOfLabels); List <double> fscore = CalculateFScore(pres, recall); PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, SVMpara, new List <Feature> { }, answers.ToList(), guesses.ConvertAll(x => (int)x)); finalResults.Add(pR); } return(finalResults); }
public List <PredictionResult> CrossValidateWithBoosting(SAMDataPoint.FeelingModel feelingsmodel, int nFold, double[] answersFromPrevious, bool useIAPSratings = false, Normalize normalizationType = Normalize.OneMinusOne) { List <PredictionResult> predictedResults = new List <PredictionResult>(); List <List <double> > tempFeatuers = GetFeatureValues(features, samData); if (answersFromPrevious.Length != tempFeatuers.Count) { //answers from previous is not the same size as current feature list, e.g. something is wrong Log.LogMessage("The number of guessses from previous machine is the same as number of datapoints in this"); return(null); } //Split into crossvalidation parts List <List <double> > tempFeatures = tempFeatuers.NormalizeFeatureList <double>(normalizationType).ToList(); for (int i = 0; i < tempFeatuers.Count; i++) { tempFeatuers[i].Add(answersFromPrevious[i]); } List <Tuple <SVMProblem, SVMProblem> > problems = tempFeatuers.GetCrossValidationSets <double>(samData, feelingsmodel, nFold, useIAPSratings); //Get correct results int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray(); foreach (SVMParameter SVMpara in Parameters) { List <double> guesses = new List <double>(); //model and predict each nfold foreach (Tuple <SVMProblem, SVMProblem> tupleProblem in problems) { guesses.AddRange(tupleProblem.Item2.Predict(tupleProblem.Item1.Train(SVMpara))); } int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel); //Calculate scoring results double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels); List <double> pres = CalculatePrecision(confus, numberOfLabels); List <double> recall = CalculateRecall(confus, numberOfLabels); List <double> fscore = CalculateFScore(pres, recall); PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, SVMpara, features, answers.ToList(), guesses.ConvertAll(x => (int)x).ToList()); predictedResults.Add(pR); } return(predictedResults); }
public PredictionResult DoVoting(SAMDataPoint.FeelingModel feelingsmodel, int nFold, bool useIAPSratings = false, Normalize normalizeFormat = Normalize.OneMinusOne) { List <PredictionResult> classifiers = new List <PredictionResult>(); //For each classifier run a crossvalidation and find the best params int prg = 0; foreach (StdClassifier classifier in standardClassifiers) { if (UpdateCallback != null) { UpdateCallback(prg++, standardClassifiers.Count); } List <PredictionResult> results = classifier.OldCrossValidate(feelingsmodel, 1, useIAPSratings, normalizeFormat); classifiers.Add(results.OrderBy(x => x.GetAverageFScore()).First()); } if (UpdateCallback != null) { UpdateCallback(standardClassifiers.Count, standardClassifiers.Count); } int labelCount = SAMData.GetNumberOfLabels(feelingsmodel); //Full List of indicies List <int> counter = new List <int>(); for (int k = 0; k < samData.dataPoints.Count(); k++) { counter.Add(k); } //Divide indicies into correct nfold List <List <int> > trainIndicies = new List <List <int> >(); List <List <int> > predictIndicies = new List <List <int> >(); for (int i = 0; i < samData.dataPoints.Count(); i += nFold) { var temp = counter.Skip(i).Take(nFold).ToList(); predictIndicies.Add(temp); trainIndicies.Add(counter.Except(temp).ToList()); } List <Dictionary <int, double> > weightedGuesses = new List <Dictionary <int, double> >(); //Fill up weightedGuesses List for (int nGuesses = 0; nGuesses < samData.dataPoints.Count; nGuesses++) { Dictionary <int, double> tempGuess = new Dictionary <int, double>(); for (int indexClass = 0; indexClass < labelCount; indexClass++) { tempGuess.Add(indexClass, 0); } weightedGuesses.Add(tempGuess); } //Split classifiers for (int i = 0; i < trainIndicies.Count; i++) { foreach (PredictionResult predictResult in classifiers) { double correct = 0; //calculate weights for (int trainingIndex = 0; trainingIndex < trainIndicies[i].Count; trainingIndex++) { if (predictResult.guesses[trainIndicies[i][trainingIndex]] == samData.dataPoints[trainIndicies[i][trainingIndex]].ToAVCoordinate(feelingsmodel)) { correct++; } } //Add weight from the trainingset to each of the guesses weightedGuesses[i][predictResult.guesses[i]] += (correct / trainIndicies.Count); } } //Calculate final answers List <double> guesses = new List <double>(); foreach (Dictionary <int, double> answer in weightedGuesses) { int tempKey = -1; double tempMax = -1; foreach (int key in answer.Keys) { if (answer[key] > tempMax) { tempKey = key; tempMax = answer[key]; } } guesses.Add(tempKey); } //Get correct results int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray(); int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel); //Calculate scoring results double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels); List <double> pres = CalculatePrecision(confus, numberOfLabels); List <double> recall = CalculateRecall(confus, numberOfLabels); List <double> fscore = CalculateFScore(pres, recall); return(new PredictionResult(confus, recall, pres, fscore, new SVMParameter(), new List <Feature> { }, answers.ToList(), guesses.ConvertAll(x => (int)x))); }
/// <summary> /// Run crossvalidation for each combination of the features for this machine /// </summary> /// <param name="feelingsmodel"></param> /// <param name="nFold"></param> /// <param name="useIAPSratings"></param> public List <PredictionResult> CrossValidateCombinations(SAMDataPoint.FeelingModel feelingsmodel, int nFold, bool useIAPSratings = false, Normalize normalizationType = Normalize.OneMinusOne) { List <List <bool> > combinations = CalculateCombinations(new List <bool>() { }, features.Count); //Get different combination of problems List <Tuple <List <Tuple <SVMProblem, SVMProblem> >, List <Feature> > > featureCombinationProblems = new List <Tuple <List <Tuple <SVMProblem, SVMProblem> >, List <Feature> > >(); for (int i = 0; i < combinations.Count; i++) { List <Feature> tempFeatures = new List <Feature>(); for (int j = 0; j < combinations[i].Count; j++) { // For each feature combination save the different problems for crossvalidation if (combinations[i][j] == true) { tempFeatures.Add(features[j]); } } featureCombinationProblems.Add ( new Tuple <List <Tuple <SVMProblem, SVMProblem> >, List <Feature> > ( GetFeatureValues(tempFeatures, samData).NormalizeFeatureList <double>(normalizationType).GetCrossValidationSets <double>(samData, feelingsmodel, nFold, useIAPSratings), tempFeatures ) ); } //Get correct results int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray(); int progressCounter = 0; List <PredictionResult> predictionResults = new List <PredictionResult>(); foreach (SVMParameter SVMpara in Parameters) { //For each feature setup for (int n = 0; n < featureCombinationProblems.Count; n++) { if (UpdateCallback != null) { UpdateCallback(progressCounter, Parameters.Count * featureCombinationProblems.Count); } //PrintProgress(progressCounter, featureCombinationProblems.Count); List <double> guesses = new List <double>(); //model and predict each nfold foreach (var tupleProblem in featureCombinationProblems[n].Item1) { guesses.AddRange(tupleProblem.Item2.Predict(tupleProblem.Item1.Train(SVMpara))); } int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel); //Calculate scoring results double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels); List <double> pres = CalculatePrecision(confus, numberOfLabels); List <double> recall = CalculateRecall(confus, numberOfLabels); List <double> fscore = CalculateFScore(pres, recall); PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, SVMpara, featureCombinationProblems[n].Item2, answers.ToList(), guesses.ConvertAll(x => (int)x)); predictionResults.Add(pR); progressCounter++; } } if (UpdateCallback != null) { UpdateCallback(progressCounter, Parameters.Count * featureCombinationProblems.Count); } return(predictionResults); }
public List <PredictionResult> CrossValidate(SAMDataPoint.FeelingModel feelingsmodel, bool useIAPSratings = false, Normalize normalizationType = Normalize.OneMinusOne) { List <PredictionResult> predictedResults = new List <PredictionResult>(); //Split into crossvalidation parts SVMProblem problems = GetFeatureValues(features, samData).NormalizeFeatureList <double>(normalizationType).CreateCompleteProblem(samData, feelingsmodel); //Get correct results int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray(); int progressCounter = 0; if (answers.Distinct().Count() <= 1) { int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel); //Calculate scoring results double[,] confus = CalculateConfusion(answers.ToList().ConvertAll(x => (double)x).ToArray(), answers, numberOfLabels); List <double> pres = CalculatePrecision(confus, numberOfLabels); List <double> recall = CalculateRecall(confus, numberOfLabels); List <double> fscore = CalculateFScore(pres, recall); PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, new SVMParameter(), features, answers.ToList(), answers.ToList().ConvertAll(x => (int)x)); predictedResults.Add(pR); progressCounter++; Log.LogMessage(ONLY_ONE_CLASS); Log.LogMessage(""); return(predictedResults); } else if (problems.X.Count == 0) { Log.LogMessage("Empty problem in " + Name); return(null); } foreach (SVMParameter SVMpara in Parameters) { if (UpdateCallback != null) { UpdateCallback(progressCounter, Parameters.Count); } double[] guesses = new double[samData.dataPoints.Count]; //model and predict each nfold try { problems.CrossValidation(SVMpara, samData.dataPoints.Count, out guesses); } catch (Exception e) { for (int i = 0; i < samData.dataPoints.Count; i++) { guesses[i] = -1; } } int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel); //Calculate scoring results double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels); List <double> pres = CalculatePrecision(confus, numberOfLabels); List <double> recall = CalculateRecall(confus, numberOfLabels); List <double> fscore = CalculateFScore(pres, recall); PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, SVMpara, features, answers.ToList(), Array.ConvertAll(guesses, (x => (int)x)).ToList()); predictedResults.Add(pR); progressCounter++; if (UpdateCallback != null) { UpdateCallback(progressCounter, Parameters.Count); } } return(predictedResults); }
/// <summary> /// Run crossvalidation for the feature setup for this machine /// </summary> /// <param name="feelingsmodel"></param> /// <param name="nFold"></param> /// <param name="useIAPSratings"></param> public List <PredictionResult> OldCrossValidate(SAMDataPoint.FeelingModel feelingsmodel, int nFold, bool useIAPSratings = false, Normalize normalizationType = Normalize.OneMinusOne) { List <PredictionResult> predictedResults = new List <PredictionResult>(); //Split into crossvalidation parts List <Tuple <SVMProblem, SVMProblem> > problems = GetFeatureValues(features, samData).NormalizeFeatureList <double>(normalizationType).GetCrossValidationSets <double>(samData, feelingsmodel, nFold, useIAPSratings); //Get correct results int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray(); int progressCounter = 0; bool postedOneClassError = false; if (answers.Distinct().Count() <= 1) { int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel); //Calculate scoring results double[,] confus = CalculateConfusion(answers.ToList().ConvertAll(x => (double)x).ToArray(), answers, numberOfLabels); List <double> pres = CalculatePrecision(confus, numberOfLabels); List <double> recall = CalculateRecall(confus, numberOfLabels); List <double> fscore = CalculateFScore(pres, recall); PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, new SVMParameter(), features, answers.ToList(), answers.ToList().ConvertAll(x => (int)x)); predictedResults.Add(pR); progressCounter++; Log.LogMessage(ONLY_ONE_CLASS); Log.LogMessage(""); return(predictedResults); } foreach (SVMParameter SVMpara in Parameters) { if (UpdateCallback != null) { UpdateCallback(progressCounter, Parameters.Count); } List <double> guesses = new List <double>(); //model and predict each nfold try { foreach (Tuple <SVMProblem, SVMProblem> tupleProblem in problems) { SVMModel trainingModel = tupleProblem.Item1.Train(SVMpara); if (trainingModel.ClassCount <= 1) { if (!postedOneClassError) { Log.LogMessage(ONLY_ONE_CLASS_IN_TRAINING); postedOneClassError = true; } guesses.AddRange(tupleProblem.Item1.Y.ToList().Take(tupleProblem.Item2.Y.Count()).ToList()); } else { double[] d = tupleProblem.Item2.Predict(trainingModel); guesses.AddRange(d); } } } catch (Exception e) { for (int i = 0; i < samData.dataPoints.Count; i++) { guesses.Add(-1); } } int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel); //Calculate scoring results double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels); List <double> pres = CalculatePrecision(confus, numberOfLabels); List <double> recall = CalculateRecall(confus, numberOfLabels); List <double> fscore = CalculateFScore(pres, recall); PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, SVMpara, features, answers.ToList(), guesses.ConvertAll(x => (int)x)); predictedResults.Add(pR); progressCounter++; if (UpdateCallback != null) { UpdateCallback(progressCounter, Parameters.Count); } } return(predictedResults); }