public BPM( string[] labels, double sparsityApproxThresh) { Debug.Assert(labels != null, "The labels must not be null."); Debug.Assert(labels.Length == 2, "The labels must have two possible values."); Debug.Assert(sparsityApproxThresh >= 0, "The sparsityApproxThresh must be greater than or equal to zero."); // Initialise the validations _validate = new Validate(); // Initialise the BPM _engine = new Dictionary <DistributionType, InferenceEngine>(); _w = new Dictionary <DistributionType, Variable <Vector> >(); _w[DistributionType.Prior] = null; _w[DistributionType.Posterior] = null; _d = new Dictionary <DistributionType, DistributionName>(); _yPredicDistrib = Enumerable.Empty <IDictionary <string, double> >(); _yPredicLabel = new string[] { }; _mapping = new GenericClassifierMapping(labels); // TO DO // Evaluator mapping var evaluatorMapping = _mapping.ForEvaluation(); _evaluator = new ClassifierEvaluator <IList <Vector>, int, IList <string>, string>(evaluatorMapping); // Other initialisations _availableDatasetName = new DatasetName(); _numObservations = 0; _numFeatures = 0; }
public BPMMapped( string[] labels) { Debug.Assert(labels != null, "The labels must not be null."); Debug.Assert(labels.Length == 2, "The labels must have two possible values."); // Initialise the validations _validate = new Validate(); // Create a BPM from the mapping _mapping = new GenericClassifierMapping(labels); _classifier = BayesPointMachineClassifier.CreateBinaryClassifier(_mapping); // Evaluator mapping var evaluatorMapping = _mapping.ForEvaluation(); _evaluator = new ClassifierEvaluator <IList <Vector>, int, IList <string>, string>(evaluatorMapping); // Other initialisations _availableDatasetName = new DatasetName(); _numObservations = 0; _numFeatures = 0; }
/// <summary> /// CrossValidate diagnosis /// </summary> /// <param name="x"></param> /// <param name="y"></param> /// <param name="mapping"></param> /// <param name="reportFileName"></param> /// <param name="crossValidationFoldCount"></param> /// <param name="iterationCount"></param> /// <param name="computeModelEvidence"></param> /// <param name="batchCount"></param> /// <remarks>Adapted from MicrosoftResearch.Infer.Learners</remarks> public CrossValidateMapped( Vector[] x, IList <string> y, GenericClassifierMapping mapping, string reportFileName, int crossValidationFoldCount, //folds int iterationCount, bool computeModelEvidence, int batchCount) { Debug.Assert(x != null, "The feature vector must not be null."); Debug.Assert(y != null, "The targe variable must not be null."); Debug.Assert(mapping != null, "The classifier map must not be null."); Debug.Assert(!string.IsNullOrEmpty(reportFileName), "The report file name must not be null/empty."); Debug.Assert(iterationCount > 0, "The iteration count must be greater than zero."); Debug.Assert(batchCount > 0, "The batch count must be greater than zero."); // Shuffle dataset shuffleVector(x); // Create evaluator var evaluatorMapping = mapping.ForEvaluation(); var evaluator = new ClassifierEvaluator < IList <Vector>, // the type of the instance source, int, // the type of an instance IList <string>, // the type of the label source string>( // the type of a label. evaluatorMapping); // Create performance metrics var accuracy = new List <double>(); var negativeLogProbability = new List <double>(); var auc = new List <double>(); var evidence = new List <double>(); var iterationCounts = new List <double>(); var trainingTime = new List <double>(); // Run cross-validation int validationSetSize = x.Length / crossValidationFoldCount; int trainingSetSize = x.Length - validationSetSize; int validationFoldSetSize = 0; int trainingFoldSetSize = 0; Console.WriteLine( "Running {0}-fold cross-validation", crossValidationFoldCount); if (validationSetSize == 0 || trainingSetSize == 0) { Console.WriteLine("Invalid number of folds"); Console.ReadKey(); System.Environment.Exit(1); } for (int fold = 0; fold < crossValidationFoldCount; fold++) { // Construct training and validation sets for fold int validationSetStart = fold * validationSetSize; int validationSetEnd = (fold + 1 == crossValidationFoldCount) ? x.Length : (fold + 1) * validationSetSize; validationFoldSetSize = validationSetEnd - validationSetStart; trainingFoldSetSize = x.Length - validationFoldSetSize; Vector[] trainingSet = new Vector[trainingFoldSetSize]; Vector[] validationSet = new Vector[validationFoldSetSize]; IList <string> trainingSetLabels = new List <string>(); IList <string> validationSetLabels = new List <string>(); for (int instance = 0, iv = 0, it = 0; instance < x.Length; instance++) { if (validationSetStart <= instance && instance < validationSetEnd) { validationSet[iv++] = x[instance]; validationSetLabels.Add(y[instance]); } else { trainingSet[it++] = x[instance]; trainingSetLabels.Add(y[instance]); } } // Print info Console.WriteLine(" Fold {0} [validation set instances {1} - {2}]", fold + 1, validationSetStart, validationSetEnd - 1); // Create classifier var classifier = BayesPointMachineClassifier.CreateBinaryClassifier(mapping); classifier.Settings.Training.IterationCount = iterationCount; classifier.Settings.Training.BatchCount = batchCount; classifier.Settings.Training.ComputeModelEvidence = computeModelEvidence; int currentIterationCount = 0; classifier.IterationChanged += (sender, eventArgs) => { currentIterationCount = eventArgs.CompletedIterationCount; }; // Train classifier var stopWatch = new Stopwatch(); stopWatch.Start(); classifier.Train(trainingSet, trainingSetLabels); stopWatch.Stop(); // Produce predictions IEnumerable <IDictionary <string, double> > predictions = classifier.PredictDistribution(validationSet); var predictedLabels = classifier.Predict(validationSet); // Iteration count iterationCounts.Add(currentIterationCount); // Training time trainingTime.Add(stopWatch.ElapsedMilliseconds); // Compute accuracy accuracy.Add(1 - (evaluator.Evaluate(validationSet, validationSetLabels, predictedLabels, Metrics.ZeroOneError) / predictions.Count())); // Compute mean negative log probability negativeLogProbability.Add(evaluator.Evaluate(validationSet, validationSetLabels, predictions, Metrics.NegativeLogProbability) / predictions.Count()); // Compute M-measure (averaged pairwise AUC) auc.Add(evaluator.AreaUnderRocCurve(validationSet, validationSetLabels, predictions)); // Compute log evidence if desired evidence.Add(computeModelEvidence ? classifier.LogModelEvidence : double.NaN); // Persist performance metrics Console.WriteLine( " Accuracy = {0,5:0.0000} NegLogProb = {1,5:0.0000} AUC = {2,5:0.0000}{3} Iterations = {4} Training time = {5}", accuracy[fold], negativeLogProbability[fold], auc[fold], computeModelEvidence ? string.Format(" Log evidence = {0,5:0.0000}", evidence[fold]) : string.Empty, iterationCounts[fold], FormatElapsedTime(trainingTime[fold])); SavePerformanceMetrics( reportFileName, accuracy, negativeLogProbability, auc, evidence, iterationCounts, trainingTime); } }