/// <summary>
        /// Runs the module.
        /// </summary>
        /// <param name="args">The command line arguments for the module.</param>
        /// <param name="usagePrefix">The prefix to print before the usage string.</param>
        /// <returns>True if the run was successful, false otherwise.</returns>
        public override bool Run(string[] args, string usagePrefix)
        {
            string testSetFile     = string.Empty;
            string modelFile       = string.Empty;
            string predictionsFile = string.Empty;

            var parser = new CommandLineParser();

            parser.RegisterParameterHandler("--test-set", "FILE", "File with test data", v => testSetFile = v, CommandLineParameterType.Required);
            parser.RegisterParameterHandler("--model", "FILE", "File with a trained binary Bayes point machine model", v => modelFile    = v, CommandLineParameterType.Required);
            parser.RegisterParameterHandler("--predictions", "FILE", "File to store predictions for the test data", v => predictionsFile = v, CommandLineParameterType.Required);
            if (!parser.TryParse(args, usagePrefix))
            {
                return(false);
            }

            var testSet = ClassifierPersistenceUtils.LoadLabeledFeatureValues(testSetFile);

            BayesPointMachineClassifierModuleUtilities.WriteDataSetInfo(testSet);

            var classifier =
                BayesPointMachineClassifier.LoadBinaryClassifier <IList <LabeledFeatureValues>, LabeledFeatureValues, IList <LabelDistribution>, string, IDictionary <string, double> >(modelFile);

            // Predict labels
            var predictions = classifier.PredictDistribution(testSet);

            // Write labels to file
            ClassifierPersistenceUtils.SaveLabelDistributions(predictionsFile, predictions);

            return(true);
        }
Beispiel #2
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        /// <summary>
        /// Samples weights from the learned weight distribution of the Bayes point machine classifier.
        /// </summary>
        /// <typeparam name="TTrainingSettings">The type of the settings for training.</typeparam>
        /// <param name="classifier">The Bayes point machine used to sample weights from.</param>
        /// <param name="samplesFile">The name of the file to which the weights will be written.</param>
        public static void SampleWeights <TTrainingSettings>(
            IBayesPointMachineClassifier <IList <LabeledFeatureValues>, LabeledFeatureValues, IList <LabelDistribution>, string, IDictionary <string, double>, TTrainingSettings, IBayesPointMachineClassifierPredictionSettings <string> > classifier,
            string samplesFile)
            where TTrainingSettings : BayesPointMachineClassifierTrainingSettings
        {
            // Sample weights
            var samples = SampleWeights(classifier);

            // Write samples to file
            if (!string.IsNullOrEmpty(samplesFile))
            {
                ClassifierPersistenceUtils.SaveVectors(samplesFile, samples);
            }
        }
        /// <summary>
        /// Runs the module.
        /// </summary>
        /// <param name="args">The command line arguments for the module.</param>
        /// <param name="usagePrefix">The prefix to print before the usage string.</param>
        /// <returns>True if the run was successful, false otherwise.</returns>
        public override bool Run(string[] args, string usagePrefix)
        {
            string trainingSetFile      = string.Empty;
            string modelFile            = string.Empty;
            int    iterationCount       = BayesPointMachineClassifierTrainingSettings.IterationCountDefault;
            int    batchCount           = BayesPointMachineClassifierTrainingSettings.BatchCountDefault;
            bool   computeModelEvidence = BayesPointMachineClassifierTrainingSettings.ComputeModelEvidenceDefault;

            var parser = new CommandLineParser();

            parser.RegisterParameterHandler("--training-set", "FILE", "File with training data", v => trainingSetFile = v, CommandLineParameterType.Required);
            parser.RegisterParameterHandler("--model", "FILE", "File to store the trained multi-class Bayes point machine model", v => modelFile = v, CommandLineParameterType.Required);
            parser.RegisterParameterHandler("--iterations", "NUM", "Number of training algorithm iterations (defaults to " + iterationCount + ")", v => iterationCount = v, CommandLineParameterType.Optional);
            parser.RegisterParameterHandler("--batches", "NUM", "Number of batches to split the training data into (defaults to " + batchCount + ")", v => batchCount  = v, CommandLineParameterType.Optional);
            parser.RegisterParameterHandler("--compute-evidence", "Compute model evidence (defaults to " + computeModelEvidence + ")", () => computeModelEvidence      = true);

            if (!parser.TryParse(args, usagePrefix))
            {
                return(false);
            }

            var trainingSet = ClassifierPersistenceUtils.LoadLabeledFeatureValues(trainingSetFile);

            BayesPointMachineClassifierModuleUtilities.WriteDataSetInfo(trainingSet);

            var featureSet = trainingSet.Count > 0 ? trainingSet.First().FeatureSet : null;
            var mapping    = new ClassifierMapping(featureSet);
            var classifier = BayesPointMachineClassifier.CreateMulticlassClassifier(mapping);

            classifier.Settings.Training.IterationCount       = iterationCount;
            classifier.Settings.Training.BatchCount           = batchCount;
            classifier.Settings.Training.ComputeModelEvidence = computeModelEvidence;

            classifier.Train(trainingSet);

            if (classifier.Settings.Training.ComputeModelEvidence)
            {
                Console.WriteLine("Log evidence = {0,10:0.0000}", classifier.LogModelEvidence);
            }

            classifier.Save(modelFile);

            return(true);
        }
Beispiel #4
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        /// <summary>
        /// Runs the module.
        /// </summary>
        /// <param name="args">The command line arguments for the module.</param>
        /// <param name="usagePrefix">The prefix to print before the usage string.</param>
        /// <returns>True if the run was successful, false otherwise.</returns>
        public override bool Run(string[] args, string usagePrefix)
        {
            string trainingSetFile = string.Empty;
            string inputModelFile  = string.Empty;
            string outputModelFile = string.Empty;
            int    iterationCount  = BayesPointMachineClassifierTrainingSettings.IterationCountDefault;
            int    batchCount      = BayesPointMachineClassifierTrainingSettings.BatchCountDefault;

            var parser = new CommandLineParser();

            parser.RegisterParameterHandler("--training-set", "FILE", "File with training data", v => trainingSetFile = v, CommandLineParameterType.Required);
            parser.RegisterParameterHandler("--input-model", "FILE", "File with the trained binary Bayes point machine model", v => inputModelFile = v, CommandLineParameterType.Required);
            parser.RegisterParameterHandler("--model", "FILE", "File to store the incrementally trained binary Bayes point machine model", v => outputModelFile        = v, CommandLineParameterType.Required);
            parser.RegisterParameterHandler("--iterations", "NUM", "Number of training algorithm iterations (defaults to " + iterationCount + ")", v => iterationCount = v, CommandLineParameterType.Optional);
            parser.RegisterParameterHandler("--batches", "NUM", "Number of batches to split the training data into (defaults to " + batchCount + ")", v => batchCount  = v, CommandLineParameterType.Optional);

            if (!parser.TryParse(args, usagePrefix))
            {
                return(false);
            }

            var trainingSet = ClassifierPersistenceUtils.LoadLabeledFeatureValues(trainingSetFile);

            BayesPointMachineClassifierModuleUtilities.WriteDataSetInfo(trainingSet);

            var classifier = BayesPointMachineClassifier.LoadBinaryClassifier <IList <LabeledFeatureValues>, LabeledFeatureValues, IList <LabelDistribution>, string, IDictionary <string, double> >(inputModelFile);

            classifier.Settings.Training.IterationCount = iterationCount;
            classifier.Settings.Training.BatchCount     = batchCount;

            classifier.TrainIncremental(trainingSet);

            classifier.Save(outputModelFile);

            return(true);
        }
Beispiel #5
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        /// <summary>
        /// Runs the module.
        /// </summary>
        /// <param name="args">The command line arguments for the module.</param>
        /// <param name="usagePrefix">The prefix to print before the usage string.</param>
        /// <returns>True if the run was successful, false otherwise.</returns>
        public override bool Run(string[] args, string usagePrefix)
        {
            string groundTruthFileName          = string.Empty;
            string predictionsFileName          = string.Empty;
            string reportFileName               = string.Empty;
            string calibrationCurveFileName     = string.Empty;
            string rocCurveFileName             = string.Empty;
            string precisionRecallCurveFileName = string.Empty;
            string positiveClassLabel           = string.Empty;

            var parser = new CommandLineParser();

            parser.RegisterParameterHandler("--ground-truth", "FILE", "File with ground truth labels", v => groundTruthFileName = v, CommandLineParameterType.Required);
            parser.RegisterParameterHandler("--predictions", "FILE", "File with label predictions", v => predictionsFileName    = v, CommandLineParameterType.Required);
            parser.RegisterParameterHandler("--report", "FILE", "File to store the evaluation report", v => reportFileName      = v, CommandLineParameterType.Optional);
            parser.RegisterParameterHandler("--calibration-curve", "FILE", "File to store the empirical calibration curve", v => calibrationCurveFileName     = v, CommandLineParameterType.Optional);
            parser.RegisterParameterHandler("--roc-curve", "FILE", "File to store the receiver operating characteristic curve", v => rocCurveFileName         = v, CommandLineParameterType.Optional);
            parser.RegisterParameterHandler("--precision-recall-curve", "FILE", "File to store the precision-recall curve", v => precisionRecallCurveFileName = v, CommandLineParameterType.Optional);
            parser.RegisterParameterHandler("--positive-class", "STRING", "Label of the positive class to use in curves", v => positiveClassLabel             = v, CommandLineParameterType.Optional);
            if (!parser.TryParse(args, usagePrefix))
            {
                return(false);
            }

            // Read ground truth
            var groundTruth = ClassifierPersistenceUtils.LoadLabeledFeatureValues(groundTruthFileName);

            // Read predictions using ground truth label dictionary
            var predictions = ClassifierPersistenceUtils.LoadLabelDistributions(predictionsFileName, groundTruth.First().LabelDistribution.LabelSet);

            // Check that there are at least two distinct class labels
            if (predictions.First().LabelSet.Count < 2)
            {
                throw new InvalidFileFormatException("Ground truth and predictions must contain at least two distinct class labels.");
            }

            // Distill distributions and point estimates
            var predictiveDistributions  = predictions.Select(i => i.ToDictionary()).ToList();
            var predictivePointEstimates = predictions.Select(i => i.GetMode()).ToList();

            // Create evaluator
            var evaluatorMapping = Mappings.Classifier.ForEvaluation();
            var evaluator        = new ClassifierEvaluator <IList <LabeledFeatureValues>, LabeledFeatureValues, IList <LabelDistribution>, string>(evaluatorMapping);

            // Write evaluation report
            if (!string.IsNullOrEmpty(reportFileName))
            {
                using (var writer = new StreamWriter(reportFileName))
                {
                    this.WriteReportHeader(writer, groundTruthFileName, predictionsFileName);
                    this.WriteReport(writer, evaluator, groundTruth, predictiveDistributions, predictivePointEstimates);
                }
            }

            // Compute and write the empirical probability calibration curve
            positiveClassLabel = this.CheckPositiveClassLabel(groundTruth, positiveClassLabel);
            if (!string.IsNullOrEmpty(calibrationCurveFileName))
            {
                this.WriteCalibrationCurve(calibrationCurveFileName, evaluator, groundTruth, predictiveDistributions, positiveClassLabel);
            }

            // Compute and write the precision-recall curve
            if (!string.IsNullOrEmpty(precisionRecallCurveFileName))
            {
                this.WritePrecisionRecallCurve(precisionRecallCurveFileName, evaluator, groundTruth, predictiveDistributions, positiveClassLabel);
            }

            // Compute and write the receiver operating characteristic curve
            if (!string.IsNullOrEmpty(rocCurveFileName))
            {
                this.WriteRocCurve(rocCurveFileName, evaluator, groundTruth, predictiveDistributions, positiveClassLabel);
            }

            return(true);
        }
Beispiel #6
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        /// <summary>
        /// Runs the module.
        /// </summary>
        /// <param name="args">The command line arguments for the module.</param>
        /// <param name="usagePrefix">The prefix to print before the usage string.</param>
        /// <returns>True if the run was successful, false otherwise.</returns>
        public override bool Run(string[] args, string usagePrefix)
        {
            string dataSetFile = string.Empty;
            string resultsFile = string.Empty;
            int    crossValidationFoldCount = 5;
            int    iterationCount           = BayesPointMachineClassifierTrainingSettings.IterationCountDefault;
            int    batchCount           = BayesPointMachineClassifierTrainingSettings.BatchCountDefault;
            bool   computeModelEvidence = BayesPointMachineClassifierTrainingSettings.ComputeModelEvidenceDefault;

            var parser = new CommandLineParser();

            parser.RegisterParameterHandler("--data-set", "FILE", "File with training data", v => dataSetFile           = v, CommandLineParameterType.Required);
            parser.RegisterParameterHandler("--results", "FILE", "File with cross-validation results", v => resultsFile = v, CommandLineParameterType.Required);
            parser.RegisterParameterHandler("--folds", "NUM", "Number of cross-validation folds (defaults to " + crossValidationFoldCount + ")", v => crossValidationFoldCount = v, CommandLineParameterType.Optional);
            parser.RegisterParameterHandler("--iterations", "NUM", "Number of training algorithm iterations (defaults to " + iterationCount + ")", v => iterationCount         = v, CommandLineParameterType.Optional);
            parser.RegisterParameterHandler("--batches", "NUM", "Number of batches to split the training data into (defaults to " + batchCount + ")", v => batchCount          = v, CommandLineParameterType.Optional);
            parser.RegisterParameterHandler("--compute-evidence", "Compute model evidence (defaults to " + computeModelEvidence + ")", () => computeModelEvidence = true);

            if (!parser.TryParse(args, usagePrefix))
            {
                return(false);
            }

            // Load and shuffle data
            var dataSet = ClassifierPersistenceUtils.LoadLabeledFeatureValues(dataSetFile);

            BayesPointMachineClassifierModuleUtilities.WriteDataSetInfo(dataSet);

            Rand.Restart(562);
            Rand.Shuffle(dataSet);

            // Create evaluator
            var evaluatorMapping = Mappings.Classifier.ForEvaluation();
            var evaluator        = new ClassifierEvaluator <IList <LabeledFeatureValues>, LabeledFeatureValues, IList <LabelDistribution>, string>(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 = dataSet.Count / crossValidationFoldCount;

            Console.WriteLine("Running {0}-fold cross-validation on {1}", crossValidationFoldCount, dataSetFile);

            // TODO: Use chained mapping to implement cross-validation
            for (int fold = 0; fold < crossValidationFoldCount; fold++)
            {
                // Construct training and validation sets for fold
                int validationSetStart = fold * validationSetSize;
                int validationSetEnd   = (fold + 1 == crossValidationFoldCount)
                                           ? dataSet.Count
                                           : (fold + 1) * validationSetSize;

                var trainingSet   = new List <LabeledFeatureValues>();
                var validationSet = new List <LabeledFeatureValues>();

                for (int instance = 0; instance < dataSet.Count; instance++)
                {
                    if (validationSetStart <= instance && instance < validationSetEnd)
                    {
                        validationSet.Add(dataSet[instance]);
                    }
                    else
                    {
                        trainingSet.Add(dataSet[instance]);
                    }
                }

                // Print info
                Console.WriteLine("   Fold {0} [validation set instances {1} - {2}]", fold + 1, validationSetStart, validationSetEnd - 1);

                // Create classifier
                var classifier = BayesPointMachineClassifier.CreateBinaryClassifier(Mappings.Classifier);
                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);
                stopWatch.Stop();

                // Produce predictions
                var predictions     = classifier.PredictDistribution(validationSet).ToList();
                var predictedLabels = predictions.Select(
                    prediction => prediction.Aggregate((aggregate, next) => next.Value > aggregate.Value ? next : aggregate).Key).ToList();

                // Iteration count
                iterationCounts.Add(currentIterationCount);

                // Training time
                trainingTime.Add(stopWatch.ElapsedMilliseconds);

                // Compute accuracy
                accuracy.Add(1 - (evaluator.Evaluate(validationSet, predictedLabels, Metrics.ZeroOneError) / predictions.Count));

                // Compute mean negative log probability
                negativeLogProbability.Add(evaluator.Evaluate(validationSet, predictions, Metrics.NegativeLogProbability) / predictions.Count);

                // Compute M-measure (averaged pairwise AUC)
                auc.Add(evaluator.AreaUnderRocCurve(validationSet, 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],
                    BayesPointMachineClassifierModuleUtilities.FormatElapsedTime(trainingTime[fold]));

                BayesPointMachineClassifierModuleUtilities.SavePerformanceMetrics(
                    resultsFile, accuracy, negativeLogProbability, auc, evidence, iterationCounts, trainingTime);
            }

            return(true);
        }