/// <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 binary 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.CreateBinaryClassifier(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);
}
/// <summary>
/// Initializes static members of the <see cref="Mappings"/> class.
/// </summary>
static Mappings()
{
StarRatingRecommender = new StarRatingRecommenderMapping();
Classifier = new ClassifierMapping();
}
