private void Train(
string outputModelFileName,
int iterationCount,
bool computeModelEvidence,
int batchCount)
{
// Set settings
_classifier.Settings.Training.ComputeModelEvidence = computeModelEvidence;
_classifier.Settings.Training.IterationCount = iterationCount;
_classifier.Settings.Training.BatchCount = batchCount;
// train
_classifier.Train(_x, _y);
_classifier.Save(outputModelFileName);
}
public override void TrainIncremental(
string inputModelFileName,
string outputModelFileName,
int iterationCount,
bool computeModelEvidence,
int batchCount,
DistributionName distributionName,
InferenceAlgorithm inferenceEngineAlgorithm,
double noise)
{
TraceListeners.Log(TraceEventType.Warning, 0,
"Advanced setting will not be used: " +
"distributionName, inferenceEngineAlgorithm & noise.", false, true);
// Validate
_validate.TrainIncremental(
inputModelFileName: inputModelFileName,
outputModelFileName: outputModelFileName,
iterationCount: iterationCount,
batchCount: batchCount);
// Load model
IBayesPointMachineClassifier <
IList <Vector>, int, IList <string>, string, IDictionary <string, double>,
BayesPointMachineClassifierTrainingSettings,
BinaryBayesPointMachineClassifierPredictionSettings <string> > classifier =
BayesPointMachineClassifier.LoadBinaryClassifier <
IList <Vector>, int, IList <string>, string, IDictionary <string, double> >
(inputModelFileName);
// Set settings
classifier.Settings.Training.ComputeModelEvidence = computeModelEvidence;
classifier.Settings.Training.IterationCount = iterationCount;
classifier.Settings.Training.BatchCount = batchCount;
// train
classifier.TrainIncremental(_x, _y);
classifier.Save(outputModelFileName);
}
/// <summary>
/// Diagnoses the Bayes point machine classifier on the specified data set.
/// </summary>
/// <typeparam name="TTrainingSettings">The type of the settings for training.</typeparam>
/// <param name="classifier">The Bayes point machine classifier.</param>
/// <param name="trainingSet">The dataset.</param>
/// <param name="maxParameterChangesFileName">The name of the file to store the maximum parameter differences.</param>
/// <param name="modelFileName">The name of the file to store the trained Bayes point machine model.</param>
public static void DiagnoseClassifier <TTrainingSettings>(
IBayesPointMachineClassifier <IList <LabeledFeatureValues>, LabeledFeatureValues, IList <LabelDistribution>, string, IDictionary <string, double>, TTrainingSettings, IBayesPointMachineClassifierPredictionSettings <string> > classifier,
IList <LabeledFeatureValues> trainingSet,
string maxParameterChangesFileName,
string modelFileName)
where TTrainingSettings : BayesPointMachineClassifierTrainingSettings
{
// Create prior distributions over weights
int classCount = trainingSet[0].LabelDistribution.LabelSet.Count;
int featureCount = trainingSet[0].GetDenseFeatureVector().Count;
var priorWeightDistributions = Util.ArrayInit(classCount, c => Util.ArrayInit(featureCount, f => new Gaussian(0.0, 1.0)));
// Create IterationChanged handler
var watch = new Stopwatch();
classifier.IterationChanged += (sender, eventArgs) =>
{
watch.Stop();
double maxParameterChange = MaxDiff(eventArgs.WeightPosteriorDistributions, priorWeightDistributions);
if (!string.IsNullOrEmpty(maxParameterChangesFileName))
{
SaveMaximumParameterDifference(
maxParameterChangesFileName,
eventArgs.CompletedIterationCount,
maxParameterChange,
watch.ElapsedMilliseconds);
}
Console.WriteLine(
"[{0}] Iteration {1,-4} dp = {2,-20} dt = {3,5}ms",
DateTime.Now.ToLongTimeString(),
eventArgs.CompletedIterationCount,
maxParameterChange,
watch.ElapsedMilliseconds);
// Copy weight marginals
for (int c = 0; c < eventArgs.WeightPosteriorDistributions.Count; c++)
{
for (int f = 0; f < eventArgs.WeightPosteriorDistributions[c].Count; f++)
{
priorWeightDistributions[c][f] = eventArgs.WeightPosteriorDistributions[c][f];
}
}
watch.Restart();
};
// Write file header
if (!string.IsNullOrEmpty(maxParameterChangesFileName))
{
using (var writer = new StreamWriter(maxParameterChangesFileName))
{
writer.WriteLine("# time, # iteration, # maximum absolute parameter difference, # iteration time in milliseconds");
}
}
// Train the Bayes point machine classifier
Console.WriteLine("[{0}] Starting training...", DateTime.Now.ToLongTimeString());
watch.Start();
classifier.Train(trainingSet);
// Compute evidence
if (classifier.Settings.Training.ComputeModelEvidence)
{
Console.WriteLine("Log evidence = {0,10:0.0000}", classifier.LogModelEvidence);
}
// Save trained model
if (!string.IsNullOrEmpty(modelFileName))
{
classifier.Save(modelFileName);
}
}
