protected SampleSet <T> ApplyCrossValidated <P>(P preprocessor, Func <T, P, T> sampleProcessing, int xValidationStart, int xValidationLength) where P : Preprocessor
{
// you need to train your Trainable preprocessor externally if you aren't using cross validation (cross validation will apply its own training)
if (!IsCrossValidatedSampleSet() && preprocessor is ITrainable && !((ITrainable)preprocessor).IsTrained())
{
throw new ArgumentException("Trainable preprocessor must be trained for non-cross validated sets");
}
Preprocessor copy = preprocessor.Copy();
// perform preprocessor training in parallel
// TODO - include imposterTrainingSize
ParallelQuery <int> crossValidationRange = Enumerable.Range(xValidationStart, xValidationLength).AsParallel();
Dictionary <int, Preprocessor> trainedPreprocessors = crossValidationRange.Select(x => new { x, preprocessorHead = preprocessor.Copy() })
.Select(p => new { p.x, trainablePreprocessor = p.preprocessorHead as ITrainable, p.preprocessorHead })
.Select(t => new { t.x, preprocessorHead = t.trainablePreprocessor != null && IsCrossValidatedSampleSet() ? t.trainablePreprocessor.Train <P>(SampleSetHelpers.GetSampleSetTrainingSamples(this, trainingSize.Value, t.x)) : t.preprocessorHead }) // only do training when cross validated
.Select(trained => new { trained.x, preprocessorHead = trained.preprocessorHead.SetPredecessor(preprocessorHeads.ContainsKey(trained.x) ? preprocessorHeads[trained.x] : null) }) // set the current preprocessor as the predecessor to the newly applied one for initializing the new SampleSet
.ToDictionary(trained => trained.x, trained => trained.preprocessorHead);
// apply preprocessor head to each cross validation (or total sample set if this is not a cross validated set)
SampleSet <T> preprocessedSampleSet = new SampleSet <T>(trainedPreprocessors, trainingSize);
if (IsCrossValidatedSampleSet())
{
preprocessedSampleSet.AddRange(crossValidationRange
.SelectMany(x => SampleSetHelpers.GetCrossValidation(this as SampleSet <CrossValidatedSample>, x).Cast <T>().Select(sample => sampleProcessing.Invoke(sample, (P)trainedPreprocessors[x]))));
}
else
{
preprocessedSampleSet.AddRange(this.Select(sample => sampleProcessing.Invoke(sample, (P)trainedPreprocessors[DEFAULT_PREPROCESSOR_KEY])));
}
return(preprocessedSampleSet);
}
public P Train <P>(List <ISample> trainingSamples) where P : Preprocessor
{
List <int> dimensionCounts = trainingSamples.Select(sample => sample.GetDimensionCount()).Distinct().ToList();
if (dimensionCounts.Count != 1)
{
throw new ArgumentException("All samples must have the same number of dimensions"); // TODO - create parent or global method assertEqualDimensionality(samples)
}
trainingDimensions = trainingSamples[0].GetDimensionCount();
// calculate the per feature means
string[] identifiers = SampleSetHelpers.GetIdentifiers(trainingSamples, true).ToArray();
Dictionary <string, double[]> means = CalculateDimensionMeans(trainingSamples, trainingDimensions.Value, identifiers);
// calculate membership score for each feature then order by best associated indices
featureRankings = Enumerable.Range(0, trainingDimensions.Value).AsParallel().Select(featureIndex => new { featureIndex, score = CalculateMembership(featureIndex, trainingSamples, means, identifiers) })
.OrderBy(member => member.score)
.Reverse()
.Take(numberOfOutputs)
.Select(member => member.featureIndex).ToArray();
return(this as P);
}
public ClassifierInstance CreateInstance(List <ISample> trainingSamples, double standardMin, double standardMax)
{
// don't train imposter samples (if you know the sample's identifier they are not an "imposter" as far as classifier training is concerned)
List <ISample> samplesToTrain = trainingSamples.Where(sample => !sample.IsImposter()).ToList();
// validate training data
ValidateTrainingData(samplesToTrain);
// generate scaling/shift per feature to acheive a standard min/max for the training data
double range = standardMax - standardMin;
double[] featureMin = SampleSetHelpers.GetMinimumFeatureValues(samplesToTrain);
double[] featureMax = SampleSetHelpers.GetMaximumFeatureValues(samplesToTrain);
double[] featureScaling = featureMax.Select((max, i) => range / (max - featureMin[i])).ToArray();
double[] featureShift = featureScaling.Select((scale, i) => standardMin - (featureMin[i] * scale)).ToArray();
// apply scaling/shift to the features in each training sample
List <ISample> standardizedSamples = samplesToTrain.Select(sample => sample.AcceptVisitor(
new SampleScaledDimensionVisitor(featureScaling, featureShift))).ToList();
string[] trainingIdentifiers = SampleSetHelpers.GetIdentifiers(samplesToTrain, false);
return(CreateInstance(standardizedSamples, featureScaling, featureShift, trainingIdentifiers));
}
public ClassifierFactory <T> Train(SampleSet <T> trainingSamples, int trainingSize, int xValidationStart = 0, int xValidationLength = 1)
{
// train with a dataset that may require additional cross validation classifier instances
xClassifierInstances = Enumerable.Range(xValidationStart, xValidationLength).AsParallel().Select(x => new { x, instance = CreateInstance(SampleSetHelpers.GetSampleSetTrainingSamples(trainingSamples, trainingSize, x), standardMin, standardMax) })
.ToDictionary(validation => validation.x, validation => validation.instance);
return(this);
}
private static Tuple <ErrorRatePair, List <ErrorRatePair> > CalculateErrorRate <T>(SampleSet <T> sampleSet, ClassifierFactory <T> classifier, int trainingSize, double threshold, int xValidationStart, int xValidationLength) where T : ISample
{
// test classifier instances with testing samples and threshold asynchronously to get cross validated result
List <ErrorRatePair> errorRates = Enumerable.Range(xValidationStart, xValidationLength).AsParallel()
.Select(x => CalculateErrorRate(classifier.GetInstance(x), SampleSetHelpers.GetSampleSetTestingSamples(sampleSet, trainingSize, x), threshold))
.ToList();
ErrorRatePair totalError = errorRates[0];
for (int i = 1; i < errorRates.Count; i++)
{
totalError = totalError + errorRates[i];
}
return(new Tuple <ErrorRatePair, List <ErrorRatePair> >(totalError, errorRates));
}
