def crossValidate(ftb, project, featureThreshold, imagesOnly, numSplits):
message = ''
fullSet = FeatureSet_Discrete()
fullSet.source_path = project.getName()
classId = 0
for ds in project.listChildren():
message += 'Processing dataset id:%d\n' % ds.getId()
message += addToFeatureSet(ftb, ds, fullSet, classId, imagesOnly)
classId += 1
tmp = fullSet.ContiguousDataMatrix()
experiment = DiscreteClassificationExperimentResult(training_set=fullSet)
for i in range(numSplits):
trainSet, testSet = fullSet.Split()
trainSet.Normalize()
testSet.Normalize(trainSet)
weights = FisherFeatureWeights.NewFromFeatureSet(trainSet)
nFeatures = ceil(len(weights.names) * featureThreshold)
message += 'Selecting top %d features\n' % nFeatures
weights = weights.Threshold(nFeatures)
trainSet = reduceFeatures(trainSet, weights)
reducedTestSet = reduceFeatures(testSet, weights)
reducedTrainSet = reduceFeatures(trainSet, weights)
batchResult = DiscreteBatchClassificationResult.New(
reducedTrainSet, reducedTestSet, weights, batch_number=i)
experiment.individual_results.append(batchResult)
out = StringIO()
experiment.Print(output_stream=out)
experiment.PerSampleStatistics(output_stream=out)
pid = project.getId()
WndcharmStorage.addTextFileAnnotationTo(
ftb.conn, out.getvalue(), 'Project', pid,
'Wndcharm_Cross_Validation_Results.txt',
'Wndcharm Cross Validation Results for Project:%d' % pid)
message += 'Attached cross-validation results\n'
#return experiment
return message
