def evaluate(classifierPath, testPath):
# Evaluate whole
actualLabels = classifier.loadLabels(testPath)
predictedLabels = test(classifierPath, testPath)
resultByName = evaluation_process.evaluateClassifier(actualLabels, predictedLabels, 'Boosted convolutional neural network')
# Evaluate parts
for weakClassifierPath in glob.glob(os.path.join(classifierPath, 'weak*.info')):
weakName = store.extractFileBaseName(weakClassifierPath)
weakClassifierInformation = store.loadInformation(weakClassifierPath)
resultByName[weakName] = weakClassifierInformation['performance']
# Return
return resultByName
def saveSamples(sampleDataset, sampleIDs, featureSet):
# Initialize
sampleCount = len(sampleIDs)
sampleDatasetPath = sampleDataset.getDatasetPath()
sampleInformation = {
'source dataset': {
'path': sampleDatasetPath,
'sample ids': ' '.join(str(x) for x in sampleIDs),
},
'feature': {
'module name': featureSet.__module__,
'class name': featureSet.__class__.__name__,
}
}
targetSampleName = '%s-count%s-min%s' % (folder_store.getFolderName(sampleDatasetPath), sampleCount, min(sampleIDs))
targetSamplePath = os.path.join(store.makeFolderSafely(os.path.join(store.temporaryPath, 'cnn_datasets')), targetSampleName)
# If targetDatasetPath exists, return
if store.loadInformation(targetSamplePath) == sampleInformation:
print 'Using existing samples...\n\ttargetSamplePath = ' + targetSamplePath
return targetSamplePath
# Save
print 'Saving samples...\n\ttargetSamplePath = ' + targetSamplePath
sampleGenerator = makeSampleLabelGeneratorFromSampleDataset(sampleDataset, sampleIDs, featureSet)
sampleFile, labelFile = [open(x, 'wt') for x in makeSampleLabelPaths(targetSamplePath)]
for sampleIndex, (sample, label) in enumerate(sampleGenerator):
# If we are starting, write header
if sampleIndex == 0:
sampleFile.write(makeLushMatrixHeaderFromPart(sample, sampleCount))
labelFile.write(makeLushMatrixHeaderFromPart(label, sampleCount))
# Write content
sampleFile.write(makeLushMatrixContent(sample))
labelFile.write(makeLushMatrixContent(label))
if sampleIndex % 100 == 0:
view.printPercentUpdate(sampleIndex + 1, sampleCount)
view.printPercentFinal(sampleCount)
# Return
labelFile.close(); sampleFile.close()
store.saveInformation(targetSamplePath, sampleInformation)
return targetSamplePath
def __init__(self, informationPath):
informationPath = os.path.join(os.path.dirname(informationPath), folder_store.fileNameByFolderName['patches'])
self.information = store.loadInformation(informationPath)
def __init__(self, informationPath):
self.information = store.loadInformation(informationPath)
def __init__(self, informationPath):
self.information = store.loadInformation(informationPath)
def __init__(self, informationPath):
self.informationPath = informationPath
self.information = store.loadInformation(informationPath)
self.parameterByName = self.information['parameters']
