def fillPath(self, baseFolderName, folderName):
'Returns a function that expands a fileName into a filePath'
# Make sure that folderName is valid
match = pattern_name.match(folderName)
if not match: raise FolderError('Invalid name: %s\n%s' % (folderName, 'Names can have letters, digits, underscores, hyphens, spaces, parentheses, commas and periods.'))
# Fill path
baseFolderPath = store.makeFolderSafely(self.folderPathByName[baseFolderName])
stampedFolderName = '%s-%s' % (store.makeTimestamp(), folderName)
folderPath = store.makeFolderSafely(os.path.join(baseFolderPath, stampedFolderName))
# Return
return lambda fileName: os.path.join(folderPath, fileName)
def fillPath(self, baseFolderName, folderName):
# Make sure that folderName is valid
match = pattern_name.match(folderName)
if not match:
explanation = 'Names can have letters, digits, underscores, hyphens, spaces, parentheses, commas and periods.'
raise FolderError('Invalid name: %s\n%s' % (folderName, explanation))
# Fill path
baseFolderPath = store.makeFolderSafely(self.folderPathByName[baseFolderName])
template = templateByFolderName[baseFolderName]
fileName = fileNameByFolderName[baseFolderName]
folderPath = os.path.join(baseFolderPath, template % (store.makeTimestamp(), folderName))
filePath = os.path.join(store.makeFolderSafely(folderPath), fileName)
# Return
return filePath
def train(targetClassifierPath, trainingPath, testPath, parameterByName):
# Set paths
store.makeFolderSafely(targetClassifierPath)
# Count the number of training samples
trainingCount = classifier.getSampleCount(trainingPath)
# Initialize weights uniformly over training samples
sampleWeights = numpy.array([1 / float(trainingCount)] * trainingCount)
# Initialize
alphas = []; boostCount = parameterByName['boost count']
# For each boost,
for boostIndex in xrange(boostCount):
# Show feedback
print '--- Boosting %d/%d ---' % (boostIndex + 1, boostCount)
# Normalize sampleWeights
sampleWeights = sampleWeights / float(sum(sampleWeights))
# Weight samples
weightedTrainingPath, sampleMultipliers = weightSamples(trainingPath, sampleWeights)
# Train weak classifier
weakClassifierPath = os.path.join(targetClassifierPath, 'weak%d' % boostIndex)
weakResultByName = classifier.train(weakClassifierPath, weightedTrainingPath, testPath, parameterByName)
shutil.rmtree(os.path.split(weightedTrainingPath)[0])
# Test weak classifier on training set
predictedLabels = classifier.test(weakClassifierPath, trainingPath)
actualLabels = classifier.loadLabels(trainingPath)
errorDistribution = predictedLabels != actualLabels
# Compute training error weighted according to sampleWeights
weightedError = numpy.dot(sampleWeights, errorDistribution)
# Prevent zero division or zero log
if weightedError == 0: weightedError = 1e-9
elif weightedError == 1: weightedError = 1 - 1e-9
# Get alpha from training error
alpha = 0.5 * numpy.log(float(1 - weightedError) / weightedError)
coefficients = [numpy.exp(alpha) if isWrong else numpy.exp(-alpha) for isWrong in errorDistribution]
# Save weakClassifier performance
print 'weighted training error = %s' % (100 * weightedError)
print 'alpha = %s' % alpha
weakResultByName['alpha'] = alpha
weakResultByName['weighted training error'] = weightedError
weakResultByName['sample multipliers'] = ' '.join(str(x) for x in sampleMultipliers)
weakResultByName['error distribution'] = ''.join(str(int(x)) for x in errorDistribution)
saveWeakClassifierInformation(weakClassifierPath, trainingPath, testPath, parameterByName, {'performance': weakResultByName})
# Update sampleWeights
sampleWeights = coefficients * sampleWeights
# Append
alphas.append(alpha)
# Save classifier
save(targetClassifierPath, alphas)
# Return evaluation
return evaluate(targetClassifierPath, testPath)
def fillPath(self, baseFolderName, folderName):
'Returns a function that expands a fileName into a filePath'
# Make sure that folderName is valid
match = pattern_name.match(folderName)
if not match:
raise FolderError('Invalid name: %s\n%s' % (
folderName,
'Names can have letters, digits, underscores, hyphens, spaces, parentheses, commas and periods.'
))
# Fill path
baseFolderPath = store.makeFolderSafely(
self.folderPathByName[baseFolderName])
stampedFolderName = '%s-%s' % (store.makeTimestamp(), folderName)
folderPath = store.makeFolderSafely(
os.path.join(baseFolderPath, stampedFolderName))
# Return
return lambda fileName: os.path.join(folderPath, fileName)
def __init__(self, basePath='.'):
# Make the folder if it doesn't exist
self.basePath = store.makeFolderSafely(basePath)
# Set folderPathByName
self.folderPathByName = dict(
(folderName,
os.path.join(basePath, '%d-%s' % (folderIndex + 1, folderName)))
for folderIndex, folderName in enumerate(folderNames))
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, basePath='.'):
# Make the folder if it doesn't exist
self.basePath = store.makeFolderSafely(basePath)
# Set folderPathByName
self.folderPathByName = dict((folderName, os.path.join(basePath, '%d-%s' % (folderIndex + 1, folderName))) for folderIndex, folderName in enumerate(folderNames))
def getPath(self, fileType, fileName):
fileTypePath = os.path.join(self.basePath, folderNameByName[fileType])
return store.makeFolderSafely(os.path.join(fileTypePath, str(fileName)))
def __init__(self, basePath):
# Make the folder if it doesn't exist
self.basePath = store.makeFolderSafely(basePath)
# Set folderPathByName
self.folderPathByName = dict((key, os.path.join(basePath, folderNameByName[key])) for key in folderNameByName)
'Simple framework for logging'
# Import system modules
import logging
import os
# Import custom modules
import config
import model_notes
import store
# Prepare
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(message)s', filename=os.path.join(store.makeFolderSafely(config.logPath), 'log.txt'), filemode='a')
def error(message):
# Save in log
logging.error(message)
# Show on screen
print message
# Email administrator
model_notes.Model(config.mailHost, config.mailPath, config.mailPassword).write(config.logEmail, '[tco-helper] Fatal error', message)
