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 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 train(targetClassifierPath, trainingPath, testPath, parameterByName):
# Set paths
temporaryFolder = tempfile.mkdtemp()
trainingSamplePath, trainingLabelPath = makeSampleLabelPaths(trainingPath)
testSamplePath, testLabelPath = makeSampleLabelPaths(testPath)
# Make layer combinations
ratioRange = store.unstringifyFloatList(parameterByName['ratio range'])
kernelRange = store.unstringifyIntegerList(parameterByName['kernel range'])
samplePixelLength = getSamplePixelLength(testPath)
layerCombinations = makeLayerCombinations(samplePixelLength, ratioRange, kernelRange)
length0, ratio0, length1, ratio1 = layerCombinations[parameterByName['which layer combination']]
# Convert connection tables if necessary
connectionTable0Path = verifyConnectionTable(parameterByName['connection table0 path'])
connectionTable1Path = verifyConnectionTable(parameterByName['connection table1 path'])
# Run
timestamp = store.makeTimestamp()
runLush('trainClassifier', trainingSamplePath, trainingLabelPath, testSamplePath, testLabelPath,
length0, ratio0, connectionTable0Path,
length1, ratio1, connectionTable1Path,
parameterByName['hidden count'], parameterByName['iteration count'],
os.path.join(temporaryFolder, timestamp))
errorPacks = getClassifierPacksByTimestamp(temporaryFolder, timestamp)
# Save the best classifier
bestTestPercentError, bestTrainingPercentError, bestIterationIndex, bestClassifierPath = errorPacks[0]
parameterByName.update(length0=length0, ratio0=ratio0, length1=length1, ratio1=ratio1)
shutil.copy(bestClassifierPath, targetClassifierPath)
shutil.rmtree(temporaryFolder)
# Evaluate
resultByName = evaluate(targetClassifierPath, testPath)
resultByName['iteration index'] = bestIterationIndex
resultByName['iteration history'] = errorPacks
resultByName['sample pixel length'] = samplePixelLength
resultByName['layer combinations'] = layerCombinations
resultByName['layer combination count'] = len(layerCombinations)
# Plot
plotIterationHistory(targetClassifierPath, errorPacks)
# Return
return resultByName
