def runLushWithPipes(scriptName, *arguments, **keywordArguments):
# Initialize
scriptPath = os.path.join(DIRECTORY, store.replaceFileExtension(scriptName, 'lsh'))
terms = ['lush', scriptPath, DIRECTORY] + [str(x) for x in arguments]
standardError = True
errorCount = 0
errorLimit = 5
standardInput = keywordArguments.get('standardInput')
# While there is standardError,
while standardError:
# Try again
if standardInput:
process = subprocess.Popen(terms, stdin=PIPE, stdout=PIPE, stderr=PIPE)
standardOutput, standardError = process.communicate(standardInput)
else:
process = subprocess.Popen(terms, stdout=PIPE, stderr=PIPE)
standardOutput, standardError = process.communicate()
# If we have an error,
if standardError:
# Count
errorCount += 1
print 'Failed (%d/%d): %s' % (errorCount, errorLimit, store.extractFileBaseName(scriptPath))
if errorCount >= errorLimit: raise ClassifierError(standardError)
# Return
return standardOutput
def getPaths(self, baseFolderName, folderName):
'Return a list of paths with the latest first'
# Get paths
baseFolderPath = self.folderPathByName[baseFolderName]
folderPaths = glob.glob(os.path.join(baseFolderPath, '*-%s' % folderName))
# Sort by timestamp
folderPacks = [(pattern_timestamp.match(store.extractFileBaseName(x)).group(1), x) for x in folderPaths]
folderPacks.sort()
folderPaths = [x[1] for x in reversed(folderPacks)]
# Append fileNames
return folderPaths
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 getPaths(self, baseFolderName, folderName):
'Return a list of paths with the latest first'
# Get paths
baseFolderPath = self.folderPathByName[baseFolderName]
folderPaths = glob.glob(
os.path.join(baseFolderPath, '*-%s' % folderName))
# Sort by timestamp
folderPacks = [
(pattern_timestamp.match(store.extractFileBaseName(x)).group(1), x)
for x in folderPaths
]
folderPacks.sort()
folderPaths = [x[1] for x in reversed(folderPacks)]
# Append fileNames
return folderPaths
