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
Esempio n. 4
0
 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