Question

在预处理某些数据时出现此有效错误：

 9:46:56.323 PM default_model Function execution took 6008 ms, finished with status: 'crash'
 9:46:56.322 PM default_model Traceback (most recent call last):
  File "/user_code/main.py", line 31, in default_model
    train, endog, exog, _, _, rawDf = preprocess(ledger, apps)
  File "/user_code/Wrangling.py", line 73, in preprocess
    raise InsufficientTimespanError(args=(appDf, locDf))

在这里发生

async def default_model(request):
    request_json = request.get_json()
    if not request_json:
        return '{"error": "empty body." }'
    if 'transaction_id' in request_json:
        transaction_id = request_json['transaction_id']

        apps = []  # array of apps whose predictions we want, or uempty for all
        if 'apps' in request_json:
            apps = request_json['apps']

        modelUrl = None
        if 'files' in request_json:
            try:
                files = request_json['files']
                modelUrl = getModelFromFiles(files)
            except:
                return package(transaction_id, error="no model to execute")
        else:
            return package(transaction_id, error="no model to execute")

        if 'ledger' in request_json:
            ledger = request_json['ledger']

            try:
                train, endog, exog, _, _, rawDf = preprocess(ledger, apps)
            # ...
            except InsufficientTimespanError as err:
                return package(transaction_id, error=err.message, appDf=err.args[0], locDf=err.args[1])

预处理正确抛出了我的自定义错误：

def preprocess(ledger, apps=[]):
    """
    convert ledger from the server, which comes in as an array of csv entries.
    normalize/resample timeseries, returning dataframes
    """
    appDf, locDf = splitLedger(ledger)

    if len(appDf) < 3 or len(locDf) < 3:
        raise InsufficientDataError(args=(appDf, locDf))

    endog = appDf['app_id'].unique().tolist()
    exog = locDf['location_id'].unique().tolist()

    rawDf = normalize(appDf, locDf)
    trainDf = cutoff(rawDf.copy(), apps)
    rawDf = cutoff(rawDf.copy(), apps, trim=False)

    # TODO - uncomment when on realish data
    if len(trainDf) < 2 * WEEKS:
        raise InsufficientTimespanError(args=(appDf, locDf))

问题是，它恰好位于try``except块中，因为我想捕获错误并返回带有错误的有效负载，而不是因500错误而崩溃。但是无论如何，它在try块中崩溃于我的自定义错误。就在该行上，呼叫preprocess。

这一定是我无法遵守正确的python代码的原因。但是我不确定自己在做什么错。环境是python 3.7

在Wrangling.py中定义错误的地方：

class WranglingError(Exception):
    """Base class for other exceptions"""
    pass


class InsufficientDataError(WranglingError):
    """insufficient data to make a prediction"""

    def __init__(self, message='insufficient data to make a prediction', args=None):
        super().__init__(message)
        self.message = message
        self.args = args


class InsufficientTimespanError(WranglingError):
    """insufficient timespan to make a prediction"""

    def __init__(self, message='insufficient timespan to make a prediction', args=None):
        super().__init__(message)
        self.message = message
        self.args = args

这是main.py声明（导入）它的方式：

from Wrangling import preprocess, InsufficientDataError, InsufficientTimespanError, DataNotNormal, InappropriateValueToPredict

Answer 1

您的preprocess函数被声明为async。这意味着其中的代码实际上不会在您调用preprocess的地方运行，而是在最终await被执行或传递到主循环（如asyncio.run）时运行。因为default_model的try块中不再运行它，所以不会捕获到异常。

您可以通过以下几种方法解决此问题：

使preprocess不同步
也使default_model异步，并在await上preprocess。

Answer 2

错误中的行号与代码中的行号匹配吗？如果不是，那么在添加try ... except之前，您可能会从代码版本中看到错误。

尝试除了不赶上功能？

2 个答案: