在不更改方法工作原理的情况下修补方法? [英] Patching a method without changing how the method works?

问题描述

我正在尝试测试是否使用某些值调用了pandas方法.

I'm trying to test that a pandas method gets called with some values.

但是，仅通过应用@patch装饰器会导致修补的方法在熊猫中抛出ValueError，而实际方法却没有.我只是想测试Stock.calc_sma正在调用基础pandas.rolling_mean函数.

However, just by applying a @patch decorator causes the patched method to throw a ValueError within pandas, when the actual method does not. I'm just trying to test that Stock.calc_sma is calling the underlying pandas.rolling_mean function.

我假设@patch装饰器基本上在我正在修补的东西中添加了一些魔术"方法，使我可以检查该函数是否被调用.如果是这种情况，为什么pandas.rolling_mean函数无论是否已修补与未修补，其行为都不相同?

I'm under the assumption that the @patch decorator basically adds some "magic" methods to the thing I'm patching that allow me to check if the function was called. If this is the case, why doesn't the pandas.rolling_mean function behave the same whether it's patched vs. not patched?

app/models.py

app/models.py

import pandas as pd
class Stock:  # i've excluded a bunch of class methods, including the one that sets self.data, which is a DataFrame of stock prices.
    def calc_sma(self, num_days)
        if self.data.shape[0] > num_days:  # Stock.data holds a DataFrame of stock prices
                column_title = 'sma' + str(num_days)
                self.data[column_title] = pd.rolling_mean(self.data['Adj Close'], num_days)

app/tests/TestStockModel.py

app/tests/TestStockModel.py

def setUp(self):
    self.stock = MagicMock(Stock)
    self.stock.ticker = "AAPL"
    self.stock.data = DataFrame(aapl_test_data.data)

@patch('app.models.pd.rolling_mean')
def test_calc_sma(self, patched_rolling_mean):
    Stock.calc_sma(self.stock, 3)
    assert(isinstance(self.stock.data['sma3'], Series))
    patched_rolling_mean.assert_any_call()

错误:test_calc_sma(TestStockModel.TestStockModel)

Traceback (most recent call last):
  File "/Users/grant/Code/python/chartflux/env/lib/python2.7/site-packages/mock.py", line 1201, in patched
    return func(*args, **keywargs)
  File "/Users/grant/Code/python/chartflux/app/tests/TestStockModel.py", line 26, in test_calc_sma
    Stock.calc_sma(self.stock, 3)
  File "/Users/grant/Code/python/chartflux/app/models.py", line 27, in calc_sma
    self.data[column_title] = pd.rolling_mean(self.data['Adj Close'], num_days)
  File "/Users/grant/Code/python/chartflux/env/lib/python2.7/site-packages/pandas/core/frame.py", line 1887, in __setitem__
    self._set_item(key, value)
  File "/Users/grant/Code/python/chartflux/env/lib/python2.7/site-packages/pandas/core/frame.py", line 1967, in _set_item
    value = self._sanitize_column(key, value)
  File "/Users/grant/Code/python/chartflux/env/lib/python2.7/site-packages/pandas/core/frame.py", line 2017, in _sanitize_column
    raise ValueError('Length of values does not match length of '
ValueError: Length of values does not match length of index

推荐答案

>>> import os
>>> os.getcwd()
'/'
>>> from unittest.mock import patch
>>> with patch('os.getcwd'):
...     print(os.getcwd)
...     print(os.getcwd())
...     print(len(os.getcwd()))
...
<MagicMock name='getcwd' id='4472112296'>
<MagicMock name='getcwd()' id='4472136928'>
0

默认情况下，patch用真正的通用模拟对象替换事物.如您所见，调用该模拟只返回另一个模拟.即使替换的对象没有len，它的len也为0.它的属性也是通用模拟.

By default patch replaces things with really generic mock objects. As you can see, calling the mock just returns another mock. It has a len of 0 even if the replaced object wouldn't have a len. Its attributes are also generic mocks.

因此，模拟行为需要一些额外的参数，例如:

So to simulate behavior requires things extra arguments like:

>>> with patch('os.getcwd', return_value='/a/wonderful/place'):
...     os.getcwd()
...
'/a/wonderful/place'

或通过":

>>> _cwd = os.getcwd
>>> with patch('os.getcwd') as p:
...     p.side_effect = lambda: _cwd()
...     print(os.getcwd())
...
/

https://docs.python中也有类似的示例. org/3.5/library/unittest.mock-examples.html

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