[Tutor] unitest with random inputs

[Sydney Shall]

On 19/07/2017 17:43, Peter Otten wrote:
> Sydney Shall wrote:
> 
>> I am learning to use unittest.
>>
>> I have written a program that runs as it should.
>> 247 tests give me a satisfactory answer.
>>
>> I have now added one more test and I get an error which I do not
>> understand.
>>
>> The two relevant tests are:
>>
>>    def test_type_capitalsadvanced(self):
>>           self.assertEqual(type(self.capitalsadvanced), numpy.ndarray)
>>
>>    def test_zero_in_capitalsadvanced(self):
>>           self.assertIn(self.capitalsadvanced, 0.0)
>>
>> The error message is:
>>
>> Traceback (most recent call last):
>>     File
>>
> "/Users/sydney/Capital/Capital_with_productivity/Current_Versions/testPOCWP_V2.py",
>> line 320, in test_zero_in_capitalsadvanced
>>       self.assertIn(self.capitalsadvanced, 0.0)
>>     File "/Users/sydney/anaconda/lib/python3.6/unittest/case.py", line
>> 1077, in assertIn
>>       if member not in container:
>> TypeError: argument of type 'float' is not iterable
>>
>> Final output from the tests is :
>>
>> Ran 247 tests in 1.179s
>>
>> FAILED (failures=9, errors=1)
>>
>> The failures all arise from a 'nan'.
> 
> I don't think so. To fix the traceback shown above you have to swap the
> arguments in assertIn():
> 
>      def test_zero_in_capitalsadvanced(self):
>          self.assertIn(0.0, self.capitalsadvanced)
> 
> Digression: once this is done you may still get failures when searching for
> NaN in a sequence as it does not compare equal to itself:
> 
>>>> nan = float("nan")
>>>> nan == nan
> False
>>>> a = numpy.array([nan])
>>>> nan in a
> False
> 
> This is how it should be (*), but unfortunately Python's builtin
> list/set/dict/tuple all assume that obj is obj implies obj == obj which
> makes the result of containment test hard to predict:
> 
>>>> nan in [nan]
> True
>>>> nan in [float("nan")]
> False
> 
> 
> (*) See for example
> <https://en.wikipedia.org/wiki/Floating-point_arithmetic>:
> "every NaN compares unequal to every value, including itself"
> 
>> It is this problem that I am trying to resolve.
>>
>> My problem is that the first test tells me correctly that the object
>> capitalsadvanced is a numpy.ndarray. But the second test error message
>> says it is a float.
>>
>> I should add that the program creates the initial data set by making use
>> of the random function which is given a mean to work with. Thus each
>> test run will be with different input data. But repeated tests show the
>> same errors.
>>
>> When I run the same tests with predetermined, fixed data I get no errors
>> and no 'nan' errors.
>>
>> Any guidance will be very welcome.
>>
>>
> 
> 
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> 
Peter,

Thanks for your comment.
This has helped to cure the first error.

For the second error, regarding 'nan' finding 'nan', I received the 
following output.


FAIL: test_nan_in_capitalsadvanced (__main__.TestPOC)
----------------------------------------------------------------------
Traceback (most recent call last):
   File 
"/Users/sydney/Capital/Capital_with_productivity/Current_Versions/testPOCWP_V2.py", 
line 323, in test_nan_in_capitalsadvanced
     self.assertIn('nan', self.capitalsadvanced)
AssertionError: 'nan' not found in array([ 12026.72641072, 
12434.53700436,  12185.56314119, ...,
         12488.04714281,  12479.4662866 ,  12310.66016998])

Could I use assertNotIn with the other problems?

Thanks again.

Sydney

-- 
Sydney