numpy FloatingPointError: invalid value encountered in subtract - not reproducible?

numpy, python

Solution

Numpy has configurable behaviour as to how floating point errors are treated. By default underflow errors are ignored, whereas other errors trigger a warning. For each category users can change this behaviour with `numpy.seterr`. These settings are global — there are no namespaces here; so if a library calls `numpy.seterr(all='raise')` then this will affect the entire program until `numpy.seterr` is called again.

You can confirm this is indeed the cause of your problem with

print(numpy.seterr())

which should output something like

{'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}

If some of those categories have the value `raise`, in particular the key `'invalid'`, then that would explain the behaviour you are observing.

You can suppress this exception by calling `numpy.seterr(invalid='warn')`, or, alternatively, `invalid='ignore'`. For a full list of possible errors, read through the documentation of `numpy.seterr`.

You can also use a context-manager to temporarily change the behaviour:

In [12]: x = np.arange(-5, 5,dtype='float64')

In [13]: with np.errstate(divide="raise"):
    print(1/x)
   ....:     
---------------------------------------------------------------------------
FloatingPointError                        Traceback (most recent call last)
<ipython-input-13-881589fdcb7a> in <module>()
      1 with np.errstate(divide="raise"):
----> 2     print(1/x)
      3 

FloatingPointError: divide by zero encountered in true_divide

In [14]: with np.errstate(divide="warn"):
    print(1/x)
   ....:     
/home/users/gholl/venv/stable-3.5/bin/ipython3:2: RuntimeWarning: divide by zero encountered in true_divide
  
[-0.2        -0.25       -0.33333333 -0.5        -1.                 inf
  1.          0.5         0.33333333  0.25      ]

In [15]: with np.errstate(divide="ignore"):
    print(1/x)
   ....:     
[-0.2        -0.25       -0.33333333 -0.5        -1.                 inf
  1.          0.5         0.33333333  0.25      ]

I tend to wrap my entire code inside a `with np.errstate(all="raise")` block, and then use a context-manager ignoring a particular condition if I am sure that the problem is not hiding a bug — it usually is, though.

If there was indeed a library changing the state permanently, I would raise an issue with the maintainers or send a pull request, because they really should be using a context manager, so that their changing setting only applies to their block of code and not to the rest of the program.

Problem

Original source