Numpy - AttributeError: 'Zero' object has no attribute 'exp'

I'm having trouble solving a discrepancy between something breaking at runtime, but using the exact same data and operations in the python console, having it work fine.

# f_err - currently has value 1.11819388872025
# l_scales - currently a numpy array [1.17840183376334 1.13456764589809]
sq_euc_dists = self.se_term(x1, x2, l_scales) # this is fine. It calls cdists on x1/l_scales, x2/l_scales vectors
return (f_err**2) * np.exp(-0.5 * sq_euc_dists) # <-- errors on this line

The error that I get is

AttributeError: 'Zero' object has no attribute 'exp'

However, calling those exact same lines, with the same f_err, l_scales, and x1, x2 in the console right after it errors out, somehow does not produce errors.

I was not able to find a post referring to the 'Zero' object error specifically, and the non-'Zero' ones I found didn't seem to apply to my case here.

EDIT: It was a bit lacking in info, so here's an actual (extracted) runnable example with sample data I took straight out of a failed run, which when run in isolation works fine/I can't reproduce the error except in runtime.

Note that the sqeucld_dist function below is quite bad and I should be using scipy's cdist instead. However, because I'm using sympy's symbols for matrix elementwise gradients with over 15 partial derivatives in my real data, cdist is not an option as it doesn't deal with arbitrary objects.

import numpy as np

def se_term(x1, x2, l):
    return sqeucl_dist(x1/l, x2/l)

def sqeucl_dist(x, xs):
    return np.sum([(i-j)**2 for i in x for j in xs], axis=1).reshape(x.shape[0], xs.shape[0])


x = np.array([[-0.29932052, 0.40997373], [0.40203481, 2.19895326], [-0.37679417, -1.11028267], [-2.53012051, 1.09819485], [0.59390005, 0.9735], [0.78276777, -1.18787904], [-0.9300892, 1.18802775], [0.44852545, -1.57954101], [1.33285028, -0.58594779], [0.7401607, 2.69842268], [-2.04258086, 0.43581565], [0.17353396, -1.34430191], [0.97214259, -1.29342284], [-0.11103534, -0.15112815], [0.41541759, -1.51803154], [-0.59852383, 0.78442389], [2.01323359, -0.85283772], [-0.14074266, -0.63457529], [-0.49504797, -1.06690869], [-0.18028754, -0.70835799], [-1.3794126, 0.20592016], [-0.49685373, -1.46109525], [-1.41276934, -0.66472598], [-1.44173868, 0.42678815], [0.64623684, 1.19927771], [-0.5945761, -0.10417961]])
f_err = 1.11466725760716
l = [1.18388412685279, 1.02290811104357]
result = (f_err**2) * np.exp(-0.5 * se_term(x, x, l)) # This runs fine, but fails with the exact same calls and data during runtime

Any help greatly appreciated!

Solution

Here is how to reproduce the error you are seeing:

import sympy
import numpy

zero = sympy.sympify('0')

numpy.exp(zero)

You will see the same exception you are seeing.

You can fix this (inefficiently) by changing your code to the following to make things floating point.

def sqeucl_dist(x, xs):
    return np.sum([np.vectorize(float)(i-j)**2 for i in x for j in xs], 
                  axis=1).reshape(x.shape[0], xs.shape[0])

It will be better to fix your gradient function using lambdify.

Here's an example of how lambdify can be used on partial d

from sympy.abc import x, y, z
expression = x**2 + sympy.sin(y) + z
derivatives = [expression.diff(var, 1) for var in [x, y, z]]

derivatives is now [2*x, cos(y), 1], a list of Sympy expressions. To create a function which will evaluate this numerically at a particular set of values, we use lambdify as follows (passing 'numpy' as an argument like that means to use numpy.cos rather than sympy.cos):

derivative_calc = sympy.lambdify((x, y, z), derivatives, 'numpy')

Now derivative_calc(1, 2, 3) will return [2, -0.41614683654714241, 1]. These are ints and numpy.float64s.

A side note: np.exp(M) will calculate the element-wise exponent of each of the elements of M. If you are trying to do a matrix exponential, you need np.linalg.exmp.