How can I make my class more robust to operator/function overloading?-CodePudding

I am writing a class for which objects are initialised with two parameters (a, b). The intention is to assign instances of this class to variables so that I can have an equation written symbolically in Python code, but have operator overloading perform unique operations on a and b.

import numpy as np


class my_class(object):

    def __init__(self, a, b):
        self.value1 = a
        self.value2 = b

    # Example of an overloaded operator that works just fine
    def __mul__(self, other):
        new_a = self.value1 * other
        new_b = self.value2 * np.absolute(other)
        return my_class(new_a, new_b)


if __name__ == "__main__":
    my_object = my_class(100, 1)

    print(np.exp(my_object))    # This doesn't work!

In running the above example code, I encountered the following output:

TypeError: loop of ufunc does not support argument 0 of type my_class which has no callable exp method

Through guesswork, I was able to see that a complaint about no callable exp method probably meant I needed to define a method using:

def exp(self):
    ...

which ended up working just fine. But now I will have to write another method for np.expm1() and so on as I require. Thankfully I only need np.exp() and np.log() to work, but I also tried math.exp() on my object and I started getting a type error.

So now my question is: The custom exp method in the class seemed to work for overloading the NumPy function, but how am I supposed to handle math.exp() not working? It must be possible because somehow when calling math.exp() on a NumPy array, NumPy understands that a 1-element array can be turned into a scalar and then passed to math.exp() without issue. I mean I guess this technically is about overloading a function, but before I realised defining a new exp was the fix to my first problem, I had no idea why a method like __rpow__ wasn't being called.

CodePudding user response：

First, on why math.exp works with 1-element Numpy arrays: the numpy.ndarray class has a __float__ method; this method is part of the Python data model for numeric types, and is used when float(x) is called. I couldn't spot anything in the math docs that says that math.exp casts its argument to float, but it's not unreasonable behaviour.

As for customizing behaviour of Numpy's ufuncs: the recommended way to implement "array-like" objects that override ufunc behaviour is somewhat complicated. I couldn't find documentation on providing exp, log, etc. methods to customize ufuncs. Supplying methods like this doesn't work in all cases, for example np.heaviside; this example

import numpy as np


class foo:
    
    def exp(self):
        return foo()

    def heaviside(self, other):
        return foo()


print(f'{np.exp(foo()) = }')
print(f'{np.heaviside(foo(), foo()) = }')

gives this output:

np.exp(foo()) = <__main__.foo object at 0x7efcdbba1ac0>
Traceback (most recent call last):
  File "/home/rory/hack/stackoverflow/q70312146/heaviside.py", line 14, in <module>
    print(f'{np.heaviside(foo(), foo()) = }')
TypeError: ufunc 'heaviside' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

CodePudding user response：

np.exp(my_object) is implemented as np.exp(np.array(my_object)).

np.array(my_object) is a object dtype array. np.exp tries elmt.exp() for each element of the array. That doesn't work for most classes, since they don't implement such a method.

Same applies for operators and other ufunc.

math.exp is an unrelated implementation. It apparently works for something that gives a single numeric value, but I haven't explored that much. numpy will raise an error if it can't do that.

Implementing * with a class __mul__ is done by interpreter.

Same error message when using array in math.exp and with __float__()

In [52]: math.exp(np.array([1,2,3]))
Traceback (most recent call last):
  File "<ipython-input-52-40503a52084a>", line 1, in <module>
    math.exp(np.array([1,2,3]))
TypeError: only size-1 arrays can be converted to Python scalars

In [53]: np.array([1,2,3]).__float__()
Traceback (most recent call last):
  File "<ipython-input-53-0bacdf9df4e7>", line 1, in <module>
    np.array([1,2,3]).__float__()
TypeError: only size-1 arrays can be converted to Python scalars

Similarly when an array is used in a boolean context (e.g if), we can get an error generated with

In [55]: np.array([1,2,3]).__bool__()
Traceback (most recent call last):
  File "<ipython-input-55-04aca1612817>", line 1, in <module>
    np.array([1,2,3]).__bool__()
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Similarly using a sympy Relational in an if results in the error produced by

In [110]: (x>0).__bool__()
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-110-d88b76ce6b22> in <module>
----> 1 (x>0).__bool__()

/usr/local/lib/python3.8/dist-packages/sympy/core/relational.py in __bool__(self)
    396 
    397     def __bool__(self):
--> 398         raise TypeError("cannot determine truth value of Relational")
    399 
    400     def _eval_as_set(self):

TypeError: cannot determine truth value of Relational

pandas Series produce a similar error.