如何比较持有numpy.ndarray（bool（a == b）引发ValueError）的数据类的相等性？

Question

If I create a Python dataclass containing a Numpy ndarray, I can no longer use the automatically generated __eq__ anymore.

import numpy as np

@dataclass
class Instr:
    foo: np.ndarray
    bar: np.ndarray

arr = np.array([1])
arr2 = np.array([1, 2])
print(Instr(arr, arr) == Instr(arr2, arr2))

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

This is because ndarray.__eq__ sometimes returns a ndarray of truth values, by comparing a[0] to b[0] , and so on and so forth to the longer of the 2. This is quite complex and unintuitive, and in fact only raises an error when the arrays are different shapes, or have different values or something.

How do I safely compare @dataclass es holding Numpy arrays?

---

@dataclass 's implementation of __eq__ is generated using eval() . Its source is missing from the stacktrace and cannot be viewed using inspect , but it's actually using a tuple comparison , which calls bool(foo).

import dis
dis.dis(Instr.__eq__)

excerpt:

  3 12 LOAD_FAST 0 (self) 14 LOAD_ATTR 1 (foo) 16 LOAD_FAST 0 (self) 18 LOAD_ATTR 2 (bar) 20 BUILD_TUPLE 2 22 LOAD_FAST 1 (other) 24 LOAD_ATTR 1 (foo) 26 LOAD_FAST 1 (other) 28 LOAD_ATTR 2 (bar) 30 BUILD_TUPLE 2 32 COMPARE_OP 2 (==) 34 RETURN_VALUE 

Answer 1

The solution is to put in your own __eq__ method and set eq=False so the dataclass doesn't generate its own (although checking the docs that last step isn't necessary but I think it's nice to be explicit anyway).

import numpy as np

def array_eq(arr1, arr2):
    return (isinstance(arr1, np.ndarray) and
            isinstance(arr2, np.ndarray) and
            arr1.shape == arr2.shape and
            (arr1 == arr2).all())

@dataclass(eq=False)
class Instr:

    foo: np.ndarray
    bar: np.ndarray

    def __eq__(self, other):
        if not isinstance(other, Instr):
            return NotImplemented
        return array_eq(self.foo, other.foo) and array_eq(self.bar, other.bar)

---

Edit

A general and quick solution for generic dataclasses where some values are numpy arrays and some others are not

import numpy as np
from dataclasses import dataclass, astuple

def array_safe_eq(a, b) -> bool:
    """Check if a and b are equal, even if they are numpy arrays"""
    if a is b:
        return True
    if isinstance(a, np.ndarray) and isinstance(b, np.ndarray):
        return a.shape == b.shape and (a == b).all()
    try:
        return a == b
    except TypeError:
        return NotImplemented

def dc_eq(dc1, dc2) -> bool:
   """checks if two dataclasses which hold numpy arrays are equal"""
   if dc1 is dc2:
        return True
   if dc1.__class__ is not dc2.__class__:
       return NotImplmeneted  # better than False
   t1 = astuple(dc1)
   t2 = astuple(dc2)
   return all(array_safe_eq(a1, a2) for a1, a2 in zip(t1, t2))

# usage
@dataclass(eq=False)
class T:

   a: int
   b: np.ndarray
   c: np.ndarray

   def __eq__(self, other):
        return dc_eq(self, other)