如何使 python 数据类可散列而不使它们不可变？

Question

Say a I have a dataclass in python3. I want to be able to hash and order these objects. I do not want these to be immutable.

I only want them ordered/hashed on id.

I see in the docs that I can just implement _ hash _ and all that but I'd like to get datacalsses to do the work for me because they are intended to handle this.

from dataclasses import dataclass, field

@dataclass(eq=True, order=True)
class Category:
    id: str = field(compare=True)
    name: str = field(default="set this in post_init", compare=False)

a = sorted(list(set([ Category(id='x'), Category(id='y')])))

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'Category'

Answer 1

From the docs :

Here are the rules governing implicit creation of a __hash__() method:

[...]

If eq and frozen are both true, by default dataclass() will generate a __hash__() method for you. If eq is true and frozen is false, __hash__() will be set to None , marking it unhashable (which it is, since it is mutable). If eq is false, __hash__() will be left untouched meaning the __hash__() method of the superclass will be used (if the superclass is object, this means it will fall back to id-based hashing).

Since you set eq=True and left frozen at the default ( False ), your dataclass is unhashable.

You have 3 options:

• Set frozen=True (in addition to eq=True ), which will make your class immutable and hashable.

• Set unsafe_hash=True , which will create a __hash__ method but leave your class mutable, thus risking problems if an instance of your class is modified while stored in a dict or set:

   cat = Category('foo', 'bar') categories = {cat} cat.id = 'baz' print(cat in categories) # False

• Manually implement a __hash__ method.

Answer 2

TL;DR

Use frozen=True in conjunction to eq=True (which will make the instances immutable).

Long Answer

From the docs :

__hash__() is used by built-in hash() , and when objects are added to hashed collections such as dictionaries and sets. Having a __hash__() implies that instances of the class are immutable. Mutability is a complicated property that depends on the programmer's intent, the existence and behavior of __eq__() , and the values of the eq and frozen flags in the dataclass() decorator.

By default, dataclass() will not implicitly add a __hash__() method unless it is safe to do so. Neither will it add or change an existing explicitly defined __hash__() method. Setting the class attribute __hash__ = None has a specific meaning to Python, as described in the __hash__() documentation.

If __hash__() is not explicit defined, or if it is set to None, then dataclass() may add an implicit __hash__() method. Although not recommended, you can force dataclass() to create a __hash__() method with unsafe_hash=True . This might be the case if your class is logically immutable but can nonetheless be mutated. This is a specialized use case and should be considered carefully.

Here are the rules governing implicit creation of a __hash__() method. Note that you cannot both have an explicit __hash__() method in your dataclass and set unsafe_hash=True ; this will result in a TypeError .

If eq and frozen are both true, by default dataclass() will generate a __hash__() method for you. If eq is true and frozen is false, __hash__() will be set to None, marking it unhashable (which it is, since it is mutable). If eq is false, __hash__() will be left untouched meaning the __hash__() method of the superclass will be used (if the superclass is object, this means it will fall back to id-based hashing).

Answer 3

I'd like to add a special note for use of unsafe_hash.

You can exclude fields from being compared by hash by setting compare=False, or hash=False. (hash by default inherits from compare).

This might be useful if you store nodes in a graph but want to mark them visited without breaking their hashing (eg if they're in a set of unvisited nodes..).

from dataclasses import dataclass, field
@dataclass(unsafe_hash=True)
class node:
    x:int
    visit_count: int = field(default=10, compare=False)  # hash inherits compare setting. So valid.
    # visit_count: int = field(default=False, hash=False)   # also valid. Arguably easier to read, but can break some compare code.
    # visit_count: int = False   # if mutated, hashing breaks. (3* printed)

s = set()
n = node(1)
s.add(n)
if n in s: print("1* n in s")
n.visit_count = 11
if n in s:
    print("2* n still in s")
else:
    print("3* n is lost to the void because hashing broke.")

This took me hours to figure out... Useful further readings I found is the python doc on dataclasses. Specifically see the field documentation and dataclass arg documentations. https://docs.python.org/3/library/dataclasses.html