如何加快数百万个对象的python实例初始化? [英] How to speed up python instance initialization for millions of objects?

问题描述

我已经定义了一个名为 Edge 的python class ，如下所示:

I have defined a python class named Edge as follows:

class Edge:
    def __init__(self):
        self.node1 = 0
        self.node2 = 0
        self.weight = 0

现在，我必须使用以下方法创建Edge的大约10 ^ 6至10 ^ 7个实例:

Now I have to create approximately 10^6 to 10^7 instances of Edge using:

edges= []
for (i,j,w) in ijw:
    edge = Edge()
    edge.node1 = i
    edge.node2 = j
    edge.weight = w
    edges.append(edge)

我在台式机上花了大约2秒钟的时间.有什么更快的方法吗?

I took me approximately 2 seconds in Desktop. Is there any faster way to do?

推荐答案

您不能使其更快很多，但我当然会使用

You can't make it much faster, but I certainly would use __slots__ to save on memory allocations. Also make it possible to pass in the attribute values when creating the instance:

class Edge:
    __slots__ = ('node1', 'node2', 'weight')
    def __init__(self, node1=0, node2=0, weight=0):
        self.node1 = node1
        self.node2 = node2
        self.weight = weight

使用更新后的 __ init __ ，您可以使用列表理解:

With the updated __init__ you can use a list comprehension:

edges = [Edge(*args) for args in ijw]

这些可以一起节省创建对象的大量时间，大约将所需的时间减半.

Together these can shave off a decent amount of time creating the objects, roughly halve the time needed.

比较创建100万个对象；设置:

Comparison creating 1 million objects; the setup:

>>> from random import randrange
>>> ijw = [(randrange(100), randrange(100), randrange(1000)) for _ in range(10 ** 6)]
>>> class OrigEdge:
...     def __init__(self):
...         self.node1 = 0
...         self.node2 = 0
...         self.weight = 0
...
>>> origloop = '''\
... edges= []
... for (i,j,w) in ijw:
...     edge = Edge()
...     edge.node1 = i
...     edge.node2 = j
...     edge.weight = w
...     edges.append(edge)
... '''
>>> class SlotsEdge:
...     __slots__ = ('node1', 'node2', 'weight')
...     def __init__(self, node1=0, node2=0, weight=0):
...         self.node1 = node1
...         self.node2 = node2
...         self.weight = weight
...
>>> listcomploop = '''[Edge(*args) for args in ijw]'''

和时间:

>>> from timeit import Timer
>>> count, total = Timer(origloop, 'from __main__ import OrigEdge as Edge, ijw').autorange()
>>> (total / count) * 1000 # milliseconds
722.1121070033405
>>> count, total = Timer(listcomploop, 'from __main__ import SlotsEdge as Edge, ijw').autorange()
>>> (total / count) * 1000 # milliseconds
386.6706900007557

那快将近2倍.

将随机输入列表增加到10 ^ 7个项目，时间差保持不变:

Increasing the random input list to 10^7 items, and the timing difference holds:

>>> ijw = [(randrange(100), randrange(100), randrange(1000)) for _ in range(10 ** 7)]
>>> count, total = Timer(origloop, 'from __main__ import OrigEdge as Edge, ijw').autorange()
>>> (total / count)
7.183759553998243
>>> count, total = Timer(listcomploop, 'from __main__ import SlotsEdge as Edge, ijw').autorange()
>>> (total / count)
3.8709938440006226

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