Question

我已经定义了一个名为class的Python Edge，如下所示：

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大约10 ^ 6至10 ^ 7个Edge实例：

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。有什么更快的方法吗？

Answer 1

您无法使其更快，但我当然会使用__slots__来节省内存分配。还可以在创建实例时传递属性值：

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：

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

这些可以一起节省创建对象的大量时间，大约将所需时间减半。

比较创建100万个对象；设置：

>>> 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项，时间差保持不变：

>>> 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

Answer 2

另一种选择是跳过Edge类，并通过表或邻接矩阵实现边。

例如

A = create_adjacency_graph(ijw)  # Implement to return a IxJ (sparse?) matrix of weights
edge_a_weight = A[3, 56]
edge_b_weight = A[670, 1023]
# etc...

尽管这样做确实消除了一些灵活性，但是创建和使用时都应该非常快。

Answer 3

还有另外一种使用recordclass library的内存节省方法：

from recordclass import dataobject

from random import randrange
import sys
ijw = [(randrange(100), randrange(100), randrange(1000)) for _ in range(10 ** 7)]

class EdgeDO(dataobject):
    __fields__ = 'node1', 'node2', 'weight'

class EdgeSlots:
    __slots__ = 'node1', 'node2', 'weight'

    def __init__(self, node1, node2, weight):
         self.node1 = node1
         self.node2 = node2
         self.weight = weight

def list_size(lst):
    return sum(sys.getsizeof(o) for o in lst)

%time list_do = [EdgeDO(n1, n2, w) for n1, n2, w in ijw]
%time list_slots = [EdgeSlots(n1, n2, w) for n1, n2, w in ijw]

print('size (dataobject):', list_size(list_do))
print('size (__slots__): ', list_size(list_slots))

有输出：

CPU times: user 2.23 s, sys: 20 ms, total: 2.25 s
Wall time: 2.25 s
CPU times: user 6.79 s, sys: 84.1 ms, total: 6.87 s
Wall time: 6.87 s
size (dataobject): 400000000
size (__slots__):  640000000

如何加快数百万个对象的python实例初始化？

3 个答案: