使用cython在图形上进行框覆盖 [英] Perform the box covering on a graph using cython

问题描述

我编写了一个python脚本来执行对图形的覆盖，但是在小图形（100个节点）上运行它需要一分钟以上的时间。
今天有人推荐cython来提高效率，所以我遵循本指南来适应我的代码。

I wrote a python script to perform the box covering on a graph but it takes more than a minute when I run it on small graphs (100 nodes). Today someone recommend cython to improve its efficiency so I follow this guide to adadpt the code that I had.

运行python代码的结果如下：

Running the python code the results where:

In [6]: %timeit test.test()
1000 loops, best of 3: 1.88 ms per loop

遵循指南后，结果为：

In [7]: %timeit c_test.test()
1000 loops, best of 3: 1.05 ms per loop

性能更好，但我相信可以改进很多。鉴于我今天刚刚遇到cython，我想问你如何改善此代码：

The performance was better but I am sure that it is a lot that can be improved. Given that I just meet cython today, I want to ask you how can I improve this code:

import random as rnd
import numpy as np

cimport cython
cimport numpy as np

DTYPE = np.int
ctypedef np.int_t DTYPE_t


def choose_color(not_valid_colors, valid_colors):
    possible_values = list(valid_colors - not_valid_colors)

    if possible_values:
        return rnd.choice(possible_values)
    else:
        return max(valid_colors.union(not_valid_colors)) + 1


@cython.boundscheck(False)
cdef np.ndarray[DTYPE_t, ndim=2] greedy_coloring(np.ndarray[DTYPE_t, ndim=2] distances, int num_nodes, int diameter):
    cdef int  i, lb, j
    cdef np.ndarray[DTYPE_t, ndim=2] c = np.empty((num_nodes+1, diameter+2), dtype=DTYPE)

    c.fill(-1)
    # Matrix C will not use the 0 column and 0 row to
    # let the algorithm look very similar to the paper
    # pseudo-code

    nodes = list(range(1, num_nodes+1))
    rnd.shuffle(nodes)

    c[nodes[0], :] = 0

    # Algorithm
    for i in nodes[1:]:
        for lb in range(2, diameter+1):
            not_valid_colors = set()
            valid_colors = set()

            for j in nodes[:i]:

                if distances[i-1, j-1] >= lb:
                    not_valid_colors.add(c[j, lb])
                else:
                    valid_colors.add(c[j, lb])

            c[i, lb] = choose_color(not_valid_colors, valid_colors)

    return c


def test():
    distances = np.matrix('0 3 2 4 1 1; \
                           3 0 1 1 3 2; \
                           2 1 0 2 2 1; \
                           4 1 2 0 4 3; \
                           1 3 2 4 0 1; \
                           1 2 1 3 1 0')

    c = greedy_coloring(distances, 6, 4)

推荐答案

在Cython中，随着在Cython函数中删除更多Python调用，您将获得更快的速度。

In Cython, you will get faster speed as you remove more Python calls inside your Cython functions.

例如，浏览代码，您正在<的嵌套循环中调用 choose_color（） code> greedy_coloring（）。以及在函数内部定义的变量也应键入。由于将反复调用它，因此会带来很多开销。

For example, skimming through your code, you are making calls to choose_color() inside the nested loop in greedy_coloring(). That should be typed as well, along with variables defined inside the function. Since it will be called repeatedly, it will bring a lot of overhead.

您可以将 cython 与 -a 选项（例如 cython -a file.pyx ）来生成带注释的html文件，该文件可以直观地显示您的哪一部分代码正在进行Python调用（黄线）。

You can use cython with -a option (e.g., cython -a file.pyx) to generate an annotated html file which shows visually which part of your code is making Python calls (yellow lines). This will help you a lot in terms of improving your Cython code.

很抱歉，由于缺少具体的指针，希望对您有所帮助。

I'm sorry for lack of specific pointers - hope this is helpful.

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