使用Numpy和Cython加速距离矩阵计算

使用Numpy和Cython加速距离矩阵计算

Cython的关键是避免使用Python对象和函数调用，包括对numpy数组进行矢量化 *** 作。这通常意味着手动写出所有循环并一次对单个数组元素进行 *** 作。

这里有一个非常有用的教程，涵盖了将numpy代码转换为Cython并对其进行优化的过程。

这是您距离功能的更优化Cython版本的快速入门：

import numpy as npcimport numpy as npcimport cython# don't use np.sqrt - the sqrt function from the C standard library is much# fasterfrom libc.math cimport sqrt# disable checks that ensure that array indices don't go out of bounds. this is# faster, but you'll get a segfault if you mess up your [email protected](False)# this disables 'wraparound' indexing from the end of the array using negative# [email protected](False)def dist(double [:, :] A):    # declare C types for as many of our variables as possible. note that we    # don't necessarily need to assign a value to them at declaration time.    cdef:        # Py_ssize_t is just a special platform-specific type for indices        Py_ssize_t nrow = A.shape[0]        Py_ssize_t ncol = A.shape[1]        Py_ssize_t ii, jj, kk        # this line is particularly expensive, since creating a numpy array        # involves unavoidable Python API overhead        np.ndarray[np.float64_t, ndim=2] D = np.zeros((nrow, nrow), np.double)        double tmpss, diff    # another advantage of using Cython rather than broadcasting is that we can    # exploit the symmetry of D by only looping over its upper triangle    for ii in range(nrow):        for jj in range(ii + 1, nrow): # we use tmpss to accumulate the SSD over each pair of rows tmpss = 0 for kk in range(ncol):     diff = A[ii, kk] - A[jj, kk]     tmpss += diff * diff tmpss = sqrt(tmpss) D[ii, jj] = tmpss D[jj, ii] = tmpss  # because D is symmetric    return D

我将其保存在名为的文件中

fastdist.pyx

。我们可以

pyximport

用来简化构建过程：

import pyximportpyximport.install()import fastdistimport numpy as npA = np.random.randn(100, 200)D1 = np.sqrt(np.square(A[np.newaxis,:,:]-A[:,np.newaxis,:]).sum(2))D2 = fastdist.dist(A)print np.allclose(D1, D2)# True

因此，至少有效。让我们使用

%timeit

魔术进行一些基准测试：

%timeit np.sqrt(np.square(A[np.newaxis,:,:]-A[:,np.newaxis,:]).sum(2))# 100 loops, best of 3: 10.6 ms per loop%timeit fastdist.dist(A)# 100 loops, best of 3: 1.21 ms per loop

约9倍的加速比不错，但不能真正改变游戏规则。正如您所说，广播方法的最大问题是构造中间阵列的内存需求。

A2 = np.random.randn(1000, 2000)%timeit fastdist.dist(A2)# 1 loops, best of 3: 1.36 s per loop

我不建议尝试使用广播…

我们可以做的另一件事是使用以下

prange

函数在最外层的循环中将其并行化：

from cython.parallel cimport prange...for ii in prange(nrow, nogil=True, schedule='guided'):...

为了编译并行版本，您需要告诉编译器启用OpenMP。我还没有弄清楚如何使用来执行此 *** 作

pyximport

，但是如果您正在使用

gcc

，则可以像这样手动进行编译：

$ cython fastdist.pyx$ gcc -shared -pthread -fPIC -fwrapv -fopenmp -O3    -Wall -fno-strict-aliasing  -I/usr/include/python2.7 -o fastdist.so fastdist.c

具有并行性，使用8个线程：

%timeit D2 = fastdist.dist_parallel(A2)1 loops, best of 3: 509 ms per loop