Python Line_profiler 和 Cython 函数

Question

所以我尝试使用 line_profiler 在我自己的 python 脚本中分析一个函数，因为我想要逐行计时。唯一的问题是该函数是 Cython 函数，并且 line_profiler 无法正常工作。在第一次运行时，它只是因错误而崩溃。然后我添加了

!python
cython: profile=True
cython: linetrace=True
cython: binding=True

在我的脚本的顶部，现在它运行正常，除了时间和统计数据是空白的!

有没有办法将 line_profiler 与 Cythonized 函数一起使用？

我可以分析非 Cythonized 函数，但它比 Cythonized 慢得多，以至于我无法使用来自分析的信息 - 纯 python 函数的缓慢使得我无法改进 Cython一个。

这是我要分析的函数的代码:

class motif_hit(object):
__slots__ = ['position', 'strand']

def __init__(self, int position=0, int strand=0):
    self.position = position
    self.strand = strand

#the decorator for line_profiler
@profile
def find_motifs_cython(list bed_list, list matrices=None, int limit=0, int mut=0):
    cdef int q = 3
    cdef list bg = [0.25, 0.25, 0.25, 0.25]
    cdef int matrices_length = len(matrices)
    cdef int results_length = 0
    cdef int results_length_shuffled = 0
    cdef np.ndarray upper_adjust_list = np.zeros(matrices_length, np.int)
    cdef np.ndarray lower_adjust_list = np.zeros(matrices_length, np.int)
    #this one need to be a list for MOODS
    cdef list threshold_list = [None for _ in xrange(matrices_length)]
    cdef list matrix_list = [None for _ in xrange(matrices_length)]
    cdef np.ndarray results_list = np.zeros(matrices_length, np.object)
    cdef int count_seq = len(bed_list)
    cdef int mat
    cdef int i, j, k
    cdef int position, strand
    cdef list result, results, results_shuffled
    cdef dict result_temp
    cdef int length
    if count_seq > 0:
        for mat in xrange(matrices_length):
            matrix_list[mat] = matrices[mat]['matrix'].tolist()
            #change that for a class
            results_list[mat] = {'kmer': matrices[mat]['kmer'],
                                 'motif_count': 0,
                                 'pos_seq_count': 0,
                                 'motif_count_shuffled': 0,
                                 'pos_seq_count_shuffled': 0,
                                 'ratio': 0,
                                 'sequence_positions': np.empty(count_seq, np.object)}
            length = len(matrices[mat]['kmer'])
            #wrong with imbalanced matrices
            upper_adjust_list[mat] = int(ceil(length / 2.0))
            lower_adjust_list[mat] = int(floor(length / 2.0))
            #upper_adjust_list[mat] = 0
            #lower_adjust_list[mat] = 0
            #-0.1 to adjust for a division floating point bug (4.99999 !< 5, but is < 4.9!)
            threshold_list[mat] = MOODS.max_score(matrix_list[mat]) - float(mut) - 0.1

        #for each sequence
        for i in xrange(count_seq):
            item = bed_list[i]
            #TODO: remove the Ns, but it might unbalance
            results = MOODS.search(str(item.sequence[limit:item.total_length - limit]), matrix_list, threshold_list, q=q, bg=bg, absolute_threshold=True, both_strands=True)
            results_shuffled = MOODS.search(str(item.sequence_shuffled[limit:item.total_length - limit]), matrix_list, threshold_list, q=q, bg=bg, absolute_threshold=True, both_strands=True)
            results = results[0:len(matrix_list)]
            results_shuffled = results_shuffled[0:len(matrix_list)]
            results_length = len(results)
            #for each matrix
            for j in xrange(results_length):
                result = results[j]
                result_shuffled = results_shuffled[j]
                upper_adjust = upper_adjust_list[j]
                lower_adjust = lower_adjust_list[j]
                result_length = len(result)
                result_length_shuffled = len(result_shuffled)
                if result_length > 0:
                    results_list[j]['pos_seq_count'] += 1
                    results_list[j]['sequence_positions'][i] = np.empty(result_length, np.object)
                    #for each motif
                    for k in xrange(result_length):
                        position = result[k][0]
                        strand = result[k][1]
                        if position >= 0:
                                strand = 0
                                adjust = upper_adjust
                        else:
                                position = -position
                                strand = 1
                                adjust = lower_adjust
                        results_list[j]['motif_count'] += 1
                        results_list[j]['sequence_positions'][i][k] = motif_hit(position + adjust + limit, strand)

                if result_length_shuffled > 0:
                    results_list[j]['pos_seq_count_shuffled'] += 1
                    #for each motif
                    for k in xrange(result_length_shuffled):
                        results_list[j]['motif_count_shuffled'] += 1

                #j = j + 1
            #i = i + 1

        for i in xrange(results_length):
            result_temp = results_list[i]
            result_temp['ratio'] = float(result_temp['pos_seq_count']) / float(count_seq)
    return results_list

我很确定三重嵌套循环是主要的缓慢部分 - 它的工作只是重新排列来自 MOODS 的结果，C 模块执行主要工作。

Answer 1

Till Hoffmann 在此处提供了有关在 Cython 中使用 line_profiler 的有用信息:How to profile cython functions line-by-line .

我引用他的解决方案:

Robert Bradshaw 帮助我获得了用于 cdef 函数的 Robert Kern 的 line_profiler 工具，我想我应该在 stackoverflow 上分享结果。

简而言之，设置一个常规的 .pyx 文件并构建脚本和 pass to cythonize linetrace compiler directive启用分析和线路跟踪:

from Cython.Build import cythonize

cythonize('hello.pyx', compiler_directives={'linetrace': True})

您可能还想设置 ( undocumented ) directive binding为 True。

另外，您应该通过修改您的扩展设置来定义 C 宏 CYTHON_TRACE=1

extensions = [
    Extension('test', ['test.pyx'], define_macros=[('CYTHON_TRACE', '1')])
]

在 iPython 笔记本中使用 %%cython 魔法的工作示例如下: http://nbviewer.ipython.org/gist/tillahoffmann/296501acea231cbdf5e7