Python Line_profiler和Cython函数

Question

所以我试图在我自己使用的python脚本中分析一个函数line_profiler,因为我想要逐行时序.唯一的问题是该函数是一个Cython函数,并且line_profiler无法正常工作.在第一次运行时,它只是崩溃了一个错误.然后我补充道

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

在我的脚本的顶部,现在运行正常,除了时间和统计是空白的!

有没有办法使用line_profilerCythonized函数？

我可以分析非Cythonized函数,但它比Cythonized函数慢得多,我无法使用来自分析的信息 - 纯python的慢速将使我无法改进Cython的一个.

这是我想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慢速部分 - 它的工作就是重新排列来自MOODS的结果,C模块正在完成主要工作.

Answer 1

Till Hoffmann在这里有关于使用line_profiler和Cython的有用信息:如何逐行剖析cython函数.

我引用他的解决方案:

罗伯特布拉德肖帮助我让Robert Kern的line_profiler工具为cdef函数工作,我想我会分享结果stackoverflow.

总之,建立经常性的.pyx文件,构建脚本,并传递给cythonize了linetrace编译器指令,以使两者分析和线条跟踪:

from Cython.Build import cythonize

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

您可能还想将(未记录的)指令设置binding为True.

此外,您应该CYTHON_TRACE=1通过修改您的extensions设置来定义C宏

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

%%cython在iPython笔记本中使用魔术的一个工作示例如下:http: //nbviewer.ipython.org/gist/tillahoffmann/296501acea231cbdf5e7

Answer 2

Api改变了.现在:

from Cython.Compiler.Options import get_directive_defaults
directive_defaults = get_directive_defaults()
directive_defaults['linetrace'] = True
directive_defaults['binding'] = True