Python判断列表是否已排序的各种方法及其性能分析_python教程-查字典教程网
Python判断列表是否已排序的各种方法及其性能分析
Python判断列表是否已排序的各种方法及其性能分析
发布时间:2016-12-28 来源:查字典编辑
摘要:声明本文基于Python2.7语言,给出判断列表是否已排序的多种方法,并在作者的WindowsXP主机(PentiumG6302.7GHz主...

声明

本文基于Python2.7语言,给出判断列表是否已排序的多种方法,并在作者的Windows XP主机(Pentium G630 2.7GHz主频2GB内存)上对比和分析其性能表现。

一. 问题提出

Haskell培训老师提出一个问题:如何判断列表是否已经排序?

排序与否实际只是相邻元素间的某种二元关系,即a->a->Bool。所以第一步可以把二元组列表找出来;第二步是把这个函数作用于每个元组,然后用and操作。老师给出的实现代码如下:

pair lst = zip lst ( tail lst ) sorted lst predict = and [ predict x y | (x,y) <- pair lst]

Haskell中,等号前面是函数的名称和参数,后面是函数的定义体。pair函数将列表lst错位一下(tail除去列表的第一个元素)后,和原列表在zip的作用下形成前后相邻元素二元组列表。predict函数接受两个数字,根据大小返回True或False。and对类型为[Bool]的列表中所有元素求与,其语义类似Python的all()函数。

随后,老师请大家思考性能问题。作者提出,若准确性要求不高,可生成三组随机数排序后作为下标,提取原列表相应的三组元素组成三个新的子列表("采样")。若判断三个子列表遵循同样的排序规则时,则认为原列表已排序。当列表很大且前段已排序时,选取适当数目的随机数,可在保障一定准确率的同时显著地降低运算规模。

此外,实际应用中还应考虑数据来源。例如,Python语言的os.listdir()方法在Windows系统中返回的列表条目满足字母序,在Linux系统中则返回乱序条目。因此,若判断系统平台(os.platform)为win32,则条目必然已经排序。

为对比验证随机采样方式的可行性,作者先在网上搜集判断列表排序的现有方法,主要参考Stack Overflow网站上"Pythonic way to check if a list is sorted or not"问题的答案,并在本文第二节给出相关的代码示例。注意,本文所述的"排序"不要求严格排序,即相邻元素允许相等。例如,[1,2,2,3]视为升序,[3,3,2,2]视为降序。

二. 代码实现

本节判断列表排序的函数名格式为IsListSorted_XXX()。为简洁起见,除代码片段及其输出外,一律以_XXX()指代。

2.1 guess

def IsListSorted_guess(lst): listLen = len(lst) if listLen <= 1: return True #由首个元素和末尾元素猜测可能的排序规则 if lst[0] == lst[-1]: #列表元素相同 for elem in lst: if elem != lst[0]: return False elif lst[0] < lst[-1]: #列表元素升序 for i, elem in enumerate(lst[1:]): if elem < lst[i]: return False else: #列表元素降序 for i, elem in enumerate(lst[1:]): if elem > lst[i]: return False return True

_guess()是最通用的实现,几乎与语言无关。值得注意的是,该函数内会猜测给定列表可能的排序规则,因此无需外部调用者指明排序规则。

2.2 sorted

def IsListSorted_sorted(lst): return sorted(lst) == lst or sorted(lst, reverse=True) == lst

_sorted()使用Python内置函数sorted()。由于sorted()会对未排序的列表排序,_sorted()函数主要适用于已排序列表。

若想判断列表未排序后再对其排序,不如直接调用列表的sort()方法,因为该方法内部会判断列表是否排序。对于已排序列表,该方法的时间复杂度为线性阶O(n)——判断为O(n)而排序为O(nlgn)。

2.3 for-loop

def IsListSorted_forloop(lst, key=lambda x, y: x <= y): for i, elem in enumerate(lst[1:]): #注意,enumerate默认迭代下标从0开始 if not key(lst[i], elem): #if elem > lst[i]更快,但通用性差 return False return True

无论列表是否已排序,本函数的时间复杂度均为线性阶O(n)。注意,参数key表明缺省的排序规则为升序。

2.4 all

def IsListSorted_allenumk(lst, key=lambda x, y: x <= y): return all(key(lst[i], elem) for i, elem in enumerate(lst[1:])) import operator def IsListSorted_allenumo(lst, oCmp=operator.le): return all(oCmp(lst[i], elem) for i, elem in enumerate(lst[1:])) def IsListSorted_allenumd(lst): return all((lst[i] <= elem) for i, elem in enumerate(lst[1:])) def IsListSorted_allxran(lst, key=lambda x,y: x <= y): return all(key(lst[i],lst[i+1]) for i in xrange(len(lst)-1)) def IsListSorted_allzip(lst, key=lambda x,y: x <= y): from itertools import izip #Python 3中zip返回生成器(generator),而izip被废弃 return all(key(a, b) for (a, b) in izip(lst[:-1],lst[1:]))

lambda表达式与operator运算符速度相当,前者简单灵活,后者略为高效(实测并不一定)。但两者速度均不如列表元素直接比较(可能存在调用开销)。亦即,_allenumd()快于_allenumo()快于_allenumk()。

若使用lambda表达式指示排序规则,更改规则时只需要改变x和y之间的比较运算符;若使用operator模块指示排序规则,更改规则时需要改变对象比较方法。具体地,lt(x, y)等效于x < y,le(x, y)等效于x <= y,eq(x, y)等效于x == y,ne(x, y)等效于x != y,gt(x, y)等效于x > y,ge(x, y)等效于x >= y。例如,_allenumo()函数若要严格升序可设置oCmp=operator.lt。

此外,由all()函数的帮助信息可知,_allenumk()其实是_forloop()的等效形式。

2.5 numpy

def IsListSorted_numpy(arr, key=lambda dif: dif >= 0): import numpy try: if arr.dtype.kind == 'u': #无符号整数数组执行np.diff时存在underflow风险 arr = numpy.int64(lst) except AttributeError: pass #无dtype属性,非数组 return (key(numpy.diff(arr))).all() #numpy.diff(x)返回相邻数组元素的差值构成的数组

NumPy是用于科学计算的Python基础包,可存储和处理大型矩阵。它包含一个强大的N维数组对象,比Python自身的嵌套列表结构(nested list structure)高效得多。第三节的实测数据表明,_numpy()处理大型列表时性能非常出色。

在Windows系统中可通过pip install numpy命令安装NumPy包,不建议登录官网下载文件自行安装。

2.6 reduce

def IsListSorted_reduce(iterable, key=lambda x, y: x <= y): cmpFunc = lambda x, y: y if key(x, y) else float('inf') return reduce(cmpFunc, iterable, .0) < float('inf')

reduce实现是all实现的变体。累加器(accumulator)中仅存储最后一个检查的列表元素,或者Infinity(若任一元素小于前个元素值)。

前面2.1~2.5小节涉及下标操作的函数适用于列表等可迭代对象(Iterable)。对于通用迭代器(Iterator)对象,即可以作用于next()函数或方法的对象,可使用_reduce()及后面除_rand()外各小节的函数。迭代器的计算是惰性的,只有在需要返回下一个数据时才会计算,以避免不必要的计算。而且,迭代器方式无需像列表那样切片为两个迭代对象。

2.7 imap

def IsListSorted_itermap(iterable, key=lambda x, y: x <= y): from itertools import imap, tee a, b = tee(iterable) #为单个iterable创建两个独立的iterator next(b, None) return all(imap(key, a, b))

2.8 izip

def IsListSorted_iterzip(iterable, key=lambda x, y: x <= y): from itertools import tee, izip a, b = tee(iterable) next(b, None) return all(key(x, y) for x, y in izip(a, b)) def pairwise(iterable): from itertools import tee, izip a, b = tee(iterable) next(b, None) return izip(a, b) #"s -> (s0,s1), (s1,s2), (s2, s3), ..." def IsListSorted_iterzipf(iterable, key=lambda x, y: x <= y): return all(key(a, b) for a, b in pairwise(iterable))

第三节的实测数据表明,虽然存在外部函数调用,_iterzipf()却比_iterzip()略为高效。

2.9 fast

def IsListSorted_fastd(lst): it = iter(lst) try: prev = it.next() except StopIteration: return True for cur in it: if prev > cur: return False prev = cur return True def IsListSorted_fastk(lst, key=lambda x, y: x <= y): it = iter(lst) try: prev = it.next() except StopIteration: return True for cur in it: if not key(prev, cur): return False prev = cur return True

_fastd()和_fastk()是Stack Overflow网站回答里据称执行最快的。实测数据表明,在列表未排序时,它们的性能表现确实优异。

2.10 random

import random def IsListSorted_rand(lst, randNum=3, randLen=100): listLen = len(lst) if listLen <= 1: return True #由首个元素和末尾元素猜测可能的排序规则 if lst[0] < lst[-1]: #列表元素升序 key = lambda dif: dif >= 0 else: #列表元素降序或相等 key = lambda dif: dif <= 0 threshold, sortedFlag = 10000, True import numpy if listLen <= threshold or listLen <= randLen*2 or not randNum: return (key(numpy.diff(numpy.array(lst)))).all() from random import sample for i in range(randNum): sortedRandList = sorted(sample(xrange(listLen), randLen)) flag = (key(numpy.diff(numpy.array([lst[x] for x in sortedRandList])))).all() sortedFlag = sortedFlag and flag return sortedFlag

_rand()借助随机采样降低运算规模,并融入其他判断函数的优点。例如,猜测列表可能的排序规则,并在随机采样不适合时使用相对快速的判断方式,如NumPy。

通过line_profiler分析可知,第20行和第21行与randLen有关,但两者耗时接近。因此randLen应小于listLen的一半,以抵消sorted开销。除内部限制外,用户可以调节随机序列个数和长度,如定制单个但较长的序列。

注意,_rand()不适用于存在微量异常数据的长列表。因为这些数据很可能被随机采样遗漏,从而影响判断结果的准确性。

三. 性能分析

本节借助Python标准库random模块,生成各种随机列表,以对比和分析上节列表排序判断函数的性能。

可通过sample()、randint()等方法生成随机列表。例如:

>>>import random >>> random.sample(range(10), 10); random.sample(range(10), 5) [9, 1, 6, 3, 0, 8, 4, 2, 7, 5] [1, 2, 5, 6, 0] >>> rand = [random.randint(1,10) for i in range(10)]; rand [7, 3, 7, 5, 7, 2, 4, 4, 9, 8] >>> random.sample(rand, 5); random.sample(rand, 5) [4, 7, 7, 9, 8] [3, 9, 2, 5, 7] >>> randGen = (random.randint(1,10) for i in range(10)); randGen <generator object <genexpr> at 0x0192C878>

sample()方法从列表中随机选择数字,结合range()函数可生产不含重复元素的随机列表;而randint()方法结合列表解析生成的随机列表可能包含重复元素。Python文档规定,首次导入random模块时使用当前系统时间作为种子初始化随机数生成器。因此,本文并未显式地调用seed()方法设置种子。

为度量性能表现,定义如下计时装饰器:

from time import clock TimeList = [] def FuncTimer(repeats=1000): def decorator(func): def wrapper(*args, **kwargs): try: startTime = clock() for i in xrange(repeats): ret = func(*args, **kwargs) except Exception, e: print '%s() Error: %s!' %(func.__name__, e) ret = None finally: #当目标函数发生异常时,仍旧输出计时信息 endTime = clock() timeElasped = (endTime-startTime)*1000.0 msg = '%21s(): %s =>Time Elasped: %.3f msec, repeated %d time(s).' %(func.__name__, ret, timeElasped, repeats) global TimeList; TimeList.append([timeElasped, msg]) return ret return wrapper return decorator def ReportTimer(): global TimeList; TimeList.sort(key=lambda x:x[0]) for entry in TimeList: print entry[1] TimeList = [] #Flush

该装饰器允许对输出进行排序,以便更直观地观察性能。基于该装饰器,下文将分别测试不同的排序场景。注意,第二节各函数头部需添加FuncTimer()装饰。

3.1 列表前段乱序

测试代码如下:

def TestHeadUnorderedList(): TEST_NAME = 'HeadUnorderedList'; scale = int(1e5) List = random.sample(xrange(scale), scale) + range(scale) print 'Test 1: %s, list len: %d' %(TEST_NAME, len(List)) IsListSorted_guess(List) IsListSorted_sorted(List) IsListSorted_allenumk(List) IsListSorted_allenumo(List) IsListSorted_allenumd(List) IsListSorted_allxran(List) IsListSorted_allzip(List) IsListSorted_forloop(List) IsListSorted_itermap(List) IsListSorted_iterzipf(List) IsListSorted_iterzip(List) IsListSorted_reduce(List) IsListSorted_numpy(numpy.array(List)) #若不先转换为数组,则耗时骤增 IsListSorted_fastd(List) IsListSorted_fastk(List) ReportTimer()

运行输出如下:

Test 1: HeadUnorderedList, list len: 200 IsListSorted_fastd(): False =>Time Elasped: 0.757 msec, repeated 1000 time(s). IsListSorted_fastk(): False =>Time Elasped: 1.091 msec, repeated 1000 time(s). IsListSorted_forloop(): False =>Time Elasped: 2.080 msec, repeated 1000 time(s). IsListSorted_guess(): False =>Time Elasped: 2.123 msec, repeated 1000 time(s). IsListSorted_allxran(): False =>Time Elasped: 2.255 msec, repeated 1000 time(s). IsListSorted_allenumd(): False =>Time Elasped: 2.672 msec, repeated 1000 time(s). IsListSorted_allenumo(): False =>Time Elasped: 3.021 msec, repeated 1000 time(s). IsListSorted_allenumk(): False =>Time Elasped: 3.207 msec, repeated 1000 time(s). IsListSorted_itermap(): False =>Time Elasped: 5.845 msec, repeated 1000 time(s). IsListSorted_allzip(): False =>Time Elasped: 7.793 msec, repeated 1000 time(s). IsListSorted_iterzip(): False =>Time Elasped: 9.667 msec, repeated 1000 time(s). IsListSorted_iterzipf(): False =>Time Elasped: 9.969 msec, repeated 1000 time(s). IsListSorted_numpy(): False =>Time Elasped: 16.203 msec, repeated 1000 time(s). IsListSorted_sorted(): False =>Time Elasped: 45.742 msec, repeated 1000 time(s). IsListSorted_reduce(): False =>Time Elasped: 145.447 msec, repeated 1000 time(s). Test 1: HeadUnorderedList, list len: 200000 IsListSorted_fastd(): False =>Time Elasped: 0.717 msec, repeated 1000 time(s). IsListSorted_fastk(): False =>Time Elasped: 0.876 msec, repeated 1000 time(s). IsListSorted_allxran(): False =>Time Elasped: 2.104 msec, repeated 1000 time(s). IsListSorted_itermap(): False =>Time Elasped: 6.062 msec, repeated 1000 time(s). IsListSorted_iterzip(): False =>Time Elasped: 7.244 msec, repeated 1000 time(s). IsListSorted_iterzipf(): False =>Time Elasped: 8.491 msec, repeated 1000 time(s). IsListSorted_numpy(): False =>Time Elasped: 801.916 msec, repeated 1000 time(s). IsListSorted_forloop(): False =>Time Elasped: 2924.755 msec, repeated 1000 time(s). IsListSorted_guess(): False =>Time Elasped: 2991.756 msec, repeated 1000 time(s). IsListSorted_allenumo(): False =>Time Elasped: 3025.864 msec, repeated 1000 time(s). IsListSorted_allenumk(): False =>Time Elasped: 3062.792 msec, repeated 1000 time(s). IsListSorted_allenumd(): False =>Time Elasped: 3190.896 msec, repeated 1000 time(s). IsListSorted_allzip(): False =>Time Elasped: 6586.183 msec, repeated 1000 time(s). IsListSorted_sorted(): False =>Time Elasped: 119974.955 msec, repeated 1000 time(s). IsListSorted_reduce(): False =>Time Elasped: 154747.659 msec, repeated 1000 time(s).

可见,对于前段乱序的列表,无论其长短_fastd()和_fastk()的表现均为最佳。对于未排序列表,_sorted()需要进行排序,故性能非常差。然而,_reduce()性能最差。

实际上除_guess()和_sorted()外,其他函数都按升序检查列表。为安全起见,可仿照_guess()实现,先猜测排序方式,再进一步检查。

因为短列表耗时差异大多可以忽略,后续测试将不再包含短列表(但作者确实测试过),仅关注长列表。除非特别说明,列表长度为10万级,重复计时1000次。

3.2 列表中段乱序

测试代码及结果如下:

def TestMiddUnorderedList(): TEST_NAME = 'MiddUnorderedList'; scale = int(1e5) List = range(scale) + random.sample(xrange(scale), scale) + range(scale) print 'Test 2: %s, list len: %d' %(TEST_NAME, len(List)) IsListSorted_numpy(numpy.array(List)) # 1572.295 msec IsListSorted_guess(List) # 14540.637 msec IsListSorted_itermap(List) # 21013.096 msec IsListSorted_fastk(List) # 23840.582 msec IsListSorted_allxran(List) # 31014.215 msec IsListSorted_iterzip(List) # 33386.059 msec IsListSorted_forloop(List) # 34228.006 msec IsListSorted_allenumk(List) # 34416.802 msec IsListSorted_allzip(List) # 42370.528 msec IsListSorted_sorted(List) # 142592.756 msec IsListSorted_reduce(List) # 187514.967 msec ReportTimer()

为节省篇幅,已根据运行输出调整函数的调用顺序。下文也作如此处理。

3.3 列表后段乱序

测试代码及结果如下:

def TestTailUnorderedList(): TEST_NAME = 'TailUnorderedList'; scale = int(1e5) List = range(scale, 0, -1) + random.sample(xrange(scale), scale) print 'Test 3: %s, list len: %d' %(TEST_NAME, len(List)) IsListSorted_numpy(numpy.array(List), key=lambda dif: dif <= 0) # 980.789 msec IsListSorted_guess(List) # 13273.862 msec IsListSorted_itermap(List, key=lambda x, y: x >= y) # 21603.315 msec IsListSorted_fastk(List, key=lambda x, y: x >= y) # 24183.548 msec IsListSorted_allxran(List, key=lambda x, y: x >= y) # 32850.254 msec IsListSorted_forloop(List, key=lambda x, y: x >= y) # 33918.848 msec IsListSorted_iterzip(List, key=lambda x, y: x >= y) # 34505.809 msec IsListSorted_allenumk(List, key=lambda x, y: x >= y) # 35631.625 msec IsListSorted_allzip(List, key=lambda x, y: x >= y) # 40076.330 msec ReportTimer()

3.4 列表完全乱序

测试代码及结果如下:

def TestUnorderedList(): TEST_NAME = 'UnorderedList'; scale = int(1e5) List = random.sample(xrange(scale), scale) print 'Test 4: %s, list len: %d' %(TEST_NAME, len(List)) IsListSorted_fastk(List) # 0.856 msec IsListSorted_allxran(List) # 3.438 msec IsListSorted_iterzip(List) # 7.233 msec IsListSorted_itermap(List) # 7.595 msec IsListSorted_numpy(numpy.array(List)) # 207.222 msec IsListSorted_allenumk(List) # 953.423 msec IsListSorted_guess(List) # 1023.575 msec IsListSorted_forloop(List) # 1076.986 msec IsListSorted_allzip(List) # 2062.821 msec ReportTimer()

3.5 列表元素相同

测试代码及结果如下:

```python def TestSameElemList(): TEST_NAME = 'SameElemList'; scale = int(1e5) List = [5]*scale print 'Test 5: %s, list len: %d' %(TEST_NAME, len(List)) IsListSorted_numpy(numpy.array(List)) # 209.324 msec IsListSorted_sorted(List) # 2760.139 msec IsListSorted_guess(List) # 5843.942 msec IsListSorted_itermap(List) # 20609.704 msec IsListSorted_fastk(List) # 23035.760 msec IsListSorted_forloop(List) # 29043.206 msec IsListSorted_allenumk(List) # 29553.716 msec IsListSorted_allxran(List) # 30348.549 msec IsListSorted_iterzip(List) # 32806.217 msec ReportTimer()

3.6 列表升序

测试代码及结果如下:

def TestAscendingList(): TEST_NAME = 'AscendingList'; scale = int(1e5) List = range(scale) print 'Test 6: %s, list len: %d' %(TEST_NAME, len(List)) IsListSorted_numpy(numpy.array(List)) # 209.217 msec IsListSorted_sorted(List) # 2845.166 msec IsListSorted_fastd(List) # 5977.520 msec IsListSorted_guess(List) # 10408.204 msec IsListSorted_allenumd(List) # 15812.754 msec IsListSorted_itermap(List) # 21244.476 msec IsListSorted_fastk(List) # 23900.196 msec IsListSorted_allenumo(List) # 28607.724 msec IsListSorted_forloop(List) # 30075.538 msec IsListSorted_allenumk(List) # 30274.017 msec IsListSorted_allxran(List) # 31126.404 msec IsListSorted_reduce(List) # 32940.108 msec IsListSorted_iterzip(List) # 34188.312 msec IsListSorted_iterzipf(List) # 34425.097 msec IsListSorted_allzip(List) # 37967.447 msec ReportTimer()

可见,列表已排序时,_sorted()的性能较占优势。

3.7 列表降序

剔除不支持降序的函数,测试代码及结果如下:

def TestDescendingList(): TEST_NAME = 'DescendingList'; scale = int(1e2) List = range(scale, 0, -1) print 'Test 7: %s, list len: %d' %(TEST_NAME, len(List)) IsListSorted_numpy(numpy.array(List), key=lambda dif: dif <= 0) # 209.318 msec IsListSorted_sorted(List) # 5707.067 msec IsListSorted_guess(List) # 10549.928 msec IsListSorted_itermap(List, key=lambda x, y: x >= y) # 21529.547 msec IsListSorted_fastk(List, key=lambda x, y: x >= y) # 24264.465 msec import operator; IsListSorted_allenumo(List, oCmp=operator.ge) # 28093.035 msec IsListSorted_forloop(List, key=lambda x, y: x >= y) # 30745.943 msec IsListSorted_allenumk(List, key=lambda x, y: x >= y) # 32696.205 msec IsListSorted_allxran(List, key=lambda x, y: x >= y) # 33431.473 msec IsListSorted_allzip(List, key=lambda x, y: x >= y) # 34837.019 msec IsListSorted_iterzip(List, key=lambda x, y: x >= y) # 35237.475 msec IsListSorted_reduce(List, key=lambda x, y: x >= y) # 37035.270 msec ReportTimer()

3.8 迭代器测试

参数为列表的函数,需要先将列表���过iter()函数转换为迭代器。注意,当iterable参数为iterator时,只能计时一次,因为该次执行将用尽迭代器。

测试代码如下:

def TestIter(): TEST_NAME = 'Iter'; scale = int(1e7) List = range(scale) #升序 # List = random.sample(xrange(scale), scale) #乱序 print 'Test 8: %s, list len: %d' %(TEST_NAME, len(List)) iterL = iter(List); IsListSorted_guess(list(iterL)) iterL = iter(List); IsListSorted_sorted(iterL) iterL = iter(List); IsListSorted_itermap(iterL) iterL = iter(List); IsListSorted_iterzipf(iterL) iterL = iter(List); IsListSorted_iterzip(iterL) iterL = iter(List); IsListSorted_reduce(iterL) iterL = iter(List); IsListSorted_fastd(iterL) iterL = iter(List); IsListSorted_fastk(iterL, key=lambda x, y: x >= y) ReportTimer()

运行结果如下:

Test 8: Iter, list len: 10000000 ---升序 IsListSorted_fastd(): True =>Time Elasped: 611.028 msec, repeated 1 time(s). IsListSorted_sorted(): False =>Time Elasped: 721.751 msec, repeated 1 time(s). IsListSorted_guess(): True =>Time Elasped: 1142.065 msec, repeated 1 time(s). IsListSorted_itermap(): True =>Time Elasped: 2097.696 msec, repeated 1 time(s). IsListSorted_fastk(): True =>Time Elasped: 2337.233 msec, repeated 1 time(s). IsListSorted_reduce(): True =>Time Elasped: 3307.361 msec, repeated 1 time(s). IsListSorted_iterzipf(): True =>Time Elasped: 3354.034 msec, repeated 1 time(s). IsListSorted_iterzip(): True =>Time Elasped: 3442.520 msec, repeated 1 time(s). Test 8: Iter, list len: 10000000 ---乱序 IsListSorted_fastk(): False =>Time Elasped: 0.004 msec, repeated 1 time(s). IsListSorted_fastd(): False =>Time Elasped: 0.010 msec, repeated 1 time(s). IsListSorted_iterzip(): False =>Time Elasped: 0.015 msec, repeated 1 time(s). IsListSorted_itermap(): False =>Time Elasped: 0.055 msec, repeated 1 time(s). IsListSorted_iterzipf(): False =>Time Elasped: 0.062 msec, repeated 1 time(s). IsListSorted_guess(): False =>Time Elasped: 736.810 msec, repeated 1 time(s). IsListSorted_reduce(): False =>Time Elasped: 8919.611 msec, repeated 1 time(s). IsListSorted_sorted(): False =>Time Elasped: 12273.018 msec, repeated 1 time(s).

其中,_itermap()、_iterzip()、_iterzipf()、_reduce()、_fastd()、_fastk()可直接判断迭代器是否已排序。其他函数需将迭代器转换为列表后才能处理。_sorted()虽然接受迭代器参数,但sorted()返回列表,故无法正确判断迭代器顺序。

3.9 随机采样测试

综合以上测试,可知_fastk()和_numpy()性能较为突出,而且_rand()内置numpy方式。因此,以_fastk()和_numpy()为参照对象,测试代码如下(计时1次):

def TestRandList(): scale = int(1e6) List = random.sample(xrange(scale), scale) + range(scale) print 'Test 1: %s, list len: %d' %('HeadUnorderedList', len(List)) IsListSorted_fastk(List) IsListSorted_numpy(numpy.array(List)) IsListSorted_rand(List, randNum=1) ReportTimer() List = range(scale) + random.sample(xrange(scale), scale) + range(scale) print 'Test 2: %s, list len: %d' %('MiddUnorderedList', len(List)) IsListSorted_fastk(List) IsListSorted_numpy(numpy.array(List)) IsListSorted_rand(List, randNum=1) ReportTimer() List = range(scale, 0, -1) + random.sample(xrange(scale), scale) print 'Test 3: %s, list len: %d' %('TailUnorderedList', len(List)) IsListSorted_fastk(List, key=lambda x, y: x >= y) IsListSorted_numpy(numpy.array(List), key=lambda dif: dif <= 0) IsListSorted_rand(List, randNum=1) ReportTimer() List = [random.randint(1,scale) for i in xrange(scale)] #random.sample(xrange(scale), scale) print 'Test 4: %s, list len: %d' %('UnorderedList', len(List)) IsListSorted_fastk(List) IsListSorted_numpy(numpy.array(List)) IsListSorted_rand(List, randNum=1) ReportTimer() List = [5]*scale print 'Test 5: %s, list len: %d' %('SameElemList', len(List)) IsListSorted_fastk(List) IsListSorted_numpy(numpy.array(List)) IsListSorted_rand(List, randNum=1) ReportTimer() List = range(scale) print 'Test 6: %s, list len: %d' %('AscendingList', len(List)) IsListSorted_fastk(List) IsListSorted_numpy(numpy.array(List)) IsListSorted_rand(List, randNum=1) ReportTimer() List = range(scale, 0, -1) print 'Test 7: %s, list len: %d' %('DescendingList', len(List)) IsListSorted_fastk(List, key=lambda x, y: x >= y) IsListSorted_numpy(numpy.array(List), key=lambda dif: dif <= 0) IsListSorted_rand(List, randNum=1) ReportTimer() List = range(scale, 0, -1); List[scale/2]=0 print 'Test 8: %s, list len: %d' %('MiddleNotchList', len(List)) IsListSorted_fastk(List, key=lambda x, y: x >= y) IsListSorted_numpy(numpy.array(List), key=lambda dif: dif <= 0) IsListSorted_rand(List, randNum=1) IsListSorted_rand(List, randNum=1, randLen=scale/2) ReportTimer()

运行输出如下:

Test 1: HeadUnorderedList, list len: 2000000 IsListSorted_fastk(): False =>Time Elasped: 0.095 msec, repeated 1 time(s). IsListSorted_rand(): False =>Time Elasped: 0.347 msec, repeated 1 time(s). IsListSorted_numpy(): False =>Time Elasped: 7.893 msec, repeated 1 time(s). Test 2: MiddUnorderedList, list len: 3000000 IsListSorted_rand(): False =>Time Elasped: 0.427 msec, repeated 1 time(s). IsListSorted_numpy(): False =>Time Elasped: 11.868 msec, repeated 1 time(s). IsListSorted_fastk(): False =>Time Elasped: 210.842 msec, repeated 1 time(s). Test 3: TailUnorderedList, list len: 2000000 IsListSorted_rand(): False =>Time Elasped: 0.355 msec, repeated 1 time(s). IsListSorted_numpy(): False =>Time Elasped: 7.548 msec, repeated 1 time(s). IsListSorted_fastk(): False =>Time Elasped: 280.416 msec, repeated 1 time(s). Test 4: UnorderedList, list len: 1000000 IsListSorted_fastk(): False =>Time Elasped: 0.074 msec, repeated 1 time(s). IsListSorted_rand(): False =>Time Elasped: 0.388 msec, repeated 1 time(s). IsListSorted_numpy(): False =>Time Elasped: 3.757 msec, repeated 1 time(s). Test 5: SameElemList, list len: 1000000 IsListSorted_rand(): True =>Time Elasped: 0.304 msec, repeated 1 time(s). IsListSorted_numpy(): True =>Time Elasped: 3.955 msec, repeated 1 time(s). IsListSorted_fastk(): True =>Time Elasped: 210.977 msec, repeated 1 time(s). Test 6: AscendingList, list len: 1000000 IsListSorted_rand(): True =>Time Elasped: 0.299 msec, repeated 1 time(s). IsListSorted_numpy(): True =>Time Elasped: 4.822 msec, repeated 1 time(s). IsListSorted_fastk(): True =>Time Elasped: 214.171 msec, repeated 1 time(s). Test 7: DescendingList, list len: 1000000 IsListSorted_rand(): True =>Time Elasped: 0.336 msec, repeated 1 time(s). IsListSorted_numpy(): True =>Time Elasped: 3.867 msec, repeated 1 time(s). IsListSorted_fastk(): True =>Time Elasped: 279.322 msec, repeated 1 time(s). Test 8: MiddleNotchList, list len: 1000000 IsListSorted_rand(): True =>Time Elasped: 0.387 msec, repeated 1 time(s). IsListSorted_numpy(): False =>Time Elasped: 4.792 msec, repeated 1 time(s). IsListSorted_rand(): False =>Time Elasped: 78.903 msec, repeated 1 time(s). IsListSorted_fastk(): False =>Time Elasped: 110.444 msec, repeated 1 time(s).

可见,在绝大部分测试场景中,_rand()性能均为最佳,且不失正确率。注意测试8,当降序列表中间某个元素被置0(开槽)时,随机采样很容易遗漏该元素,导致误判。然而,这种场景在实际使用中非常罕见。

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