你知道是否有办法让 pythonrandom.sample使用生成器对象。我正在尝试从一个非常大的文本语料库中获取随机样本。问题是random.sample()引发以下错误。
TypeError: object of type 'generator' has no len()
我在想也许有一些方法可以用一些东西来做这件事，itertools但通过一些搜索却找不到任何东西。
一个有点编造的例子：
import random
def list_item(ls):
for item in ls:
yield item
random.sample( list_item(range(100)), 20 )
更新
根据MartinPieters的要求，我对目前提出的三种方法做了一些时间安排。结果如下。
Sampling 1000 from 10000
Using iterSample 0.0163 s
Using sample_from_iterable 0.0098 s
Using iter_sample_fast 0.0148 s
Sampling 10000 from 100000
Using iterSample 0.1786 s
Using sample_from_iterable 0.1320 s
Using iter_sample_fast 0.1576 s
Sampling 100000 from 1000000
Using iterSample 3.2740 s
Using sample_from_iterable 1.9860 s
Using iter_sample_fast 1.4586 s
Sampling 200000 from 1000000
Using iterSample 7.6115 s
Using sample_from_iterable 3.0663 s
Using iter_sample_fast 1.4101 s
Sampling 500000 from 1000000
Using iterSample 39.2595 s
Using sample_from_iterable 4.9994 s
Using iter_sample_fast 1.2178 s
Sampling 2000000 from 5000000
Using iterSample 798.8016 s
Using sample_from_iterable 28.6618 s
Using iter_sample_fast 6.6482 s
所以事实证明，array.insert当涉及到大样本时，它有一个严重的缺点。我用来计时方法的代码
from heapq import nlargest
import random
import timeit
def iterSample(iterable, samplesize):
results = []
for i, v in enumerate(iterable):
r = random.randint(0, i)
if r < samplesize:
if i < samplesize:
results.insert(r, v) # add first samplesize items in random order
else:
results[r] = v # at a decreasing rate, replace random items
if len(results) < samplesize:
raise ValueError("Sample larger than population.")
return results
def sample_from_iterable(iterable, samplesize):
return (x for _, x in nlargest(samplesize, ((random.random(), x) for x in iterable)))
def iter_sample_fast(iterable, samplesize):
results = []
iterator = iter(iterable)
# Fill in the first samplesize elements:
for _ in xrange(samplesize):
results.append(iterator.next())
random.shuffle(results) # Randomize their positions
for i, v in enumerate(iterator, samplesize):
r = random.randint(0, i)
if r < samplesize:
results[r] = v # at a decreasing rate, replace random items
if len(results) < samplesize:
raise ValueError("Sample larger than population.")
return results
if __name__ == '__main__':
pop_sizes = [int(10e+3),int(10e+4),int(10e+5),int(10e+5),int(10e+5),int(10e+5)*5]
k_sizes = [int(10e+2),int(10e+3),int(10e+4),int(10e+4)*2,int(10e+4)*5,int(10e+5)*2]
for pop_size, k_size in zip(pop_sizes, k_sizes):
pop = xrange(pop_size)
k = k_size
t1 = timeit.Timer(stmt='iterSample(pop, %i)'%(k_size), setup='from __main__ import iterSample,pop')
t2 = timeit.Timer(stmt='sample_from_iterable(pop, %i)'%(k_size), setup='from __main__ import sample_from_iterable,pop')
t3 = timeit.Timer(stmt='iter_sample_fast(pop, %i)'%(k_size), setup='from __main__ import iter_sample_fast,pop')
print 'Sampling', k, 'from', pop_size
print 'Using iterSample', '%1.4f s'%(t1.timeit(number=100) / 100.0)
print 'Using sample_from_iterable', '%1.4f s'%(t2.timeit(number=100) / 100.0)
print 'Using iter_sample_fast', '%1.4f s'%(t3.timeit(number=100) / 100.0)
print ''
我还进行了一项测试，以检查所有方法确实确实采用了生成器的无偏样本。因此，对于所有方法，我从时间中采样1000元素10000 100000并计算总体中每个项目的平均出现频率，结果证明这~.1与所有三种方法的预期相同。