快速方法Hash Numpy对象进行缓存

Question

实施一个系统,当涉及到繁重的数学提升时,我想尽可能少地做.

我知道存在与numpy对象进行memoisation的问题,因此实现了一个惰性密钥缓存以避免整个"过早优化"参数.

def magic(numpyarg,intarg):
    key = str(numpyarg)+str(intarg)

    try:
        ret = self._cache[key]
        return ret
    except:
        pass

    ... here be dragons ...
    self._cache[key]=value
    return value

但由于字符串转换需要很长时间......

t=timeit.Timer("str(a)","import numpy;a=numpy.random.rand(10,10)")
t.timeit(number=100000)/100000 = 0.00132s/call

人们认为做"更好的方式"是什么意思？

Answer 1

借用这个答案 ......所以我猜这是重复的:

>>> import hashlib
>>> import numpy
>>> a = numpy.random.rand(10, 100)
>>> b = a.view(numpy.uint8)
>>> hashlib.sha1(b).hexdigest()
'15c61fba5c969e5ed12cee619551881be908f11b'
>>> t=timeit.Timer("hashlib.sha1(a.view(numpy.uint8)).hexdigest()", 
                   "import hashlib;import numpy;a=numpy.random.rand(10,10)") 
>>> t.timeit(number=10000)/10000
2.5790500640869139e-05

Answer 2

有一个名为joblib的包。从这个问题中发现。

from joblib import Memory
location = './cachedir'
memory = Memory(location)

# Create caching version of magic
magic_cached = memory.cache(magic)
result = magic_cached(...)

# Or (for one-time use)
result = memory.eval(magic, ...)