Python 想象一下索引比numpy快得多？

我在许多不同的地方读到过

numpy.take

是一种比花哨的索引更快的方法，例如和

然而，我发现情况并非如此。。。完全下面是我在调试过程中拨弄代码时的一个示例：

knn_idx
Out[2]: 
array([ 3290,  5847,  7682,  6957, 22660,  5482, 22661, 10965,     7,
        1477,  7681,     3, 17541, 15717,  9139,  1475, 14251,  4400,
        7680,  9140,  4758, 22289,  7679,  8407, 20101, 15718, 15716,
        8405, 15710, 20829, 22662], dtype=uint32)
%timeit X.take(knn_idx, axis=0)
100 loops, best of 3: 3.14 ms per loop
%timeit X[knn_idx]
The slowest run took 60.61 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 5.48 µs per loop
X.shape
Out[5]: 
(23011, 30)
X.dtype
Out[6]: 
dtype('float64')

这表明，奇特的索引速度要快得多！使用

numpy.arange

生成索引，我得到了类似的结果：

idx = np.arange(0, len(X), 100)
%timeit X.take(idx, axis=0)
100 loops, best of 3: 3.04 ms per loop
%timeit X[idx]
The slowest run took 9.41 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 20.7 µs per loop

为什么花式索引比使用

numpy.take

要快得多？我是不是碰到了什么边缘案件

我正在通过Anaconda使用Python 3.6，如果相关的话，这里是我的numpy信息：

np.__version__
Out[11]: 
'1.11.3'
np.__config__.show()
blas_mkl_info:
    libraries = ['mkl_core_dll', 'mkl_intel_lp64_dll', 'mkl_intel_thread_dll']
    library_dirs = ['C:/Users/pbreach/Continuum/Anaconda3\\Library\\lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['C:/Users/pbreach/Continuum/Anaconda3\\Library\\include']
blas_opt_info:
    libraries = ['mkl_core_dll', 'mkl_intel_lp64_dll', 'mkl_intel_thread_dll']
    library_dirs = ['C:/Users/pbreach/Continuum/Anaconda3\\Library\\lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['C:/Users/pbreach/Continuum/Anaconda3\\Library\\include']
openblas_lapack_info:
  NOT AVAILABLE
lapack_mkl_info:
    libraries = ['mkl_core_dll', 'mkl_intel_lp64_dll', 'mkl_intel_thread_dll']
    library_dirs = ['C:/Users/pbreach/Continuum/Anaconda3\\Library\\lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['C:/Users/pbreach/Continuum/Anaconda3\\Library\\include']
lapack_opt_info:
    libraries = ['mkl_core_dll', 'mkl_intel_lp64_dll', 'mkl_intel_thread_dll']
    library_dirs = ['C:/Users/pbreach/Continuum/Anaconda3\\Library\\lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['C:/Users/pbreach/Continuum/Anaconda3\\Library\\include']

---

在我的测试中，

take

稍微快一些；但是，由于时间很短，加上“缓存”的警告，我并没有对差异进行过多的估计：

In [192]: timeit X.take(idx2, axis=0).shape
The slowest run took 23.29 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 3.66 µs per loop
In [193]: timeit X[idx2,:].shape
The slowest run took 16.75 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 5.58 µs per loop

但是您的索引数组是

uint32

。这在索引方面还可以，但take给了我一个施法错误；所以我的

idx2

是

astype（int）

使用arange idx，时间是11.5µs，16µs

请注意，我正在使用

.shape

计时；我不完全确定这会有什么不同

我不知道你为什么会得到

ms

的拍摄时间。这感觉更像是一个时间问题，而不是

拍摄的实际差异

我不认为图书馆、BLAS等会带来不同。基本上，底层任务都是相同的—逐步通过数据缓冲区并复制选定的字节。不需要进行复杂的计算。但是我还没有研究take的C代码

Numpy版本“1.12.0”，Linux，4gb翻新桌面。
奇怪的是，在问题的第二次测试中，我使用了
int32
的索引数组，但仍然得到了与第一次测试相似的计时。即使在运行最慢的情况下，使用
int32
索引，速度仍然快了约15倍。这一点很好现在我想知道在我的具体情况下，与一般情况相比，
take
会有什么问题。我还将尝试在计时中包含
shape
。