花式索引比numpy.take快得多？

I have read in a number of of different places that numpy.take is a much faster alternative to fancy indexing, for example here and here.

我已经在很多不同的地方读过numpy.take是一种比花式索引更快的替代方法，例如这里和这里。

However, I am not finding this to be the case... at all. Here is an example from when I was poking around my code during some debugging:

但是，我根本没有发现这种情况......这是一个例子，当我在一些调试过程中探索我的代码时：

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')

Which is showing that fancy indexing is much faster! Using numpy.arange to generate the indices I get a similar result:

这表明花哨的索引要快得多！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

Why is fancy indexing so much faster than using numpy.take now? Am I hitting some kind of edge case?

为什么花哨的索引比现在使用numpy.take快得多？我打的是某种边缘情况吗？

I'm using Python 3.6 through Anaconda and here is my numpy info if relevant:

我正在使用Python 3.6通过Anaconda，如果相关，这是我的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']

1 个解决方案

#1

In my tests take is modestly faster; but with the small time and 'cached' warning I don't put a lot of stock in the difference:

在我的测试中，采取适度更快;但是由于时间和“缓存”警告，我不会在差异上投入大量资金：

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

But your index array is uint32. That worked ok with indexing, but take gave me a casting error; So my idx2 is astype(int).

但你的索引数组是uint32。这对索引起作用了，但是拿了一个转换错误;所以我的idx2是astype（int）。

And with the arange idx, times are 11.5 µs, 16 µs.

使用arange idx，时间为11.5μs，16μs。

Notice I'm timing with a .shape; I'm not entirely sure that makes a difference.

请注意我的时间是.shape;我不完全确定这会有所作为。

I don't know why you are getting ms times for your take. It feels like more of a timing issue than an actual difference in take.

我不知道你为什么要花费ms时间。感觉更多的是时间问题，而不是实际的差异。

I don't the libraries, BLAS etc will make a difference. The underlying task is basically the same - step through the data buffer and copy out selected bytes. There's no complicated computation to farm out. But I haven't studied the C code for take.

我不是图书馆，BLAS等会有所作为。底层任务基本相同 - 逐步通过数据缓冲区并复制出选定的字节。农场没有复杂的计算。但我还没有研究过C代码。

Numpy version '1.12.0', Linux, 4gb refurbished desktop.

Numpy版本'1.12.0'，Linux，4gb翻新桌面。

#1