numpy,在列表优化上应用函数
numpy , applying function over list optimization
我有这两个代码,它们做同样的事情,但数据结构不同
res = np.array([np.array([2.0, 4.0, 6.0]), np.array([8.0, 10.0, 12.0])], dtype=np.int)
%timeit np.sum(res, axis=1)
4.08 µs ± 728 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
list_obj_array = np.ndarray((2,), dtype=np.object)
list_obj_array[0] = [2.0, 4.0, 6.0]
list_obj_array[1] = [8.0, 10.0, 12.0]
v_func = np.vectorize(np.sum, otypes=[np.int])
%timeit v_func(list_obj_array)
20.6 µs ± 486 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
第二个慢了5倍,有没有更好的优化方法?
@nb.jit()
def nb_np_sum(arry_list):
return [np.sum(row) for row in arry_list]
%timeit nb_np_sum(list_obj_array)
30.8 µs ± 5.88 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
@nb.jit()
def nb_sum(arry_list):
return [sum(row) for row in arry_list]
%timeit nb_sum(list_obj_array)
13.6 µs ± 669 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
迄今为止最好的(感谢@hpaulj)
%timeit [sum(l) for l in list_obj_array]
850 ns ± 115 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
@nb.njit()
def nb_sum(arry_list):
return [sum(row) for row in arry_list]
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Untyped global name 'sum': cannot determine Numba type of <class 'builtin_function_or_method'>
File "<ipython-input-54-3bb48c5273bb>", line 3:
def nb_sum(arry_list):
return [sum(row) for row in arry_list]
对于更长的数组
list_obj_array = np.ndarray((n,), dtype=np.object)
for i in range(n):
list_obj_array[i] = list(range(7))
vectorized
版本更接近最佳选择(列表理解)
%timeit [sum(l) for l in list_obj_array]
23.4 µs ± 4.19 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
%timeit v_func(list_obj_array)
29.6 µs ± 4.91 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
numba 还是比较慢
%timeit nb_sum(list_obj_array)
74.4 µs ± 6.11 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
自从您使用 otypes
后,您阅读了足够多的 vectorize
文档,知道它不是性能工具。
In [430]: timeit v_func(list_obj_array)
38.3 µs ± 894 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
列表理解速度更快:
In [431]: timeit [sum(l) for l in list_obj_array]
2.08 µs ± 62.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
如果您从列表列表而不是对象 dtype 数组开始,那就更好了:
In [432]: alist = list_obj_array.tolist()
In [433]: timeit [sum(l) for l in alist]
542 ns ± 11.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
编辑
np.frompyfunc
比 np.vectorize
快,尤其是在处理对象 dtype 数组时:
In [459]: np.frompyfunc(sum,1,1)(list_obj_array)
Out[459]: array([12.0, 30.0], dtype=object)
In [460]: timeit np.frompyfunc(sum,1,1)(list_obj_array)
2.22 µs ± 16.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
正如我在其他地方看到的那样 frompyfunc
与列表理解具有竞争力。
有趣的是,使用 np.sum
而不是 sum
会减慢速度。我认为这是因为应用于列表的 np.sum
具有将列表转换为数组的开销。 sum
应用于数字列表非常好,使用 python 自己的编译代码。
In [461]: timeit np.frompyfunc(np.sum,1,1)(list_obj_array)
30.3 µs ± 165 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
所以让我们在矢量化中尝试 sum
:
In [462]: v_func = np.vectorize(sum, otypes=[int])
In [463]: timeit v_func(list_obj_array)
8.7 µs ± 331 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
好多了。
我有这两个代码,它们做同样的事情,但数据结构不同
res = np.array([np.array([2.0, 4.0, 6.0]), np.array([8.0, 10.0, 12.0])], dtype=np.int)
%timeit np.sum(res, axis=1)
4.08 µs ± 728 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
list_obj_array = np.ndarray((2,), dtype=np.object)
list_obj_array[0] = [2.0, 4.0, 6.0]
list_obj_array[1] = [8.0, 10.0, 12.0]
v_func = np.vectorize(np.sum, otypes=[np.int])
%timeit v_func(list_obj_array)
20.6 µs ± 486 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
第二个慢了5倍,有没有更好的优化方法?
@nb.jit()
def nb_np_sum(arry_list):
return [np.sum(row) for row in arry_list]
%timeit nb_np_sum(list_obj_array)
30.8 µs ± 5.88 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
@nb.jit()
def nb_sum(arry_list):
return [sum(row) for row in arry_list]
%timeit nb_sum(list_obj_array)
13.6 µs ± 669 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
迄今为止最好的(感谢@hpaulj)
%timeit [sum(l) for l in list_obj_array]
850 ns ± 115 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
@nb.njit()
def nb_sum(arry_list):
return [sum(row) for row in arry_list]
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Untyped global name 'sum': cannot determine Numba type of <class 'builtin_function_or_method'>
File "<ipython-input-54-3bb48c5273bb>", line 3:
def nb_sum(arry_list):
return [sum(row) for row in arry_list]
对于更长的数组
list_obj_array = np.ndarray((n,), dtype=np.object)
for i in range(n):
list_obj_array[i] = list(range(7))
vectorized
版本更接近最佳选择(列表理解)
%timeit [sum(l) for l in list_obj_array]
23.4 µs ± 4.19 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
%timeit v_func(list_obj_array)
29.6 µs ± 4.91 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
numba 还是比较慢
%timeit nb_sum(list_obj_array)
74.4 µs ± 6.11 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
自从您使用 otypes
后,您阅读了足够多的 vectorize
文档,知道它不是性能工具。
In [430]: timeit v_func(list_obj_array)
38.3 µs ± 894 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
列表理解速度更快:
In [431]: timeit [sum(l) for l in list_obj_array]
2.08 µs ± 62.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
如果您从列表列表而不是对象 dtype 数组开始,那就更好了:
In [432]: alist = list_obj_array.tolist()
In [433]: timeit [sum(l) for l in alist]
542 ns ± 11.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
编辑
np.frompyfunc
比 np.vectorize
快,尤其是在处理对象 dtype 数组时:
In [459]: np.frompyfunc(sum,1,1)(list_obj_array)
Out[459]: array([12.0, 30.0], dtype=object)
In [460]: timeit np.frompyfunc(sum,1,1)(list_obj_array)
2.22 µs ± 16.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
正如我在其他地方看到的那样 frompyfunc
与列表理解具有竞争力。
有趣的是,使用 np.sum
而不是 sum
会减慢速度。我认为这是因为应用于列表的 np.sum
具有将列表转换为数组的开销。 sum
应用于数字列表非常好,使用 python 自己的编译代码。
In [461]: timeit np.frompyfunc(np.sum,1,1)(list_obj_array)
30.3 µs ± 165 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
所以让我们在矢量化中尝试 sum
:
In [462]: v_func = np.vectorize(sum, otypes=[int])
In [463]: timeit v_func(list_obj_array)
8.7 µs ± 331 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
好多了。