Numba jit与scipy

问题描述

所以我想加快我在numba 结构除外）

I assume this is due to jit not being able to handle try except (it also does not work if I only put jit on the curve_fit and integrate.quad parts and work around my own try except structure)

import scipy.integrate as integrate
from scipy optimize import curve_fit
from numba import jit

def fitfunction():
    ...

@jit
def integral(lower, upper):
    return integrate.quad(lambda x: fitfunction(fit_param), lower, upper)

@jit
def fitting(x, y, pzero, max_fev)
    return curve_fit(fitfunction, x, y, p0=pzero, maxfev=max_fev)


def function(x):
    # do some stuff
    try:
        fit_param, fit_cov = fitting(x, y, (0,0,0), 500)
        for idx in some_list:
            integrated = integral(lower, upper)
    except:
        fit_param=(0,0,0)
        ...

有没有办法使用 jit 和 scipy.integrate.quad 和 curve_fit ，而无需手动删除所有尝试 除了scipy代码中的结构？

Is there a way to use jit with scipy.integrate.quad and curve_fit without manually deleting all try except structures from the scipy code?

它甚至会加快速度吗？

And would it even speed up the code?

推荐答案

Numba根本不是不是一个通用库来加速代码。使用numba可以更快地解决一类问题（特别是如果您在数组上循环，进行数字运算），但是（1）不支持或（2）只是稍微快一些甚至很多，

Numba simply is not a general-purpose library to speed code up. There is a class of problems that can be solved in a much faster way with numba (especially if you have loops over arrays, number crunching) but everything else is either (1) not supported or (2) only slightly faster or even a lot slower.

SciPy已经是一个高性能的库，因此在大多数情况下，我希望numba的性能更差（或者很少：稍微好一点）。您可能需要进行一些，以了解瓶颈是否确实存在于您所编写的代码中 jit ted，那么您可以得到一些改进。但是我怀疑瓶颈将在SciPy的编译代码中，并且该编译代码可能已经进行了优化（因此，确实不太可能您发现可以仅与该代码竞争的实现）

SciPy is already a high-performance library so in most cases I would expect numba to perform worse (or rarely: slightly better). You might do some profiling to find out if the bottleneck is really in the code that you jitted, then you could get some improvements. But I suspect the bottleneck will be in the compiled code of SciPy and that compiled code is probably already heavily optimized (so it's really unlikely that you find an implementation that could "only" compete with that code).

正如您正确地假定 try 和 except 一样，

As you correctly assumed try and except is simply not supported by numba at this time.

[...]

• 异常处理（ try .. 除了， try .. 最后）

• Exception handling (try .. except, try .. finally)

所以这里的答案是否。

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