使用cython没有真正的收益

Question

我正在尝试使用RANSAC回归算法的修改版本，但没有更改sklearn的函数（为了避免使用sklearn 0.24.1的问题和避免函数中的一些警告），我在github中找到了一个算法，并尝试对其进行Cythonization以提高速度，然后才能进行修改。令我惊讶的是，速度提升非常差，是因为代码中充满了numpy调用，还是我做错了什么？以下代码是python版本、cython版本（经过适当修改以避免错误）：

##Python version
import numpy as np
def ransac_polyfit(x,y,order, t, n=0.8,k=100,f=0.9):
    
    besterr = np.inf
    bestfit = np.array([None])
    for kk in range(k):
        maybeinliers = np.random.randint(len(x), size=int(n*len(x)))
        maybemodel = np.polyfit(x[maybeinliers], y[maybeinliers], order)
        alsoinliers = np.abs(np.polyval(maybemodel, x)-y) < t
        if sum(alsoinliers) > len(x)*f:
            bettermodel = np.polyfit(x[alsoinliers], y[alsoinliers], order)
            thiserr = np.sum(np.abs(np.polyval(bettermodel, x[alsoinliers])-y[alsoinliers]))
            if thiserr < besterr:
                bestfit = bettermodel
                besterr = thiserr
                     
    return bestfit

##Cython version
cimport cython
cimport numpy as np
import numpy as np

np.import_array()

@cython.boundscheck(False)
@cython.wraparound(False)
cdef ransac_polyfit(np.ndarray[np.npy_int64, ndim=1] x,
                    np.ndarray[np.npy_int64, ndim=1] y,
                    np.npy_intp order, 
                    np.npy_float32 t, 
                    np.npy_float32 n=0.8,
                    np.npy_intp k=100,
                    np.npy_float32 f=0.9):
    
    cdef np.npy_intp kk, i, ransac_control
    cdef np.npy_float64 thiserr, besterr = -1.0
    cdef np.ndarray[np.npy_int64, ndim=1] maybeinliers
    cdef np.ndarray[np.npy_bool, ndim=1] alsoinliers
    cdef np.ndarray[np.npy_float64, ndim=1] bestfit, maybemodel, bettermodel
    
    bestfit = np.zeros(1, dtype = np.float64)
    for kk in range(k):
        maybeinliers = np.random.randint(len(x), size=int(n*len(x)))
        maybemodel = np.polyfit(x[maybeinliers], y[maybeinliers], order)
        alsoinliers = np.abs(polyval(maybemodel, x)-y) < t
        if sum(alsoinliers) > len(x)*f:
            bettermodel = np.polyfit(x[alsoinliers], y[alsoinliers], order)
            thiserr = np.sum(np.abs(polyval(bettermodel, x[alsoinliers])-y[alsoinliers]))
            if (thiserr < besterr) or (besterr == -1.0):
                bestfit = bettermodel
                besterr = thiserr
    
    #Since I can't return an empty array, I had to set it as an array with a zero and the ransac_control variable will tell if the function was able or not to find a good model
    if (besterr == -1.0):
        ransac_control = 0
        return ransac_control, bestfit
    else:
        ransac_control = 1
        return ransac_control, bestfit

**PS：无法发送cython代码的HTML页面图像，因为这是我的第一个问题