perpplot比较多个函数的性能与多个参数（多个）

发布于 2025-02-10 23:12:31 字数 824 浏览 3 评论 0原文

我想通过使用多个参数的perpplot来显示某些功能的性能，但是我不知道该怎么做（我对perppertplot不太熟悉代码>）。一个人说 >，但是我很困惑，但如何使用它。假设我们具有以下功能：

from scipy.ndimage import zoom
import numpy as np

f = 5
n = 3
arr = np.random.random_integers(0, 1, (f, f))

def scipy_zoom(arr, n):
    return zoom(arr, n)

def numpy_rep(arr, n):
    return arr.repeat(n, 0).repeat(n, 1)

f和n需要评估更改，并且似乎最好单独绘制它们，即一旦f保持恒定，n变化，一次反之亦然。我不知道如果强> perpplot 可以绘制（如果在答案中显示，但不是这个问题的主要目的）。

原文

I would like to show performance of some functions by perfplot that uses more than one arguments, but I don't know how could I do this (I'm not familiar much with perfplot). One said broadcast all args to just x, but I am confused yet how to use it. Assume we have the following functions:

from scipy.ndimage import zoom
import numpy as np

f = 5
n = 3
arr = np.random.random_integers(0, 1, (f, f))

def scipy_zoom(arr, n):
    return zoom(arr, n)

def numpy_rep(arr, n):
    return arr.repeat(n, 0).repeat(n, 1)

Both f and n changes need to be evaluated and it seems it will be better to plot them separately i.e. once f be constant and n varies and once vice-versa. I don't know how understandable it will be if the 3D perfplot could be plotted (this will be useful if be shown in the answers, but not the main aim of this question).

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鹿港巷口少年归 2025-02-17 23:12:31

对于两个参数，我们可以收集所有基准时间安排，以 factials.partials.partial 和 N 在perpplot中

import perfplot
from functools import partial

res = []
for f in range(5,100,20):
    arr = np.random.randint(0, 2, (f, f))
    g = perfplot.bench(
        setup=lambda n: n,
        kernels=[partial(scipy_zoom, arr),
                 partial(numpy_rep, arr)],
        labels=['zoom','numpy'],
        n_range=[2,3,4,5,6,7,8,9,10],
        equality_check=False # results are not equal!
    )
    res.append(g)

，并绘制了2D行绘图层的3D图。

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from cycler import cycler


fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111, projection='3d')
cc = cycler(color=list('rg'))

for i, g in zip(range(5,100,20)[::-1],res[::-1]):  # plot from back to front
    ax.plot(g.n_range,g.timings_s.T, i, zdir='y', alpha=0.8)

plt.rc('axes', prop_cycle=cc)
ax.set_xlabel('n')
ax.set_ylabel('f')
ax.set_zlabel('seconds')
ax.legend(g.labels)
plt.show()

输出

For two arguments we can collect all benchmark timings, setting the range of f for arr size with functools.partial and the range of n within perfplot

import perfplot
from functools import partial

res = []
for f in range(5,100,20):
    arr = np.random.randint(0, 2, (f, f))
    g = perfplot.bench(
        setup=lambda n: n,
        kernels=[partial(scipy_zoom, arr),
                 partial(numpy_rep, arr)],
        labels=['zoom','numpy'],
        n_range=[2,3,4,5,6,7,8,9,10],
        equality_check=False # results are not equal!
    )
    res.append(g)

and plot a 3D graph of 2D line plot layers.

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from cycler import cycler


fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111, projection='3d')
cc = cycler(color=list('rg'))

for i, g in zip(range(5,100,20)[::-1],res[::-1]):  # plot from back to front
    ax.plot(g.n_range,g.timings_s.T, i, zdir='y', alpha=0.8)

plt.rc('axes', prop_cycle=cc)
ax.set_xlabel('n')
ax.set_ylabel('f')
ax.set_zlabel('seconds')
ax.legend(g.labels)
plt.show()

Output