当目标函数有多个参数时,如何使用scipy.optimize minimize_scalar？

Question

我有多个参数的功能.我希望针对单个变量优化它,同时保持其他变量不变.为此,我想使用来自spicy.optimize的minimize_scalar.我阅读了文档,但我仍然很困惑如何告诉minim_scalar我想要最小化变量:w1.下面是一个最小的工作代码.

import numpy as np
from scipy.optimize import minimize_scalar

def error(w0,w1,x,y_actual):
    y_pred = w0+w1*x
    mse = ((y_actual-y_pred)**2).mean()
    return mse

w0=50
x = np.array([1,2,3])
y = np.array([52,54,56])
minimize_scalar(error,args=(w0,x,y),bounds=(-5,5))

Answer 1

您可以使用lambda函数

minimize_scalar(lambda w1: error(w0,w1,x,y),bounds=(-5,5))

Answer 2

您还可以使用部分功能.

from functools import partial
error_partial = partial(error, w0=w0, x=x, y_actual=y)
minimize_scalar(error_partial, bounds=(-5, 5))

如果你想知道性能......它与lambdas相同.

import time
from functools import partial
import numpy as np
from scipy.optimize import minimize_scalar

def error(w1, w0, x, y_actual):
    y_pred = w0 + w1 * x
    mse = ((y_actual - y_pred) ** 2).mean()
    return mse

w0 = 50
x = np.arange(int(1e5))
y = np.arange(int(1e5)) + 52

error_partial = partial(error, w0=w0, x=x, y_actual=y)

p_time = []
for _ in range(100):
    p_time_ = time.time()
    p = minimize_scalar(error_partial, bounds=(-5, 5))
    p_time_ = time.time() - p_time_
    p_time.append(p_time_  / p.nfev)

l_time = []
for _ in range(100):
    l_time_ = time.time()
    l = minimize_scalar(lambda w1: error(w1, w0, x, y), bounds=(-5, 5))
    l_time_ = time.time() - l_time_
    l_time.append(l_time_ / l.nfev)

print(f'Same performance? {np.median(p_time) == np.median(l_time)}')
# Same performance? True