Hyperopt工具包 贝叶斯优化

本文主要是介绍Hyperopt工具包 贝叶斯优化，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

Hyperopt工具包

一. Bayesian Optimization 贝叶斯优化四大步骤

1. Objective 目标函数
2. Domain space 指定参数空间
3. Hyperparameter optimizationfunction 可选择的采样算法,随机或者贝叶斯优化
4. Trials 记录结果的保存

1. Objective 自定义目标, 这里得有一个最小值

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inlinedef objective(x):f = np.poly1d([1, -3, -28, 29, 12, -22, 99]) # np.poly1d生成多项式, 如np.poly1d([2,3,4])生成2x^2 + 3x + 4return f(x) * 0.05x = np.linspace(-5, 7, 10000)
y = objective(x)miny = min(y)
minx = x[np.argmin(y)]plt.figure(figsize = (8, 6))
plt.style.use('fivethirtyeight')
plt.title('Objective Function')
plt.xlabel('x')
plt.ylabel('f(x)')
plt.vlines(minx, min(y)-50, max(y), linestyles='--', colors='r')
plt.plot(x, y)
print('Minimum of %0.4f occurs at %0.4f' % (miny, minx))

Minimum of -394.7324 occurs at 5.4266

2. Domain 参数取值范围

from hyperopt import hp
space = hp.uniform('x', -5, 7)from hyperopt.pyll.stochastic import sample
samples = []
for _ in range(10000):samples.append(sample(space))plt.hist(samples, bins = 20, edgecolor = 'black')
plt.xlabel('x'); plt.ylabel('Frequency'); plt.title('Domain Space')

3. 选择采样算法

Hyperparameter Optimization Algorithm 随机或者贝叶斯优化

from hyperopt import rand, tpe
tpe_algo = tpe.</

这篇关于Hyperopt工具包 贝叶斯优化的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！

---