Python实现哈里斯鹰优化算法(HHO)优化XGBoost分类模型(XGBClassifier算法)项目实战

哈里斯鹰优化算法(HHO)是一种基于仿生学的优化算法，它受到了鹰的捕猎行为启发，通过模拟鹰的捕猎过程来寻求最优解。本项目中，我们将使用Python实现HHO算法，并将其应用于XGBoost分类模型(XGBClassifier算法)的优化。

首先，我们需要导入所需的库和模块：

```python
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
```

接着，我们加载乳腺癌数据集，并将其划分为训练集和测试集：

```python
data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, random_state=42)
```

然后，我们定义HHO算法的主要函数，其中包括初始化、计算适应度值、更新鹰的位置等步骤：

```python
def HHO(X_train, X_test, y_train, y_test, pop_size, max_iter, lb, ub):

dim = X_train.shape[1]
f_best = np.inf
pos_best = np.zeros(dim)

# 初始化
pop = np.random.uniform(lb, ub, (pop_size, dim))
fit = np.zeros(pop_size)
for i in range(pop_size):
model = XGBClassifier(n_estimators=int(round(pop[i][0])), max_depth=int(round(pop[i][1])),
learning_rate=pop[i][2])
model.fit(X_train, y_train)
fit[i] = 1 - model.score(X_test, y_test)

# 记录最佳适应度值和对应位置
if fit[i] < f_best:
f_best = fit[i]
pos_best = pop[i]

for iter in range(max_iter):

# 计算适应度值
for i in range(pop_size):
model = XGBClassifier(n_estimators=int(round(pop[i][0])), max_depth=int(round(pop[i][1])),
learning_rate=pop[i][2])
model.fit(X_train, y_train)
fit[i] = 1 - model.score(X_test, y_test)

# 更新鹰的位置
for i in range(pop_size):
if np.random.rand() > 0.5:
# 捕食行为
E0 = 2 * (1 - (iter / max_iter)) * np.random.rand() - 1
Esc = 2 * np.random.rand() - 1
F = np.abs(E0 * pos_best - pop[i])
X1 = pos_best - Esc * F
X1[X1 < lb] = lb
X1[X1 > ub] = ub
pop[i] = X1
else:
# 探索行为
F1 = np.abs(pos_best - pop[i])
E1 = 2 * np.random.rand() - 1
X2 = pop[i] - E1 * F1
X2[X2 < lb] = lb
X2[X2 > ub] = ub
pop[i] = X2

# 更新最佳适应度值和对应位置
for i in range(pop_size):
if fit[i] < f_best:
f_best = fit[i]
pos_best = pop[i]

print('Iteration:', iter, 'Best fitness:', f_best)

return f_best, pos_best
```

最后，我们可以调用HHO函数来训练和优化XGBClassifier模型：

```python
f_best, pos_best = HHO(X_train, X_test, y_train, y_test, pop_size=20, max_iter=50, lb=[50, 1, 0.01], ub=[150, 10, 0.5])

best_model = XGBClassifier(n_estimators=int(round(pos_best[0])), max_depth=int(round(pos_best[1])), learning_rate=pos_best[2])
best_model.fit(X_train, y_train)
accuracy = best_model.score(X_test, y_test)

print('Best accuracy:', accuracy)
```

在上述代码中，我们设置了种群大小(pop_size)为20，迭代次数(max_iter)为50次，搜索范围(lb和ub)分别为：n_estimators(50至150)、max_depth(1至10)和learning_rate(0.01至0.5)。最后，我们输出了优化后的最佳准确率。

总之，本项目基于Python实现了HHO算法，并将其应用于优化XGBClassifier分类模型，希望对您有所帮助。