接着上面的回答，请告诉鲸鱼优化算法WOA原始代码优化XGBoost模型，优化模型寻找最优参数的过程

好的，以下是鲸鱼优化算法WOA原始代码优化XGBoost模型的过程：

1.导入必要的库和模块

import numpy as np
import xgboost as xgb
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

2.加载数据集并进行训练集和测试集的划分

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)

3.定义XGBoost模型

def xgb_model(params):
    model = xgb.XGBClassifier(n_estimators=100,
                              learning_rate=params[0],
                              max_depth=int(params[1]),
                              subsample=params[2],
                              gamma=params[3],
                              reg_lambda=params[4],
                              colsample_bytree=params[5],
                              objective='binary:logistic')
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    return accuracy

4.定义WOA算法

def WOA(obj_func, lb, ub, dim, search_agents=30, max_iter=100):
    # 初始化搜索代理的位置和速度
    positions = np.zeros((search_agents, dim))
    for i in range(dim):
        positions[:, i] = np.random.uniform(0, 1, size=search_agents) * (ub - lb) + lb
    for i in range(search_agents):
        fitness = obj_func(positions[i, :])
        if i == 0:
            best_fitness = fitness
            best_position = positions[i, :]
        elif fitness > best_fitness:
            best_fitness = fitness
            best_position = positions[i, :]
    
    # 开始迭代
    for t in range(max_iter):
        a = 2 - 2 * t / max_iter  # 调整参数a，使得搜索代理的行为更加随机
        
        for i in range(search_agents):
            r1 = np.random.uniform(size=dim)
            r2 = np.random.uniform(size=dim)
            A = 2 * a * r1 - a
            C = 2 * r2
            b = 1

            if np.random.uniform() < 0.5:
                D = np.abs(C * best_position - positions[i, :])
                new_position = best_position - A * D
            else:
                rand_leader_index = np.random.randint(0, search_agents)
                X_rand = positions[rand_leader_index, :]
                D = np.abs(C * X_rand - positions[i, :])
                new_position = X_rand - A * D

            # 边界处理
            new_position = np.clip(new_position, lb, ub)
            new_fitness = obj_func(new_position)

            # 更新最优位置
            if new_fitness > best_fitness:
                best_fitness = new_fitness
                best_position = new_position

            # 更新当前位置
            positions[i, :] = new_position
            
    return best_position, best_fitness

5.定义搜索空间和运行WOA算法

search_space = [(0.01, 1),  # learning_rate
                (1, 10),  # max_depth
                (0.1, 1),  # subsample
                (0, 1),  # gamma
                (0, 10),  # reg_lambda
                (0.1, 1)]  # colsample_bytree

dim = len(search_space)
lb = [item[0] for item in search_space]
ub = [item[1] for item in search_space]

best_position, best_fitness = WOA(xgb_model, lb, ub, dim, search_agents=30, max_iter=100)
print('Best accuracy:', best_fitness)
print('Best parameters:', best_position)

经过WOA算法的优化寻找，可以得到最优的XGBoost模型参数，从而达到优化模型的目的。