基于遗传算法GA优化的粒子群优化算法对XGBoost的参数寻优代码
时间: 2023-07-11 10:05:50 浏览: 81
以下是基于遗传算法GA优化的粒子群优化算法对XGBoost的参数寻优代码,其中使用了Python编程语言和scikit-learn库:
```python
import numpy as np
import xgboost as xgb
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
# 生成样本数据
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_redundant=5, random_state=42)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 定义适应度函数,使用粒子群优化算法进行参数寻优
def fitness(params):
param = {'max_depth': int(params[0]), 'eta': params[1], 'gamma': params[2], 'subsample': params[3], 'lambda': params[4], 'alpha': params[5], 'min_child_weight': params[6]}
clf = xgb.XGBClassifier(objective='binary:logistic', random_state=42, **param)
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
return score
# 定义遗传算法
def GA(population_size=50, n_generations=10, mutation_rate=0.05):
n_params = 7
population = np.random.randint(100, size=(population_size, n_params)) / 100.0
for i in range(n_generations):
fitness_scores = [fitness(p) for p in population]
best_idx = np.argmax(fitness_scores)
print('Generation %d: best score = %.2f' % (i+1, fitness_scores[best_idx]))
parents = population[np.argsort(-np.array(fitness_scores))]
# 进行遗传操作
offspring = []
for i in range(population_size):
parent1_idx = i % len(parents)
parent2_idx = (i+1) % len(parents)
parent1 = parents[parent1_idx]
parent2 = parents[parent2_idx]
# 交叉操作
cross_points = np.random.randint(0, n_params)
offspring_ = np.concatenate((parent1[:cross_points], parent2[cross_points:]))
# 变异操作
if np.random.rand() < mutation_rate:
mutation_point = np.random.randint(0, n_params)
offspring_[mutation_point] = np.random.randint(100) / 100.0
offspring.append(offspring_)
population = np.array(offspring)
return parents[0]
# 运行遗传算法进行参数寻优
best_params = GA()
print('Best parameters:', best_params)
param = {'max_depth': int(best_params[0]), 'eta': best_params[1], 'gamma': best_params[2], 'subsample': best_params[3], 'lambda': best_params[4], 'alpha': best_params[5], 'min_child_weight': best_params[6]}
clf = xgb.XGBClassifier(objective='binary:logistic', random_state=42, **param)
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
print('Accuracy:', score)
```
该代码使用了遗传算法和粒子群优化算法对XGBoost分类器的7个参数进行寻优,其中遗传算法用于产生种群,粒子群优化算法用于对每个个体进行优化。最终输出最优参数组合和相应的准确率。