鹈鹕算法.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mealpy.swarm_based.POA import OriginalPOA
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import cross_val_score
from sklearn import datasets, metrics
from sklearn.svm import SVR

# 读入Excel文件到数据框
df = pd.read_excel('data.xlsx')

# 将数据框转换为NumPy数组
X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values

# 训练集和测试集
X_train = X[:125]
y_train = y[:125]
X_test = X[125:]
y_test = y[125:]

# 对训练集和测试集的输入特征进行归一化处理
scaler = MinMaxScaler()
X_train_norm = scaler.fit_transform(X_train)
X_test_norm = scaler.transform(X_test)

def fitness_function(solution):
    c = solution[0]  # 第一个变量为C参数
    e = solution[1]  # 第二个变量为epsilon参数
    regressor = SVR(kernel='rbf', C=c, epsilon=e)
    # 使用交叉验证计算模型的均方误差
    cv_scores = cross_val_score(regressor, X_train_norm, y_train, cv=5, scoring='neg_mean_squared_error')
    fitness = -np.mean(cv_scores)  # 取平均均方误差的负值作为适应度值
    return fitness
    #使用交叉验证计算模型的均方误差
    #cv_scores = cross_val_score(regressor, X_train_norm, y_train, cv=5, scoring='neg_mean_squared_error')
    #fitness = -np.mean(cv_scores) 
    
    # 取平均均方误差的负值作为适应度值
    #regressor.fit(X_train_norm, y_train)
    #y_pred = regressor.predict(X_test_norm)
    #accuracy = sum(abs((y_test - y_pred) / y_test) <= 0.2) / len(y_test)
    #return accuracy

problem = {
   "fit_func": fitness_function,
   "lb": [0.01, 0.001, 0.01],
   "ub": [100, 10, 100],
   "minmax": "min",
   #"obj_weights": [1, 1, 1, 1, 1]  # Adjust the number of weights to match the number of objectives
}



epoch = 120
pop_size = 100
model = OriginalPOA(epoch, pop_size)
best_position, best_fitness = model.solve(problem)
print(f"Solution: {best_position}, Fitness: {best_fitness}")

regressor = SVR(kernel='rbf', C=best_position[0], epsilon=best_position[1])
regressor.fit(X_train_norm, y_train)

#预测测试集的目标值
y_pred = regressor.predict(X_test_norm)
#相关系数
corr_coef = np.corrcoef(y_test, y_pred)

#准确率
accuracy = sum(abs((y_test - y_pred) / y_test) <= 0.2) / len(y_test)

#计算测试集的均方误差和平均绝对误差
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)

# 交叉验证评估模型性能
scores = cross_val_score(regressor, X_train, y_train, cv=5)

#输出到屏幕
print('POA')
print('最优的参数C:', best_position[0])
print('最优的参数epsilon:', best_position[1])
print("准确率:", accuracy)
print('均方误差：{:.2f}'.format(mse))
print('平均绝对误差：{:.2f}'.format(mae))
print("相关系数:", corr_coef[0][1])
print('平均交叉验证:', np.mean(scores))

model.history.save_global_objectives_chart(filename="POA/goc")
model.history.save_local_objectives_chart(filename="POA/loc")

model.history.save_global_best_fitness_chart(filename="POA/gbfc")
model.history.save_local_best_fitness_chart(filename="POA/lbfc")

model.history.save_runtime_chart(filename="POA/rtc")
model.history.save_exploration_exploitation_chart(filename="POA/eec")
model.history.save_diversity_chart(filename="POA/dc")

# 绘制预测结果和测试集真实结果的折线图
plt.plot(y_test, label='True')
plt.plot(y_pred, label='Predicted')
plt.title("True vs Predicted")
plt.legend()
plt.show()