ML-exp-2/code/bp_neural_network-wine_quality.py

import numpy as np
from ucimlrepo import fetch_ucirepo
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler


# 定义BP神经网络类
class BPNeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size, learning_rate=0.1):
        # 初始化输入层、隐藏层和输出层的大小
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.learning_rate = learning_rate

        # 初始化权重和偏置
        self.weights_input_hidden = np.random.randn(self.input_size, self.hidden_size)  # 输入层到隐藏层的权重
        self.bias_hidden = np.random.randn(self.hidden_size)  # 隐藏层的偏置
        self.weights_hidden_output = np.random.randn(self.hidden_size, self.output_size)  # 隐藏层到输出层的权重
        self.bias_output = np.random.randn(self.output_size)  # 输出层的偏置

    def sigmoid(self, x):
        # Sigmoid激活函数
        return 1 / (1 + np.exp(-x))

    def sigmoid_derivative(self, x):
        # Sigmoid激活函数的导数
        return x * (1 - x)

    def forward(self, X):
        # 前向传播
        self.hidden_input = np.dot(X, self.weights_input_hidden) + self.bias_hidden  # 隐藏层的输入
        self.hidden_output = self.sigmoid(self.hidden_input)  # 隐藏层的输出
        self.final_input = np.dot(self.hidden_output, self.weights_hidden_output) + self.bias_output  # 输出层的输入
        self.final_output = self.sigmoid(self.final_input)  # 输出层的输出
        return self.final_output

    def backward(self, X, y, output):
        # 反向传播
        output_error = y - output  # 输出层的误差
        output_delta = output_error * self.sigmoid_derivative(output)  # 输出层的误差信号

        hidden_error = output_delta.dot(self.weights_hidden_output.T)  # 隐藏层的误差
        hidden_delta = hidden_error * self.sigmoid_derivative(self.hidden_output)  # 隐藏层的误差信号

        # 更新权重和偏置
        self.weights_hidden_output += self.hidden_output.T.dot(output_delta) * self.learning_rate  # 更新隐藏层到输出层的权重
        self.bias_output += np.sum(output_delta, axis=0) * self.learning_rate  # 更新输出层的偏置
        self.weights_input_hidden += X.T.dot(hidden_delta) * self.learning_rate  # 更新输入层到隐藏层的权重
        self.bias_hidden += np.sum(hidden_delta, axis=0) * self.learning_rate  # 更新隐藏层的偏置

    def train(self, X, y, epochs):
        # 训练网络
        for epoch in range(epochs):
            output = self.forward(X)  # 前向传播
            self.backward(X, y, output)  # 反向传播
            if epoch % 1000 == 0:
                loss = np.mean(np.square(y - output))  # 计算损失
                print(f'Epoch {epoch}, Loss: {loss}')  # 打印损失

    def predict(self, X):
        # 预测
        return np.round(self.forward(X))  # 四舍五入预测结果

# 将标签转换为one-hot编码
def one_hot_encode(y):
    # 确保 y 中的每个元素是字符串
    y = np.array([str(label) for label in y])
    # 创建一个标签到整数的映射
    label_to_int = {label: i for i, label in enumerate(np.unique(y))}
    # 将标签转换为整数
    y_int = np.array([label_to_int[label] for label in y])
    n_values = np.max(y_int) + 1
    return np.eye(n_values)[y_int]  # 返回one-hot编码

# fetch dataset 
wine_quality = fetch_ucirepo(id=186) 
  
# data (as pandas dataframes) 
X = wine_quality.data.features.values  # 特征数据
y = wine_quality.data.targets.values  # 标签数据

# metadata 
print(wine_quality.metadata) 
  
# variable information 
print(wine_quality.variables) 

# 特征缩放
scaler = StandardScaler()
X = scaler.fit_transform(X)  # 标准化特征数据

# 对标签进行one-hot编码
y_encoded = one_hot_encode(y)

# 十折交叉验证
kf = KFold(n_splits=10, shuffle=True, random_state=42)
accuracies = []

for train_index, test_index in kf.split(X):
    X_train, X_test = X[train_index], X[test_index]  # 训练集和测试集的特征数据
    y_train, y_test = y_encoded[train_index], y_encoded[test_index]  # 训练集和测试集的标签数据

    # 创建并训练BP神经网络
    nn = BPNeuralNetwork(input_size=X_train.shape[1], hidden_size=20, output_size=y_train.shape[1], learning_rate=0.0001)  # 修改隐藏层大小和学习率
    nn.train(X_train, y_train, epochs=50000)  # 增加训练轮数

    # 预测并计算准确率
    predictions = nn.predict(X_test)
    accuracy = accuracy_score(np.argmax(y_test, axis=1), np.argmax(predictions, axis=1))  # 计算准确率
    accuracies.append(accuracy)  # 存储每次交叉验证的准确率

print(f'Average Accuracy: {np.mean(accuracies)}')  # 打印平均准确率