import numpy as np
import os
from sklearn.preprocessing import StandardScaler

"""
实验说明:
- 自行编写实现数据加载与预测病马死亡的对率回归算法。

注意事项:
- 在 function.py 中实现对率回归算法。
- 可以自行导入其他库，需要在 requirements.txt 添加依赖，若未在 requirements.txt 中添加使用到的依赖，则无法自动评测，成绩为零。
- 自动评测脚本仅会加载 train_logreg、predict_logreg 和 load_data 函数，请勿修改函数名。

测评环境:
- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
- OS: Ubuntu 22.04.4 LTS
- Python: 3.10.12
- numpy: 2.1.1
"""

"""
请在这里填写学号和姓名，不填写即成绩无效。
姓名请直接**填写中文**
例如: name = "张三"
"""
id = "202210423110"
name = "罗浩"


def sigmoid(z):
    """计算sigmoid函数"""
    return 1.0 / (1 + np.exp(-z))

def compute_gradient(X_batch, y_batch, weights, lambda_reg):
    """计算梯度，包含L2正则化"""
    z = np.dot(X_batch, weights)
    h = sigmoid(z)
    gradient = np.dot(X_batch.T, (h - y_batch)) / len(y_batch)
    gradient += (lambda_reg / len(y_batch)) * weights  # L2正则化
    return gradient

def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):
    """逻辑回归训练函数。"""
    m, n = X.shape
    weights = np.random.randn(n) * np.sqrt(2. / n)  # He初始化
    print("开始训练逻辑回归模型")

    for i in range(iterations):
        indices = np.random.permutation(m)
        X_shuffled = X[indices]
        y_shuffled = y[indices]

        for j in range(0, m, batch_size):
            X_batch = X_shuffled[j:j + batch_size]
            y_batch = y_shuffled[j:j + batch_size]
            gradient = compute_gradient(X_batch, y_batch, weights, lambda_reg)
            weights -= learning_rate * gradient
            
        if i % 1000 == 0:  # 每1000次迭代打印一次信息
            print(f"迭代 {i}: 当前权重形状: {weights.shape}")

    return weights

def predict_logreg(inX, weights):
    """逻辑回归预测函数。"""
    z = np.dot(inX, weights)
    h = sigmoid(z)
    return 1 if h >= 0.5 else 0

def load_data(filepath):
    """从文件中加载数据。"""
    if not os.path.exists(filepath):
        raise FileNotFoundError(f"文件 {filepath} 不存在！")

    data, labels = [], []
    print(f"加载数据中: {filepath}")  
    with open(filepath, 'r') as f:  
        for line in f:  
            line = line.strip().split()  
            if len(line) >= 1 and line[-1].isdigit():  
                line_data = [0 if x == '?' else float(x) for x in line[:-1]]  
                data.append(line_data)  
                labels.append(int(line[-1]))  
    data = np.array(data)  
    labels = np.array(labels)  
    num_features = data.shape[1]  
    indices_to_remove = [2]  
    if num_features > 23:  
        indices_to_remove.extend(range(23, min(28, num_features)))  
    data = np.delete(data, indices_to_remove, axis=1)  
    target_variable = np.where((labels == 2) | (labels == 3), 0, 1)  
    print(f"数据加载成功，数据形状: {data.shape}, 标签形状: {target_variable.shape}")  
    return data, target_variable  

def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):  
    """K折交叉验证。"""  
    m = X.shape[0]  
    fold_size = m // k  
    accuracies = []  
    for i in range(k):  
        val_start = i * fold_size  
        val_end = (i + 1) * fold_size if i != k - 1 else m  
        X_val = X[val_start:val_end]  
        y_val = y[val_start:val_end]  
        X_train = np.concatenate((X[:val_start], X[val_end:]))  
        y_train = np.concatenate((y[:val_start], y[val_end:]))  
        weights = train_logreg(X_train, y_train, learning_rate, iterations, batch_size, lambda_reg)  
        val_accuracy = np.mean([predict_logreg(X_val[j], weights) == y_val[j] for j in range(len(y_val))])  
        accuracies.append(val_accuracy)  
        print(f"Fold {i + 1}/{k} - 验证准确率: {val_accuracy:.4f}")  
    return np.mean(accuracies)  

if __name__ == "__main__":  
    # 加载训练集  
    try:  
        X_train, y_train = load_data("./data/horse-colic.data")  
    except FileNotFoundError as e:  
        print(e)  
        exit()  
    
    # 数据归一化处理  
    scaler = StandardScaler()  
    X_train = scaler.fit_transform(X_train)  # 使用训练集进行标准化  
    
    # k折交叉验证  
    avg_accuracy = k_fold_cross_validation(X_train, y_train, k=5, learning_rate=0.01, iterations=1000)  
    print(f"K折交叉验证的平均准确率: {avg_accuracy:.4f}")  
    
    # 加载测试集  
    try:  
        X_test, y_test = load_data("./data/horse-colic.test")  
    except FileNotFoundError as e:  
        print(e)  
        exit()  
    
    # 测试集也需要进行相同的归一化  
    X_test = scaler.transform(X_test)  # 使用相同的Scaler进行测试集的标准化  
    
    # 训练逻辑回归模型  
    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)  
    
    # 测试单个数据  
    y_pred = predict_logreg(X_test[11], weights)  
    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")