import numpy as np

id = "202210423213"
name = "熊浩燃"

# 对率回归训练函数，包含加权损失、正则化、学习率调度和提前停止
def train_logreg(X, y, learning_rate=0.01, iterations=1000, regularization=0.01, early_stopping_rounds=100, class_weights={0: 1, 1: 1}):
    weights = np.zeros(X.shape[1])
    min_loss = float('inf')
    stopping_step = 0

    for i in range(iterations):
        z = np.dot(X, weights)
        predictions = 1 / (1 + np.exp(-z))

        loss = -np.mean(class_weights[1] * y * np.log(predictions) + 
                        class_weights[0] * (1 - y) * np.log(1 - predictions)) + regularization * np.sum(weights**2)

        if loss < min_loss:
            min_loss = loss
            stopping_step = 0
        else:
            stopping_step += 1
        if stopping_step >= early_stopping_rounds:
            print("提前停止于迭代:", i)
            break

        gradient = (np.dot(X.T, (predictions - y)) + regularization * weights) / y.size
        weights -= learning_rate * gradient

        if i % 100 == 0 and i != 0:
            learning_rate *= 0.9  # 每100轮衰减学习率
            print(f"迭代 {i}，损失: {loss}，学习率: {learning_rate}")

    return weights

# 对率回归预测函数，带有自定义阈值
def predict_logreg(inX, weights, threshold=0.6):
    probability = 1 / (1 + np.exp(-np.dot(inX, weights)))
    return 1 if probability >= threshold else 0

# 数据加载函数，包含缺失值填充
def load_data(filepath):
    data = []
    labels = []
    with open(filepath, 'r') as file:
        for line in file:
            fields = line.strip().split()
            features = [float(x) if x != '?' else None for x in fields[:-1]]
            label = float(fields[-1])
            data.append(features)
            labels.append(label)

    data = np.array(data, dtype=float)
    labels = np.array(labels, dtype=float)

    col_means = np.nanmean(data, axis=0)
    indices = np.where(np.isnan(data))
    data[indices] = np.take(col_means, indices[1])

    return data, labels

# 暴力搜索函数
def brute_force_search(X, y, X_test, y_test, learning_rates, iterations_list, regularizations, class_weights_list, thresholds):
    best_params = {}
    best_weights = None
    best_accuracy = 0

    for lr in learning_rates:
        for iters in iterations_list:
            for reg in regularizations:
                for cw in class_weights_list:
                    for threshold in thresholds:
                        print(f"参数组合: 学习率={lr}, 迭代次数={iters}, 正则化={reg}, 类别权重={cw}, 阈值={threshold}")
                        weights = train_logreg(X, y, learning_rate=lr, iterations=iters, regularization=reg, class_weights=cw)
                        y_pred = [predict_logreg(x, weights, threshold=threshold) for x in X_test]
                        accuracy = np.mean(y_pred == y_test)
                        print(f"当前组合的测试集准确率: {accuracy}")

                        if accuracy > best_accuracy:
                            best_accuracy = accuracy
                            best_weights = weights
                            best_params = {'learning_rate': lr, 'iterations': iters, 'regularization': reg, 'class_weights': cw, 'threshold': threshold}

    print("最佳参数组合:", best_params)
    print("最佳准确率:", best_accuracy)
    return best_weights, best_params

if __name__ == "__main__":
    # 加载训练集
    X_train, y_train = load_data("./data/horse-colic.data")
    X_test, y_test = load_data("./data/horse-colic.test")

    # 调整后的超参数组合
    learning_rates = [0.0001, 0.001, 0.01, 0.05, 0.1]
    iterations_list = [1000, 2000, 5000, 10000]
    regularizations = [0.01, 0.1, 1.0, 10.0]
    class_weights_list = [{0: 1, 1: 2}, {0: 1, 1: 3}, {0: 1, 1: 4}, {0: 1, 1: 5}]
    thresholds = [0.5, 0.55, 0.6, 0.65, 0.7]

    # 进行暴力搜索找到最佳参数组合
    best_weights, best_params = brute_force_search(X_train, y_train, X_test, y_test, learning_rates, iterations_list, regularizations, class_weights_list, thresholds)
    print("最佳权重:", best_weights)

    # 使用最佳权重和阈值在测试集上评估
    y_pred = [predict_logreg(x, best_weights, threshold=best_params['threshold']) for x in X_test]
    accuracy = np.mean(y_pred == y_test)
    print(f"最终测试集准确率: {accuracy}")