forked from LufsX/Experiment-LogReg
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import numpy as np
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import os
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from sklearn.preprocessing import StandardScaler # 导入标准化库
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"""
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实验说明:
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- 自行编写实现数据加载与预测病马死亡的对率回归算法。
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注意事项:
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- 在 function.py 中实现对率回归算法。
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- 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。
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- 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。
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测评环境:
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- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
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- OS: Ubuntu 22.04.4 LTS
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- Python: 3.10.12
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- numpy: 2.1.1
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"""
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"""
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请在这里填写学号和姓名，不填写即成绩无效。
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姓名请直接**填写中文**
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例如: name = "张三"
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"""
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# 学号和姓名
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id = "202210423220"
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name = "方子涵"
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def sigmoid(z):
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"""计算sigmoid函数"""
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return 1.0 / (1 + np.exp(-z))
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# 这里写自己的代码实现
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def train_logreg(X, y, learning_rate=0.01, iterations=1000):
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"""
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对率回归训练函数。
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def compute_gradient(X_batch, y_batch, weights, lambda_reg):
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"""计算梯度，包含L2正则化"""
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z = np.dot(X_batch, weights)
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h = sigmoid(z)
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gradient = np.dot(X_batch.T, (h - y_batch)) / len(y_batch)
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gradient += (lambda_reg / len(y_batch)) * weights # L2正则化
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return gradient
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Args:
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- X (numpy array): 特征数据。
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- y (numpy array): 标签数据。
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- learning_rate (float): 学习率，默认为0.01。
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- iterations (int): 迭代次数，默认为1000。
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Returns:
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- numpy array: 训练得到的权重。
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"""
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def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):
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"""对率回归训练函数。"""
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m, n = X.shape
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weights = np.zeros(n)
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for _ in range(iterations):
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linear_model = np.dot(X, weights)
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y_predicted = 1 / (1 + np.exp(-linear_model))
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gradient = np.dot(X.T, (y_predicted - y)) / m
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weights = np.random.randn(n) * np.sqrt(2. / n) # He初始化
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print("开始训练对率回归模型")
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for i in range(iterations):
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indices = np.random.permutation(m)
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X_shuffled = X[indices]
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y_shuffled = y[indices]
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for j in range(0, m, batch_size):
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X_batch = X_shuffled[j:j + batch_size]
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y_batch = y_shuffled[j:j + batch_size]
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gradient = compute_gradient(X_batch, y_batch, weights, lambda_reg)
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weights -= learning_rate * gradient
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if i % 1000 == 0: # 每1000次迭代打印一次信息
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print(f"迭代 {i}: 当前权重形状: {weights.shape}")
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return weights
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def predict_logreg(inX, weights):
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"""
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对率回归预测函数。
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"""对率回归预测函数。"""
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z = np.dot(inX, weights)
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h = sigmoid(z)
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return 1 if h >= 0.5 else 0
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Args:
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- inX (numpy array): 输入待分类的样本。
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- weights (numpy array): 训练得到的权重。
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Returns:
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- int: 预测的分类标签。
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"""
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linear_model = np.dot(inX, weights)
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y_predicted = 1 / (1 + np.exp(-linear_model))
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return 1 if y_predicted > 0.5 else 0
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# 这里也写自己的代码实现
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def load_data(filepath):
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"""
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从文件中加载数据，若不自行正确实现，则无法通过评测，**不记录成绩**。
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"""从文件中加载数据。"""
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if not os.path.exists(filepath):
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raise FileNotFoundError(f"文件 {filepath} 不存在！")
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Args:
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- filepath (str): 数据文件路径。
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data, labels = [], []
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print(f"加载数据中: {filepath}")
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with open(filepath, 'r') as f:
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for line in f:
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line = line.strip().split()
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if len(line) >= 1 and line[-1].isdigit():
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line_data = [0 if x == '?' else float(x) for x in line[:-1]]
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data.append(line_data)
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labels.append(int(line[-1]))
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Returns:
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- np.array: 特征数据。
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- np.array: 标签数据。
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"""
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with open(filepath, 'r') as file:
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lines = file.readlines()
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features = []
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labels = []
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for line in lines:
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parts = line.strip().split(',')
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label = int(parts[-1])
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feature_list = []
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for x in parts[:-1]:
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if x == '?':
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feature_list.append(0) # 用0代替缺失值
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else:
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feature_list.append(float(x))
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features.append(feature_list)
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labels.append(label)
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return np.array(features), np.array(labels)
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data = np.array(data)
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labels = np.array(labels)
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num_features = data.shape[1]
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indices_to_remove = [2] # 去掉特征3（索引2）
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if num_features > 23:
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indices_to_remove.extend(range(23, min(28, num_features)))
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data = np.delete(data, indices_to_remove, axis=1)
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target_variable = np.where((labels == 2) | (labels == 3), 0, 1)
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print(f"数据加载成功，数据形状: {data.shape}, 标签形状: {target_variable.shape}")
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return data, target_variable
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def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):
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"""K折交叉验证。"""
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m = X.shape[0]
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fold_size = m // k
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accuracies = []
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for i in range(k):
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val_start = i * fold_size
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val_end = (i + 1) * fold_size if i != k - 1 else m
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X_val = X[val_start:val_end]
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y_val = y[val_start:val_end]
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X_train = np.concatenate((X[:val_start], X[val_end:]))
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y_train = np.concatenate((y[:val_start], y[val_end:]))
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weights = train_logreg(X_train, y_train, learning_rate, iterations, batch_size, lambda_reg)
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val_accuracy = np.mean([predict_logreg(X_val[j], weights) == y_val[j] for j in range(len(y_val))])
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accuracies.append(val_accuracy)
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print(f"Fold {i + 1}/{k} - 验证准确率: {val_accuracy:.4f}")
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return np.mean(accuracies)
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if __name__ == "__main__":
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# 以下代码可用于测试，可自行随意修改
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# 加载训练集
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try:
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X_train, y_train = load_data("./data/horse-colic.data")
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# 训练模型
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weights = train_logreg(X_train, y_train)
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# 预测
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predictions = [predict_logreg(X_train[i], weights) for i in range(X_train.shape[0])]
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# 打印预测结果
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print(predictions)
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except FileNotFoundError as e:
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print(e)
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exit()
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# 数据归一化处理
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train) # 使用训练集进行标准化
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# k折交叉验证
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avg_accuracy = k_fold_cross_validation(X_train, y_train, k=5, learning_rate=0.01, iterations=1000)
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print(f"K折交叉验证的平均准确率: {avg_accuracy:.4f}")
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# 加载测试集
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try:
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X_test, y_test = load_data("./data/horse-colic.test")
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except FileNotFoundError as e:
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print(e)
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exit()
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# 测试集也需要进行相同的归一化
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X_test = scaler.transform(X_test) # 使用相同的Scaler进行测试集的标准化
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# 训练对率回归模型
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weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
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# 测试单个数据
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y_pred = predict_logreg(X_test[11], weights)
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print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
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