From b01025720db2b31786387855d940d17cd7ae74bd Mon Sep 17 00:00:00 2001 From: Ulysses-ui <3233559397@qq.com> Date: Sun, 3 Nov 2024 17:40:57 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- function.py | 176 +++++++++++++++++++++++++++++++--------------------- 1 file changed, 104 insertions(+), 72 deletions(-) diff --git a/function.py b/function.py index 25f3901..c6dc977 100644 --- a/function.py +++ b/function.py @@ -1,98 +1,130 @@ -import numpy as np -from sklearn.preprocessing import StandardScaler, PolynomialFeatures +""" +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。 +""" id = "202210423124" name = "刘子豪" -def train_logreg(X, y, learning_rate=0.005, iterations=5000, reg_lambda=0.001): - """ - 对率回归训练函数。 +import numpy as np +from sklearn.preprocessing import MinMaxScaler +from sklearn.model_selection import train_test_split - Args: - - X (numpy array): 特征数据。 - - y (numpy array): 标签数据。 - - learning_rate (float): 学习率。 - - iterations (int): 迭代次数。 - - reg_lambda (float): 正则化强度。 +def sigmoid(z): + return 1.0 / (1 + np.exp(-z)) - Returns: - - numpy array: 训练得到的权重。 - """ +def compute_gradient(X_batch, y_batch, weights, lambda_reg): + 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): m, n = X.shape - weights = np.zeros(n) + weights = np.random.randn(n) * np.sqrt(2. / n) # He初始化 + best_weights = weights.copy() + best_accuracy = 0 for _ in range(iterations): - z = np.dot(X, weights) - predictions = 1 / (1 + np.exp(-z)) # 计算sigmoid - errors = y - predictions - gradient = np.dot(X.T, errors) / m - # 加入正则化 - gradient -= reg_lambda * weights / m - weights += learning_rate * gradient + indices = np.random.permutation(m) + X_shuffled = X[indices] + y_shuffled = y[indices] - return weights + 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 -def predict_logreg(inX, weights): - """ - 对率回归预测函数。 + # 计算训练集准确率 + train_accuracy = accuracy(X, y, weights) + if train_accuracy > best_accuracy: + best_accuracy = train_accuracy + best_weights = weights.copy() - Args: - - inX (numpy array): 输入待分类的样本。 - - weights (numpy array): 训练得到的权重。 + # 动态调整学习率 + if _ % 100 == 0 and train_accuracy > 0.95: # 达到高准确率时减小学习率 + learning_rate *= 0.9 - Returns: - - int: 预测的分类标签。 - """ - z = np.dot(inX, weights) - probability = 1 / (1 + np.exp(-z)) - return 1 if probability >= 0.5 else 0 + return best_weights + +def predict_logreg(X, weights): + z = np.dot(X, weights) + h = sigmoid(z) + return np.where(h >= 0.5, 1, 0) def load_data(filepath): - """ - 从文件中加载数据。 + data, labels = [], [] + 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])) - Args: - - filepath (str): 数据文件路径。 - - Returns: - - np.array: 特征数据。 - - np.array: 标签数据。 - """ - data = [] - with open(filepath, 'r') as file: - for line in file: - line_data = line.strip().split() - if line_data: - try: - data.append([float(x) if x !='?' else np.nan for x in line_data]) - except ValueError as e: - print(f"Error processing line: {line}. Error: {e}") data = np.array(data) - data = np.nan_to_num(data, nan=np.nanmean(data, axis=0)) # 将缺失值用平均值代替 - X = data[:, :-1] # 特征 - y = data[:, -1] # 标签 - return X, y + labels = np.array(labels) + # 检查特征数量 + num_features = data.shape[1] + + # 特征选择：去掉特征3（索引2）和特征24~28（索引23~26），确保索引有效 + indices_to_delete = [2] # 特征3 + if num_features > 24: # 确保特征数量足够 + indices_to_delete += list(range(24, 27)) # 特征24到27（索引23到26） + + # 删除特征 + data = np.delete(data, indices_to_delete, axis=1) + + # 将标签二分类化，特征23（索引22）取值2和3合并为0，其余为1 + target_variable = np.where((labels == 2) | (labels == 3), 0, 1) + + return data, target_variable + +def accuracy(X, y, weights): + predictions = predict_logreg(X, weights) + return np.mean(predictions == y) + +# 其余代码保持不变 if __name__ == "__main__": - # 加载训练集 - X_train, y_train = load_data("./data/horse-colic.data") + # 加载数据 + X, y = load_data("./data/horse-colic.data") - # 特征标准化 - scaler = StandardScaler() + # 拆分训练集和验证集 + X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) + + # 数据归一化 + scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) + X_val = scaler.transform(X_val) - # 使用 PolynomialFeatures 生成多项式特征 - poly = PolynomialFeatures(degree=2, include_bias=False) - X_train_poly = poly.fit_transform(X_train) + # 训练模型 + final_weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=5000, batch_size=64, lambda_reg=0.1) + + # 验证模型 + val_accuracy = accuracy(X_val, y_val, final_weights) + print(f"验证集的准确率: {val_accuracy:.4f}") # 加载测试集 X_test, y_test = load_data("./data/horse-colic.test") - X_test = scaler.transform(X_test) # 使用同一标准化参数 - X_test_poly = poly.transform(X_test) # 应用相同的多项式特征转换 + X_test = scaler.transform(X_test) - # 训练对率回归模型 - weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000, reg_lambda=0.01) - - # 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]}") + # 测试模型 + final_test_accuracy = accuracy(X_test, y_test, final_weights) + print(f"最终测试集的准确率: {final_test_accuracy:.4f}") \ No newline at end of file