From 4c9f84d6d9c5b9583a44772b15551586e97a17e3 Mon Sep 17 00:00:00 2001 From: heart20003 <1789588961@qq.com> Date: Wed, 13 Nov 2024 16:17:51 +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 | 60 ++++++++++++++++++----------------------------------- 1 file changed, 20 insertions(+), 40 deletions(-) diff --git a/function.py b/function.py index 5c890e9..7aac7d6 100644 --- a/function.py +++ b/function.py @@ -1,41 +1,15 @@ -# function.py import numpy as np import os from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split - -""" -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 -""" - - - -# 学号和姓名 -id = "202210423204" -name = "陈垣豪" - -import numpy as np -import os -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split +import joblib # 用于保存模型 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) @@ -44,12 +18,13 @@ def compute_gradient(X_batch, y_batch, weights, lambda_reg): 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] @@ -60,23 +35,25 @@ def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lam 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: @@ -89,30 +66,31 @@ def load_data(filepath): data = np.array(data) labels = np.array(labels) - + num_features = data.shape[1] - indices_to_remove = [2] - + 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 bagging_predict(X, models): """集成学习预测函数。""" predictions = np.zeros((X.shape[0], len(models))) - + for i, model in enumerate(models): predictions[:, i] = [predict_logreg(x, model) for x in X] - + # 投票 final_predictions = np.round(np.mean(predictions, axis=1)) return final_predictions + 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] @@ -122,7 +100,7 @@ def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, ba 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:])) @@ -143,11 +121,11 @@ def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, ba return np.mean(accuracies) + if __name__ == "__main__": # 加载训练集 X_train, y_train = load_data("./data/horse-colic.data") - # 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 @@ -159,7 +137,6 @@ if __name__ == "__main__": # 加载测试集 X_test, y_test = load_data("./data/horse-colic.test") - # 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 @@ -176,3 +153,6 @@ if __name__ == "__main__": accuracy = np.mean(y_pred == y_test) print(f"测试集准确率: {accuracy:.4f}") + # 保存训练的模型 + joblib.dump(models, "bagging_models.pkl") + print("模型已保存为 bagging_models.pkl")