generated from LufsX/Experiment-LogReg
92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
import os
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import numpy as np
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import pandas as pd
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.model_selection import train_test_split
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from sklearn.impute import SimpleImputer
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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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# Sigmoid 函数
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def sigmoid(z):
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return 1 / (1 + np.exp(-z))
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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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"""
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id = "202210423223"
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name = "李顺潮"
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# 对率回归训练函数
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def train_logreg(X, y, learning_rate=0.01, iterations=1000):
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X = np.hstack((np.ones((X.shape[0], 1)), X)) # 在特征前添加一列全为1的列
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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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z = np.dot(X, weights)
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predictions = sigmoid(z) # 使用sigmoid函数
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gradient = np.dot(X.T, (predictions - y)) / m
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weights -= learning_rate * gradient
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return weights
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# 对率回归预测函数
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def predict_logreg(inX, weights):
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inX = np.insert(inX, 0, 1) # 在特征前添加1作为偏置项
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prob = sigmoid(np.dot(inX, weights)) # 使用sigmoid函数
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return 1 if prob >= 0.5 else 0
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# 从文件中加载数据
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def load_data(filepath):
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data = pd.read_csv(filepath, header=None, na_values='?', delim_whitespace=True)
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# 使用更合理的缺失值填充策略，例如用均值或中位数填充
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imputer = SimpleImputer(strategy='mean') # 使用均值填充
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data = pd.DataFrame(imputer.fit_transform(data))
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X = data.iloc[:, :-1].to_numpy() # 特征
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y = data.iloc[:, -1].to_numpy().astype(int) # 标签,确保y是整数类型
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return X, y
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# 准确率计算函数
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def calculate_accuracy(X, y, weights):
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predictions = np.array([predict_logreg(x, weights) for x in X])
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accuracy = np.mean(predictions == y)
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return accuracy
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if __name__ == "__main__":
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# 加载训练集和测试集
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X_train, y_train = load_data("E:\\Edge站内文件下载\\病马数据集\\horse-colic.data")
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X_test, y_test = load_data("E:\\Edge站内文件下载\\病马数据集\\horse-colic.test")
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# 特征缩放 (可选，但建议尝试)
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scaler = MinMaxScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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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=10000) #增加迭代次数
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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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# 计算并输出模型的准确率
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accuracy = calculate_accuracy(X_test, y_test, weights)
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print(f"模型的准确率: {accuracy:.4f}") |