generated from LufsX/Experiment-LogReg
68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
"""
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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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id = "202210423211"
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name = "梅晓东"
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import pandas as pd
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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def load_data(file_path):
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"""
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加载数据
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:param file_path: 数据文件路径
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:return: 特征和标签
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"""
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data = pd.read_csv(file_path)
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X = data.drop('death', axis=1) # 假设 'death' 列为标签
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y = data['death']
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return X, y
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def train_logreg(X, y):
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"""
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训练对率回归模型
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:param X: 特征
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:param y: 标签
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:return: 训练好的模型
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"""
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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model = LogisticRegression()
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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print(f'Accuracy: {accuracy:.2f}')
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return model
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def predict_logreg(model, X_new):
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"""
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使用训练好的模型进行预测
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:param model: 训练好的对率回归模型
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:param X_new: 新的特征数据
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:return: 预测结果
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"""
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predictions = model.predict(X_new)
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return predictions
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