generated from LufsX/Experiment-LogReg
108 lines
3.1 KiB
Python
108 lines
3.1 KiB
Python
# function.py
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import numpy as np
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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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"""
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id = "202210423222"
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name = "邓龙树"
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# user_code.py
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import numpy as np
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def sigmoid(z):
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"""计算sigmoid函数"""
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return 1 / (1 + np.exp(-z))
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def train_logreg(X, y, learning_rate=0.01, iterations=1000):
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"""对率回归训练函数。"""
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weights = np.zeros(X.shape[1])
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for _ in range(iterations):
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z = np.dot(X, weights)
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predictions = sigmoid(z)
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gradient = np.dot(X.T, (predictions - y)) / y.size
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weights -= learning_rate * gradient
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return weights
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def predict_logreg(X, weights):
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"""使用训练好的权重预测标签"""
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z = np.dot(X, weights)
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predictions = sigmoid(z)
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return np.where(predictions >= 0.5, 1, 0)
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def load_data(filepath):
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"""从文件中加载数据，处理不规则列数问题"""
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with open(filepath, 'r') as file:
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lines = file.readlines()
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max_columns = 0
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data = []
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for line in lines:
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parts = line.strip().split()
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max_columns = max(max_columns, len(parts))
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data.append(parts)
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# 填充缺失的列
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for i in range(len(data)):
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if len(data[i]) < max_columns:
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data[i].extend(['nan'] * (max_columns - len(data[i])))
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# 转换成numpy数组，处理缺失值
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data_array = np.array(data, dtype=float)
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data_array[np.isnan(data_array)] = np.nanmean(data_array, axis=0)
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return data_array[:, :-1], data_array[:, -1]
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def preprocess_data(X, y):
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"""数据预处理，合并特征23的值，并删除特征3和特征24~28。"""
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# 合并特征23的值：将2（死亡）和3（安乐死）合并为0（死亡）
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y = np.where(y == 2, 0, y) # 将值2替换为0
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y = np.where(y == 3, 0, y) # 将值3替换为0
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# 删除特征3和特征24~28（假设特征索引从0开始）
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X = np.delete(X, [2] + list(range(23, 28)), axis=1)
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return X, y
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if __name__ == "__main__":
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# 加载数据
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X, y = load_data("./data/horse-colic.data")
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# 数据预处理
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X, y = preprocess_data(X, y)
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# 训练模型
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weights = train_logreg(X, y)
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# 加载测试数据并进行预处理
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X_test, y_test = load_data("./data/horse-colic.test")
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X_test, y_test = preprocess_data(X_test, y_test)
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# 预测
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predictions = predict_logreg(X_test, weights)
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# 打印预测结果和实际结果
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for i in range(min(10, len(y_test))):
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print(f"预测结果: {predictions[i]}, 实际结果: {y_test[i]}")
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