generated from LufsX/Experiment-LogReg
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
import numpy as np
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from sklearn.preprocessing import StandardScaler
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import train_test_split, GridSearchCV
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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 = "202210423221"
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name = "廖子豪"
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def load_data(filepath):
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data = np.genfromtxt(filepath, delimiter=' ', missing_values='?', filling_values=0)
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labels = data[:, -1].astype(int)
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features = data[:, :-1]
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# 将标签转换为二分类问题
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labels = np.where((labels == 2) | (labels == 3), 1, 0)
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# 数据标准化
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scaler = StandardScaler()
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features = scaler.fit_transform(features)
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return features, labels
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def train_logreg(X, y, random_state=42):
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# 使用 GridSearchCV 进行参数调优
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param_grid = {'C': [0.01, 0.1, 1, 10, 100], 'penalty': ['l1', 'l2']}
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model = LogisticRegression(solver='liblinear', random_state=random_state)
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grid_search = GridSearchCV(model, param_grid, cv=5)
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grid_search.fit(X, y)
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return grid_search.best_estimator_
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def predict_logreg(inX, model):
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return int(model.predict([inX])[0])
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# 示例使用
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if __name__ == "__main__":
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# 加载训练数据和测试数据
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X_train, y_train = load_data("./data/horse-colic.data")
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X_test, y_test = load_data("./data/horse-colic.test")
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# 训练模型
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model = train_logreg(X_train, y_train, random_state=42)
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# 进行预测
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y_pred = [predict_logreg(x, model) for x in X_test]
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# 计算准确率
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accuracy = np.mean(y_pred == y_test)
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print(f"Test Accuracy: {accuracy:.2f}") |