generated from LufsX/Experiment-LogReg
zhangchenghong
e2f5b6ad04
Gitea Actions Experiment Test / Experiment-Actions (push) Successful in 25s
128 lines
3.8 KiB
Python
128 lines
3.8 KiB
Python
import os
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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 = "202210423215"
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name = "章程鸿"
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# 这里写自己的代码实现
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import numpy as np
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import pandas as pd
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split
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def sigmoid(z):
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"""Sigmoid 激活函数。"""
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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, lambda_reg=0.01, batch_size=32):
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"""逻辑回归训练函数，随机初始化权重，并在每次迭代前打乱数据。"""
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m, n = X.shape
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weights = np.random.randn(n) * 0.01 # 随机初始化权重，取较小值
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for i in range(iterations):
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# 每次迭代打乱数据
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indices = np.random.permutation(m)
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X = X[indices]
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y = y[indices]
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for j in range(0, m, batch_size): # 按照 batch size 迭代
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X_batch = X[j:j + batch_size]
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y_batch = y[j:j + batch_size]
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z = np.dot(X_batch, weights)
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predictions = sigmoid(z)
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gradient = np.dot(X_batch.T, (predictions - y_batch)) / X_batch.shape[0]
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# L2正则化
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gradient += (lambda_reg / m) * weights
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# 更新权重
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weights -= learning_rate * gradient
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# 动态调整学习率，逐渐减小
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learning_rate *= 0.99
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return weights
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def predict_logreg(inX, weights):
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"""逻辑回归预测函数。"""
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z = np.dot(inX, weights)
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prediction_prob = sigmoid(z)
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return 1 if prediction_prob >= 0.5 else 0
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def load_data(filepath):
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"""加载数据并处理缺失值。"""
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data = []
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labels = []
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with open(filepath, 'r') as f:
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for line in f:
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values = line.strip().replace('?', '0').split()
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try:
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values = [float(v) for v in values]
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# 合并特征23的取值
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if values[22] == 2 or values[22] == 3: # 特征23（索引22）
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labels.append(0) # 死亡
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elif values[22] == 1:
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labels.append(1) # 生存
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else:
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continue # 无效标签，丢弃
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# 用0填充缺失值
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values = [0 if v == 0 else v for v in values] # 直接用0填充
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data.append(values[:2] + values[3:23]) # 保留特征1, 2 和特征4-23
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except ValueError:
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continue
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data = np.array(data)
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labels = np.array(labels)
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return data, labels
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def preprocess_data(X):
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"""特征标准化。"""
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scaler = StandardScaler()
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return scaler.fit_transform(X)
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if __name__ == "__main__":
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# 加载数据集
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X, y = load_data("./data/horse-colic.data")
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# 归一化数据
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X = preprocess_data(X)
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# 使用 train_test_split 打乱并随机抽取数据
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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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# 训练逻辑回归模型，设置 batch size
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batch_size = 32
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weights = train_logreg(X_train, y_train, learning_rate=0.1, iterations=1000, lambda_reg=0.01, batch_size=batch_size)
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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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