generated from LufsX/Experiment-LogReg
151 lines
4.6 KiB
Python
151 lines
4.6 KiB
Python
import os
|
||
import numpy as np
|
||
|
||
"""
|
||
实验说明:
|
||
- 自行编写实现数据加载与预测病马死亡的对率回归算法。
|
||
|
||
注意事项:
|
||
- 在 function.py 中实现对率回归算法。
|
||
- 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。
|
||
- 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。
|
||
|
||
测评环境:
|
||
- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
|
||
- OS: Ubuntu 22.04.4 LTS
|
||
- Python: 3.10.12
|
||
- numpy: 2.1.1
|
||
"""
|
||
|
||
"""
|
||
请在这里填写学号和姓名，不填写即成绩无效。
|
||
"""
|
||
id = "202210423120"
|
||
name = "袁焜"
|
||
|
||
# 这里写自己的代码实现
|
||
import numpy as np
|
||
from sklearn.preprocessing import MinMaxScaler
|
||
from sklearn.model_selection import train_test_split
|
||
|
||
|
||
def sigmoid(z):
|
||
return 1.0 / (1 + np.exp(-z))
|
||
|
||
|
||
def compute_gradient(X_batch, y_batch, weights, lambda_reg):
|
||
z = np.dot(X_batch, weights)
|
||
h = sigmoid(z)
|
||
gradient = np.dot(X_batch.T, (h - y_batch)) / len(y_batch)
|
||
gradient += (lambda_reg / len(y_batch)) * weights # L2正则化
|
||
return gradient
|
||
|
||
|
||
def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):
|
||
m, n = X.shape
|
||
weights = np.random.randn(n) * np.sqrt(2. / n) # He初始化
|
||
best_weights = weights.copy()
|
||
best_accuracy = 0
|
||
|
||
for _ in range(iterations):
|
||
indices = np.random.permutation(m)
|
||
X_shuffled = X[indices]
|
||
y_shuffled = y[indices]
|
||
|
||
for j in range(0, m, batch_size):
|
||
X_batch = X_shuffled[j:j + batch_size]
|
||
y_batch = y_shuffled[j:j + batch_size]
|
||
gradient = compute_gradient(X_batch, y_batch, weights, lambda_reg)
|
||
weights -= learning_rate * gradient
|
||
|
||
# 计算训练集准确率
|
||
train_accuracy = accuracy(X, y, weights)
|
||
if train_accuracy > best_accuracy:
|
||
best_accuracy = train_accuracy
|
||
best_weights = weights.copy()
|
||
|
||
# 动态调整学习率
|
||
if _ % 100 == 0 and train_accuracy > 0.95: # 达到高准确率时减小学习率
|
||
learning_rate *= 0.9
|
||
|
||
return best_weights
|
||
|
||
|
||
def predict_logreg(X, weights):
|
||
z = np.dot(X, weights)
|
||
h = sigmoid(z)
|
||
return np.where(h >= 0.5, 1, 0)
|
||
|
||
|
||
def load_data(filepath):
|
||
data, labels = [], []
|
||
with open(filepath, 'r') as f:
|
||
for line in f:
|
||
line = line.strip().split()
|
||
if len(line) >= 1 and line[-1].isdigit():
|
||
line_data = [0 if x == '?' else float(x) for x in line[:-1]]
|
||
data.append(line_data)
|
||
labels.append(int(line[-1]))
|
||
|
||
data = np.array(data)
|
||
labels = np.array(labels)
|
||
|
||
# 检查特征数量
|
||
num_features = data.shape[1]
|
||
|
||
# 特征选择：去掉特征3（索引2）和特征24~28（索引23~26），确保索引有效
|
||
indices_to_delete = [2] # 特征3
|
||
if num_features > 24: # 确保特征数量足够
|
||
indices_to_delete += list(range(24, 27)) # 特征24到27（索引23到26）
|
||
|
||
# 删除特征
|
||
data = np.delete(data, indices_to_delete, axis=1)
|
||
|
||
# 将标签二分类化，特征23（索引22）取值2和3合并为0，其余为1
|
||
target_variable = np.where((labels == 2) | (labels == 3), 0, 1)
|
||
|
||
return data, target_variable
|
||
|
||
|
||
def accuracy(X, y, weights):
|
||
predictions = predict_logreg(X, weights)
|
||
return np.mean(predictions == y)
|
||
|
||
|
||
# 其余代码保持不变
|
||
if __name__ == "__main__":
|
||
# 加载数据
|
||
X, y = load_data("./data/horse-colic.data")
|
||
|
||
# 拆分训练集和验证集
|
||
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
|
||
|
||
# 数据归一化
|
||
scaler = MinMaxScaler()
|
||
X_train = scaler.fit_transform(X_train)
|
||
X_val = scaler.transform(X_val)
|
||
|
||
# 训练模型
|
||
final_weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=5000, batch_size=64, lambda_reg=0.1)
|
||
|
||
# 验证模型
|
||
val_accuracy = accuracy(X_val, y_val, final_weights)
|
||
print(f"验证集的准确率: {val_accuracy:.4f}")
|
||
|
||
# 加载测试集
|
||
X_test, y_test = load_data("./data/horse-colic.test")
|
||
X_test = scaler.transform(X_test)
|
||
|
||
# 测试模型
|
||
final_test_accuracy = accuracy(X_test, y_test, final_weights)
|
||
print(f"最终测试集的准确率: {final_test_accuracy:.4f}")
|
||
# 加载测试集
|
||
X_test, y_test = load_data("./data/horse-colic.test")
|
||
|
||
# 训练对率回归模型
|
||
weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
|
||
|
||
# 测试单个数据
|
||
y_pred = predict_logreg(X_test[11], weights)
|
||
print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
|