import logging
import matplotlib.pyplot as plt
# Setting up basic logging configuration
logging.basicConfig(filename='predictions.log', level=logging.INFO)
# Make predictions on the test set and log the results
for i in range(X_test.shape[0]):
instance = X_test[i,:].reshape(1, -1)
prediction = model.predict(instance)
logging.info(f'Inst. {i} - PredClass: {prediction[0]}, RealClass: {y_test[i]}')
使用 Python 记录日志（续）
# Function to visualize the predictions from log
with open(logfile, 'r') as f:
lines = f.readlines()
predicted_classes = [int(line.split("Predicted Class: ")[1].split(",")[0]) \
for line in lines]
# Perform data analysis, visualization, etc.
...
用 Python 日志追踪模型表现
可视化
按时间检查性能
将原始输入/预测数据转化为洞见
import matplotlib.pyplot as plt
# Sample data: Random accuracy values for 12 months
months = ["Jan", "Feb", "Mar", ...]
accuracies = [0.86, 0.91, 0.74, ...]
plt.plot(months, accuracies, '-o')
plt.title("Model Accuracy Over Months")
plt.xlabel("Months")
plt.ylabel("Accuracy")
plt.show()
可视化示例
日志记录（Logging）
事件记录
变量值、函数调用跟踪
支持执行与性能的信息
监控可跟踪：
使用情况、性能、错误/异常
2023-08-04 09:15:20 [INFO] Model version 1.2.7 started
2023-08-04 09:15:45 [INFO] Preprocessing input data for prediction
2023-08-04 09:15:47 [DEBUG] Input data shape: (1, 12)
2023-08-04 09:15:48 [INFO] Making prediction
2023-08-04 09:15:50 [DEBUG] Output prediction: [0.78]
...
Preparing Video For Download...
