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 記錄日誌（續）
# 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 日誌追蹤模型效能
視覺化
檢視長期效能
將輸入／預測的原始資料轉為洞見
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()
視覺化範例
日誌紀錄
事件紀錄
追蹤變數值、函式呼叫
有助執行與效能的資訊
監控可追蹤：
使用量、效能、錯誤／異常
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]
...
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