Advanced Resource plotting with AI: Predictive Analytics, and Enhanced Visualization #1247
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This commit updates the `plot.py` script to include advanced AI-driven features for resource analysis and prediction. The enhancements provide deeper insights into CPU and memory usage, anomaly detection, and future resource predictions. The refactoring also improves code readability and adheres to best practices.
Key Updates
1. **Exception Handling Improvement**
- Replaced the generic `Exception` with a more specific `FileNotFoundError` to provide clearer error handling when the profile result file does not exist.
2. **Cognitive Complexity Reduction**
- Refactored the `get_each_method_maximun_cpu_mem` method to reduce cognitive complexity. This refactoring involved breaking down the method into smaller, more manageable functions:
- **`update_max_values`**: Updates the maximum CPU and memory usage values for the current method execution period.
- **`update_method_exec_info`**: Updates the method execution information with the maximum values and manages the transition to the next method execution period.
3. **Redundant `continue` Removal**
- Cleaned up unnecessary `continue` statements to streamline the code logic and improve readability.
4. **AI-Driven Features**
- **Anomaly Detection**: Added the `analyze_data` method using Isolation Forest to detect anomalies in CPU and memory usage. This helps identify unusual patterns or outliers in the resource usage data.
- **Predictive Analytics**: Introduced the `predict_resource_usage` method employing Linear Regression to forecast future CPU and memory usage based on historical data. This feature aids in predicting resource demands and planning accordingly.
- **Automated Insights**: Implemented the `generate_insights` method to provide textual insights from the data analysis and predictions. This method summarizes detected anomalies and provides future usage predictions.
5. **Improved Plotting**
- **Refactored Plotting Code**: Abstracted common plotting logic into helper methods:
- **`plot_method_exec_info`**: Handles the plotting of method execution information, including vertical lines and annotation points.
- **`prepare_method_data`**: Prepares data for plotting the key points, including color coding based on results.
- **Enhanced Visualization**: Updated the plot to include AI-generated insights and predictions, providing a more comprehensive view of the resource usage data.
These updates enhance the functionality of the script by integrating advanced AI techniques for resource analysis, improving code maintainability, and delivering actionable insights based on the collected data.
Integrate AI-Driven Anomaly Detection and Predictive Analytics into Resource Plotting Script
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1. Summary:
The script introduces AI-enabled resource plotting and predictive analytics to better monitor CPU and memory usage. It combines anomaly detection (using Isolation Forest) with predictive analysis (using Linear Regression) for effective management of resources. Refactored code, specific exception handling, lower cognitive complexity, and simplified plotting logic are the key updates.
2. Discussions:
On refactoring, anomaly detection, predictive insights, and cleaner execution logic. More at its core
3. QA Instructions:
Test predictive analytics and anomaly detection on different resource consumption logs. Ensure that refactored methods are providing accurate output and improved readability.
** 4. Merge Plan:**
Merge after post-QA approval to confirm the correctness of the new AI features and refactored methods.