Enhance Performance Metrics Collection for Adaptive Learning Process

[sopheakim]

Enhance Performance Metrics Collection for Adaptive Learning Process

Description

Task

Add basic performance metrics collection

Acceptance Criteria

• Lightweight metrics collection
• Performance tracking
• Thread-safe implementation
• Configurable history
• Multiple metric types
• Error handling
• Logging support

Summary of Work

Implement and document a comprehensive performance metrics collection system for the Adaptive Learning Process (ALP).

Key Improvements:

• Implemented a flexible, thread-safe metrics collection mechanism
• Supports multiple metric types (performance, resource usage, error rate, learning progress)
• Provides configurable history tracking and logging
• Offers methods for recording, retrieving, and analyzing metrics

Detailed Changes:

1. Created PerformanceMetricsCollector class with robust metric tracking
2. Implemented configurable max history for metrics
3. Added support for different metric types via MetricType enum
4. Provided methods for:
   • Recording metrics (record_metric)
   • Retrieving metric history (get_metric_history)
   • Getting latest metric (get_latest_metric)
   • Calculating metric averages (calculate_metric_average)

Acceptance Criteria:

• Implement a lightweight metrics collection system
• Support tracking performance characteristics of learning cycles
• Ensure thread-safe metric recording
• Provide configurable history tracking
• Support multiple metric types
• Implement comprehensive error handling
• Add logging capabilities for metrics

Changes Made

• Created src/metrics_collector.py with PerformanceMetricsCollector class
• Implemented MetricType enum for categorizing metrics
• Added MetricRecord dataclass for structured metric storage
• Implemented methods for recording, retrieving, and analyzing metrics
• Added comprehensive logging and error handling

Tests

• Verified basic metric recording functionality
• Tested max history limitation for metrics
• Checked latest metric retrieval
• Validated metric average calculation
• Tested recording different metric types
• Verified behavior with non-existent metrics

Signatures

Staking Key

8hAhdRrEs8mbVnN2douviNWjQ2xXgeb31KQNAa2yTbVg: 2PtxXmBSv7GPnojZ6A9xgGzvM8FJTX544PBP2p7X3aKSw29MHFHaCByocnXdUSHBkkehwUqYh6dP6FS8TWyfxXTBLfMk6bp54xTfRudjrzfK5knzfKGS1jSrgRVMxbhSUPop7PENFfw33T2AMDRufJrdmFv4cuvQtTys92x3HDySBycL9MRCEENK46VrKe3oxCWGWdTgRLByq8xRkLFh9mmMsyYRkAneZhfy65s1tSLb4ftLGNkK3LiYEYZieSjhhG4eGFbF3nruQuu7DxLSne7gKR97RFRoq3Tjw9665cFmj57ZdtdGeyMHUQzEmjCfdmGD7ag8mrGQD39KT89N21zLruBZbFigZs8FUW6invENHwiZvgiVm9uSCh4i9TMLCtdfEvRarGNQ7Hb96TKvm8s4R8yd5ngavvA8

Public Key

43bZxBBPosBCxfy982VaGcrTgT3Ff4wwK3hcZSEXGVUn: 33KeH2sJjSzvGkLganJumJ7Fm7sSmyYazC27kut5EutWZSSTxdD461tNjpufYhETdJUs3PKL9aAaYTcDwc1C9jkVjsmmiGd5yhHazJuYFitySokBTeSKmEFpCE2crQ7NnyzGkjpTje2FhJ7jN11gY5qxkWVKkeCQWdpoAStpXccmH83ThU6VS2kW4xE7FEwWMTRmAHXZgdHdaARUGGcfFGXawjpcEifdHdbUzcx5hiKqz5PFzrrToz15a3zmEUk9qYpExG4cU9CzyFxQMSMrZA3YVhWPHHHcSSwkamGWFfnkgr4UaWB4WZFaSv9vJ5ey7rZsFxXzjq2Mpx2eXLQDgu13Ak5MHm1hmJ2y5KqMtshVTcSuTyvZq9CZ51NZDihWdVLgNM7sp6Qqvqm9VZ5J1vd96DkKRSBemwAk