Implement Core ALP Loop Execution Strategy with Performance Tracking

[valin1993]

Implement Core ALP Loop Execution Strategy with Performance Tracking

Description

Task

Implement core loop execution strategy for running ALP learning cycles, including iteration flow control and basic performance tracking.

Acceptance Criteria

• Implements flexible iteration control mechanism
• Provides comprehensive performance tracking
• Supports configurable runtime and iteration limits
• Handles errors gracefully with logging
• Tracks and reports detailed loop execution metrics
• Allows custom iteration function integration
• Supports multiple loop termination scenarios

Summary of Work

Overview

This pull request implements the core execution strategy for the Adaptive Learning Process (ALP) learning cycles, providing a robust and flexible mechanism for running iterative machine learning processes.

Key Implementation Details

• Created CoreLoopExecutor class to manage learning cycle execution
• Implemented comprehensive performance tracking with PerformanceMetrics
• Added flexible configuration options for iteration control
• Developed robust error handling and logging mechanism
• Introduced LoopStatus enum for tracking loop state

Core Components

1. Execution Strategy

   • Supports configurable iteration function
   • Tracks iterations, runtime, and performance metrics
   • Allows setting maximum iterations and runtime
   • Provides graceful error handling and logging

2. Performance Metrics

   • Track total iterations
   • Monitor total runtime
   • Calculate average iteration time
   • Count encountered errors

3. Error Handling

   • Custom exceptions for loop execution
   • Configurable error tolerance
   • Logging of critical errors
   • Ability to stop execution on excessive errors

Configuration Flexibility

• Optional configuration dictionary
• Configurable max iterations and runtime
• Extensible through inheritance (e.g., custom _should_terminate() method)

Testing Approach

• Comprehensive error handling tests
• Performance metric calculation verification
• Status transition validation
• Iteration function execution tracking

Potential Future Improvements

• Add more granular performance metrics
• Implement more sophisticated termination conditions
• Enhance error recovery strategies

Notes

• Logging level can be adjusted as needed
• Designed for extensibility and customization

Changes Made

• Created CoreLoopExecutor class in src/alp/core_loop.py
• Implemented LoopStatus enum for tracking loop state
• Added PerformanceMetrics dataclass for tracking iteration metrics
• Developed execute() method with comprehensive iteration and error handling
• Created custom exceptions for loop execution errors
• Added logging and metrics collection mechanisms
• Implemented configurable iteration and runtime limits

Tests

• Verify CoreLoopExecutor can run iterations successfully
• Test max iterations and runtime limits
• Validate performance metrics calculation
• Check error handling and logging mechanisms
• Ensure loop status transitions work correctly
• Test custom iteration function execution

Signatures

Staking Key

AfoZUkZfJSxTqy9XdG9qCSUsfqzySwuexqZzpD6oeYva: 3G1bd2fgpRq5CdsAZtANdcH8fk8sRJriJ8XBwEEBYBXtsBwSpg8MpC2Kq7SNCMTVyVzo4kruUmkajdD2RD2in2QHwMcKYdwbiKB7ZXiuM3TXMNyDRcugjU8M9M5Q7SJJttvGVhgYVm3h7S91WjNXMccNGBhATj8Wfd42u7xUQmfhFYhMaF3up9sN3TNM1sDT3MK9WgCKLUgMDA7dqPhcfrsu9JzKQaJGyQj43y5WDGuCiHjqz8h1b9hQJHPhHzZsScwwAv5Lgx5fHHGCg5SngFjvd9QnubrNmW8hwUCBWhw9TBiPukbvYkFVZdTx4j1vMPEoZTwTDTqpsjr7cy1DyyD3Gixfj2XnN2JXKuviYyqUyoxdsH9xGeDnBdVDrUxVTm7jW4aMMFx1sHcAF8WPu7HRHFHMhHjFC9C8

Public Key

D5VNc6HXDCJwfNP7nbNk7yTC96mW9m7UFtU4oHiK2H8T: 3avtgCHSwMEaUFLG3NKjqCXhMgUKkZX7B47biJttkfYijjprTVM7EQDX3HMe3kLzriWqnWsNifdpSYRcGkNako1L956J1LwuXSdek4jZZNfwBbvXcNexFHjE8c9higbLU79sW8Fb4qvyVvwVqbj932igMhu7z2TFvdfB3XyRta8xvhsBiCnW9gsdEtWghKxv4vK8suAiF73c4YgtwyKeJYFnTUC4dKJqzotWtbfu1imMD2JHM3Ero2MeZadwxdbFXreLMQ7jfmFQkbE8iia2NPWkSbn83iVBFfEfLC7ZW1Z4EF7XJWrbCuNPk4V8yXeCDsUCcQ5mwVZ4CvH7ijFn8F5Z8JpDbwAcAdN6DFP7w6xgDFhdh6z9iTUMPtNpodycpwc2JfaLAtJKVarT996G9B7aXofMrDScuxL4