Implement Abstract Base Class for Adaptive Learning Process (ALP) Loop Mechanism

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Implement Abstract Base Class for Adaptive Learning Process (ALP) Loop Mechanism

Description

Task

Create abstract base class for ALP Loop mechanism

Acceptance Criteria

• Provides a flexible, extensible abstract base class
• Supports configurable learning loop parameters
• Implements comprehensive error handling
• Provides robust logging and metrics tracking
• Allows easy extension for specific learning scenarios

Summary of Work

This pull request introduces a robust and flexible abstract base class for the Adaptive Learning Process (ALP) Loop Mechanism, providing a standardized template for implementing iterative, self-improving learning cycles.

Key Implementation Details

Core Components

• LoopStatus Enum: Defines the possible states of the learning loop (INITIALIZED, RUNNING, PAUSED, COMPLETED, FAILED)
• LoopMetrics Dataclass: Tracks performance metrics including iterations, runtime, and custom metrics
• AdaptiveLearningProcessLoop Abstract Base Class: Provides the core structure for learning loops

Key Features

• Dynamic iteration control with configurable max iterations
• Flexible logging and error handling
• Extensible design with abstract methods for initialization and iteration
• Comprehensive status and metrics tracking
• Pause and resume functionality

Abstract Methods

• _initialize(): Prepares resources and learning environment before loop starts
• _iteration(): Defines a single learning cycle, returns whether to continue

Error Handling

• Robust exception management
• Automatic status tracking during loop execution
• Detailed logging of loop events and errors

Metrics Tracking

• Tracks iterations, runtime, and performance
• Supports custom additional metrics
• Returns comprehensive metrics upon loop completion

Considerations

• Implementations must override _initialize() and _iteration() methods
• Max iterations can be configured or set to infinity
• Custom loggers can be provided for advanced logging needs

Changes Made

• Created LoopStatus enum for tracking loop states
• Implemented LoopMetrics dataclass for performance tracking
• Developed AdaptiveLearningProcessLoop abstract base class
• Added initialization, iteration, and error handling methods
• Implemented pause and resume functionality
• Added comprehensive logging configuration
• Supported flexible iteration and metrics tracking

Tests

• Verify LoopStatus enum covers all expected states
• Test LoopMetrics initialization and metric tracking
• Validate abstract method requirements for subclasses
• Check error handling and logging mechanisms
• Test pause and resume functionality
• Verify metrics collection during loop execution

Signatures

Staking Key

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Public Key

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