Implement Core Iteration Tracking Mechanism for Adaptive Learning Process

[Merango]

Implement Core Iteration Tracking Mechanism for Adaptive Learning Process

Description

Task

Implement core iteration tracking mechanism for Adaptive Learning Process (ALP), creating a robust system to track and manage iterations with comprehensive state management.

Acceptance Criteria

• Support tracking of current iteration count
• Implement iteration status tracking
• Provide methods for starting and completing iterations
• Support optional maximum iteration limit
• Enable manual termination of iteration process
• Add reset functionality
• Include comprehensive logging

Summary of Work

This pull request implements a robust iteration tracking mechanism for the Adaptive Learning Process (ALP) that provides flexible state management and iteration control.

Key Implementation Details

Core Components

• Created IterationState dataclass to manage iteration tracking
• Implemented IterationStatus enum to represent different iteration states
• Provided methods for starting, completing, and terminating iterations

Features

• Dynamic iteration tracking with optional maximum iteration limit
• Comprehensive state management
• Logging of iteration events
• Metadata storage for additional context
• Flexible termination and reset capabilities

Implemented Methods

• start_iteration(): Begins a new iteration, respecting max iteration limit
• complete_iteration(): Marks current iteration as complete
• terminate(): Forcibly stops iteration process with optional reason
• reset(): Restores iteration state to initial configuration

Testing Approach

• Comprehensive unit tests covering various scenarios
• Validated initialization, iteration progression, termination, and reset
• Tested edge cases like max iteration limits
• Ensured proper state transitions and logging

Important Notes

• Logging is implemented to track iteration events
• Metadata can store additional context about iterations
• Designed for extensibility and easy integration with other ALP components

Changes Made

• Created src/iteration_tracker.py with IterationState class
• Implemented IterationStatus enum for tracking iteration states
• Added methods for start_iteration(), complete_iteration(), terminate(), and reset()
• Included logging for iteration events
• Supported optional max_iterations configuration
• Implemented metadata storage for additional iteration context

Tests

• Verified initial state initialization
• Tested iteration progression with and without max iterations
• Validated iteration start and complete methods
• Confirmed termination functionality
• Checked reset method restores initial state
• Ensured proper logging and metadata tracking

Signatures

Staking Key

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Public Key

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