Implement Adaptive Learning Process State Management System

[gapcomputer2]

Implement Adaptive Learning Process State Management System

Description

Task

Design basic iteration state management, develop a state management system that can capture and store the state of each learning iteration, allowing for potential resume/rollback capabilities.

Acceptance Criteria

• Supports capturing iteration state details
• Enables state transitions between different stages
• Provides serialization and deserialization of states
• Supports persistent state storage
• Allows querying states by status
• Minimal performance overhead
• Thread-safe implementation

Summary of Work

Overview

This pull request implements a comprehensive state management system for the Adaptive Learning Process (ALP), enabling robust tracking, serialization, and management of learning iteration states.

Key Components

1. IterationStatus Enum:

   • Defines possible states for learning iterations
   • Includes: PENDING, RUNNING, COMPLETED, FAILED, INTERRUPTED

2. IterationState Dataclass:

   • Captures detailed state information for each learning iteration
   • Supports dynamic configuration and metrics tracking
   • Provides methods for state transitions and serialization
   • Tracks start/end times, error details, and iteration context

3. IterationStateManager:

   • Manages persistent storage of iteration states
   • Supports saving, loading, and querying states
   • Uses JSON file-based storage for flexibility
   • Provides methods to retrieve states by status

Implementation Details

• Used type hints and dataclasses for type safety
• Implemented comprehensive error handling
• Created flexible serialization methods (to_dict/from_dict)
• Supported file-based state persistence
• Ensured minimal dependencies

Testing Approach

• Comprehensive unit tests covering:
  • State creation and transitions
  • Serialization and deserialization
  • State management operations
  • Error handling scenarios

Notes

• Default state storage directory: 'iteration_states'
• Easily extensible for future requirements
• Minimal performance overhead
• Thread-safe state management

Changes Made

• Created IterationStatus enum for standardized iteration states
• Implemented IterationState dataclass with comprehensive state tracking
• Developed IterationStateManager for state persistence and retrieval
• Added methods for state marking (started, completed, failed, interrupted)
• Implemented JSON-based serialization and deserialization
• Created methods to query states by status

Tests

• Test iteration state creation and initial state
• Verify state transition methods
• Test error handling and state marking
• Validate serialization and deserialization
• Verify state management and persistence operations

Signatures

Staking Key

EKjJikFMaWp6Kx3JPi6PRNqANutX8LCAsQnCd2UCu621: 3KRiqYq2zgGSK8of7mcSFhMNNjWNiMdW5YAT9f6ajDgUnL9UyRmwLTwX6nTCA4BVTQ3b7PiSfBMVVUQWzUTN6YCXzKWYw6zDF8asz3yY1ypcupzaDmx6R2pgoTif67K4XTraZS8Svdu5A1sokxwqtChqtTJw6uMEati46QYfZhGZUfY4XMDAairR1V6puyyWNkmqrgkoS7hP8TBwjQZY2uSHeYyxEhM4B8boPxd48jyhficgXWcXgomGfCYow5BMrMNUzJpKs9ihYP5CcE3UULLFwyjpeMQmbavCJfKNjNmXeap8uDAoWYGbJhiHnryxNinLjM4iYMsJhAziJhMChtoH8YwMd3YjfnpMfL4dUsEk8kPf2U29Zj2UrqQhzSvrfCBujARVs4KgiwrQfqegNMPdkXfFJh3odfXZgtur

Public Key

CXaf1KgZ3emsz9DoERxggja4xrtNYvWn8CDHzcybTqqc: 28umMH9ePCPu7brHWD6k1g6jLNBgRtSb3iXVLFK2AZJuYKq9sXZ4t6wuz7hkGsoZfexA5sceW3co293oqBvEcXyXfdFBGvgwzpERe3UYmNngPdgf6fyzaTp8y1FYzMznV3CogStmAbc1SnMQGwiTFinUoDD7gQY7j8iQGFFTpnwGB8XTznyawzwNnRbQbaXDo5Cx51ekipuEu7BHLi8HrEGPcBhjG25RoFYqzAW6cCr7QRA4c8dUfJJuHY4X2gUZdR3rtLNfUkUTuqBbzrJDBSSuo6nxwyg5Zwf1WBs1T6po1RjuCxRpJtkcaLewUDBT4dDimvqhG1AhjadAtw5vr4ipfpXxQxVXNtyD3fz1NG8zEpAgCYTRjfbSPaV8yRZXaUeuxXMqpuQZbQDtdJsqiJGxTZCBDonadkD5LJf6