Create Visual Interface for Real-Time Training Visualization and Improve Evaluation Methods

[mentatbot]

Description

This issue aims to achieve the following:

1. Create a Visual Interface for Real-Time Training Visualization:

   • Develop a comprehensive visual interface to visualize the training process in real-time.
   • Display evaluation metrics and their changes over time, including loss, accuracy, and other relevant metrics.
   • Show real-time examples of problems extracted from the training, including the problem, level, topic, and the model's responses.
   • Compare the model's formulation with the actual formulation for better understanding and debugging.
   • Provide an option to export the model's generated formulas to an Excel file, with proper formatting and filtering options (e.g., by moment, year, topic).
   • Include interactive elements to allow users to pause, resume, and adjust training parameters on-the-fly.
   • Implement visualizations for the model's internal states and memory usage to provide insights into the learning process.
   • Ensure the interface is user-friendly and provides meaningful insights into the training process.

2. Improve Evaluation Methods and Testing:

   • Enhance evaluation methods to cover preprocessing, pipelines, and model transformations.
   • Implement tests to verify that data transformations are correct both independently and as a group.
   • Ensure the model's security, reliability, optimization, hyperparameters, architecture, and scalability.
   • Develop automatic node creation for training flow and problem dataset creation.
   • Implement dynamic adjustments based on the model's learning progress to optimize topic transitions.
   • Ensure comprehensive testing for all aspects to improve the model's performance and reliability.
   • Develop a suite of tests to evaluate the model's robustness against various types of manipulations.
   • Implement mechanisms to track and log the model's performance over time, providing historical data for analysis.
   • Create detailed documentation for the evaluation methods and testing procedures to ensure reproducibility and transparency.

Tasks

1. Create a Visual Interface for Real-Time Training Visualization:

   • Develop a visual interface for real-time training visualization using a suitable framework (e.g., Dash, Flask, or a custom web application).
   • Display evaluation metrics and their changes over time, including loss, accuracy, and other relevant metrics.
   • Show real-time examples of problems, including problem, level, topic, and model's responses.
   • Compare the model's formulation with the actual formulation for better understanding and debugging.
   • Provide an option to export generated formulas to an Excel file with filtering options.
   • Include interactive elements to allow users to pause, resume, and adjust training parameters on-the-fly.
   • Implement visualizations for the model's internal states and memory usage.
   • Ensure the interface is user-friendly and provides meaningful insights into the training process.

2. Improve Evaluation Methods and Testing:

   • Enhance evaluation methods for preprocessing, pipelines, and model transformations.
   • Implement tests to verify data transformations independently and as a group.
   • Ensure the model's security, reliability, optimization, hyperparameters, architecture, and scalability.
   • Develop automatic node creation for training flow and problem dataset creation.
   • Implement dynamic adjustments based on the model's learning progress.
   • Ensure comprehensive testing for all aspects to improve the model's performance and reliability.
   • Develop a suite of tests to evaluate the model's robustness against various types of manipulations.
   • Implement mechanisms to track and log the model's performance over time, providing historical data for analysis.
   • Create detailed documentation for the evaluation methods and testing procedures to ensure reproducibility and transparency.

Additional Features

• Real-Time Problem Visualization:

  • Display real-time examples of problems being solved, including the problem statement, level, topic, and the model's response.
  • Provide a side-by-side comparison of the model's formulation and the actual formulation.
  • Allow users to filter and search for specific problems based on various criteria (e.g., difficulty, topic, date).

• Export and Reporting:

  • Implement functionality to export generated formulas and other relevant data to Excel.
  • Ensure the exported data is well-formatted and includes filtering options for specific criteria (e.g., moment, year, topic).
  • Provide summary reports and visualizations of the model's performance over time.

• Interactive Training Control:

  • Allow users to pause, resume, and adjust training parameters in real-time.
  • Implement controls for adjusting hyperparameters, learning rates, and other training settings on-the-fly.
  • Provide visual feedback on the impact of these adjustments on the training process.

• Internal State and Memory Visualization:

  • Visualize the model's internal states, including memory usage and activations.
  • Provide insights into how the model is processing and storing information during training.
  • Implement tools for debugging and understanding the model's behavior.

• Advanced Evaluation Metrics:

  • Develop and integrate advanced evaluation metrics to provide deeper insights into the model's performance.
  • Implement metrics for measuring the model's generalization ability, robustness, and efficiency.
  • Provide visualizations for these metrics to help users understand the model's strengths and weaknesses.

• User Authentication and Data Security:

  • Implement user authentication to ensure secure access to the visual interface.
  • Ensure that all data is securely stored and transmitted, following best practices for data security.

• Scalability and Performance Optimization:

  • Optimize the visual interface and backend systems for scalability and performance.
  • Ensure that the system can handle large datasets and high volumes of real-time data without performance degradation.

References

• Kistmath_AI/models/kistmat_ai.py
• Kistmath_AI/models/external_memory.py
• Kistmath_AI/training/curriculum_learning.py

Additional Notes

Please ensure that the new features and improvements are well-documented and include appropriate unit tests to verify their functionality. The visual interface should be intuitive and provide meaningful insights into the training process, while the evaluation methods should be comprehensive and ensure the model's robustness and reliability. Additionally, ensure that the system is secure, scalable, and optimized for performance.