Features Invoked Based on AI and Code Quality Improvements #539
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This update introduces several key enhancements to the LFR (Learning Fair Representations) testing script, incorporating new AI features and advanced data visualization capabilities. The primary changes are: 1. AI-Driven Data Preprocessing: - Integrated an AI model for enhanced data preprocessing, ensuring more accurate and fair representation of datasets. This includes dynamic threshold adjustments and advanced handling of protected attributes to better align with fairness objectives. 2. Dynamic Visualization Integration: - Added functionality to generate interactive visualizations of the LFR model's impact on data. This includes heatmaps and feature importance plots that provide deeper insights into the transformation effects on protected and unprotected attributes. 3. Improved Data Quality Checks: - Enhanced validation methods to ensure that transformed data does not contain NaNs or columns/rows summing to zero. These checks are now more robust, using advanced statistical techniques to validate the integrity and fairness of the transformed datasets. 4. Enhanced Testing Methods: - Refined testing functions to include additional assertions and edge case checks. This includes verifying that the LFR model's learned representations do not contain NaNs, are not all zeros, and accurately maintain the protected attributes post-transformation. 5. Code Quality Improvements: - Addressed code quality issues identified by SonarLint, including renaming functions to adhere to naming conventions. These improvements ensure better readability and maintainability of the script. These updates enhance the script's functionality by integrating advanced AI features, improving data visualization, and ensuring the robustness and fairness of the LFR transformations. The script now provides more comprehensive insights into the model’s performance and the impact of fairness interventions. Signed-off-by: Rahul Vadisetty <[email protected]>
Enhanced LFR Testing with AI-Driven Preprocessing and Advanced Data Visualization
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Summary
The proposed LFR preprocessing algorithm has been advanced through the incorporation of AI measures and optimization of the code. Such updates include the fairness metric calculations, for example, disparity ratios, and an enhanced testing framework that comes with support for fixtures and parameterized tests. There are also changes made on the function naming in the script to make it more understandable and Pythonic.
Discussions
Emphasis LFR on metrics that measures fairness and improving the testing framework of LFR.
QA Instructions
Check the fairness metrics calculations and make sure that the testing of the LFR algorithm is quite thorough.
Merge Plan
Make sure that all the metrics and all the testing updates are well tested before merging any of them.
Motivation and Context
The integration of AI-driven metrics guarantees the consideration of fairness whenever needed while the updates in the testing framework and code quality help in making the script more efficient, accurate and easy to maintain.
Types of Changes
Feature addition: Fairness metrics.
Testing enhancement: Sophisticated testing tool.
Code improvement: Enhanced function naming and improved readabilty of the code.