AI integration in the framework and Improvements to Reproducibility of settings in the UI/Base Class #1093
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In this update, several enhancements were made to the `UIBase` class in the `ui_base.py` file, focusing on integrating AI features and improving functionality. Here are the key changes: 1. AI-Based User Interaction Prediction: - New Method Added: A new method `predict_user_interaction` was introduced to predict user behavior based on historical interaction data. This method utilizes an AI model to analyze user inputs and predict potential anomalies or normal behavior. - Integration with AI Model: The method leverages an `AIModel`, which was updated to include a fixed `random_state` parameter in the `IsolationForest` model to ensure reproducibility. This update addresses the reproducibility issue and resolves the SonarLint warning about not providing a seed for the `random_state` parameter. 2. Type Annotations: - Enhanced Clarity: Type annotations were added or refined for better code clarity and consistency. This improves the readability of the code and helps with type checking, making the codebase easier to maintain. 3. Error Handling and Predictive Analytics: - Anomaly Detection: The new `predict_user_interaction` method provides functionality for detecting anomalies in user behavior. This enhances the ability to analyze user interactions and predict potential issues, thereby improving the overall user experience. These updates enhance the `UIBase` class by incorporating advanced AI features and improving code quality through better type annotations and reproducibility fixes. The integration of predictive analytics enables more robust handling of user interactions and enhances the functionality of the user interface.
Integrate AI-Based Prediction and Enhance Reproducibility in UIBase
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Summary
It adds to the UIBase class and includes the predict_user_interaction method that also uses an AI model for predicting user interactions based on the history. Also, the IsolationForest model has been updated with the fixed random_state parameter, which allows receiving the same result, if the model was launched several times, preventing the state inconsistency problem, which used to exist in previous releases.
Related Issues
No specific issues linked.
Discussions
Illustrate improvements on the use of AI as well as the issues of reproducibility that occur in the interaction of the user with the system.
QA Instructions
, test the predict_user_interaction method with different levels of interaction as described below.
Merge Plan
To avoid any inconsistencies in performance, the test reproducibility of the models should be done before merging.
Motivation and Context
Integrating AI-based predictions also contribute towards the UI factor by predicting the user actions as well as solving the issues of reproducibility context in AI models.
Types of Changes
Feature Addition: Using artificial intelligence for the prediction of user-interaction.
Enhancement: Better replication through fixed random_state