G.O.D Framework

Introduction

The ai_feedback_loop.py module is a key component of the G.O.D Framework that facilitates seamless integration of user feedback into AI workflows for active learning and model refinement. This helps bridge the gap between user expectations and model predictions, ensuring continuous improvement and relevance in dynamic applications.

Using structured pipelines, this module processes feedback and iteratively updates models, either in real time or at scheduled intervals.

Purpose

• Enable active learning workflows by integrating user feedback into model training pipelines.
• Automate the evaluation and prioritization of useful feedback for improving model accuracy.
• Reduce human intervention by closing the loop between inference, feedback, and retraining.
• Provide mechanisms for integrating feedback in both supervised and unsupervised learning scenarios.

Key Features

• Active Learning: Dynamically selects relevant feedback for incremental learning.
• Model Retraining: Automates the retraining process based on user-controlled thresholds.
• Feedback Prioritization: Ranks and filters feedback entries based on predefined criteria.
• Multi-Model Support: Supports pipelines working with multiple model types and frameworks (e.g., PyTorch, TensorFlow).
• Insights Dashboard: Dashboards for visualizing feedback efficiency and its impact on model performance.

Logic and Implementation

The core functionality of ai_feedback_loop.py revolves around a feedback processor which validates and prioritizes feedback before sending it to retrain machine learning models. Below is a simplified example illustrating how feedback is processed and applied in an active learning loop:

            import numpy as np
            from sklearn.linear_model import LogisticRegression

            class FeedbackLoop:
                """
                Feedback loop for incorporating user feedback into AI model training.
                """
                def __init__(self, model, feedback_storage, retrain_threshold=10):
                    """
                    Initialize the feedback loop.
                    :param model: AI model to retrain (e.g., scikit-learn or custom pipeline).
                    :param feedback_storage: A storage system for user feedback (e.g., database, in-memory).
                    :param retrain_threshold: Number of feedback entries required to trigger retraining.
                    """
                    self.model = model
                    self.feedback_storage = feedback_storage
                    self.retrain_threshold = retrain_threshold

                def process_feedback(self, feedback_entry):
                    """
                    Validate and store user feedback.
                    :param feedback_entry: Feedback data submitted by the user.
                    Example: {"input": X, "target": y, "corrected_prediction": True/False}
                    """
                    if "input" in feedback_entry and "target" in feedback_entry:
                        self.feedback_storage.append(feedback_entry)
                        print("Feedback stored successfully.")
                    else:
                        raise ValueError("Invalid feedback format")

                def retrain_model(self):
                    """
                    Retrains the model if the feedback threshold is met.
                    """
                    if len(self.feedback_storage) >= self.retrain_threshold:
                        print(f"Retraining model using {len(self.feedback_storage)} feedback samples...")
                        inputs = [fb["input"] for fb in self.feedback_storage]
                        targets = [fb["target"] for fb in self.feedback_storage]
                        self.model.fit(np.array(inputs), np.array(targets))
                        self.feedback_storage.clear()  # Clear feedback after retraining
                        print("Model retraining completed!")
                    else:
                        print(f"Insufficient feedback for retraining. {len(self.feedback_storage)}/{self.retrain_threshold} collected.")

            # Example execution
            if __name__ == "__main__":
                # Create a mock model and feedback loop
                model = LogisticRegression()
                feedback_storage = []

                feedback_loop = FeedbackLoop(model, feedback_storage, retrain_threshold=5)

                # Simulate user feedback
                feedback_1 = {"input": [0.5, 1.0, 2.1], "target": 1}
                feedback_2 = {"input": [0.2, 0.8, 1.5], "target": 0}

                feedback_loop.process_feedback(feedback_1)
                feedback_loop.process_feedback(feedback_2)

                feedback_loop.retrain_model()  # Will not retrain as threshold not met
            

Dependencies

This module relies on the following external and core libraries:

• Numpy: For numerical operations and data preprocessing.
• Scikit-learn: Example model framework; supports lightweight experimentation.
• Custom Model Frameworks: Replaceable with any ML framework (e.g., PyTorch, TensorFlow).

Usage

Incorporate **ai_feedback_loop** into your application by initializing the FeedbackLoop class with the desired ML Model and feedback storage interface.

            # Feedback Loop Usage:
            model = PretrainedModel()  # Replace with your pretrained AI model
            feedback_storage = []     # Use appropriate storage for production systems

            feedback_loop = FeedbackLoop(model, feedback_storage, retrain_threshold=10)

            # Process feedback
            new_feedback = {"input": [1.2, 0.5, 3.1], "target": 1}
            feedback_loop.process_feedback(new_feedback)

            # Trigger retraining (if threshold met)
            feedback_loop.retrain_model()
            

System Integration

• Web Interface: Accept feedback directly through dashboards or APIs and auto-trigger updates.
• Real-time Processing: Integrate with active systems for real-time or asynchronous feedback evaluation.
• Cloud Pipelines: Deploy as part of CI/CD pipelines for large-scale models requiring collaboration.

Future Enhancements

• Automated Threshold Adjustments: Dynamically adjust retraining thresholds based on feedback distribution.
• Pipeline-as-a-Service: Expose the feedback loop via a REST service for easier external integrations.
• Explainability Injection: Integrate explainability tools to justify continuous refinements to AI predictions.