G.O.D Framework

Introduction

The ai_real_time_learner.py module in the G.O.D Framework is responsible for enabling real-time processing and learning from streaming data. Its primary goal is to continuously adapt to evolving data patterns and improve its decision-making capabilities without requiring retraining cycles. This module is ideal for scenarios like fraud detection, personalized recommendations, and dynamic environment adaptation.

Purpose

The purpose of the ai_real_time_learner.py is to:

• Provide a framework for adaptive and incremental learning.
• Handle continuous data streams efficiently.
• Enable fast, real-time predictions and decision-making.
• Minimize latency while updating model parameters on the fly.
• Reduce resource intensity compared to frequent retraining cycles.

Key Features

• Incremental Learning: Continuously updates model weights as new data is ingested.
• Stream Processing: Handles data streams in real-time using libraries such as Kafka or RabbitMQ.
• Custom Model Support: Supports a range of machine learning algorithms, such as online SGD and decision trees.
• Real-Time Feedback Loop: Incorporates feedback from predictions to improve model performance over time.
• Resource Optimization: Uses memory and CPU resources efficiently for real-time operation.

Logic and Implementation

The ai_real_time_learner.py module primarily relies on stream processing frameworks alongside incremental learning algorithms. Below is the skeleton implementation:

            from sklearn.linear_model import SGDClassifier
            import numpy as np
            import threading

            class RealTimeLearner:
                """
                Real-Time Learner for continuous adaptation to streaming data.
                """
                def __init__(self, n_features, loss="log"):
                    """
                    Initializes the learner with an online SGD model.

                    Args:
                        n_features (int): Number of features in the dataset.
                        loss (str): Loss function type, e.g., 'log' for logistic regression.
                    """
                    self.model = SGDClassifier(loss=loss)
                    self.n_features = n_features
                    self.lock = threading.Lock()

                def train_on_batch(self, X_batch, y_batch):
                    """
                    Trains the model on the incoming data batch.

                    Args:
                        X_batch (np.ndarray): Features of the data.
                        y_batch (np.ndarray): Labels of the data.
                    """
                    with self.lock:
                        self.model.partial_fit(X_batch, y_batch, classes=np.unique(y_batch))
                        print("Model updated on new batch.")

                def predict(self, X):
                    """
                    Make predictions on input features.

                    Args:
                        X (np.ndarray): Features for prediction.

                    Returns:
                        np.ndarray: Predicted class labels.
                    """
                    with self.lock:
                        return self.model.predict(X)

            # Example Usage
            if __name__ == "__main__":
                learner = RealTimeLearner(n_features=5, loss="log")

                # Simulate streaming data
                X_stream = np.random.randn(100, 5)  # 100 samples, 5 features
                y_stream = np.random.randint(0, 2, 100)  # Binary classification labels

                # Train on streaming data in batches of 10
                for i in range(0, 100, 10):
                    learner.train_on_batch(X_stream[i:i+10], y_stream[i:i+10])

                # Predict on new sample
                new_sample = np.random.randn(1, 5)
                prediction = learner.predict(new_sample)
                print(f"Prediction for the new sample: {prediction}")
            

Dependencies

• scikit-learn: Provides the online learning implementation (SGDClassifier).
• numpy: For numerical computations and batch handling.
• threading: Ensures thread-safe model updates during real-time data ingestion.

Integration with the G.O.D Framework

This module integrates seamlessly with the following G.O.D components:

• ai_feedback_loop.py: Provides real-time feedback for improving predictions.
• ai_monitoring.py: Tracks model performance on live data streams.
• ai_data_ingestion.py: Supplies continuous batches of streaming data.
• ai_error_tracker.py: Logs errors and discrepancies in real-time predictions.

Usage

To use ai_real_time_learner.py, follow these steps:

            # Run the module standalone or as part of the pipeline:
            python ai_real_time_learner.py

            # Example Output:
            Model updated on new batch.
            Prediction for the new sample: [1]
            

Future Enhancements

Planned future improvements for this module include:

• Integration with distributed stream processing platforms like Kafka, Apache Flink, or Spark Streaming.
• Support for advanced algorithms such as online neural networks (LSTM, Transformer-based learners).
• Visualization tools to observe real-time model updates and predictions.
• Integration with reinforcement learning for dynamic reward-based adaptive learning.