G.O.D Framework

Introduction

The ai_model_drift_monitoring.py script is an integral component of the G.O.D Framework's monitoring system. Its primary goal is to detect, quantify, and respond to instances of model drift. Model drift occurs when the behavior of a predictive model changes over time due to shifting data distributions, resulting in degraded performance.

This module ensures that models maintain accuracy and reliability in real-world use cases by providing tools for consistent monitoring and automated alerts when drift is detected.

Purpose

The primary objectives of the ai_model_drift_monitoring.py script include:

• Detecting data distribution changes and concept drift in deployed models.
• Maintaining a robust predictive system by alerting stakeholders to critical drift events.
• Providing visual insights into model performance degradation over time.
• Streamlining the retraining process by pinpointing the cause and severity of drift.

Key Features

1. Drift Detection: Implements statistical tests like KS Test or Chi-Square Test to detect data drift.
2. Performance Degradation Monitoring: Tracks model accuracy, precision, recall, and other metrics over time.
3. Visualization Support: Provides graphical insights into metric trends and drift events.
4. Notification System: Sends alerts when drift thresholds are breached.
5. Integration Ready: Works seamlessly with data pipelines and model retraining workflows.

Logic and Implementation

The module collects and compares incoming data distributions against historical baselines to identify drift (distributional or concept). Based on threshold breaches, it triggers alerts and logs incidents. Developers can integrate it with CI/CD pipelines for model retraining.

            import numpy as np
            from scipy.stats import ks_2samp
            import logging

            class ModelDriftMonitor:
                """
                AI Model Drift Monitoring class for detecting and responding to performance drift.
                """

                def __init__(self, baseline_data, threshold=0.05):
                    self.baseline_data = baseline_data  # Historical data as a baseline
                    self.threshold = threshold          # Drift detection threshold
                    self.logger = logging.getLogger("ModelDriftMonitor")
                    self.logger.setLevel(logging.INFO)

                def ks_test(self, new_data):
                    """
                    Perform KS Test to detect drift in new data compared to baseline data.
                    """
                    test_results = []
                    for column in new_data.columns:
                        stat, p_value = ks_2samp(new_data[column], self.baseline_data[column])
                        test_results.append((column, p_value))
                        self.logger.info(f"Column: {column}, P-Value: {p_value}")

                    return test_results

                def check_drift(self, new_data):
                    """
                    Check for drift in new data using KS Test.
                    """
                    drifted_columns = []
                    results = self.ks_test(new_data)
                    for column, p_value in results:
                        if p_value < self.threshold:
                            drifted_columns.append(column)

                    if drifted_columns:
                        self.logger.warning(f"Drift detected in columns: {drifted_columns}")
                    else:
                        self.logger.info("No drift detected.")

                    return drifted_columns

            # Example Usage
            if __name__ == "__main__":
                import pandas as pd

                # Mock data (baseline and new)
                baseline_data = pd.DataFrame({
                    "feature_1": np.random.normal(0, 1, 100),
                    "feature_2": np.random.normal(5, 1, 100)
                })

                new_data = pd.DataFrame({
                    "feature_1": np.random.normal(0.5, 1.2, 100),
                    "feature_2": np.random.normal(5, 1, 100)
                })

                monitor = ModelDriftMonitor(baseline_data)
                drift_columns = monitor.check_drift(new_data)
                print(f"Columns with Drift: {drift_columns}")
            

Dependencies

• numpy: For numerical operations and random data generation for baseline/new data.
• scipy.stats: Provides KS Test for detecting statistical differences in distributions.
• logging: Enables logging of drift events and results.
• pandas: Used to structure and manage data for drift monitoring.

Usage

To use the ai_model_drift_monitoring.py module:

1. Initialize the ModelDriftMonitor class with baseline data.
2. Use the check_drift() method with new data to identify drift.

Example:

            from ai_model_drift_monitoring import ModelDriftMonitor

            baseline_data = ...
            new_data = ...

            monitor = ModelDriftMonitor(baseline_data)
            drift_columns = monitor.check_drift(new_data)
            print(f"Drift detected in the following columns: {drift_columns}")
            

Future Enhancements

• Integrate advanced statistical tests such as Chi-Square or Jensen-Shannon Divergence.
• Add automatic retraining triggers when drift thresholds are exceeded.
• Expand logging to include graphical reports of drift over time.
• Introduce drift severity quantification for prioritized optimization.