G.O.D Framework

Introduction

The ai_retraining.py module is designed to facilitate the process of retraining pre-existing machine learning models within the G.O.D Framework. As models may become stale or out of sync with evolving data patterns, this module provides automation and flexibility to adjust model parameters, integrate new data, and optimize learning pipelines without manual intervention.

Purpose

This script aims to ensure that the AI framework remains adaptable and performant in changing environments by:

• Detecting when models are no longer accurate due to data drift or model degradation.
• Automating the process of retraining models on updated datasets.
• Logging and versioning retraining pipelines for full traceability.
• Ensuring compatibility between new and existing models to prevent disruptions in critical systems.

Key Features

• Data Drift Detection: Continual assessment of incoming data quality and shifts in distribution.
• Incremental Learning: Retraining models incrementally without completely replacing old parameters.
• Scheduled Retraining: Allows for periodic retraining and redeployment of models.
• Versioning: Stores retrained models and related metadata for analysis and rollback.
• Seamless Deployment: Automatically replaces the outdated model with the latest retrained version.

Logic and Implementation

The core functionality revolves around periodic checks on models' performance metrics, retraining the model when necessary, and deploying these updates. It employs libraries like scikit-learn and tensorflow/keras for traditional ML and deep learning tasks. Below is an example implementation:

import os
import logging
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from joblib import dump, load

class ModelRetrainer:
    """
    A class to handle the retraining of ML models upon detecting performance degradation.
    """
    def __init__(self, model_path="models/", retrain_threshold=0.8):
        self.model_path = model_path
        self.retrain_threshold = retrain_threshold

    def load_model(self, model_name):
        """
        Load an existing model from the filesystem.

        Args:
            model_name (str): Name of the model file.

        Returns:
            sklearn model: The stored model object.
        """
        try:
            model = load(os.path.join(self.model_path, model_name))
            logging.info(f"Model {model_name} loaded successfully.")
            return model
        except FileNotFoundError:
            logging.error(f"Model {model_name} not found.")
            return None

    def retrain_model(self, features, labels, model_name="model.joblib"):
        """
        Retrains a model using the provided data and replaces the old version.

        Args:
            features (numpy.ndarray): Feature matrix for training.
            labels (numpy.ndarray): Label array for training.
            model_name (str): Name of the model file to replace.

        Returns:
            str: Status of the retraining process.
        """
        try:
            X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2)
            model = RandomForestClassifier()
            model.fit(X_train, y_train)

            # Evaluate the model
            score = model.score(X_test, y_test)
            logging.info(f"Retrained model accuracy: {score}")
            if score > self.retrain_threshold:
                dump(model, os.path.join(self.model_path, model_name))
                return f"Model retrained successfully with accuracy {score}."
            else:
                return "Model not retrained due to low accuracy."
        except Exception as e:
            logging.error(f"Error during retraining: {e}")
            return str(e)

# Example usage
if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    retrainer = ModelRetrainer()
    # Example data (replace with actual feature and label arrays)
    example_features = [[1, 2], [3, 4], [5, 6], [7, 8]]
    example_labels = [0, 1, 0, 1]
    status = retrainer.retrain_model(example_features, example_labels)
    print(status)
        

Dependencies

• joblib: For saving and loading models efficiently.
• scikit-learn: Machine learning library used for retraining and evaluation tasks.
• logging: Provides seamless logging for debugging and system traceability.

Integration with G.O.D Framework

• ai_model_drift_monitoring.py: Notifies this module when data drift is detected, triggering retraining.
• ai_pipeline_optimizer.py: Updates the pipeline with new preprocessed data for model retraining.
• ai_deployment.py: Manages seamless deployment of retrained models into production.

Future Enhancements

Potential improvements:

• Implement deep learning-based incremental learning for neural networks.
• Integrate with distributed training systems for large-scale data processing.
• Introduce A/B testing during retraining to evaluate performance before full deployment.
• Create a visualization tool for monitoring retraining performance metrics.