G.O.D. Framework

Introduction

The ai_data_balancer.py script is responsible for ensuring that datasets are well-balanced before they are fed into machine learning models. Balancing datasets is crucial in rectifying issues caused by class imbalances, such as biased predictions or reduced model performance on minority classes.

Purpose

• Class Balancing: Balance data for binary or multi-class classification problems.
• Bias Mitigation: Reduce overfitting and bias towards majority classes.
• Dataset Resampling: Perform oversampling, undersampling, or hybrid sampling based on use-case requirements.
• Quality Preprocessing: Ensure robust machine learning results by providing preprocessed input.

Key Features

• Oversampling Techniques: Use methods like SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic samples of minority classes.
• Undersampling: Drop a portion of majority class data to make it balanced with the minority class.
• Hybrid Approaches: Combine both oversampling and undersampling for optimal dataset balancing.
• Custom Balancing Rules: Provide the flexibility to define custom balancing strategies based on class proportions.

Logic and Implementation

The ai_data_balancer.py module preprocesses datasets by analyzing class distribution and applying data resampling techniques to ensure balance. The process follows these steps:

1. Analyze dataset and calculate class distributions.
2. Select a balancing method: oversampling, undersampling, or hybrid.
3. Transform the dataset based on the chosen method.
4. Return the balanced dataset for subsequent use in training or validation.

            from imblearn.over_sampling import SMOTE
            from imblearn.under_sampling import RandomUnderSampler
            from imblearn.combine import SMOTEENN
            import numpy as np
            import pandas as pd

            class DataBalancer:
                def __init__(self, method="smote"):
                    """
                    Initialize the dataset balancer with the chosen method.
                    :param method: The resampling method ('smote', 'undersample', 'hybrid').
                    """
                    self.method = method

                def balance_data(self, X, y):
                    """
                    Balances the dataset using the specified method.
                    :param X: Feature matrix (numpy array or pandas DataFrame).
                    :param y: Target vector (numpy array or pandas Series).
                    :return: Balanced feature matrix and target vector.
                    """
                    if self.method == "smote":
                        sampler = SMOTE()
                    elif self.method == "undersample":
                        sampler = RandomUnderSampler()
                    elif self.method == "hybrid":
                        sampler = SMOTEENN()
                    else:
                        raise ValueError("Invalid method. Choose 'smote', 'undersample', or 'hybrid'.")

                    X_resampled, y_resampled = sampler.fit_resample(X, y)
                    return X_resampled, y_resampled

            if __name__ == "__main__":
                # Example dataset
                X = np.array([[i] for i in range(100)])
                y = np.array([0] * 90 + [1] * 10)  # Imbalanced target vector

                # Balance the data
                balancer = DataBalancer(method="smote")
                X_balanced, y_balanced = balancer.balance_data(X, y)

                print("Original class distribution:", dict(pd.Series(y).value_counts()))
                print("Balanced class distribution:", dict(pd.Series(y_balanced).value_counts()))
            

Dependencies

This script relies on the following external Python libraries:

• imblearn: For resampling techniques like SMOTE, RandomUnderSampler, and SMOTEENN.
• scikit-learn: Underlying functionalities for dataset handling and pipeline integration.
• numpy: For numerical operations on datasets.
• pandas: For structured dataset manipulations.

How to Use This Script

1. Install necessary libraries using pip install imbalanced-learn scikit-learn.
2. Load your feature matrix (X) and target vector (y).
3. Select a resampling method: smote, undersample, or hybrid.
4. Run the balance_data method to process the dataset.
5. Use the balanced dataset for model training or analysis.

            # Usage Example
            balancer = DataBalancer(method="hybrid")
            X_balanced, y_balanced = balancer.balance_data(X, y)
            print("Distribution after balancing:", dict(pd.Series(y_balanced).value_counts()))
            

Role in the G.O.D. Framework

• Preprocessing: Prepares balanced datasets for training modules like ai_training_data.py or ai_training_model.py.
• Bias Reduction: Mitigates bias propagated to models monitored by modules such as ai_anomaly_detection.py.
• Pipeline Coordination: Integrates into automated data handling pipelines like ai_automated_data_pipeline.py.

Future Enhancements

• Automated Balancing Mode: Introduce an auto-detection mode to determine the best balancing strategy based on dataset characteristics.
• Class Adaptation: Adjust balancing thresholds dynamically as classes evolve or new ones are introduced.
• Visualization: Include graphs or charts to illustrate pre- and post-distribution states.