G.O.D. Framework

Introduction

The ai_data_detection.py script is a pivotal component in the G.O.D. Framework, designed to detect patterns, anomalies, or inconsistencies in datasets. Whether working with structured or unstructured data, this module applies state-of-the-art algorithms to ensure high-quality results and identify problematic trends.

Purpose

• Data Integrity Checks: Ensure data accuracy by detecting errors, duplicates, or gaps.
• Anomaly Detection: Identify unusual patterns or values that could indicate fraud, system faults, or outliers.
• Pattern Recognition: Enhance downstream models by revealing hidden data structures or trends.
• Dataset Validation: Validate input data before pipeline execution to minimize downstream disruptions.

Key Features

• Anomaly Identification: Detect anomalies across various domains, including financial fraud, network intrusions, and biological data.
• Pattern Analysis: Employ clustering and density-based techniques for discovering consistent patterns.
• Automated Thresholding: Dynamically calculate thresholds for anomalies based on statistical measures (e.g., z-scores).
• Customizable Rules: Allow developers to define domain-specific detection rules or algorithms.

Logic and Implementation

This script applies statistical, machine learning, and deep learning methodologies to analyze data streams. The workflow simplifies the following sequence:

1. Dataset Preparation: Load the input dataset (CSV, database, or API input).
2. Feature Analysis: Extract numerical and categorical features for anomaly/pattern detection.
3. Algorithm Selection: Offer pre-set options for detection (e.g., Z-score, Isolation Forest, or DBSCAN).
4. Execution: Apply selected detection algorithms and identify instances outside the normal patterns.
5. Results Interpretation: Provide user-friendly reports and visuals outlining anomalies or recognized patterns.

            from sklearn.ensemble import IsolationForest
            from sklearn.cluster import DBSCAN
            from scipy.stats import zscore
            import numpy as np
            import pandas as pd

            class DataDetector:
                def __init__(self, method="zscore", threshold=3):
                    """
                    Initialize the data detection module with the desired method.
                    :param method: Detection method ('zscore', 'isolation_forest', 'dbscan').
                    :param threshold: Threshold value (applicable for z-score).
                    """
                    self.method = method
                    self.threshold = threshold

                def detect(self, X):
                    """
                    Detect anomalies or patterns in the given dataset.
                    :param X: Feature matrix (numpy array or pandas DataFrame).
                    :return: Anomaly labels or cluster assignments.
                    """
                    if self.method == "zscore":
                        # Compute Z-scores
                        z_scores = np.abs(zscore(X))
                        anomalies = np.where(z_scores > self.threshold, 1, 0)
                        return anomalies

                    elif self.method == "isolation_forest":
                        # Isolation Forest model
                        model = IsolationForest(contamination=0.1)
                        model.fit(X)
                        labels = model.predict(X)  # -1 for anomaly, 1 for normal
                        return labels

                    elif self.method == "dbscan":
                        # DBSCAN clustering
                        model = DBSCAN(eps=1.5, min_samples=5)
                        labels = model.fit_predict(X)
                        return labels

                    else:
                        raise ValueError("Invalid method specified. Use 'zscore', 'isolation_forest', or 'dbscan'.")

            if __name__ == "__main__":
                # Example dataset
                data = np.random.rand(100, 2)  # Randomly generated 2D dataset
                detector = DataDetector(method="zscore", threshold=2.5)
                anomaly_labels = detector.detect(data)

                print("Anomaly Labels:", anomaly_labels)
            

Dependencies

This script depends on the following Python libraries:

• scikit-learn: Provides models like Isolation Forest and DBSCAN for detection.
• scipy: Assists with statistical computations for z-score analysis.
• numpy: Essential for numerical array manipulations.
• pandas (optional): Handles data in tabular formats like CSVs or DataFrames.

How to Use This Script

1. Prepare your feature matrix (X), ensuring it contains numerical features.
2. Create an instance of the DataDetector class with your preferred detection method.
3. Run the detect method to generate anomaly labels or clusters.
4. Interpret the output and use it for downstream processes, such as reporting or corrective actions.

            # Usage Example
            data = pd.read_csv("dataset.csv")  # Load your dataset
            detector = DataDetector(method="isolation_forest")
            results = detector.detect(data)
            print("Detection Results:", results)
            

Role in the G.O.D. Framework

• Preprocessing: Liaises with ai_data_preparation.py to refine datasets for anomaly detection.
• Alerting: Works with ai_alerting.py to trigger notifications for detected anomalies.
• Monitoring: Assists modules like ai_data_monitoring_reporting.py by supplying insights into irregular data trends.

Future Enhancements

• Deep Learning Integration: Leverage autoencoders or GAN-based techniques for anomaly detection.
• Real-Time Detection: Incorporate streaming compatibility for real-time datasets.
• Visualization: Create interactive dashboards for anomaly visualizations.