Metadata-Version: 2.1
Name: detect-anomalies-package
Version: 0.0.1
Summary: A Python package for anomaly detection using various techniques.
Home-page: UNKNOWN
Author: Nikhil Mugganawar
Author-email: nikhil.mugganawar@gmail.com
License: MIT
Description: Provides various methods for anomaly detection.
        1. Traditional Statistical Methods:
           - Boxplot Anomaly Detection: Identifies anomalies based on the interquartile range (IQR) method.
           - Gaussian Anomaly Detection: Detects anomalies based on deviations from the mean using standard deviation.
           - Grubbs Test: Identifies outliers using the Grubbs test.
           - Dixon's Q Test: Identifies outliers using Dixon's Q test.
           - Percentile Thresholding: Detects anomalies based on a specified percentile threshold.
        
        2. Histogram-Based Methods:
           - Histogram Anomaly Detection: Detects anomalies based on histogram peaks, indicating multimodal distributions.
           - Histogram-Based Outlier Score: Identifies anomalies based on the density of data points in the histogram.
        
        3. Model-Based Methods:
           - Gaussian Mixture Model: Uses Gaussian mixture models to identify anomalies.
           - Kernel Density Estimation: Estimates the probability density function of the data using kernel density estimation.
           - Exponential Distribution: Detects anomalies based on extreme values using exponential distribution.
        
        4. Nearest Neighbors and Clustering:
           - K Nearest Neighbors (KNN): Identifies anomalies based on distances to nearest neighbors.
           - Local Outlier Factor (LOF): Computes the local density deviation of a given data point with respect to its neighbors.
           - Isolation Forest: Constructs an ensemble of isolation trees for anomaly detection.
           - One-Class SVM: Identifies anomalies by separating data points from the origin in a high-dimensional space.
        
        5. Clustering-Based Methods:
           - KMeans: Uses KMeans clustering to identify anomalies based on centroid distances.
           - DBSCAN: Density-based spatial clustering of applications with noise (DBSCAN) for anomaly detection.
        
        6. Additional Techniques:
           - Z-Score Anomaly Detection: Detects anomalies based on z-scores.
           - Quantile Regression: Identifies anomalies based on quantiles of the data distribution.
           - Anderson-Darling Test: A non-parametric test to assess whether a sample comes from a specified distribution.
        
        
        Sample Usage
        
        import numpy as np
        from detect_anomalies_package.detect_anomalies import DetectAnomalies
        
        # Sample data
        data = np.random.normal(loc=0, scale=1, size=100)
        
        # Initialize DetectAnomalies object
        anomaly_detector = DetectAnomalies(data)
        
        # Detect anomalies using various methods
        anomalies = anomaly_detector.detect_anomalies()
        
        # Print anomalies detected by each method
        for method, result in anomalies.items():
            if result[0] is not None:
                print(f"Anomalies detected by {method}: {result[0]}")
            else:
                print(f"No anomalies detected by {method}: {result[1]}")
Platform: UNKNOWN
Description-Content-Type: text/plain
