autoanomalydetection 0.1.0

The First Fully Unified, Automated, and Scalable Python Library for Intelligent Anomaly Detection

🔍 AutoAnomalyDetection

The First Fully Unified, Automated & Scalable Python Library for Intelligent Anomaly Detection

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🚀 Overview

AutoAnomalyDetection is the world's first end-to-end, unified anomaly detection library that:

• Automatically determines detection mode (supervised, semi-supervised, unsupervised)
• Orchestrates ensembles of multiple algorithms
• Handles missing values intelligently
• Produces severity-based anomaly scoring
• Generates context-aware visualizations
• Provides a simple and clean Python API

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✨ Key Features

🎯 Automatic Mode Selection

• Detects if data is supervised, semi-supervised, or unsupervised
• Seamlessly switches modes without user intervention

🤖 Multi-Algorithm Ensemble

Supports a wide range of methods:

Category	Algorithms
Statistical	Z-Score, IQR, Gaussian Deviation
Distance-Based	Euclidean, Mahalanobis, Cosine, DTW
Proximity-Based	KNN, LOF, Isolation Forest
Clustering	KMeans, DBSCAN
Deep Learning	Autoencoders, LSTM, VAEs
Supervised	Random Forest, XGBoost, SVM

🧠 Intelligent Data Handling

• Smart missing value detection & imputation
• Type inference for mixed numerical/categorical data
• Scales to millions of rows with optimized performance

📊 Advanced Visualization

• Context-aware anomaly plots
• Auto-zooming on abnormal regions
• Multi-panel layouts
• Downsampling for large datasets

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🛠️ Installation

Basic Install

pip install autoanomalydetection

With Optional Deep Learning Dependencies

pip install autoanomalydetection[deeplearning]

Install From Source

git clone https://github.com/atharvjadhav1112/AutoAnomalyDetection.git
cd AutoAnomalyDetection
pip install -e .

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📖 Quick Start

🔥 Basic Usage (3 Lines!)

import numpy as np
from autoanomalydetection import AutoAnomalyDetector

data = np.random.randn(1000, 5)

detector = AutoAnomalyDetector()
results = detector.fit_detect(data)

anomalies = detector.get_anomalies()
print(f"Detected {len(anomalies)} anomalies")

detector.visualize_results('overview')

🎯 Supervised Detection

import pandas as pd
from autoanomalydetection import AutoAnomalyDetector

df = pd.DataFrame(np.random.randn(1000, 4))
df['is_anomaly'] = np.random.choice([0, 1], 1000, p=[0.95, 0.05])

detector = AutoAnomalyDetector()
results = detector.fit_detect(df, target='is_anomaly')

print(f"Performance: {results['performance']}")

⚙️ Custom Configuration

config = {
    'contamination': 0.05,
    'n_models': 3,
    'severity_levels': 5,
    'use_deep_learning': False,
    'ensemble_method': 'voting'
}

detector = AutoAnomalyDetector(config)
results = detector.fit_detect(data)

📈 Real-World Example (IoT Sensors)

import pandas as pd
import numpy as np
from autoanomalydetection import AutoAnomalyDetector

np.random.seed(42)

time_index = pd.date_range('2023-01-01', periods=1000, freq='H')
sensor_data = pd.DataFrame({
    'timestamp': time_index,
    'temperature': np.random.normal(25, 5, 1000),
    'pressure': np.random.normal(100, 10, 1000),
    'vibration': np.random.normal(5, 2, 1000)
})

sensor_data.loc[100:105, 'temperature'] = 50
sensor_data.loc[500:502, 'pressure'] = 200

detector = AutoAnomalyDetector({
    'contamination': 0.01,
    'n_models': 4,
    'severity_levels': 3
})

results = detector.fit_detect(sensor_data[['temperature','pressure','vibration']])

print(f"Detected {results['summary']['total_anomalies']} sensor anomalies")

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🏗️ Architecture

autoanomalydetection/
├── core/
│   ├── data_handler.py
│   ├── mode_selector.py
│   ├── model_manager.py
│   ├── scorer.py
│   └── visualizer.py
├── models/
│   ├── supervised.py
│   ├── semi_supervised.py
│   ├── unsupervised.py
│   ├── distance_based.py
│   └── deep_learning.py
└── utils/
    ├── config.py
    ├── metrics.py
    └── logger.py

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📊 Supported Data Types

Type	Description	Examples
Tabular	Feature matrices	Fraud, QC
Time Series	Sequential data	IoT, network logs
High-Dimensional	Sparse/dense data	Genomics, images
Mixed-Type	Numeric + categorical	Healthcare, CRM
Large-Scale	Millions+ rows	Finance, IoT

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🎯 Use Cases

🔒 Cybersecurity

detector = AutoAnomalyDetector({
    'contamination': 0.001,
    'n_models': 5,
    'severity_levels': 4
})

💰 Finance

detector = AutoAnomalyDetector({
    'contamination': 0.01,
    'use_deep_learning': True,
    'ensemble_method': 'stacking'
})

🏭 IoT & Manufacturing

detector = AutoAnomalyDetector({
    'contamination': 0.05,
    'n_models': 3,
    'severity_levels': ৩
})

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⚡ Performance

Metric	Value	Description
Max Dataset Size	10M+ rows	Large-scale datasets
Processing Speed	1M rows/min	Optimized engine
Memory Usage	Smart chunking	Memory-safe
Parallelization	Multi-core	Faster runtime

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🔬 Advanced Usage

Custom Model Selection

custom_methods = ['isolation_forest','local_outlier_factor','autoencoder']
detector = AutoAnomalyDetector()
results = detector.fit_detect(data, custom_methods=custom_methods)

Results Analysis

anomalies = detector.get_anomalies(threshold=0.95)
report = detector.get_detection_report()
detector.save_results('detection_results.pkl')

Visualization Options

fig1 = detector.visualize_results('overview')
fig2 = detector.visualize_results('severity_distribution')
fig3 = detector.visualize_results('feature_importance')

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📋 Comparison with Existing Libraries

Feature	PyOD	ADTK	Anomalib	AutoAnomalyDetection
Unified supervised + unsupervised	❌	❌	❌	✅
Automatic mode selection	❌	❌	❌	✅
Multi-Algorithm Ensemble	Partial	Partial	❌	✅
Missing value intelligence	❌	Partial	❌	✅
Context-aware visualization	❌	Partial	❌	✅
Large-scale production ready	Partial	Partial	❌	✅

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🤝 Contributing

We welcome PRs! Clone and install:

git clone https://github.com/atharvjadhav1112/AutoAnomalyDetection.git
cd AutoAnomalyDetection
pip install -e .
pip install -r requirements-dev.txt

Running Tests

pytest tests/
pytest --cov=autoanomalydetection tests/
pytest tests/test_core.py -v

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📜 License

MIT License — see LICENSE file.

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🙏 Acknowledgments

Built on scikit-learn, NumPy, Pandas

Inspired by PyOD, ADTK, Anomalib

Thanks to the open-source community ❤️

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🔮 Roadmap

• AutoFeatureSelector
• Hyperparameter AutoML
• Streaming anomaly detection
• Distributed support (Dask/Spark)
• Streamlit/Dash dashboard
• Transformer-based anomaly detectors
• Anomaly explanation module
• Automated benchmarking

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📊 Citation

@software{autonanomalydetection2024,
  title={AutoAnomalyDetection: Unified Automated Anomaly Detection Library},
  author={Jadhav, Atharv},
  year={2024},
  url={https://github.com/atharvjadhav1112/AutoAnomalyDetection}
}

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⭐ If you find AutoAnomalyDetection useful, please give it a star!

Making anomaly detection accessible, automated, and accurate for everyone. 🚀