PyODDS

Official Website: http://pyodds.com/

PyODDS is an end-to end Python system for outlier detection with database support. PyODDS provides outlier detection algorithms which meet the demands for users in different fields, w/wo data science or machine learning background. PyODDS gives the ability to execute machine learning algorithms in-database without moving data out of the database server or over the network. It also provides access to a wide range of outlier detection algorithms, including statistical analysis and more recent deep learning based approaches.

PyODDS is featured for:

• Full Stack Service which supports operations and maintenances from light-weight SQL based database to back-end machine learning algorithms and makes the throughput speed faster;

• State-of-the-art Anomaly Detection Approaches including Statistical/Machine Learning/Deep Learning models with unified APIs and detailed documentation;

• Powerful Data Analysis Mechanism which supports both static and time-series data analysis with flexible time-slice(sliding-window) segmentation.

The Full API Reference can be found in handbook.

API Demo:

from utils.import_algorithm import algorithm_selection
from utils.utilities import output_performance,connect_server,query_data

# connect to the database
conn,cursor=connect_server(host, user, password)

# query data from specific time range
data = query_data(database_name,table_name,start_time,end_time)

# train the anomaly detection algorithm
clf = algorithm_selection(algorithm_name)
clf.fit(X_train)

# get outlier result and scores
prediction_result = clf.predict(X_test)
outlierness_score = clf.decision_function(test)

#visualize the prediction_result
visualize_distribution(X_test,prediction_result,outlierness_score)

Cite this work

Yuening Li, Daochen Zha, Na Zou, Xia Hu. "PyODDS: An End-to-End Outlier Detection System" (Download)

Biblatex entry:

@article{li2019pyodds,
  author = {Li, Yuening and Zha, Daochen and Zou, Na and Hu, Xia},
  title = {PyODDS: An End-to-End Outlier Detection System},
  year = {2019},
  eprint = {arXiv:1910.02575},
}

Quick Start

python demo.py --ground_truth --visualize_distribution

Results are shown as

connect to TDengine success
Load dataset and table
Loading cost: 0.151061 seconds
Load data successful
Start processing:
100%|████████████████████████████████████| 10/10 [00:00<00:00, 14.02it/s]
==============================
Results in Algorithm dagmm are:
accuracy_score: 0.98
precision_score: 0.99
recall_score: 0.99
f1_score: 0.99
roc_auc_score: 0.99
processing time: 15.330137 seconds
==============================
connection is closed

Installation

To install the package, please use the pip installation as follows:

pip install pyodds
pip install git+git@github.com:datamllab/PyODDS.git

Note: PyODDS is only compatible with Python 3.6 and above.

Required Dependencies

- pandas>=0.25.0
- taos==1.4.15
- tensorflow==2.0.0b1
- numpy>=1.16.4
- seaborn>=0.9.0
- torch>=1.1.0
- luminol==0.4
- tqdm>=4.35.0
- matplotlib>=3.1.1
- scikit_learn>=0.21.3

To compile and package the JDBC driver source code, you should have a Java jdk-8 or higher and Apache Maven 2.7 or higher installed. To install openjdk-8 on Ubuntu:

sudo apt-get install openjdk-8-jdk

To install Apache Maven on Ubuntu:

sudo apt-get install maven

To install the TDengine as the back-end database service, please refer to this instruction.

To enable the Python client APIs for TDengine, please follow this handbook.

To insure the locale in config file is valid:

sudo locale-gen "en_US.UTF-8"
export LC_ALL="en_US.UTF-8"
locale

To start the service after installation, in a terminal, use:

taosd

Implemented Algorithms

Statistical Based Methods

Deep Learning Based Methods

Time Serie Methods

APIs Cheatsheet

The Full API Reference can be found in handbook.

• connect_server(hostname,username,password): Connect to Apache backend TDengine Service.

• query_data(connection,cursor,database_name,table_name,start_time,end_time): Query data from table table_name in database database_name within a given time range.

• algorithm_selection(algorithm_name,contamination): Select an algorithm as detector.

• fit(X): Fit X to detector.

• predict(X): Predict if instance in X is outlier or not.

• decision_function(X): Output the anomaly score of instances in X.

• output_performance(algorithm_name,ground_truth,prediction_result,outlierness_score): Output the prediction result as evaluation matrix in Accuracy, Precision, Recall, F1 Score, ROC-AUC Score, Cost time.

• visualize_distribution(X,prediction_result,outlierness_score): Visualize the detection result with the the data distribution.

• visualize_outlierscore(outlierness_score,prediction_result,contamination) Visualize the detection result with the outlier score.

License

You may use this software under the MIT License.