Python Outlier Detection (PyOD)

Deployment & Documentation & Stats

Build Status & Coverage & Maintainability & License

PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as Outlier Detection or Anomaly Detection. Since 2017, PyOD has been successfully used in various academic researches and commercial products¹ ² ³ ⁴. It is also well acknowledged by the machine learning community with various dedicated posts/tutorials, including Analytics Vidhya, KDnuggets, Towards Data Science, Computer Vision News, and awesome-machine-learning.

PyOD is featured for:

Unified APIs, detailed documentation, and interactive examples across various algorithms.
Advanced models, including Neural Networks/Deep Learning and Outlier Ensembles.
Optimized performance with JIT and parallelization when possible, using numba and joblib.
Compatible with both Python 2 & 3.

Note on Python 2.7: The maintenance of Python 2.7 will be stopped by January 1, 2020 (see official announcement). To be consistent with the Python change and PyOD's dependent libraries, e.g., scikit-learn, we will stop supporting Python 2.7 in the near future (dates are still to be decided). We encourage you to use Python 3.5 or newer for the latest functions and bug fixes. More information can be found at Moving to require Python 3.

API Demo:

# train the KNN detector
from pyod.models.knn import KNN
clf = KNN()
clf.fit(X_train)

# get outlier scores
y_train_scores = clf.decision_scores_  # raw outlier scores
y_test_scores = clf.decision_function(X_test)  # outlier scores

Citing PyOD:

PyOD paper is published in JMLR (machine learning open-source software track). If you use PyOD in a scientific publication, we would appreciate citations to the following paper:

@article{zhao2019pyod,
  author  = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
  title   = {PyOD: A Python Toolbox for Scalable Outlier Detection},
  journal = {Journal of Machine Learning Research},
  year    = {2019},
  volume  = {20},
  number  = {96},
  pages   = {1-7},
  url     = {http://jmlr.org/papers/v20/19-011.html}
}

or:

Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.

Key Links and Resources:

Table of Contents:

Installation
API Cheatsheet & Reference
Implemented Algorithms
Algorithm Benchmark
Quick Start for Outlier Detection
Quick Start for Combining Outlier Scores from Various Base Detectors
How to Contribute
Inclusion Criteria

Installation

It is recommended to use pip for installation. Please make sure the latest version is installed, as PyOD is updated frequently:

pip install pyod            # normal install
pip install --upgrade pyod  # or update if needed
pip install --pre pyod      # or include pre-release version for new features

Alternatively, you could clone and run setup.py file:

git clone https://github.com/yzhao062/pyod.git
cd pyod
pip install .

Note on Python 2.7: The maintenance of Python 2.7 will be stopped by January 1, 2020 (see official announcement) To be consistent with the Python change and PyOD's dependent libraries, e.g., scikit-learn, we will stop supporting Python 2.7 in the near future (dates are still to be decided). We encourage you to use Python 3.5 or newer for the latest functions and bug fixes. More information can be found at Moving to require Python 3.

Required Dependencies:

Python 2.7, 3.5, 3.6, or 3.7
combo>=0.0.8
numpy>=1.13
numba>=0.35
scipy>=0.19.1
scikit_learn>=0.19.1

Optional Dependencies (see details below):

keras (optional, required for AutoEncoder)
matplotlib (optional, required for running examples)
pandas (optional, required for running benchmark)
tensorflow (optional, required for AutoEncoder, other backend works)
xgboost (optional, required for XGBOD)

Warning 1: PyOD has multiple neural network based models, e.g., AutoEncoders, which are implemented in Keras. However, PyOD does NOT install keras and/or tensorflow for you. This reduces the risk of interfering with your local copies. If you want to use neural-net based models, please make sure Keras and a backend library, e.g., TensorFlow, are installed. Instructions are provided: neural-net FAQ. Similarly, models depending on xgboost, e.g., XGBOD, would NOT enforce xgboost installation by default.

Warning 2: Running examples needs matplotlib, which may throw errors in conda virtual environment on mac OS. See reasons and solutions mac_matplotlib.

Warning 3: PyOD contains multiple models that also exist in scikit-learn. However, these two libraries' API is not exactly the same--it is recommended to use only one of them for consistency but not mix the results. Refer Differences between sckit-learn and PyOD for more information.

API Cheatsheet & Reference

Full API Reference: (https://pyod.readthedocs.io/en/latest/pyod.html). API cheatsheet for all detectors:

fit(X): Fit detector.
decision_function(X): Predict raw anomaly score of X using the fitted detector.
predict(X): Predict if a particular sample is an outlier or not using the fitted detector.
predict_proba(X): Predict the probability of a sample being outlier using the fitted detector.
fit_predict(X): [Deprecated in V0.6.9] Fit detector first and then predict whether a particular sample is an outlier or not.
fit_predict_score(X, y): [Deprecated in V0.6.9] Fit the detector, predict on samples, and evaluate the model by predefined metrics, e.g., ROC.

Key Attributes of a fitted model:

decision_scores_: The outlier scores of the training data. The higher, the more abnormal. Outliers tend to have higher scores.
labels_: The binary labels of the training data. 0 stands for inliers and 1 for outliers/anomalies.

Note : fit_predict() and fit_predict_score() are deprecated in V0.6.9 due to consistency issue and will be removed in V0.8.0. To get the binary labels of the training data X_train, one should call clf.fit(X_train) and use clf.labels_, instead of calling clf.predict(X_train).

Implemented Algorithms

PyOD toolkit consists of three major functional groups:

(i) Individual Detection Algorithms :

Type	Abbr	Algorithm	Year	Ref
Linear Model	PCA	Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes)	2003	⁵
Linear Model	MCD	Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores)	1999	⁶ ⁷
Linear Model	OCSVM	One-Class Support Vector Machines	2001	⁸
Linear Model	LMDD	Deviation-based Outlier Detection (LMDD)	1996	⁹
Proximity-Based	LOF	Local Outlier Factor	2000	¹⁰
Proximity-Based	COF	Connectivity-Based Outlier Factor	2002	¹¹
Proximity-Based	CBLOF	Clustering-Based Local Outlier Factor	2003	¹²
Proximity-Based	LOCI	LOCI: Fast outlier detection using the local correlation integral	2003	¹³
Proximity-Based	HBOS	Histogram-based Outlier Score	2012	¹⁴
Proximity-Based	kNN	k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score)	2000	¹⁵
Proximity-Based	AvgKNN	Average kNN (use the average distance to k nearest neighbors as the outlier score)	2002	¹⁶
Proximity-Based	MedKNN	Median kNN (use the median distance to k nearest neighbors as the outlier score)	2002	¹⁷
Proximity-Based	SOD	Subspace Outlier Detection	2009	¹⁸
Probabilistic	ABOD	Angle-Based Outlier Detection	2008	¹⁹
Probabilistic	FastABOD	Fast Angle-Based Outlier Detection using approximation	2008	²⁰
Probabilistic	SOS	Stochastic Outlier Selection	2012	²¹
Outlier Ensembles Outlier Ensembles	IForest	Isolation Forest Feature Bagging	2008 2005	²² ²³
Outlier Ensembles	LSCP	LSCP: Locally Selective Combination of Parallel Outlier Ensembles	2019	²⁴
Outlier Ensembles Neural Networks	XGBOD AutoEncoder	Extreme Boosting Based Outlier Detection (Supervised) Fully connected AutoEncoder (use reconstruction error as the outlier score)	2018	²⁵ ²⁶ [Ch.3]
Neural Networks	SO_GAAL	Single-Objective Generative Adversarial Active Learning	2019	²⁷
Neural Networks	MO_GAAL	Multiple-Objective Generative Adversarial Active Learning	2019	²⁸

(ii) Outlier Ensembles & Outlier Detector Combination Frameworks:

Type	Abbr	Algorithm	Year	Ref
Outlier Ensembles		Feature Bagging	2005	²⁹
Outlier Ensembles	LSCP	LSCP: Locally Selective Combination of Parallel Outlier Ensembles	2019	³⁰
Combination	Average	Simple combination by averaging the scores	2015	³¹
Combination	Weighted Average	Simple combination by averaging the scores with detector weights	2015	³²
Combination	Maximization	Simple combination by taking the maximum scores	2015	³³
Combination	AOM	Average of Maximum	2015	³⁴
Combination	MOA	Maximization of Average	2015	³⁵
Combination	Median	Simple combination by taking the median of the scores	2015	³⁶
Combination	majority Vote	Simple combination by taking the majority vote of the labels (weights can be used)	2015	³⁷

(iii) Utility Functions:

Type	Name	Function	Documentation
Data	generate_data	Synthesized data generation; normal data is generated by a multivariate Gaussian and outliers are generated by a uniform distribution	generate_data
Data	generate_data_clusters	Synthesized data generation in clusters; more complex data patterns can be created with multiple clusters	generate_data_clusters
Stat	wpearsonr	Calculate the weighted Pearson correlation of two samples	wpearsonr
Utility	get_label_n	Turn raw outlier scores into binary labels by assign 1 to top n outlier scores	get_label_n
Utility	precision_n_scores	calculate precision @ rank n	precision_n_scores

Algorithm Benchmark

The comparison among of implemented models is made available below (Figure, compare_all_models.py, Interactive Jupyter Notebooks). For Jupyter Notebooks, please navigate to "/notebooks/Compare All Models.ipynb".

A benchmark is supplied for select algorithms to provide an overview of the implemented models. In total, 17 benchmark datasets are used for comparison, which can be downloaded at ODDS.

For each dataset, it is first split into 60% for training and 40% for testing. All experiments are repeated 10 times independently with random splits. The mean of 10 trials is regarded as the final result. Three evaluation metrics are provided:

The area under receiver operating characteristic (ROC) curve
Precision @ rank n (P@N)
Execution time

Check the latest benchmark. You could replicate this process by running benchmark.py.

Quick Start for Outlier Detection

PyOD has been well acknowledged by the machine learning community with a few featured posts and tutorials.

Analytics Vidhya: An Awesome Tutorial to Learn Outlier Detection in Python using PyOD Library

KDnuggets: Intuitive Visualization of Outlier Detection Methods, An Overview of Outlier Detection Methods from PyOD

Towards Data Science: Anomaly Detection for Dummies

Computer Vision News (March 2019): Python Open Source Toolbox for Outlier Detection

"examples/knn_example.py" demonstrates the basic API of using kNN detector. It is noted that the API across all other algorithms are consistent/similar.

More detailed instructions for running examples can be found in examples directory.

Initialize a kNN detector, fit the model, and make the prediction.

from pyod.models.knn import KNN   # kNN detector

# train kNN detector
clf_name = 'KNN'
clf = KNN()
clf.fit(X_train)

# get the prediction label and outlier scores of the training data
y_train_pred = clf.labels_  # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_  # raw outlier scores

# get the prediction on the test data
y_test_pred = clf.predict(X_test)  # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test)  # outlier scores

Evaluate the prediction by ROC and Precision @ Rank n (p@n).

# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)

See a sample output & visualization.

On Training Data:
KNN ROC:1.0, precision @ rank n:1.0

On Test Data:
KNN ROC:0.9989, precision @ rank n:0.9

visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
    y_test_pred, show_figure=True, save_figure=False)

Visualization (knn_figure):

Quick Start for Combining Outlier Scores from Various Base Detectors

Outlier detection often suffers from model instability due to its unsupervised nature. Thus, it is recommended to combine various detector outputs, e.g., by averaging, to improve its robustness. Detector combination is a subfield of outlier ensembles; refer³⁸ for more information.

Four score combination mechanisms are shown in this demo:

Average: average scores of all detectors.
maximization: maximum score across all detectors.
Average of Maximum (AOM): divide base detectors into subgroups and take the maximum score for each subgroup. The final score is the average of all subgroup scores.
Maximum of Average (MOA): divide base detectors into subgroups and take the average score for each subgroup. The final score is the maximum of all subgroup scores.

"examples/comb_example.py" illustrates the API for combining the output of multiple base detectors (comb_example.py, Jupyter Notebooks). For Jupyter Notebooks, please navigate to "/notebooks/Model Combination.ipynb"

Import models and generate sample data.

from pyod.models.knn import KNN
from pyod.models.combination import aom, moa, average, maximization
from pyod.utils.data import generate_data

X, y = generate_data(train_only=True)  # load data

First initialize 20 kNN outlier detectors with different k (10 to 200), and get the outlier scores.

# initialize 20 base detectors for combination
k_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,
            150, 160, 170, 180, 190, 200]

train_scores = np.zeros([X_train.shape[0], n_clf])
test_scores = np.zeros([X_test.shape[0], n_clf])

for i in range(n_clf):
    k = k_list[i]

    clf = KNN(n_neighbors=k, method='largest')
    clf.fit(X_train_norm)

    train_scores[:, i] = clf.decision_scores_
    test_scores[:, i] = clf.decision_function(X_test_norm)

Then the output scores are standardized into zero mean and unit variance before combination. This step is crucial to adjust the detector outputs to the same scale.
```
from pyod.utils.utility import standardizer
train_scores_norm, test_scores_norm = standardizer(train_scores, test_scores)
```

Then four different combination algorithms are applied as described above.

comb_by_average = average(test_scores_norm)
comb_by_maximization = maximization(test_scores_norm)
comb_by_aom = aom(test_scores_norm, 5) # 5 groups
comb_by_moa = moa(test_scores_norm, 5)) # 5 groups

Finally, all four combination methods are evaluated with ROC and Precision @ Rank n.

Combining 20 kNN detectors
Combination by Average ROC:0.9194, precision @ rank n:0.4531
Combination by Maximization ROC:0.9198, precision @ rank n:0.4688
Combination by AOM ROC:0.9257, precision @ rank n:0.4844
Combination by MOA ROC:0.9263, precision @ rank n:0.4688

How to Contribute

You are welcome to contribute to this exciting project:

Please first check Issue lists for "help wanted" tag and comment the one you are interested. We will assign the issue to you.
Fork the master branch and add your improvement/modification/fix.
Create a pull request to development branch and follow the pull request template PR template
Automatic tests will be triggered. Make sure all tests are passed. Please make sure all added modules are accompanied with proper test functions.

To make sure the code has the same style and standard, please refer to abod.py, hbos.py, or feature_bagging.py for example.

You are also welcome to share your ideas by opening an issue or dropping me an email at zhaoy@cmu.edu :)

Inclusion Criteria

Similarly to scikit-learn, We mainly consider well-established algorithms for inclusion. A rule of thumb is at least two years since publication, 50+ citations, and usefulness.

However, we encourage the author(s) of newly proposed models to share and add your implementation into PyOD for boosting ML accessibility and reproducibility. This exception only applies if you could commit to the maintenance of your model for at least two year period.

Reference

Gopalan, P., Sharan, V. and Wieder, U., 2019. PIDForest: Anomaly Detection via Partial Identification. In Advances in Neural Information Processing Systems, pp. 15783-15793.↩
Li, D., Chen, D., Jin, B., Shi, L., Goh, J. and Ng, S.K., 2019, September. MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In International Conference on Artificial Neural Networks (pp. 703-716). Springer, Cham.↩
Wang, X., Du, Y., Lin, S., Cui, P., Shen, Y. and Yang, Y., 2019. adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection. Knowledge-Based Systems.↩
Zhao, Y., Nasrullah, Z., Hryniewicki, M.K. and Li, Z., 2019, May. LSCP: Locally selective combination in parallel outlier ensembles. In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), pp. 585-593. Society for Industrial and Applied Mathematics.↩
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Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.ACM SIGKDD Explorations Newsletter, 17(1), pp.24-47.↩
Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.ACM SIGKDD Explorations Newsletter, 17(1), pp.24-47.↩
Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.ACM SIGKDD Explorations Newsletter, 17(1), pp.24-47.↩
Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.ACM SIGKDD Explorations Newsletter, 17(1), pp.24-47.↩
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yuliusd / pyod