README.rst
Python Outlier Detection (PyOD)
===============================
**Deployment & Documentation & Stats & License**
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-----
Read Me First
^^^^^^^^^^^^^
Welcome to PyOD, a versatile Python library for detecting anomalies in multivariate data. Whether you're tackling a small-scale project or large datasets, PyOD offers a range of algorithms to suit your needs.
* **For time-series outlier detection**, please use `TODS <https://github.com/datamllab/tods>`_.
* **For graph outlier detection**, please use `PyGOD <https://pygod.org/>`_.
* **Performance Comparison \& Datasets**: We have a 45-page, the most comprehensive `anomaly detection benchmark paper <https://www.andrew.cmu.edu/user/yuezhao2/papers/22-neurips-adbench.pdf>`_. The fully `open-sourced ADBench <https://github.com/Minqi824/ADBench>`_ compares 30 anomaly detection algorithms on 57 benchmark datasets.
* **Learn more about anomaly detection** \@ `Anomaly Detection Resources <https://github.com/yzhao062/anomaly-detection-resources>`_
* **PyOD on Distributed Systems**: you could also run `PyOD on databricks <https://www.databricks.com/blog/2023/03/13/unsupervised-outlier-detection-databricks.html>`_.
----
About PyOD
^^^^^^^^^^
PyOD, established in 2017, has become a go-to **Python library** for **detecting anomalous/outlying objects** in
multivariate data. This exciting yet challenging field is commonly referred as
`Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_
or `Anomaly Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_.
PyOD includes more than 50 detection algorithms, from classical LOF (SIGMOD 2000) to
the cutting-edge ECOD and DIF (TKDE 2022 and 2023). Since 2017, PyOD has been successfully used in numerous academic researches and
commercial products with more than `17 million downloads <https://pepy.tech/project/pyod>`_.
It is also well acknowledged by the machine learning community with various dedicated posts/tutorials, including
`Analytics Vidhya <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>`_,
`KDnuggets <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>`_, and
`Towards Data Science <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>`_.
**PyOD is featured for**:
* **Unified, User-Friendly Interface** across various algorithms.
* **Wide Range of Models**\, from classic techniques to the latest deep learning methods.
* **High Performance & Efficiency**, leveraging `numba <https://github.com/numba/numba>`_ and `joblib <https://github.com/joblib/joblib>`_ for JIT compilation and parallel processing.
* **Fast Training & Prediction**, achieved through the SUOD framework [#Zhao2021SUOD]_.
**Outlier Detection with 5 Lines of Code**\ :
.. code-block:: python
# Example: Training an ECOD detector
from pyod.models.ecod import ECOD
clf = ECOD()
clf.fit(X_train)
y_train_scores = clf.decision_scores_ # Outlier scores for training data
y_test_scores = clf.decision_function(X_test) # Outlier scores for test data
**Selecting the Right Algorithm:**. Unsure where to start? Consider these robust and interpretable options:
- `ECOD <https://github.com/yzhao062/pyod/blob/master/examples/ecod_example.py>`_: Example of using ECOD for outlier detection
- `Isolation Forest <https://github.com/yzhao062/pyod/blob/master/examples/iforest_example.py>`_: Example of using Isolation Forest for outlier detection
Alternatively, explore `MetaOD <https://github.com/yzhao062/MetaOD>`_ for a data-driven approach.
**Citing PyOD**\ :
`PyOD paper <http://www.jmlr.org/papers/volume20/19-011/19-011.pdf>`_ is published in
`Journal of Machine Learning Research (JMLR) <http://www.jmlr.org/>`_ (MLOSS 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.
For a broader perspective on anomaly detection, see our NeurIPS papers
`ADBench: Anomaly Detection Benchmark Paper <https://viterbi-web.usc.edu/~yzhao010/papers/22-neurips-adbench.pdf>`_ \& `ADGym: Design Choices for Deep Anomaly Detection <https://viterbi-web.usc.edu/~yzhao010/papers/23-neurips-adgym.pdf>`_::
@article{han2022adbench,
title={Adbench: Anomaly detection benchmark},
author={Han, Songqiao and Hu, Xiyang and Huang, Hailiang and Jiang, Minqi and Zhao, Yue},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={32142--32159},
year={2022}
}
@article{jiang2023adgym,
title={ADGym: Design Choices for Deep Anomaly Detection},
author={Jiang, Minqi and Hou, Chaochuan and Zheng, Ao and Han, Songqiao and Huang, Hailiang and Wen, Qingsong and Hu, Xiyang and Zhao, Yue},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2023}
}
**Table of Contents**\ :
* `Installation <#installation>`_
* `API Cheatsheet & Reference <#api-cheatsheet--reference>`_
* `ADBench Benchmark and Datasets <#adbench-benchmark-and-datasets>`_
* `Model Save & Load <#model-save--load>`_
* `Fast Train with SUOD <#fast-train-with-suod>`_
* `Thresholding Outlier Scores <#thresholding-outlier-scores>`_
* `Implemented Algorithms <#implemented-algorithms>`_
* `Quick Start for Outlier Detection <#quick-start-for-outlier-detection>`_
* `How to Contribute <#how-to-contribute>`_
* `Inclusion Criteria <#inclusion-criteria>`_
----
Installation
^^^^^^^^^^^^
PyOD is designed for easy installation using either **pip** or **conda**.
We recommend using the latest version of PyOD due to frequent updates and enhancements:
.. code-block:: bash
pip install pyod # normal install
pip install --upgrade pyod # or update if needed
.. code-block:: bash
conda install -c conda-forge pyod
Alternatively, you could clone and run setup.py file:
.. code-block:: bash
git clone https://github.com/yzhao062/pyod.git
cd pyod
pip install .
**Required Dependencies**\ :
* Python 3.8 or higher
* joblib
* matplotlib
* numpy>=1.19
* numba>=0.51
* scipy>=1.5.1
* scikit_learn>=0.22.0
**Optional Dependencies (see details below)**\ :
* combo (optional, required for models/combination.py and FeatureBagging)
* keras/tensorflow (optional, required for AutoEncoder, and other deep learning models)
* suod (optional, required for running SUOD model)
* xgboost (optional, required for XGBOD)
* pythresh (optional, required for thresholding)optional
----
API Cheatsheet & Reference
^^^^^^^^^^^^^^^^^^^^^^^^^^
The full API Reference is available at `PyOD Documentation <https://pyod.readthedocs.io/en/latest/pyod.html>`_. Below is a quick cheatsheet for all detectors:
* **fit(X)**\ : Fit the detector. The parameter y is ignored in unsupervised methods.
* **decision_function(X)**\ : Predict raw anomaly scores for X using the fitted detector.
* **predict(X)**\ : Determine whether a sample is an outlier or not as binary labels using the fitted detector.
* **predict_proba(X)**\ : Estimate the probability of a sample being an outlier using the fitted detector.
* **predict_confidence(X)**\ : Assess the model's confidence on a per-sample basis (applicable in predict and predict_proba) [#Perini2020Quantifying]_.
**Key Attributes of a fitted model**:
* **decision_scores_**\ : Outlier scores of the training data. Higher scores typically indicate more abnormal behavior. Outliers usually have higher scores.
* **labels_**\ : Binary labels of the training data, where 0 indicates inliers and 1 indicates outliers/anomalies.
----
ADBench Benchmark and Datasets
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We just released a 45-page, the most comprehensive `ADBench: Anomaly Detection Benchmark <https://arxiv.org/abs/2206.09426>`_ [#Han2022ADBench]_.
The fully `open-sourced ADBench <https://github.com/Minqi824/ADBench>`_ compares 30 anomaly detection algorithms on 57 benchmark datasets.
The organization of **ADBench** is provided below:
.. image:: https://github.com/Minqi824/ADBench/blob/main/figs/ADBench.png?raw=true
:target: https://github.com/Minqi824/ADBench/blob/main/figs/ADBench.png?raw=true
:alt: benchmark-fig
For a simpler visualization, we make **the comparison of selected models** via
`compare_all_models.py <https://github.com/yzhao062/pyod/blob/master/examples/compare_all_models.py>`_\.
.. image:: https://github.com/yzhao062/pyod/blob/development/examples/ALL.png?raw=true
:target: https://github.com/yzhao062/pyod/blob/development/examples/ALL.png?raw=true
:alt: Comparison_of_All
----
Model Save & Load
^^^^^^^^^^^^^^^^^
PyOD takes a similar approach of sklearn regarding model persistence.
See `model persistence <https://scikit-learn.org/stable/modules/model_persistence.html>`_ for clarification.
In short, we recommend to use joblib or pickle for saving and loading PyOD models.
See `"examples/save_load_model_example.py" <https://github.com/yzhao062/pyod/blob/master/examples/save_load_model_example.py>`_ for an example.
In short, it is simple as below:
.. code-block:: python
from joblib import dump, load
# save the model
dump(clf, 'clf.joblib')
# load the model
clf = load('clf.joblib')
It is known that there are challenges in saving neural network models.
Check `#328 <https://github.com/yzhao062/pyod/issues/328#issuecomment-917192704>`_
and `#88 <https://github.com/yzhao062/pyod/issues/88#issuecomment-615343139>`_
for temporary workaround.
----
Fast Train with SUOD
^^^^^^^^^^^^^^^^^^^^
**Fast training and prediction**: it is possible to train and predict with
a large number of detection models in PyOD by leveraging SUOD framework [#Zhao2021SUOD]_.
See `SUOD Paper <https://www.andrew.cmu.edu/user/yuezhao2/papers/21-mlsys-suod.pdf>`_
and `SUOD example <https://github.com/yzhao062/pyod/blob/master/examples/suod_example.py>`_.
.. code-block:: python
from pyod.models.suod import SUOD
# initialized a group of outlier detectors for acceleration
detector_list = [LOF(n_neighbors=15), LOF(n_neighbors=20),
LOF(n_neighbors=25), LOF(n_neighbors=35),
COPOD(), IForest(n_estimators=100),
IForest(n_estimators=200)]
# decide the number of parallel process, and the combination method
# then clf can be used as any outlier detection model
clf = SUOD(base_estimators=detector_list, n_jobs=2, combination='average',
verbose=False)
----
Thresholding Outlier Scores
^^^^^^^^^^^^^^^^^^^^^^^^^^^
A more data based approach can be taken when setting the contamination level.
By using a thresholding method, guessing an abritrary value can be replaced
with tested techniques for seperating inliers and outliers. Refer to
`PyThresh <https://github.com/KulikDM/pythresh>`_ for
a more in depth look at thresholding.
.. code-block:: python
from pyod.models.knn import KNN
from pyod.models.thresholds import FILTER
# Set the outlier detection and thresholding methods
clf = KNN(contamination=FILTER())
----
Implemented Algorithms
^^^^^^^^^^^^^^^^^^^^^^
PyOD toolkit consists of four major functional groups:
**(i) Individual Detection Algorithms** :
=================== ================== ====================================================================================================== ===== ========================================
Type Abbr Algorithm Year Ref
=================== ================== ====================================================================================================== ===== ========================================
Probabilistic ECOD Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions 2022 [#Li2021ECOD]_
Probabilistic ABOD Angle-Based Outlier Detection 2008 [#Kriegel2008Angle]_
Probabilistic FastABOD Fast Angle-Based Outlier Detection using approximation 2008 [#Kriegel2008Angle]_
Probabilistic COPOD COPOD: Copula-Based Outlier Detection 2020 [#Li2020COPOD]_
Probabilistic MAD Median Absolute Deviation (MAD) 1993 [#Iglewicz1993How]_
Probabilistic SOS Stochastic Outlier Selection 2012 [#Janssens2012Stochastic]_
Probabilistic QMCD Quasi-Monte Carlo Discrepancy outlier detection 2001 [#Fang2001Wrap]_
Probabilistic KDE Outlier Detection with Kernel Density Functions 2007 [#Latecki2007Outlier]_
Probabilistic Sampling Rapid distance-based outlier detection via sampling 2013 [#Sugiyama2013Rapid]_
Probabilistic GMM Probabilistic Mixture Modeling for Outlier Analysis [#Aggarwal2015Outlier]_ [Ch.2]
Linear Model PCA Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes) 2003 [#Shyu2003A]_
Linear Model KPCA Kernel Principal Component Analysis 2007 [#Hoffmann2007Kernel]_
Linear Model MCD Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores) 1999 [#Hardin2004Outlier]_ [#Rousseeuw1999A]_
Linear Model CD Use Cook's distance for outlier detection 1977 [#Cook1977Detection]_
Linear Model OCSVM One-Class Support Vector Machines 2001 [#Scholkopf2001Estimating]_
Linear Model LMDD Deviation-based Outlier Detection (LMDD) 1996 [#Arning1996A]_
Proximity-Based LOF Local Outlier Factor 2000 [#Breunig2000LOF]_
Proximity-Based COF Connectivity-Based Outlier Factor 2002 [#Tang2002Enhancing]_
Proximity-Based (Incremental) COF Memory Efficient Connectivity-Based Outlier Factor (slower but reduce storage complexity) 2002 [#Tang2002Enhancing]_
Proximity-Based CBLOF Clustering-Based Local Outlier Factor 2003 [#He2003Discovering]_
Proximity-Based LOCI LOCI: Fast outlier detection using the local correlation integral 2003 [#Papadimitriou2003LOCI]_
Proximity-Based HBOS Histogram-based Outlier Score 2012 [#Goldstein2012Histogram]_
Proximity-Based kNN k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score) 2000 [#Ramaswamy2000Efficient]_
Proximity-Based AvgKNN Average kNN (use the average distance to k nearest neighbors as the outlier score) 2002 [#Angiulli2002Fast]_
Proximity-Based MedKNN Median kNN (use the median distance to k nearest neighbors as the outlier score) 2002 [#Angiulli2002Fast]_
Proximity-Based SOD Subspace Outlier Detection 2009 [#Kriegel2009Outlier]_
Proximity-Based ROD Rotation-based Outlier Detection 2020 [#Almardeny2020A]_
Outlier Ensembles IForest Isolation Forest 2008 [#Liu2008Isolation]_
Outlier Ensembles INNE Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles 2018 [#Bandaragoda2018Isolation]_
Outlier Ensembles DIF Deep Isolation Forest for Anomaly Detection 2023 [#Xu2023Deep]_
Outlier Ensembles FB Feature Bagging 2005 [#Lazarevic2005Feature]_
Outlier Ensembles LSCP LSCP: Locally Selective Combination of Parallel Outlier Ensembles 2019 [#Zhao2019LSCP]_
Outlier Ensembles XGBOD Extreme Boosting Based Outlier Detection **(Supervised)** 2018 [#Zhao2018XGBOD]_
Outlier Ensembles LODA Lightweight On-line Detector of Anomalies 2016 [#Pevny2016Loda]_
Outlier Ensembles SUOD SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection **(Acceleration)** 2021 [#Zhao2021SUOD]_
Neural Networks AutoEncoder Fully connected AutoEncoder (use reconstruction error as the outlier score) [#Aggarwal2015Outlier]_ [Ch.3]
Neural Networks VAE Variational AutoEncoder (use reconstruction error as the outlier score) 2013 [#Kingma2013Auto]_
Neural Networks Beta-VAE Variational AutoEncoder (all customized loss term by varying gamma and capacity) 2018 [#Burgess2018Understanding]_
Neural Networks SO_GAAL Single-Objective Generative Adversarial Active Learning 2019 [#Liu2019Generative]_
Neural Networks MO_GAAL Multiple-Objective Generative Adversarial Active Learning 2019 [#Liu2019Generative]_
Neural Networks DeepSVDD Deep One-Class Classification 2018 [#Ruff2018Deep]_
Neural Networks AnoGAN Anomaly Detection with Generative Adversarial Networks 2017 [#Schlegl2017Unsupervised]_
Neural Networks ALAD Adversarially learned anomaly detection 2018 [#Zenati2018Adversarially]_
Graph-based R-Graph Outlier detection by R-graph 2017 [#You2017Provable]_
Graph-based LUNAR LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks 2022 [#Goodge2022Lunar]_
=================== ================== ====================================================================================================== ===== ========================================
**(ii) Outlier Ensembles & Outlier Detector Combination Frameworks**:
=================== ================ ===================================================================================================== ===== ========================================
Type Abbr Algorithm Year Ref
=================== ================ ===================================================================================================== ===== ========================================
Outlier Ensembles FB Feature Bagging 2005 [#Lazarevic2005Feature]_
Outlier Ensembles LSCP LSCP: Locally Selective Combination of Parallel Outlier Ensembles 2019 [#Zhao2019LSCP]_
Outlier Ensembles XGBOD Extreme Boosting Based Outlier Detection **(Supervised)** 2018 [#Zhao2018XGBOD]_
Outlier Ensembles LODA Lightweight On-line Detector of Anomalies 2016 [#Pevny2016Loda]_
Outlier Ensembles SUOD SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection **(Acceleration)** 2021 [#Zhao2021SUOD]_
Outlier Ensembles INNE Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles 2018 [#Bandaragoda2018Isolation]_
Combination Average Simple combination by averaging the scores 2015 [#Aggarwal2015Theoretical]_
Combination Weighted Average Simple combination by averaging the scores with detector weights 2015 [#Aggarwal2015Theoretical]_
Combination Maximization Simple combination by taking the maximum scores 2015 [#Aggarwal2015Theoretical]_
Combination AOM Average of Maximum 2015 [#Aggarwal2015Theoretical]_
Combination MOA Maximization of Average 2015 [#Aggarwal2015Theoretical]_
Combination Median Simple combination by taking the median of the scores 2015 [#Aggarwal2015Theoretical]_
Combination majority Vote Simple combination by taking the majority vote of the labels (weights can be used) 2015 [#Aggarwal2015Theoretical]_
=================== ================ ===================================================================================================== ===== ========================================
**(iii) Outlier Detection Score Thresholding Methods**:
================================== ================ ================================================================ ====================================================================================================================
Type Abbr Algorithm Documentation
================================== ================ ================================================================ ====================================================================================================================
Kernel-Based AUCP Area Under Curve Percentage `AUCP <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.AUCP>`_
Statistical Moment-Based BOOT Bootstrapping `BOOT <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.BOOT>`_
Normality-Based CHAU Chauvenet's Criterion `CHAU <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.CHAU>`_
Linear Model CLF Trained Linear Classifier `CLF <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.CLF>`_
cluster-Based CLUST Clustering Based `CLUST <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.CLUST>`_
Kernel-Based CPD Change Point Detection `CPD <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.CPD>`_
Transformation-Based DECOMP Decomposition `DECOMP <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.DECOMP>`_
Normality-Based DSN Distance Shift from Normal `DSN <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.DSN>`_
Curve-Based EB Elliptical Boundary `EB <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.EB>`_
Kernel-Based FGD Fixed Gradient Descent `FGD <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.FGD>`_
Filter-Based FILTER Filtering Based `FILTER <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.FILTER>`_
Curve-Based FWFM Full Width at Full Minimum `FWFM <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.FWFM>`_
Statistical Test-Based GESD Generalized Extreme Studentized Deviate `GESD <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.GESD>`_
Filter-Based HIST Histogram Based `HIST <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.HIST>`_
Quantile-Based IQR Inter-Quartile Region `IQR <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.IQR>`_
Statistical Moment-Based KARCH Karcher mean (Riemannian Center of Mass) `KARCH <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.KARCH>`_
Statistical Moment-Based MAD Median Absolute Deviation `MAD <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.MAD>`_
Statistical Test-Based MCST Monte Carlo Shapiro Tests `MCST <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.MCST>`_
Ensembles-Based META Meta-model Trained Classifier `META <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.META>`_
Transformation-Based MOLL Friedrichs' Mollifier `MOLL <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.MOLL>`_
Statistical Test-Based MTT Modified Thompson Tau Test `MTT <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.MTT>`_
Linear Model OCSVM One-Class Support Vector Machine `OCSVM <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.OCSVM>`_
Quantile-Based QMCD Quasi-Monte Carlo Discrepancy `QMCD <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.QMCD>`_
Linear Model REGR Regression Based `REGR <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.REGR>`_
Neural Networks VAE Variational Autoencoder `VAE <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.VAE>`_
Curve-Based WIND Topological Winding Number `WIND <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.WIND>`_
Transformation-Based YJ Yeo-Johnson Transformation `YJ <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.YJ>`_
Normality-Based ZSCORE Z-score `ZSCORE <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.ZSCORE>`_
================================== ================ ================================================================ ====================================================================================================================
**(iV) 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 <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.data.generate_data>`_
Data generate_data_clusters Synthesized data generation in clusters; more complex data patterns can be created with multiple clusters `generate_data_clusters <https://pyod.readthedocs.io/en/latest/pyod.utils.html#pyod.utils.data.generate_data_clusters>`_
Stat wpearsonr Calculate the weighted Pearson correlation of two samples `wpearsonr <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.stat_models.wpearsonr>`_
Utility get_label_n Turn raw outlier scores into binary labels by assign 1 to top n outlier scores `get_label_n <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.get_label_n>`_
Utility precision_n_scores calculate precision @ rank n `precision_n_scores <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.precision_n_scores>`_
=================== ====================== ===================================================================================================================================================== ======================================================================================================================================
----
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 <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>`_
**KDnuggets**: `Intuitive Visualization of Outlier Detection Methods <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>`_, `An Overview of Outlier Detection Methods from PyOD <https://www.kdnuggets.com/2019/06/overview-outlier-detection-methods-pyod.html>`_
**Towards Data Science**: `Anomaly Detection for Dummies <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>`_
**Computer Vision News (March 2019)**: `Python Open Source Toolbox for Outlier Detection <https://rsipvision.com/ComputerVisionNews-2019March/18/>`_
`"examples/knn_example.py" <https://github.com/yzhao062/pyod/blob/master/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 <https://github.com/yzhao062/pyod/blob/master/examples>`_.
#. Initialize a kNN detector, fit the model, and make the prediction.
.. code-block:: python
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
# it is possible to get the prediction confidence as well
y_test_pred, y_test_pred_confidence = clf.predict(X_test, return_confidence=True) # outlier labels (0 or 1) and confidence in the range of [0,1]
#. Evaluate the prediction by ROC and Precision @ Rank n (p@n).
.. code-block:: python
from pyod.utils.data import evaluate_print
# 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.
.. code-block:: python
On Training Data:
KNN ROC:1.0, precision @ rank n:1.0
On Test Data:
KNN ROC:0.9989, precision @ rank n:0.9
.. code-block:: python
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 <https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png>`_\ ):
.. image:: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png
:target: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png
:alt: kNN example figure
----
Reference
^^^^^^^^^
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