Metadata-Version: 2.1
Name: pyod
Version: 0.5.5
Summary: A Python Outlier Detection (Anomaly Detection) Toolbox
Home-page: https://github.com/yzhao062/Pyod
Author: Yue Zhao
Author-email: yuezhao@cs.toronto.edu
License: UNKNOWN
Download-URL: https://github.com/yzhao062/Pyod/archive/master.zip
Description: # Python Outlier Detection (PyOD)
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        --------------------------
        
        PyOD is a comprehensive **Python toolkit** to **identify outlying objects** in 
        multivariate data with both unsupervised and supervised approaches. 
        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)*** .
        The toolkit has been successfully used in various academic researches [4, 8] and commercial products.
        Unlike existing libraries, PyOD provides:
        
        - **Unified and consistent APIs** across various anomaly detection algorithms for easy use.
        - **Compatibility with both Python 2 and 3**. All implemented algorithms are also **scikit-learn compatible**.
        - **Advanced functions**, e.g., **Outlier Ensemble Frameworks** to combine multiple detectors.
        - **Detailed API Reference, Interactive Examples in Jupyter Notebooks** for better reliability.
        
        
        **Table of Contents**:
        <!-- TOC -->
        
        - [Quick Introduction](#quick-introduction)
        - [Installation](#installation)
        - [API Cheatsheet & Reference](#api-cheatsheet-reference)
        - [Quick Start for Outlier Detection](#quick-start-for-outlier-detection)
        - [Quick Start for Combining Multiple Outlier Detectors](#quick-start-for-combining-outlier-scores-from-various-base-detectors)
        - [How to Contribute and Collaborate?](#how-to-contribute-and-collaborate)
        - [Benchmark](#benchmark)
        
        <!-- /TOC -->
        
        ------------------------------
        # Key Links & Resources
        
        - **[Documentation & API Reference](https://pyod.readthedocs.io)** [![Documentation Status](https://readthedocs.org/projects/pyod/badge/?version=latest)](https://pyod.readthedocs.io/en/latest/?badge=latest)
        
        - **[Interactive Jupyter Notebooks](https://mybinder.org/v2/gh/yzhao062/Pyod/master/)** [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/yzhao062/Pyod/master)
        
        - **[Github repository with examples](https://github.com/yzhao062/Pyod/tree/master/examples)** | **[Example Documentation](https://pyod.readthedocs.io/en/latest/example.html)**
        
        - **[Anomaly detection resources](https://github.com/yzhao062/anomaly-detection-resources)**, e.g., courses, books, papers and videos.
        
        -----------------------------------
        
        ### Quick Introduction
        
        PyOD toolkit consists of three major groups of functionalities: (i) outlier 
        detection algorithms; (ii) outlier ensemble frameworks and (iii) outlier 
        detection utility functions.
        
        ***Individual Detection Algorithms***:
          1. Linear Models for Outlier Detection:
             1. **PCA: Principal Component Analysis** use the sum of
               weighted projected distances to the eigenvector hyperplane 
               as the outlier outlier scores) [10]
             2. **MCD: Minimum Covariance Determinant** (use the mahalanobis distances 
               as the outlier scores) [11, 12]
             3. **One-Class Support Vector Machines** [3]
             
          2. Proximity-Based Outlier Detection Models:
             1. **LOF: Local Outlier Factor** [1]
             2. **CBLOF: Clustering-Based Local Outlier Factor** [15]
             3. **HBOS: Histogram-based Outlier Score** [5]
             4. **kNN: k Nearest Neighbors** (use the distance to the kth nearest 
             neighbor as the outlier score) [13]
             5. **Average kNN or kNN Sum** Outlier Detection (use the average distance to k 
             nearest neighbors as the outlier score or sum all k distances) [14]
             6. **Median kNN** Outlier Detection (use the median distance to k nearest 
             neighbors as the outlier score)
             
          3. Probabilistic Models for Outlier Detection:
             1. **ABOD: Angle-Based Outlier Detection** [7]
             2. **FastABOD: Fast Angle-Based Outlier Detection using approximation** [7]
          
          4. Outlier Ensembles and Combination Frameworks
             1. **Isolation Forest** [2]
             2. **Feature Bagging** [9]
        
        ***Outlier Detector/Scores Combination Frameworks***:
          1. **Feature Bagging**: build various detectors on random selected features [9]
          2. **Average** & **Weighted Average**: simply combine scores by averaging [6]
          3. **Maximization**: simply combine scores by taking the maximum across all 
          base detectors [6]
          4. **Average of Maximum (AOM)** [6]
          5. **Maximum of Average (MOA)** [6]
          6. **Threshold Sum (Thresh)** [6]
        
        ***Utility Functions for Outlier Detection***:
          1. score_to_lable(): convert raw outlier scores to binary labels
          2. precision_n_scores(): one of the popular evaluation metrics for outlier 
          mining (precision @ rank n)
          3. generate_data(): generate pseudo data for outlier detection experiment
          4. wpearsonr(): weighted pearson is useful in pseudo ground truth generation
          
        **Comparison of all implemented models** are made available below:
         ([Figure](https://github.com/yzhao062/Pyod/blob/master/examples/ALL.png), 
         [Code](https://github.com/yzhao062/Pyod/blob/master/examples/compare_all_models.py),
         [Jupyter Notebooks](https://mybinder.org/v2/gh/yzhao062/Pyod/master)):
         
        For Jupyter Notebooks, please navigate to **"/notebooks/Compare All Models.ipynb"**
         
        ![Comparision_of_All](https://github.com/yzhao062/Pyod/blob/master/examples/ALL.png)
         
        ------------
        
        ### Installation
        
        It is recommended to use **pip** for installation. Please make sure 
        **the latest version** is installed since PyOD is currently updated on **a daily basis**:
        ````cmd
        pip install pyod
        pip install --upgrade pyod # make sure the latest version is installed!
        ````
        Alternatively,install from github directly (**NOT Recommended**)
        
        ````cmd
        git clone https://github.com/yzhao062/pyod.git
        python setup.py install
        ````
        **Required Dependency**: 
        
        - Python 2.7, 3.4, 3.5 or 3.6
        - numpy>=1.13
        - scipy>=0.19.1
        - scikit_learn>=0.19.1
        - matplotlib                      
        - nose                      
        
        **Known Issue**: PyOD depends on matplotlib, which would throw errors in conda 
        virtual environment on mac OS. See reasons and solutions [here](https://github.com/yzhao062/Pyod/issues/6).
        
        ------------
        
        ### API Cheatsheet & Reference
        
        Full API Reference: (https://pyod.readthedocs.io/en/latest/pyod.html). API cheatsheet for all detectors:
        
        - **fit(X)**: Fit detector.
        - **fit_predict(X)**: Fit detector and predict if a particular sample is an outlier or not.
        - **fit_predict_score(X, y)**: Fit, predict and then evaluate with predefined metrics (ROC and precision @ rank n).
        - **decision_function(X)**: Predict anomaly score of X of the base classifiers.
        - **predict(X)**: Predict if a particular sample is an outlier or not. The model must be fitted first.
        - **predict_proba(X)**: Predict the probability of a sample being outlier. The model must be fitted first.
        
        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.
        
        Full package structure can be found below:
        - http://pyod.readthedocs.io/en/latest/genindex.html
        - http://pyod.readthedocs.io/en/latest/py-modindex.html
        
        ------------
        
        ### Quick Start for Outlier Detection
        See **examples directory** for more demos. ["examples/knn_example.py"](https://github.com/yzhao062/Pyod/blob/master/examples/knn_example.py)
        demonstrates the basic APIs of PyOD using kNN detector. **It is noted the APIs for other detectors are similar**. 
        
        More detailed instruction of running examples can be found [here.](https://github.com/yzhao062/Pyod/blob/master/examples)
        1. Initialize a kNN detector, fit the model, and make the prediction.
            ```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
            ```
        2. Evaluate the prediction by ROC and Precision@rank *n* (p@n):
            ```python
            # 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)
            ```
         3. See a sample output & visualization
            ````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
            ````
            ````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://github.com/yzhao062/Pyod/blob/master/examples/KNN.png)):
        ![kNN example figure](https://github.com/yzhao062/Pyod/blob/master/examples/KNN.png)
        
        ---
        ### Quick Start for Combining Outlier Scores from Various Base Detectors
        
        "examples/comb_example.py" illustrates the APIs for combining multiple base detectors 
        ([Code](https://github.com/yzhao062/Pyod/blob/master/examples/comb_example.py),
        [Jupyter Notebooks](https://mybinder.org/v2/gh/yzhao062/Pyod/master)).
        
        For Jupyter Notebooks, please navigate to **"/notebooks/Model Combination.ipynb"**
        
        Given we have *n* individual outlier detectors, each of them generates an individual score for all samples. 
        The task is to combine the outputs from these detectors effectively 
        **Key Step: conducting Z-score normalization on raw scores before the combination.** 
        Four combination mechanisms are shown in this demo:
        
        1. Average: take the average of all base detectors.
        2. maximization : take the maximum score across all detectors as the score.
        3. Average of Maximum (AOM): first randomly split n detectors in to p groups. For each group, use the maximum within the group as the group output. Use the average of all group outputs as the final output.
        4. Maximum of Average (MOA): similarly to AOM, the same grouping is introduced. However, we use the average of a group as the group output, and use maximum of all group outputs as the final output.
        To better understand the merging techniques, refer to [6].
        
        The walkthrough of the code example is provided:
        
        0. Import models and generate sample data
            ````python
            
            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
            ````
            
        1. First initialize 20 kNN outlier detectors with different k (10 to 200), and get the outlier scores:
            ```python
            # 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)
            ```
        2. Then the output codes are standardized into zero mean and unit variance before combination.
            ```python
            from pyod.utils.utility import standardizer
            train_scores_norm, test_scores_norm = standardizer(train_scores, test_scores)
            ```
        3. Then four different combination algorithms are applied as described above:
            ```python
            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
            ```
        4. Finally, all four combination methods are evaluated with ROC and Precision
           @ Rank n:
            ````bash
            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 and Collaborate
        
        You are welcome to contribute to this exciting project, and we are pursuing 
        to publish the toolkit in prestigious academic venues, e.g., 
        [JMLR](http://www.jmlr.org/mloss/) (Track for open-source software).
        
        If you are interested in contributing: 
        
        - Please first check Issue lists for "help wanted" tag and comment the one 
        you are interested
        
        - Fork the repository and add your improvement/modification/fix
        
        - Create a pull request
        
        To make sure the code has the same style and standard, please refer to models,
        such as abod.py, hbos.py or feature bagging for example.
        
        You are also welcome to propose your ideas by open an issue or drop me an email
        at yuezhao@cs.toronto.edu
        
        
        ---
        
        ### Benchmark
        
        To provide an overview and quick guidance of the implemented models, benchmark
        is supplied.
        
        In total, 17 benchmark data are used for comparision, all datasets could be
        downloaded at [ODDS](http://odds.cs.stonybrook.edu/#table1).
        
        For each dataset, it is first split into 60% for training and 40% for testing.
        All experiments are repeated 20 times independently with different samplings.
        The mean of 20 trials are taken as the final result. Three evaluation metrics
        are provided:
        
        - The area under receiver operating characteristic (ROC) curve
        - Precision @ rank n (P@N)
        - Execution time
        
        You are welcome to replicate this process by running
        [benchmark.py](https://github.com/yzhao062/Pyod/blob/master/notebooks/benchmark.py).
        
        #### ROC Performance
        
        TO ADD
        
        #### P@N Performance
        
        TO ADD
        
        #### Execution Time
        
        TO ADD
        
        #### Conclusion
        
        TO ADD
        
        ---
        
        ### Reference
        [1] Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. In *ACM SIGMOD Record*, pp. 93-104. ACM.
        
        [2] Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In *ICDM '08*, pp. 413-422. IEEE.
        
        [3] Ma, J. and Perkins, S., 2003, July. Time-series novelty detection using one-class support vector machines. In *IJCNN' 03*, pp. 1741-1745. IEEE.
        
        [4] Y. Zhao and M.K. Hryniewicki, "DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles," *ACM SIGKDD Workshop on Outlier Detection De-constructed (ODD v5.0)*, 2018.
        
        [5] Goldstein, M. and Dengel, A., 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In *KI-2012: Poster and Demo Track*, pp.59-63.
        
        [6] Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.*ACM SIGKDD Explorations Newsletter*, 17(1), pp.24-47.
        
        [7] Kriegel, H.P. and Zimek, A., 2008, August. Angle-based outlier detection in high-dimensional data. In *KDD '08*, pp. 444-452. ACM.
        
        [8] Y. Zhao and M.K. Hryniewicki, "XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning," *IEEE International Joint Conference on Neural Networks*, 2018.
        
        [9] Lazarevic, A. and Kumar, V., 2005, August. Feature bagging for outlier detection. In *KDD '05*. 2005.
        
        [10] Shyu, M.L., Chen, S.C., Sarinnapakorn, K. and Chang, L., 2003. A novel anomaly detection scheme based on principal component classifier. *MIAMI UNIV CORAL GABLES FL DEPT OF ELECTRICAL AND COMPUTER ENGINEERING*.
        
        [11] Rousseeuw, P.J. and Driessen, K.V., 1999. A fast algorithm for the minimum covariance determinant estimator. *Technometrics*, 41(3), pp.212-223.
        
        [12] Hardin, J. and Rocke, D.M., 2004. Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. *Computational Statistics & Data Analysis*, 44(4), pp.625-638.
        
        [13] Ramaswamy, S., Rastogi, R. and Shim, K., 2000, May. Efficient algorithms for mining outliers from large data sets. *ACM Sigmod Record*, 29(2), pp. 427-438).
        
        [14] Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In *European Conference on Principles of Data Mining and Knowledge Discovery* pp. 15-27.
        
        [15] He, Z., Xu, X. and Deng, S., 2003. Discovering cluster-based local outliers. *Pattern Recognition Letters*, 24(9-10), pp.1641-1650.
        
Keywords: outlier detection,anomaly detection,outlier ensembles,data mining
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
