kenchi 0.9.0

A set of python modules for anomaly detection

kenchi

This is a set of python modules for anomaly detection.

Dependencies

• Python (>=3.6)

• matplotlib (>=2.1.1)

• networkx (>=2.0)

• numpy (>=1.14.0)

• scikit-learn (>=0.19.1)

• scipy (>=1.0.0)

Installation

You can install via pip

pip install kenchi

or conda.

conda install -c y_ohr_n kenchi

Anomaly detection methods

1. FastABOD [6]

2. MiniBatchKMeans

3. KNN [1], [8]

4. OneTimeSampling [9]

5. LOF [2]

6. IForest [7]

7. PCA

8. GMM

9. HBOS [3]

10. KDE

11. SparseStructureLearning [4]

Examples

import matplotlib.pyplot as plt
from kenchi.datasets import load_wdbc
from kenchi.outlier_detection import *

# Load the breast cancer wisconsin dataset
X, y      = load_wdbc(random_state=0)

f, ax     = plt.subplots()
detectors = [
    FastABOD(),
    MiniBatchKMeans(random_state=0),
    LOF(),
    KNN(),
    IForest(random_state=0),
    PCA(),
    KDE()
]

for det in detectors:
    # Fit the model, and plot the ROC curve
    det.fit(X).plot_roc_curve(X=None, y=y, ax=ax)

plt.show()

License

BSD 3-Clause “New” or “Revised” License

Copyright (c) 2018, Kon

References

[1]

Angiulli, F., and Pizzuti, C., “Fast outlier detection in high dimensional spaces,” In Proceedings of PKDD’02, pp. 15-27, 2002.

[2]

Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J., “LOF: identifying density-based local outliers,” In ACM sigmod record, pp. 93-104, 2000.

[3]

Goldstein, M., and Dengel, A., “Histogram-based outlier score (HBOS): A fast unsupervised anomaly detection algorithm,” KI’12: Poster and Demo Track, pp. 59-63, 2012.

[4]

Ide, T., Lozano, C., Abe N., and Liu, Y., “Proximity-based anomaly detection using sparse structure learning,” In Proceedings of SDM’09, pp. 97-108, 2009.

[5]

Kriegel, H.-P., Kroger, P., Schubert E., and Zimek, A., “Interpreting and unifying outlier scores,” In Proceedings of SDM’11, pp. 13-24, 2011.

[6]

Kriegel, H.-P., Schubert M., and Zimek, A., “Angle-based outlier detection in high-dimensional data,” In Proceedings of SIGKDD’08, pp. 444-452, 2008.

[7]

Liu, F. T., Ting K. M., and Zhou, Z.-H., “Isolation forest,” In Proceedings of ICDM’08, pp. 413-422, 2008.

[8]

Ramaswamy, S., Rastogi R., and Shim, K., “Efficient algorithms for mining outliers from large data sets,” In Proceedings of SIGMOD’00, pp. 427-438, 2000.

[9]

Sugiyama M., and Borgwardt, K., “Rapid distance-based outlier detection via sampling,” Advances in NIPS’13, pp. 467-475, 2013.