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This is a scikit-learn compatible library for anomaly detection.
numpy>=1.13.3 <http://www.numpy.org/>
_ (BSD 3-Clause License)scikit-learn>=0.20.0 <http://scikit-learn.org/>
_ (BSD 3-Clause License)scipy>=0.19.1 <https://www.scipy.org/scipylib/>
_ (BSD 3-Clause License)matplotlib>=2.1.2 <https://matplotlib.org/>
_ (PSF-based License)networkx>=2.2 <https://networkx.github.io/>
_ (BSD 3-Clause License)You can install via pip
::
pip install kenchi
or conda
.
::
conda install -c y_ohr_n kenchi
.. code:: python
import matplotlib.pyplot as plt
import numpy as np
from kenchi.datasets import load_pima
from kenchi.outlier_detection import *
from kenchi.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
np.random.seed(0)
scaler = StandardScaler()
detectors = [
FastABOD(novelty=True, n_jobs=-1), OCSVM(),
MiniBatchKMeans(), LOF(novelty=True, n_jobs=-1),
KNN(novelty=True, n_jobs=-1), IForest(n_jobs=-1),
PCA(), KDE()
]
# Load the Pima Indians diabetes dataset.
X, y = load_pima(return_X_y=True)
X_train, X_test, _, y_test = train_test_split(X, y)
# Get the current Axes instance
ax = plt.gca()
for det in detectors:
# Fit the model according to the given training data
pipeline = make_pipeline(scaler, det).fit(X_train)
# Plot the Receiver Operating Characteristic (ROC) curve
pipeline.plot_roc_curve(X_test, y_test, ax=ax)
# Display the figure
plt.show()
.. figure:: https://raw.githubusercontent.com/HazureChi/kenchi/master/docs/images/readme.png :align: center
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_
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.. [#kriegel08] Kriegel, H.-P., Schubert, M., and Zimek, A.,
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_
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.. [#lee03] Lee, W. S, and Liu, B., "Learning with positive and unlabeled examples using weighted Logistic Regression," In Proceedings of ICML, pp. 448-455, 2003.
.. [#liu08] Liu, F. T., Ting, K. M., and Zhou, Z.-H.,
"Isolation forest," <https://doi.org/10.1109/ICDM.2008.17>
_
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.. [#parzen62] Parzen, E.,
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.. [#ramaswamy00] Ramaswamy, S., Rastogi, R., and Shim, K.,
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_
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.. [#scholkopf01] Scholkopf, B., Platt, J. C., Shawe-Taylor, J. C., Smola, A. J., and Williamson, R. C.,
"Estimating the Support of a High-Dimensional Distribution," <https://doi.org/10.1162/089976601750264965>
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.. [#sugiyama13] Sugiyama, M., and Borgwardt, K.,
"Rapid distance-based outlier detection via sampling," <http://papers.nips.cc/paper/5127-rapid-distance-based-outlier-detection-via-sampling>
_
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