lsanomaly is a flexible, fast, probabilistic method for calculating outlier scores on test data, given training examples of inliers. Out of the box it works well with scikit-learn packages. See the features section for why you might chose this model over other options.
- Compatible with scikit-learn package modules
- Probabilistic outlier detection model
- Robust classifier when given multiple inlier classes
- Easy to install and get started
The best way to install lsanomaly is to:
pip install lsanomaly
Because lsanomaly requires scikit-learn it also requires numpy and scipy inherintly. Make sure you have successfully installed these packages if you're having trouble getting lsanomaly to install.
For those familiar with scikit-learn the interface will be familiar, in fact lsanomaly was built to be compatible with sklearn modules where applicable. Here is basic usage of lsanomaly to get started quick as possible.
Configuring the Model
The LSAnomaly provides reasonable default parameters when given an empty init or it can be passed values for rho and sigma. The value rho controls sensitivity to outliers and sigma determines the ‘smoothness’ of the boundary. These values can be tuned to improve your results using lsanomaly.
from lsanomaly import LSAnomaly
# At train time lsanomaly calculates parameters rho and sigma
lsanomaly = LSAnomaly()
# or alternatively
lsanomaly = LSAnomaly(sigma=3, rho=0.1)Training the Model
After the model is configured the training data can be fit.
import numpy as np
lsanomaly.fit(np.array([[1],[2],[3],[1],[2],[3]]))Making Predictions
Now that the data is fit, we will probably want to try and predict on some data not in the training set.
>>> lsanomaly.predict(np.array([[0]]))
[0.0]
>>> lsanomaly.predict_proba(np.array([[0]]))
array([[ 0.7231233, 0.2768767]])J.A. Quinn, M. Sugiyama. A least-squares approach to anomaly detection in static and sequential data. Pattern Recognition Letters 40:36-40, 2014.
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To check out the complete release notes see the changelog.
The MIT License (MIT)
Copyright (c) 2016 John Quinn
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