scikit-sos 0.1.10

Install

pip install scikit-sos

Usage

>>> import pandas as pd
>>> from sksos import SOS
>>> iris = pd.read_csv("http://bit.ly/iris-csv")
>>> X = iris.drop("Name", axis=1).values
>>> iris["score"] = detector.predict(X)
>>> iris.sort_values("score", ascending=False).head(10)
     SepalLength  SepalWidth  PetalLength  PetalWidth             Name     score
41           4.5         2.3          1.3         0.3      Iris-setosa  0.981898
106          4.9         2.5          4.5         1.7   Iris-virginica  0.964381
22           4.6         3.6          1.0         0.2      Iris-setosa  0.957945
134          6.1         2.6          5.6         1.4   Iris-virginica  0.897970
24           4.8         3.4          1.9         0.2      Iris-setosa  0.871733
114          5.8         2.8          5.1         2.4   Iris-virginica  0.831610
62           6.0         2.2          4.0         1.0  Iris-versicolor  0.821141
108          6.7         2.5          5.8         1.8   Iris-virginica  0.819842
44           5.1         3.8          1.9         0.4      Iris-setosa  0.773301
100          6.3         3.3          6.0         2.5   Iris-virginica  0.765657

Selecting outliers from the command line

This module also includes a command-line tool called sos. To illustrate, we apply SOS with a perplexity of 10 to the Iris dataset:

$ curl -sL http://bit.ly/iris-csv |
> tail -n +2 | cut -d, -f1-4 |
> sos -p 10 |
> sort -nr | head
0.98189840
0.96438132
0.95794492
0.89797043
0.87173299
0.83161045
0.82114072
0.81984209
0.77330148
0.76565738

Adding a threshold causes SOS to output 0s and 1s instead of outlier probabilities. If we set the threshold to 0.8 then we see that out of the 150 data points, 8 are selected as outliers:

$ curl -sL http://bit.ly/iris-csv |
> tail -n +2 | cut -d, -f1-4 |
> sos -p 10 -t 0.8 |
> paste -sd+ | bc
8

License

All software in this repository is distributed under the terms of the BSD Simplified License. The full license is in the LICENSE file.