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        "description": "scikit-sos\n==========\n\nscikit-sos is a Python module for Stochastic Outlier Selection (SOS). It\nis compatible with scikit-learn. SOS is an unsupervised outlier selection\nalgorithm. It uses the concept of affinity to compute an outlier probability\nfor each data point.\n\n.. figure:: https://github.com/jeroenjanssens/scikit-sos/raw/master/doc/sos.png\n   :alt: SOS\n\nFor more information about SOS, see the technical report: J.H.M.\nJanssens, F. Huszar, E.O. Postma, and H.J. van den Herik. `Stochastic\nOutlier\nSelection <https://github.com/jeroenjanssens/sos/blob/master/doc/sos-ticc-tr-2012-001.pdf?raw=true>`__.\nTechnical Report TiCC TR 2012-001, Tilburg University, Tilburg, the\nNetherlands, 2012.\n\nInstall\n-------\n\n.. code:: bash\n\n   pip install scikit-sos\n\n\nUsage\n-----\n\n.. code:: python\n\n    >>> import pandas as pd\n    >>> from sksos import SOS\n    >>> iris = pd.read_csv(\"http://bit.ly/iris-csv\")\n    >>> X = iris.drop(\"Name\", axis=1).values\n    >>> iris[\"score\"] = detector.predict(X)\n    >>> iris.sort_values(\"score\", ascending=False).head(10)\n         SepalLength  SepalWidth  PetalLength  PetalWidth             Name     score\n    41           4.5         2.3          1.3         0.3      Iris-setosa  0.981898\n    106          4.9         2.5          4.5         1.7   Iris-virginica  0.964381\n    22           4.6         3.6          1.0         0.2      Iris-setosa  0.957945\n    134          6.1         2.6          5.6         1.4   Iris-virginica  0.897970\n    24           4.8         3.4          1.9         0.2      Iris-setosa  0.871733\n    114          5.8         2.8          5.1         2.4   Iris-virginica  0.831610\n    62           6.0         2.2          4.0         1.0  Iris-versicolor  0.821141\n    108          6.7         2.5          5.8         1.8   Iris-virginica  0.819842\n    44           5.1         3.8          1.9         0.4      Iris-setosa  0.773301\n    100          6.3         3.3          6.0         2.5   Iris-virginica  0.765657\n\n\nSelecting outliers from the command line\n----------------------------------------\n\nThis module also includes a command-line tool called `sos`.\nTo illustrate, we apply SOS with a perplexity of 10 to the Iris dataset:\n\n.. code:: bash\n\n    $ curl -sL http://bit.ly/iris-csv |\n    > tail -n +2 | cut -d, -f1-4 |\n    > sos -p 10 |\n    > sort -nr | head\n    0.98189840\n    0.96438132\n    0.95794492\n    0.89797043\n    0.87173299\n    0.83161045\n    0.82114072\n    0.81984209\n    0.77330148\n    0.76565738\n\n\nAdding a threshold causes SOS to output 0s and 1s instead of outlier\nprobabilities. If we set the threshold to 0.8 then we see that out of\nthe 150 data points, 8 are selected as outliers:\n\n.. code:: bash\n\n    $ curl -sL http://bit.ly/iris-csv |\n    > tail -n +2 | cut -d, -f1-4 |\n    > sos -p 10 -t 0.8 |\n    > paste -sd+ | bc\n    8\n\n\nLicense\n-------\n\nAll software in this repository is distributed under the terms of the\nBSD Simplified License. The full license is in the LICENSE file.",
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