.. currentmodule:: wildboar
.. _guide-outlier-generation:

############################
Outlier detection benchmarks
############################


.. warning::

   The :mod:`datasets.outlier` module optionally requires the package `networkx
   <https://pypi.org/projects/networkx>`_ to generate outliers for multi-class
   problems using :func:`~datasets.outlier.emmott_outliers`. We can
   install the packages using `pip`:

   .. code-block:: shell

      pip install networkx

Wildboar is bundled with support for outlier detection. The primary focus is on
detecting samples that deviate from what is expected, i.e., outlier samples, as
opposed to detecting deviating patterns or time points. Since there are no good
benchmark datasets for outlier detection (except for ``wildboar/outlier``,
which we construct using the :class:`~datasets.outlier.emmott_outliers`), we
support the constructing benchmark outlier datasets from traditional
classification tasks. In the following sections, we will use the `GunPoint`
dataset.

.. execute::
   :context:

   from wildboar.datasets import load_gun_point
   x, y = load_gun_point()

*****************
Minority outliers
*****************

Perhaps the simplest approach to generate outliers implemented in Wildboar is
the :func:`~datasets.outlier.minority_outliers`. The minority outliers selects a
small fraction, specified as ``n_outliers``, of the minority class as the outlier
samples:

.. execute::
   :context:

   from wildboar.datasets.outlier import minority_outliers
   x_outlier, y_outlier = minority_outliers(x, y, n_outliers=0.05)


*****************
Majority outliers
*****************

Another equally simple approach to generate outliers implemented in Wildboar is
the :func:`~datasets.outlier.majority_outliers`. The majority outliers selects a
small fraction, specified as ``n_outliers``, of the majority class as the outlier
samples:

.. execute::
   :context:

   from wildboar.datasets.outlier import majority_outliers
   x_outlier, y_outlier = majority_outliers(x, y, n_outliers=0.05)


***************
Emmott outliers
***************

Wildboar implements a more sophisticated approach to generate outliers, first
published by Emmott et.al (2013) [#emmott]_. Wildboar implements the default
version, but, by default, converts the the time series to an interval
representation using :class:`transform.IntervalTransform` for both the confusion
and difficulty estimator. The default confusion estimator is a
:class:`sklearn:sklearn.ensemble.RandomForestClassifier` and the default
difficulty estimator is a kernel logistic regression model with RBF-kernel. The
:func:`~datasets.outlier.emmott_outliers` can construct synthetic outliers from
both binary and multi-class problems, and allows the user to specify a desired
difficulty, i.e., the separation between inliers and outliers. By default, it
returns a tight cluster of the simplest outliers:

.. execute::
   :context:

   from wildboar.datasets.outlier import emmott_outliers
   x_outlier, y_outlier = emmott_outliers(
      x,
      y,
      n_outliers=0.05,
      variation="tight",     # default
      difficulty="simplest", # default
   )


but we can request a dispersed collection of the most difficult samples:

.. execute::
   :context:

   from wildboar.datasets.outlier import emmott_outliers
   x_outlier, y_outlier = emmott_outliers(
      x,
      y,
      n_outliers=0.05,
      variation="dispersed",
      difficulty="hardest",
   )

.. note::
   To generate outliers for multiclass problems, we must install the package
   ``networkx``.

The :func:`~datasets.outlier.emmott_outliers` discretizes the difficulty scores
assigned by the difficulty estimator into `n` discrete bins determined by the
:python:`scale` parameter. By default, the difficulty scores are assigned into
four bins according to the ranges ``[0, 0.16]``, ``[0.16, 0.3]``, ``[0.3,
0.5]`` and ``[0.5, 1.0]`` (``scale=[0, 0.16, 0.3, 0.5]``), but we can specify other
scales as either a list or an :python:`int`. If we specify an integer, the difficulty
scores are quantized into ``scale`` percentiles, e.g., :python:`scale=4` results in the
ranges ``[0, 0.25, 0.5, 0.75]``. We can then request outliers of specified
difficulty with the ``difficulty``-parameter, e.g., :python:`difficulty=1` selects
samples with difficulty scores in the first quantile. We can also specify
multiple difficulties by passing a list, e.g., :python:`difficulty=[1, 4]` selects the
simplest and the hardest outliers.

.. [#emmott]
    Emmott, A. F., Das, S., Dietterich, T., Fern, A., &Wong, W. K. (2013).
    Systematic construction of anomaly detection benchmarks from real data. In
    Proceedings of the ACM SIGKDD workshop on outlier detection and description
    (pp. 16-21).