.. _outlier_detection:
===================================================
Novelty and Outlier Detection
===================================================
.. currentmodule:: sklearn
Many applications require being able to decide whether a new observation
belongs to the same distribution as existing observations (it is an
*inlier*), or should be considered as different (it is an *outlier*).
Often, this ability is used to clean real data sets. Two important
distinctions must be made:
:outlier detection:
The training data contains outliers which are defined as observations that
are far from the others. Outlier detection estimators thus try to fit the
regions where the training data is the most concentrated, ignoring the
deviant observations.
:novelty detection:
The training data is not polluted by outliers and we are interested in
detecting whether a **new** observation is an outlier. In this context an
outlier is also called a novelty.
Outlier detection and novelty detection are both used for anomaly
detection, where one is interested in detecting abnormal or unusual
observations. Outlier detection is then also known as unsupervised anomaly
detection and novelty detection as semi-supervised anomaly detection. In the
context of outlier detection, the outliers/anomalies cannot form a
dense cluster as available estimators assume that the outliers/anomalies are
located in low density regions. On the contrary, in the context of novelty
detection, novelties/anomalies can form a dense cluster as long as they are in
a low density region of the training data, considered as normal in this
context.
The scikit-learn project provides a set of machine learning tools that
can be used both for novelty or outlier detection. This strategy is
implemented with objects learning in an unsupervised way from the data::
estimator.fit(X_train)
new observations can then be sorted as inliers or outliers with a
``predict`` method::
estimator.predict(X_test)
Inliers are labeled 1, while outliers are labeled -1. The predict method
makes use of a threshold on the raw scoring function computed by the
estimator. This scoring function is accessible through the ``score_samples``
method, while the threshold can be controlled by the ``contamination``
parameter.
The ``decision_function`` method is also defined from the scoring function,
in such a way that negative values are outliers and non-negative ones are
inliers::
estimator.decision_function(X_test)
Note that :class:`neighbors.LocalOutlierFactor` does not support
``predict``, ``decision_function`` and ``score_samples`` methods by default
but only a ``fit_predict`` method, as this estimator was originally meant to
be applied for outlier detection. The scores of abnormality of the training
samples are accessible through the ``negative_outlier_factor_`` attribute.
If you really want to use :class:`neighbors.LocalOutlierFactor` for novelty
detection, i.e. predict labels or compute the score of abnormality of new
unseen data, you can instantiate the estimator with the ``novelty`` parameter
set to ``True`` before fitting the estimator. In this case, ``fit_predict`` is
not available.
.. warning:: **Novelty detection with Local Outlier Factor**
When ``novelty`` is set to ``True`` be aware that you must only use
``predict``, ``decision_function`` and ``score_samples`` on new unseen data
and not on the training samples as this would lead to wrong results.
The scores of abnormality of the training samples are always accessible
through the ``negative_outlier_factor_`` attribute.
The behavior of :class:`neighbors.LocalOutlierFactor` is summarized in the
following table.
===================== ================================ =====================
Method Outlier detection Novelty detection
===================== ================================ =====================
``fit_predict`` OK Not available
``predict`` Not available Use only on new data
``decision_function`` Not available Use only on new data
``score_samples`` Use ``negative_outlier_factor_`` Use only on new data
===================== ================================ =====================
Overview of outlier detection methods
=====================================
A comparison of the outlier detection algorithms in scikit-learn. Local
Outlier Factor (LOF) does not show a decision boundary in black as it
has no predict method to be applied on new data when it is used for outlier
detection.
.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_anomaly_comparison_001.png
:target: ../auto_examples/miscellaneous/plot_anomaly_comparison.html
:align: center
:scale: 50
:class:`ensemble.IsolationForest` and :class:`neighbors.LocalOutlierFactor`
perform reasonably well on the data sets considered here.
The :class:`svm.OneClassSVM` is known to be sensitive to outliers and thus
does not perform very well for outlier detection. Finally,
:class:`covariance.EllipticEnvelope` assumes the data is Gaussian and learns
an ellipse. For more details on the different estimators refer to the example
:ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` and the
sections hereunder.
.. topic:: Examples:
* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
for a comparison of the :class:`svm.OneClassSVM`, the
:class:`ensemble.IsolationForest`, the
:class:`neighbors.LocalOutlierFactor` and
:class:`covariance.EllipticEnvelope`.
Novelty Detection
=================
Consider a data set of :math:`n` observations from the same
distribution described by :math:`p` features. Consider now that we
add one more observation to that data set. Is the new observation so
different from the others that we can doubt it is regular? (i.e. does
it come from the same distribution?) Or on the contrary, is it so
similar to the other that we cannot distinguish it from the original
observations? This is the question addressed by the novelty detection
tools and methods.
In general, it is about to learn a rough, close frontier delimiting
the contour of the initial observations distribution, plotted in
embedding :math:`p`-dimensional space. Then, if further observations
lay within the frontier-delimited subspace, they are considered as
coming from the same population than the initial
observations. Otherwise, if they lay outside the frontier, we can say
that they are abnormal with a given confidence in our assessment.
The One-Class SVM has been introduced by Schölkopf et al. for that purpose
and implemented in the :ref:`svm` module in the
:class:`svm.OneClassSVM` object. It requires the choice of a
kernel and a scalar parameter to define a frontier. The RBF kernel is
usually chosen although there exists no exact formula or algorithm to
set its bandwidth parameter. This is the default in the scikit-learn
implementation. The :math:`\nu` parameter, also known as the margin of
the One-Class SVM, corresponds to the probability of finding a new,
but regular, observation outside the frontier.
.. topic:: References:
* `Estimating the support of a high-dimensional distribution
`_ Schölkopf,
Bernhard, et al. Neural computation 13.7 (2001): 1443-1471.
.. topic:: Examples:
* See :ref:`sphx_glr_auto_examples_svm_plot_oneclass.py` for visualizing the
frontier learned around some data by a
:class:`svm.OneClassSVM` object.
* :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`
.. figure:: ../auto_examples/svm/images/sphx_glr_plot_oneclass_001.png
:target: ../auto_examples/svm/plot_oneclass.html
:align: center
:scale: 75%
Outlier Detection
=================
Outlier detection is similar to novelty detection in the sense that
the goal is to separate a core of regular observations from some
polluting ones, called *outliers*. Yet, in the case of outlier
detection, we don't have a clean data set representing the population
of regular observations that can be used to train any tool.
Fitting an elliptic envelope
----------------------------
One common way of performing outlier detection is to assume that the
regular data come from a known distribution (e.g. data are Gaussian
distributed). From this assumption, we generally try to define the
"shape" of the data, and can define outlying observations as
observations which stand far enough from the fit shape.
The scikit-learn provides an object
:class:`covariance.EllipticEnvelope` that fits a robust covariance
estimate to the data, and thus fits an ellipse to the central data
points, ignoring points outside the central mode.
For instance, assuming that the inlier data are Gaussian distributed, it
will estimate the inlier location and covariance in a robust way (i.e.
without being influenced by outliers). The Mahalanobis distances
obtained from this estimate is used to derive a measure of outlyingness.
This strategy is illustrated below.
.. figure:: ../auto_examples/covariance/images/sphx_glr_plot_mahalanobis_distances_001.png
:target: ../auto_examples/covariance/plot_mahalanobis_distances.html
:align: center
:scale: 75%
.. topic:: Examples:
* See :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py` for
an illustration of the difference between using a standard
(:class:`covariance.EmpiricalCovariance`) or a robust estimate
(:class:`covariance.MinCovDet`) of location and covariance to
assess the degree of outlyingness of an observation.
.. topic:: References:
* Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the minimum
covariance determinant estimator" Technometrics 41(3), 212 (1999)
.. _isolation_forest:
Isolation Forest
----------------------------
One efficient way of performing outlier detection in high-dimensional datasets
is to use random forests.
The :class:`ensemble.IsolationForest` 'isolates' observations by randomly selecting
a feature and then randomly selecting a split value between the maximum and
minimum values of the selected feature.
Since recursive partitioning can be represented by a tree structure, the
number of splittings required to isolate a sample is equivalent to the path
length from the root node to the terminating node.
This path length, averaged over a forest of such random trees, is a
measure of normality and our decision function.
Random partitioning produces noticeably shorter paths for anomalies.
Hence, when a forest of random trees collectively produce shorter path
lengths for particular samples, they are highly likely to be anomalies.
The implementation of :class:`ensemble.IsolationForest` is based on an ensemble
of :class:`tree.ExtraTreeRegressor`. Following Isolation Forest original paper,
the maximum depth of each tree is set to :math:`\lceil \log_2(n) \rceil` where
:math:`n` is the number of samples used to build the tree (see (Liu et al.,
2008) for more details).
This algorithm is illustrated below.
.. figure:: ../auto_examples/ensemble/images/sphx_glr_plot_isolation_forest_001.png
:target: ../auto_examples/ensemble/plot_isolation_forest.html
:align: center
:scale: 75%
.. _iforest_warm_start:
The :class:`ensemble.IsolationForest` supports ``warm_start=True`` which
allows you to add more trees to an already fitted model::
>>> from sklearn.ensemble import IsolationForest
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [0, 0], [-20, 50], [3, 5]])
>>> clf = IsolationForest(n_estimators=10, warm_start=True)
>>> clf.fit(X) # fit 10 trees # doctest: +SKIP
>>> clf.set_params(n_estimators=20) # add 10 more trees # doctest: +SKIP
>>> clf.fit(X) # fit the added trees # doctest: +SKIP
.. topic:: Examples:
* See :ref:`sphx_glr_auto_examples_ensemble_plot_isolation_forest.py` for
an illustration of the use of IsolationForest.
* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
for a comparison of :class:`ensemble.IsolationForest` with
:class:`neighbors.LocalOutlierFactor`,
:class:`svm.OneClassSVM` (tuned to perform like an outlier detection
method) and a covariance-based outlier detection with
:class:`covariance.EllipticEnvelope`.
.. topic:: References:
* Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest."
Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.
Local Outlier Factor
--------------------
Another efficient way to perform outlier detection on moderately high dimensional
datasets is to use the Local Outlier Factor (LOF) algorithm.
The :class:`neighbors.LocalOutlierFactor` (LOF) algorithm computes a score
(called local outlier factor) reflecting the degree of abnormality of the
observations.
It measures the local density deviation of a given data point with respect to
its neighbors. The idea is to detect the samples that have a substantially
lower density than their neighbors.
In practice the local density is obtained from the k-nearest neighbors.
The LOF score of an observation is equal to the ratio of the
average local density of his k-nearest neighbors, and its own local density:
a normal instance is expected to have a local density similar to that of its
neighbors, while abnormal data are expected to have much smaller local density.
The number k of neighbors considered, (alias parameter n_neighbors) is typically
chosen 1) greater than the minimum number of objects a cluster has to contain,
so that other objects can be local outliers relative to this cluster, and 2)
smaller than the maximum number of close by objects that can potentially be
local outliers.
In practice, such informations are generally not available, and taking
n_neighbors=20 appears to work well in general.
When the proportion of outliers is high (i.e. greater than 10 \%, as in the
example below), n_neighbors should be greater (n_neighbors=35 in the example
below).
The strength of the LOF algorithm is that it takes both local and global
properties of datasets into consideration: it can perform well even in datasets
where abnormal samples have different underlying densities.
The question is not, how isolated the sample is, but how isolated it is
with respect to the surrounding neighborhood.
When applying LOF for outlier detection, there are no ``predict``,
``decision_function`` and ``score_samples`` methods but only a ``fit_predict``
method. The scores of abnormality of the training samples are accessible
through the ``negative_outlier_factor_`` attribute.
Note that ``predict``, ``decision_function`` and ``score_samples`` can be used
on new unseen data when LOF is applied for novelty detection, i.e. when the
``novelty`` parameter is set to ``True``. See :ref:`novelty_with_lof`.
This strategy is illustrated below.
.. figure:: ../auto_examples/neighbors/images/sphx_glr_plot_lof_outlier_detection_001.png
:target: ../auto_examples/neighbors/sphx_glr_plot_lof_outlier_detection.html
:align: center
:scale: 75%
.. topic:: Examples:
* See :ref:`sphx_glr_auto_examples_neighbors_plot_lof_outlier_detection.py`
for an illustration of the use of :class:`neighbors.LocalOutlierFactor`.
* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
for a comparison with other anomaly detection methods.
.. topic:: References:
* Breunig, Kriegel, Ng, and Sander (2000)
`LOF: identifying density-based local outliers.
`_
Proc. ACM SIGMOD
.. _novelty_with_lof:
Novelty detection with Local Outlier Factor
===========================================
To use :class:`neighbors.LocalOutlierFactor` for novelty detection, i.e.
predict labels or compute the score of abnormality of new unseen data, you
need to instantiate the estimator with the ``novelty`` parameter
set to ``True`` before fitting the estimator::
lof = LocalOutlierFactor(novelty=True)
lof.fit(X_train)
Note that ``fit_predict`` is not available in this case.
.. warning:: **Novelty detection with Local Outlier Factor`**
When ``novelty`` is set to ``True`` be aware that you must only use
``predict``, ``decision_function`` and ``score_samples`` on new unseen data
and not on the training samples as this would lead to wrong results.
The scores of abnormality of the training samples are always accessible
through the ``negative_outlier_factor_`` attribute.
Novelty detection with Local Outlier Factor is illustrated below.
.. figure:: ../auto_examples/neighbors/images/sphx_glr_plot_lof_novelty_detection_001.png
:target: ../auto_examples/neighbors/sphx_glr_plot_lof_novelty_detection.html
:align: center
:scale: 75%