scikit-learn · glemaitre · Jul 19, 2018 · Feb 25, 2018 · Apr 2, 2018 · Apr 2, 2018
diff --git a/doc/modules/outlier_detection.rst b/doc/modules/outlier_detection.rst
@@ -12,13 +12,27 @@ belongs to the same distribution as existing observations (it is an
 Often, this ability is used to clean real data sets. Two important
 distinction must be made:
 
-:novelty detection:
-  The training data is not polluted by outliers, and we are interested in
-  detecting anomalies in new observations.
-
 :outlier detection:
-  The training data contains outliers, and we need to fit the central
-  mode of the training data, ignoring the deviant observations.
+  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 outliers detection. This strategy is
@@ -44,19 +58,55 @@ inliers::
     estimator.decision_function(X_test)
 
 Note that :class:`neighbors.LocalOutlierFactor` does not support
-``predict`` and ``decision_function`` methods, as this algorithm is
-purely transductive and is thus not designed to deal with new data.
+``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.
+
 
 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/images/sphx_glr_plot_anomaly_comparison_001.png
    :target: ../auto_examples/plot_anomaly_comparison.html
    :align: center
    :scale: 50
 
-   A comparison of the outlier detection algorithms in scikit-learn
+: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_plot_anomaly_comparison.py` and the sections
+hereunder.
+
+.. topic:: Examples:
 
+  * See :ref:`sphx_glr_auto_examples_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
 =================
@@ -189,7 +239,7 @@ This strategy is illustrated below.
    * 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_covariance_plot_outlier_detection.py` for a
+   * See :ref:`sphx_glr_auto_examples_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
@@ -237,20 +287,29 @@ 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_001.png
-   :target: ../auto_examples/neighbors/plot_lof.html
+.. 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.py` for
-     an illustration of the use of :class:`neighbors.LocalOutlierFactor`.
+   * 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_covariance_plot_outlier_detection.py` for a
-     comparison with other anomaly detection methods.
+   * See :ref:`sphx_glr_auto_examples_plot_anomaly_comparison.py` for a
+   comparison with other anomaly detection methods.
 
 .. topic:: References:
 
@@ -259,72 +318,45 @@ This strategy is illustrated below.
       <http://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf>`_
       Proc. ACM SIGMOD
 
-One-class SVM versus Elliptic Envelope versus Isolation Forest versus LOF
--------------------------------------------------------------------------
-
-Strictly-speaking, the One-class SVM is not an outlier-detection method,
-but a novelty-detection method: its training set should not be
-contaminated by outliers as it may fit them. That said, outlier detection
-in high-dimension, or without any assumptions on the distribution of the
-inlying data is very challenging, and a One-class SVM gives useful
-results in these situations.
-
-The examples below illustrate how the performance of the
-:class:`covariance.EllipticEnvelope` degrades as the data is less and
-less unimodal. The :class:`svm.OneClassSVM` works better on data with
-multiple modes and :class:`ensemble.IsolationForest` and
-:class:`neighbors.LocalOutlierFactor` perform well in every cases.
-
-.. |outlier1| image:: ../auto_examples/covariance/images/sphx_glr_plot_outlier_detection_001.png
-   :target: ../auto_examples/covariance/plot_outlier_detection.html
-   :scale: 50%
-
-.. |outlier2| image:: ../auto_examples/covariance/images/sphx_glr_plot_outlier_detection_002.png
-   :target: ../auto_examples/covariance/plot_outlier_detection.html
-   :scale: 50%
-
-.. |outlier3| image:: ../auto_examples/covariance/images/sphx_glr_plot_outlier_detection_003.png
-   :target: ../auto_examples/covariance/plot_outlier_detection.html
-   :scale: 50%
-
-.. list-table:: **Comparing One-class SVM, Isolation Forest, LOF, and Elliptic Envelope**
-   :widths: 40 60
-
-   *
-      - For a inlier mode well-centered and elliptic, the
-        :class:`svm.OneClassSVM` is not able to benefit from the
-        rotational symmetry of the inlier population. In addition, it
-        fits a bit the outliers present in the training set. On the
-        opposite, the decision rule based on fitting an
-        :class:`covariance.EllipticEnvelope` learns an ellipse, which
-        fits well the inlier distribution. The :class:`ensemble.IsolationForest`
-        and :class:`neighbors.LocalOutlierFactor` perform as well.
-      - |outlier1| 
-
-   *
-      - As the inlier distribution becomes bimodal, the
-        :class:`covariance.EllipticEnvelope` does not fit well the
-        inliers. However, we can see that :class:`ensemble.IsolationForest`,
-        :class:`svm.OneClassSVM` and :class:`neighbors.LocalOutlierFactor`
-        have difficulties to detect the two modes,
-        and that the :class:`svm.OneClassSVM`
-        tends to overfit: because it has no model of inliers, it
-        interprets a region where, by chance some outliers are
-        clustered, as inliers.
-      - |outlier2|
-
-   *
-      - If the inlier distribution is strongly non Gaussian, the
-        :class:`svm.OneClassSVM` is able to recover a reasonable
-        approximation as well as :class:`ensemble.IsolationForest`
-        and :class:`neighbors.LocalOutlierFactor`,
-        whereas the :class:`covariance.EllipticEnvelope` completely fails.
-      - |outlier3|
+.. _novelty_with_lof:
 
-.. topic:: Examples:
+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.
+
+The behavior of LOF is summarized in the following table.
+
+====================  ================================  =====================
+Method                Outlier detection                 Novelty detection
+====================  ================================  =====================
+`fit_predict`         OK                                Not available
+`predict`             Not available                     Use only on test data
+`decision_function`   Not available                     Use only on test data
+`score_samples`       Use `negative_outlier_factor_`    Use only on test data
+====================  ================================  =====================
+
+
+This strategy 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%
 
-   * See :ref:`sphx_glr_auto_examples_covariance_plot_outlier_detection.py` for a
-     comparison of the :class:`svm.OneClassSVM` (tuned to perform like
-     an outlier detection method), the :class:`ensemble.IsolationForest`,
-     the :class:`neighbors.LocalOutlierFactor`
-     and a covariance-based outlier detection :class:`covariance.EllipticEnvelope`.
diff --git a/doc/whats_new/v0.20.rst b/doc/whats_new/v0.20.rst
@@ -861,6 +861,15 @@ Outlier Detection models
   ``raw_values`` parameter is deprecated as the shifted Mahalanobis distance
   will be always returned in 0.22. :issue:`9015` by `Nicolas Goix`_.
 
+- Novelty detection with :class:`neighbors.LocalOutlierFactor`:
+  Add a ``novelty`` parameter to :class:`neighbors.LocalOutlierFactor`. When
+  ``novelty`` is set to True, :class:`neighbors.LocalOutlierFactor` can then 
+  be used for novelty detection, i.e. predict on new unseen data. Available
+  prediction methods are ``predict``, ``decision_function`` and
+  ``score_samples``. By default, ``novelty`` is set to ``False``, and only
+  the ``fit_predict`` method is avaiable.
+  By :user:`Albert Thomas <albertcthomas>`.
+
 Covariance
 
 - The :func:`covariance.graph_lasso`, :class:`covariance.GraphLasso` and

diff --git a/examples/covariance/plot_outlier_detection.py b/examples/covariance/plot_outlier_detection.py