8000 DOC Ensures that LocalOutlierFactor passes numpydoc validation by Pinky-Chaudhary · Pull Request #21180 · scikit-learn/scikit-learn · GitHub
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Merged
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Sep 30, 2021
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DOC Ensures that LocalOutlierFactor passes numpydoc validation #21180

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c09572e
fixed pytest errorts for PLSCanonical
Pinky-Chaudhary Sep 28, 2021
f5e09e1
Merge branch 'scikit-learn:main' into doc_PLSCanonical
Pinky-Chaudhary Sep 28, 2021
01c5edd
Merge pull request #1 from Pinky-Chaudhary/doc_PLSCanonical
Pinky-Chaudhary Sep 28, 2021
c751ce7
Fix docs for LocalOutlierFactor
Pinky-Chaudhary Sep 28, 2021
3e21575
Remove LocalOutlierFactor from DOCSTRING_IGNORE_LIST
Pinky-Chaudhary Sep 28, 2021
990fd90
Update sklearn/neighbors/_lof.py
Pinky-Chaudhary Sep 29, 2021
8882323
Update recommended changes
Pinky-Chaudhary Sep 29, 2021
944e0f1
Merge branch 'scikit-learn:main' into main
Pinky-Chaudhary Sep 29, 2021
93c5ea7
Merge branch 'scikit-learn:main' into main
Pinky-Chaudhary Sep 30, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,6 @@
"KNNImputer",
"LabelPropagation",
"LabelSpreading",
"LocalOutlierFactor",
"LocallyLinearEmbedding",
"MultiLabelBinarizer",
"MultiTaskElasticNet",
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37 changes: 21 additions & 16 deletions sklearn/neighbors/_lof.py
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Expand Up @@ -17,18 +17,18 @@


class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):
"""Unsupervised Outlier Detection using Local Outlier Factor (LOF)
"""Unsupervised Outlier Detection using the Local Outlier Factor (LOF).

The anomaly score of each sample is called Local Outlier Factor.
It measures the local deviation of density of a given sample with
respect to its neighbors.
The anomaly score of each sample is called the Local Outlier Factor.
It measures the local deviation of the density of a given sample with respect
to its neighbors.
It is local in that the anomaly score depends on how isolated the object
is with respect to the surrounding neighborhood.
More precisely, locality is given by k-nearest neighbors, whose distance
is used to estimate the local density.
By comparing the local density of a sample to the local densities of
its neighbors, one can identify samples that have a substantially lower
density than their neighbors. These are considered outliers.
By comparing the local density of a sample to the local densities of its
neighbors, one can identify samples that have a substantially lower density
than their neighbors. These are considered outliers.

.. versionadded:: 0.19

Expand All @@ -52,13 +52,13 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):
this parameter, using brute force.

leaf_size : int, default=30
Leaf size passed to :class:`BallTree` or :class:`KDTree`. This can
Leaf is size passed to :class:`BallTree` or :class:`KDTree`. This can
affect the speed of the construction and query, as well as the memory
required to store the tree. The optimal value depends on the
nature of the problem.

metric : str or callable, default='minkowski'
metric used for the distance computation. Any metric from scikit-learn
The metric is used for distance computation. Any metric from scikit-learn
or scipy.spatial.distance can be used.

If metric is "precomputed", X is assumed to be a distance matrix and
Expand All @@ -84,7 +84,7 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):

See the documentation for scipy.spatial.distance for details on these
metrics:
https://docs.scipy.org/doc/scipy/reference/spatial.distance.html
https://docs.scipy.org/doc/scipy/reference/spatial.distance.html.

p : int, default=2
Parameter for the Minkowski metric from
Expand Down Expand Up @@ -170,6 +170,16 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):
n_samples_fit_ : int
It is the number of samples in the fitted data.

See also
----------
sklearn.svm.OneClassSVM: Unsupervised Outlier Detection using
Support Vector Machine.

References
----------
.. [1] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May).
LOF: identifying density-based local outliers. In ACM sigmod record.

Examples
--------
>>> import numpy as np
Expand All @@ -180,11 +190,6 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):
array([ 1, 1, -1, 1])
>>> clf.negative_outlier_factor_
array([ -0.9821..., -1.0370..., -73.3697..., -0.9821...])

References
----------
.. [1] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May).
LOF: identifying density-based local outliers. In ACM sigmod record.
"""

def __init__(
Expand Down Expand Up @@ -223,7 +228,7 @@ def _check_novelty_fit_predict(self):

@available_if(_check_novelty_fit_predict)
def fit_predict(self, X, y=None):
"""Fits the model to the training set X and returns the labels.
"""Fit the model to the training set X and return the labels.

**Not available for novelty detection (when novelty is set to True).**
Label is 1 for an inlier and -1 for an outlier according to the LOF
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