class_docstr: Wrapper of scikit-learn LOF Class with more functionalities.
  Unsupervised Outlier Detection using 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.
  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.
  See :cite:`breunig2000lof` for details.
  
  Parameters
  ----------
  n_neighbors : int, optional (default=20)
      Number of neighbors to use by default for `kneighbors` queries.
      If n_neighbors is larger than the number of samples provided,
      all samples will be used.
  
  algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
      Algorithm used to compute the nearest neighbors:
  
      - 'ball_tree' will use BallTree
      - 'kd_tree' will use KDTree
      - 'brute' will use a brute-force search.
      - 'auto' will attempt to decide the most appropriate algorithm
        based on the values passed to :meth:`fit` method.
  
      Note: fitting on sparse input will override the setting of
      this parameter, using brute force.
  
  leaf_size : int, optional (default=30)
      Leaf size passed to `BallTree` or `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 : string or callable, default 'minkowski'
      metric used for the distance computation. Any metric from scikit-learn
      or scipy.spatial.distance can be used.
  
      If 'precomputed', the training input X is expected to be a distance
      matrix.
  
      If metric is a callable function, it is called on each
      pair of instances (rows) and the resulting value recorded. The callable
      should take two arrays as input and return one value indicating the
      distance between them. This works for Scipy's metrics, but is less
      efficient than passing the metric name as a string.
  
      Valid values for metric are:
  
      - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
        'manhattan']
  
      - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
        'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
        'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto',
        'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
        'sqeuclidean', 'yule']
  
      See the documentation for scipy.spatial.distance for details on these
      metrics:
      http://docs.scipy.org/doc/scipy/reference/spatial.distance.html
  
  p : integer, optional (default = 2)
      Parameter for the Minkowski metric from
      sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
      equivalent to using manhattan_distance (l1), and euclidean_distance
      (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
      See http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.pairwise_distances
  
  metric_params : dict, optional (default = None)
      Additional keyword arguments for the metric function.
  
  contamination : float in (0., 0.5), optional (default=0.1)
      The amount of contamination of the data set, i.e. the proportion
      of outliers in the data set. When fitting this is used to define the
      threshold on the decision function.
  
  n_jobs : int, optional (default = 1)
      The number of parallel jobs to run for neighbors search.
      If ``-1``, then the number of jobs is set to the number of CPU cores.
      Affects only kneighbors and kneighbors_graph methods.
  
  novelty : bool (default=False)
      By default, LocalOutlierFactor is only meant to be used for outlier
      detection (novelty=False). Set novelty to True if you want to use
      LocalOutlierFactor for novelty detection. In this case be aware that
      that you should only use predict, decision_function and score_samples
      on new unseen data and not on the training set.
  
  Attributes
  ----------
  n_neighbors_ : int
      The actual number of neighbors used for `kneighbors` queries.
  
  decision_scores_ : numpy array of shape (n_samples,)
      The outlier scores of the training data.
      The higher, the more abnormal. Outliers tend to have higher
      scores. This value is available once the detector is
      fitted.
  
  threshold_ : float
      The threshold is based on ``contamination``. It is the
      ``n_samples * contamination`` most abnormal samples in
      ``decision_scores_``. The threshold is calculated for generating
      binary outlier labels.
  
  labels_ : int, either 0 or 1
      The binary labels of the training data. 0 stands for inliers
      and 1 for outliers/anomalies. It is generated by applying
      ``threshold_`` on ``decision_scores_``.
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docstr: The actual number of neighbors used for kneighbors queries.
  Decorator for scikit-learn LOF attributes.
docstr_tok: ["The","actual","number","of","neighbors","used","for","kneighbors","queries",".","Decorator","for","scikit-learn","LOF","attributes","."]
code: def n_neighbors_(self):
      """The actual number of neighbors used for kneighbors queries.
      Decorator for scikit-learn LOF attributes.
      """
      return self.detector_.n_neighbors_
  
file_path: pyod/pyod/models/lof.py
imports: from __future__ import division
  from __future__ import print_function
  from sklearn.neighbors import LocalOutlierFactor
  from sklearn.utils.validation import check_array
  from sklearn.utils.validation import check_is_fitted
  from .base import BaseDetector
  from ..utils.utility import invert_order
label_desc: Use image node_id 4 for calling the LOF obj's underlying member method code with example usage: obj.n_neighbors_() and returns: self