class_docstr: kNN class for outlier detection.
  For an observation, its distance to its kth nearest neighbor could be
  viewed as the outlying score. It could be viewed as a way to measure
  the density. See :cite:`ramaswamy2000efficient,angiulli2002fast` for
  details.
  
  Three kNN detectors are supported:
  largest: use the distance to the kth neighbor as the outlier score
  mean: use the average of all k neighbors as the outlier score
  median: use the median of the distance to k neighbors as the outlier score
  
  Parameters
  ----------
  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. Used when fitting to
      define the threshold on the decision function.
  
  n_neighbors : int, optional (default = 5)
      Number of neighbors to use by default for k neighbors queries.
  
  method : str, optional (default='largest')
      {'largest', 'mean', 'median'}
  
      - 'largest': use the distance to the kth neighbor as the outlier score
      - 'mean': use the average of all k neighbors as the outlier score
      - 'median': use the median of the distance to k neighbors as the
        outlier score
  
  radius : float, optional (default = 1.0)
      Range of parameter space to use by default for `radius_neighbors`
      queries.
  
  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.
  
      .. deprecated:: 0.74
         ``algorithm`` is deprecated in PyOD 0.7.4 and will not be
         possible in 0.7.6. It has to use BallTree for consistency.
  
  leaf_size : int, optional (default = 30)
      Leaf size passed to BallTree. 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 to use for distance computation. Any metric from scikit-learn
      or scipy.spatial.distance can be used.
  
      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.
  
      Distance matrices are not supported.
  
      Valid values for metric are:
  
      - from scikit-learn: ['cityblock', '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.
  
  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.
  
  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.
  
  Attributes
  ----------
  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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code: def __init__(
      self,
      contamination=0.1,
      n_neighbors=5,
      method="largest",
      radius=1.0,
      algorithm="auto",
      leaf_size=30,
      metric="minkowski",
      p=2,
      metric_params=None,
      n_jobs=1,
      **kwargs,
  ):
      super(KNN, self).__init__(contamination=contamination)
      self.n_neighbors = n_neighbors
      self.method = method
      self.radius = radius
      self.algorithm = algorithm
      self.leaf_size = leaf_size
      self.metric = metric
      self.p = p
      self.metric_params = metric_params
      self.n_jobs = n_jobs
  
      if self.algorithm != "auto" and self.algorithm != "ball_tree":
          warn(
              "algorithm parameter is deprecated and will be removed "
              "in version 0.7.6. By default, ball_tree will be used.",
              FutureWarning,
          )
  
      self.neigh_ = NearestNeighbors(
          n_neighbors=self.n_neighbors,
          radius=self.radius,
          algorithm=self.algorithm,
          leaf_size=self.leaf_size,
          metric=self.metric,
          p=self.p,
          metric_params=self.metric_params,
          n_jobs=self.n_jobs,
          **kwargs,
      )
  
file_path: pyod/pyod/models/knn.py
imports: from __future__ import division
  from __future__ import print_function
  from warnings import warn
  import numpy
  from sklearn.neighbors import BallTree
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from .base import BaseDetector
label_desc: Use image node_id 1 to create a new KNN object from inherited base classes: BaseDetector with example: obj = KNN(contamination, n_neighbors, method, radius, algorithm, leaf_size, metric, p, metric_params, n_jobs)