text: """k-Nearest Neighbors Detector (kNN)"""
  # Author: Yue Zhao <zhaoy@cmu.edu>
  # License: BSD 2 clause
  from __future__ import division, print_function
  from warnings import warn
  
  import numpy as np
  from sklearn.neighbors import BallTree, NearestNeighbors
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  
  from .base import BaseDetector
  
  # TODO: algorithm parameter is deprecated and will be removed in 0.7.6.
  # Warning has been turned on.
  # TODO: since Ball_tree is used by default, may introduce its parameters.
  
  
  class KNN(BaseDetector):
      # noinspection PyPep8
      """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', '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.
  
      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_``.
      """
  
      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)
  
      def fit(self, X, y=None):
          """Fit detector. y is ignored in unsupervised methods.
  
          Parameters
          ----------
          X : numpy array of shape (n_samples, n_features)
              The input samples.
  
          y : Ignored
              Not used, present for API consistency by convention.
  
          Returns
          -------
          self : object
              Fitted estimator.
          """
  
          # validate inputs X and y (optional)
          X = check_array(X)
          self._set_n_classes(y)
  
          self.neigh_.fit(X)
  
          # In certain cases, _tree does not exist for NearestNeighbors
          # See Issue #158 (https://github.com/yzhao062/pyod/issues/158)
          # n_neighbors = 100
          if self.neigh_._tree is not None:
              self.tree_ = self.neigh_._tree
  
          else:
              if self.metric_params is not None:
                  self.tree_ = BallTree(
                      X,
                      leaf_size=self.leaf_size,
                      metric=self.metric,
                      **self.metric_params)
              else:
                  self.tree_ = BallTree(
                      X, leaf_size=self.leaf_size, metric=self.metric)
  
          dist_arr, _ = self.neigh_.kneighbors(
              n_neighbors=self.n_neighbors, return_distance=True)
          dist = self._get_dist_by_method(dist_arr)
  
          self.decision_scores_ = dist.ravel()
          self._process_decision_scores()
  
          return self
  
      def decision_function(self, X):
          """Predict raw anomaly score of X using the fitted detector.
  
          The anomaly score of an input sample is computed based on different
          detector algorithms. For consistency, outliers are assigned with
          larger anomaly scores.
  
          Parameters
          ----------
          X : numpy array of shape (n_samples, n_features)
              The training input samples. Sparse matrices are accepted only
              if they are supported by the base estimator.
  
          Returns
          -------
          anomaly_scores : numpy array of shape (n_samples,)
              The anomaly score of the input samples.
          """
          check_is_fitted(self,
                          ['tree_', 'decision_scores_', 'threshold_', 'labels_'])
  
          X = check_array(X)
  
          # initialize the output score
          pred_scores = np.zeros([X.shape[0], 1])
  
          for i in range(X.shape[0]):
              x_i = X[i, :]
              x_i = np.asarray(x_i).reshape(1, x_i.shape[0])
  
              # get the distance of the current point
              dist_arr, _ = self.tree_.query(x_i, k=self.n_neighbors)
              dist = self._get_dist_by_method(dist_arr)
              pred_score_i = dist[-1]
  
              # record the current item
              pred_scores[i, :] = pred_score_i
  
          return pred_scores.ravel()
  
      def _get_dist_by_method(self, dist_arr):
          """Internal function to decide how to process passed in distance array.
  
          Parameters
          ----------
          dist_arr : numpy array of shape (n_samples, n_neighbors)
              Distance matrix.
  
          Returns
          -------
          dist : numpy array of shape (n_samples,)
              The outlier scores by distance.
          """
  
          if self.method == 'largest':
              return dist_arr[:, -1]
          elif self.method == 'mean':
              return np.mean(dist_arr, axis=1)
          elif self.method == 'median':
              return np.median(dist_arr, axis=1)
  