class_docstr: KDE class for outlier detection.
  
  For an observation, its negative log probability density could be viewed
  as the outlying score.
  
  See :cite:`latecki2007outlier` for details.
  
  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.
  
  bandwidth : float, optional (default=1.0)
      The bandwidth of the kernel.
  
  algorithm : {'auto', 'ball_tree', 'kd_tree'}, optional
      Algorithm used to compute the kernel density estimator:
  
      - 'ball_tree' will use BallTree
      - 'kd_tree' will use KDTree
      - 'auto' will attempt to decide the most appropriate algorithm
        based on the values passed to :meth:`fit` method.
  
  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.
  
  metric_params : dict, optional (default = None)
      Additional keyword arguments for the metric function.
  
  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_``.
docstr: 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.
code: 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, ["decision_scores_", "threshold_", "labels_"]
      )
  
      X = check_array(X)
  
      # invert outlier scores. Outliers comes with higher outlier scores.
      return invert_order(self.kde_.score_samples(X))
  
file_path: pyod/pyod/models/kde.py
imports: from __future__ import division, print_function
  from warnings import warn
  from sklearn.neighbors import KernelDensity
  from sklearn.utils 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 3 for calling the KDE obj's underlying member method code with example usage: obj.decision_function(X) and returns: invert_order