class_docstr: Subspace outlier detection (SOD) schema aims to detect outlier in
  varying subspaces of a high dimensional feature space. For each data
  object, SOD explores the axis-parallel subspace spanned by the data
  object's neighbors and determines how much the object deviates from the
  neighbors in this subspace.
  
  See :cite:`kriegel2009outlier` for details.
  
  Parameters
  ----------
  n_neighbors : int, optional (default=20)
      Number of neighbors to use by default for k neighbors queries.
  
  ref_set: int, optional (default=10)
      specifies the number of shared nearest neighbors to create the
      reference set. Note that ref_set must be smaller than n_neighbors.
  
  alpha: float in (0., 1.), optional (default=0.8)
         specifies the lower limit for selecting subspace.
         0.8 is set as default as suggested in the original paper.
  
  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.
  
  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: 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.
docstr_tok: ["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","."]
code: 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.decision_scores_ = self.decision_function(X)
      self._process_decision_scores()
  
      return self
  
file_path: pyod/pyod/models/sod.py
imports: import numba
  import numpy
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils import check_array
  from .base import BaseDetector
  from ..utils.utility import check_parameter
label_desc: Use image node_id 2 for calling the SOD obj's underlying member method code with example usage: obj.fit(X, y) and returns: self