class_docstr: ECOD class for Unsupervised Outlier Detection Using Empirical
  Cumulative Distribution Functions (ECOD)
  ECOD is a parameter-free, highly interpretable outlier detection algorithm
  based on empirical CDF functions.
  See :cite:`Li2021ecod` 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.
  
  n_jobs : optional (default=1)
      The number of jobs to run in parallel for both `fit` and
      `predict`. If -1, then the number of jobs is set to the
      number of cores.
  
  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_``.
class_docstr_tok: ["ECOD","class","for","Unsupervised","Outlier","Detection","Using","Empirical","Cumulative","Distribution","Functions","(","ECOD",")","ECOD","is","a","parameter-free",",","highly","interpretable","outlier","detection","algorithm","based","on","empirical","CDF","functions",".","See",":","cite",":","`","Li2021ecod","`","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",".","n_jobs",":","optional","(","default=1",")","The","number","of","jobs","to","run","in","parallel","for","both","`","fit","`","and","`","predict","`",".","If","-1",",","then","the","number","of","jobs","is","set","to","the","number","of","cores",".","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.
      """
      X = check_array(X)
      self._set_n_classes(y)
      self.decision_scores_ = self.decision_function(X)
      self.X_train = X
      self._process_decision_scores()
      return self
  
file_path: pyod/pyod/models/ecod.py
imports: from __future__ import division
  from __future__ import print_function
  import warnings
  import matplotlib.pyplot
  import numpy
  from joblib import Parallel, delayed
  from scipy.stats import skew
  from sklearn.utils import check_array
  from .base import BaseDetector
  from .sklearn_base import _partition_estimators
  from ..utils.stat_models import column_ecdf
label_desc: Use image node_id 2 for calling the ECOD obj's underlying member method code with example usage: obj.fit(X, y) and returns: self