class_docstr: COPOD class for Copula Based Outlier Detector.
  COPOD is a parameter-free, highly interpretable outlier detection algorithm
  based on empirical copula models.
  See :cite:`li2020copod` 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_``.
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code: def __init__(self, contamination=0.1, n_jobs=1):
      super(COPOD, self).__init__(contamination=contamination)
      self.n_jobs = n_jobs
  
file_path: pyod/pyod/models/copod.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 1 to create a new COPOD object from inherited base classes: BaseDetector with example: obj = COPOD(contamination, n_jobs)