class_docstr: Abstract class for all outlier detection algorithms.
  
  .. warning::
  pyod would stop supporting Python 2 in the future. Consider move to
  Python 3.5+.
  
  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.
  
  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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docstr: Get parameters for this estimator.
  
  See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
  and sklearn/base.py for more information.
  
  Parameters
  ----------
  deep : bool, optional (default=True)
      If True, will return the parameters for this estimator and
      contained subobjects that are estimators.
  
  Returns
  -------
  params : mapping of string to any
      Parameter names mapped to their values.
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code: def get_params(self, deep=True):
      """Get parameters for this estimator.
  
      See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
      and sklearn/base.py for more information.
  
      Parameters
      ----------
      deep : bool, optional (default=True)
          If True, will return the parameters for this estimator and
          contained subobjects that are estimators.
  
      Returns
      -------
      params : mapping of string to any
          Parameter names mapped to their values.
      """
  
      out = dict()
      for key in self._get_param_names():
          # We need deprecation warnings to always be on in order to
          # catch deprecated param values.
          # This is set in utils/__init__.py but it gets overwritten
          # when running under python3 somehow.
          warnings.simplefilter("always", DeprecationWarning)
          try:
              with warnings.catch_warnings(record=True) as w:
                  value = getattr(self, key, None)
              if len(w) and w[0].category == DeprecationWarning:
                  # if the parameter is deprecated, don't show it
                  continue
          finally:
              warnings.filters.pop(0)
  
          # XXX: should we rather test if instance of estimator?
          if deep and hasattr(value, "get_params"):
              deep_items = value.get_params().items()
              out.update((key + "__" + k, val) for k, val in deep_items)
          out[key] = value
      return out
  
imports: from __future__ import division
  from __future__ import print_function
  import abc
  import warnings
  from collections import defaultdict
  from inspect import signature
  import numpy
  import six
  from numpy import percentile
  from scipy.special import erf
  from scipy.stats import binom
  from sklearn.metrics import roc_auc_score
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.utils import deprecated
  from sklearn.utils.multiclass import check_classification_targets
  from sklearn.utils.validation import check_is_fitted
  from .sklearn_base import _pprint
  from ..utils.utility import precision_n_scores
label_desc: Use image node_id 13 for calling the BaseDetector obj's underlying member method code with example usage: obj.get_params(deep) and returns: out