class_docstr: SUOD (Scalable Unsupervised Outlier Detection) is an acceleration
  framework for large scale unsupervised outlier detector training and
  prediction. The corresponding paper is under review in KDD 2020.
  
  Parameters
  ----------
  base_estimators: list, length must be greater than 1
          A list of base estimators. Certain methods must be present, e.g.,
          `fit` and `predict`.
  
  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
          the number of jobs that can actually run in parallel.
  
  rp_clf_list : list, optional (default=None)
          The list of outlier detection models to use random projection. The
          detector name should be consistent with PyOD.
  
  rp_ng_clf_list : list, optional (default=None)
          The list of outlier detection models NOT to use random projection. The
          detector name should be consistent with PyOD.
  
  rp_flag_global : bool, optional (default=True)
          If set to False, random projection is turned off for all base models.
  
  target_dim_frac : float in (0., 1), optional (default=0.5)
          The target compression ratio.
  
  jl_method : string, optional (default = 'basic')
          The JL projection method:
  
          - "basic": each component of the transformation matrix is taken at
            random in N(0,1).
          - "discrete", each component of the transformation matrix is taken at
            random in {-1,1}.
          - "circulant": the first row of the transformation matrix is taken at
            random in N(0,1), and each row is obtained from the previous one
            by a one-left shift.
          - "toeplitz": the first row and column of the transformation matrix
            is taken at random in N(0,1), and each diagonal has a constant value
            taken from these first vector.
  
  bps_flag : bool, optional (default=True)
          If set to False, balanced parallel scheduling is turned off.
  
  approx_clf_list : list, optional (default=None)
          The list of outlier detection models to use pseudo-supervised
          approximation. The detector name should be consistent with PyOD.
  
  approx_ng_clf_list : list, optional (default=None)
          The list of outlier detection models NOT to use pseudo-supervised
          approximation. The detector name should be consistent with PyOD.
  
  approx_flag_global : bool, optional (default=True)
          If set to False, pseudo-supervised approximation is turned off.
  
  approx_clf : object, optional (default: sklearn RandomForestRegressor)
          The supervised model used to approximate unsupervised models.
  
  cost_forecast_loc_fit : str, optional
          The location of the pretrained cost prediction forecast for training.
  
  cost_forecast_loc_pred : str, optional
          The location of the pretrained cost prediction forecast for prediction.
  
  verbose : bool, optional (default=False)
          Controls the verbosity of the building process.
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 : boolean, optional
          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.
docstr_tok: ["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",":","boolean",",","optional","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","."]
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 : boolean, optional
              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: import os
  import sys
  import time
  import numbers
  import numpy
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.utils import check_array
  import joblib
  from joblib import Parallel, delayed, effective_n_jobs
  from joblib.externals.loky.backend.context import set_start_method
  from pyod.models.sklearn_base import _pprint
  from pyod.utils.utility import check_parameter
  from suod.models.parallel_processes import cost_forecast_meta
  from suod.models.parallel_processes import balanced_scheduling
  from suod.models.parallel_processes import _parallel_fit
  from suod.models.parallel_processes import _parallel_predict
  from suod.models.parallel_processes import _parallel_predict_proba
  from suod.models.parallel_processes import _parallel_decision_function
  from suod.models.parallel_processes import _partition_estimators
  from suod.models.parallel_processes import _parallel_approx_estimators
  from ..utils.utility import _unfold_parallel, build_codes, _get_sklearn_version
  import warnings
  from collections import defaultdict
  from inspect import signature
label_desc: Use image node_id 12 for calling the SUOD obj's underlying member method code with example usage: obj.get_params(deep) and returns: out