class_docstr: Automatically drops ids and drifting variables between train and test datasets.
  
  Drops on train and test datasets. The list of drift coefficients is available and
  saved as "drifts.txt". To get familiar with drift:
  https://github.com/AxeldeRomblay/MLBox/blob/master/docs/webinars/features.pdf
  
  Parameters
  ----------
  threshold : float, defaut = 0.6
      Drift threshold under which features are kept. Must be between 0. and 1.
      The lower the more you keep non-drifting/stable variables: a feature with
      a drift measure of 0. is very stable and a one with 1. is highly unstable.
  
  inplace : bool, default = False
      If True, train and test datasets are transformed. Returns self.
      Otherwise, train and test datasets are not transformed. Returns a new dictionnary with
      cleaned datasets.
  
  verbose : bool, default = True
      Verbose mode
  
  to_path : str, default = "save"
      Name of the folder where the list of drift coefficients is saved.
docstr: Returns the univariate drifts for all variables.
  
  Returns
  -------
  dict
      Dictionnary containing the drifts for each feature
code: def drifts(self):
      """Returns the univariate drifts for all variables.
  
      Returns
      -------
      dict
          Dictionnary containing the drifts for each feature
      """
  
      if self.__fitOK:
          return self.__Ddrifts
      else:
          raise ValueError("Call the fit_transform function before !")
  
file_path: MLBox/mlbox/preprocessing/drift_thresholder.py
imports: import os
  import time
  from sklearn.pipeline import Pipeline
  from .drift import DriftThreshold
  from ..encoding.na_encoder import NA_encoder
  from ..encoding.categorical_encoder import Categorical_encoder
label_desc: Use image node_id 3 for calling the Drift_thresholder obj's underlying member method code with example usage: obj.drifts() and returns: self