class_docstr: Estimate the univariate drift between two datasets.
  
  Estimate the univariate drift between two datasets
  and select features with low drifts
  
  Parameters
  ----------
  threshold : float, defaut = 0.6
      The drift threshold (univariate drift below are kept)
      Must be between 0. and 1.
  
  subsample : float, defaut = 1.
      Subsampling parameter for the datasets.
      Must be between 0. and 1.
  
  estimator : classifier, default = DecisionTreeClassifier(max_depth=6)
      The estimator that estimates the drift between two datasets.
  
  n_folds : int, default = 2
      Number of folds used to estimate the drift.
  
  stratify : bool, default = True
      Whether the cv is stratified (same number of train and test samples
      within each fold)
  
  random_state : int, default = 1
      Seed for for cv and subsampling.
  
  n_jobs : int, defaut = -1
      Number of cores used for processing (-1 for all cores)
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docstr: Select the features with low drift.
  
  Parameters
  ----------
  df : pandas dataframe
      A dataset with the same features
  
  Returns
  -------
  pandas DataFrame
      The transformed dataframe
docstr_tok: ["Select","the","features","with","low","drift",".","Parameters","--","--","--","--","--","df",":","pandas","dataframe","A","dataset","with","the","same","features","Returns","--","--","--","-","pandas","DataFrame","The","transformed","dataframe"]
code: def transform(self, df):
      """Select the features with low drift.
  
      Parameters
      ----------
      df : pandas dataframe
          A dataset with the same features
  
      Returns
      -------
      pandas DataFrame
          The transformed dataframe
  
      """
      if self.__fitOK:
          selected_col = []
  
          for i, col in enumerate(df.columns):
              if self.__Ddrifts[col] < self.threshold:
                  selected_col.append(col)
  
          return df[selected_col]
  
      else:
          raise ValueError("Call the fit function before !")
  
file_path: MLBox/mlbox/preprocessing/drift/drift_threshold.py
imports: import sys
  from joblib import Parallel, delayed
  from sklearn.tree import DecisionTreeClassifier
  from .drift_estimator import DriftEstimator
label_desc: Use image node_id 5 for calling the DriftThreshold obj's underlying member method code with example usage: obj.transform(df) and returns: df