code: def predict_features(self, df_features, df_target, idx=0, C=.1, **kwargs):
          
          lsvc = LinearSVR(C=C).fit(df_features.values, df_target.values)
  
          return np.abs(lsvc.coef_)
docs: For one variable, predict its neighbouring nodes.
  
          Args:
              df_features (pandas.DataFrame):
              df_target (pandas.Series):
              idx (int): (optional) for printing purposes
              kwargs (dict): additional options for algorithms
              C (float): Penalty parameter of the error term
  
          Returns:
              list: scores of each feature relatively to the target