class_docstr: Contains the class for the Xcessiv stacked ensemble
docstr: Process using secondary learner meta-feature generator
  
  Since secondary learner meta-feature generator can be anything e.g. predict, predict_proba,
  this internal method gives the ability to use any string. Just make sure secondary learner
  has the method.
  
  Args:
      X (array-like): Features array
  
      meta_feature_generator (str, unicode): Method for use by secondary learner
code: def _process_using_meta_feature_generator(
      self, X, meta_feature_generator
  ):
      """Process using secondary learner meta-feature generator
  
      Since secondary learner meta-feature generator can be anything e.g. predict, predict_proba,
      this internal method gives the ability to use any string. Just make sure secondary learner
      has the method.
  
      Args:
          X (array-like): Features array
  
          meta_feature_generator (str, unicode): Method for use by secondary learner
      """
  
      all_learner_meta_features = []
      for idx, base_learner in enumerate(self.base_learners):
          single_learner_meta_features = getattr(
              base_learner, self.meta_feature_generators[idx]
          )(X)
  
          if len(single_learner_meta_features.shape) == 1:
              single_learner_meta_features = (
                  single_learner_meta_features.reshape(-1, 1)
              )
          all_learner_meta_features.append(single_learner_meta_features)
  
      all_learner_meta_features = np.concatenate(
          all_learner_meta_features, axis=1
      )
  
      out = getattr(self.secondary_learner, meta_feature_generator)(
          all_learner_meta_features
      )
  
      return out
  
file_path: xcessiv/xcessiv/stacker.py
imports: import sklearn
  import numpy
label_desc: Use image node_id 5 for calling the XcessivStackedEnsemble obj's underlying member method code with example usage: obj._process_using_meta_feature_generator(X, meta_feature_generator) and returns: out